System

Abstract

An object of the system according to the embodiment is to compensate for a shortage of care plans and support creation and implementation of an effective care manager.SOLUTION: A system according to an embodiment includes a consultation handling unit, a care plan creation unit, an intracerebral age measurement unit, and a training unit. The consultation handling unit receives a consultation from the counselor. The care plan preparation unit prepares a care plan based on the consultation received by the consultation handling unit. The intracerebral age measurement part measures an intracerebral age on the basis of the care plan created by the care plan creation part. The training unit performs training based on the intracerebral age measured by the intracerebral age measurement unit.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] With conventional technology, elderly care faced issues such as a shortage of care managers and delays in creating care plans.

[0005] The system according to the embodiment aims to compensate for the shortage of care managers and to support the creation and implementation of effective care plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a consultation response unit, a care plan creation unit, a brain age measurement unit, and a training unit. The consultation response unit receives consultations from clients. The care plan creation unit creates a care plan based on the consultations received by the consultation response unit. The brain age measurement unit measures the brain age based on the care plan created by the care plan creation unit. The training unit performs training based on the brain age measured by the brain age measurement unit. [Effects of the Invention]

[0007] The system according to the embodiment can compensate for the shortage of care managers and support the creation and implementation of effective care plans. [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) The care manager assistance system according to an embodiment of the present invention is a system that operates a robot equipped with a generative AI as a care manager and assists in maintaining a healthy lifespan by creating and implementing a dementia prevention plan. As a result, the care manager assistance system can compensate for the shortage of care managers and support the maintenance of a healthy lifespan for the elderly.

[0029] A care manager assistance system according to an embodiment includes a consultation unit, a care plan creation unit, a mental age measurement unit, and a training unit. The consultation unit receives consultations from clients. For example, the consultation unit uses a generation AI to provide appropriate answers to the clients' questions. The consultation unit can also analyze the clients' past consultation history and provide individually optimized answers. The consultation unit can also analyze the client's tone of voice and facial expression to respond according to their emotions. For example, the generation AI can analyze the client's tone of voice in real time to estimate their emotional state. The generation AI can also capture the client's facial expression with a camera and perform emotional analysis. The care plan creation unit creates a care plan based on the consultation received by the consultation unit. For example, the generation AI can analyze the client's lifestyle and health condition in detail to create an individually optimized care plan. The generation AI can also use an emotion estimation function to create a care plan that takes the client's emotions into consideration, thereby increasing their emotional satisfaction. The mental age measurement unit measures the client's mental age based on the care plan created by the care plan creation unit. For example, the generation AI periodically measures the elderly person's brain age and tracks long-term changes. The generation AI can also perform a detailed analysis of the elderly person's cognitive function and propose an individually optimized training plan. The training unit conducts training based on the elderly person's brain age measured by the brain age measurement unit. For example, the generation AI analyzes the elderly person's exercise history and proposes an individually optimized exercise plan. The generation AI can also perform a detailed analysis of the elderly person's physical condition and propose appropriate exercise intensity and type. Furthermore, the generation AI can use an emotion estimation function to create an exercise plan that takes the elderly person's emotions into consideration and increase their motivation to exercise. This allows the care manager assistance system according to the embodiment to compensate for the shortage of care managers and support the maintenance of healthy life expectancy for the elderly. For example, by having the generation AI create a care plan, the burden on care managers can be reduced, allowing them to handle more consultations. Furthermore, dementia prevention plans can prevent the elderly person's cognitive function from declining and extend their healthy life expectancy.

[0030] The consultation response unit can analyze the consultation history of the person making the request and provide an individually optimized answer. For example, the generation AI retrieves the consultation history of the person making the request from a database and analyzes similar consultation content. For example, if the person has previously consulted about how to use nursing care insurance, the generation AI can provide the optimal answer based on that history. The generation AI can also analyze the consultation history of the person making the request and find specific patterns and trends. For example, it can provide more detailed information on frequently asked topics. Furthermore, the generation AI can generate individually optimized answers based on the consultation history of the person making the request. For example, it can extract keywords that match the content of past consultations and customize answers based on those. This allows the user to receive a more appropriate answer.

