System

Abstract

An object of a system according to an embodiment is to analyze needs of an elderly person and provide an optimal activity site and support.SOLUTION: A system according to an embodiment includes a need analysis unit, a suggestion unit, a community construction unit, a health management unit, a re-employment support unit, and a social participation support unit. The need analysis unit analyzes the needs of the elderly person using the generated AI. The proposal unit proposes an optimal activity field based on the need analyzed by the need analysis unit. The community construction unit constructs an online community on the basis of the activity site proposed by the proposal unit. The health management unit analyzes health data of the elderly person and supports health management. The re-employment support unit analyzes the work experience and skill of the elderly person and supports reemployment. The social participation support unit supports social participation and volunteer activities of elderly people.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately grasp the needs of the elderly and provide appropriate activities and support, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze the needs of elderly people and provide them with optimal activity opportunities and support. [Means for solving the problem]

[0006] The system according to the embodiment includes a needs analysis unit, a proposal unit, a community building unit, a health management unit, a reemployment support unit, and a social participation support unit. The needs analysis unit analyzes the needs of elderly people using a generative AI. The proposal unit proposes optimal activity areas based on the needs analyzed by the needs analysis unit. The community building unit builds an online community based on the activity areas proposed by the proposal unit. The health management unit analyzes the health data of elderly people and supports their health management. The reemployment support unit analyzes the work history and skills of elderly people and supports their reemployment. The social participation support unit supports social participation and volunteer activities for elderly people. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the needs of elderly people and provide optimal activity opportunities and support. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 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. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, 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).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. 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).

[0015] 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a 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.

[0018] 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, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact 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.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process 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 process 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.

[0025] 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.

[0026] 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 together 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 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. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] 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. Furthermore, 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.

[0028] (Example 1) The elderly support system according to the embodiment of the present invention is a system that uses generative AI to provide a place where elderly people can live comfortably and actively participate. As a result, the elderly support system can provide a place where elderly people can live comfortably and actively participate.

[0029] An elderly support system according to an embodiment includes a needs analysis unit, a proposal unit, a community building unit, a health management unit, a reemployment support unit, and a social participation support unit. The needs analysis unit analyzes the needs of the elderly using a generation AI. For example, the needs analysis unit collects and analyzes data such as the elderly's health condition, hobbies, and past work history. Furthermore, the needs analysis unit analyzes the needs based on the elderly's profile information, health data, and information about hobbies and interests using the generation AI. The proposal unit proposes optimal activity venues based on the needs analyzed by the needs analysis unit. For example, the proposal unit proposes appropriate activity venues based on the elderly's health condition and interests using the generation AI. Furthermore, the proposal unit can also propose appropriate activity venues based on the elderly's health condition and interests using the generation AI. The community building unit builds an online community based on the activity venues proposed by the proposal unit. For example, the community building unit proposes appropriate online groups and forums based on the elderly's interests using the generation AI. Furthermore, the community building unit can also promote active interaction by automatically generating topics and discussions within the community using the generation AI. The health management department analyzes the health data of elderly people and supports their health management. For example, in the health management department, the generating AI analyzes the health data of elderly people and provides appropriate exercise and dietary advice. In addition, in the health management department, the generating AI can conduct regular health checks and, if abnormalities are detected, encourage them to visit a medical institution. The out-employment support department analyzes the work history and skills of elderly people and supports their re-employment. For example, in the out-employment support department, the generating AI analyzes the work history and skills of elderly people and suggests appropriate re-employment opportunities based on that analysis. In addition, in the out-employment support department, the generating AI can suggest skill-up courses and training programs for elderly people and support their preparation for re-employment. The social participation support department supports elderly people's social participation and volunteer activities. For example, in the social participation support department, the generating AI suggests appropriate volunteer activities and local events based on the elderly's interests. In addition, in the social participation support department, the generating AI can manage volunteer activity schedules and match participants, supporting elderly people to participate in activities smoothly.As a result, the elderly support system according to the embodiment can provide a place where elderly people can live comfortably and actively participate. For example, elderly people can realize self-actualization by utilizing their interests and skills to contribute to society. Furthermore, elderly people can live healthy and fulfilling lives through health management and community participation.

[0030] The needs analysis unit monitors the elderly's lifestyle patterns in real time and can dynamically suggest optimal activity areas in response to daily changes. For example, the needs analysis unit monitors the elderly's lifestyle patterns using sensors or wearable devices, and the generation AI analyzes the data. For example, it suggests optimal activity areas based on daily activity levels and sleep patterns. The needs analysis unit also collects the elderly's dietary and exercise data in real time, and the generation AI analyzes the data to suggest activity areas based on their health condition. For example, it suggests light exercise when exercise is lacking. The needs analysis unit also monitors the elderly's social activity history, and the generation AI uses the data to suggest new opportunities for social participation. For example, it suggests community activities for elderly people who tend to be isolated. This makes it possible to dynamically suggest activity areas in response to the elderly's lifestyle patterns.

[0031] The needs analysis unit can analyze the elderly person's past activity history and suggest new activity areas based on successful experiences and activities that generated high satisfaction. For example, the needs analysis unit stores the elderly person's past activity history in a database, and the generation AI analyzes that data. For example, it suggests new activity areas based on the satisfaction level of events and activities that the elderly participated in in the past. The needs analysis unit also collects feedback on activities that the elderly person has participated in in the past, and the generation AI analyzes that data to suggest new activity areas based on successful experiences. For example, it prioritizes activities that have a lot of positive feedback. The needs analysis unit also analyzes the elderly person's past activity history in combination with emotional data, and the generation AI suggests new activity areas. For example, it makes suggestions based on emotional scores obtained from past activities. This makes it possible to suggest activity areas based on the elderly person's past successful experiences and satisfaction levels.

[0032] The proposal unit can be applied to elderly people from different cultural areas and regions and propose optimal activity spaces from a global perspective. For example, the proposal unit collects data on elderly people from different cultural areas and regions, and the generation AI analyzes that data. For example, the proposal unit proposes activity spaces that take cultural background and regional characteristics into consideration. The proposal unit also analyzes the needs of elderly people from a global perspective, and the generation AI proposes activity spaces that are suitable for elderly people from different regions. For example, the proposal is made based on traditional events and cultural activities in each region. The proposal unit also analyzes the lifestyle patterns and interests of elderly people from different cultural areas, and the generation AI proposes optimal activity spaces based on that data. For example, it proposes intercultural exchange events. This makes it possible to propose activity spaces that are applicable to elderly people from different cultural areas and regions.

[0033] The proposal unit can introduce a process for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the optimal activity space. For example, the proposal unit builds a system for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the optimal activity space. For example, the proposal unit reflects opinions from family meetings. The proposal unit also introduces a process for the generation AI to notify family members and caregivers of the results of the elderly person's needs analysis, and for them to jointly select the activity space. For example, the proposal unit adjusts the proposal based on feedback from family members and caregivers. The proposal unit also provides an online platform for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the activity space. For example, a function is added that allows family members and caregivers to comment on the proposal. This makes it possible to introduce a process for them to jointly select the optimal activity space with family members and caregivers.

[0034] The community building unit can provide a personalized community experience based on the interests and concerns of the elderly and suggest groups that meet their individual needs. For example, the community building unit analyzes the interest and concern data of the elderly, and the generation AI provides a personalized community experience. For example, it suggests groups based on specific hobbies and interests. The community building unit also uses the profile information of the elderly to have the generation AI suggest community groups that meet their individual needs. For example, it matches elderly people who share the same hobbies. The community building unit also combines and analyzes the interest and concern data of the elderly with activity history within the community, and the generation AI provides a personalized experience. For example, it suggests new groups based on past activity history. This makes it possible to provide a personalized community experience based on the interests and concerns of the elderly.

