JP2025048978A - system - Google Patents

Abstract

To provide a system for performing in an integrated manner, mental care, medicine-taking management, risk detection in walking of an elderly person, for reducing a burden applied to a care giver.SOLUTION: A system in an embodiment comprises: a conversation part; a management part; and a detection part. The conversion part performs mental care through a conversation with an elderly person. The management part outputs alert of medicine taking management on the basis of the information obtained by the conversation part. The detection part detects risk in walking on the basis of the information managed by the management part, and notifies the elderly person of the detected risk.SELECTED DRAWING: Figure 1

Description

The technology disclosed herein relates to a system.

Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description of the chatbot character and an associated instruction sentence, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

JP 2022-180282 A

Conventional technology has made it difficult to provide a unified approach to mental care, medication management, and risk detection for elderly people while they are walking, which places a heavy burden on caregivers.

The system of this embodiment aims to reduce the burden on caregivers by providing mental care for elderly people, managing their medication, and detecting dangers while walking in an integrated manner.

The system according to the embodiment includes a conversation unit, a management unit, and a detection unit. The conversation unit provides mental care through conversation with the elderly person. The management unit issues an alert for medication management based on information obtained by the conversation unit and reports it to the caregiver. The detection unit detects dangers while walking based on information managed by the management unit and notifies the elderly person.

The system according to the embodiment can provide mental care for the elderly, manage their medication, and detect dangers while walking in an integrated manner, reducing the burden on caregivers.

1 is a conceptual diagram showing an example of a configuration of a data processing system according to a first embodiment. 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. FIG. 11 is a conceptual diagram showing an example of a configuration of a data processing system according to a second embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped.

Below, an example of an embodiment of a system related to the technology disclosed herein is described with reference to the attached drawings.

First, let us explain the terminology used in the following explanation.

In the following embodiments, the signed processor (hereinafter simply referred to as the "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. The processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

In the following embodiments, a signed random access memory (RAM) is a memory in which information is temporarily stored and is used by the processor as a working memory.

In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (Solid State Drive (SSD)), magnetic disks (e.g., hard disks), and magnetic tapes.

In the following embodiments, a communication I/F (Interface) with a code is an interface including a communication processor and an antenna. The communication I/F controls communication between multiple computers. Examples of communication standards applied to the communication I/F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

In the following embodiments, "A and/or B" is synonymous with "at least one of A and B." In other words, "A and/or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same concept as "A and/or B" is also applied when three or more things are expressed by connecting them with "and/or."

[First embodiment]
FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form of expression that the user can perceive (e.g., voice and/or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

As shown in FIG. 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

(Example 1)
The care partner system according to the embodiment of the present invention is a system that aims to improve the satisfaction of each elderly person while reducing the burden on caregivers who care for the elderly. This care partner system provides mental care for elderly people who suffer from loneliness through conversation with AI, and provides medication management alerts, reports to caregivers, detects dangers while walking and notifies the elderly, and even provides tool support for communication with family members who live far away. For example, the care partner system allows elderly people to enjoy daily conversations with AI, which can reduce the sense of loneliness. AI provides topics based on the hobbies and interests of the elderly, and eases the feelings of the elderly through dialogue. In addition, the care partner system has an alert function for medication management, where AI manages the elderly's medication schedule and issues an alert to encourage them to take medication at the appropriate time. In addition, it also has a reporting function to the caregiver, and notifies the caregiver if the elderly forgets to take their medication or if there is an abnormality. In addition, the care partner system has a danger detection function during walking, where AI monitors the elderly's walking and notifies the elderly if there is a risk of falling. For example, AI analyzes the elderly's walking pattern and immediately issues a warning if their balance is lost. In addition, the care partner system also provides tools to support communication with family members who live far away. AI supports video calls and messaging between the elderly and their families, helping them maintain connections with their families. For example, AI can read messages from family members to the elderly and help set up video calls. In this way, the care partner system using AI can reduce the burden on caregivers and improve the satisfaction of the elderly by providing a wide range of functions, such as mental care for the elderly, medication management, danger detection while walking, and communication support with family members. As a result, the care partner system can consistently provide mental care for the elderly, medication management, danger detection while walking, and communication support with family members.

The care partner system according to the embodiment includes a conversation unit, a management unit, and a detection unit. The conversation unit provides mental care through conversation with the elderly. The conversation unit, for example, uses AI to have daily conversations with the elderly. Conversations with the elderly include, for example, daily conversations, conversations about health, and conversations about hobbies, but are not limited to such examples. For example, the conversation unit provides topics based on the hobbies and interests of the elderly, and eases the feelings of the elderly through dialogue. The conversation unit can also estimate the emotions of the elderly using AI and select a conversation topic based on the estimated emotions of the elderly. For example, the AI analyzes the voice and facial expressions of the elderly to estimate the emotions. The management unit issues an alert for medication management based on the information obtained by the conversation unit and reports to the caregiver. For example, the management unit uses AI to manage the elderly's medication schedule and issues an alert to encourage medication at an appropriate time. The medication schedule includes, for example, setting time periods and managing the dosage, but is not limited to such examples. The management unit can also use AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. For example, AI analyzes the elderly person's medication history and detects abnormalities. The detection unit detects dangers when walking based on the information managed by the management unit and notifies the elderly person. The detection unit, for example, uses AI to monitor the elderly person's walking and notifies the elderly person if there is a risk of falling. Walking monitoring includes, for example, step counting, walking speed measurement, posture detection, etc., but is not limited to such examples. The detection unit can also use AI to analyze the elderly person's walking pattern and issue a warning if balance is lost. For example, AI analyzes the elderly person's walking data and evaluates the risk of falling. As a result, the care partner system according to the embodiment can consistently provide mental care for the elderly person, medication management, and danger detection when walking.

The conversation unit provides mental care through conversation with the elderly. The conversation unit, for example, uses AI to have daily conversations with the elderly. Specifically, the AI uses natural language processing technology to understand what the elderly say and generate appropriate responses. Daily conversations include topics such as the weather, news, and family topics, and respond flexibly according to the interests and concerns of the elderly. In health-related conversations, the AI asks questions about the elderly's health and lifestyle habits, and provides advice as necessary. In hobbies-related conversations, topics related to hobbies and interests are provided based on the elderly's past comments and profile information. For example, for an elderly person whose hobby is gardening, topics about how to grow seasonal flowers and plants are provided. In addition, the AI can analyze the elderly's voice and facial expressions to estimate emotions. In voice analysis, changes in tone, speed, and volume of the voice are detected, and in facial expression analysis, emotions are estimated by analyzing the movement of facial muscles. This makes it possible to select appropriate conversation topics according to emotions, such as when the elderly person is stressed or relaxed. For example, if the elderly person looks sad, the AI provides a fun topic to ease their mood. This allows the conversation section to effectively provide mental care for the elderly and reduce feelings of loneliness and stress.

The management unit issues an alert for medication management based on the information obtained by the conversation unit and reports it to the caregiver. For example, the management unit uses AI to manage the elderly person's medication schedule and issues an alert to encourage them to take the medication at the appropriate time. Specifically, the AI registers the elderly person's medication schedule in a database and issues an alert at the set time. The alert is issued through voice notification, smartphone notification, or even a dedicated device. The medication schedule includes the type of medication, the dosage, the time of taking the medication, etc., and is set based on the elderly person's health condition and the doctor's instructions. The AI can analyze the medication history and detect forgetting to take the medication or abnormalities. For example, if the elderly person forgets to take the medication, the AI issues another alert and notifies the caregiver. In addition, if the amount of medication is excessive or insufficient, or if the timing of taking the medication is off, the AI detects abnormalities and reports them to the caregiver. This allows the management unit to efficiently manage the elderly person's medication and prevent medication errors. In addition, the management unit can collect data on the elderly person's health condition and provide it to doctors and caregivers. This allows doctors and caregivers to understand the health status of elderly people in real time and take appropriate measures.

