System

Abstract

An object of a system according to an embodiment is to continuously evaluate a health condition of an elderly person or a person to be cared for and provide an optimal care plan.SOLUTION: A system includes a data collection part, an analysis part, a care plan generation part, and a provision part. The AI collector continuously collects date of the elderly person or the person to be cared for by utilizing the generated date. The analysis unit analyzes the data collected by the data collection unit and evaluates a health condition of the elderly person or the person to be cared for. The care plan generator automatically generates an optimum care plan based on the health condition evaluated by the analyzer. The provider provides the care plan generated by the care plan generator to the caregiver or the concerned party.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies do not adequately assess the health status of elderly people and those receiving care and provide optimal care plans, so there is room for improvement.

[0005] The system according to the embodiment aims to continuously evaluate the health status of elderly people and care recipients and provide an optimal care plan. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a care plan generation unit, and a provision unit. The data collection unit continuously collects data on the elderly person or care recipient using a generation AI. The analysis unit analyzes the data collected by the data collection unit and evaluates the health condition of the elderly person or care recipient. The care plan generation unit automatically generates an optimal care plan based on the health condition evaluated by the analysis unit. The provision unit provides the care plan generated by the care plan generation unit to a caregiver or related party. [Effects of the Invention]

[0007] The system according to the embodiment can continuously evaluate the health status of elderly people and those receiving care, and provide an optimal care plan. [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 plan generation system according to an embodiment of the present invention continuously collects data on elderly people and care recipients, analyzes the data using a generation AI, automatically generates optimal care plans based on the individual health conditions and needs, and provides the plans to caregivers and other relevant parties. As a result, the care plan generation system can provide optimal care plans according to the health conditions of elderly people and care recipients.

[0029] A care plan generation system according to an embodiment includes a data collection unit, an analysis unit, a care plan generation unit, and a provision unit. The data collection unit continuously collects data on elderly people and care recipients. For example, the data collection unit collects vital signs such as heart rate and blood pressure using a wearable device. The data collection unit can also collect daily behavior data using a smart home system. The data collection unit can also use a dedicated application to collect data such as dietary content and exercise amount. The analysis unit analyzes the collected data and evaluates the health status of the elderly people and care recipients. For example, the analysis unit scores the health status based on the collected vital sign data. The analysis unit can also analyze the daily behavior data and evaluate the activity level. The analysis unit can also analyze data on dietary content and exercise amount to evaluate the nutritional status and physical strength. The care plan generation unit automatically generates an optimal care plan based on the health status evaluated by the analysis unit. For example, the care plan generation unit suggests a balanced meal menu if the nutritional status is insufficient. The care plan generation unit can also suggest an appropriate exercise program if the amount of exercise is insufficient. Furthermore, the care plan generation unit can also suggest relaxation methods and measures to improve the sleep environment if the quality of sleep is poor. The provision unit provides the care plan generated by the care plan generation unit to caregivers and related parties. For example, the provision unit notifies the care plan through a smartphone app. The provision unit can also provide the care plan through a web portal. Furthermore, if feedback is desired in paper form, the provision unit can print the results using a printer. This allows the care plan generation system to provide an optimal care plan based on the health condition of the elderly person or the person receiving care.

[0030] The data collection unit can collect data on the dietary content, exercise volume, sleep patterns, body temperature, blood pressure, and heart rate of elderly people or those receiving care. For example, the data collection unit scans handwritten answers and saves them as image data. It then converts the image data into text data using OCR technology. The data collection unit also takes a photo of the handwritten answer using a smartphone camera and converts the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The data collection unit also writes the handwritten answer with a dedicated digital pen, which converts the data into digital data in real time. For example, a sensor detects the movement of the pen and saves it as text data. Converting the handwritten answer into digital data makes it easier for generative AI to analyze.

[0031] The analysis unit can evaluate nutritional status or physical strength, and can evaluate sleep quality or physical condition from sleep patterns or body temperature. The analysis unit, for example, uses a pen pressure sensor to analyze how an answer is written. For example, it collects data on the strength of pen pressure and extracts the writer's characteristics. The analysis unit also tracks the movement of a digital pen to collect data on the writer's stroke order in order to analyze the stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. The analysis unit also combines the pen pressure and stroke order data to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in pen pressure and stroke order. In this way, the writer's characteristics can be grasped in detail by analyzing the pen pressure and stroke order.

[0032] The care plan generation unit can suggest a balanced meal menu if the student's nutritional status is insufficient, and can suggest an appropriate exercise program if the student's exercise volume is insufficient. For example, the care plan generation unit can capture the student's facial expression while writing an answer sheet with a camera and analyze the student's emotion using an emotion estimation algorithm. For example, the care plan generation unit can calculate an emotion score based on changes in facial expression. The care plan generation unit can also record the student's voice while writing an answer sheet and estimate the student's emotion using voice analysis technology. For example, the care plan generation unit can analyze the tone and speed of the voice and calculate an emotion score. The care plan generation unit can also collect the student's biometric data (heart rate and electrodermal activity) while writing an answer sheet with a sensor and analyze the student's emotion using an emotion estimation algorithm. For example, the care plan generation unit can calculate an emotion score based on fluctuations in heart rate. This allows the student's emotion to be reflected in the evaluation, enabling a more comprehensive evaluation.

[0033] The provision unit can notify the care plan through a smartphone app or web portal. For example, the provision unit records what the student dictates with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The provision unit also creates a system that recognizes what the student dictates in real time and displays it as text data. For example, the text is displayed on a screen simultaneously with the voice input. The provision unit also uses voice input to convert what the student dictates into text and saves the text data as an answer sheet. For example, voice recognition technology is used to perform highly accurate text conversion. This allows even students who have difficulty writing by hand or typing to submit answer sheets by using voice input.

[0034] The data collection unit can analyze images and diagrams and include visual information in the evaluation. For example, the data collection unit analyzes images and diagrams included in answer sheets using image recognition technology and converts the content into text data. For example, the content of the diagrams is automatically analyzed and reflected in the evaluation. The data collection unit also analyzes images and diagrams included in answer sheets and builds a system for evaluation based on visual information. For example, the content of the images is analyzed and reflected in the evaluation of the answer. The data collection unit also analyzes answer sheets that include images and diagrams and integrates the visual information with the text data for evaluation. For example, the visual information is analyzed using image recognition technology and reflected in the evaluation of the answer. In this way, analyzing answer sheets that include images and diagrams enables more detailed evaluation.

