System

Abstract

An object of a system according to an embodiment is to propose and execute an effective care plan by integrating health data and life data of an elderly person.SOLUTION: A system includes a health data collection part, a life data collection part, a care plan suggestion part, a care plan execution support part, and an evaluation feedback part. The health data collection unit collects health data of the elderly person. The life data collection unit collects life data of an elderly person. The care plan proposing section proposes a care plan based on the data collected by the health data collecting section and the living data collecting section. The care plan execution supporter supports the execution of the care plan proposed by the care plan proposer. The evaluation feedback part evaluates the effect of the care plan and performs feedback.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 have not adequately integrated health and lifestyle data of elderly people to propose and implement effective care plans, and there is room for improvement.

[0005] The system according to the embodiment aims to propose and implement effective care plans by integrating health data and lifestyle data of elderly people. [Means for solving the problem]

[0006] The system according to the embodiment includes a health data collection unit, a lifestyle data collection unit, a care plan proposal unit, a care plan execution support unit, and an evaluation feedback unit. The health data collection unit collects health data of the elderly. The lifestyle data collection unit collects lifestyle data of the elderly. The care plan proposal unit proposes a care plan based on the data collected by the health data collection unit and the lifestyle data collection unit. The care plan execution support unit supports the implementation of the care plan proposed by the care plan proposal unit. The evaluation feedback unit evaluates the effectiveness of the care plan and provides feedback. [Effects of the Invention]

[0007] The system according to the embodiment can integrate health data and lifestyle data of elderly people to propose and implement effective care plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The personal care system according to an embodiment of the present invention uses AI to monitor the health and living conditions of elderly people, and proposes and implements individually optimized care plans. This allows the personal care system to improve the quality of life of elderly people.

[0029] A personal care system according to an embodiment includes a health data collection unit, a lifestyle data collection unit, a care plan proposal unit, a care plan implementation support unit, and an evaluation feedback unit. The health data collection unit collects health data of the elderly. For example, it collects data such as heart rate, blood pressure, and body temperature using a wearable device. The health data collection unit can also collect health data manually entered using a smartphone app. The health data collection unit can also collect data by linking with home medical devices (e.g., blood glucose meters and oximeters). The lifestyle data collection unit collects lifestyle data of the elderly. For example, it can detect the elderly's movements using smart home devices and issue an alert if an abnormality is detected. The lifestyle data collection unit can also learn daily behavior patterns and detect abnormal behavior early. The lifestyle data collection unit can also link with smart home appliances and IoT devices to collect more detailed data. The care plan proposal unit proposes a care plan based on the data collected by the health data collection unit and the lifestyle data collection unit. For example, it can propose individually customized meal menus, exercise plans, and medication schedules. The care plan proposal unit can also provide a more personalized plan by taking into account past medical history and genetic information. Furthermore, the care plan proposal unit can incorporate the opinions of family members and caregivers to jointly create a plan. The care plan execution support unit supports the implementation of the care plan proposed by the care plan proposal unit. For example, it can use a reminder function to notify the elderly person of the timing of taking medication and exercising. The care plan execution support unit can also periodically check the elderly person's physical condition and mood using a voice assistant to supplement the data. Furthermore, the care plan execution support unit can automatically consult with a medical professional using the generation AI and arrange for remote medical treatment if necessary. The evaluation feedback unit evaluates the effectiveness of the care plan and provides feedback. For example, it can regularly evaluate the effectiveness of the care plan and revise the plan as necessary. The evaluation feedback unit can also monitor the elderly person's emotional state using an emotion estimation function to detect early signs of stress and anxiety.Furthermore, the evaluation feedback unit can provide emotional feedback to improve the motivation of the elderly. As a result, the personal care system according to the embodiment can comprehensively manage the health of the elderly and provide lifestyle support. For example, by monitoring the health status, abnormalities can be detected early and appropriate measures can be taken. Furthermore, by providing an individually optimized care plan, the quality of life of the elderly can be improved. Furthermore, by providing regular evaluation and feedback, it is possible to always provide optimal care.

[0030] The health data collection unit can collect heart rate, blood pressure, and body temperature data using a wearable device. For example, the health data collection unit collects heart rate, blood pressure, and body temperature data using a wearable device. For example, the heart rate can be monitored in real time using a smart watch. The health data collection unit can also periodically measure blood pressure using a fitness tracker. The health data collection unit can also continuously measure body temperature using a smart band. This allows for accurate collection of health data for the elderly.

[0031] The life data collection unit can detect the movements of the elderly using smart home devices and issue an alert if an abnormality is detected. The life data collection unit, for example, detects the movements of the elderly using smart home devices and issue an alert if an abnormality is detected. For example, the life data collection unit can detect the movements of the elderly using a motion sensor and issue an alert if an abnormality is detected. The life data collection unit can also monitor the movements of the elderly using a smart camera and issue an alert if an abnormality is detected. The life data collection unit can also detect when the elderly goes out using a smart door sensor and issue an alert if an abnormality is detected. This allows for accurate monitoring of the elderly's living conditions.

[0032] The care plan proposal unit can individually customize and propose meal menus, exercise plans, and medication schedules. The care plan proposal unit, for example, individually customizes and proposes meal menus, exercise plans, and medication schedules. For example, the generation AI can propose a nutritionally balanced meal menu based on the elderly person's health data. The care plan proposal unit can also propose an appropriate exercise plan based on the elderly person's lifestyle data. The care plan proposal unit can also manage the elderly person's medication schedule and propose medication at the appropriate time. This makes it possible to provide the elderly with the optimal care plan.

[0033] The care plan execution support unit can notify the elderly of the timing to take medication or exercise using a reminder function. The care plan execution support unit, for example, notifies the elderly of the timing to take medication or exercise using a reminder function. For example, the care plan execution support unit notifies the elderly of the timing to take medication using a smartphone app. The care plan execution support unit can also notify the elderly of the timing to exercise using a smart watch. The care plan execution support unit can also notify the elderly of the timing to take medication or exercise using a voice assistant. This allows the elderly to reliably carry out the care plan.

[0034] The evaluation feedback unit can periodically evaluate the effectiveness of the care plan and revise the plan as necessary. For example, the evaluation feedback unit can periodically evaluate the effectiveness of the care plan and revise the plan as necessary. For example, the generation AI analyzes health data and evaluates the effectiveness of the care plan. The evaluation feedback unit can also evaluate the effectiveness of the care plan based on lifestyle data and revise the plan as necessary. The evaluation feedback unit can also monitor the emotional state of the elderly person using an emotion estimation function and evaluate the effectiveness of the care plan. This allows the provision of optimal care at all times.