The detection unit detects dangers during walking based on the information managed by the management unit and notifies the elderly person. For example, the detection unit uses AI to monitor the elderly person's walking and notifies the elderly person if there is a risk of falling. Specifically, the AI analyzes the elderly person's walking data in real time and detects changes in walking patterns and posture. Walking monitoring includes step counting, walking speed measurement, posture detection, etc. In step counting, the AI counts the elderly person's steps and grasps the amount of daily activity. In measuring walking speed, the AI analyzes the walking speed and rhythm and detects abnormal changes. In posture detection, the AI analyzes the elderly person's body inclination and changes in balance to evaluate the risk of falling. For example, if an elderly person suddenly loses balance, the AI immediately issues a warning and alerts the elderly person. The detection unit can also accumulate the elderly person's walking data and analyze long-term changes in walking patterns. This makes it possible to detect the risk of falling early and plan preventive measures. Furthermore, the detection unit can provide elderly people's walking data to caregivers and doctors to use as a reference for rehabilitation and treatment. This allows the detection unit to ensure the safety of elderly people and minimize the risk of falls.

The care partner system includes a tool unit that supports communication with family members. The tool unit, for example, uses AI to support video calls and message exchanges between the elderly and family members. For example, the tool unit uses AI to read messages from family members to the elderly and assists in setting up video calls. Specific methods of supporting communication include, but are not limited to, video calls, messaging, and voice calls. In this way, the care partner system can maintain a connection with family members by supporting communication between the elderly and family members.

The conversation unit can use AI to have everyday conversations with the elderly. The conversation unit, for example, uses AI to have everyday conversations with the elderly. Daily conversations include, but are not limited to, for example, talking about the weather, food, and hobbies. For example, the conversation unit uses AI to provide topics based on the elderly's hobbies and interests, and eases the elderly's feelings through dialogue. In this way, by using AI, mental care can be provided through everyday conversations with the elderly.

The management unit can use AI to manage the elderly person's medication schedule and issue alerts to encourage them to take their medication. The management unit, for example, uses AI to manage the elderly person's medication schedule and issue alerts to encourage them to take their medication at appropriate times. The medication schedule includes, for example, setting time periods and managing the amount of medication taken, but is not limited to these examples. For example, the management unit uses AI to analyze the elderly person's medication history and determine the appropriate timing for taking medication. In this way, by using AI, it is possible to efficiently manage the elderly person's medication.

The management unit can use AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. The management unit, for example, uses AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. Abnormalities include, for example, forgetting to take medication, taking too much medication, feeling unwell, etc., but are not limited to these examples. For example, the management unit uses AI to analyze the elderly person's medication history and detect abnormalities. In this way, by using AI, it is possible to quickly notify the caregiver if medication is forgotten or an abnormality occurs.

The detection unit can monitor the walking of an elderly person using AI and notify the elderly person if there is a risk of falling. The detection unit can, for example, monitor the walking of an elderly person using AI and notify the elderly person if there is a risk of falling. Walking monitoring includes, for example, but is not limited to, counting steps, measuring walking speed, detecting posture, and the like. For example, the detection unit uses AI to analyze the walking data of the elderly person and evaluate the risk of falling. In this way, by using AI, it is possible to detect and notify the elderly person of the risk of falling at an early stage.

The detection unit can use AI to analyze the walking pattern of an elderly person and issue a warning if balance is lost. The detection unit can, for example, use AI to analyze the walking pattern of an elderly person and issue a warning if balance is lost. Walking patterns include, for example, stride length, walking speed, walking rhythm, etc., but are not limited to such examples. For example, the detection unit uses AI to analyze the walking data of an elderly person and evaluate the risk of falling. In this way, by using AI, it is possible to analyze the walking pattern of an elderly person and issue a warning if balance is lost.

The conversation unit can analyze the elderly person's past conversation history and select a method for progressing the conversation. The conversation unit can, for example, use AI to analyze the elderly person's past conversation history and select an optimal method for progressing the conversation. Methods for progressing the conversation include, for example, but are not limited to, topic selection, questioning methods, and conversation tempo. For example, the conversation unit can retake topics that the elderly person has shown interest in in the past and progress the conversation. The conversation unit can also avoid topics that the elderly person has avoided in the past and focus on positive topics. Furthermore, the conversation unit can provide new related topics based on topics that the elderly person has frequently talked about in the past. This makes it possible to provide more effective mental care by selecting an optimal method for progressing the conversation based on past conversation history.

The conversation unit can provide personalized topics based on the elderly person's hobbies and interests during conversation. The conversation unit, for example, uses AI to provide personalized topics based on the elderly person's hobbies and interests during conversation. Personalized topics include, but are not limited to, hobbies, interests, past experiences, and the like. For example, if the elderly person is interested in gardening, the conversation unit can provide the AI with topics about seasonal flowers and plants. Also, if the elderly person is interested in music, the conversation unit can talk about favorite artists and new music. Furthermore, if the elderly person is interested in traveling, the conversation unit can talk about past travel experiences and places the elderly person wants to go. This improves the quality of the conversation by providing topics based on the elderly person's hobbies and interests.

The conversation unit can provide topics related to the area during the conversation, taking into account the geographical location information of the elderly person. The conversation unit, for example, uses AI to provide topics related to the area, taking into account the geographical location information of the elderly person during the conversation. Geographical location information includes, for example, GPS data, address information, characteristics of the area, etc., but is not limited to such examples. For example, the conversation unit talks about events and news in the area where the elderly person lives. The conversation unit can also talk about memories and history of the area where the elderly person lived in the past. Furthermore, the conversation unit can also talk about tourist spots and culture in the area where the elderly person wants to travel. This improves the quality of the conversation by providing topics based on the geographical location information of the elderly person.

The conversation unit can analyze the social media activity of the elderly person during the conversation and provide related topics. The conversation unit can, for example, use AI to analyze the social media activity of the elderly person during the conversation and provide related topics. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. The conversation unit can, for example, talk about photos and posts shared by the elderly person on social media. The conversation unit can also provide topics about accounts and groups that the elderly person follows. Furthermore, the conversation unit can talk about online events and communities in which the elderly person participates. This improves the quality of the conversation by providing topics based on the elderly person's social media activity.

The management unit can analyze the elderly person's past medication history and propose a medication schedule. The management unit can, for example, use AI to analyze the elderly person's past medication history and propose an optimal medication schedule. Medication schedules include, for example, past medication history, doctor's instructions, changes in physical condition, and the like, but are not limited to these examples. For example, the management unit can propose a schedule that avoids time periods when the elderly person has forgotten to take medication in the past. The management unit can also propose a schedule based on time periods when the elderly person has taken medication in the past. Furthermore, the management unit can analyze the elderly person's past medication patterns and propose an optimal schedule. This improves the accuracy of medication management by proposing an optimal medication schedule based on past medication history.

The management unit can adjust the frequency of alerts based on the elderly person's health condition during medication management. The management unit, for example, uses AI to adjust the frequency of alerts based on the elderly person's health condition during medication management. The frequency of alerts includes, for example, health condition, importance of medication, emotional state, and the like, but is not limited to such examples. For example, the management unit increases the frequency of alerts when the elderly person's health condition is deteriorating. The management unit can also issue alerts at a normal frequency when the elderly person's health condition is stable. Furthermore, the management unit can also reduce the frequency of alerts when the elderly person's health condition is improving. This enables more appropriate medication management by adjusting the frequency of alerts according to the elderly person's health condition.

The management unit can provide relevant pharmaceutical information taking into account the geographic location information of the elderly person during medication management. The management unit, for example, uses AI to provide relevant pharmaceutical information taking into account the geographic location information of the elderly person during medication management. Pharmaceutical information includes, for example, drug efficacy, side effects, and usage methods, but is not limited to such examples. For example, the management unit provides information on pharmacies in the area where the elderly person lives. In addition, if the elderly person is traveling, the management unit can also provide information on nearby pharmacies. Furthermore, if the elderly person is in a specific area, the management unit can also provide information on pharmaceuticals available in that area. This improves the accuracy of medication management by providing pharmaceutical information based on the geographic location information of the elderly person.

The management unit can analyze the social media activity of the elderly person during medication management and provide related health information. The management unit can, for example, use AI to analyze the social media activity of the elderly person during medication management and provide related health information. Health information includes, for example, health management advice, disease prevention methods, health food information, and the like, but is not limited to such examples. The management unit can, for example, issue an alert based on health information shared by the elderly person on social media. The management unit can also provide information on health-related accounts that the elderly person follows. Furthermore, the management unit can provide information on online health communities in which the elderly person participates. This improves the accuracy of medication management by providing health information based on the elderly person's social media activity.

