System

Abstract

An object of the system according to the embodiment is to enable an elderly person to feel a social connection and receive psychological support.SOLUTION: A system includes an information provision part, a psychological support part, a health information provision part, a frailty prevention part, and an expert consultation part. The information providing unit provides information on the community and hobbies based on the interest and concern of the elderly person. The psychological support unit performs a conversation for supporting the psychological health condition of the elderly person. The health information providing unit provides information on health to the elderly person. The frailty prevention section provides advice for the elderly person to prevent frailty. The expert consultation unit recommends the elderly person to consult with an expert as necessary.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 has made it difficult for elderly people to feel socially connected or receive psychological support.

[0005] The system according to the embodiment aims to help elderly people feel socially connected and receive psychological support. [Means for solving the problem]

[0006] The system according to the embodiment includes an information providing unit, a psychological support unit, a health information providing unit, a frailty prevention unit, and an expert consultation unit. The information providing unit provides information about communities and hobbies based on the interests and concerns of the elderly. The psychological support unit engages in dialogue to support the elderly's psychological health. The health information providing unit provides health information to the elderly. The frailty prevention unit provides advice to prevent frailty for the elderly. The expert consultation unit recommends that the elderly consult with an expert if necessary. [Effects of the Invention]

[0007] The system according to the embodiment allows elderly people to feel socially connected and receive psychological 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A conversation app according to an embodiment of the present invention is a system that introduces information about communities and hobbies to help elderly people feel socially connected, and also provides psychological support. As a result, the conversation app can improve the psychological health of elderly people and contribute to preventing frailty.

[0029] A conversation app according to an embodiment includes an information providing unit, a psychological support unit, a health information providing unit, a frailty prevention unit, and an expert consultation unit. The information providing unit provides information about communities and hobbies based on the interests and concerns of the elderly. For example, the generation AI suggests local clubs and online communities based on the elderly's interests. The generation AI can also provide information about hobbies. For example, the generation AI suggests hobbies such as gardening and reading. The psychological support unit engages in dialogue to support the elderly's psychological health. For example, the generation AI provides words of encouragement based on the elderly's mood. The generation AI can also suggest relaxation methods. For example, the generation AI suggests deep breathing techniques and meditation. The health information providing unit provides health information to the elderly. For example, the generation AI provides advice on a balanced diet and nutrition. The generation AI can also provide information on exercise and relaxation methods. For example, the generation AI suggests an appropriate exercise plan. The frailty prevention unit provides advice to prevent frailty for the elderly. For example, the generation AI explains the importance of appropriate exercise, diet, and social activity. The generating AI can also provide specific advice for preventing frailty. For example, the generating AI can suggest strength training and balance exercises. The expert consultation unit recommends that the elderly consult with an expert if necessary. For example, the generating AI can suggest consulting with an expert such as a doctor or counselor. The generating AI can also provide information on how to consult with an expert. For example, the generating AI can explain how to conduct online or telephone consultations. As a result, the conversation app according to the embodiment can promote social connections for the elderly, provide psychological support, provide health information, prevent frailty, and provide expert consultations in an integrated manner.

[0030] The information provision unit can analyze the elderly person's past activity history and suggest the most suitable communities and hobbies. For example, the information provision unit uses a generation AI to analyze the elderly person's past activity history and evaluate their level of interest in specific hobbies and activities. For example, it can suggest the most suitable communities and hobbies based on data on events and club activities they have participated in in the past. The information provision unit can also suggest new hobbies and activities that the elderly person may be interested in based on their past activity history. For example, it can analyze the history of hobby clubs and events that the elderly person has participated in in the past and introduce new related activities. This makes it possible to suggest the most suitable communities and hobbies based on the elderly person's past activity history.