[0035] The community building unit analyzes activity history within the community, identifies the most active members and topics, and can form a new community based on that. For example, the community building unit stores activity history within the community in a database, and the generation AI analyzes that data. For example, it identifies the most active members and topics and forms a new community. The community building unit also monitors activity history within the elderly community in real time, and the generation AI proposes new communities based on that data. For example, it forms new groups based on active discussions. The community building unit also combines and analyzes activity history within the community with emotional data, and the generation AI forms new communities. For example, it proposes new groups based on topics with a high level of positive emotion. This makes it possible to form new communities based on the most active members and topics.

[0036] The community building unit can make the online community compatible with seniors from different languages ​​and cultural backgrounds, thereby promoting international exchange. For example, the community building unit builds an online community that seniors from different languages ​​and cultural backgrounds can participate in, and the generation AI analyzes the data. For example, it provides a multilingual discussion forum. The community building unit also uses the generation AI to match seniors with seniors from different cultural backgrounds based on their profile information. For example, it can suggest intercultural exchange events. The community building unit also collects data on seniors from different languages ​​and cultural backgrounds, and the generation AI uses that data to promote international exchange. For example, it can suggest topics that take cultural backgrounds into consideration. This makes it possible to promote international exchange that is compatible with seniors from different languages ​​and cultural backgrounds.

[0037] The community building department allows young and middle-aged people to participate in online communities for the elderly, thereby promoting intergenerational exchange. For example, the community building department allows young and middle-aged people to participate in online communities for the elderly, and the generation AI analyzes the data. For example, it suggests intergenerational exchange events. Furthermore, the community building department uses profile information from the elderly, young, and middle-aged people to suggest groups to promote intergenerational exchange. For example, it matches members with common hobbies and interests. Furthermore, the community building department allows young and middle-aged people to participate in online communities for the elderly, and the generation AI promotes intergenerational exchange based on that data. For example, it provides a discussion forum between different generations. This makes it possible to promote intergenerational exchange in which young and middle-aged people can also participate.

[0038] The health management unit monitors the elderly person's health data in real time and can immediately notify medical institutions if an abnormality is detected. For example, the health management unit monitors the elderly person's health data in real time using sensors or wearable devices, and the generation AI analyzes the data. For example, if an abnormality in heart rate or blood pressure is detected, a medical institution is notified. The health management unit also collects the elderly person's health data in real time, and the generation AI detects abnormalities based on the data. For example, if an abnormal body temperature or respiratory rate is detected, a medical institution is notified. The health management unit also analyzes the elderly person's health data in combination with past health history, and if the generation AI detects an abnormality, a medical institution is notified immediately. For example, a notification is made if an abnormality is found by comparing with past data. This makes it possible to monitor the elderly person's health data in real time and immediately notify medical institutions if an abnormality is detected.

[0039] The health management unit can analyze the elderly's lifestyle habits in detail and provide a customized health management plan based on their individual health risks. For example, the health management unit collects the elderly's lifestyle data, and the generation AI analyzes that data. For example, it identifies individual health risks based on diet, exercise, and sleep data and provides a customized health management plan. The health management unit also combines and analyzes the elderly's lifestyle data and health data, and the generation AI provides a health management plan based on their individual health risks. For example, it proposes a plan to improve lack of exercise and unhealthy eating habits. The health management unit also monitors the elderly's lifestyle data in real time, and the generation AI provides a customized health management plan based on that data. For example, it adjusts the plan according to changes in daily lifestyle habits. This allows for a detailed analysis of the elderly's lifestyle habits and provides a customized health management plan based on their individual health risks.

[0040] The health management department can introduce a system for sharing the health management data of elderly people with their families and caregivers and for collaborative health management. For example, the health management department builds a system for sharing the health management data of elderly people with their families and caregivers, and the generating AI analyzes the data. For example, it allows family members and caregivers to participate in health management. The health management department also introduces a process for notifying family members and caregivers of the elderly person's health data in real time and for the generating AI to collaboratively manage health. For example, it notifies family members and caregivers if an abnormality is detected. The health management department also provides an online platform for sharing the health management data of elderly people with their families and caregivers and for collaborative health management. For example, it adds a function that allows family members and caregivers to comment on health management plans. This allows the elderly person's health management data to be shared with family members and caregivers, and for collaborative health management.

[0041] The health management department can link the elderly health management system with different medical institutions and experts to provide comprehensive health support. For example, the health management department links the elderly health management system with different medical institutions and experts, and the generating AI analyzes the data. For example, the health management plan is adjusted based on feedback from medical institutions. The health management department also shares the elderly's health data with different medical institutions and experts, and the generating AI provides comprehensive health support. For example, it provides health management advice based on the experts' opinions. The health management department also links the elderly health management system with different medical institutions and experts, and the generating AI provides comprehensive health support based on the data. For example, it integrates and analyzes data from multiple medical institutions. This allows the elderly health management system to link with different medical institutions and experts to provide comprehensive health support.

[0042] The reemployment support department can analyze the skills of elderly people in detail and propose new job types and roles through skill combinations. For example, the reemployment support department collects skill data of elderly people and the generation AI analyzes that data. For example, it proposes new job types and roles by combining multiple skills. The reemployment support department also combines and analyzes the elderly person's work history data and skill data, and the generation AI proposes new job types and roles. For example, it makes proposals based on past work experience and skill sets. The reemployment support department also monitors the elderly person's skill data in real time, and the generation AI proposes new job types and roles based on that data. For example, it proposes new career paths through skill combinations. This makes it possible to analyze the skills of elderly people in detail and propose new job types and roles through skill combinations.

[0043] The re-employment support department can support elderly people's re-employment activities in real time and dynamically provide appropriate job information. For example, the re-employment support department monitors elderly people's re-employment activity data in real time, and the generation AI provides appropriate job information based on that data. For example, it automatically notifies them of the latest job information. The re-employment support department also combines and analyzes elderly people's work history data and re-employment activity data, and the generation AI dynamically provides job information. For example, it makes suggestions based on past work experience and current job information. The re-employment support department also collects elderly people's re-employment activity data in real time, and the generation AI provides appropriate job information based on that data. For example, it adjusts suggestions according to updates to job information. This makes it possible to support elderly people's re-employment activities in real time and dynamically provide appropriate job information.

[0044] The reemployment support department can apply the skills of older people to different industries and fields, discovering new jobs and roles. For example, the reemployment support department applies the skill data of older people to different industries and fields, and the generation AI analyzes the data. For example, it can propose jobs that apply technical skills to other industries. The reemployment support department also combines and analyzes the older person's work history data and skill data, and the generation AI proposes new jobs and roles that can be applied to different industries and fields. For example, it explores the possibility of changing jobs to a different industry. The reemployment support department also applies the skill data of older people to different industries and fields, and the generation AI discovers new jobs and roles based on that data. For example, it proposes new career paths based on the versatility of skills. This makes it possible to apply the skills of older people to different industries and fields, discovering new jobs and roles.