[0035] The health data collection unit takes medical history and genetic information into account, allowing for more accurate assessments of health status. For example, when the generation AI analyzes health data, the health data collection unit refers to past medical history and takes specific medical and treatment histories into account. For example, if a person has had heart disease in the past, abnormalities in heart rate and blood pressure can be detected earlier. The health data collection unit also allows the generation AI to evaluate specific genetic risks based on genetic information and reflect this in health status monitoring. For example, if there is a family history of diabetes, fluctuations in blood sugar levels can be monitored particularly carefully. The health data collection unit also integrates past medical history and genetic information when analyzing health data to create an individual health risk profile. This allows the generation AI to more accurately assess health status. This allows for more accurate assessments of the health status of elderly people.

[0036] The health data collection unit allows the generating AI to automatically consult with a medical professional based on the results of health data analysis, and arrange for remote medical treatment if necessary. For example, the health data collection unit builds a system in which the generating AI analyzes health data and automatically notifies a medical professional if an abnormality is detected. For example, if the heart rate is abnormally high, it will contact a cardiologist. The health data collection unit also allows the generating AI to automatically arrange a remote medical treatment appointment based on the results of health data analysis. For example, if blood pressure is high, it will schedule a video call with an internist. The health data collection unit also automates the process in which the generating AI analyzes health data and consults with a medical professional if necessary. For example, if abnormal temperature fluctuations are detected, it will contact an infectious disease specialist. This allows for prompt consultation with a medical professional and appropriate action to be taken if an elderly person's health condition is abnormal.

[0037] The health data collection unit can compare the data with that of other elderly people and set benchmarks for early detection of abnormal values. The health data collection unit, for example, sets benchmarks for the generation AI to compare with the health data of other elderly people and detect abnormal values ​​early. For example, it detects abnormalities by comparing with the average heart rate of people of the same age. In addition, the health data collection unit refers to the data of other elderly people when analyzing the health data and sets standards for abnormal values. For example, it compares with the average blood pressure to evaluate the risk of high blood pressure. In addition, the health data collection unit builds benchmarks based on the data of other elderly people for the generation AI to detect abnormal values ​​early. For example, it detects signs of fever based on the range of body temperature fluctuations. This enables early detection of abnormalities in the health status of elderly people.

[0038] The care plan proposal unit can build an individual health risk prediction model based on health data and predict future health risks. The care plan proposal unit builds an individual health risk prediction model based on health data collected by the generation AI, for example. For example, it predicts the risk of heart disease using heart rate and blood pressure data. The care plan proposal unit also develops a system in which the generation AI predicts future health risks based on the results of health data analysis. For example, it evaluates the risk of diabetes based on fluctuations in blood sugar levels. The care plan proposal unit also uses the health data collected by the generation AI to create an individual health risk profile and predict future health risks. For example, it evaluates the risk of infectious diseases based on fluctuations in body temperature. This makes it possible to predict the future health risks of elderly people and propose appropriate care plans.

[0039] The care plan proposal unit allows the generating AI to automatically provide health advice based on the results of health data analysis, thereby supporting health management in daily life. The care plan proposal unit, for example, builds a system in which the generating AI automatically provides health advice based on the results of health data analysis. For example, it may recommend improving diet and exercise. The care plan proposal unit also supports health management in daily life based on the results of health data analysis. For example, it may provide advice on appropriate sleep duration and stress management. The care plan proposal unit also develops a system in which the generating AI automatically provides health advice based on the results of health data analysis, thereby supporting health management in daily life. For example, it may recommend regular health checks and vaccinations. This can support the health management of elderly people in their daily lives.

[0040] The health data collection unit can use a voice assistant to periodically check the physical condition and mood of the elderly and supplement the data. For example, the health data collection unit uses a voice assistant to collect health data and builds a system to periodically check the physical condition and mood of the elderly. For example, a health check is performed by voice every morning. The health data collection unit also uses the voice assistant to check the physical condition and mood of the elderly and sends that data to the generation AI. For example, if the elderly feels unwell, they report it by voice. The health data collection unit also uses a voice assistant to collect health data and develops a system to periodically check the physical condition and mood of the elderly. For example, the voice assistant periodically asks about their physical condition and mood and sends that data to the generation AI. This allows the physical condition and mood of the elderly to be periodically checked and supplement the data.

[0041] The health data collection unit shares the collected health data with family members and caregivers, allowing them to jointly manage their health. For example, the health data collection unit builds a system for sharing health data collected by the generation AI with family members and caregivers. For example, health data is shared in real time, allowing them to jointly manage their health. The health data collection unit also uses the health data sharing function to allow family members and caregivers to understand the health status of the elderly person and jointly manage their health. For example, health data is shared via a smartphone app. The health data collection unit also develops a system for sharing health data collected by the generation AI with family members and caregivers, allowing them to jointly manage their health. For example, health data is stored in the cloud, allowing family members and caregivers to access it. This allows health data to be shared with family members and caregivers, allowing them to jointly manage their health.

[0042] The lifestyle data collection unit learns daily behavior patterns and can detect abnormal behavior early. For example, the lifestyle data collection unit uses a generation AI to learn the daily behavior patterns of elderly people and build a system that detects abnormal behavior early. For example, an alert is issued if the person does not wake up at their usual wake-up time. The lifestyle data collection unit also uses the generation AI to detect abnormal behavior through learning of behavior patterns. For example, an abnormality is detected if the person does not eat at their usual mealtime. The lifestyle data collection unit also uses a generation AI to learn daily behavior patterns and develop a system that detects abnormal behavior early. For example, an alert is issued if the person deviates from their usual walking route. This makes it possible to learn the daily behavior patterns of elderly people and detect abnormal behavior early.

[0043] The lifestyle data collection unit enables the generating AI to automatically make lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. The lifestyle data collection unit, for example, builds a system in which the generating AI automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it detects lack of exercise and suggests an exercise plan. The lifestyle data collection unit also automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it suggests a nutritionally balanced meal menu if the diet is unbalanced. The lifestyle data collection unit also develops a system in which the generating AI automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it suggests relaxation methods if the quality of sleep is poor. In this way, the living situation of the elderly can be monitored and lifestyle improvement suggestions can be automatically made.

[0044] The lifestyle data collection unit can collect more detailed data by linking smart home appliances and IoT devices to monitor lifestyle conditions. For example, the lifestyle data collection unit builds a system that links smart home appliances to monitor lifestyle conditions and collects detailed data. For example, it collects data on when a refrigerator is opened and closed to monitor meal frequency. The lifestyle data collection unit also uses IoT devices to collect detailed data on lifestyle conditions. For example, it collects data on smart lighting usage and analyzes sleep patterns. The lifestyle data collection unit also develops a system that links smart home appliances and IoT devices to collect detailed data on lifestyle conditions. For example, it collects data on smart speaker usage and monitors communication frequency. This allows for more detailed monitoring of the elderly's lifestyle conditions.