[0031] The information provision unit can analyze the lifestyle rhythm and daily behavior patterns of the elderly person and provide information on communities and hobbies at the optimal timing. For example, the information provision unit uses a generation AI to analyze the lifestyle rhythm of the elderly person and provide information on hobbies and communities at the optimal timing. For example, information is provided during times when the elderly person is relaxing, such as after a morning walk or after dinner. The information provision unit also analyzes the elderly person's daily behavior patterns and provides information on communities and hobbies that may interest them at the appropriate timing. For example, it suggests event information during times when the elderly person has free weekend schedules. This makes it possible to provide information at the optimal timing based on the elderly person's lifestyle rhythm and behavior patterns.

[0032] The health information provision unit can analyze the elderly person's past health data and provide individually customized health information. For example, the generation AI analyzes the elderly person's past health data and provides individually customized health information. For example, it provides appropriate dietary and exercise advice based on past health checkup results and medical records. The health information provision unit also provides individually customized health information based on the elderly person's past health data. For example, it proposes nutritional advice and exercise plans according to specific health conditions. This makes it possible to provide individually customized health information based on the elderly person's past health data.

[0033] The health information provision unit can monitor the diet and exercise history of the elderly person and provide health information in real time. For example, the generation AI can monitor the diet and exercise history of the elderly person and provide health information in real time. For example, it can analyze the content of meals and frequency of exercise and provide appropriate advice. The health information provision unit also provides health information in real time based on the diet and exercise history of the elderly person. For example, it can evaluate the balance of meals and the effects of exercise and support a healthy lifestyle. This makes it possible to provide health information in real time based on the diet and exercise history of the elderly person.

[0034] The frailty prevention department can analyze an elderly person's past health data and provide individually customized advice on frailty prevention. For example, the generative AI analyzes an elderly person's past health data and provides individually customized advice on frailty prevention. For example, it provides appropriate exercise and dietary advice based on past health checkup results and medical records. The frailty prevention department also provides individually customized advice on frailty prevention based on an elderly person's past health data. For example, it suggests nutritional advice and exercise plans according to specific health conditions. This makes it possible to provide individually customized advice on frailty prevention based on an elderly person's past health data.

[0035] The frailty prevention unit can analyze the lifestyle rhythms and daily behavioral patterns of elderly people and provide frailty prevention advice at the optimal time. For example, the generative AI analyzes the lifestyle rhythms of elderly people and provides frailty prevention advice at the optimal time. For example, advice is provided during times when people are relaxed, such as after a morning walk or after dinner. The frailty prevention unit also analyzes the daily behavioral patterns of elderly people and provides frailty prevention advice at the optimal time. For example, it suggests exercise and dietary advice during times when people have free weekend schedules. This makes it possible to provide frailty prevention advice at the optimal time based on the lifestyle rhythms and behavioral patterns of elderly people.

[0036] The expert consultation unit can analyze the elderly person's past health data and issue an alert when consultation with a specialist is necessary. For example, the generation AI analyzes the elderly person's past health data and issues an alert when consultation with a specialist is necessary. For example, an alert is issued when an abnormality is detected based on health checkup results or medical records. The expert consultation unit also issues an alert when consultation with a specialist is necessary based on the elderly person's past health data. For example, it suggests consulting a doctor or counselor depending on a specific health condition. This makes it possible to issue an alert when consultation with a specialist is necessary based on the elderly person's past health data.

[0037] The expert consultation unit can provide information in a format that is visually easy to understand when an elderly person consults with an expert. For example, when the generation AI consults with an expert to an elderly person, the expert consultation unit provides information in a format that is visually easy to understand. For example, it uses infographics and videos to explain how to consult. Furthermore, the expert consultation unit provides information in a format that is visually easy to understand when an elderly person consults with an expert. For example, it uses diagrams and illustrations to explain the procedures and contents of the consultation. This makes it possible to provide information in a format that is visually easy to understand when an elderly person consults with an expert.