[0045] The reemployment support department can apply the reemployment support system for the elderly to younger and middle-aged people, promoting intergenerational skill sharing. For example, the reemployment support department could apply the reemployment support system for the elderly to younger and middle-aged people, and the generation AI would analyze the data. For example, it could provide a platform to promote intergenerational skill sharing. The reemployment support department could also combine and analyze skill data from older, younger, and middle-aged people, and the generation AI would promote intergenerational skill sharing. For example, it could propose a mentoring program between different generations. The reemployment support department could also apply the reemployment support system for the elderly to younger and middle-aged people, and the generation AI would use the data to promote intergenerational skill sharing. For example, it could match members with common skills. This would allow the reemployment support system for the elderly to be applied to younger and middle-aged people, promoting intergenerational skill sharing.

[0046] The social participation support unit can analyze the elderly person's past social participation history and suggest new activities based on successful experiences and activities with high satisfaction. For example, the social participation support unit stores the elderly person's past social participation history in a database, and the generation AI analyzes that data. For example, it suggests new activities based on the satisfaction level of past activities. The social participation support unit also collects feedback on social participation activities that the elderly person has participated in in the past, and the generation AI analyzes that data to suggest new activities based on successful experiences. For example, it prioritizes activities with a lot of positive feedback. The social participation support unit also combines and analyzes the elderly person's past social participation history with emotional data, and the generation AI suggests new activities. For example, it makes suggestions based on the emotional scores obtained from past activities. This makes it possible to analyze the elderly person's past social participation history and suggest new activities based on successful experiences and activities with high satisfaction.

[0047] The social participation support unit can monitor the social participation activities of elderly people in real time and dynamically evaluate the progress and results of the activities. For example, the social participation support unit monitors the social participation activities of elderly people in real time using sensors or wearable devices, and the generation AI analyzes the data. For example, it dynamically evaluates the progress and results of the activities. The social participation support unit also collects social participation activity data of elderly people in real time, and the generation AI evaluates the progress and results of the activities based on that data. For example, it quantifies and evaluates the effectiveness of the activities. The social participation support unit also combines and analyzes the social participation activity data of elderly people with past activity history, and the generation AI dynamically evaluates the progress and results of the activities. For example, it evaluates progress by comparing it with past data. This makes it possible to monitor the social participation activities of elderly people in real time and dynamically evaluate the progress and results of the activities.

[0048] The social participation support unit can apply social participation for the elderly to different regions and cultural spheres, providing opportunities for social participation from a global perspective. For example, the social participation support unit collects data on elderly people from different regions and cultural spheres, and the generation AI analyzes that data. For example, it proposes social participation opportunities for each region. The social participation support unit also analyzes social participation for the elderly from a global perspective, and the generation AI proposes social participation opportunities suitable for elderly people in different regions. For example, it makes proposals based on cultural activities for each region. The social participation support unit also analyzes the lifestyle patterns and interests of elderly people in different cultural spheres, and the generation AI proposes optimal social participation opportunities based on that data. For example, it proposes intercultural exchange events. This allows social participation for the elderly to be applied to different regions and cultural spheres, providing opportunities for social participation from a global perspective.

[0049] The social participation support unit provides opportunities for elderly people to participate in social activities together with family and friends, thereby strengthening social ties. For example, the social participation support unit builds a system that provides opportunities for elderly people to participate in social activities together with family and friends, and the generation AI analyzes the data. For example, it suggests activities that family and friends can participate in together. The social participation support unit also introduces a process in which elderly people's social participation activity data is shared with family and friends, and the generation AI jointly carries out activities. For example, it adjusts the suggestions based on feedback from family and friends. The social participation support unit also provides an online platform for elderly people to participate in social activities together with family and friends, and the generation AI makes suggestions based on the data. For example, it suggests events that family and friends can participate in. This provides opportunities for elderly people to participate in social activities together with family and friends, thereby strengthening social ties.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The proposal unit can introduce a process for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the optimal activity space. For example, the proposal unit builds a system for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the optimal activity space. For example, the proposal unit reflects opinions from family meetings. The proposal unit also introduces a process for the generation AI to notify family members and caregivers of the results of the elderly person's needs analysis, and for them to jointly select the activity space. For example, the proposal unit adjusts the proposal based on feedback from family members and caregivers. The proposal unit also provides an online platform for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the activity space. For example, a function is added that allows family members and caregivers to comment on the proposal. This makes it possible to introduce a process for them to jointly select the optimal activity space with family members and caregivers.

[0052] The proposal unit can be applied to elderly people from different cultural areas and regions and propose optimal activity spaces from a global perspective. For example, the proposal unit collects data on elderly people from different cultural areas and regions, and the generation AI analyzes that data. For example, the proposal unit proposes activity spaces that take cultural background and regional characteristics into consideration. The proposal unit also analyzes the needs of elderly people from a global perspective, and the generation AI proposes activity spaces that are suitable for elderly people from different regions. For example, the proposal is made based on traditional events and cultural activities in each region. The proposal unit also analyzes the lifestyle patterns and interests of elderly people from different cultural areas, and the generation AI proposes optimal activity spaces based on that data. For example, it proposes intercultural exchange events. This makes it possible to propose activity spaces that are applicable to elderly people from different cultural areas and regions.

[0053] The health management unit monitors the elderly person's health data in real time and can immediately notify medical institutions if an abnormality is detected. For example, the health management unit monitors the elderly person's health data in real time using sensors or wearable devices, and the generation AI analyzes the data. For example, if an abnormality in heart rate or blood pressure is detected, a medical institution is notified. The health management unit also collects the elderly person's health data in real time, and the generation AI detects abnormalities based on the data. For example, if an abnormal body temperature or respiratory rate is detected, a medical institution is notified. The health management unit also analyzes the elderly person's health data in combination with past health history, and if the generation AI detects an abnormality, a medical institution is notified immediately. For example, a notification is made if an abnormality is found by comparing with past data. This makes it possible to monitor the elderly person's health data in real time and immediately notify medical institutions if an abnormality is detected.

[0054] The reemployment support department can analyze the skills of elderly people in detail and propose new job types and roles through skill combinations. For example, the reemployment support department collects skill data of elderly people and the generation AI analyzes that data. For example, it proposes new job types and roles by combining multiple skills. The reemployment support department also combines and analyzes the elderly person's work history data and skill data, and the generation AI proposes new job types and roles. For example, it makes proposals based on past work experience and skill sets. The reemployment support department also monitors the elderly person's skill data in real time, and the generation AI proposes new job types and roles based on that data. For example, it proposes new career paths through skill combinations. This makes it possible to analyze the skills of elderly people in detail and propose new job types and roles through skill combinations.

[0055] The social participation support unit can monitor the social participation activities of elderly people in real time and dynamically evaluate the progress and results of the activities. For example, the social participation support unit monitors the social participation activities of elderly people in real time using sensors or wearable devices, and the generation AI analyzes the data. For example, it dynamically evaluates the progress and results of the activities. The social participation support unit also collects social participation activity data of elderly people in real time, and the generation AI evaluates the progress and results of the activities based on that data. For example, it quantifies and evaluates the effectiveness of the activities. The social participation support unit also combines and analyzes the social participation activity data of elderly people with past activity history, and the generation AI dynamically evaluates the progress and results of the activities. For example, it evaluates progress by comparing it with past data. This makes it possible to monitor the social participation activities of elderly people in real time and dynamically evaluate the progress and results of the activities.