[0031] The care plan creation unit can perform a detailed analysis of the client's lifestyle habits or health condition and create an individually optimized care plan. The generation AI, for example, performs a detailed analysis of the client's lifestyle habits and creates a care plan based on that. For example, it can propose a plan that takes into account dietary and exercise habits. The generation AI also performs a detailed analysis of the client's health condition and creates an individually optimized care plan. For example, it can propose specific nursing care services to address specific medical conditions. Furthermore, the generation AI comprehensively analyzes the client's lifestyle habits and health condition and creates an optimal care plan. For example, it can propose a plan that takes into account the client's living environment and family support situation. This allows the client to receive a more appropriate care plan.

[0032] The brain age measurement unit can periodically measure the brain age of an elderly person and track long-term changes. The generation AI, for example, periodically measures the brain age of an elderly person and accumulates the data. For example, it records monthly measurement results and tracks long-term changes. The generation AI also measures the brain age of an elderly person and adjusts the training plan based on the results. For example, if the brain age increases, it can suggest more effective training. Furthermore, the generation AI periodically measures the brain age of an elderly person and visualizes long-term changes in graphs and charts. For example, it can compare the progress with past data. This makes it possible to track changes in the brain age of an elderly person over the long term.

[0033] The brain age measurement unit can perform a detailed analysis of the elderly person's cognitive function and propose an individually optimized training plan. The generation AI, for example, can perform a detailed analysis of the elderly person's cognitive function and propose a training plan based on the results. For example, it can provide specific training to improve memory and attention. The generation AI also measures the elderly person's cognitive function and creates an individually optimized training plan. For example, it can propose training specialized for areas where cognitive function is declining. The generation AI also analyzes the elderly person's cognitive function in detail and generates an individually optimized training plan. For example, it can propose specific training methods such as puzzles and memory games. This makes it possible to provide a training plan tailored to the elderly person's cognitive function.

[0034] The training department can analyze the exercise history of the elderly person and propose an individually optimized exercise plan. The generation AI, for example, retrieves the elderly person's exercise history from a database and proposes an optimal exercise plan based on that. For example, it proposes an effective exercise method based on past exercise data. The generation AI also analyzes the elderly person's exercise history and creates an individually optimized exercise plan. For example, it can propose a plan that adjusts the frequency and intensity of exercise. Furthermore, the generation AI generates an individually optimized exercise plan based on the elderly person's exercise history. For example, if a particular exercise was effective, it creates a plan centered around that exercise. This makes it possible to provide an exercise plan based on the elderly person's exercise history.

[0035] The training unit can perform a detailed analysis of the elderly person's physical condition and suggest appropriate exercise intensity or type. The generation AI, for example, can perform a detailed analysis of the elderly person's physical condition and suggest appropriate exercise intensity and type based on that analysis. For example, it can create an exercise plan that takes muscle strength and flexibility into account. The generation AI can also analyze the elderly person's physical condition and suggest an individually optimized exercise plan. For example, it can select the type of exercise based on the condition of the joints and muscle strength. The generation AI can also perform a detailed analysis of the elderly person's physical condition and suggest appropriate exercise intensity and type. For example, it can create an exercise plan that takes cardiopulmonary function and physical strength into account. This makes it possible to provide an exercise plan based on the elderly person's physical condition.

[0036] The training department can collaborate with other elderly people to suggest group exercise or recreational activities. The generation AI can, for example, collaborate with other elderly people to suggest group exercise plans. For example, it can suggest group exercise or team sports. The generation AI can also collaborate with other elderly people to create group recreational plans. For example, it can suggest games or activities to play together. Furthermore, the generation AI can collaborate with other elderly people to build a system that suggests group exercise and recreational activities. For example, it can suggest group activities through an online platform. This allows elderly people to enjoy group exercise and recreational activities.

[0037] The training department can work with local sports or recreation facilities to propose exercise plans optimized for the area. The generation AI, for example, works with local sports facilities to propose exercise plans optimized for the area. For example, it creates a plan that uses local gyms and fitness centers. The generation AI can also work with local recreation facilities to propose recreation plans specific to the area. For example, it can create a plan that uses local parks and community centers. The generation AI can also work with local sports or recreation facilities to jointly create exercise plans optimized for the area. For example, it can propose a plan that incorporates local events and activities. This makes it possible to provide exercise plans optimized for the area.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The care manager assistance system may further include a voice recognition unit. The voice recognition unit can convert the voice of the client into text in real time and record the consultation content. For example, what the client speaks can be automatically converted into text so that it can be referenced later. The voice recognition unit can also support multiple languages ​​and can also accommodate clients who speak foreign languages. Furthermore, the voice recognition unit can analyze the client's voice data and extract speech patterns and characteristics. This allows the client's speech content to be accurately recorded and used for later analysis and reference.