[0038] The expert consultation unit can also use audio explanations when an elderly person consults with an expert. For example, when the generation AI consults with an expert to an elderly person, the expert consultation unit also uses audio explanations. For example, it explains the procedure and content of the consultation by audio to help understanding. The expert consultation unit also uses audio explanations when an elderly person consults with an expert. For example, it provides information about the consultation by audio to promote understanding both visually and aurally. This makes it possible to also use audio explanations when an elderly person consults with an expert.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The information providing unit can analyze the user's past travel history and suggest the user's next travel destination. For example, it can suggest new travel destinations that the user might be interested in based on data on tourist spots and accommodations visited in the past. The information providing unit can also provide travel plans tailored to the season or event based on the user's travel history. For example, it can suggest cherry blossom viewing spots in the spring and beach resorts in the summer. Furthermore, the information providing unit can also provide information on activities and restaurants during the trip based on the user's travel history. This makes it possible to suggest the user's next travel destination and plan based on their past travel history.

[0041] The psychological support unit can analyze the user's past reading history and suggest books that provide psychological support. For example, it can suggest new books that the user might be interested in based on the genres and themes of books that the user has read in the past. The psychological support unit can also suggest books that provide support for specific psychological issues based on the user's reading history. For example, it can introduce books related to stress relief and self-development. Furthermore, the psychological support unit can also provide information about reading-related communities and events based on the user's reading history. This makes it possible to suggest books and information that provide psychological support based on the user's past reading history.

[0042] The health information providing unit can analyze the user's past exercise history and provide an individually customized exercise plan. For example, it can suggest an optimal exercise plan for the user based on the type and frequency of exercise performed in the past. The health information providing unit can also provide an exercise plan tailored to a specific health goal based on the user's exercise history. For example, it can suggest an appropriate exercise plan for a user aiming to lose weight or increase muscle strength. Furthermore, the health information providing unit can also provide points to note during exercise and effective training methods based on the user's exercise history. This makes it possible to provide an individually customized exercise plan based on the user's past exercise history.

[0043] The frailty prevention unit can analyze the user's past dietary history and provide individually customized nutritional advice. For example, it can propose an optimal meal plan to the user based on the content and nutritional balance of meals previously consumed. The frailty prevention unit can also provide nutritional advice tailored to specific health goals based on the user's dietary history. For example, it can propose an appropriate meal plan to a user aiming to manage their weight or control their blood sugar levels. Furthermore, the frailty prevention unit can also provide dietary precautions and effective nutritional intake methods based on the user's dietary history. This makes it possible to provide individually customized nutritional advice based on the user's past dietary history.

[0044] The expert consultation unit can analyze the user's past medical history and issue an alert when consultation with a specialist is necessary. For example, an alert can be issued when an abnormality is detected based on past diagnosis results and treatment history. The expert consultation unit can also suggest consultation with a specialist according to a specific health condition based on the user's medical history. For example, when a specific symptom appears, it can recommend consultation with an appropriate specialist. Furthermore, the expert consultation unit can also provide information on how to consult with a specialist and what preparations should be made based on the user's medical history. This makes it possible to issue an alert when consultation with a specialist is necessary based on the user's past medical history.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The information provider provides information about communities and hobbies based on the elderly person's interests. For example, the generator AI suggests local clubs and online communities based on the elderly person's interests. The generator AI also provides information about hobbies such as gardening and reading. Step 2: The psychological support unit engages in dialogue to support the elderly person's psychological well-being. For example, the generative AI may provide encouraging words based on the elderly person's mood and suggest deep breathing exercises or meditation as relaxation methods. Step 3: The health information provider provides health information to the elderly. For example, the AI ​​generator provides advice on balanced meals and nutrition, and suggests appropriate exercise plans. Step 4: The Frailty Prevention Unit provides advice to help elderly people prevent frailty. For example, the generative AI explains the importance of proper exercise, diet, and social activity, and suggests strength training and balance exercises. Step 5: The expert consultation section recommends that the elderly seek professional advice if necessary. For example, the AI ​​generator will suggest consulting a doctor, counselor, or other specialist, and explain how to seek online or telephone advice.