[0035] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0036] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0037] The data collection unit can collect data not only from wearable devices but also from IoT devices in the home. For example, the data collection unit uses a smart refrigerator to automatically record meal contents and intake. For example, it monitors food consumption in the refrigerator and collects data. The data collection unit also uses a smart bed to monitor sleep patterns and body movements. For example, it measures sleep quality and body movements using sensors built into the bed and collects data. The data collection unit also integrates IoT devices in the home, and the generation AI analyzes the data. For example, data from the smart refrigerator and smart bed can be combined to evaluate health status. This makes it possible to collect data from IoT devices in the home and evaluate health status more comprehensively.

[0038] The analysis unit can be equipped with a function to detect abnormal values ​​or sudden changes by comparing with past data and issue an early warning. For example, the analysis unit develops an algorithm that the generation AI compares with past data to detect abnormal values ​​or sudden changes. For example, it detects sudden fluctuations in heart rate or blood pressure. The analysis unit also builds a system that issues an early warning when an abnormal value or sudden change is detected. For example, it notifies the warning via a smartphone app or web portal. The analysis unit also learns from past data so that the generation AI can detect abnormal values ​​or sudden changes. For example, it automatically distinguishes between normal and abnormal ranges. This enables early detection of abnormal values ​​or sudden changes and issue of a warning.

[0039] The analysis unit can build a health condition prediction model based on the analysis results and predict future health risks. In the analysis unit, for example, the generative AI builds a health condition prediction model based on the analysis results. For example, it learns from past data and develops an algorithm to predict future health risks. The analysis unit also uses the health condition prediction model to build a system that predicts future health risks. For example, it predicts the risk of heart disease and diabetes. In the analysis unit, the generative AI also continuously updates the health condition prediction model. For example, it re-learns the model every time new data is collected, improving prediction accuracy. This makes it possible to predict future health risks and take preventative measures.

[0040] The analysis unit can also include long-term data such as genetic information or family history in the evaluation of health status. For example, the analysis unit collects genetic test data to include genetic information in the evaluation of health status. For example, the analysis unit obtains genetic information using a genetic test kit and stores it in a database. The analysis unit also collects family health data to evaluate health status taking family history into consideration. For example, the analysis unit registers family medical history and health status in the database. The analysis unit also analyzes long-term data including genetic information and family history to evaluate health status. For example, health risk is evaluated based on genetic risk and family history. This makes it possible to evaluate health status taking genetic information and family history into consideration.

[0041] Based on the analysis results, the analysis unit can not only evaluate health status but also make suggestions for lifestyle improvement. For example, the generation AI uses the analysis results to evaluate health status and make suggestions for lifestyle improvement. For example, if exercise is lacking, an appropriate exercise program will be suggested. The analysis unit also makes suggestions for dietary improvement based on the health status evaluation results. For example, if nutritional balance is unbalanced, a balanced meal menu will be suggested. The analysis unit also analyzes individual data to make suggestions for lifestyle improvement. For example, if sleep quality is poor, it will suggest relaxation methods and measures to improve the sleeping environment. This makes it possible to make suggestions for lifestyle improvement based on the health status evaluation.

[0042] The care plan generation unit can evaluate the effectiveness of past care plans and optimize new care plans based on the results. For example, the care plan generation unit collects feedback data so that the generation AI can evaluate the effectiveness of past care plans. For example, it records the health and emotional state after the implementation of a care plan and analyzes the data. The care plan generation unit also evaluates the effectiveness of past care plans and develops an algorithm to optimize new care plans based on the results. For example, it incorporates elements of care plans that were highly effective. The care plan generation unit also allows the generation AI to learn data from past care plans and generate an optimal care plan. For example, it proposes a new care plan based on past success stories. This allows new care plans to be optimized based on the effectiveness of past care plans.

[0043] The care plan generation unit can incorporate local medical resources or community support into the generation of the care plan. For example, the care plan generation unit collects data on local medical institutions and experts in order to incorporate local medical resources into the generation of the care plan. For example, it incorporates information on nearby hospitals and clinics. The care plan generation unit also collects information on local support groups and volunteer activities in order to generate a care plan that incorporates community support. For example, it suggests activities of local support groups. The care plan generation unit also uses the generation AI to analyze the data in order to generate a care plan that incorporates local medical resources and community support. For example, it suggests a care plan that utilizes local medical resources. This makes it possible to provide a care plan that utilizes local medical resources and community support.

[0044] The care plan generation unit can also make suggestions to reduce the caregiver's burden when generating a care plan. For example, when the generation AI generates a care plan, the care plan generation unit makes suggestions to reduce the caregiver's burden. For example, it proposes a schedule to ensure that the caregiver has time to rest. The care plan generation unit also incorporates support resources into the care plan to reduce the caregiver's burden. For example, it proposes support from local support groups and volunteers. The care plan generation unit also analyzes the caregiver's stress level and health condition so that the generation AI can generate a care plan to reduce the caregiver's burden. For example, if stress is high, it suggests relaxation methods. This makes it possible to provide a care plan that includes suggestions to reduce the caregiver's burden.

[0045] The provision unit can evaluate the effectiveness of the care plan in real time based on the feedback and modify the plan as necessary. For example, the provision unit builds a system in which the generation AI evaluates the effectiveness of the care plan in real time based on the feedback. For example, the health and emotional states after the care plan is implemented are recorded and the data is analyzed. The provision unit also collects feedback data and the generation AI analyzes that data. For example, if the effectiveness of the care plan is low, the content of the plan is modified. The provision unit also allows the generation AI to dynamically adjust the content of the care plan based on the feedback. For example, elements of the care plan are added or deleted based on the feedback data. This allows the effectiveness of the care plan to be evaluated in real time based on the feedback and the plan to be modified as necessary.