[0040] The care manager assistance system can further include a reminder unit. The reminder unit can remind the client of important appointments and tasks. For example, it can notify the client not to forget to make a doctor's appointment or take medication. The reminder unit can also manage the client's schedule and remind the client at an appropriate time. Furthermore, the reminder unit can customize the reminder method according to the client's preferences. For example, the client can select voice notification or text message. This can help the client not forget to complete important appointments and tasks.

[0041] The care manager assistance system can further include a health monitoring unit. The health monitoring unit can monitor the client's health condition in real time and issue an alert if an abnormality is detected. For example, it can periodically measure the client's heart rate and blood pressure and notify a doctor if an abnormal value is detected. The health monitoring unit can also accumulate the client's health data and track changes in the client's health condition over the long term. Furthermore, the health monitoring unit can analyze the client's health data and provide advice for maintaining health. This allows the client's health condition to be constantly monitored and appropriate measures to be taken.

[0042] The care manager assistance system can further include a nutrition management unit. The nutrition management unit can record the dietary content of the client and analyze the nutritional balance. For example, it can automatically calculate the calories and nutrients of the meals consumed by the client and propose a balanced meal plan. The nutrition management unit can also customize the meal plan according to the client's health condition and goals. Furthermore, the nutrition management unit can provide the client with dietary advice and recipes. This can help the client maintain a healthy diet.

[0043] The care manager assistance system may further include a learning support unit. The learning support unit may support the client in acquiring new knowledge and skills. For example, the learning support unit may suggest online courses or workshops. The learning support unit may also monitor the client's learning progress and provide appropriate feedback. Furthermore, the learning support unit may customize a learning plan according to the client's interests and goals. This allows the client to acquire new knowledge and skills and promote self-growth.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The consultation response unit receives a consultation from a client. For example, it uses generation AI to provide an appropriate answer to the client's question. It can also analyze the client's past consultation history and provide individually optimized answers. It can also analyze the client's tone of voice and facial expressions and respond according to their emotions. For example, generation AI can analyze the client's tone of voice in real time and estimate their emotional state. It can also capture the client's facial expressions with a camera and perform emotional analysis. Step 2: The care plan creation unit creates a care plan based on the consultation received by the consultation response unit. For example, the generation AI can perform a detailed analysis of the client's lifestyle and health condition to create an individually optimized care plan. It can also use an emotion estimation function to create a care plan that takes the client's emotions into account, increasing emotional satisfaction. Step 3: The brain age measurement unit measures the brain age based on the care plan created by the care plan creation unit. For example, the generation AI can periodically measure the brain age of an elderly person and track long-term changes. It can also perform a detailed analysis of the elderly person's cognitive function and propose individually optimized training plans. Step 4: The training unit conducts training based on the brain age measured by the brain age measurement unit. For example, the generation AI analyzes the elderly person's exercise history and proposes an individually optimized exercise plan. It can also perform a detailed analysis of the elderly person's physical condition and propose appropriate exercise intensity and type. Furthermore, it can use an emotion estimation function to create an exercise plan that takes the elderly person's emotions into consideration, increasing their motivation to exercise.

[0046] (Example 2) The care manager assistance system according to an embodiment of the present invention is a system that operates a robot equipped with a generative AI as a care manager and assists in maintaining a healthy lifespan by creating and implementing a dementia prevention plan. As a result, the care manager assistance system can compensate for the shortage of care managers and support the maintenance of a healthy lifespan for the elderly.