[0047] (Example 2) A conversation app according to an embodiment of the present invention is a system that introduces information about communities and hobbies to help elderly people feel socially connected, and also provides psychological support. As a result, the conversation app can improve the psychological health of elderly people and contribute to preventing frailty.

[0048] A conversation app according to an embodiment includes an information providing unit, a psychological support unit, a health information providing unit, a frailty prevention unit, and an expert consultation unit. The information providing unit provides information about communities and hobbies based on the interests and concerns of the elderly. For example, the generation AI suggests local clubs and online communities based on the elderly's interests. The generation AI can also provide information about hobbies. For example, the generation AI suggests hobbies such as gardening and reading. The psychological support unit engages in dialogue to support the elderly's psychological health. For example, the generation AI provides words of encouragement based on the elderly's mood. The generation AI can also suggest relaxation methods. For example, the generation AI suggests deep breathing techniques and meditation. The health information providing unit provides health information to the elderly. For example, the generation AI provides advice on a balanced diet and nutrition. The generation AI can also provide information on exercise and relaxation methods. For example, the generation AI suggests an appropriate exercise plan. The frailty prevention unit provides advice to prevent frailty for the elderly. For example, the generation AI explains the importance of appropriate exercise, diet, and social activity. The generating AI can also provide specific advice for preventing frailty. For example, the generating AI can suggest strength training and balance exercises. The expert consultation unit recommends that the elderly consult with an expert if necessary. For example, the generating AI can suggest consulting with an expert such as a doctor or counselor. The generating AI can also provide information on how to consult with an expert. For example, the generating AI can explain how to conduct online or telephone consultations. As a result, the conversation app according to the embodiment can promote social connections for the elderly, provide psychological support, provide health information, prevent frailty, and provide expert consultations in an integrated manner.

[0049] The information provision unit can analyze the elderly person's past activity history and suggest the most suitable communities and hobbies. For example, the information provision unit uses a generation AI to analyze the elderly person's past activity history and evaluate their level of interest in specific hobbies and activities. For example, it can suggest the most suitable communities and hobbies based on data on events and club activities they have participated in in the past. The information provision unit can also suggest new hobbies and activities that the elderly person may be interested in based on their past activity history. For example, it can analyze the history of hobby clubs and events that the elderly person has participated in in the past and introduce new related activities. This makes it possible to suggest the most suitable communities and hobbies based on the elderly person's past activity history.

[0050] The information provision unit can analyze the lifestyle rhythm and daily behavior patterns of the elderly person and provide information on communities and hobbies at the optimal timing. For example, the information provision unit uses a generation AI to analyze the lifestyle rhythm of the elderly person and provide information on hobbies and communities at the optimal timing. For example, information is provided during times when the elderly person is relaxing, such as after a morning walk or after dinner. The information provision unit also analyzes the elderly person's daily behavior patterns and provides information on communities and hobbies that may interest them at the appropriate timing. For example, it suggests event information during times when the elderly person has free weekend schedules. This makes it possible to provide information at the optimal timing based on the elderly person's lifestyle rhythm and behavior patterns.

[0051] The information providing unit can use the emotion estimation function to suggest communities and hobbies that are likely to pique the elderly person's interest based on the elderly person's current emotional state. For example, the information providing unit uses the emotion estimation function to analyze the elderly person's current emotional state and suggest communities and hobbies that are likely to pique the elderly person's interest. For example, it suggests a new hobby when the elderly person has strong positive emotions. The information providing unit also monitors the elderly person's emotional state in real time and suggests communities and hobbies that are likely to pique the elderly person's interest. For example, it introduces hobbies that have a relaxing effect when the elderly person is feeling depressed. In this way, it is possible to suggest communities and hobbies that are likely to pique the elderly person's interest based on the elderly person's emotional state.