[0046] The provision unit analyzes the feedback, identifies common problems or areas for improvement, and can reflect these in the next care plan. In the provision unit, for example, the generation AI analyzes the feedback and identifies common problems or areas for improvement. For example, it analyzes multiple pieces of feedback data and extracts common issues. The provision unit also builds a system that reflects this in the next care plan based on the feedback data. For example, it makes suggestions to solve common problems. In addition, the provision unit has the generation AI continuously collect feedback and reflect this in the next care plan. For example, it learns from the feedback data and optimizes the content of the care plan. This allows the feedback to be analyzed and common problems or areas for improvement to be reflected in the next care plan.

[0047] The provision unit can provide the care plan in an interactive format using a voice assistant or a robot. The provision unit, for example, uses a voice assistant to provide the care plan. For example, the care plan is notified by voice through a smart speaker. The provision unit also builds an interactive care plan provision system using a robot. For example, a robot explains the care plan to an elderly person or a care recipient and answers questions. The provision unit also provides the care plan interactively using a voice assistant or a robot. For example, the care plan is implemented according to voice instructions. This makes it possible to provide the care plan in an interactive format using a voice assistant or a robot.

[0048] The providing department can collect feedback in the form of periodic questionnaires or interviews to obtain more detailed information. The providing department, for example, collects feedback in the form of periodic questionnaires. For example, a questionnaire is sent after a care plan is implemented to obtain detailed feedback. The providing department also builds a system for collecting feedback in the form of interviews. For example, the providing department directly interacts with caregivers and related parties to hear about the effectiveness of the care plan and areas for improvement. The providing department also collects detailed feedback through periodic questionnaires and interviews. For example, the providing department asks for specific opinions on each element of the care plan. This makes it possible to collect detailed feedback in the form of periodic questionnaires and interviews.

[0049] The care plan generation unit can optimize the care plan by referring to past data when customizing the care plan based on an individual's health condition or lifestyle habits. The care plan generation unit, for example, refers to past data so that the generation AI can customize the care plan based on an individual's health condition or lifestyle habits. For example, it proposes an optimal care plan based on past health data or lifestyle data. The care plan generation unit also refers to past data to develop an algorithm that optimizes the customization of the care plan. For example, it adjusts the care plan based on past success stories. The care plan generation unit also allows the generation AI to learn from past data and customize the care plan based on an individual's health condition or lifestyle habits. For example, it proposes optimal meal menus and exercise programs based on past data. This allows the customization of the care plan to be optimized by referring to past data.

[0050] When customizing, the care plan generation unit can refer to the latest medical information or guidelines and provide the optimal care plan. In the care plan generation unit, for example, the generation AI refers to the latest medical information and guidelines and customizes the care plan. For example, it proposes a meal menu based on the latest nutritional guidelines. The care plan generation unit also regularly updates the medical information and guidelines, and the generation AI optimizes the care plan based on that information. For example, it incorporates new treatment and drug information. In addition, the care plan generation unit refers to medical information and guidelines and provides the optimal care plan according to each individual's health condition. For example, it proposes a care plan specialized for a specific disease or disability. This allows the optimal care plan to be provided by referring to the latest medical information and guidelines.

[0051] The care plan generation unit can customize the plan to meet the needs of not only the elderly person or the care recipient, but also the caregiver. For example, the care plan generation unit monitors the caregiver's health condition and stress level so that the generation AI can customize the plan to meet the caregiver's needs. For example, it measures the caregiver's heart rate and electrodermal activity and collects data. The care plan generation unit also collects feedback from the caregiver to generate a care plan that meets the caregiver's needs. For example, it adjusts the care plan based on the caregiver's opinions and requests. The care plan generation unit also suggests stress management and support for the caregiver so that the generation AI can meet the caregiver's needs. For example, it suggests relaxation methods and counseling. This makes it possible to customize the plan to meet the caregiver's needs.

[0052] The care plan generation unit can share the customized care plan with other medical institutions or experts and obtain feedback. For example, the care plan generation unit builds a data sharing platform to share the care plan customized by the generation AI with other medical institutions and experts. For example, the care plan is shared using a cloud-based system. The care plan generation unit also holds regular reviews and meetings to obtain feedback from other medical institutions and experts. For example, it listens to the opinions of experts on the content of the care plan. The care plan generation unit also optimizes the care plan based on feedback from other medical institutions and experts by the generation AI. For example, it analyzes the feedback data and adjusts the content of the care plan. This allows the care plan to be optimized by sharing it with other medical institutions and experts and obtaining feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The care plan generation system may further include an environmental monitoring unit. The environmental monitoring unit can collect environmental data such as indoor temperature, humidity, and illuminance, and provide the data to the analysis unit. For example, the environmental monitoring unit can suggest using an air conditioner if the indoor temperature is too high. It can also suggest using a humidifier if the humidity is low. This makes it possible to evaluate health conditions and propose care plans based on environmental data.

[0055] The care plan generation system can further include an exercise monitoring unit. The exercise monitoring unit can collect exercise data of the elderly person or care recipient and provide it to the analysis unit. For example, the exercise monitoring unit can record the number of steps and exercise time. The exercise monitoring unit can also evaluate the type and intensity of exercise and propose an appropriate exercise program. This makes it possible to evaluate the health status and propose a care plan based on the exercise data.

[0056] The care plan generation system can further include a nutrition monitoring unit. The nutrition monitoring unit can collect dietary data of the elderly person or care recipient and provide it to the analysis unit. For example, the nutrition monitoring unit can record the contents of meals and calorie intake. The nutrition monitoring unit can also evaluate nutritional balance and suggest appropriate meal menus. This makes it possible to evaluate health status and suggest care plans based on dietary data.

[0057] The care plan generation system may further include a sleep monitoring unit. The sleep monitoring unit can collect sleep data of the elderly person or care recipient and provide it to the analysis unit. For example, the sleep monitoring unit may record sleep duration and sleep quality. The sleep monitoring unit may also evaluate the sleep environment and propose appropriate improvement measures. This makes it possible to evaluate health conditions and propose care plans based on sleep data.