[0056] The health management unit can analyze the elderly's lifestyle habits in detail and provide a customized health management plan based on their individual health risks. For example, the health management unit collects the elderly's lifestyle data, and the generation AI analyzes that data. For example, it identifies individual health risks based on diet, exercise, and sleep data and provides a customized health management plan. The health management unit also combines and analyzes the elderly's lifestyle data and health data, and the generation AI provides a health management plan based on their individual health risks. For example, it proposes a plan to improve lack of exercise and unhealthy eating habits. The health management unit also monitors the elderly's lifestyle data in real time, and the generation AI provides a customized health management plan based on that data. For example, it adjusts the plan according to changes in daily lifestyle habits. This allows for a detailed analysis of the elderly's lifestyle habits and provides a customized health management plan based on their individual health risks.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The needs analysis unit uses the generation AI to analyze the elderly person's needs. For example, the needs analysis unit collects and analyzes data such as the elderly person's health condition, hobbies, and past work history. The generation AI also analyzes the elderly person's needs based on their profile information, health data, and information on hobbies and interests. Step 2: The proposal unit proposes an optimal activity field based on the needs analyzed by the needs analysis unit. For example, the generation AI proposes an appropriate activity field based on the elderly person's health condition and interests. Step 3: The community construction unit builds an online community based on the activity areas proposed by the suggestion unit. For example, the generation AI can suggest appropriate online groups and forums based on the interests of seniors. The generation AI can also automatically generate topics and discussions within the community to promote active interaction. Step 4: The health management department analyzes the elderly person's health data and supports their health management. For example, the generating AI analyzes the elderly person's health data and provides appropriate exercise and dietary advice. The generating AI can also conduct regular health checks and encourage them to visit a medical institution if any abnormalities are detected. Step 5: The reemployment support department analyzes the elderly person's work history and skills and supports them in finding new employment. For example, the generative AI can analyze the elderly person's work history and skills and suggest appropriate new employment opportunities based on that analysis. The generative AI can also suggest skill-up courses and training programs for the elderly to support their preparation for reemployment. Step 6: The Social Participation Support Department supports elderly people's social participation and volunteer activities. For example, the Generative AI can suggest appropriate volunteer activities and local events based on the interests and concerns of the elderly. The Generative AI can also manage volunteer activity schedules and match participants, helping elderly people to participate in activities smoothly.

[0059] (Example 2) The elderly support system according to the embodiment of the present invention is a system that uses generative AI to provide a place where elderly people can live comfortably and actively participate. As a result, the elderly support system can provide a place where elderly people can live comfortably and actively participate.

[0060] An elderly support system according to an embodiment includes a needs analysis unit, a proposal unit, a community building unit, a health management unit, a reemployment support unit, and a social participation support unit. The needs analysis unit analyzes the needs of the elderly using a generation AI. For example, the needs analysis unit collects and analyzes data such as the elderly's health condition, hobbies, and past work history. Furthermore, the needs analysis unit analyzes the needs based on the elderly's profile information, health data, and information about hobbies and interests using the generation AI. The proposal unit proposes optimal activity venues based on the needs analyzed by the needs analysis unit. For example, the proposal unit proposes appropriate activity venues based on the elderly's health condition and interests using the generation AI. Furthermore, the proposal unit can also propose appropriate activity venues based on the elderly's health condition and interests using the generation AI. The community building unit builds an online community based on the activity venues proposed by the proposal unit. For example, the community building unit proposes appropriate online groups and forums based on the elderly's interests using the generation AI. Furthermore, the community building unit can also promote active interaction by automatically generating topics and discussions within the community using the generation AI. The health management department analyzes the health data of elderly people and supports their health management. For example, in the health management department, the generating AI analyzes the health data of elderly people and provides appropriate exercise and dietary advice. In addition, in the health management department, the generating AI can conduct regular health checks and, if abnormalities are detected, encourage them to visit a medical institution. The out-employment support department analyzes the work history and skills of elderly people and supports their re-employment. For example, in the out-employment support department, the generating AI analyzes the work history and skills of elderly people and suggests appropriate re-employment opportunities based on that analysis. In addition, in the out-employment support department, the generating AI can suggest skill-up courses and training programs for elderly people and support their preparation for re-employment. The social participation support department supports elderly people's social participation and volunteer activities. For example, in the social participation support department, the generating AI suggests appropriate volunteer activities and local events based on the elderly's interests. In addition, in the social participation support department, the generating AI can manage volunteer activity schedules and match participants, supporting elderly people to participate in activities smoothly.As a result, the elderly support system according to the embodiment can provide a place where elderly people can live comfortably and actively participate. For example, elderly people can realize self-actualization by utilizing their interests and skills to contribute to society. Furthermore, elderly people can live healthy and fulfilling lives through health management and community participation.

[0061] The needs analysis unit can collect emotional data from the elderly and suggest activity areas based on their emotions. For example, the needs analysis unit collects emotional data from the elderly, and the generation AI analyzes that data. For example, it records daily emotional fluctuations and suggests activity areas that bring out positive emotions. The needs analysis unit also analyzes emotional data from activities the elderly has previously participated in and suggests new activity areas based on activities that generated high satisfaction. For example, it compares past activity history with emotional scores. The needs analysis unit also monitors the elderly's real-time emotional state and dynamically suggests activity areas based on their emotions at that time. For example, it suggests relaxation activities when stress is high. This makes it possible to suggest activity areas based on the elderly's emotions.

[0062] The needs analysis unit monitors the elderly's lifestyle patterns in real time and can dynamically suggest optimal activity areas in response to daily changes. For example, the needs analysis unit monitors the elderly's lifestyle patterns using sensors or wearable devices, and the generation AI analyzes the data. For example, it suggests optimal activity areas based on daily activity levels and sleep patterns. The needs analysis unit also collects the elderly's dietary and exercise data in real time, and the generation AI analyzes the data to suggest activity areas based on their health condition. For example, it suggests light exercise when exercise is lacking. The needs analysis unit also monitors the elderly's social activity history, and the generation AI uses the data to suggest new opportunities for social participation. For example, it suggests community activities for elderly people who tend to be isolated. This makes it possible to dynamically suggest activity areas in response to the elderly's lifestyle patterns.

[0063] The needs analysis unit can analyze the elderly person's past activity history and suggest new activity areas based on successful experiences and activities that generated high satisfaction. For example, the needs analysis unit stores the elderly person's past activity history in a database, and the generation AI analyzes that data. For example, it suggests new activity areas based on the satisfaction level of events and activities that the elderly participated in in the past. The needs analysis unit also collects feedback on activities that the elderly person has participated in in the past, and the generation AI analyzes that data to suggest new activity areas based on successful experiences. For example, it prioritizes activities that have a lot of positive feedback. The needs analysis unit also analyzes the elderly person's past activity history in combination with emotional data, and the generation AI suggests new activity areas. For example, it makes suggestions based on emotional scores obtained from past activities. This makes it possible to suggest activity areas based on the elderly person's past successful experiences and satisfaction levels.

[0064] The proposal unit can be applied to elderly people from different cultural areas and regions and propose optimal activity spaces from a global perspective. For example, the proposal unit collects data on elderly people from different cultural areas and regions, and the generation AI analyzes that data. For example, the proposal unit proposes activity spaces that take cultural background and regional characteristics into consideration. The proposal unit also analyzes the needs of elderly people from a global perspective, and the generation AI proposes activity spaces that are suitable for elderly people from different regions. For example, the proposal is made based on traditional events and cultural activities in each region. The proposal unit also analyzes the lifestyle patterns and interests of elderly people from different cultural areas, and the generation AI proposes optimal activity spaces based on that data. For example, it proposes intercultural exchange events. This makes it possible to propose activity spaces that are applicable to elderly people from different cultural areas and regions.