[0052] The psychological support unit can analyze the elderly person's past conversation history, identify patterns for specific psychological problems, and provide appropriate support. For example, the psychological support unit uses a generative AI to analyze the elderly person's past conversation history and identify patterns for specific psychological problems. For example, it extracts frequently appearing negative keywords and provides appropriate support. The psychological support unit can also identify patterns for specific psychological problems based on the elderly person's past conversation history and provide appropriate support. For example, it analyzes past conversation data and detects signs of psychological problems. This makes it possible to provide appropriate support for specific psychological problems based on the elderly person's past conversation history.

[0053] The psychological support unit can regularly monitor the psychological state of the elderly person and provide customized support as needed. For example, the generative AI can regularly monitor the psychological state of the elderly person and provide customized support as needed. For example, it can conduct regular check-ins and evaluate the psychological state. The psychological support unit can also regularly monitor the psychological state of the elderly person and provide customized support. For example, it can suggest words of encouragement or relaxation methods depending on the psychological state. This allows the psychological state of the elderly person to be regularly monitored and customized support to be provided as needed.

[0054] The psychological support unit can use the emotion estimation function to suggest optimal words of encouragement and relaxation methods based on the emotional state of the elderly person. For example, the psychological support unit uses the emotion estimation function to analyze the emotional state of the elderly person and suggest optimal words of encouragement and relaxation methods. For example, it can introduce relaxation methods when the emotional score is low. The psychological support unit also monitors the emotional state of the elderly person in real time and suggests optimal words of encouragement and relaxation methods. For example, it can provide words of encouragement when the elderly person is feeling depressed. This makes it possible to suggest optimal words of encouragement and relaxation methods based on the emotional state of the elderly person.

[0055] The health information provision unit can analyze the elderly person's past health data and provide individually customized health information. For example, the generation AI analyzes the elderly person's past health data and provides individually customized health information. For example, it provides appropriate dietary and exercise advice based on past health checkup results and medical records. The health information provision unit also provides individually customized health information based on the elderly person's past health data. For example, it proposes nutritional advice and exercise plans according to specific health conditions. This makes it possible to provide individually customized health information based on the elderly person's past health data.

[0056] The health information provision unit can monitor the diet and exercise history of the elderly person and provide health information in real time. For example, the generation AI can monitor the diet and exercise history of the elderly person and provide health information in real time. For example, it can analyze the content of meals and frequency of exercise and provide appropriate advice. The health information provision unit also provides health information in real time based on the diet and exercise history of the elderly person. For example, it can evaluate the balance of meals and the effects of exercise and support a healthy lifestyle. This makes it possible to provide health information in real time based on the diet and exercise history of the elderly person.

[0057] The health information providing unit can use the emotion estimation function to provide optimal health information based on the emotional state of the elderly person. For example, the health information providing unit uses the emotion estimation function to analyze the emotional state of the elderly person and provide optimal health information. For example, when the elderly person is feeling depressed, the health information providing unit suggests relaxation methods or stress relief methods. The health information providing unit also monitors the elderly person's emotional state in real time and provides optimal health information. For example, when the elderly person is feeling positive, the health information providing unit suggests a new exercise plan. This makes it possible to provide optimal health information based on the elderly person's emotional state.

[0058] The frailty prevention department can analyze an elderly person's past health data and provide individually customized advice on frailty prevention. For example, the generative AI analyzes an elderly person's past health data and provides individually customized advice on frailty prevention. For example, it provides appropriate exercise and dietary advice based on past health checkup results and medical records. The frailty prevention department also provides individually customized advice on frailty prevention based on an elderly person's past health data. For example, it suggests nutritional advice and exercise plans according to specific health conditions. This makes it possible to provide individually customized advice on frailty prevention based on an elderly person's past health data.

[0059] The frailty prevention unit can analyze the lifestyle rhythms and daily behavioral patterns of elderly people and provide frailty prevention advice at the optimal time. For example, the generative AI analyzes the lifestyle rhythms of elderly people and provides frailty prevention advice at the optimal time. For example, advice is provided during times when people are relaxed, such as after a morning walk or after dinner. The frailty prevention unit also analyzes the daily behavioral patterns of elderly people and provides frailty prevention advice at the optimal time. For example, it suggests exercise and dietary advice during times when people have free weekend schedules. This makes it possible to provide frailty prevention advice at the optimal time based on the lifestyle rhythms and behavioral patterns of elderly people.