[0047] A care manager assistance system according to an embodiment includes a consultation unit, a care plan creation unit, a mental age measurement unit, and a training unit. The consultation unit receives consultations from clients. For example, the consultation unit uses a generation AI to provide appropriate answers to the clients' questions. The consultation unit can also analyze the clients' past consultation history and provide individually optimized answers. The consultation unit can also analyze the client's tone of voice and facial expression to respond according to their emotions. For example, the generation AI can analyze the client's tone of voice in real time to estimate their emotional state. The generation AI can also capture the client's facial expression with a camera and perform emotional analysis. The care plan creation unit creates a care plan based on the consultation received by the consultation unit. For example, the generation AI can analyze the client's lifestyle and health condition in detail to create an individually optimized care plan. The generation AI can also use an emotion estimation function to create a care plan that takes the client's emotions into consideration, thereby increasing their emotional satisfaction. The mental age measurement unit measures the client's mental age based on the care plan created by the care plan creation unit. For example, the generation AI periodically measures the elderly person's brain age and tracks long-term changes. The generation AI can also perform a detailed analysis of the elderly person's cognitive function and propose an individually optimized training plan. The training unit conducts training based on the elderly person's brain age measured by the brain age measurement unit. For example, the generation AI analyzes the elderly person's exercise history and proposes an individually optimized exercise plan. The generation AI can also perform a detailed analysis of the elderly person's physical condition and propose appropriate exercise intensity and type. Furthermore, the generation AI can use an emotion estimation function to create an exercise plan that takes the elderly person's emotions into consideration and increase their motivation to exercise. This allows the care manager assistance system according to the embodiment to compensate for the shortage of care managers and support the maintenance of healthy life expectancy for the elderly. For example, by having the generation AI create a care plan, the burden on care managers can be reduced, allowing them to handle more consultations. Furthermore, dementia prevention plans can prevent the elderly person's cognitive function from declining and extend their healthy life expectancy.

[0048] The consultation response unit can analyze the consultation history of the person making the request and provide an individually optimized answer. For example, the generation AI retrieves the consultation history of the person making the request from a database and analyzes similar consultation content. For example, if the person has previously consulted about how to use nursing care insurance, the generation AI can provide the optimal answer based on that history. The generation AI can also analyze the consultation history of the person making the request and find specific patterns and trends. For example, it can provide more detailed information on frequently asked topics. Furthermore, the generation AI can generate individually optimized answers based on the consultation history of the person making the request. For example, it can extract keywords that match the content of past consultations and customize answers based on those. This allows the user to receive a more appropriate answer.

[0049] The consultation response unit can analyze the caller's tone of voice or facial expression and respond according to their emotions. The generation AI, for example, analyzes the caller's tone of voice in real time to estimate their emotional state. For example, if their voice tone is low, it will provide a response that will help them relax. The generation AI also captures the caller's facial expression with a camera and performs emotional analysis. For example, if they rarely smile, it can offer words of encouragement. Furthermore, the generation AI simultaneously analyzes the caller's tone of voice and facial expression and responds according to their emotions. For example, if their voice tone is high and their facial expression is bright, it will provide positive feedback. This makes it possible to respond appropriately according to the caller's emotions.

[0050] The consultation response unit can use the emotion estimation function to estimate the client's emotions in real time and provide appropriate advice based on those emotions. The generation AI, for example, estimates the client's emotions in real time and provides advice based on the results. For example, if the client is feeling anxious, it can provide reassuring advice. The generation AI also uses the emotion estimation function to monitor the client's emotional state and provide advice at the appropriate time. For example, if the client is feeling stressed, it can suggest relaxation methods. Furthermore, the generation AI analyzes the client's emotions in real time and customizes advice based on those emotions. For example, if the client is feeling happy, it will emphasize positive feedback. This makes it possible to provide appropriate advice based on the client's emotions.

[0051] The care plan creation unit can perform a detailed analysis of the client's lifestyle habits or health condition and create an individually optimized care plan. The generation AI, for example, performs a detailed analysis of the client's lifestyle habits and creates a care plan based on that. For example, it can propose a plan that takes into account dietary and exercise habits. The generation AI also performs a detailed analysis of the client's health condition and creates an individually optimized care plan. For example, it can propose specific nursing care services to address specific medical conditions. Furthermore, the generation AI comprehensively analyzes the client's lifestyle habits and health condition and creates an optimal care plan. For example, it can propose a plan that takes into account the client's living environment and family support situation. This allows the client to receive a more appropriate care plan.

