System

Abstract

An object of a system according to an embodiment is to provide a personalized nursing care service to an elderly person.SOLUTION: A system according to an embodiment includes a generation AI, an image generation AI, and a personalized nursing care service providing unit. The generation AI performs verbal communication with the elderly person. The image generation AI performs visual communication with the elderly person. The personalized nursing care service provider provides individual nursing care services.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 technologies do not provide adequate personalized care services to the elderly, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized care services to elderly people. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an image generation AI, and a personalized care service provider. The generation AI communicates verbally with the elderly. The image generation AI communicates visually with the elderly. The personalized care service provider provides individualized care services. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized care services to elderly people. [Brief explanation of the drawings]

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

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A nursing care service system according to an embodiment of the present invention provides personalized nursing care services to individuals using generative AI and image generation AI. This system supports elderly care through verbal communication using generative AI and visual communication using image generation AI. As a result, the nursing care service system can improve the quality of nursing care services for the elderly and reduce the burden on caregivers.

[0029] A nursing care service system according to an embodiment includes a generation AI, an image generation AI, and a personalized nursing care service provider. The generation AI communicates verbally with the elderly. For example, if the elderly asks, "What's the weather like today?", the generation AI responds with, "It's sunny today. The temperature is 25 degrees, so it's a perfect day to go outside." The generation AI uses natural language processing technology to understand the elderly's questions and conversations and generate appropriate answers. The generation AI receives inputs from prompts containing instructions on what the elderly wants the generation AI to do, and the generation AI generates answers based on the prompts. The image generation AI communicates visually with the elderly. For example, if the elderly says, "Show me a picture of my grandchild," the image generation AI can generate a photo of the grandchild and show it to the elderly. The image generation AI has previously studied photos and information about the elderly's family and generates appropriate images in response to the elderly's requests. The generation AI receives inputs from prompts containing instructions on what the elderly wants the generation AI to do, and the generation AI generates an image based on the prompts. The personalized nursing care service provision unit combines generative AI and image-generative AI to provide personalized nursing care services to the elderly. For example, it monitors the elderly's health condition and daily activities and provides necessary support. The generative AI answers questions about the elderly's health condition and offers advice on daily activities. The image-generative AI also provides visual content in response to the elderly's requests, increasing their enjoyment of daily life. This enables the nursing care service system to provide personalized nursing care services to the elderly and reduce the burden on caregivers.

[0030] Generative AI can learn from the elderly person's past conversation history and generate conversations based on their preferences and interests. For example, generative AI stores the elderly person's past conversation history in a database and learns from that data. For example, it can understand the topics that a particular elderly person likes and is interested in, and generate conversations based on that. Generative AI learns the preferences and interests of the elderly person based on their past conversation history and provides conversations tailored to each individual elderly person. This makes it possible to provide more personalized conversations to the elderly.

[0031] Generative AI can mimic the tone of voice and speaking style of an elderly person, providing a more natural conversation experience. For example, generative AI records the tone of voice and speaking style of an elderly person and learns from that data. For example, it generates conversations that capture the vocal characteristics of a specific elderly person. Generative AI can mimic the tone of voice and speaking style of an elderly person, providing a more natural conversation experience. This makes it possible to provide a more natural conversation experience for the elderly.

[0032] If an elderly person wants to talk in depth about a particular topic, the generative AI can provide detailed information about that topic. For example, if an elderly person asks a question about a particular topic, the generative AI will provide detailed information about that topic. For example, it will generate information about historical events or scientific knowledge. If an elderly person wants to talk in depth about a particular topic, the generative AI will provide detailed information about that topic. This makes it possible to provide detailed information when an elderly person wants to talk in depth about a particular topic.

[0033] The generative AI can generate a virtual community where elderly people can enjoy conversations with other elderly people and support those conversations. For example, the generative AI can connect elderly people who share common hobbies and interests. The generative AI can generate a virtual community where elderly people can enjoy conversations with other elderly people and support those conversations. This makes it possible to provide a virtual community where elderly people can enjoy conversations with other elderly people.

[0034] Image generation AI can learn from the elderly's past photos and memories and generate new images based on them. For example, image generation AI stores the elderly's past photos and memories in a database and learns from that data. For example, it generates new images based on family photos or travel photos of a specific elderly person. Image generation AI learns from the elderly's past photos and memories and generates new images based on them. This makes it possible to provide new images based on the elderly's past photos and memories.