[0065] The proposal unit can introduce a process for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the optimal activity space. For example, the proposal unit builds a system for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the optimal activity space. For example, the proposal unit reflects opinions from family meetings. The proposal unit also introduces a process for the generation AI to notify family members and caregivers of the results of the elderly person's needs analysis, and for them to jointly select the activity space. For example, the proposal unit adjusts the proposal based on feedback from family members and caregivers. The proposal unit also provides an online platform for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the activity space. For example, a function is added that allows family members and caregivers to comment on the proposal. This makes it possible to introduce a process for them to jointly select the optimal activity space with family members and caregivers.

[0066] The suggestion unit can use the elderly person's emotion estimation function to suggest joint activities with family and friends, strengthening social ties. For example, the suggestion unit analyzes the elderly person's emotion data, and the generation AI suggests joint activities with family and friends. For example, joint activities are suggested based on activities with high emotion scores. The suggestion unit also monitors the elderly person's emotional state in real time, and the generation AI suggests joint activities with family and friends based on that data. For example, activities that elicit positive emotions are suggested. The suggestion unit also analyzes the elderly person's emotion data in combination with data from family and friends, and the generation AI suggests joint activities. For example, suggestions are made based on emotion scores of past joint activities. This makes it possible to strengthen social ties through joint activities with family and friends.

[0067] The community building unit can use the emotion estimation function to generate topics and discussions to strengthen emotional connections within the community. For example, the community building unit analyzes the emotional data of elderly people, and the generation AI generates topics to strengthen emotional connections within the community. For example, it suggests topics that elicit positive emotions. The community building unit also monitors the emotional data within the community in real time, and the generation AI generates discussions based on that data. For example, it prioritizes topics with high emotion scores. The community building unit also analyzes the emotional data of elderly people in combination with activity history within the community, and the generation AI suggests topics to strengthen emotional connections. For example, it generates topics based on past successful experiences. This strengthens emotional connections within the community.

[0068] The community building unit can provide a personalized community experience based on the interests and concerns of the elderly and suggest groups that meet their individual needs. For example, the community building unit analyzes the interest and concern data of the elderly, and the generation AI provides a personalized community experience. For example, it suggests groups based on specific hobbies and interests. The community building unit also uses the profile information of the elderly to have the generation AI suggest community groups that meet their individual needs. For example, it matches elderly people who share the same hobbies. The community building unit also combines and analyzes the interest and concern data of the elderly with activity history within the community, and the generation AI provides a personalized experience. For example, it suggests new groups based on past activity history. This makes it possible to provide a personalized community experience based on the interests and concerns of the elderly.

[0069] The community building unit analyzes activity history within the community, identifies the most active members and topics, and can form a new community based on that. For example, the community building unit stores activity history within the community in a database, and the generation AI analyzes that data. For example, it identifies the most active members and topics and forms a new community. The community building unit also monitors activity history within the elderly community in real time, and the generation AI proposes new communities based on that data. For example, it forms new groups based on active discussions. The community building unit also combines and analyzes activity history within the community with emotional data, and the generation AI forms new communities. For example, it proposes new groups based on topics with a high level of positive emotion. This makes it possible to form new communities based on the most active members and topics.

[0070] The community building unit can make the online community compatible with seniors from different languages ​​and cultural backgrounds, thereby promoting international exchange. For example, the community building unit builds an online community that seniors from different languages ​​and cultural backgrounds can participate in, and the generation AI analyzes the data. For example, it provides a multilingual discussion forum. The community building unit also uses the generation AI to match seniors with seniors from different cultural backgrounds based on their profile information. For example, it can suggest intercultural exchange events. The community building unit also collects data on seniors from different languages ​​and cultural backgrounds, and the generation AI uses that data to promote international exchange. For example, it can suggest topics that take cultural backgrounds into consideration. This makes it possible to promote international exchange that is compatible with seniors from different languages ​​and cultural backgrounds.

[0071] The community building department allows young and middle-aged people to participate in online communities for the elderly, thereby promoting intergenerational exchange. For example, the community building department allows young and middle-aged people to participate in online communities for the elderly, and the generation AI analyzes the data. For example, it suggests intergenerational exchange events. Furthermore, the community building department uses profile information from the elderly, young, and middle-aged people to suggest groups to promote intergenerational exchange. For example, it matches members with common hobbies and interests. Furthermore, the community building department allows young and middle-aged people to participate in online communities for the elderly, and the generation AI promotes intergenerational exchange based on that data. For example, it provides a discussion forum between different generations. This makes it possible to promote intergenerational exchange in which young and middle-aged people can also participate.

[0072] The community building unit can use the emotion estimation function to detect negative emotions within the community early and provide appropriate support. For example, the community building unit monitors emotion data within the community in real time, and the generation AI detects negative emotions early. For example, support is provided when emotion scores for stress or anxiety are high. The community building unit also analyzes the emotion data of elderly people, and the generation AI identifies negative emotions within the community. For example, support is suggested based on negative comments and discussions. The community building unit also combines and analyzes emotion data and activity history within the community, and the generation AI detects negative emotions early and provides appropriate support. For example, counseling or mental health support is suggested. This makes it possible to detect negative emotions within the community early and provide appropriate support.

[0073] The health management unit can use the emotion estimation function to provide health management advice according to the emotional state of the elderly person. For example, the health management unit analyzes the emotional data of the elderly person, and the generation AI provides health management advice based on that data. For example, if stress is high, it will suggest relaxation methods. The health management unit also monitors the emotional state of the elderly person in real time, and the generation AI provides health management advice based on that data. For example, it will suggest exercises and meals that will elicit positive emotions. The health management unit also combines and analyzes the emotional data and health data of the elderly person, and the generation AI provides health management advice according to their emotional state. For example, if the emotion score is low, it will suggest mental health support. This makes it possible to provide health management advice according to the emotional state of the elderly person.

[0074] The health management unit monitors the elderly person's health data in real time and can immediately notify medical institutions if an abnormality is detected. For example, the health management unit monitors the elderly person's health data in real time using sensors or wearable devices, and the generation AI analyzes the data. For example, if an abnormality in heart rate or blood pressure is detected, a medical institution is notified. The health management unit also collects the elderly person's health data in real time, and the generation AI detects abnormalities based on the data. For example, if an abnormal body temperature or respiratory rate is detected, a medical institution is notified. The health management unit also analyzes the elderly person's health data in combination with past health history, and if the generation AI detects an abnormality, a medical institution is notified immediately. For example, a notification is made if an abnormality is found by comparing with past data. This makes it possible to monitor the elderly person's health data in real time and immediately notify medical institutions if an abnormality is detected.

[0075] The health management unit can analyze the elderly's lifestyle habits in detail and provide a customized health management plan based on their individual health risks. For example, the health management unit collects the elderly's lifestyle data, and the generation AI analyzes that data. For example, it identifies individual health risks based on diet, exercise, and sleep data and provides a customized health management plan. The health management unit also combines and analyzes the elderly's lifestyle data and health data, and the generation AI provides a health management plan based on their individual health risks. For example, it proposes a plan to improve lack of exercise and unhealthy eating habits. The health management unit also monitors the elderly's lifestyle data in real time, and the generation AI provides a customized health management plan based on that data. For example, it adjusts the plan according to changes in daily lifestyle habits. This allows for a detailed analysis of the elderly's lifestyle habits and provides a customized health management plan based on their individual health risks.