[0045] The lifestyle data collection unit can compare with the data of other elderly people and set a benchmark for early detection of abnormal behavior. The lifestyle data collection unit, for example, sets a benchmark for the generation AI to compare with the lifestyle data of other elderly people and detect abnormal behavior early. For example, it detects abnormalities by comparing with the average activity level of people of the same age. In addition, the lifestyle data collection unit references the data of other elderly people when monitoring their lifestyle conditions and sets standards for abnormal behavior. For example, it detects abnormalities by comparing with the usual number of meals. In addition, the lifestyle data collection unit builds a benchmark based on the data of other elderly people for the generation AI to detect abnormal behavior early. For example, it detects abnormalities by comparing with the usual frequency of going out. This enables early detection of abnormalities in the elderly person's lifestyle conditions.

[0046] The lifestyle data collection unit builds a predictive model for abnormal behavior based on past lifestyle data, enabling early response. In the lifestyle data collection unit, for example, the generation AI builds a predictive model for abnormal behavior based on past lifestyle data. For example, it analyzes past data and identifies patterns of abnormal behavior. The lifestyle data collection unit also builds a system in which the generation AI responds early based on the predictive model for abnormal behavior. For example, it issues an alert when abnormal behavior is predicted. The lifestyle data collection unit also develops a system in which the generation AI builds a predictive model for abnormal behavior based on past lifestyle data, enabling early response. For example, it notifies family members or caregivers when abnormal behavior is predicted. This makes it possible to predict abnormal behavior in elderly people and respond early.

[0047] The lifestyle data collection unit enables the generating AI to automatically make lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. The lifestyle data collection unit, for example, builds a system in which the generating AI automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it detects lack of exercise and suggests an exercise plan. The lifestyle data collection unit also automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it suggests a nutritionally balanced meal menu if the diet is unbalanced. The lifestyle data collection unit also develops a system in which the generating AI automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it suggests relaxation methods if the quality of sleep is poor. In this way, the living situation of the elderly can be monitored and lifestyle improvement suggestions can be automatically made.

[0048] The lifestyle data collection unit can collect more detailed data by linking smart home appliances and IoT devices to monitor lifestyle conditions. For example, the lifestyle data collection unit builds a system that links smart home appliances to monitor lifestyle conditions and collects detailed data. For example, it collects data on when a refrigerator is opened and closed to monitor meal frequency. The lifestyle data collection unit also uses IoT devices to collect detailed data on lifestyle conditions. For example, it collects data on smart lighting usage and analyzes sleep patterns. The lifestyle data collection unit also develops a system that links smart home appliances and IoT devices to collect detailed data on lifestyle conditions. For example, it collects data on smart speaker usage and monitors communication frequency. This allows for more detailed monitoring of the elderly's lifestyle conditions.

[0049] The lifestyle data collection unit can compare with the data of other elderly people and set a benchmark for early detection of abnormal behavior. The lifestyle data collection unit, for example, sets a benchmark for the generation AI to compare with the lifestyle data of other elderly people and detect abnormal behavior early. For example, it detects abnormalities by comparing with the average activity level of people of the same age. In addition, the lifestyle data collection unit references the data of other elderly people when monitoring their lifestyle conditions and sets standards for abnormal behavior. For example, it detects abnormalities by comparing with the usual number of meals. In addition, the lifestyle data collection unit builds a benchmark based on the data of other elderly people for the generation AI to detect abnormal behavior early. For example, it detects abnormalities by comparing with the usual frequency of going out. This enables early detection of abnormalities in the elderly person's lifestyle conditions.

[0050] The care plan proposal unit can provide a more personalized care plan by taking medical history and genetic information into account when creating the care plan. For example, in the care plan proposal unit, the generation AI proposes a personalized care plan based on past medical history. For example, if the patient has had heart disease in the past, it proposes a heart-friendly exercise plan. The care plan proposal unit also considers genetic information and provides a personalized care plan. For example, if diabetes runs in the family, it proposes a diet plan for diabetes prevention. The care plan proposal unit also builds a system in which the generation AI integrates past medical history and genetic information to provide a more personalized care plan. For example, it proposes a care plan that takes specific medical history and genetic risks into account. This makes it possible to provide a more personalized care plan for the elderly.

[0051] The care plan proposal unit can incorporate the opinions of family members and caregivers when proposing a care plan, and jointly create the plan. The care plan proposal unit, for example, builds a system that incorporates the opinions of family members and caregivers when proposing a care plan. For example, the generation AI takes into account meal menus proposed by family members. The care plan proposal unit also jointly creates a care plan based on the opinions of family members and caregivers. For example, the generation AI incorporates an exercise plan proposed by a caregiver. The care plan proposal unit also develops a system in which the generation AI incorporates the opinions of family members and caregivers when proposing a care plan, and jointly creates a plan. For example, the generation AI takes into account reminder functions proposed by family members. This makes it possible to provide a care plan that reflects the opinions of family members and caregivers.

[0052] The care plan proposal unit can compare the proposed care plan with data on other elderly people and select the optimal plan. For example, the care plan proposal unit builds a system that compares the care plan proposed by the generation AI with data on other elderly people and selects the optimal plan. For example, it selects an exercise plan by comparing it with the average amount of exercise for people of the same age. The care plan proposal unit also selects the optimal care plan based on data on other elderly people. For example, it selects a meal plan by referring to data on elderly people with the same medical history. The care plan proposal unit also develops a system that compares the care plan proposed by the generation AI with data on other elderly people and selects the optimal plan. For example, it selects a medication schedule by referring to data on elderly people with the same genetic risk. This makes it possible to provide the optimal care plan by comparing it with data on other elderly people.

[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] Personal care systems can also monitor the social activities of seniors to help prevent isolation. For example, the system can monitor how often seniors communicate with friends and family and issue an alert if there is no contact for a long period of time. The system can also provide information about local events and club activities to help seniors actively participate. Furthermore, the system can promote participation in online communities and support interactions with other seniors. This can prevent social isolation and maintain mental health in seniors.

[0055] The health data collection unit can also collect environmental data and evaluate factors that affect health status. For example, it can monitor indoor temperature, humidity, and air quality and provide advice on maintaining an appropriate environment. It can also collect external environmental data (e.g., weather and air pollution information) and suggest precautions to take when going out. Furthermore, based on the environmental data, the generative AI can suggest a living environment that is optimal for the elderly's health. This will help optimize the elderly's living environment and maintain their health.

[0056] The lifestyle data collection unit can also collect data on the elderly's hobbies and interests and make suggestions to improve their quality of life. For example, it can collect data on the elderly's favorite music and movies and suggest content for relaxation and enjoyment. It can also provide information on activities and events that interest the elderly and encourage active participation. Furthermore, based on the hobbies and interests, the generation AI can suggest new hobbies and activities that are suitable for the elderly. This can improve the quality of life of the elderly and maintain their mental health.

[0057] The care plan proposal unit can also propose more personalized meal plans by taking into account the elderly person's food preferences and allergy information. For example, if an elderly person has a preference for a particular ingredient, it can propose recipes using that ingredient. It can also provide safe meal plans based on allergy information. Furthermore, it is possible to build a system in which the generation AI learns the elderly person's food preferences and allergy information and always proposes the most optimal meal plan. This allows the elderly to enjoy their meals with peace of mind.