[0035] When an elderly person requests a specific place or event, the image generation AI can generate images related to that place or event. For example, when an elderly person requests a specific place or event, the image generation AI can generate images related to that place or event. For example, it can generate images of travel destinations or family gatherings. When an elderly person requests a specific place or event, the image generation AI can generate images related to that place or event. This makes it possible to provide images related to the specific place or event requested by the elderly.

[0036] Image generation AI can be provided as a tool for elderly people to generate their own artworks. Image generation AI can be provided as a tool for elderly people to generate their own artworks. For example, it can provide an interface for creating paintings or digital art. Image generation AI can be provided as a tool for elderly people to generate their own artworks. This can provide a tool for elderly people to generate their own artworks.

[0037] Image generation AI can build a platform for elderly people to share images with other elderly people and exchange comments. Image generation AI can build a platform for elderly people to share images with other elderly people and exchange comments. For example, it can provide a function for sharing family photos or travel photos. Image generation AI can build a platform for elderly people to share images with other elderly people and exchange comments. This can provide a platform for elderly people to share images with other elderly people and exchange comments.

[0038] The personalized nursing care service providing unit can monitor the health data of the elderly in real time and immediately notify if an abnormality is detected. The personalized nursing care service providing unit, for example, builds a system that monitors the health data of the elderly in real time and immediately notifies if an abnormality is detected. For example, it detects abnormalities in heart rate or blood pressure. The personalized nursing care service providing unit monitors the health data of the elderly in real time and immediately notifies if an abnormality is detected. This makes it possible to monitor the health data of the elderly in real time and immediately notify if an abnormality is detected.

[0039] The personalized nursing care service provision unit can use a generation AI to provide a rehabilitation program that the elderly can perform at home. The personalized nursing care service provision unit, for example, uses a generation AI to provide a rehabilitation program that the elderly can perform at home. For example, an exercise program tailored to the health condition of each elderly person is generated. The personalized nursing care service provision unit uses a generation AI to provide a rehabilitation program that the elderly can perform at home. In this way, a rehabilitation program that the elderly can perform at home can be provided.

[0040] The personalized nursing care service provision unit can use a generation AI to plan and manage online events for elderly people to interact with other elderly people. The personalized nursing care service provision unit, for example, uses a generation AI to plan and manage online events for elderly people to interact with other elderly people. For example, online clubs and workshops based on hobbies and interests are held. The personalized nursing care service provision unit can use a generation AI to plan and manage online events for elderly people to interact with other elderly people. This makes it possible to provide online events for elderly people to interact with other elderly people.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The nursing care service system can further include a nutrition management unit that manages the elderly's diet. The nutrition management unit proposes an appropriate meal plan based on the elderly's health condition and food preferences. For example, it can propose low-carbohydrate meals to elderly people with diabetes and provide menus that take nutritional balance into consideration. It can also record meal times and amounts and adjust meal plans according to changes in the elderly's health condition. This makes it possible to maintain the health of the elderly and provide them with nutritionally balanced meals.

[0043] The nursing care service system can further include an exercise management unit that manages the elderly's exercise. The exercise management unit proposes an appropriate exercise plan based on the elderly's physical strength and health condition. For example, it can suggest stretching to increase joint flexibility and light strength training to maintain muscle strength. It can also record exercise progress and provide feedback to increase motivation to achieve goals. This helps maintain the elderly's physical strength and support a healthy lifestyle.

[0044] The nursing care service system can further include a sleep management unit that manages the sleep of the elderly. The sleep management unit monitors the sleep patterns of the elderly and provides advice to promote quality sleep. For example, it can suggest ways to relax before bed and how to create an appropriate bedroom environment. It can also provide music and meditation guides to improve sleep quality. This can improve the quality of sleep of the elderly and enhance the quality of daily life.

[0045] The nursing care service system can further include a social participation support unit to promote social participation among the elderly. The social participation support unit provides opportunities for the elderly to participate in local events and volunteer activities. For example, it introduces local cultural festivals and hobby club activities and supports their participation. It can also plan online social events to increase opportunities for the elderly to interact with other people. This can promote social participation among the elderly and reduce feelings of isolation.