[0058] The care plan generation system can further include a rehabilitation monitoring unit. The rehabilitation monitoring unit can collect rehabilitation data of elderly people and care recipients and provide it to the analysis unit. For example, the rehabilitation monitoring unit can record the progress and effects of rehabilitation. The rehabilitation monitoring unit can also evaluate the content of rehabilitation and propose an appropriate program. This makes it possible to evaluate health conditions and propose care plans based on rehabilitation data.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The data collection unit continuously collects data on the elderly person or care recipient. For example, the data collection unit may use a wearable device to collect vital signs such as heart rate and blood pressure. The data collection unit may also use a smart home system to collect daily behavior data. Furthermore, the data collection unit may use a dedicated application to collect data such as dietary content and exercise volume. Step 2: The analysis unit analyzes the collected data and evaluates the health status of the elderly person or person receiving care. For example, the analysis unit may score the health status based on the collected vital sign data. The analysis unit may also analyze data on daily life behavior and evaluate activity levels. Furthermore, the analysis unit may analyze data on dietary content and exercise volume to evaluate nutritional status and physical strength. Step 3: The care plan generation unit automatically generates an optimal care plan based on the health condition evaluated by the analysis unit. For example, if the nutritional status is insufficient, the care plan generation unit can suggest a balanced meal menu. If the amount of exercise is insufficient, the care plan generation unit can also suggest an appropriate exercise program. Furthermore, if the quality of sleep is poor, the care plan generation unit can also suggest relaxation methods or measures to improve the sleeping environment. Step 4: The providing unit provides the care plan generated by the care plan generating unit to caregivers and other relevant parties. For example, the providing unit notifies the care plan through a smartphone app. The providing unit can also provide the care plan through a web portal. Furthermore, if feedback is desired in paper form, the providing unit can print out the results using a printer.

[0061] (Example 2) The care plan generation system according to an embodiment of the present invention continuously collects data on elderly people and care recipients, analyzes the data using a generation AI, automatically generates optimal care plans based on the individual health conditions and needs, and provides the plans to caregivers and other relevant parties. As a result, the care plan generation system can provide optimal care plans according to the health conditions of elderly people and care recipients.

[0062] A care plan generation system according to an embodiment includes a data collection unit, an analysis unit, a care plan generation unit, and a provision unit. The data collection unit continuously collects data on elderly people and care recipients. For example, the data collection unit collects vital signs such as heart rate and blood pressure using a wearable device. The data collection unit can also collect daily behavior data using a smart home system. The data collection unit can also use a dedicated application to collect data such as dietary content and exercise amount. The analysis unit analyzes the collected data and evaluates the health status of the elderly people and care recipients. For example, the analysis unit scores the health status based on the collected vital sign data. The analysis unit can also analyze the daily behavior data and evaluate the activity level. The analysis unit can also analyze data on dietary content and exercise amount to evaluate the nutritional status and physical strength. The care plan generation unit automatically generates an optimal care plan based on the health status evaluated by the analysis unit. For example, the care plan generation unit suggests a balanced meal menu if the nutritional status is insufficient. The care plan generation unit can also suggest an appropriate exercise program if the amount of exercise is insufficient. Furthermore, the care plan generation unit can also suggest relaxation methods and measures to improve the sleep environment if the quality of sleep is poor. The provision unit provides the care plan generated by the care plan generation unit to caregivers and related parties. For example, the provision unit notifies the care plan through a smartphone app. The provision unit can also provide the care plan through a web portal. Furthermore, if feedback is desired in paper form, the provision unit can print the results using a printer. This allows the care plan generation system to provide an optimal care plan based on the health condition of the elderly person or the person receiving care.

[0063] The data collection unit can collect data on the dietary content, exercise volume, sleep patterns, body temperature, blood pressure, and heart rate of elderly people or those receiving care. For example, the data collection unit scans handwritten answers and saves them as image data. It then converts the image data into text data using OCR technology. The data collection unit also takes a photo of the handwritten answer using a smartphone camera and converts the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The data collection unit also writes the handwritten answer with a dedicated digital pen, which converts the data into digital data in real time. For example, a sensor detects the movement of the pen and saves it as text data. Converting the handwritten answer into digital data makes it easier for generative AI to analyze.

[0064] The analysis unit can evaluate nutritional status or physical strength, and can evaluate sleep quality or physical condition from sleep patterns or body temperature. The analysis unit, for example, uses a pen pressure sensor to analyze how an answer is written. For example, it collects data on the strength of pen pressure and extracts the writer's characteristics. The analysis unit also tracks the movement of a digital pen to collect data on the writer's stroke order in order to analyze the stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. The analysis unit also combines the pen pressure and stroke order data to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in pen pressure and stroke order. In this way, the writer's characteristics can be grasped in detail by analyzing the pen pressure and stroke order.

[0065] The care plan generation unit can suggest a balanced meal menu if the student's nutritional status is insufficient, and can suggest an appropriate exercise program if the student's exercise volume is insufficient. For example, the care plan generation unit can capture the student's facial expression while writing an answer sheet with a camera and analyze the student's emotion using an emotion estimation algorithm. For example, the care plan generation unit can calculate an emotion score based on changes in facial expression. The care plan generation unit can also record the student's voice while writing an answer sheet and estimate the student's emotion using voice analysis technology. For example, the care plan generation unit can analyze the tone and speed of the voice and calculate an emotion score. The care plan generation unit can also collect the student's biometric data (heart rate and electrodermal activity) while writing an answer sheet with a sensor and analyze the student's emotion using an emotion estimation algorithm. For example, the care plan generation unit can calculate an emotion score based on fluctuations in heart rate. This allows the student's emotion to be reflected in the evaluation, enabling a more comprehensive evaluation.

[0066] The provision unit can notify the care plan through a smartphone app or web portal. For example, the provision unit records what the student dictates with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The provision unit also creates a system that recognizes what the student dictates in real time and displays it as text data. For example, the text is displayed on a screen simultaneously with the voice input. The provision unit also uses voice input to convert what the student dictates into text and saves the text data as an answer sheet. For example, voice recognition technology is used to perform highly accurate text conversion. This allows even students who have difficulty writing by hand or typing to submit answer sheets by using voice input.

[0067] The data collection unit can analyze images and diagrams and include visual information in the evaluation. For example, the data collection unit analyzes images and diagrams included in answer sheets using image recognition technology and converts the content into text data. For example, the content of the diagrams is automatically analyzed and reflected in the evaluation. The data collection unit also analyzes images and diagrams included in answer sheets and builds a system for evaluation based on visual information. For example, the content of the images is analyzed and reflected in the evaluation of the answer. The data collection unit also analyzes answer sheets that include images and diagrams and integrates the visual information with the text data for evaluation. For example, the visual information is analyzed using image recognition technology and reflected in the evaluation of the answer. In this way, analyzing answer sheets that include images and diagrams enables more detailed evaluation.