[0076] The health management department can introduce a system for sharing the health management data of elderly people with their families and caregivers and for collaborative health management. For example, the health management department builds a system for sharing the health management data of elderly people with their families and caregivers, and the generating AI analyzes the data. For example, it allows family members and caregivers to participate in health management. The health management department also introduces a process for notifying family members and caregivers of the elderly person's health data in real time and for the generating AI to collaboratively manage health. For example, it notifies family members and caregivers if an abnormality is detected. The health management department also provides an online platform for sharing the health management data of elderly people with their families and caregivers and for collaborative health management. For example, it adds a function that allows family members and caregivers to comment on health management plans. This allows the elderly person's health management data to be shared with family members and caregivers, and for collaborative health management.

[0077] The health management department can link the elderly health management system with different medical institutions and experts to provide comprehensive health support. For example, the health management department links the elderly health management system with different medical institutions and experts, and the generating AI analyzes the data. For example, the health management plan is adjusted based on feedback from medical institutions. The health management department also shares the elderly's health data with different medical institutions and experts, and the generating AI provides comprehensive health support. For example, it provides health management advice based on the experts' opinions. The health management department also links the elderly health management system with different medical institutions and experts, and the generating AI provides comprehensive health support based on the data. For example, it integrates and analyzes data from multiple medical institutions. This allows the elderly health management system to link with different medical institutions and experts to provide comprehensive health support.

[0078] The health management unit uses the emotion estimation function to provide health management advice according to the emotional state of the elderly person, thereby supporting their emotional health. For example, the health management unit analyzes the elderly person's emotional data, and the generation AI uses that data to provide health management advice according to their emotional state. For example, if stress is high, it will suggest relaxation methods. The health management unit also monitors the elderly person's emotional state in real time, and the generation AI uses that data to provide advice to support their emotional health. For example, it will suggest activities that bring out positive emotions. The health management unit also combines and analyzes the elderly person's emotional data and health data, and the generation AI provides health management advice according to their emotional state. For example, if the emotion score is low, it will suggest mental health support. This makes it possible to provide health management advice according to the elderly person's emotional state, thereby supporting their emotional health.

[0079] The outplacement support unit can use the emotion estimation function to suggest occupations and roles that will give the elderly the most satisfaction. For example, the outplacement support unit analyzes the elderly's emotional data, and the generation AI uses that data to suggest occupations and roles that will give the elderly the most satisfaction. For example, suggestions are made by matching past work history with emotional scores. The outplacement support unit also monitors the elderly's emotional state in real time, and the generation AI uses that data to suggest occupations and roles that will give the elderly the most satisfaction. For example, it identifies occupations that elicit positive emotions. The outplacement support unit also combines and analyzes the elderly's emotional data and work history data, and the generation AI suggests occupations and roles that will give the elderly the most satisfaction. For example, it makes suggestions based on past work experience and emotional scores. This makes it possible to suggest occupations and roles that will give the elderly the most satisfaction.

[0080] The reemployment support department can analyze the skills of elderly people in detail and propose new job types and roles through skill combinations. For example, the reemployment support department collects skill data of elderly people and the generation AI analyzes that data. For example, it proposes new job types and roles by combining multiple skills. The reemployment support department also combines and analyzes the elderly person's work history data and skill data, and the generation AI proposes new job types and roles. For example, it makes proposals based on past work experience and skill sets. The reemployment support department also monitors the elderly person's skill data in real time, and the generation AI proposes new job types and roles based on that data. For example, it proposes new career paths through skill combinations. This makes it possible to analyze the skills of elderly people in detail and propose new job types and roles through skill combinations.

[0081] The re-employment support department can support elderly people's re-employment activities in real time and dynamically provide appropriate job information. For example, the re-employment support department monitors elderly people's re-employment activity data in real time, and the generation AI provides appropriate job information based on that data. For example, it automatically notifies them of the latest job information. The re-employment support department also combines and analyzes elderly people's work history data and re-employment activity data, and the generation AI dynamically provides job information. For example, it makes suggestions based on past work experience and current job information. The re-employment support department also collects elderly people's re-employment activity data in real time, and the generation AI provides appropriate job information based on that data. For example, it adjusts suggestions according to updates to job information. This makes it possible to support elderly people's re-employment activities in real time and dynamically provide appropriate job information.

[0082] The reemployment support department can apply the skills of older people to different industries and fields, discovering new jobs and roles. For example, the reemployment support department applies the skill data of older people to different industries and fields, and the generation AI analyzes the data. For example, it can propose jobs that apply technical skills to other industries. The reemployment support department also combines and analyzes the older person's work history data and skill data, and the generation AI proposes new jobs and roles that can be applied to different industries and fields. For example, it explores the possibility of changing jobs to a different industry. The reemployment support department also applies the skill data of older people to different industries and fields, and the generation AI discovers new jobs and roles based on that data. For example, it proposes new career paths based on the versatility of skills. This makes it possible to apply the skills of older people to different industries and fields, discovering new jobs and roles.

[0083] The reemployment support department can apply the reemployment support system for the elderly to younger and middle-aged people, promoting intergenerational skill sharing. For example, the reemployment support department could apply the reemployment support system for the elderly to younger and middle-aged people, and the generation AI would analyze the data. For example, it could provide a platform to promote intergenerational skill sharing. The reemployment support department could also combine and analyze skill data from older, younger, and middle-aged people, and the generation AI would promote intergenerational skill sharing. For example, it could propose a mentoring program between different generations. The reemployment support department could also apply the reemployment support system for the elderly to younger and middle-aged people, and the generation AI would use the data to promote intergenerational skill sharing. For example, it could match members with common skills. This would allow the reemployment support system for the elderly to be applied to younger and middle-aged people, promoting intergenerational skill sharing.

[0084] The outplacement support unit uses the emotion estimation function to suggest occupations and roles that will give the elderly the most satisfaction, thereby improving the success rate of re-employment. For example, the outplacement support unit analyzes the elderly's emotional data, and the generation AI uses that data to suggest occupations and roles that will give the elderly the most satisfaction. For example, suggestions are made by matching past work history with emotional scores. The outplacement support unit also monitors the elderly's emotional state in real time, and the generation AI uses that data to suggest occupations and roles that will give the elderly the most satisfaction. For example, it identifies occupations that elicit positive emotions. The outplacement support unit also combines and analyzes the elderly's emotional data and work history data, and the generation AI suggests occupations and roles that will give the elderly the most satisfaction. For example, it makes suggestions based on past work experience and emotional scores. This allows the generation AI to suggest occupations and roles that will give the elderly the most satisfaction, thereby improving the success rate of re-employment.

[0085] The social participation support unit can use the emotion estimation function to suggest volunteer activities and social participation opportunities that will evoke the most positive emotions in the elderly. For example, the social participation support unit analyzes the elderly's emotional data, and the generation AI uses that data to suggest volunteer activities and social participation opportunities that will evoke the most positive emotions. For example, suggestions are made by comparing past activity history with emotion scores. The social participation support unit also monitors the elderly's emotional state in real time, and the generation AI uses that data to suggest volunteer activities that will evoke the most positive emotions. For example, activities with a high emotion score are prioritized. The social participation support unit also analyzes the elderly's emotional data in combination with their past social participation history, and the generation AI suggests activities that will evoke the most positive emotions. For example, it suggests new activities based on past successful experiences. This makes it possible to suggest volunteer activities and social participation opportunities that will evoke the most positive emotions in the elderly.