[0060] The frailty prevention unit can use the emotion estimation function to provide optimal frailty prevention advice based on the emotional state of the elderly person. For example, the frailty prevention unit uses the emotion estimation function to analyze the emotional state of the elderly person and provide optimal frailty prevention advice. For example, when the elderly person is feeling depressed, it suggests relaxation methods and stress relief methods. The frailty prevention unit also monitors the elderly person's emotional state in real time and provides optimal frailty prevention advice. For example, when the elderly person is feeling positive, it suggests a new exercise plan. This makes it possible to provide optimal frailty prevention advice based on the elderly person's emotional state.

[0061] The expert consultation unit can analyze the elderly person's past health data and issue an alert when consultation with a specialist is necessary. For example, the generation AI analyzes the elderly person's past health data and issues an alert when consultation with a specialist is necessary. For example, an alert is issued when an abnormality is detected based on health checkup results or medical records. The expert consultation unit also issues an alert when consultation with a specialist is necessary based on the elderly person's past health data. For example, it suggests consulting a doctor or counselor depending on a specific health condition. This makes it possible to issue an alert when consultation with a specialist is necessary based on the elderly person's past health data.

[0062] The expert consultation unit monitors the psychological state of the elderly and can recommend consultation with a specialist when necessary. For example, the generative AI monitors the psychological state of the elderly and recommends consultation with a specialist when necessary. For example, if the emotion score is low, it suggests consulting a counselor. The expert consultation unit also monitors the psychological state of the elderly in real time and issues an alert when consultation with a specialist is necessary. For example, it recommends consultation with a specialist when a psychological problem is detected. This makes it possible to monitor the psychological state of the elderly and recommend consultation with a specialist when necessary.

[0063] The expert consultation unit can use the emotion estimation function to identify the consultation method with an expert that will evoke the most positive emotions from the elderly person, and make a proposal based on that. The expert consultation unit can, for example, use the emotion estimation function to identify the consultation method with an expert that will evoke the most positive emotions from the elderly person, and make a proposal based on that. For example, it can analyze past emotion data and propose a consultation method that will evoke strong positive emotions. The expert consultation unit can also identify the consultation method with an expert that will evoke the most positive emotions from the elderly person's emotion data, and make a proposal based on that. For example, it can preferentially propose a consultation method with a high emotion score. This makes it possible to identify the consultation method that will evoke the most positive emotions from the elderly person, and make a proposal based on that.

[0064] The expert consultation unit can provide information in a format that is visually easy to understand when an elderly person consults with an expert. For example, when the generation AI consults with an expert to an elderly person, the expert consultation unit provides information in a format that is visually easy to understand. For example, it uses infographics and videos to explain how to consult. Furthermore, the expert consultation unit provides information in a format that is visually easy to understand when an elderly person consults with an expert. For example, it uses diagrams and illustrations to explain the procedures and contents of the consultation. This makes it possible to provide information in a format that is visually easy to understand when an elderly person consults with an expert.

[0065] The expert consultation unit can also use audio explanations when an elderly person consults with an expert. For example, when the generation AI consults with an expert to an elderly person, the expert consultation unit also uses audio explanations. For example, it explains the procedure and content of the consultation by audio to help understanding. The expert consultation unit also uses audio explanations when an elderly person consults with an expert. For example, it provides information about the consultation by audio to promote understanding both visually and aurally. This makes it possible to also use audio explanations when an elderly person consults with an expert.