[0052] The care plan creation unit uses the emotion estimation function to create a care plan based on the client's emotions, thereby increasing emotional satisfaction. The generation AI, for example, estimates the client's emotions in real time and creates a care plan based on the results. For example, it proposes a plan that gives the client a sense of security. The generation AI also uses the emotion estimation function to create a care plan that takes the client's emotional state into consideration. For example, it can propose specific support methods to reduce stress. Furthermore, the generation AI analyzes the client's emotions and generates a care plan to increase emotional satisfaction. For example, it proposes nursing care services that match the client's preferences. This makes it possible to provide a care plan that takes the client's emotions into consideration.

[0053] The brain age measurement unit can periodically measure the brain age of an elderly person and track long-term changes. The generation AI, for example, periodically measures the brain age of an elderly person and accumulates the data. For example, it records monthly measurement results and tracks long-term changes. The generation AI also measures the brain age of an elderly person and adjusts the training plan based on the results. For example, if the brain age increases, it can suggest more effective training. Furthermore, the generation AI periodically measures the brain age of an elderly person and visualizes long-term changes in graphs and charts. For example, it can compare the progress with past data. This makes it possible to track changes in the brain age of an elderly person over the long term.

[0054] The brain age measurement unit can perform a detailed analysis of the elderly person's cognitive function and propose an individually optimized training plan. The generation AI, for example, can perform a detailed analysis of the elderly person's cognitive function and propose a training plan based on the results. For example, it can provide specific training to improve memory and attention. The generation AI also measures the elderly person's cognitive function and creates an individually optimized training plan. For example, it can propose training specialized for areas where cognitive function is declining. The generation AI also analyzes the elderly person's cognitive function in detail and generates an individually optimized training plan. For example, it can propose specific training methods such as puzzles and memory games. This makes it possible to provide a training plan tailored to the elderly person's cognitive function.

[0055] The brain age measurement unit uses the emotion estimation function to create a training plan based on the emotions of the elderly, thereby improving the effectiveness of the training. The generation AI, for example, estimates the emotions of the elderly in real time and creates a training plan based on the results. For example, it suggests training that brings out positive emotions. The generation AI also uses the emotion estimation function to create a training plan that takes into account the emotional state of the elderly. For example, it can suggest training that incorporates relaxation techniques to reduce stress. Furthermore, the generation AI analyzes the emotions of the elderly and generates a training plan to increase emotional satisfaction. For example, it adjusts the intensity and content of the training according to the emotional state. This makes it possible to provide a training plan that suits the emotions of the elderly.

[0056] The training department can analyze the exercise history of the elderly person and propose an individually optimized exercise plan. The generation AI, for example, retrieves the elderly person's exercise history from a database and proposes an optimal exercise plan based on that. For example, it proposes an effective exercise method based on past exercise data. The generation AI also analyzes the elderly person's exercise history and creates an individually optimized exercise plan. For example, it can propose a plan that adjusts the frequency and intensity of exercise. Furthermore, the generation AI generates an individually optimized exercise plan based on the elderly person's exercise history. For example, if a particular exercise was effective, it creates a plan centered around that exercise. This makes it possible to provide an exercise plan based on the elderly person's exercise history.

[0057] The training unit can perform a detailed analysis of the elderly person's physical condition and suggest appropriate exercise intensity or type. The generation AI, for example, can perform a detailed analysis of the elderly person's physical condition and suggest appropriate exercise intensity and type based on that analysis. For example, it can create an exercise plan that takes muscle strength and flexibility into account. The generation AI can also analyze the elderly person's physical condition and suggest an individually optimized exercise plan. For example, it can select the type of exercise based on the condition of the joints and muscle strength. The generation AI can also perform a detailed analysis of the elderly person's physical condition and suggest appropriate exercise intensity and type. For example, it can create an exercise plan that takes cardiopulmonary function and physical strength into account. This makes it possible to provide an exercise plan based on the elderly person's physical condition.

[0058] The training unit can use the emotion estimation function to create an exercise plan based on the elderly person's emotions, increasing their motivation to exercise. The generation AI, for example, estimates the elderly person's emotions in real time and creates an exercise plan based on the results. For example, it can suggest exercises that elicit positive emotions. The generation AI also uses the emotion estimation function to create an exercise plan that takes into account the elderly person's emotional state. For example, it can incorporate relaxation exercises to reduce stress. Furthermore, the generation AI analyzes the elderly person's emotions and generates an exercise plan to increase emotional satisfaction. For example, it can adjust the intensity and content of the exercise according to the emotional state. This makes it possible to provide an exercise plan that suits the elderly person's emotions.