The detection unit can analyze the elderly person's past walking history and apply a danger detection algorithm. The detection unit can, for example, use AI to analyze the elderly person's past walking history and apply an optimal danger detection algorithm. The danger detection algorithm can include, for example, a method for analyzing walking patterns and criteria for evaluating the risk of falling, but is not limited to such examples. The detection unit can adjust the danger detection algorithm based on, for example, the location and time of day when the elderly person has fallen in the past. The detection unit can also analyze the elderly person's past walking patterns and identify situations with a high risk of falling. Furthermore, the detection unit can apply an optimal danger detection algorithm based on the elderly person's past walking history. This makes it possible to effectively detect the risk of falling by applying an optimal danger detection algorithm based on the past walking history.

The detection unit can adjust the frequency of notifications based on the elderly person's health condition when danger is detected while walking. The detection unit adjusts the frequency of notifications based on the elderly person's health condition when danger is detected while walking, for example, by using AI. The frequency of notifications includes, for example, health condition, degree of danger, emotional state, and the like, but is not limited to such examples. For example, the detection unit increases the frequency of notifications when the elderly person's health condition is deteriorating. Furthermore, the detection unit can also notify at a normal frequency when the elderly person's health condition is stable. Furthermore, the detection unit can also reduce the frequency of notifications when the elderly person's health condition is improving. In this way, more appropriate danger notifications can be provided by adjusting the frequency of notifications according to the elderly person's health condition.

When detecting danger while walking, the detection unit can provide relevant safety information taking into account the geographical location information of the elderly person. When detecting danger while walking, the detection unit, for example, uses AI to provide relevant safety information taking into account the geographical location information of the elderly person. Safety information includes, for example, local safety information, evacuation routes, emergency contact information, etc., but is not limited to such examples. For example, the detection unit provides safety information for the area where the elderly person lives. In addition, if the elderly person is traveling, the detection unit can also provide information on nearby safe places. Furthermore, if the elderly person is in a specific area, the detection unit can also provide safety information for that area. In this way, by providing safety information based on the geographical location information of the elderly person, danger can be avoided.

The detection unit can analyze the social media activity of the elderly person when danger is detected while walking, and provide related safety information. The detection unit can, for example, use AI to analyze the social media activity of the elderly person when danger is detected while walking, and provide related safety information. Safety information includes, for example, local safety information, evacuation routes, emergency contacts, and the like, but is not limited to such examples. The detection unit can, for example, issue a notification based on safety information shared by the elderly person on social media. The detection unit can also provide information on safety-related accounts that the elderly person follows. Furthermore, the detection unit can provide information on online safety communities in which the elderly person participates. This makes it possible to avoid danger by providing safety information based on the elderly person's social media activity.

The tool unit can analyze the elderly person's past communication history and suggest how to use the tools. The tool unit can, for example, use AI to analyze the elderly person's past communication history and suggest the optimal way to use the tools. Examples of how to use the tools include, but are not limited to, past usage history, purpose of use, and frequency of use. For example, the tool unit preferentially suggests tools that the elderly person has used frequently in the past. The tool unit can also analyze the elderly person's past communication patterns and suggest the optimal tool. Furthermore, the tool unit can also suggest tools that avoid tools that the elderly person has avoided in the past. In this way, the quality of communication is improved by suggesting the optimal way to use the tools based on the past communication history.

The tool unit can adjust the frequency of use based on the elderly person's health condition when using the communication tool. The tool unit, for example, uses AI to adjust the frequency of use based on the elderly person's health condition when using the communication tool. The frequency of use includes, for example, health condition, purpose of use, emotional state, and the like, but is not limited to such examples. For example, the tool unit reduces the frequency of use when the elderly person's health condition is deteriorating. The tool unit can also be used at a normal frequency when the elderly person's health condition is stable. Furthermore, the tool unit can increase the frequency of use when the elderly person's health condition is improving. This allows more appropriate communication by adjusting the frequency of use according to the elderly person's health condition.

The tool unit can provide relevant family information in consideration of the geographic location information of the elderly person when using the communication tool. The tool unit, for example, uses AI to provide relevant family information in consideration of the geographic location information of the elderly person when using the communication tool. Family information includes, for example, family contact information, family health status, family interests, etc., but is not limited to such examples. The tool unit provides, for example, family information in the area where the elderly person lives. In addition, the tool unit can also provide information on family members living nearby when the elderly person is traveling. Furthermore, the tool unit can also provide information on family members living in a specific area when the elderly person is in that area. This improves the quality of communication by providing family information based on the geographic location information of the elderly person.

The tool unit can analyze the social media activity of the elderly person when using the communication tool and provide related family information. The tool unit can, for example, use AI to analyze the social media activity of the elderly person when using the communication tool and provide related family information. Family information includes, for example, family contact information, family health status, family interests, etc., but is not limited to such examples. The tool unit can provide tools based on, for example, family information shared by the elderly person on social media. The tool unit can also provide information on family accounts that the elderly person follows. Furthermore, the tool unit can provide information on family-related online communities in which the elderly person participates. This improves the quality of communication by providing family information based on the elderly person's social media activity.

The system according to the embodiment is not limited to the above-mentioned example, and various modifications are possible, for example, as follows:

The care partner system further includes a reminder unit. The reminder unit can manage important schedules and tasks in the elderly person's daily life and provide reminders at appropriate times. For example, the reminder unit can manage the elderly person's medical appointments and social activity schedules and notify them in advance. The reminder unit can also remind the elderly person of daily exercise and meal times that they should be doing. Furthermore, the reminder unit can also notify the elderly person so that they do not forget appointments with family and friends. This makes it easier for the elderly person to manage their daily life and improves their quality of life.

The care partner system further includes an entertainment unit. The entertainment unit can provide appropriate entertainment content based on the hobbies and interests of the elderly. For example, the entertainment unit can recommend and play music and movies that the elderly like. The entertainment unit can also provide games and puzzles that the elderly can enjoy. Furthermore, the entertainment unit can introduce online events and community activities that the elderly can participate in. This can provide fun to the lives of the elderly and improve their mental satisfaction.

The care partner system further includes a health monitoring unit. The health monitoring unit monitors the health condition of the elderly person in real time, and can take appropriate action if an abnormality is detected. For example, the health monitoring unit measures the elderly person's heart rate and blood pressure, and notifies the elderly person if an abnormality is detected. The health monitoring unit can also analyze the elderly person's sleep patterns and provide advice on how to improve the quality of sleep. Furthermore, the health monitoring unit can manage the elderly person's weight and dietary records, and support healthy lifestyle habits. This allows for efficient health management of the elderly person and reduces health risks.

The care partner system further includes a learning support unit. The learning support unit can support the elderly in acquiring new knowledge and skills. For example, the learning support unit can provide online courses and teaching materials in areas that interest the elderly. The learning support unit can also introduce workshops and seminars that the elderly can participate in. Furthermore, the learning support unit can provide quizzes and tests for the elderly to review what they have learned. This can satisfy the intellectual curiosity of the elderly and promote their personal growth.

The care partner system further includes a relaxation section. The relaxation section can reduce stress for the elderly and provide an environment in which they can relax. For example, the relaxation section plays music or sounds of nature that can help the elderly relax. The relaxation section can also guide the elderly in simple meditation or breathing techniques that they can do. Furthermore, the relaxation section can introduce aromatherapy and massage methods that can help the elderly relax. This can reduce stress for the elderly and maintain their physical and mental health.

The processing flow of Example 1 is briefly explained below.