[0046] The care service system can further include a hobby activity support unit that supports elderly people's hobby activities. The hobby activity support unit suggests appropriate activities based on the elderly person's hobbies and interests. For example, it supports hobby activities such as painting, handicrafts, and gardening, and provides the necessary materials and tools. It can also record the progress of hobby activities and provide feedback that gives the elderly a sense of accomplishment. This supports elderly people's hobby activities and increases the enjoyment of daily life.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The generation AI communicates verbally with the elderly. For example, if the elderly person asks, "What's the weather like today?", the generation AI responds with something like, "It's sunny today. The temperature is 25 degrees, so it's a perfect day to go outside." The generation AI uses natural language processing technology to understand the elderly person's questions and conversations and generate appropriate answers. The input to the generation AI is a prompt containing instructions on what the elderly person wants the generation AI to do, and the generation AI generates an answer based on that prompt. Step 2: The image generation AI engages in visual communication with the elderly. For example, if an elderly person says, "Show me a photo of my grandchild," it can generate a photo of the grandchild and show it to the elderly. The image generation AI has previously studied photos and information about the elderly person's family, and generates an appropriate image in response to the elderly person's request. The input to the generation AI is a prompt containing instructions on what the elderly person wants the generation AI to do, and the generation AI generates an image based on that prompt. Step 3: The personalized care service provider combines the generation AI and image generation AI to provide personalized care services to the elderly. For example, it monitors the elderly's health status and daily activities and provides necessary support. The generation AI answers questions about the elderly's health status and provides advice on daily activities. The image generation AI also provides visual content at the elderly's request, increasing their enjoyment of daily life. This enables the care service system to provide personalized care services to the elderly and reduce the burden on caregivers.

[0049] (Example 2) A nursing care service system according to an embodiment of the present invention provides personalized nursing care services to individuals using generative AI and image generation AI. This system supports elderly care through verbal communication using generative AI and visual communication using image generation AI. As a result, the nursing care service system can improve the quality of nursing care services for the elderly and reduce the burden on caregivers.

[0050] A nursing care service system according to an embodiment includes a generation AI, an image generation AI, and a personalized nursing care service provider. The generation AI communicates verbally with the elderly. For example, if the elderly asks, "What's the weather like today?", the generation AI responds with, "It's sunny today. The temperature is 25 degrees, so it's a perfect day to go outside." The generation AI uses natural language processing technology to understand the elderly's questions and conversations and generate appropriate answers. The generation AI receives inputs from prompts containing instructions on what the elderly wants the generation AI to do, and the generation AI generates answers based on the prompts. The image generation AI communicates visually with the elderly. For example, if the elderly says, "Show me a picture of my grandchild," the image generation AI can generate a photo of the grandchild and show it to the elderly. The image generation AI has previously studied photos and information about the elderly's family and generates appropriate images in response to the elderly's requests. The generation AI receives inputs from prompts containing instructions on what the elderly wants the generation AI to do, and the generation AI generates an image based on the prompts. The personalized nursing care service provision unit combines generative AI and image-generative AI to provide personalized nursing care services to the elderly. For example, it monitors the elderly's health condition and daily activities and provides necessary support. The generative AI answers questions about the elderly's health condition and offers advice on daily activities. The image-generative AI also provides visual content in response to the elderly's requests, increasing their enjoyment of daily life. This enables the nursing care service system to provide personalized nursing care services to the elderly and reduce the burden on caregivers.

[0051] Generative AI can learn from the elderly person's past conversation history and generate conversations based on their preferences and interests. For example, generative AI stores the elderly person's past conversation history in a database and learns from that data. For example, it can understand the topics that a particular elderly person likes and is interested in, and generate conversations based on that. Generative AI learns the preferences and interests of the elderly person based on their past conversation history and provides conversations tailored to each individual elderly person. This makes it possible to provide more personalized conversations to the elderly.

[0052] Generative AI can mimic the tone of voice and speaking style of an elderly person, providing a more natural conversation experience. For example, generative AI records the tone of voice and speaking style of an elderly person and learns from that data. For example, it generates conversations that capture the vocal characteristics of a specific elderly person. Generative AI can mimic the tone of voice and speaking style of an elderly person, providing a more natural conversation experience. This makes it possible to provide a more natural conversation experience for the elderly.

[0053] The generative AI can use its emotion estimation function to analyze the emotional state of the elderly in real time and generate an appropriate response. For example, the generative AI can analyze the elderly's facial expressions and tone of voice to estimate their emotional state in real time. For example, it can detect emotions such as joy or sadness and generate an appropriate response. The generative AI can use its emotion estimation function to analyze the elderly's emotional state in real time and generate an appropriate response. This makes it possible to provide an appropriate response according to the elderly's emotional state.