[0059] The training department can collaborate with other elderly people to suggest group exercise or recreational activities. The generation AI can, for example, collaborate with other elderly people to suggest group exercise plans. For example, it can suggest group exercise or team sports. The generation AI can also collaborate with other elderly people to create group recreational plans. For example, it can suggest games or activities to play together. Furthermore, the generation AI can collaborate with other elderly people to build a system that suggests group exercise and recreational activities. For example, it can suggest group activities through an online platform. This allows elderly people to enjoy group exercise and recreational activities.

[0060] The training department can work with local sports or recreation facilities to propose exercise plans optimized for the area. The generation AI, for example, works with local sports facilities to propose exercise plans optimized for the area. For example, it creates a plan that uses local gyms and fitness centers. The generation AI can also work with local recreation facilities to propose recreation plans specific to the area. For example, it can create a plan that uses local parks and community centers. The generation AI can also work with local sports or recreation facilities to jointly create exercise plans optimized for the area. For example, it can propose a plan that incorporates local events and activities. This makes it possible to provide exercise plans optimized for the area.

[0061] The training department can use the emotion estimation function to collect recreation feedback based on the emotions of the elderly and use it to improve the plan. The generation AI, for example, estimates the emotions of the elderly in real time and collects recreation feedback based on the results. For example, it measures their satisfaction with the recreation. The generation AI also uses the emotion estimation function to collect feedback that takes into account the elderly's emotional state and use it to improve the recreation plan. For example, it can make specific suggestions to reduce stress. Furthermore, the generation AI analyzes the emotions of the elderly and collects feedback to increase their emotional satisfaction. For example, it collects specific opinions to adjust the content of the recreation. This allows feedback based on the emotions of the elderly to be collected and used to improve the recreation plan.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The care manager assistance system may further include a voice recognition unit. The voice recognition unit can convert the voice of the client into text in real time and record the consultation content. For example, what the client speaks can be automatically converted into text so that it can be referenced later. The voice recognition unit can also support multiple languages ​​and can also accommodate clients who speak foreign languages. Furthermore, the voice recognition unit can analyze the client's voice data and extract speech patterns and characteristics. This allows the client's speech content to be accurately recorded and used for later analysis and reference.

[0064] The care manager assistance system can further include a reminder unit. The reminder unit can remind the client of important appointments and tasks. For example, it can notify the client not to forget to make a doctor's appointment or take medication. The reminder unit can also manage the client's schedule and remind the client at an appropriate time. Furthermore, the reminder unit can customize the reminder method according to the client's preferences. For example, the client can select voice notification or text message. This can help the client not forget to complete important appointments and tasks.

[0065] The care manager assistance system can further include a health monitoring unit. The health monitoring unit can monitor the client's health condition in real time and issue an alert if an abnormality is detected. For example, it can periodically measure the client's heart rate and blood pressure and notify a doctor if an abnormal value is detected. The health monitoring unit can also accumulate the client's health data and track changes in the client's health condition over the long term. Furthermore, the health monitoring unit can analyze the client's health data and provide advice for maintaining health. This allows the client's health condition to be constantly monitored and appropriate measures to be taken.

[0066] The care manager assistance system can further include a nutrition management unit. The nutrition management unit can record the dietary content of the client and analyze the nutritional balance. For example, it can automatically calculate the calories and nutrients of the meals consumed by the client and propose a balanced meal plan. The nutrition management unit can also customize the meal plan according to the client's health condition and goals. Furthermore, the nutrition management unit can provide the client with dietary advice and recipes. This can help the client maintain a healthy diet.

[0067] The care manager assistance system may further include a communication support unit. The communication support unit can support the client in smoothly communicating with others. For example, speech recognition technology can be used to convert the client's speech into text, allowing communication with others in chat format. The communication support unit also has a translation function and can support communication between people who speak different languages. Furthermore, the communication support unit can analyze the client's emotions and suggest appropriate communication methods. This allows the client to smoothly communicate with others.