[0066] The expert consultation unit can use the emotion estimation function to identify the consultation method that will evoke the most positive emotions from the elderly, and provide information based on that. The expert consultation unit can, for example, use the emotion estimation function to identify the consultation method that will evoke the most positive emotions from the elderly, and provide information based on that. For example, it can analyze past emotion data and provide a consultation method that will evoke strong positive emotions. The expert consultation unit can also identify the consultation method that will evoke the most positive emotions from the elderly's emotion data, and provide information based on that. For example, it can provide a consultation method with a high emotion score preferentially. This makes it possible to identify the consultation method that will evoke the most positive emotions from the elderly, and provide information based on that.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The information providing unit can analyze the user's past travel history and suggest the user's next travel destination. For example, it can suggest new travel destinations that the user might be interested in based on data on tourist spots and accommodations visited in the past. The information providing unit can also provide travel plans tailored to the season or event based on the user's travel history. For example, it can suggest cherry blossom viewing spots in the spring and beach resorts in the summer. Furthermore, the information providing unit can also provide information on activities and restaurants during the trip based on the user's travel history. This makes it possible to suggest the user's next travel destination and plan based on their past travel history.

[0069] The psychological support unit can analyze the user's past reading history and suggest books that provide psychological support. For example, it can suggest new books that the user might be interested in based on the genres and themes of books that the user has read in the past. The psychological support unit can also suggest books that provide support for specific psychological issues based on the user's reading history. For example, it can introduce books related to stress relief and self-development. Furthermore, the psychological support unit can also provide information about reading-related communities and events based on the user's reading history. This makes it possible to suggest books and information that provide psychological support based on the user's past reading history.

[0070] The health information providing unit can analyze the user's past exercise history and provide an individually customized exercise plan. For example, it can suggest an optimal exercise plan for the user based on the type and frequency of exercise performed in the past. The health information providing unit can also provide an exercise plan tailored to a specific health goal based on the user's exercise history. For example, it can suggest an appropriate exercise plan for a user aiming to lose weight or increase muscle strength. Furthermore, the health information providing unit can also provide points to note during exercise and effective training methods based on the user's exercise history. This makes it possible to provide an individually customized exercise plan based on the user's past exercise history.

[0071] The frailty prevention unit can analyze the user's past dietary history and provide individually customized nutritional advice. For example, it can propose an optimal meal plan to the user based on the content and nutritional balance of meals previously consumed. The frailty prevention unit can also provide nutritional advice tailored to specific health goals based on the user's dietary history. For example, it can propose an appropriate meal plan to a user aiming to manage their weight or control their blood sugar levels. Furthermore, the frailty prevention unit can also provide dietary precautions and effective nutritional intake methods based on the user's dietary history. This makes it possible to provide individually customized nutritional advice based on the user's past dietary history.

[0072] The expert consultation unit can analyze the user's past medical history and issue an alert when consultation with a specialist is necessary. For example, an alert can be issued when an abnormality is detected based on past diagnosis results and treatment history. The expert consultation unit can also suggest consultation with a specialist according to a specific health condition based on the user's medical history. For example, when a specific symptom appears, it can recommend consultation with an appropriate specialist. Furthermore, the expert consultation unit can also provide information on how to consult with a specialist and what preparations should be made based on the user's medical history. This makes it possible to issue an alert when consultation with a specialist is necessary based on the user's past medical history.

[0073] The information providing unit can use the emotion estimation function to suggest relaxing travel destinations based on the user's current emotional state. For example, when the user is feeling down, the information providing unit can recommend hot spring resorts that have a relaxing effect. The information providing unit can also monitor the user's emotional state in real time and suggest travel destinations based on the user's emotions. For example, when the user is feeling positive, the information providing unit can suggest active adventure tours. Furthermore, the information providing unit can also provide information on travel activities and accommodations based on the user's emotional state. This makes it possible to suggest relaxing travel destinations and plans based on the user's emotional state.

[0074] The psychological support unit can use the emotion estimation function to suggest optimal reading lists based on the user's emotional state. For example, it can recommend self-help books to lift the user's spirits when they are feeling down. The psychological support unit can also monitor the user's emotional state in real time and suggest reading lists based on the user's emotions. For example, it can suggest novels in a new genre when the user is feeling positive. Furthermore, the psychological support unit can provide information about reading-related communities and events based on the user's emotional state. This makes it possible to suggest optimal reading lists and information based on the user's emotional state.