Step 1: The conversation unit provides mental care through conversation with the elderly. For example, AI is used to have daily conversations with the elderly, including daily conversations, conversations about health, and conversations about hobbies. The conversation unit provides topics based on the elderly's hobbies and interests, and eases the elderly's feelings through dialogue. AI can also be used to estimate the elderly's emotions and select conversation topics based on the estimated emotions. For example, AI analyzes the elderly's voice and facial expressions to estimate emotions.
Step 2: The management unit issues a medication management alert based on the information obtained by the conversation unit and reports to the caregiver. For example, AI can be used to manage the elderly person's medication schedule and issue an alert to encourage them to take medication at the appropriate time. The medication schedule includes setting time periods and managing the amount of medication taken. The management unit can also use AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. For example, AI can analyze the elderly person's medication history and detect abnormalities.
Step 3: The detection unit detects dangers during walking based on the information managed by the management unit and notifies the elderly person. For example, the AI is used to monitor the elderly person's walking and notifies the elderly person if there is a risk of falling. The walking monitoring includes counting steps, measuring walking speed, detecting posture, etc. The detection unit can also use the AI to analyze the elderly person's walking pattern and issue a warning if balance is lost. For example, the AI analyzes the elderly person's walking data and evaluates the risk of falling.

(Example 2)
The care partner system according to the embodiment of the present invention is a system that aims to improve the satisfaction of each elderly person while reducing the burden on caregivers who care for the elderly. This care partner system provides mental care for elderly people who suffer from loneliness through conversation with AI, and provides medication management alerts, reports to caregivers, detects dangers while walking and notifies the elderly, and even provides tool support for communication with family members who live far away. For example, the care partner system allows elderly people to enjoy daily conversations with AI, which can reduce the sense of loneliness. AI provides topics based on the hobbies and interests of the elderly, and eases the feelings of the elderly through dialogue. In addition, the care partner system has an alert function for medication management, where AI manages the elderly's medication schedule and issues an alert to encourage them to take medication at the appropriate time. In addition, it also has a reporting function to the caregiver, and notifies the caregiver if the elderly forgets to take their medication or if there is an abnormality. In addition, the care partner system has a danger detection function during walking, where AI monitors the elderly's walking and notifies the elderly if there is a risk of falling. For example, AI analyzes the elderly's walking pattern and immediately issues a warning if their balance is lost. In addition, the care partner system also provides tools to support communication with family members who live far away. AI supports video calls and messaging between the elderly and their families, helping them maintain connections with their families. For example, AI can read messages from family members to the elderly and help set up video calls. In this way, the care partner system using AI can reduce the burden on caregivers and improve the satisfaction of the elderly by providing a wide range of functions, such as mental care for the elderly, medication management, danger detection while walking, and communication support with family members. As a result, the care partner system can consistently provide mental care for the elderly, medication management, danger detection while walking, and communication support with family members.

The care partner system according to the embodiment includes a conversation unit, a management unit, and a detection unit. The conversation unit provides mental care through conversation with the elderly. The conversation unit, for example, uses AI to have daily conversations with the elderly. Conversations with the elderly include, for example, daily conversations, conversations about health, and conversations about hobbies, but are not limited to such examples. For example, the conversation unit provides topics based on the hobbies and interests of the elderly, and eases the feelings of the elderly through dialogue. The conversation unit can also estimate the emotions of the elderly using AI and select a conversation topic based on the estimated emotions of the elderly. For example, the AI analyzes the voice and facial expressions of the elderly to estimate the emotions. The management unit issues an alert for medication management based on the information obtained by the conversation unit and reports to the caregiver. For example, the management unit uses AI to manage the elderly's medication schedule and issues an alert to encourage medication at an appropriate time. The medication schedule includes, for example, setting time periods and managing the dosage, but is not limited to such examples. The management unit can also use AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. For example, AI analyzes the elderly person's medication history and detects abnormalities. The detection unit detects dangers when walking based on the information managed by the management unit and notifies the elderly person. The detection unit, for example, uses AI to monitor the elderly person's walking and notifies the elderly person if there is a risk of falling. Walking monitoring includes, for example, step counting, walking speed measurement, posture detection, etc., but is not limited to such examples. The detection unit can also use AI to analyze the elderly person's walking pattern and issue a warning if balance is lost. For example, AI analyzes the elderly person's walking data and evaluates the risk of falling. As a result, the care partner system according to the embodiment can consistently provide mental care for the elderly person, medication management, and danger detection when walking.

The conversation unit provides mental care through conversation with the elderly. The conversation unit, for example, uses AI to have daily conversations with the elderly. Specifically, the AI uses natural language processing technology to understand what the elderly say and generate appropriate responses. Daily conversations include topics such as the weather, news, and family topics, and respond flexibly according to the interests and concerns of the elderly. In health-related conversations, the AI asks questions about the elderly's health and lifestyle habits, and provides advice as necessary. In hobbies-related conversations, topics related to hobbies and interests are provided based on the elderly's past comments and profile information. For example, for an elderly person whose hobby is gardening, topics about how to grow seasonal flowers and plants are provided. In addition, the AI can analyze the elderly's voice and facial expressions to estimate emotions. In voice analysis, changes in tone, speed, and volume of the voice are detected, and in facial expression analysis, emotions are estimated by analyzing the movement of facial muscles. This makes it possible to select appropriate conversation topics according to emotions, such as when the elderly person is stressed or relaxed. For example, if the elderly person looks sad, the AI provides a fun topic to ease their mood. This allows the conversation section to effectively provide mental care for the elderly and reduce feelings of loneliness and stress.

The management unit issues an alert for medication management based on the information obtained by the conversation unit and reports it to the caregiver. For example, the management unit uses AI to manage the elderly person's medication schedule and issues an alert to encourage them to take the medication at the appropriate time. Specifically, the AI registers the elderly person's medication schedule in a database and issues an alert at the set time. The alert is issued through voice notification, smartphone notification, or even a dedicated device. The medication schedule includes the type of medication, the dosage, the time of taking the medication, etc., and is set based on the elderly person's health condition and the doctor's instructions. The AI can analyze the medication history and detect forgetting to take the medication or abnormalities. For example, if the elderly person forgets to take the medication, the AI issues another alert and notifies the caregiver. In addition, if the amount of medication is excessive or insufficient, or if the timing of taking the medication is off, the AI detects abnormalities and reports them to the caregiver. This allows the management unit to efficiently manage the elderly person's medication and prevent medication errors. In addition, the management unit can collect data on the elderly person's health condition and provide it to doctors and caregivers. This allows doctors and caregivers to understand the health status of elderly people in real time and take appropriate measures.

The detection unit detects dangers during walking based on the information managed by the management unit and notifies the elderly person. For example, the detection unit uses AI to monitor the elderly person's walking and notifies the elderly person if there is a risk of falling. Specifically, the AI analyzes the elderly person's walking data in real time and detects changes in walking patterns and posture. Walking monitoring includes step counting, walking speed measurement, posture detection, etc. In step counting, the AI counts the elderly person's steps and grasps the amount of daily activity. In measuring walking speed, the AI analyzes the walking speed and rhythm and detects abnormal changes. In posture detection, the AI analyzes the elderly person's body inclination and changes in balance to evaluate the risk of falling. For example, if an elderly person suddenly loses balance, the AI immediately issues a warning and alerts the elderly person. The detection unit can also accumulate the elderly person's walking data and analyze long-term changes in walking patterns. This makes it possible to detect the risk of falling early and plan preventive measures. Furthermore, the detection unit can provide elderly people's walking data to caregivers and doctors to use as a reference for rehabilitation and treatment. This allows the detection unit to ensure the safety of elderly people and minimize the risk of falls.

The care partner system includes a tool unit that supports communication with family members. The tool unit, for example, uses AI to support video calls and message exchanges between the elderly and family members. For example, the tool unit uses AI to read messages from family members to the elderly and assists in setting up video calls. Specific methods of supporting communication include, but are not limited to, video calls, messaging, and voice calls. In this way, the care partner system can maintain a connection with family members by supporting communication between the elderly and family members.

The conversation unit can use AI to have everyday conversations with the elderly. The conversation unit, for example, uses AI to have everyday conversations with the elderly. Daily conversations include, but are not limited to, for example, talking about the weather, food, and hobbies. For example, the conversation unit uses AI to provide topics based on the elderly's hobbies and interests, and eases the elderly's feelings through dialogue. In this way, by using AI, mental care can be provided through everyday conversations with the elderly.

The management unit can use AI to manage the elderly person's medication schedule and issue alerts to encourage them to take their medication. The management unit, for example, uses AI to manage the elderly person's medication schedule and issue alerts to encourage them to take their medication at appropriate times. The medication schedule includes, for example, setting time periods and managing the amount of medication taken, but is not limited to these examples. For example, the management unit uses AI to analyze the elderly person's medication history and determine the appropriate timing for taking medication. In this way, by using AI, it is possible to efficiently manage the elderly person's medication.