[0058] The care plan implementation support unit can also evaluate the elderly person's physical ability and strength and propose an appropriate exercise plan. For example, the generation AI can analyze the elderly person's exercise data and propose a reasonable exercise plan. It can also adjust the exercise intensity in stages according to the elderly person's physical strength and exercise ability. Furthermore, it can build a system in which the generation AI evaluates the effectiveness of exercise based on the elderly person's exercise data and modifies the plan as necessary. This allows the elderly person to continue exercising safely.

[0059] The health data collection unit can also collect sleep data from elderly people and evaluate their sleep quality. For example, the generation AI can analyze the sleep data of elderly people and evaluate their sleep quality. It can also suggest an appropriate sleeping environment based on the sleep data. Furthermore, it can build a system in which the generation AI provides advice on improving sleep quality based on the sleep data of elderly people. This can improve the sleep quality of elderly people and maintain their health.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The health data collection unit collects health data of the elderly person. For example, it collects data such as heart rate, blood pressure, and body temperature using a wearable device. It can also collect health data manually entered using a smartphone app or by linking with home medical devices (e.g., blood glucose monitors and oximeters). Step 2: The lifestyle data collection unit collects lifestyle data about the elderly. For example, it can use smart home devices to detect the elderly's movements and issue an alert if there are any abnormalities. It can also learn daily behavioral patterns and detect abnormal behavior early. Furthermore, it can connect smart home appliances and IoT devices to collect more detailed data. Step 3: The care plan proposal unit proposes a care plan based on the data collected by the health data collection unit and lifestyle data collection unit. For example, it proposes individually customized meal menus, exercise plans, and medication schedules. It can also provide a more personalized plan by taking into account past medical history and genetic information. It can also incorporate the opinions of family members and caregivers to jointly create a plan. Step 4: The care plan implementation support unit assists in the implementation of the care plan proposed by the care plan proposal unit. For example, it uses a reminder function to notify the elderly person of the timing of taking medication and exercising. It can also periodically check the elderly person's physical condition and mood using a voice assistant to supplement the data. Furthermore, the generation AI can automatically consult with a medical professional and arrange for remote medical treatment if necessary. Step 5: The evaluation and feedback unit evaluates the effectiveness of the care plan and provides feedback. For example, the effectiveness of the care plan can be evaluated periodically and revised as necessary. The emotional estimation function can also be used to monitor the elderly person's emotional state and detect signs of stress or anxiety early. Furthermore, emotional feedback can be provided to improve the elderly person's motivation.

[0062] (Example 2) The personal care system according to an embodiment of the present invention uses AI to monitor the health and living conditions of elderly people, and proposes and implements individually optimized care plans. This allows the personal care system to improve the quality of life of elderly people.

[0063] A personal care system according to an embodiment includes a health data collection unit, a lifestyle data collection unit, a care plan proposal unit, a care plan implementation support unit, and an evaluation feedback unit. The health data collection unit collects health data of the elderly. For example, it collects data such as heart rate, blood pressure, and body temperature using a wearable device. The health data collection unit can also collect health data manually entered using a smartphone app. The health data collection unit can also collect data by linking with home medical devices (e.g., blood glucose meters and oximeters). The lifestyle data collection unit collects lifestyle data of the elderly. For example, it can detect the elderly's movements using smart home devices and issue an alert if an abnormality is detected. The lifestyle data collection unit can also learn daily behavior patterns and detect abnormal behavior early. The lifestyle data collection unit can also link with smart home appliances and IoT devices to collect more detailed data. The care plan proposal unit proposes a care plan based on the data collected by the health data collection unit and the lifestyle data collection unit. For example, it can propose individually customized meal menus, exercise plans, and medication schedules. The care plan proposal unit can also provide a more personalized plan by taking into account past medical history and genetic information. Furthermore, the care plan proposal unit can incorporate the opinions of family members and caregivers to jointly create a plan. The care plan execution support unit supports the implementation of the care plan proposed by the care plan proposal unit. For example, it can use a reminder function to notify the elderly person of the timing of taking medication and exercising. The care plan execution support unit can also periodically check the elderly person's physical condition and mood using a voice assistant to supplement the data. Furthermore, the care plan execution support unit can automatically consult with a medical professional using the generation AI and arrange for remote medical treatment if necessary. The evaluation feedback unit evaluates the effectiveness of the care plan and provides feedback. For example, it can regularly evaluate the effectiveness of the care plan and revise the plan as necessary. The evaluation feedback unit can also monitor the elderly person's emotional state using an emotion estimation function to detect early signs of stress and anxiety.Furthermore, the evaluation feedback unit can provide emotional feedback to improve the motivation of the elderly. As a result, the personal care system according to the embodiment can comprehensively manage the health of the elderly and provide lifestyle support. For example, by monitoring the health status, abnormalities can be detected early and appropriate measures can be taken. Furthermore, by providing an individually optimized care plan, the quality of life of the elderly can be improved. Furthermore, by providing regular evaluation and feedback, it is possible to always provide optimal care.

[0064] The health data collection unit can collect heart rate, blood pressure, and body temperature data using a wearable device. For example, the health data collection unit collects heart rate, blood pressure, and body temperature data using a wearable device. For example, the heart rate can be monitored in real time using a smart watch. The health data collection unit can also periodically measure blood pressure using a fitness tracker. The health data collection unit can also continuously measure body temperature using a smart band. This allows for accurate collection of health data for the elderly.

[0065] The life data collection unit can detect the movements of the elderly using smart home devices and issue an alert if an abnormality is detected. The life data collection unit, for example, detects the movements of the elderly using smart home devices and issue an alert if an abnormality is detected. For example, the life data collection unit can detect the movements of the elderly using a motion sensor and issue an alert if an abnormality is detected. The life data collection unit can also monitor the movements of the elderly using a smart camera and issue an alert if an abnormality is detected. The life data collection unit can also detect when the elderly goes out using a smart door sensor and issue an alert if an abnormality is detected. This allows for accurate monitoring of the elderly's living conditions.

[0066] The care plan proposal unit can individually customize and propose meal menus, exercise plans, and medication schedules. The care plan proposal unit, for example, individually customizes and proposes meal menus, exercise plans, and medication schedules. For example, the generation AI can propose a nutritionally balanced meal menu based on the elderly person's health data. The care plan proposal unit can also propose an appropriate exercise plan based on the elderly person's lifestyle data. The care plan proposal unit can also manage the elderly person's medication schedule and propose medication at the appropriate time. This makes it possible to provide the elderly with the optimal care plan.

[0067] The care plan execution support unit can notify the elderly of the timing to take medication or exercise using a reminder function. The care plan execution support unit, for example, notifies the elderly of the timing to take medication or exercise using a reminder function. For example, the care plan execution support unit notifies the elderly of the timing to take medication using a smartphone app. The care plan execution support unit can also notify the elderly of the timing to exercise using a smart watch. The care plan execution support unit can also notify the elderly of the timing to take medication or exercise using a voice assistant. This allows the elderly to reliably carry out the care plan.