[0075] The health information providing unit can use the emotion estimation function to provide an optimal exercise plan based on the user's emotional state. For example, when the user is feeling down, it can suggest yoga, which has a relaxing effect. The health information providing unit can also monitor the user's emotional state in real time and provide an exercise plan based on the user's emotions. For example, when the user is feeling positive, it can suggest energetic aerobics. Furthermore, the health information providing unit can also provide points to note during exercise and effective training methods based on the user's emotional state. This makes it possible to provide an optimal exercise plan based on the user's emotional state.

[0076] The frailty prevention unit can use the emotion estimation function to provide optimal nutritional advice based on the user's emotional state. For example, it can suggest a meal plan to lift the user's spirits when they are feeling down. The frailty prevention unit can also monitor the user's emotional state in real time and provide nutritional advice according to the emotion. For example, it can suggest new recipes when the user is feeling positive. Furthermore, the frailty prevention unit can also provide dietary precautions and effective nutritional intake methods based on the user's emotional state. This allows the system to provide optimal nutritional advice based on the user's emotional state.

[0077] The expert consultation unit can use the emotion estimation function to suggest the most appropriate way to consult with an expert based on the user's emotional state. For example, it can recommend consulting a counselor when the user is feeling depressed. The expert consultation unit can also monitor the user's emotional state in real time and suggest a way to consult with an expert based on the emotion. For example, it can suggest regular checkups with a doctor when the user is feeling positive. Furthermore, the expert consultation unit can also provide consultation procedures and information that should be prepared based on the user's emotional state. This makes it possible to suggest the most appropriate way to consult with an expert based on the user's emotional state.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The information provider provides information about communities and hobbies based on the elderly person's interests. For example, the generator AI suggests local clubs and online communities based on the elderly person's interests. The generator AI also provides information about hobbies such as gardening and reading. Step 2: The psychological support unit engages in dialogue to support the elderly person's psychological well-being. For example, the generative AI may provide encouraging words based on the elderly person's mood and suggest deep breathing exercises or meditation as relaxation methods. Step 3: The health information provider provides health information to the elderly. For example, the AI ​​generator provides advice on balanced meals and nutrition, and suggests appropriate exercise plans. Step 4: The Frailty Prevention Unit provides advice to help elderly people prevent frailty. For example, the generative AI explains the importance of proper exercise, diet, and social activity, and suggests strength training and balance exercises. Step 5: The expert consultation section recommends that the elderly seek professional advice if necessary. For example, the AI ​​generator will suggest consulting a doctor, counselor, or other specialist, and explain how to seek online or telephone advice.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0093] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 system that uses generation AI to provide a conversation app for elderly people, The generated AI is An information department that provides information on communities and hobbies based on the interests and concerns of the elderly; a psychological support unit that conducts dialogue to support the psychological health of the elderly person; a health information providing unit that provides health information to the elderly person; a frailty prevention department that provides advice to the elderly to prevent frailty; and an expert consultation unit that recommends that the elderly person consult with an expert as necessary. A system characterized by:

2. The information providing unit Analyzing the elderly person's past activity history and suggesting the most suitable community and hobby 2. The system of claim 1.

3. The information providing unit Analyzing the lifestyle rhythm and daily behavior patterns of the elderly person and providing information on the community and hobbies at the optimal timing 2. The system of claim 1.

4. The information providing unit Suggesting the community and hobby that are likely to attract the elderly person's interest based on the elderly person's current emotional state 2. The system of claim 1.

5. The psychological support department: Analyzing the elderly person's past conversation history, identifying patterns for the specific psychological problem, and providing appropriate support.

2. The system of claim 1.

6. The psychological support department: Regularly monitor the psychological status of the elderly person and provide customized support as needed.

2. The system of claim 1.

Images