[0068] The data collection unit can monitor students' emotions in real time using an emotion estimation function and provide feedback according to their emotions. For example, the data collection unit uses a camera to capture the student's facial expression when reading an answer sheet and uses an emotion estimation algorithm to analyze the student's emotions in real time. For example, the data collection unit calculates an emotion score based on changes in facial expression and provides feedback. The data collection unit also records the student's voice when reading an answer sheet and uses voice analysis technology to estimate the student's emotion in real time. For example, the data collection unit analyzes the tone and speed of the voice to calculate an emotion score and provide feedback. The data collection unit also uses a sensor to collect the student's biometric data (heart rate and electrodermal activity) when reading an answer sheet and uses an emotion estimation algorithm to analyze the student's emotion in real time. For example, the data collection unit calculates an emotion score based on fluctuations in heart rate and provides feedback. In this way, the student's emotions can be monitored in real time and appropriate feedback can be provided, thereby improving learning effectiveness.

[0069] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0070] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0071] The summary generation unit uses the emotion estimation function to generate a summary that captures the emotional nuances of the answer, allowing the emotional elements to be reflected in the evaluation. For example, when the generation AI summarizes, the summary generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The summary generation unit also develops an algorithm for the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

[0072] The data collection unit can collect data not only from wearable devices but also from IoT devices in the home. For example, the data collection unit uses a smart refrigerator to automatically record meal contents and intake. For example, it monitors food consumption in the refrigerator and collects data. The data collection unit also uses a smart bed to monitor sleep patterns and body movements. For example, it measures sleep quality and body movements using sensors built into the bed and collects data. The data collection unit also integrates IoT devices in the home, and the generation AI analyzes the data. For example, data from the smart refrigerator and smart bed can be combined to evaluate health status. This makes it possible to collect data from IoT devices in the home and evaluate health status more comprehensively.

[0073] The data collection unit can use the emotion estimation function to evaluate how the elderly or care recipient feels about data collection and improve data collection methods. The data collection unit, for example, uses the emotion estimation function to evaluate how the elderly or care recipient feels about data collection. For example, it analyzes facial expressions and voice to estimate their emotional state. The data collection unit also improves the data collection method based on the emotional evaluation about data collection. For example, if there are a lot of negative emotions, it reviews the frequency and method of data collection. The data collection unit also collects emotion estimation data, and the generation AI analyzes that data. For example, it adjusts the data collection approach depending on the emotional state. This makes it possible to collect data that takes into consideration the emotions of the elderly or care recipient.

[0074] The analysis unit uses the emotion estimation function to analyze the emotional state of the elderly or care recipient, and can evaluate the impact of changes in emotion on their health condition. For example, the analysis unit uses the generative AI to analyze the emotional state of the elderly or care recipient using the emotion estimation function. For example, it calculates an emotion score based on facial expressions and voice data. The analysis unit also integrates emotion data and health data to evaluate the impact of changes in emotional state on their health condition. For example, it combines emotion scores with heart rate and blood pressure data for analysis. The analysis unit also collects long-term data to evaluate the impact of changes in emotional state on their health condition. For example, it analyzes fluctuations in emotion scores and changes in health condition over time. This makes it possible to evaluate the impact of emotional state on their health condition.

[0075] The analysis unit can be equipped with a function to detect abnormal values ​​or sudden changes by comparing with past data and issue an early warning. For example, the analysis unit develops an algorithm that the generation AI compares with past data to detect abnormal values ​​or sudden changes. For example, it detects sudden fluctuations in heart rate or blood pressure. The analysis unit also builds a system that issues an early warning when an abnormal value or sudden change is detected. For example, it notifies the warning via a smartphone app or web portal. The analysis unit also learns from past data so that the generation AI can detect abnormal values ​​or sudden changes. For example, it automatically distinguishes between normal and abnormal ranges. This enables early detection of abnormal values ​​or sudden changes and issue of a warning.

[0076] The analysis unit can build a health condition prediction model based on the analysis results and predict future health risks. In the analysis unit, for example, the generative AI builds a health condition prediction model based on the analysis results. For example, it learns from past data and develops an algorithm to predict future health risks. The analysis unit also uses the health condition prediction model to build a system that predicts future health risks. For example, it predicts the risk of heart disease and diabetes. In the analysis unit, the generative AI also continuously updates the health condition prediction model. For example, it re-learns the model every time new data is collected, improving prediction accuracy. This makes it possible to predict future health risks and take preventative measures.

[0077] The analysis unit can also include long-term data such as genetic information or family history in the evaluation of health status. For example, the analysis unit collects genetic test data to include genetic information in the evaluation of health status. For example, the analysis unit obtains genetic information using a genetic test kit and stores it in a database. The analysis unit also collects family health data to evaluate health status taking family history into consideration. For example, the analysis unit registers family medical history and health status in the database. The analysis unit also analyzes long-term data including genetic information and family history to evaluate health status. For example, health risk is evaluated based on genetic risk and family history. This makes it possible to evaluate health status taking genetic information and family history into consideration.

[0078] Based on the analysis results, the analysis unit can not only evaluate health status but also make suggestions for lifestyle improvement. For example, the generation AI uses the analysis results to evaluate health status and make suggestions for lifestyle improvement. For example, if exercise is lacking, an appropriate exercise program will be suggested. The analysis unit also makes suggestions for dietary improvement based on the health status evaluation results. For example, if nutritional balance is unbalanced, a balanced meal menu will be suggested. The analysis unit also analyzes individual data to make suggestions for lifestyle improvement. For example, if sleep quality is poor, it will suggest relaxation methods and measures to improve the sleeping environment. This makes it possible to make suggestions for lifestyle improvement based on the health status evaluation.

[0079] The analysis unit also uses the emotion estimation function to analyze the caregiver's emotional state and can provide stress management or support for the caregiver. The analysis unit, for example, uses the emotion estimation function to analyze the caregiver's emotional state. For example, it calculates an emotion score based on facial expressions and voice data. The analysis unit also analyzes the caregiver's emotional state and builds a system for stress management and support. For example, if stress is high, it suggests relaxation methods or counseling. The analysis unit also collects emotional data from the caregiver, and the generation AI analyzes that data. For example, it monitors fluctuations in the emotion score and provides advice for stress management. This makes it possible to analyze the caregiver's emotional state and provide stress management and support.