[0068] The evaluation feedback unit can periodically evaluate the effectiveness of the care plan and revise the plan as necessary. For example, the evaluation feedback unit can periodically evaluate the effectiveness of the care plan and revise the plan as necessary. For example, the generation AI analyzes health data and evaluates the effectiveness of the care plan. The evaluation feedback unit can also evaluate the effectiveness of the care plan based on lifestyle data and revise the plan as necessary. The evaluation feedback unit can also monitor the emotional state of the elderly person using an emotion estimation function and evaluate the effectiveness of the care plan. This allows the provision of optimal care at all times.

[0069] The health data collection unit takes medical history and genetic information into account, allowing for more accurate assessments of health status. For example, when the generation AI analyzes health data, the health data collection unit refers to past medical history and takes specific medical and treatment histories into account. For example, if a person has had heart disease in the past, abnormalities in heart rate and blood pressure can be detected earlier. The health data collection unit also allows the generation AI to evaluate specific genetic risks based on genetic information and reflect this in health status monitoring. For example, if there is a family history of diabetes, fluctuations in blood sugar levels can be monitored particularly carefully. The health data collection unit also integrates past medical history and genetic information when analyzing health data to create an individual health risk profile. This allows the generation AI to more accurately assess health status. This allows for more accurate assessments of the health status of elderly people.

[0070] The health data collection unit allows the generating AI to automatically consult with a medical professional based on the results of health data analysis, and arrange for remote medical treatment if necessary. For example, the health data collection unit builds a system in which the generating AI analyzes health data and automatically notifies a medical professional if an abnormality is detected. For example, if the heart rate is abnormally high, it will contact a cardiologist. The health data collection unit also allows the generating AI to automatically arrange a remote medical treatment appointment based on the results of health data analysis. For example, if blood pressure is high, it will schedule a video call with an internist. The health data collection unit also automates the process in which the generating AI analyzes health data and consults with a medical professional if necessary. For example, if abnormal temperature fluctuations are detected, it will contact an infectious disease specialist. This allows for prompt consultation with a medical professional and appropriate action to be taken if an elderly person's health condition is abnormal.

[0071] The health data collection unit uses the emotion estimation function to monitor the emotional state of the elderly along with the health data, allowing for early detection of signs of stress and anxiety. In the health data collection unit, for example, the generation AI uses the emotion estimation function to monitor the emotional state of the elderly along with the health data. For example, signs of anxiety are detected along with an increase in heart rate. The health data collection unit also uses the emotion estimation function to analyze changes in the elderly's emotions in real time in accordance with fluctuations in health data. For example, signs of stress are detected along with an increase in blood pressure. The health data collection unit also integrates the health data and emotion data, building a system in which the generation AI detects signs of stress and anxiety early. For example, changes in emotions are analyzed along with fluctuations in body temperature. This allows for monitoring the emotional state of the elderly and early detection of signs of stress and anxiety.

[0072] The health data collection unit can compare the data with that of other elderly people and set benchmarks for early detection of abnormal values. The health data collection unit, for example, sets benchmarks for the generation AI to compare with the health data of other elderly people and detect abnormal values ​​early. For example, it detects abnormalities by comparing with the average heart rate of people of the same age. In addition, the health data collection unit refers to the data of other elderly people when analyzing the health data and sets standards for abnormal values. For example, it compares with the average blood pressure to evaluate the risk of high blood pressure. In addition, the health data collection unit builds benchmarks based on the data of other elderly people for the generation AI to detect abnormal values ​​early. For example, it detects signs of fever based on the range of body temperature fluctuations. This enables early detection of abnormalities in the health status of elderly people.

[0073] The health data collection unit can use the emotion estimation function to detect emotional changes in the elderly in real time while their health condition is being monitored and suggest relaxation methods as needed. For example, the health data collection unit uses the emotion estimation function to detect emotional changes in the elderly in real time while their health condition is being monitored. For example, it detects signs of stress and suggests relaxation methods. The health data collection unit also integrates the health data and emotion data, and a generative AI analyzes the elderly's emotional changes. For example, it detects signs of anxiety and suggests deep breathing or meditation. The health data collection unit also uses the emotion estimation function to analyze emotional changes in the elderly in real time while their health condition is being monitored and builds a system that suggests relaxation methods. For example, it suggests relaxing music or aromatherapy. This makes it possible to detect emotional changes in the elderly in real time and suggest appropriate relaxation methods.

[0074] The care plan proposal unit can build an individual health risk prediction model based on health data and predict future health risks. The care plan proposal unit builds an individual health risk prediction model based on health data collected by the generation AI, for example. For example, it predicts the risk of heart disease using heart rate and blood pressure data. The care plan proposal unit also develops a system in which the generation AI predicts future health risks based on the results of health data analysis. For example, it evaluates the risk of diabetes based on fluctuations in blood sugar levels. The care plan proposal unit also uses the health data collected by the generation AI to create an individual health risk profile and predict future health risks. For example, it evaluates the risk of infectious diseases based on fluctuations in body temperature. This makes it possible to predict the future health risks of elderly people and propose appropriate care plans.

[0075] The care plan proposal unit allows the generating AI to automatically provide health advice based on the results of health data analysis, thereby supporting health management in daily life. The care plan proposal unit, for example, builds a system in which the generating AI automatically provides health advice based on the results of health data analysis. For example, it may recommend improving diet and exercise. The care plan proposal unit also supports health management in daily life based on the results of health data analysis. For example, it may provide advice on appropriate sleep duration and stress management. The care plan proposal unit also develops a system in which the generating AI automatically provides health advice based on the results of health data analysis, thereby supporting health management in daily life. For example, it may recommend regular health checks and vaccinations. This can support the health management of elderly people in their daily lives.

[0076] The care plan proposal unit uses the emotion estimation function to analyze changes in the emotions of the elderly that accompany fluctuations in health data and can provide emotional support. The care plan proposal unit, for example, uses the emotion estimation function to analyze changes in the emotions of the elderly that accompany fluctuations in health data. For example, it detects signs of anxiety along with an increase in heart rate and provides emotional support. The care plan proposal unit also integrates health data and emotional data to build a system in which a generative AI analyzes changes in the emotions of the elderly. For example, it detects signs of stress along with fluctuations in blood pressure and suggests relaxation methods. The care plan proposal unit also uses the emotion estimation function to develop a system that analyzes changes in the emotions of the elderly that accompany fluctuations in health data in real time and provides emotional support. For example, it analyzes changes in emotions along with fluctuations in body temperature and provides emotional support. This makes it possible to analyze changes in the emotions of the elderly and provide appropriate emotional support.