The management unit can use AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. The management unit, for example, uses AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. Abnormalities include, for example, forgetting to take medication, taking too much medication, feeling unwell, etc., but are not limited to these examples. For example, the management unit uses AI to analyze the elderly person's medication history and detect abnormalities. In this way, by using AI, it is possible to quickly notify the caregiver if medication is forgotten or an abnormality occurs.

The detection unit can monitor the walking of an elderly person using AI and notify the elderly person if there is a risk of falling. The detection unit can, for example, monitor the walking of an elderly person using AI and notify the elderly person if there is a risk of falling. Walking monitoring includes, for example, but is not limited to, counting steps, measuring walking speed, detecting posture, and the like. For example, the detection unit uses AI to analyze the walking data of the elderly person and evaluate the risk of falling. In this way, by using AI, it is possible to detect and notify the elderly person of the risk of falling at an early stage.

The detection unit can use AI to analyze the walking pattern of an elderly person and issue a warning if balance is lost. The detection unit can, for example, use AI to analyze the walking pattern of an elderly person and issue a warning if balance is lost. Walking patterns include, for example, stride length, walking speed, walking rhythm, etc., but are not limited to such examples. For example, the detection unit uses AI to analyze the walking data of an elderly person and evaluate the risk of falling. In this way, by using AI, it is possible to analyze the walking pattern of an elderly person and issue a warning if balance is lost.

The conversation unit can estimate the elderly person's emotions and select a conversation topic based on the elderly person's estimated emotions. The conversation unit can, for example, use AI to estimate the elderly person's emotions and select a conversation topic based on the elderly person's estimated emotions. Emotion estimation includes, for example, voice analysis, facial expression analysis, text analysis, and the like, but is not limited to such examples. For example, when the elderly person is sad, the conversation unit can provide a topic related to past happy memories or hobbies. In addition, when the elderly person is excited, the conversation unit can also talk about topics that can relax or relaxation methods. Furthermore, when the elderly person feels lonely, the conversation unit can provide a topic related to family and friends to make the elderly person feel connected. In this way, by selecting a conversation topic according to the elderly person's emotions, more appropriate mental care can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The conversation unit can analyze the elderly person's past conversation history and select a method for progressing the conversation. The conversation unit can, for example, use AI to analyze the elderly person's past conversation history and select an optimal method for progressing the conversation. Methods for progressing the conversation include, for example, but are not limited to, topic selection, questioning methods, and conversation tempo. For example, the conversation unit can retake topics that the elderly person has shown interest in in the past and progress the conversation. The conversation unit can also avoid topics that the elderly person has avoided in the past and focus on positive topics. Furthermore, the conversation unit can provide new related topics based on topics that the elderly person has frequently talked about in the past. This makes it possible to provide more effective mental care by selecting an optimal method for progressing the conversation based on past conversation history.

The conversation unit can provide personalized topics based on the elderly person's hobbies and interests during conversation. The conversation unit, for example, uses AI to provide personalized topics based on the elderly person's hobbies and interests during conversation. Personalized topics include, but are not limited to, hobbies, interests, past experiences, and the like. For example, if the elderly person is interested in gardening, the conversation unit can provide the AI with topics about seasonal flowers and plants. Also, if the elderly person is interested in music, the conversation unit can talk about favorite artists and new music. Furthermore, if the elderly person is interested in traveling, the conversation unit can talk about past travel experiences and places the elderly person wants to go. This improves the quality of the conversation by providing topics based on the elderly person's hobbies and interests.

The conversation unit can estimate the elderly person's emotions and adjust the tempo of the conversation based on the elderly person's estimated emotions. The conversation unit can, for example, use AI to estimate the elderly person's emotions and adjust the tempo of the conversation based on the elderly person's estimated emotions. The tempo of the conversation includes, for example, the speaking speed, the pause, the timing of the response, and the like, but is not limited to such examples. For example, when the elderly person is relaxed, the conversation unit can proceed with the conversation at a slow tempo. In addition, when the elderly person is excited, the conversation unit can also proceed with the conversation at a faster tempo. Furthermore, when the elderly person is tired, the conversation unit can also proceed with the conversation at a slower tempo. In this way, by adjusting the tempo of the conversation according to the elderly person's emotions, more appropriate mental care can be provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The conversation unit can provide topics related to the area during the conversation, taking into account the geographical location information of the elderly person. The conversation unit, for example, uses AI to provide topics related to the area, taking into account the geographical location information of the elderly person during the conversation. Geographical location information includes, for example, GPS data, address information, characteristics of the area, etc., but is not limited to such examples. For example, the conversation unit talks about events and news in the area where the elderly person lives. The conversation unit can also talk about memories and history of the area where the elderly person lived in the past. Furthermore, the conversation unit can also talk about tourist spots and culture in the area where the elderly person wants to travel. This improves the quality of the conversation by providing topics based on the geographical location information of the elderly person.

The conversation unit can analyze the social media activity of the elderly person during the conversation and provide related topics. The conversation unit can, for example, use AI to analyze the social media activity of the elderly person during the conversation and provide related topics. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. The conversation unit can, for example, talk about photos and posts shared by the elderly person on social media. The conversation unit can also provide topics about accounts and groups that the elderly person follows. Furthermore, the conversation unit can talk about online events and communities in which the elderly person participates. This improves the quality of the conversation by providing topics based on the elderly person's social media activity.

The management unit can estimate the elderly person's emotions and adjust the timing of the medication alert based on the elderly person's estimated emotions. The management unit, for example, uses AI to estimate the elderly person's emotions and adjusts the timing of the medication alert based on the elderly person's estimated emotions. The timing of the medication alert includes, for example, the time to take the medication, changes in physical condition, and emotional state, but is not limited to such examples. For example, if the elderly person is stressed, the management unit issues a medication alert at a relaxed time. In addition, if the elderly person is relaxed, the management unit can also issue a medication alert at a normal time. Furthermore, if the elderly person is excited, the management unit can also issue a medication alert at a calm time. This enables more appropriate medication management by adjusting the timing of the medication alert according to the elderly person's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The management unit can analyze the elderly person's past medication history and propose a medication schedule. The management unit can, for example, use AI to analyze the elderly person's past medication history and propose an optimal medication schedule. Medication schedules include, for example, past medication history, doctor's instructions, changes in physical condition, and the like, but are not limited to these examples. For example, the management unit can propose a schedule that avoids time periods when the elderly person has forgotten to take medication in the past. The management unit can also propose a schedule based on time periods when the elderly person has taken medication in the past. Furthermore, the management unit can analyze the elderly person's past medication patterns and propose an optimal schedule. This improves the accuracy of medication management by proposing an optimal medication schedule based on past medication history.

The management unit can adjust the frequency of alerts based on the elderly person's health condition during medication management. The management unit, for example, uses AI to adjust the frequency of alerts based on the elderly person's health condition during medication management. The frequency of alerts includes, for example, health condition, importance of medication, emotional state, and the like, but is not limited to such examples. For example, the management unit increases the frequency of alerts when the elderly person's health condition is deteriorating. The management unit can also issue alerts at a normal frequency when the elderly person's health condition is stable. Furthermore, the management unit can also reduce the frequency of alerts when the elderly person's health condition is improving. This enables more appropriate medication management by adjusting the frequency of alerts according to the elderly person's health condition.

The management unit can estimate the elderly person's emotions and determine the priority of the medication alert based on the elderly person's estimated emotions. The management unit, for example, uses AI to estimate the elderly person's emotions and determines the priority of the medication alert based on the elderly person's estimated emotions. The priority of the medication alert includes, for example, the importance of taking the medication, changes in physical condition, and emotional state, but is not limited to such examples. For example, when the elderly person is stressed, the management unit can give priority to important medication alerts. In addition, when the elderly person is relaxed, the management unit can also give priority to important medication alerts. Furthermore, when the elderly person is excited, the management unit can also give priority to important medication alerts. This enables more appropriate medication management by determining the priority of medication alerts according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The management unit can provide relevant pharmaceutical information taking into account the geographic location information of the elderly person during medication management. The management unit, for example, uses AI to provide relevant pharmaceutical information taking into account the geographic location information of the elderly person during medication management. Pharmaceutical information includes, for example, drug efficacy, side effects, and usage methods, but is not limited to such examples. For example, the management unit provides information on pharmacies in the area where the elderly person lives. In addition, if the elderly person is traveling, the management unit can also provide information on nearby pharmacies. Furthermore, if the elderly person is in a specific area, the management unit can also provide information on pharmaceuticals available in that area. This improves the accuracy of medication management by providing pharmaceutical information based on the geographic location information of the elderly person.