[0080] The care plan generation unit can use the emotion estimation function to generate a care plan according to the emotional state of the elderly person or care recipient. For example, the generation AI in the care plan generation unit uses the emotion estimation function to analyze the emotional state of the elderly person or care recipient and generates a care plan based on the results. For example, if the emotions are unstable, relaxation methods are suggested. The care plan generation unit also collects emotional data to generate a care plan according to the emotional state, and the generation AI analyzes the data. For example, if the emotion score is low, positive activities are suggested. The care plan generation unit also uses the emotion estimation function to adjust the content of the care plan. For example, if the emotions are stable, active activities are suggested, and if the emotions are unstable, relaxation methods are suggested. This makes it possible to provide a care plan according to the emotional state.

[0081] The care plan generation unit can evaluate the effectiveness of past care plans and optimize new care plans based on the results. For example, the care plan generation unit collects feedback data so that the generation AI can evaluate the effectiveness of past care plans. For example, it records the health and emotional state after the implementation of a care plan and analyzes the data. The care plan generation unit also evaluates the effectiveness of past care plans and develops an algorithm to optimize new care plans based on the results. For example, it incorporates elements of care plans that were highly effective. The care plan generation unit also allows the generation AI to learn data from past care plans and generate an optimal care plan. For example, it proposes a new care plan based on past success stories. This allows new care plans to be optimized based on the effectiveness of past care plans.

[0082] The care plan generation unit can also include elements of psychological support or mental health care in the care plan. For example, the care plan generation unit suggests psychological counseling or mental health care so that the generation AI can include elements of psychological support in the care plan. For example, it suggests regular counseling sessions. The care plan generation unit also analyzes emotional data to generate a care plan that includes elements of mental health care. For example, if stress is high, it suggests relaxation methods and stress management. The care plan generation unit also generates a care plan with elements of psychological support and mental health care. For example, it suggests a care plan that incorporates positive activities and hobbies. This makes it possible to provide a care plan that includes psychological support and mental health care.

[0083] The care plan generation unit can incorporate local medical resources or community support into the generation of the care plan. For example, the care plan generation unit collects data on local medical institutions and experts in order to incorporate local medical resources into the generation of the care plan. For example, it incorporates information on nearby hospitals and clinics. The care plan generation unit also collects information on local support groups and volunteer activities in order to generate a care plan that incorporates community support. For example, it suggests activities of local support groups. The care plan generation unit also uses the generation AI to analyze the data in order to generate a care plan that incorporates local medical resources and community support. For example, it suggests a care plan that utilizes local medical resources. This makes it possible to provide a care plan that utilizes local medical resources and community support.

[0084] The care plan generation unit can also make suggestions to reduce the caregiver's burden when generating a care plan. For example, when the generation AI generates a care plan, the care plan generation unit makes suggestions to reduce the caregiver's burden. For example, it proposes a schedule to ensure that the caregiver has time to rest. The care plan generation unit also incorporates support resources into the care plan to reduce the caregiver's burden. For example, it proposes support from local support groups and volunteers. The care plan generation unit also analyzes the caregiver's stress level and health condition so that the generation AI can generate a care plan to reduce the caregiver's burden. For example, if stress is high, it suggests relaxation methods. This makes it possible to provide a care plan that includes suggestions to reduce the caregiver's burden.

[0085] The care plan generation unit can use the emotion estimation function to monitor the emotional state of the elderly person or care recipient while the care plan is being implemented and adjust the plan as needed. The care plan generation unit, for example, uses the emotion estimation function to monitor the emotional state of the elderly person or care recipient while the care plan is being implemented. For example, it analyzes facial expressions and voice data and calculates an emotion score. The care plan generation unit also builds a system that monitors the emotional state while the care plan is being implemented and adjusts the plan as needed. For example, if the emotion score is low, it adds a relaxation method. The care plan generation unit also collects emotion estimation data, and the generation AI analyzes the data. For example, it dynamically adjusts the content of the care plan according to the emotional state. This makes it possible to monitor the emotional state while the care plan is being implemented and adjust the plan as needed.

[0086] The provision unit can use the emotion estimation function to evaluate the emotional reactions of the elderly person or care recipient when providing a care plan and collect feedback. In the provision unit, for example, the generation AI uses the emotion estimation function to evaluate the emotional reactions of the elderly person or care recipient when providing a care plan. For example, the generation AI analyzes facial expressions and voice data to calculate an emotion score. The provision unit also builds a system that evaluates the emotional reactions when providing a care plan and collects the data as feedback. For example, if the emotion score is low, the content of the care plan is revised. The provision unit also collects emotion estimation data, and the generation AI analyzes the data. For example, the effectiveness of the care plan is evaluated based on the emotional reactions and feedback is provided. This makes it possible to evaluate emotional reactions and collect feedback when providing a care plan.

[0087] The provision unit can evaluate the effectiveness of the care plan in real time based on the feedback and modify the plan as necessary. For example, the provision unit builds a system in which the generation AI evaluates the effectiveness of the care plan in real time based on the feedback. For example, the health and emotional states after the care plan is implemented are recorded and the data is analyzed. The provision unit also collects feedback data and the generation AI analyzes that data. For example, if the effectiveness of the care plan is low, the content of the plan is modified. The provision unit also allows the generation AI to dynamically adjust the content of the care plan based on the feedback. For example, elements of the care plan are added or deleted based on the feedback data. This allows the effectiveness of the care plan to be evaluated in real time based on the feedback and the plan to be modified as necessary.

[0088] The provision unit analyzes the feedback, identifies common problems or areas for improvement, and can reflect these in the next care plan. In the provision unit, for example, the generation AI analyzes the feedback and identifies common problems or areas for improvement. For example, it analyzes multiple pieces of feedback data and extracts common issues. The provision unit also builds a system that reflects this in the next care plan based on the feedback data. For example, it makes suggestions to solve common problems. In addition, the provision unit has the generation AI continuously collect feedback and reflect this in the next care plan. For example, it learns from the feedback data and optimizes the content of the care plan. This allows the feedback to be analyzed and common problems or areas for improvement to be reflected in the next care plan.