[0077] The health data collection unit can use a voice assistant to periodically check the physical condition and mood of the elderly and supplement the data. For example, the health data collection unit uses a voice assistant to collect health data and builds a system to periodically check the physical condition and mood of the elderly. For example, a health check is performed by voice every morning. The health data collection unit also uses the voice assistant to check the physical condition and mood of the elderly and sends that data to the generation AI. For example, if the elderly feels unwell, they report it by voice. The health data collection unit also uses a voice assistant to collect health data and develops a system to periodically check the physical condition and mood of the elderly. For example, the voice assistant periodically asks about their physical condition and mood and sends that data to the generation AI. This allows the physical condition and mood of the elderly to be periodically checked and supplement the data.

[0078] The health data collection unit shares the collected health data with family members and caregivers, allowing them to jointly manage their health. For example, the health data collection unit builds a system for sharing health data collected by the generation AI with family members and caregivers. For example, health data is shared in real time, allowing them to jointly manage their health. The health data collection unit also uses the health data sharing function to allow family members and caregivers to understand the health status of the elderly person and jointly manage their health. For example, health data is shared via a smartphone app. The health data collection unit also develops a system for sharing health data collected by the generation AI with family members and caregivers, allowing them to jointly manage their health. For example, health data is stored in the cloud, allowing family members and caregivers to access it. This allows health data to be shared with family members and caregivers, allowing them to jointly manage their health.

[0079] The health data collection unit can use the emotion estimation function to provide emotional feedback based on the analysis results of health data, thereby improving the motivation of elderly people. For example, the health data collection unit builds a system that uses the emotion estimation function to provide emotional feedback based on the analysis results of health data. For example, it sends positive messages for progress in health improvement. The health data collection unit also provides emotional feedback based on the analysis results of health data to improve the motivation of elderly people. For example, it sends encouraging messages for exercise results. The health data collection unit also develops a system that uses the emotion estimation function to provide emotional feedback based on the analysis results of health data, thereby improving the motivation of elderly people. For example, it sends words of praise for achieving health goals. This makes it possible to provide emotional feedback to improve the motivation of elderly people.

[0080] The lifestyle data collection unit learns daily behavior patterns and can detect abnormal behavior early. For example, the lifestyle data collection unit uses a generation AI to learn the daily behavior patterns of elderly people and build a system that detects abnormal behavior early. For example, an alert is issued if the person does not wake up at their usual wake-up time. The lifestyle data collection unit also uses the generation AI to detect abnormal behavior through learning of behavior patterns. For example, an abnormality is detected if the person does not eat at their usual mealtime. The lifestyle data collection unit also uses a generation AI to learn daily behavior patterns and develop a system that detects abnormal behavior early. For example, an alert is issued if the person deviates from their usual walking route. This makes it possible to learn the daily behavior patterns of elderly people and detect abnormal behavior early.

[0081] The lifestyle data collection unit enables the generating AI to automatically make lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. The lifestyle data collection unit, for example, builds a system in which the generating AI automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it detects lack of exercise and suggests an exercise plan. The lifestyle data collection unit also automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it suggests a nutritionally balanced meal menu if the diet is unbalanced. The lifestyle data collection unit also develops a system in which the generating AI automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it suggests relaxation methods if the quality of sleep is poor. In this way, the living situation of the elderly can be monitored and lifestyle improvement suggestions can be automatically made.

[0082] The lifestyle data collection unit can collect more detailed data by linking smart home appliances and IoT devices to monitor lifestyle conditions. For example, the lifestyle data collection unit builds a system that links smart home appliances to monitor lifestyle conditions and collects detailed data. For example, it collects data on when a refrigerator is opened and closed to monitor meal frequency. The lifestyle data collection unit also uses IoT devices to collect detailed data on lifestyle conditions. For example, it collects data on smart lighting usage and analyzes sleep patterns. The lifestyle data collection unit also develops a system that links smart home appliances and IoT devices to collect detailed data on lifestyle conditions. For example, it collects data on smart speaker usage and monitors communication frequency. This allows for more detailed monitoring of the elderly's lifestyle conditions.

[0083] The lifestyle data collection unit can compare with the data of other elderly people and set a benchmark for early detection of abnormal behavior. The lifestyle data collection unit, for example, sets a benchmark for the generation AI to compare with the lifestyle data of other elderly people and detect abnormal behavior early. For example, it detects abnormalities by comparing with the average activity level of people of the same age. In addition, the lifestyle data collection unit references the data of other elderly people when monitoring their lifestyle conditions and sets standards for abnormal behavior. For example, it detects abnormalities by comparing with the usual number of meals. In addition, the lifestyle data collection unit builds a benchmark based on the data of other elderly people for the generation AI to detect abnormal behavior early. For example, it detects abnormalities by comparing with the usual frequency of going out. This enables early detection of abnormalities in the elderly person's lifestyle conditions.

[0084] The life data collection unit can use the emotion estimation function to detect emotional changes in the elderly person in real time while monitoring their living conditions and suggest relaxation methods as needed. The life data collection unit, for example, uses the emotion estimation function to detect emotional changes in the elderly person in real time while monitoring their living conditions. For example, it detects signs of stress and suggests relaxation methods. The life data collection unit also builds a system that analyzes emotional changes in the elderly person while monitoring their living conditions and suggests relaxation methods as needed. For example, it detects signs of anxiety and suggests deep breathing or meditation. The life data collection unit also develops a system that uses the emotion estimation function to analyze emotional changes in the elderly person in real time while monitoring their living conditions and suggests relaxation methods. For example, it suggests relaxing music or aromatherapy. This makes it possible to detect emotional changes in the elderly person in real time and suggest appropriate relaxation methods.

[0085] The lifestyle data collection unit builds a predictive model for abnormal behavior based on past lifestyle data, enabling early response. In the lifestyle data collection unit, for example, the generation AI builds a predictive model for abnormal behavior based on past lifestyle data. For example, it analyzes past data and identifies patterns of abnormal behavior. The lifestyle data collection unit also builds a system in which the generation AI responds early based on the predictive model for abnormal behavior. For example, it issues an alert when abnormal behavior is predicted. The lifestyle data collection unit also develops a system in which the generation AI builds a predictive model for abnormal behavior based on past lifestyle data, enabling early response. For example, it notifies family members or caregivers when abnormal behavior is predicted. This makes it possible to predict abnormal behavior in elderly people and respond early.

[0086] The lifestyle data collection unit enables the generating AI to automatically make lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. The lifestyle data collection unit, for example, builds a system in which the generating AI automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it detects lack of exercise and suggests an exercise plan. The lifestyle data collection unit also automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it suggests a nutritionally balanced meal menu if the diet is unbalanced. The lifestyle data collection unit also develops a system in which the generating AI automatically makes lifestyle improvement suggestions based on the results of monitoring the lifestyle situation. For example, it suggests relaxation methods if the quality of sleep is poor. In this way, the living situation of the elderly can be monitored and lifestyle improvement suggestions can be automatically made.