The management unit can analyze the social media activity of the elderly person during medication management and provide related health information. The management unit can, for example, use AI to analyze the social media activity of the elderly person during medication management and provide related health information. Health information includes, for example, health management advice, disease prevention methods, health food information, and the like, but is not limited to such examples. The management unit can, for example, issue an alert based on health information shared by the elderly person on social media. The management unit can also provide information on health-related accounts that the elderly person follows. Furthermore, the management unit can provide information on online health communities in which the elderly person participates. This improves the accuracy of medication management by providing health information based on the elderly person's social media activity.

The detection unit can estimate the elderly person's emotions and adjust the danger notification method based on the elderly person's estimated emotions. The detection unit can, for example, estimate the elderly person's emotions using AI and adjust the danger notification method based on the elderly person's estimated emotions. The danger notification method includes, for example, the timing of notification, the content of notification, and the means of notification, but is not limited to such examples. For example, when the elderly person is nervous, the detection unit can notify the elderly person of the danger in a calm voice. Also, when the elderly person is relaxed, the detection unit can notify the elderly person of the danger in a normal manner. Furthermore, when the elderly person is excited, the detection unit can notify the elderly person of the danger in a quick and concise manner. This allows for more appropriate danger notification by adjusting the danger notification method according to the elderly person's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The detection unit can analyze the elderly person's past walking history and apply a danger detection algorithm. The detection unit can, for example, use AI to analyze the elderly person's past walking history and apply an optimal danger detection algorithm. The danger detection algorithm can include, for example, a method for analyzing walking patterns and criteria for evaluating the risk of falling, but is not limited to such examples. The detection unit can adjust the danger detection algorithm based on, for example, the location and time of day when the elderly person has fallen in the past. The detection unit can also analyze the elderly person's past walking patterns and identify situations with a high risk of falling. Furthermore, the detection unit can apply an optimal danger detection algorithm based on the elderly person's past walking history. This makes it possible to effectively detect the risk of falling by applying an optimal danger detection algorithm based on the past walking history.

The detection unit can adjust the frequency of notifications based on the elderly person's health condition when danger is detected while walking. The detection unit adjusts the frequency of notifications based on the elderly person's health condition when danger is detected while walking, for example, by using AI. The frequency of notifications includes, for example, health condition, degree of danger, emotional state, and the like, but is not limited to such examples. For example, the detection unit increases the frequency of notifications when the elderly person's health condition is deteriorating. Furthermore, the detection unit can also notify at a normal frequency when the elderly person's health condition is stable. Furthermore, the detection unit can also reduce the frequency of notifications when the elderly person's health condition is improving. In this way, more appropriate danger notifications can be provided by adjusting the frequency of notifications according to the elderly person's health condition.

The detection unit can estimate the elderly person's emotions and determine the priority of danger notifications based on the elderly person's estimated emotions. The detection unit can estimate the elderly person's emotions using AI, for example, and determine the priority of danger notifications based on the elderly person's estimated emotions. The priority of danger notifications includes, for example, the degree of danger, changes in physical condition, and emotional state, but is not limited to such examples. For example, when the elderly person is stressed, the detection unit can give priority to important danger notifications. In addition, when the elderly person is relaxed, the detection unit can also give priority to important danger notifications. In addition, when the elderly person is excited, the detection unit can also give priority to important danger notifications. In this way, by determining the priority of danger notifications according to the elderly person's emotions, more appropriate danger notifications can be provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

When detecting danger while walking, the detection unit can provide relevant safety information taking into account the geographical location information of the elderly person. When detecting danger while walking, the detection unit, for example, uses AI to provide relevant safety information taking into account the geographical location information of the elderly person. Safety information includes, for example, local safety information, evacuation routes, emergency contact information, etc., but is not limited to such examples. For example, the detection unit provides safety information for the area where the elderly person lives. In addition, if the elderly person is traveling, the detection unit can also provide information on nearby safe places. Furthermore, if the elderly person is in a specific area, the detection unit can also provide safety information for that area. In this way, by providing safety information based on the geographical location information of the elderly person, danger can be avoided.

The detection unit can analyze the social media activity of the elderly person when danger is detected while walking, and provide related safety information. The detection unit can, for example, use AI to analyze the social media activity of the elderly person when danger is detected while walking, and provide related safety information. Safety information includes, for example, local safety information, evacuation routes, emergency contacts, and the like, but is not limited to such examples. The detection unit can, for example, issue a notification based on safety information shared by the elderly person on social media. The detection unit can also provide information on safety-related accounts that the elderly person follows. Furthermore, the detection unit can provide information on online safety communities in which the elderly person participates. This makes it possible to avoid danger by providing safety information based on the elderly person's social media activity.

The tool unit can estimate the elderly person's emotions and adjust the usage of the communication tool based on the elderly person's estimated emotions. The tool unit can, for example, use AI to estimate the elderly person's emotions and adjust the usage of the communication tool based on the elderly person's estimated emotions. The usage of the communication tool includes, for example, the frequency of use, the usage time, and the purpose of use, but is not limited to such examples. For example, when the elderly person is nervous, the tool unit provides a simple and easy-to-use interface. In addition, when the elderly person is relaxed, the tool unit can also provide detailed options. Furthermore, when the elderly person is excited, the tool unit can also provide a visually stimulating interface. This enables more appropriate communication by adjusting the usage of the communication tool according to the elderly person's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The tool unit can analyze the elderly person's past communication history and suggest how to use the tools. The tool unit can, for example, use AI to analyze the elderly person's past communication history and suggest the optimal way to use the tools. Examples of how to use the tools include, but are not limited to, past usage history, purpose of use, and frequency of use. For example, the tool unit preferentially suggests tools that the elderly person has used frequently in the past. The tool unit can also analyze the elderly person's past communication patterns and suggest the optimal tool. Furthermore, the tool unit can also suggest tools that avoid tools that the elderly person has avoided in the past. In this way, the quality of communication is improved by suggesting the optimal way to use the tools based on the past communication history.

The tool unit can adjust the frequency of use based on the elderly person's health condition when using the communication tool. The tool unit, for example, uses AI to adjust the frequency of use based on the elderly person's health condition when using the communication tool. The frequency of use includes, for example, health condition, purpose of use, emotional state, and the like, but is not limited to such examples. For example, the tool unit reduces the frequency of use when the elderly person's health condition is deteriorating. The tool unit can also be used at a normal frequency when the elderly person's health condition is stable. Furthermore, the tool unit can increase the frequency of use when the elderly person's health condition is improving. This allows more appropriate communication by adjusting the frequency of use according to the elderly person's health condition.

The tool unit can estimate the elderly person's emotions and determine the priority of communication tools based on the elderly person's estimated emotions. The tool unit can estimate the elderly person's emotions using AI, for example, and determine the priority of communication tools based on the elderly person's estimated emotions. The priority of communication tools includes, for example, the purpose of use, the frequency of use, and the emotional state, but is not limited to such examples. For example, when the elderly person is stressed, the tool unit can provide a simple and easy-to-use tool with priority. In addition, when the elderly person is relaxed, the tool unit can also provide a tool that provides detailed options with priority. Furthermore, when the elderly person is excited, the tool unit can also provide a visually stimulating tool with priority. This enables more appropriate communication by determining the priority of communication tools according to the elderly person's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The tool unit can provide relevant family information in consideration of the geographic location information of the elderly person when using the communication tool. The tool unit, for example, uses AI to provide relevant family information in consideration of the geographic location information of the elderly person when using the communication tool. Family information includes, for example, family contact information, family health status, family interests, etc., but is not limited to such examples. The tool unit provides, for example, family information in the area where the elderly person lives. In addition, the tool unit can also provide information on family members living nearby when the elderly person is traveling. Furthermore, the tool unit can also provide information on family members living in a specific area when the elderly person is in that area. This improves the quality of communication by providing family information based on the geographic location information of the elderly person.