[0086] The social participation support unit can analyze the elderly person's past social participation history and suggest new activities based on successful experiences and activities with high satisfaction. For example, the social participation support unit stores the elderly person's past social participation history in a database, and the generation AI analyzes that data. For example, it suggests new activities based on the satisfaction level of past activities. The social participation support unit also collects feedback on social participation activities that the elderly person has participated in in the past, and the generation AI analyzes that data to suggest new activities based on successful experiences. For example, it prioritizes activities with a lot of positive feedback. The social participation support unit also combines and analyzes the elderly person's past social participation history with emotional data, and the generation AI suggests new activities. For example, it makes suggestions based on the emotional scores obtained from past activities. This makes it possible to analyze the elderly person's past social participation history and suggest new activities based on successful experiences and activities with high satisfaction.

[0087] The social participation support unit can monitor the social participation activities of elderly people in real time and dynamically evaluate the progress and results of the activities. For example, the social participation support unit monitors the social participation activities of elderly people in real time using sensors or wearable devices, and the generation AI analyzes the data. For example, it dynamically evaluates the progress and results of the activities. The social participation support unit also collects social participation activity data of elderly people in real time, and the generation AI evaluates the progress and results of the activities based on that data. For example, it quantifies and evaluates the effectiveness of the activities. The social participation support unit also combines and analyzes the social participation activity data of elderly people with past activity history, and the generation AI dynamically evaluates the progress and results of the activities. For example, it evaluates progress by comparing it with past data. This makes it possible to monitor the social participation activities of elderly people in real time and dynamically evaluate the progress and results of the activities.

[0088] The social participation support unit can apply social participation for the elderly to different regions and cultural spheres, providing opportunities for social participation from a global perspective. For example, the social participation support unit collects data on elderly people from different regions and cultural spheres, and the generation AI analyzes that data. For example, it proposes social participation opportunities for each region. The social participation support unit also analyzes social participation for the elderly from a global perspective, and the generation AI proposes social participation opportunities suitable for elderly people in different regions. For example, it makes proposals based on cultural activities for each region. The social participation support unit also analyzes the lifestyle patterns and interests of elderly people in different cultural spheres, and the generation AI proposes optimal social participation opportunities based on that data. For example, it proposes intercultural exchange events. This allows social participation for the elderly to be applied to different regions and cultural spheres, providing opportunities for social participation from a global perspective.

[0089] The social participation support unit provides opportunities for elderly people to participate in social activities together with family and friends, thereby strengthening social ties. For example, the social participation support unit builds a system that provides opportunities for elderly people to participate in social activities together with family and friends, and the generation AI analyzes the data. For example, it suggests activities that family and friends can participate in together. The social participation support unit also introduces a process in which elderly people's social participation activity data is shared with family and friends, and the generation AI jointly carries out activities. For example, it adjusts the suggestions based on feedback from family and friends. The social participation support unit also provides an online platform for elderly people to participate in social activities together with family and friends, and the generation AI makes suggestions based on the data. For example, it suggests events that family and friends can participate in. This provides opportunities for elderly people to participate in social activities together with family and friends, thereby strengthening social ties.

[0090] The social participation support unit uses the emotion estimation function to suggest volunteer activities and social participation opportunities that will evoke the most positive emotions in the elderly, thereby improving activity satisfaction. For example, the social participation support unit analyzes the elderly's emotional data, and the generation AI uses that data to suggest volunteer activities and social participation opportunities that will evoke the most positive emotions. For example, the unit makes suggestions by comparing past activity history with emotion scores. The social participation support unit also monitors the elderly's emotional state in real time, and the generation AI uses that data to suggest volunteer activities that will evoke the most positive emotions. For example, activities with a high emotion score are prioritized. The social participation support unit also analyzes the elderly's emotional data in combination with their past social participation history, and the generation AI suggests activities that will evoke the most positive emotions. For example, it suggests new activities based on past successful experiences. This makes it possible to suggest volunteer activities and social participation opportunities that will evoke the most positive emotions in the elderly, thereby improving activity satisfaction.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The proposal unit can introduce a process for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the optimal activity space. For example, the proposal unit builds a system for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the optimal activity space. For example, the proposal unit reflects opinions from family meetings. The proposal unit also introduces a process for the generation AI to notify family members and caregivers of the results of the elderly person's needs analysis, and for them to jointly select the activity space. For example, the proposal unit adjusts the proposal based on feedback from family members and caregivers. The proposal unit also provides an online platform for sharing the results of the elderly person's needs analysis with family members and caregivers, and for them to jointly select the activity space. For example, a function is added that allows family members and caregivers to comment on the proposal. This makes it possible to introduce a process for them to jointly select the optimal activity space with family members and caregivers.

[0093] The proposal unit can be applied to elderly people from different cultural areas and regions and propose optimal activity spaces from a global perspective. For example, the proposal unit collects data on elderly people from different cultural areas and regions, and the generation AI analyzes that data. For example, the proposal unit proposes activity spaces that take cultural background and regional characteristics into consideration. The proposal unit also analyzes the needs of elderly people from a global perspective, and the generation AI proposes activity spaces that are suitable for elderly people from different regions. For example, the proposal is made based on traditional events and cultural activities in each region. The proposal unit also analyzes the lifestyle patterns and interests of elderly people from different cultural areas, and the generation AI proposes optimal activity spaces based on that data. For example, it proposes intercultural exchange events. This makes it possible to propose activity spaces that are applicable to elderly people from different cultural areas and regions.

[0094] The community building unit can use the emotion estimation function to generate topics and discussions to strengthen emotional connections within the community. For example, the community building unit analyzes the emotional data of elderly people, and the generation AI generates topics to strengthen emotional connections within the community. For example, it suggests topics that elicit positive emotions. The community building unit also monitors the emotional data within the community in real time, and the generation AI generates discussions based on that data. For example, it prioritizes topics with high emotion scores. The community building unit also analyzes the emotional data of elderly people in combination with activity history within the community, and the generation AI suggests topics to strengthen emotional connections. For example, it generates topics based on past successful experiences. This strengthens emotional connections within the community.

[0095] The health management unit monitors the elderly person's health data in real time and can immediately notify medical institutions if an abnormality is detected. For example, the health management unit monitors the elderly person's health data in real time using sensors or wearable devices, and the generation AI analyzes the data. For example, if an abnormality in heart rate or blood pressure is detected, a medical institution is notified. The health management unit also collects the elderly person's health data in real time, and the generation AI detects abnormalities based on the data. For example, if an abnormal body temperature or respiratory rate is detected, a medical institution is notified. The health management unit also analyzes the elderly person's health data in combination with past health history, and if the generation AI detects an abnormality, a medical institution is notified immediately. For example, a notification is made if an abnormality is found by comparing with past data. This makes it possible to monitor the elderly person's health data in real time and immediately notify medical institutions if an abnormality is detected.

[0096] The reemployment support department can analyze the skills of elderly people in detail and propose new job types and roles through skill combinations. For example, the reemployment support department collects skill data of elderly people and the generation AI analyzes that data. For example, it proposes new job types and roles by combining multiple skills. The reemployment support department also combines and analyzes the elderly person's work history data and skill data, and the generation AI proposes new job types and roles. For example, it makes proposals based on past work experience and skill sets. The reemployment support department also monitors the elderly person's skill data in real time, and the generation AI proposes new job types and roles based on that data. For example, it proposes new career paths through skill combinations. This makes it possible to analyze the skills of elderly people in detail and propose new job types and roles through skill combinations.