[0087] The life data collection unit can use the emotion estimation function to analyze changes in the emotions of elderly people due to changes in their living situations and provide emotional support. The life data collection unit, for example, uses the emotion estimation function to build a system that analyzes changes in the emotions of elderly people due to changes in their living situations. For example, emotional support is provided when they feel lonely. The life data collection unit also analyzes changes in the emotions of elderly people due to changes in their living situations and provides emotional support. For example, it suggests ways to relax when they feel stressed. The life data collection unit also uses the emotion estimation function to analyze changes in the emotions of elderly people due to changes in their living situations in real time and develops a system that provides emotional support. For example, if they feel anxious, it sends a message that gives them a sense of security. This makes it possible to analyze changes in the emotions of elderly people and provide appropriate emotional support.

[0088] The lifestyle data collection unit can collect more detailed data by linking smart home appliances and IoT devices to monitor lifestyle conditions. For example, the lifestyle data collection unit builds a system that links smart home appliances to monitor lifestyle conditions and collects detailed data. For example, it collects data on when a refrigerator is opened and closed to monitor meal frequency. The lifestyle data collection unit also uses IoT devices to collect detailed data on lifestyle conditions. For example, it collects data on smart lighting usage and analyzes sleep patterns. The lifestyle data collection unit also develops a system that links smart home appliances and IoT devices to collect detailed data on lifestyle conditions. For example, it collects data on smart speaker usage and monitors communication frequency. This allows for more detailed monitoring of the elderly's lifestyle conditions.

[0089] The lifestyle data collection unit can compare with the data of other elderly people and set a benchmark for early detection of abnormal behavior. The lifestyle data collection unit, for example, sets a benchmark for the generation AI to compare with the lifestyle data of other elderly people and detect abnormal behavior early. For example, it detects abnormalities by comparing with the average activity level of people of the same age. In addition, the lifestyle data collection unit references the data of other elderly people when monitoring their lifestyle conditions and sets standards for abnormal behavior. For example, it detects abnormalities by comparing with the usual number of meals. In addition, the lifestyle data collection unit builds a benchmark based on the data of other elderly people for the generation AI to detect abnormal behavior early. For example, it detects abnormalities by comparing with the usual frequency of going out. This enables early detection of abnormalities in the elderly person's lifestyle conditions.

[0090] The life data collection unit can use the emotion estimation function to detect emotional changes in the elderly person in real time while monitoring their living conditions and suggest relaxation methods as needed. The life data collection unit, for example, uses the emotion estimation function to detect emotional changes in the elderly person in real time while monitoring their living conditions. For example, it detects signs of stress and suggests relaxation methods. The life data collection unit also builds a system that analyzes emotional changes in the elderly person while monitoring their living conditions and suggests relaxation methods as needed. For example, it detects signs of anxiety and suggests deep breathing or meditation. The life data collection unit also develops a system that uses the emotion estimation function to analyze emotional changes in the elderly person in real time while monitoring their living conditions and suggests relaxation methods. For example, it suggests relaxing music or aromatherapy. This makes it possible to detect emotional changes in the elderly person in real time and suggest appropriate relaxation methods.

[0091] The care plan proposal unit can provide a more personalized care plan by taking medical history and genetic information into account when creating the care plan. For example, in the care plan proposal unit, the generation AI proposes a personalized care plan based on past medical history. For example, if the patient has had heart disease in the past, it proposes a heart-friendly exercise plan. The care plan proposal unit also considers genetic information and provides a personalized care plan. For example, if diabetes runs in the family, it proposes a diet plan for diabetes prevention. The care plan proposal unit also builds a system in which the generation AI integrates past medical history and genetic information to provide a more personalized care plan. For example, it proposes a care plan that takes specific medical history and genetic risks into account. This makes it possible to provide a more personalized care plan for the elderly.

[0092] The care plan proposal unit uses the emotion estimation function to consider the emotional state of the elderly person when proposing a care plan, and can provide a plan that is emotionally easy to accept. The care plan proposal unit, for example, uses the emotion estimation function to build a system that considers the emotional state of the elderly person when proposing a care plan. For example, if the elderly person is feeling stressed, it suggests relaxation methods. The care plan proposal unit also considers the emotional state of the elderly person when proposing a care plan, and provides a plan that is emotionally easy to accept. For example, if the elderly person is feeling anxious, it sends a message that gives a sense of security. The care plan proposal unit also uses the emotion estimation function to analyze the emotional state of the elderly person in real time when proposing a care plan, and develops a system that provides a plan that is emotionally easy to accept. For example, it makes suggestions that elicit positive emotions. This makes it possible to provide a care plan that is emotionally easy for the elderly person to accept.

[0093] The care plan proposal unit can incorporate the opinions of family members and caregivers when proposing a care plan, and jointly create the plan. The care plan proposal unit, for example, builds a system that incorporates the opinions of family members and caregivers when proposing a care plan. For example, the generation AI takes into account meal menus proposed by family members. The care plan proposal unit also jointly creates a care plan based on the opinions of family members and caregivers. For example, the generation AI incorporates an exercise plan proposed by a caregiver. The care plan proposal unit also develops a system in which the generation AI incorporates the opinions of family members and caregivers when proposing a care plan, and jointly creates a plan. For example, the generation AI takes into account reminder functions proposed by family members. This makes it possible to provide a care plan that reflects the opinions of family members and caregivers.

[0094] The care plan proposal unit can compare the proposed care plan with data on other elderly people and select the optimal plan. For example, the care plan proposal unit builds a system that compares the care plan proposed by the generation AI with data on other elderly people and selects the optimal plan. For example, it selects an exercise plan by comparing it with the average amount of exercise for people of the same age. The care plan proposal unit also selects the optimal care plan based on data on other elderly people. For example, it selects a meal plan by referring to data on elderly people with the same medical history. The care plan proposal unit also develops a system that compares the care plan proposed by the generation AI with data on other elderly people and selects the optimal plan. For example, it selects a medication schedule by referring to data on elderly people with the same genetic risk. This makes it possible to provide the optimal care plan by comparing it with data on other elderly people.

[0095] The care plan proposal unit uses the emotion estimation function to consider the emotional state of the elderly person when proposing a care plan, and can provide a plan that is emotionally easy to accept. The care plan proposal unit, for example, uses the emotion estimation function to build a system that considers the emotional state of the elderly person when proposing a care plan. For example, if the elderly person is feeling stressed, it suggests relaxation methods. The care plan proposal unit also considers the emotional state of the elderly person when proposing a care plan, and provides a plan that is emotionally easy to accept. For example, if the elderly person is feeling anxious, it sends a message that gives a sense of security. The care plan proposal unit also uses the emotion estimation function to analyze the emotional state of the elderly person in real time when proposing a care plan, and develops a system that provides a plan that is emotionally easy to accept. For example, it makes suggestions that elicit positive emotions. This makes it possible to provide a care plan that is emotionally easy for the elderly person to accept.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] Personal care systems can also monitor the social activities of seniors to help prevent isolation. For example, the system can monitor how often seniors communicate with friends and family and issue an alert if there is no contact for a long period of time. The system can also provide information about local events and club activities to help seniors actively participate. Furthermore, the system can promote participation in online communities and support interactions with other seniors. This can prevent social isolation and maintain mental health in seniors.