[0054] If an elderly person wants to talk in depth about a particular topic, the generative AI can provide detailed information about that topic. For example, if an elderly person asks a question about a particular topic, the generative AI will provide detailed information about that topic. For example, it will generate information about historical events or scientific knowledge. If an elderly person wants to talk in depth about a particular topic, the generative AI will provide detailed information about that topic. This makes it possible to provide detailed information when an elderly person wants to talk in depth about a particular topic.

[0055] The generative AI can generate a virtual community where elderly people can enjoy conversations with other elderly people and support those conversations. For example, the generative AI can connect elderly people who share common hobbies and interests. The generative AI can generate a virtual community where elderly people can enjoy conversations with other elderly people and support those conversations. This makes it possible to provide a virtual community where elderly people can enjoy conversations with other elderly people.

[0056] The generation AI can use the emotion estimation function to generate conversation content to reduce the sense of loneliness felt by the elderly. For example, the generation AI uses the emotion estimation function to analyze the emotional state of the elderly in real time and generate conversation content to reduce the sense of loneliness. For example, it provides conversation that includes words of encouragement and shows empathy. The generation AI uses the emotion estimation function to generate conversation content to reduce the sense of loneliness felt by the elderly. In this way, it is possible to provide conversation content to reduce the sense of loneliness felt by the elderly.

[0057] Image generation AI can learn from the elderly's past photos and memories and generate new images based on them. For example, image generation AI stores the elderly's past photos and memories in a database and learns from that data. For example, it generates new images based on family photos or travel photos of a specific elderly person. Image generation AI learns from the elderly's past photos and memories and generates new images based on them. This makes it possible to provide new images based on the elderly's past photos and memories.

[0058] When an elderly person requests a specific place or event, the image generation AI can generate images related to that place or event. For example, when an elderly person requests a specific place or event, the image generation AI can generate images related to that place or event. For example, it can generate images of travel destinations or family gatherings. When an elderly person requests a specific place or event, the image generation AI can generate images related to that place or event. This makes it possible to provide images related to the specific place or event requested by the elderly.

[0059] Image generation AI uses emotion estimation functions to generate images that correspond to the emotional state of the elderly, thereby eliciting positive emotions. For example, image generation AI analyzes the emotional state of the elderly in real time and generates images that correspond to that emotional state. For example, it provides images that elicit a sense of joy or security. Image generation AI uses emotion estimation functions to generate images that correspond to the emotional state of the elderly, thereby eliciting positive emotions. This makes it possible to provide positive images that correspond to the emotional state of the elderly.

[0060] Image generation AI can be provided as a tool for elderly people to generate their own artworks. Image generation AI can be provided as a tool for elderly people to generate their own artworks. For example, it can provide an interface for creating paintings or digital art. Image generation AI can be provided as a tool for elderly people to generate their own artworks. This can provide a tool for elderly people to generate their own artworks.

[0061] Image generation AI can build a platform for elderly people to share images with other elderly people and exchange comments. Image generation AI can build a platform for elderly people to share images with other elderly people and exchange comments. For example, it can provide a function for sharing family photos or travel photos. Image generation AI can build a platform for elderly people to share images with other elderly people and exchange comments. This can provide a platform for elderly people to share images with other elderly people and exchange comments.

[0062] Image generation AI uses emotion estimation functions to generate images that bring joy to the elderly, providing them with daily enjoyment. For example, image generation AI uses emotion estimation functions to analyze the emotional state of the elderly in real time and generate images that bring them the most joy. For example, it provides images of family photos or memorable places. Image generation AI uses emotion estimation functions to generate images that bring joy to the elderly, providing them with daily enjoyment. This provides images that bring joy to the elderly, increasing their daily enjoyment.

[0063] The personalized nursing care service providing unit can monitor the health data of the elderly in real time and immediately notify if an abnormality is detected. The personalized nursing care service providing unit, for example, builds a system that monitors the health data of the elderly in real time and immediately notifies if an abnormality is detected. For example, it detects abnormalities in heart rate or blood pressure. The personalized nursing care service providing unit monitors the health data of the elderly in real time and immediately notifies if an abnormality is detected. This makes it possible to monitor the health data of the elderly in real time and immediately notify if an abnormality is detected.