[0097] The outplacement support unit can use the emotion estimation function to suggest occupations and roles that will give the elderly the most satisfaction. For example, the outplacement support unit analyzes the elderly's emotional data, and the generation AI uses that data to suggest occupations and roles that will give the elderly the most satisfaction. For example, suggestions are made by matching past work history with emotional scores. The outplacement support unit also monitors the elderly's emotional state in real time, and the generation AI uses that data to suggest occupations and roles that will give the elderly the most satisfaction. For example, it identifies occupations that elicit positive emotions. The outplacement support unit also combines and analyzes the elderly's emotional data and work history data, and the generation AI suggests occupations and roles that will give the elderly the most satisfaction. For example, it makes suggestions based on past work experience and emotional scores. This makes it possible to suggest occupations and roles that will give the elderly the most satisfaction.

[0098] The social participation support unit can monitor the social participation activities of elderly people in real time and dynamically evaluate the progress and results of the activities. For example, the social participation support unit monitors the social participation activities of elderly people in real time using sensors or wearable devices, and the generation AI analyzes the data. For example, it dynamically evaluates the progress and results of the activities. The social participation support unit also collects social participation activity data of elderly people in real time, and the generation AI evaluates the progress and results of the activities based on that data. For example, it quantifies and evaluates the effectiveness of the activities. The social participation support unit also combines and analyzes the social participation activity data of elderly people with past activity history, and the generation AI dynamically evaluates the progress and results of the activities. For example, it evaluates progress by comparing it with past data. This makes it possible to monitor the social participation activities of elderly people in real time and dynamically evaluate the progress and results of the activities.

[0099] The social participation support unit can use the emotion estimation function to suggest volunteer activities and social participation opportunities that will evoke the most positive emotions in the elderly. For example, the social participation support unit analyzes the elderly's emotional data, and the generation AI uses that data to suggest volunteer activities and social participation opportunities that will evoke the most positive emotions. For example, suggestions are made by comparing past activity history with emotion scores. The social participation support unit also monitors the elderly's emotional state in real time, and the generation AI uses that data to suggest volunteer activities that will evoke the most positive emotions. For example, activities with a high emotion score are prioritized. The social participation support unit also analyzes the elderly's emotional data in combination with their past social participation history, and the generation AI suggests activities that will evoke the most positive emotions. For example, it suggests new activities based on past successful experiences. This makes it possible to suggest volunteer activities and social participation opportunities that will evoke the most positive emotions in the elderly.

[0100] The health management unit can analyze the elderly's lifestyle habits in detail and provide a customized health management plan based on their individual health risks. For example, the health management unit collects the elderly's lifestyle data, and the generation AI analyzes that data. For example, it identifies individual health risks based on diet, exercise, and sleep data and provides a customized health management plan. The health management unit also combines and analyzes the elderly's lifestyle data and health data, and the generation AI provides a health management plan based on their individual health risks. For example, it proposes a plan to improve lack of exercise and unhealthy eating habits. The health management unit also monitors the elderly's lifestyle data in real time, and the generation AI provides a customized health management plan based on that data. For example, it adjusts the plan according to changes in daily lifestyle habits. This allows for a detailed analysis of the elderly's lifestyle habits and provides a customized health management plan based on their individual health risks.

[0101] The health management unit can use the emotion estimation function to provide health management advice according to the emotional state of the elderly person. For example, the health management unit analyzes the emotional data of the elderly person, and the generation AI provides health management advice based on that data. For example, if stress is high, it will suggest relaxation methods. The health management unit also monitors the emotional state of the elderly person in real time, and the generation AI provides health management advice based on that data. For example, it will suggest exercises and meals that will elicit positive emotions. The health management unit also combines and analyzes the emotional data and health data of the elderly person, and the generation AI provides health management advice according to their emotional state. For example, if the emotion score is low, it will suggest mental health support. This makes it possible to provide health management advice according to the emotional state of the elderly person.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The needs analysis unit uses the generation AI to analyze the elderly person's needs. For example, the needs analysis unit collects and analyzes data such as the elderly person's health condition, hobbies, and past work history. The generation AI also analyzes the elderly person's needs based on their profile information, health data, and information on hobbies and interests. Step 2: The proposal unit proposes an optimal activity field based on the needs analyzed by the needs analysis unit. For example, the generation AI proposes an appropriate activity field based on the elderly person's health condition and interests. Step 3: The community construction unit builds an online community based on the activity areas proposed by the suggestion unit. For example, the generation AI can suggest appropriate online groups and forums based on the interests of seniors. The generation AI can also automatically generate topics and discussions within the community to promote active interaction. Step 4: The health management department analyzes the elderly person's health data and supports their health management. For example, the generating AI analyzes the elderly person's health data and provides appropriate exercise and dietary advice. The generating AI can also conduct regular health checks and encourage them to visit a medical institution if any abnormalities are detected. Step 5: The reemployment support department analyzes the elderly person's work history and skills and supports them in finding new employment. For example, the generative AI can analyze the elderly person's work history and skills and suggest appropriate new employment opportunities based on that analysis. The generative AI can also suggest skill-up courses and training programs for the elderly to support their preparation for reemployment. Step 6: The Social Participation Support Department supports elderly people's social participation and volunteer activities. For example, the Generative AI can suggest appropriate volunteer activities and local events based on the interests and concerns of the elderly. The Generative AI can also manage volunteer activity schedules and match participants, helping elderly people to participate in activities smoothly.

[0104] 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 regarding 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, 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. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and 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.

[0110] 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.

[0111] 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 audio according to instructions from the processor 46.

[0112] 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 images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a 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 carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 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.

[0115] 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.

[0116] 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.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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 regarding 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.

[0120] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and 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.

[0125] 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.

[0126] 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 audio according to instructions from the processor 46.

[0127] 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 images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a 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 carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] 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 regarding 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.

[0135] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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, RAM 30, and 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.

[0140] 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.

[0141] 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 audio according to instructions from the processor 46.

[0142] 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 images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a 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 carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. 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.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] 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 a user input regarding 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.

[0151] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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. Furthermore, 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.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, 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 by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into 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 pieces of training 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 FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] 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.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, 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 USB (Universal Serial Bus) 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.

[0162] Alternatively, the specific processing program 56 may 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an 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 above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described 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 description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A needs analysis section that uses generative AI to analyze the needs of the elderly, a suggestion unit that suggests an optimal activity field based on the needs analyzed by the needs analysis unit; a community building unit that builds an online community based on the activity field proposed by the proposal unit; a health management unit that analyzes health data of the elderly person and supports health management; A re-employment support department that analyzes the work history and skills of the elderly and supports them in finding new employment; a social participation support department that supports the elderly person's social participation and volunteer activities; A system characterized by:

2. The proposal unit Applying this to elderly people in different cultures and regions, we propose optimal activity spaces from a global perspective.

2. The system of claim 1.

3. The community building unit Generate topics and discussions to strengthen emotional connections within your community 2. The system of claim 1.

4. The health management department Providing health care advice according to the emotional state of the elderly person 2. The system of claim 1.

5. The outplacement support department Suggest the type of job or role that will give these seniors the most satisfaction 2. The system of claim 1.

6. The Social Participation Support Department: Suggest volunteer activities and social participation opportunities that evoke the most positive feelings from these seniors.

2. The system of claim 1.

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