The tool unit can analyze the social media activity of the elderly person when using the communication tool and provide related family information. The tool unit can, for example, use AI to analyze the social media activity of the elderly person when using the communication tool and provide related family information. Family information includes, for example, family contact information, family health status, family interests, etc., but is not limited to such examples. The tool unit can provide tools based on, for example, family information shared by the elderly person on social media. The tool unit can also provide information on family accounts that the elderly person follows. Furthermore, the tool unit can provide information on family-related online communities in which the elderly person participates. This improves the quality of communication by providing family information based on the elderly person's social media activity.

The system according to the embodiment is not limited to the above-mentioned example, and various modifications are possible, for example, as follows:

The care partner system further includes a reminder unit. The reminder unit can manage important schedules and tasks in the elderly person's daily life and provide reminders at appropriate times. For example, the reminder unit can manage the elderly person's medical appointments and social activity schedules and notify them in advance. The reminder unit can also remind the elderly person of daily exercise and meal times that they should be doing. Furthermore, the reminder unit can also notify the elderly person so that they do not forget appointments with family and friends. This makes it easier for the elderly person to manage their daily life and improves their quality of life.

The care partner system further includes an entertainment unit. The entertainment unit can provide appropriate entertainment content based on the hobbies and interests of the elderly. For example, the entertainment unit can recommend and play music and movies that the elderly like. The entertainment unit can also provide games and puzzles that the elderly can enjoy. Furthermore, the entertainment unit can introduce online events and community activities that the elderly can participate in. This can provide fun to the lives of the elderly and improve their mental satisfaction.

The care partner system further includes a health monitoring unit. The health monitoring unit monitors the health condition of the elderly person in real time, and can take appropriate action if an abnormality is detected. For example, the health monitoring unit measures the elderly person's heart rate and blood pressure, and notifies the elderly person if an abnormality is detected. The health monitoring unit can also analyze the elderly person's sleep patterns and provide advice on how to improve the quality of sleep. Furthermore, the health monitoring unit can manage the elderly person's weight and dietary records, and support healthy lifestyle habits. This allows for efficient health management of the elderly person and reduces health risks.

The care partner system further includes a learning support unit. The learning support unit can support the elderly in acquiring new knowledge and skills. For example, the learning support unit can provide online courses and teaching materials in areas that interest the elderly. The learning support unit can also introduce workshops and seminars that the elderly can participate in. Furthermore, the learning support unit can provide quizzes and tests for the elderly to review what they have learned. This can satisfy the intellectual curiosity of the elderly and promote their personal growth.

The care partner system further includes a relaxation section. The relaxation section can reduce stress for the elderly and provide an environment in which they can relax. For example, the relaxation section plays music or sounds of nature that can help the elderly relax. The relaxation section can also guide the elderly in simple meditation or breathing techniques that they can do. Furthermore, the relaxation section can introduce aromatherapy and massage methods that can help the elderly relax. This can reduce stress for the elderly and maintain their physical and mental health.

The care partner system further includes an emotion diary unit. The emotion diary unit can support the elderly in recording their daily emotions and deepening their self-understanding. For example, the emotion diary unit provides an interface for the elderly to record their emotions and events of the day. The emotion diary unit can also analyze the recorded emotion data and visualize emotion patterns and trends. Furthermore, the emotion diary unit can provide advice for the elderly to organize their emotions and increase positive emotions. This can improve the elderly's self-management of their emotions and maintain their mental health.

The care partner system further includes an emotion sharing unit. The emotion sharing unit can support the elderly to share their emotions with others and gain empathy. For example, the emotion sharing unit provides a message function for the elderly to communicate their emotions to family and friends. The emotion sharing unit can also introduce online communities in which the elderly can participate and interact with people who have common interests and experiences. Furthermore, the emotion sharing unit can support art and music activities for the elderly to express their emotions. This promotes the sharing of emotions among the elderly and strengthens social connections.

The care partner system further includes an emotion feedback unit. The emotion feedback unit can provide feedback on the emotions of the elderly person and support them in deepening their understanding of emotions. For example, the emotion feedback unit can analyze emotion data recorded by the elderly person and provide feedback on changes and trends in emotions. The emotion feedback unit can also analyze the cause and background of why the elderly person felt a particular emotion and provide information to deepen their understanding. Furthermore, the emotion feedback unit can provide specific advice to help the elderly person increase their positive emotions. This can improve the elderly person's self-understanding of emotions and maintain their mental health.

The care partner system further includes an emotion prediction unit. The emotion prediction unit can predict future emotions based on the elderly person's past emotion data and provide support for appropriate responses. For example, the emotion prediction unit can analyze the elderly person's past emotion data and predict emotions for specific situations or events. The emotion prediction unit can also suggest relaxation methods before the elderly person feels stressed based on the predicted emotions. Furthermore, the emotion prediction unit can also suggest activities or events to elicit positive emotions. This makes it possible to predict and respond to the elderly person's emotions, thereby maintaining their mental health.

The care partner system further includes an emotion training unit. The emotion training unit can support the elderly in learning to control their emotions and increase their positive emotions. For example, the emotion training unit can provide a training program for the elderly to learn relaxation and stress management techniques. The emotion training unit can also suggest activities for the elderly to develop a sense of gratitude. Furthermore, the emotion training unit can provide mental training for the elderly to cultivate positive thinking. This can improve the elderly's emotion control and maintain their mental health.

The process flow for Example 2 is briefly explained below.

Step 1: The conversation unit provides mental care through conversation with the elderly. For example, AI is used to have daily conversations with the elderly, including daily conversations, conversations about health, and conversations about hobbies. The conversation unit provides topics based on the elderly's hobbies and interests, and eases the elderly's feelings through dialogue. AI can also be used to estimate the elderly's emotions and select conversation topics based on the estimated emotions. For example, AI analyzes the elderly's voice and facial expressions to estimate emotions.
Step 2: The management unit issues a medication management alert based on the information obtained by the conversation unit and reports to the caregiver. For example, AI can be used to manage the elderly person's medication schedule and issue an alert to encourage them to take medication at the appropriate time. The medication schedule includes setting time periods and managing the amount of medication taken. The management unit can also use AI to notify the caregiver if the elderly person forgets to take their medication or if there is an abnormality. For example, AI can analyze the elderly person's medication history and detect abnormalities.
Step 3: The detection unit detects dangers during walking based on the information managed by the management unit and notifies the elderly person. For example, the AI is used to monitor the elderly person's walking and notifies the elderly person if there is a risk of falling. The walking monitoring includes counting steps, measuring walking speed, detecting posture, etc. The detection unit can also use the AI to analyze the elderly person's walking pattern and issue a warning if balance is lost. For example, the AI analyzes the elderly person's walking data and evaluates the risk of falling.

The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input for the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. The specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

Each of the multiple elements including the conversation unit, management unit, detection unit, and tool unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the smart device 14, and has daily conversations with the elderly. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues alerts for medication management and reports to the caregiver. The detection unit is realized, for example, by the control unit 46A of the smart device 14, and monitors the elderly's walking and notifies the elderly if there is a risk of falling. The tool unit is realized, for example, by the control unit 46A of the smart device 14, and supports video calls and message exchanges between the elderly and their families. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[Second embodiment]
FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is performed in a secure state.

Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores the specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input for the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the conversation unit, management unit, detection unit, and tool unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the smart glasses 214, and has daily conversations with the elderly. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues an alert for medication management and reports it to the caregiver. The detection unit is realized, for example, by the control unit 46A of the smart glasses 214, and monitors the elderly's walking and notifies the elderly if there is a risk of falling. The tool unit is realized, for example, by the control unit 46A of the smart glasses 214, and supports video calls and message exchanges between the elderly and their families. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[Third embodiment]
FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

As shown in FIG. 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is performed in a secure state.