[0089] The provision unit can provide the care plan in an interactive format using a voice assistant or a robot. The provision unit, for example, uses a voice assistant to provide the care plan. For example, the care plan is notified by voice through a smart speaker. The provision unit also builds an interactive care plan provision system using a robot. For example, a robot explains the care plan to an elderly person or a care recipient and answers questions. The provision unit also provides the care plan interactively using a voice assistant or a robot. For example, the care plan is implemented according to voice instructions. This makes it possible to provide the care plan in an interactive format using a voice assistant or a robot.

[0090] The providing department can collect feedback in the form of periodic questionnaires or interviews to obtain more detailed information. The providing department, for example, collects feedback in the form of periodic questionnaires. For example, a questionnaire is sent after a care plan is implemented to obtain detailed feedback. The providing department also builds a system for collecting feedback in the form of interviews. For example, the providing department directly interacts with caregivers and related parties to hear about the effectiveness of the care plan and areas for improvement. The providing department also collects detailed feedback through periodic questionnaires and interviews. For example, the providing department asks for specific opinions on each element of the care plan. This makes it possible to collect detailed feedback in the form of periodic questionnaires and interviews.

[0091] The providing unit can also use the emotion estimation function to collect caregiver feedback and provide support tailored to the caregiver's needs. The providing unit, for example, uses the emotion estimation function to collect caregiver feedback. For example, it analyzes facial expressions and voice data to calculate an emotion score. The providing unit also builds a system that provides support tailored to the caregiver's needs based on the caregiver's feedback. For example, if stress is high, it suggests relaxation methods. The providing unit also collects emotion estimation data, and the generation AI analyzes that data. For example, it adjusts the support content according to the caregiver's emotional state. This makes it possible to collect caregiver feedback and provide support tailored to their needs.

[0092] The care plan generation unit can use the emotion estimation function to customize the care plan according to the emotional state of the elderly person or care recipient. For example, the generation AI uses the emotion estimation function to analyze the emotional state of the elderly person or care recipient and customizes the care plan based on the results. For example, if the emotions are unstable, relaxation methods are suggested. The care plan generation unit also collects emotional data to customize the care plan according to the emotional state, and the generation AI analyzes the data. For example, if the emotion score is low, positive activities are suggested. The care plan generation unit also uses the emotion estimation function to adjust the content of the care plan. For example, if the emotions are stable, active activities are suggested, and if the emotions are unstable, relaxation methods are suggested. This makes it possible to customize the care plan according to the emotional state.

[0093] The care plan generation unit can optimize the care plan by referring to past data when customizing the care plan based on an individual's health condition or lifestyle habits. The care plan generation unit, for example, refers to past data so that the generation AI can customize the care plan based on an individual's health condition or lifestyle habits. For example, it proposes an optimal care plan based on past health data or lifestyle data. The care plan generation unit also refers to past data to develop an algorithm that optimizes the customization of the care plan. For example, it adjusts the care plan based on past success stories. The care plan generation unit also allows the generation AI to learn from past data and customize the care plan based on an individual's health condition or lifestyle habits. For example, it proposes optimal meal menus and exercise programs based on past data. This allows the customization of the care plan to be optimized by referring to past data.

[0094] When customizing, the care plan generation unit can refer to the latest medical information or guidelines and provide the optimal care plan. In the care plan generation unit, for example, the generation AI refers to the latest medical information and guidelines and customizes the care plan. For example, it proposes a meal menu based on the latest nutritional guidelines. The care plan generation unit also regularly updates the medical information and guidelines, and the generation AI optimizes the care plan based on that information. For example, it incorporates new treatment and drug information. In addition, the care plan generation unit refers to medical information and guidelines and provides the optimal care plan according to each individual's health condition. For example, it proposes a care plan specialized for a specific disease or disability. This allows the optimal care plan to be provided by referring to the latest medical information and guidelines.

[0095] The care plan generation unit can customize the plan to meet the needs of not only the elderly person or the care recipient, but also the caregiver. For example, the care plan generation unit monitors the caregiver's health condition and stress level so that the generation AI can customize the plan to meet the caregiver's needs. For example, it measures the caregiver's heart rate and electrodermal activity and collects data. The care plan generation unit also collects feedback from the caregiver to generate a care plan that meets the caregiver's needs. For example, it adjusts the care plan based on the caregiver's opinions and requests. The care plan generation unit also suggests stress management and support for the caregiver so that the generation AI can meet the caregiver's needs. For example, it suggests relaxation methods and counseling. This makes it possible to customize the plan to meet the caregiver's needs.

[0096] The care plan generation unit can share the customized care plan with other medical institutions or experts and obtain feedback. For example, the care plan generation unit builds a data sharing platform to share the care plan customized by the generation AI with other medical institutions and experts. For example, the care plan is shared using a cloud-based system. The care plan generation unit also holds regular reviews and meetings to obtain feedback from other medical institutions and experts. For example, it listens to the opinions of experts on the content of the care plan. The care plan generation unit also optimizes the care plan based on feedback from other medical institutions and experts by the generation AI. For example, it analyzes the feedback data and adjusts the content of the care plan. This allows the care plan to be optimized by sharing it with other medical institutions and experts and obtaining feedback.

[0097] The care plan generation unit can use the emotion estimation function to monitor the emotional state of the elderly person or care recipient while the customized care plan is being implemented and adjust the plan as needed. The care plan generation unit, for example, uses the emotion estimation function to monitor the emotional state of the elderly person or care recipient while the customized care plan is being implemented. For example, it analyzes facial expressions and voice data and calculates an emotion score. The care plan generation unit also builds a system that monitors the emotional state while the care plan is being implemented and adjusts the plan as needed. For example, if the emotion score is low, it adds a relaxation method. The care plan generation unit also collects emotion estimation data, and the generation AI analyzes the data. For example, it dynamically adjusts the content of the care plan according to the emotional state. This makes it possible to monitor the emotional state while the customized care plan is being implemented and adjust the plan as needed.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The care plan generation system can further include a voice recognition unit. The voice recognition unit can convert dictated content from the elderly person or the care recipient into text data and provide it to the analysis unit. For example, the voice recognition unit can collect daily conversations and reports on physical condition by voice and save them as text data. The voice recognition unit can also analyze the voice data and evaluate the emotional state using an emotion estimation function. This makes it possible to utilize voice data to evaluate the health condition in more detail.