[0098] The health data collection unit can also collect environmental data and evaluate factors that affect health status. For example, it can monitor indoor temperature, humidity, and air quality and provide advice on maintaining an appropriate environment. It can also collect external environmental data (e.g., weather and air pollution information) and suggest precautions to take when going out. Furthermore, based on the environmental data, the generative AI can suggest a living environment that is optimal for the elderly's health. This will help optimize the elderly's living environment and maintain their health.

[0099] The lifestyle data collection unit can also collect data on the elderly's hobbies and interests and make suggestions to improve their quality of life. For example, it can collect data on the elderly's favorite music and movies and suggest content for relaxation and enjoyment. It can also provide information on activities and events that interest the elderly and encourage active participation. Furthermore, based on the hobbies and interests, the generation AI can suggest new hobbies and activities that are suitable for the elderly. This can improve the quality of life of the elderly and maintain their mental health.

[0100] The care plan proposal unit can also propose more personalized meal plans by taking into account the elderly person's food preferences and allergy information. For example, if an elderly person has a preference for a particular ingredient, it can propose recipes using that ingredient. It can also provide safe meal plans based on allergy information. Furthermore, it is possible to build a system in which the generation AI learns the elderly person's food preferences and allergy information and always proposes the most optimal meal plan. This allows the elderly to enjoy their meals with peace of mind.

[0101] The care plan implementation support unit can also evaluate the elderly person's physical ability and strength and propose an appropriate exercise plan. For example, the generation AI can analyze the elderly person's exercise data and propose a reasonable exercise plan. It can also adjust the exercise intensity in stages according to the elderly person's physical strength and exercise ability. Furthermore, it can build a system in which the generation AI evaluates the effectiveness of exercise based on the elderly person's exercise data and modifies the plan as necessary. This allows the elderly person to continue exercising safely.

[0102] The evaluation feedback unit can also monitor the emotional state of the elderly and provide emotional feedback. For example, the generation AI can analyze the elderly's emotional data and send positive messages. It can also suggest ways for the elderly to relax if they are feeling stressed. Furthermore, the generation AI can build a system that provides emotional support based on the elderly's emotional data. This makes it possible to monitor the elderly's emotional state and provide appropriate feedback.

[0103] The health data collection unit can also collect sleep data from elderly people and evaluate their sleep quality. For example, the generation AI can analyze the sleep data of elderly people and evaluate their sleep quality. It can also suggest an appropriate sleeping environment based on the sleep data. Furthermore, it can build a system in which the generation AI provides advice on improving sleep quality based on the sleep data of elderly people. This can improve the sleep quality of elderly people and maintain their health.

[0104] The health data collection unit can also monitor the emotional state of the elderly and integrate and analyze the emotional data with health data. For example, the generation AI can analyze the emotional data of the elderly and identify emotional factors that affect their health status. It can also integrate emotional data and health data to provide a more accurate assessment of their health status. Furthermore, the generation AI can build a system that provides emotional support based on the elderly's emotional data. This allows for a more accurate assessment of the elderly's health status and the provision of appropriate support.

[0105] The lifestyle data collection unit can also monitor the emotional state of the elderly and integrate and analyze the emotional data with lifestyle data. For example, the generation AI can analyze the elderly's emotional data and identify emotional factors that affect their lifestyle. It can also integrate the emotional data and lifestyle data to more accurately evaluate their lifestyle. Furthermore, the generation AI can build a system that provides emotional support based on the elderly's emotional data. This allows for a more accurate evaluation of the elderly's lifestyle and the provision of appropriate support.

[0106] The care plan proposal unit can also monitor the emotional state of the elderly person and reflect the emotional data in the care plan. For example, the generation AI can analyze the emotional data of the elderly person and propose a care plan that corresponds to their emotional state. It can also provide a care plan that is emotionally easy to accept based on the emotional data. Furthermore, the generation AI can build a system that provides emotional support based on the emotional data of the elderly person. This makes it possible to provide a care plan that takes into account the emotional state of the elderly person, improving their quality of life.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The health data collection unit collects health data of the elderly person. For example, it collects data such as heart rate, blood pressure, and body temperature using a wearable device. It can also collect health data manually entered using a smartphone app or by linking with home medical devices (e.g., blood glucose monitors and oximeters). Step 2: The lifestyle data collection unit collects lifestyle data about the elderly. For example, it can use smart home devices to detect the elderly's movements and issue an alert if there are any abnormalities. It can also learn daily behavioral patterns and detect abnormal behavior early. Furthermore, it can connect smart home appliances and IoT devices to collect more detailed data. Step 3: The care plan proposal unit proposes a care plan based on the data collected by the health data collection unit and lifestyle data collection unit. For example, it proposes individually customized meal menus, exercise plans, and medication schedules. It can also provide a more personalized plan by taking into account past medical history and genetic information. It can also incorporate the opinions of family members and caregivers to jointly create a plan. Step 4: The care plan implementation support unit assists in the implementation of the care plan proposed by the care plan proposal unit. For example, it uses a reminder function to notify the elderly person of the timing of taking medication and exercising. It can also periodically check the elderly person's physical condition and mood using a voice assistant to supplement the data. Furthermore, the generation AI can automatically consult with a medical professional and arrange for remote medical treatment if necessary. Step 5: The evaluation and feedback unit evaluates the effectiveness of the care plan and provides feedback. For example, the effectiveness of the care plan can be evaluated periodically and revised as necessary. The emotional estimation function can also be used to monitor the elderly person's emotional state and detect signs of stress or anxiety early. Furthermore, emotional feedback can be provided to improve the elderly person's motivation.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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]

[0176] 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 health data collection unit that collects health data of elderly people; a lifestyle data collection unit that collects lifestyle data of elderly people; a care plan suggestion unit that suggests a care plan based on the data collected by the health data collection unit and the lifestyle data collection unit; a care plan execution support unit that supports the execution of the care plan proposed by the care plan proposal unit; an evaluation and feedback unit that evaluates the effectiveness of the care plan and provides feedback; A system characterized by:

2. The health data collection unit: Wearable devices collect heart rate, blood pressure, and temperature data 2. The system of claim 1.

3. The life data collection unit Using smart home devices, the elderly person's movements are detected and an alert is issued if an abnormality is detected.

2. The system of claim 1.

4. The care plan proposal unit Providing personalized recommendations for meal plans, exercise plans, and medication schedules 2. The system of claim 1.

5. The care plan execution support unit Use the reminder function to remind you to take your medication or exercise 2. The system of claim 1.

6. The evaluation feedback unit Periodically evaluate the effectiveness of the care plan and modify the plan as needed.

2. The system of claim 1.

7. The health data collection unit: Taking into account medical history and genetic information, we can provide a more accurate assessment of your health.

2. The system of claim 1.

8. The health data collection unit: Based on the analysis of the health data, the generated AI will automatically consult with a medical professional and arrange for remote medical treatment if necessary.

2. The system of claim 1.

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