[0064] The personalized nursing care service providing unit can use the emotion estimation function to provide nursing care services according to the emotional state of the elderly person and support their mental health. The personalized nursing care service providing unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly person in real time and provide nursing care services based on the data. For example, if the emotion is negative, it suggests relaxing activities. The personalized nursing care service providing unit can use the emotion estimation function to provide nursing care services according to the emotional state of the elderly person and support their mental health. This makes it possible to provide nursing care services according to the emotional state of the elderly person and support their mental health.

[0065] The personalized nursing care service provision unit can use a generation AI to provide a rehabilitation program that the elderly can perform at home. The personalized nursing care service provision unit, for example, uses a generation AI to provide a rehabilitation program that the elderly can perform at home. For example, an exercise program tailored to the health condition of each elderly person is generated. The personalized nursing care service provision unit uses a generation AI to provide a rehabilitation program that the elderly can perform at home. In this way, a rehabilitation program that the elderly can perform at home can be provided.

[0066] The personalized nursing care service provision unit can use a generation AI to plan and manage online events for elderly people to interact with other elderly people. The personalized nursing care service provision unit, for example, uses a generation AI to plan and manage online events for elderly people to interact with other elderly people. For example, online clubs and workshops based on hobbies and interests are held. The personalized nursing care service provision unit can use a generation AI to plan and manage online events for elderly people to interact with other elderly people. This makes it possible to provide online events for elderly people to interact with other elderly people.

[0067] The personalized nursing care service providing unit can use the emotion estimation function to provide advice for providing the elderly with the most relaxing environment. The personalized nursing care service providing unit, for example, uses the emotion estimation function to analyze the emotional state of the elderly in real time and provide advice for providing the elderly with the most relaxing environment. For example, it suggests adjusting music or lighting. The personalized nursing care service providing unit can use the emotion estimation function to provide advice for providing the elderly with the most relaxing environment. This makes it possible to provide advice for providing the elderly with the most relaxing environment.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The nursing care service system can further include a nutrition management unit that manages the elderly's diet. The nutrition management unit proposes an appropriate meal plan based on the elderly's health condition and food preferences. For example, it can propose low-carbohydrate meals to elderly people with diabetes and provide menus that take nutritional balance into consideration. It can also record meal times and amounts and adjust meal plans according to changes in the elderly's health condition. This makes it possible to maintain the health of the elderly and provide them with nutritionally balanced meals.

[0070] The nursing care service system can further include an exercise management unit that manages the elderly's exercise. The exercise management unit proposes an appropriate exercise plan based on the elderly's physical strength and health condition. For example, it can suggest stretching to increase joint flexibility and light strength training to maintain muscle strength. It can also record exercise progress and provide feedback to increase motivation to achieve goals. This helps maintain the elderly's physical strength and support a healthy lifestyle.

[0071] The nursing care service system can further include a sleep management unit that manages the sleep of the elderly. The sleep management unit monitors the sleep patterns of the elderly and provides advice to promote quality sleep. For example, it can suggest ways to relax before bed and how to create an appropriate bedroom environment. It can also provide music and meditation guides to improve sleep quality. This can improve the quality of sleep of the elderly and enhance the quality of daily life.

[0072] The nursing care service system can further include a social participation support unit to promote social participation among the elderly. The social participation support unit provides opportunities for the elderly to participate in local events and volunteer activities. For example, it introduces local cultural festivals and hobby club activities and supports their participation. It can also plan online social events to increase opportunities for the elderly to interact with other people. This can promote social participation among the elderly and reduce feelings of isolation.

[0073] The care service system can further include a hobby activity support unit that supports elderly people's hobby activities. The hobby activity support unit suggests appropriate activities based on the elderly person's hobbies and interests. For example, it supports hobby activities such as painting, handicrafts, and gardening, and provides the necessary materials and tools. It can also record the progress of hobby activities and provide feedback that gives the elderly a sense of accomplishment. This supports elderly people's hobby activities and increases the enjoyment of daily life.

[0074] The nursing care service system can further include a music provider that estimates the emotional state of the elderly person and provides music that corresponds to that emotion. The music provider analyzes the elderly person's emotional state in real time and selects music that corresponds to that emotion. For example, it provides calm music when the elderly person wants to relax, and upbeat music when the elderly person wants to cheer up. It can also learn the elderly person's preferences and past music history to provide a personalized music experience. This makes it possible to provide music that corresponds to the elderly person's emotional state and support their mental health.