[0100] The care plan generation system may further include an environmental monitoring unit. The environmental monitoring unit can collect environmental data such as indoor temperature, humidity, and illuminance, and provide the data to the analysis unit. For example, the environmental monitoring unit can suggest using an air conditioner if the indoor temperature is too high. It can also suggest using a humidifier if the humidity is low. This makes it possible to evaluate health conditions and propose care plans based on environmental data.

[0101] The care plan generation system can further include a social activity monitoring unit. The social activity monitoring unit can collect data on the social activities of the elderly person or care recipient and provide it to the analysis unit. For example, the social activity monitoring unit can record the frequency of going out and the number of interactions with friends. The social activity monitoring unit can also use an emotion estimation function to evaluate the impact of social activities on the emotional state. This makes it possible to evaluate the health condition and propose a care plan based on the social activity data.

[0102] The care plan generation system may further include a hobby activity monitoring unit. The hobby activity monitoring unit can collect hobby activity data of the elderly person or the care recipient and provide it to the analysis unit. For example, the hobby activity monitoring unit records activities such as reading, handicrafts, and gardening. The hobby activity monitoring unit can also use an emotion estimation function to evaluate the impact of hobby activities on the emotional state. This makes it possible to evaluate the health condition and propose a care plan based on the hobby activity data.

[0103] The care plan generation system can further include a reminder function. The reminder function can periodically send notifications to encourage the implementation of the care plan. For example, the reminder function can notify the time to take medicine or exercise. The reminder function can also adjust the timing and content of notifications using an emotion estimation function. This supports the implementation of the care plan and enables the provision of effective care.

[0104] The care plan generation system may further include a virtual assistant unit. The virtual assistant unit may interact with the elderly person or the care recipient to support the implementation of the care plan. For example, the virtual assistant unit may explain the contents of the care plan and answer questions by voice. The virtual assistant unit may also use an emotion estimation function to evaluate the emotional state during the interaction and provide appropriate feedback. This allows the system to support the implementation of the care plan through the interaction and provide effective care.

[0105] The care plan generation system can further include an exercise monitoring unit. The exercise monitoring unit can collect exercise data of the elderly person or care recipient and provide it to the analysis unit. For example, the exercise monitoring unit can record the number of steps and exercise time. The exercise monitoring unit can also evaluate the type and intensity of exercise and propose an appropriate exercise program. This makes it possible to evaluate the health status and propose a care plan based on the exercise data.

[0106] The care plan generation system can further include a nutrition monitoring unit. The nutrition monitoring unit can collect dietary data of the elderly person or care recipient and provide it to the analysis unit. For example, the nutrition monitoring unit can record the contents of meals and calorie intake. The nutrition monitoring unit can also evaluate nutritional balance and suggest appropriate meal menus. This makes it possible to evaluate health status and suggest care plans based on dietary data.

[0107] The care plan generation system may further include a sleep monitoring unit. The sleep monitoring unit can collect sleep data of the elderly person or care recipient and provide it to the analysis unit. For example, the sleep monitoring unit may record sleep duration and sleep quality. The sleep monitoring unit may also evaluate the sleep environment and propose appropriate improvement measures. This makes it possible to evaluate health conditions and propose care plans based on sleep data.

[0108] The care plan generation system can further include a rehabilitation monitoring unit. The rehabilitation monitoring unit can collect rehabilitation data of elderly people and care recipients and provide it to the analysis unit. For example, the rehabilitation monitoring unit can record the progress and effects of rehabilitation. The rehabilitation monitoring unit can also evaluate the content of rehabilitation and propose an appropriate program. This makes it possible to evaluate health conditions and propose care plans based on rehabilitation data.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The data collection unit continuously collects data on the elderly person or care recipient. For example, the data collection unit may use a wearable device to collect vital signs such as heart rate and blood pressure. The data collection unit may also use a smart home system to collect daily behavior data. Furthermore, the data collection unit may use a dedicated application to collect data such as dietary content and exercise volume. Step 2: The analysis unit analyzes the collected data and evaluates the health status of the elderly person or person receiving care. For example, the analysis unit may score the health status based on the collected vital sign data. The analysis unit may also analyze data on daily life behavior and evaluate activity levels. Furthermore, the analysis unit may analyze data on dietary content and exercise volume to evaluate nutritional status and physical strength. Step 3: The care plan generation unit automatically generates an optimal care plan based on the health condition evaluated by the analysis unit. For example, if the nutritional status is insufficient, the care plan generation unit can suggest a balanced meal menu. If the amount of exercise is insufficient, the care plan generation unit can also suggest an appropriate exercise program. Furthermore, if the quality of sleep is poor, the care plan generation unit can also suggest relaxation methods or measures to improve the sleeping environment. Step 4: The providing unit provides the care plan generated by the care plan generating unit to caregivers and other relevant parties. For example, the providing unit notifies the care plan through a smartphone app. The providing unit can also provide the care plan through a web portal. Furthermore, if feedback is desired in paper form, the providing unit can print out the results using a printer.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] 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.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the 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.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] 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.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0156] 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.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection unit that uses generative AI to continuously collect data on elderly people or care recipients; an analysis unit that analyzes the data collected by the data collection unit and evaluates the health status of the elderly person or the care recipient; a care plan generation unit that automatically generates an optimal care plan based on the health condition evaluated by the analysis unit; a providing unit that provides the care plan generated by the care plan generating unit to a caregiver or a related person. A system characterized by:

2. The data collection unit Collecting data on the diet, exercise, sleep patterns, body temperature, blood pressure, and heart rate of the elderly person or the person receiving care.

2. The system of claim 1.

3. The analysis unit Evaluate nutritional status or physical fitness, and evaluate sleep quality or physical condition based on sleep patterns or body temperature 2. The system of claim 1.

4. The care plan generation unit If nutritional status is inadequate, we will suggest a balanced meal menu, and if exercise is insufficient, we will suggest an appropriate exercise program.

2. The system of claim 1.

5. The providing unit Communicate the care plan through a smartphone app or web portal 2. The system of claim 1.

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