Figure 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Figure 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores the specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input for the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

Each of the multiple elements including the conversation unit, management unit, detection unit, and tool unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the headset type terminal 314, and has daily conversations with the elderly. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and issues alerts for medication management and reports to the caregiver. The detection unit is realized, for example, by the control unit 46A of the headset type terminal 314, and monitors the elderly's walking and notifies the elderly if there is a risk of falling. The tool unit is realized, for example, by the control unit 46A of the headset type terminal 314, and supports video calls and message exchanges between the elderly and their families. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[Fourth embodiment]
FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

As shown in FIG. 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN and/or a LAN.

The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is performed in a secure state.

The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and legs. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores the specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating the user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the conversation unit, management unit, detection unit, and tool unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the conversation unit is realized by the control unit 46A of the robot 414 and has daily conversations with the elderly. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and issues an alert for medication management and reports to the caregiver. The detection unit is realized, for example, by the control unit 46A of the robot 414 and monitors the elderly's walking and notifies the elderly if there is a risk of falling. The tool unit is realized, for example, by the control unit 46A of the robot 414 and supports video calls and message exchanges between the elderly and their family members. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

The emotion identification model 59, which serves as an emotion engine, may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. Emotions that represent states and actions arising from a state of mind are arranged on the outer sides of the concentric circles. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

These emotions are distributed in the three o'clock direction of emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

The inside of emotion map 400 represents what is going on inside the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotions become.

Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery power, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions that belong to an area called "reaction" where sensation is dominant. The right half of the emotion map is lined with emotions that belong to an area called "situation" where situation recognition is dominant.

The emotion map defines two emotions that encourage learning. The first is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input to a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple learning data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, "peace of mind," "calm," and "reassuring," have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

In the above embodiment, an example has been described in which the specific processing program 56 is stored in the storage 32, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

The specific processing program 56 may also be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The various processors listed below can be used as hardware resources for executing specific processes. Examples of processors include a CPU, which is a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically to execute specific processes. All of these processors have built-in or connected memory, and all of these processors execute specific processes by using the memory.

The hardware resource that executes the specific process may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific process may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured by combining one or more CPUs with software, and this processor functions as a hardware resource that executes a specific process. Secondly, there is a configuration in which a processor is used that realizes the functions of the entire system, including multiple hardware resources that execute a specific process, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the various processors mentioned above as hardware resources.

More specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processing described above is merely an example. It goes without saying that unnecessary steps can be deleted, new steps can be added, and the processing order can be changed without departing from the spirit of the invention.

In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are only examples, and they may be combined with each other, or may be other devices. Also, in the above example, the first and second embodiments have been described separately, but these may be combined.

The above description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above explanation of the configuration, function, action, and effect is an explanation of an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above description and illustrations, within the scope of the gist of the technology of the present disclosure. Also, in order to avoid confusion and to make it easier to understand the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require particular explanation to enable the implementation of the technology of the present disclosure.

All publications, patent applications, and technical standards mentioned in this specification are incorporated by reference into this specification to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

(Appendix 1)
The system comprises a conversation unit that provides mental care through conversation with the elderly, a management unit that issues medication management alerts based on information obtained by the conversation unit and reports them to a caregiver, and a detection unit that detects dangers while walking based on the information managed by the management unit and notifies the elderly.
(Appendix 2)
A system equipped with a tool section that assists in communication with family members.
(Appendix 3)
The conversation unit is a system that uses AI to conduct everyday conversations with elderly people.
(Appendix 4)
The management unit is a system that uses AI to manage the medication schedules of elderly people and issue alerts to encourage them to take their medication.
(Appendix 5)
The management unit is a system that uses AI to notify caregivers if an elderly person forgets to take their medication or if there is an abnormality.
(Appendix 6)
The detection unit is a system that uses AI to monitor the walking of elderly people and notify them if there is a risk of them falling.
(Appendix 7)
The detection unit is a system that uses AI to analyze the walking patterns of elderly people and issue a warning if they lose balance.
(Appendix 8)
The conversation unit is a system that estimates the emotions of the elderly person and selects a conversation topic based on the estimated emotions of the elderly person.
(Appendix 9)
The conversation unit is a system that analyzes the elderly person's past conversation history and selects how to proceed with the conversation.
(Appendix 10)
The conversation unit is a system that provides personalized topics based on the elderly person's hobbies and interests during conversation.
(Appendix 11)
The conversation unit is a system that estimates the elderly person's emotions and adjusts the tempo of the conversation based on the elderly person's estimated emotions.
(Appendix 12)
The conversation unit is a system that provides local topics during conversation by taking into account the elderly person's geographical location information.
(Appendix 13)
The conversation unit is a system that analyzes the elderly person's social media activity during the conversation and provides related topics.
(Appendix 14)
The management unit is a system that estimates the elderly person's emotions and adjusts the timing of medication alerts based on the elderly person's estimated emotions.
(Appendix 15)
The management unit is a system that analyzes the elderly person's past medication history and proposes a medication schedule.
(Appendix 16)
The management unit is a system that adjusts the frequency of alerts based on the health condition of an elderly person during medication management.
(Appendix 17)
The management unit is a system that estimates the emotions of an elderly person and determines the priority of medication alerts based on the estimated emotions of the elderly person.
(Appendix 18)
The management unit is a system that provides relevant pharmaceutical information taking into account the geographical location information of elderly people when managing medication.
(Appendix 19)
The management unit is a system that analyzes the social media activity of elderly people and provides relevant health information during medication management.
(Appendix 20)
The detection unit is a system that estimates the emotions of an elderly person and adjusts the method of danger notification based on the estimated emotions of the elderly person.
(Appendix 21)
The detection unit is a system that analyzes the elderly person's past walking history and applies a danger detection algorithm.
(Appendix 22)
The detection unit is a system that adjusts the frequency of notifications based on the elderly person's health condition when it detects danger while walking.
(Appendix 23)
The detection unit is a system that estimates the emotions of an elderly person and determines the priority of danger notifications based on the estimated emotions of the elderly person.
(Appendix 24)
The detection unit is a system that provides relevant safety information taking into account the geographical location information of an elderly person when a danger is detected while walking.
(Appendix 25)
The detection unit is a system that analyzes the social media activity of elderly people when it detects a danger while walking and provides relevant safety information.
(Appendix 26)
The tool unit is a system that estimates the emotions of the elderly person and adjusts the way in which communication tools are used based on the estimated emotions of the elderly person.
(Appendix 27)
The tool unit is a system that analyzes the elderly person's past communication history and suggests how to use the tool.
(Appendix 28)
The tool unit is a system that adjusts the frequency of use of a communication tool based on the elderly person's health condition when using the communication tool.
(Appendix 29)
The tool unit is a system that estimates the emotions of the elderly person and determines the priority order of communication tools based on the estimated emotions of the elderly person.
(Appendix 30)
The tool unit is a system that provides related family information taking into account the geographical location information of an elderly person when using a communication tool.
(Appendix 31)
The tool unit is a system that analyzes the social media activity of elderly people when they use communication tools and provides related family information.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (9)

The conversation department provides mental care through conversation with the elderly.
a management unit that issues an alert for medication management based on the information obtained by the conversation unit and reports the alert to a caregiver;
A system comprising: a detection unit that detects danger while walking based on the information managed by the management unit and notifies the elderly person of the danger. The system according to claim 1, further comprising a tool section for supporting communication with family members. The conversation unit includes:
The system according to claim 1, characterized in that it uses AI to conduct everyday conversations with elderly people. The management unit
The system described in claim 1 uses AI to manage the medication schedules of elderly people and issue alerts to encourage them to take their medication. The management unit
The system according to claim 1, characterized in that it uses AI to notify caregivers if an elderly person forgets to take their medication or if there is an abnormality. The detection unit is
The system according to claim 1, characterized in that it uses AI to monitor the gait of elderly people and notify the elderly people if they are at risk of falling. The detection unit is
The system according to claim 1, characterized in that it uses AI to analyze the walking patterns of elderly people and issue a warning if balance is lost. The conversation unit includes:
The system according to claim 1 , further comprising: estimating an emotion of the elderly person; and selecting a conversation topic based on the estimated emotion of the elderly person. The conversation unit includes:
The system according to claim 1, further comprising: analyzing the elderly person's past conversation history and selecting a method for proceeding with the conversation.

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