[0075] The care service system can further include a relaxation support unit that estimates the emotional state of the elderly person and suggests relaxation methods according to that emotion. The relaxation support unit analyzes the elderly person's emotional state in real time and suggests relaxation methods according to that emotion. For example, it suggests deep breathing or meditation when the elderly person is feeling stressed, and light stretching when the elderly person is feeling tired. It can also learn the elderly person's preferences and past relaxation history to provide a personalized relaxation experience. This makes it possible to provide relaxation methods according to the elderly person's emotional state and support their mental health.

[0076] The nursing care service system can further include an activity suggestion unit that estimates the emotional state of the elderly person and suggests activities according to that emotion. The activity suggestion unit analyzes the elderly person's emotional state in real time and suggests activities according to that emotion. For example, it suggests taking a walk or light exercise when feeling down, or reading or watching a movie when wanting to relax. It can also learn the elderly person's preferences and past activity history to provide a personalized activity experience. This makes it possible to provide activities according to the elderly person's emotional state and support their mental health.

[0077] The nursing care service system can further include a communication support unit that estimates the emotional state of the elderly person and suggests a communication method according to that emotion. The communication support unit analyzes the elderly person's emotional state in real time and suggests a communication method according to that emotion. For example, if the elderly person is feeling lonely, it may suggest a video call with family or friends, or if the elderly person is feeling stressed, it may suggest a relaxing conversation. The system can also learn the elderly person's preferences and past communication history to provide a personalized communication experience. This makes it possible to provide a communication method according to the elderly person's emotional state and support their mental health.

[0078] The nursing care service system can further include an entertainment provider that estimates the emotional state of the elderly person and provides entertainment according to that emotion. The entertainment provider analyzes the elderly person's emotional state in real time and provides entertainment according to that emotion. For example, when the elderly person is feeling down, it can provide comedy movies or fun TV shows, and when the elderly person wants to relax, it can provide videos of natural scenery. The system can also learn the elderly person's preferences and past entertainment history to provide a personalized entertainment experience. This makes it possible to provide entertainment according to the elderly person's emotional state and support their mental health.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The generation AI communicates verbally with the elderly. For example, if the elderly person asks, "What's the weather like today?", the generation AI responds with something like, "It's sunny today. The temperature is 25 degrees, so it's a perfect day to go outside." The generation AI uses natural language processing technology to understand the elderly person's questions and conversations and generate appropriate answers. The input to the generation AI is a prompt containing instructions on what the elderly person wants the generation AI to do, and the generation AI generates an answer based on that prompt. Step 2: The image generation AI engages in visual communication with the elderly. For example, if an elderly person says, "Show me a photo of my grandchild," it can generate a photo of the grandchild and show it to the elderly. The image generation AI has previously studied photos and information about the elderly person's family, and generates an appropriate image in response to the elderly person's request. The input to the generation AI is a prompt containing instructions on what the elderly person wants the generation AI to do, and the generation AI generates an image based on that prompt. Step 3: The personalized care service provider combines the generation AI and image generation AI to provide personalized care services to the elderly. For example, it monitors the elderly's health status and daily activities and provides necessary support. The generation AI answers questions about the elderly's health status and provides advice on daily activities. The image generation AI also provides visual content at the elderly's request, increasing their enjoyment of daily life. This enables the care service system to provide personalized care services to the elderly and reduce the burden on caregivers.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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 AI 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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 AI 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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 AI 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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. Generative AI and Image generation AI and A personalized care service provision department, The generated AI is Communicating with the elderly through words, The image generation AI is Visual communication with the elderly The personalized care service providing unit Providing personalized care services A system characterized by:

2. The generated AI is Analyzing the emotional state of the elderly person in real time and generating an appropriate response 2. The system of claim 1.

3. The generated AI is A virtual community is created for the elderly to enjoy conversations with other elderly people, and the conversations are supported.

2. The system of claim 1.

4. The image generation AI is Generate an image that corresponds to the emotional state of the elderly person and elicit positive emotions.

2. The system of claim 1.

5. The personalized care service providing unit Real-time monitoring of the elderly person's health data and immediate notification if any abnormalities are detected 2. The system of claim 1.

6. The personalized care service providing unit Provide care services tailored to the emotional state of the elderly to support their mental health.

2. The system of claim 1.

7. The personalized care service providing unit Provide advice on how to provide the most relaxing environment for the elderly person.

2. The system of claim 1.

8. The generated AI is Generate conversation content to reduce the sense of loneliness felt by the elderly person 2. The system of claim 1.

Images