[0068] The care manager assistance system may further include a hobby activity support unit. The hobby activity support unit can suggest appropriate activities based on the hobbies and interests of the client. For example, if the client is interested in painting or music, it can suggest related events or classes. The hobby activity support unit can also analyze the client's emotions and suggest hobby activities to reduce stress. Furthermore, the hobby activity support unit can support the client in finding a new hobby. This can help the client live a fulfilling life.

[0069] The care manager assistance system can further include a relaxation support unit. The relaxation support unit can suggest relaxation methods to reduce the client's stress. For example, it can teach meditation and deep breathing techniques. The relaxation support unit can also analyze the client's emotions and provide music and videos that have a high relaxing effect. Furthermore, the relaxation support unit can monitor the client's stress level and suggest relaxation methods at appropriate times. This can help the client reduce stress and maintain a relaxed state.

[0070] The care manager assistance system can further include a social participation support unit. The social participation support unit can support the client in actively participating in the local community. For example, it can provide information on participating in local volunteer activities and events. The social participation support unit can also analyze the client's emotions and provide advice to increase motivation to participate in society. Furthermore, the social participation support unit can support the client in making new friends. This allows the client to actively participate in the local community and form social connections.

[0071] The care manager assistance system can further include a safety management unit. The safety management unit can provide support to ensure the safety of the client. For example, it can monitor the client's living environment and issue an alert if it detects an abnormality. The safety management unit can also track the client's location when they are out to ensure their safety. Furthermore, the safety management unit can analyze the client's emotions and take appropriate action if they are feeling anxious or scared. This ensures the client's safety and helps them live with peace of mind.

[0072] The care manager assistance system may further include a learning support unit. The learning support unit may support the client in acquiring new knowledge and skills. For example, the learning support unit may suggest online courses or workshops. The learning support unit may also monitor the client's learning progress and provide appropriate feedback. Furthermore, the learning support unit may customize a learning plan according to the client's interests and goals. This allows the client to acquire new knowledge and skills and promote self-growth.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The consultation response unit receives a consultation from a client. For example, it uses generation AI to provide an appropriate answer to the client's question. It can also analyze the client's past consultation history and provide individually optimized answers. It can also analyze the client's tone of voice and facial expressions and respond according to their emotions. For example, generation AI can analyze the client's tone of voice in real time and estimate their emotional state. It can also capture the client's facial expressions with a camera and perform emotional analysis. Step 2: The care plan creation unit creates a care plan based on the consultation received by the consultation response unit. For example, the generation AI can perform a detailed analysis of the client's lifestyle and health condition to create an individually optimized care plan. It can also use an emotion estimation function to create a care plan that takes the client's emotions into account, increasing emotional satisfaction. Step 3: The brain age measurement unit measures the brain age based on the care plan created by the care plan creation unit. For example, the generation AI can periodically measure the brain age of an elderly person and track long-term changes. It can also perform a detailed analysis of the elderly person's cognitive function and propose individually optimized training plans. Step 4: The training unit conducts training based on the brain age measured by the brain age measurement unit. For example, the generation AI analyzes the elderly person's exercise history and proposes an individually optimized exercise plan. It can also perform a detailed analysis of the elderly person's physical condition and propose appropriate exercise intensity and type. Furthermore, it can use an emotion estimation function to create an exercise plan that takes the elderly person's emotions into consideration, increasing their motivation to exercise.

[0075] 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.

[0076] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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]

[0142] 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. The consultation department receives inquiries from those seeking advice, a care plan creation unit that creates a care plan based on the consultation received by the consultation response unit; a brain age measurement unit that measures a brain age based on the care plan created by the care plan creation unit; a training unit that performs training based on the brain age measured by the brain age measurement unit. A system characterized by:

2. The consultation department Analyze the consultation history of the person seeking advice and provide an individually optimized answer 2. The system of claim 1.

3. The consultation department Analyze the tone of voice or facial expression of the person seeking advice and respond accordingly 2. The system of claim 1.

4. The consultation department The system estimates the client's emotions in real time and provides appropriate advice based on those emotions.

2. The system of claim 1.

5. The care plan creation unit A detailed analysis of the client's lifestyle or health condition will be conducted to create an individually optimized care plan.

2. The system of claim 1.

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