System

Abstract

An object of a system according to an embodiment is to reduce the workload of a care manager and improve the quality of a care plan.SOLUTION: A system includes a user information acquisition part, a care plan generation part, a feedback collection part, and a care plan adjustment part. The user information acquisition unit acquires information on a user. The care plan generator generates a care plan based on the user information acquired by the user information acquirer. The feedback collector collects user feedback on the care plan generated by the care plan generator. The care plan adjuster adjusts the care plan based on the feedback collected by the feedback collector.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology places a heavy workload on care managers and can lead to inconsistencies in the quality of care plans.

[0005] The system according to the embodiment aims to reduce the workload of care managers and improve the quality of care plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information acquisition unit, a care plan generation unit, a feedback collection unit, and a care plan adjustment unit. The user information acquisition unit acquires user information. The care plan generation unit generates a care plan based on the user information acquired by the user information acquisition unit. The feedback collection unit collects user feedback on the care plan generated by the care plan generation unit. The care plan adjustment unit adjusts the care plan based on the feedback collected by the feedback collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the workload of care managers and improve the quality of 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) A care plan creation support system according to an embodiment of the present invention is a system that uses Google Gemini to generate an optimal care plan based on user information when a care manager creates a care plan, thereby reducing the workload and improving the quality of the care plan. As a result, the care plan creation support system can reduce the workload of the care manager, improve the quality of the care plan, promote information sharing among care staff, and meet the needs of the user.

[0029] A care plan creation assistance system according to an embodiment includes a user information acquisition unit, a care plan generation unit, a feedback collection unit, and a care plan adjustment unit. The user information acquisition unit acquires user information. For example, it collects data such as the user's health condition, lifestyle habits, and past care history. The user information acquisition unit can also collect the user's emotional data. For example, it collects the user's daily emotional data, and the generation AI analyzes the data to propose a care plan based on the emotions. The care plan generation unit generates a care plan based on the user information acquired by the user information acquisition unit. For example, the generation AI proposes an optimal care plan based on the user's health condition and lifestyle habits. The generation AI can also generate a care plan by referring to the latest medical guidelines. The feedback collection unit collects user feedback on the care plan generated by the care plan generation unit. For example, the generation AI collects feedback from the user's family and friends, and proposes a personalized care plan based on that information. The care plan adjustment unit adjusts the care plan based on the feedback collected by the feedback collection unit. For example, it adjusts the care plan based on the user's emotional data to provide a care plan that meets the user's needs. This allows the care plan creation assistance system to efficiently generate and adjust care plans. For example, the care plan creation assistance system analyzes the user's past care plans and their results, and the generation AI predicts the optimal care plan. The implementation results of past care plans are collected in real time, and the generation AI dynamically adjusts the next care plan based on that data. The user's past care plans and their results are analyzed over the long term, and the generation AI predicts the optimal care plan based on the user's needs and reactions based on that data.

[0030] The user information acquisition unit can acquire the user's health condition, lifestyle habits, and past care history. For example, the user information acquisition unit collects the user's daily emotional data, and the generation AI analyzes that data to propose a care plan based on the user's emotions. For example, if the user is feeling stressed, a plan including relaxation activities is proposed. The user's emotional data is also collected in real time, and the generation AI dynamically adjusts the care plan based on the user's emotions based on that data. For example, if the user is feeling anxious, activities that provide a sense of security are added. The user's emotional data is also collected over the long term, and the generation AI analyzes emotional patterns based on that data to propose an optimal care plan. For example, if the user tends to feel depressed during certain times of the day, fun activities are incorporated into those times. This allows detailed information about the user to be acquired.

[0031] The care plan generation unit can generate a care plan by referring to the latest medical guidelines. For example, the care plan generation unit collects feedback from the user's family and friends, and the generation AI proposes a personalized care plan based on that information. For example, family members provide the user's preferences and habits. The care plan generation unit also collects feedback from family and friends in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if a family member reports a change in the user's health condition, the plan can be immediately changed. The care plan generation unit also collects feedback from family and friends over the long term, and the generation AI proposes an optimal care plan based on the user's needs and preferences based on that data. For example, the generation AI incorporates the user's favorite activities into the plan based on information provided by family members. This makes it possible to provide a care plan that reflects the latest medical information.

[0032] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0033] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0034] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0035] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0036] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0037] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0038] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0039] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0040] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0041] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0042] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0043] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0044] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0045] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0046] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0047] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0048] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0049] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0050] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0051] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0052] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0053] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0054] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0055] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0056] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0057] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0058] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0059] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0060] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The care plan creation assistance system can also include a unit that analyzes the user's social network. For example, it can analyze the type of community the user belongs to and the type of friendships they have, and reflect this in the care plan. Specifically, it can incorporate local club activities and volunteer activities that the user participates in into the care plan. It can also analyze the frequency of the user's interactions with friends and family and suggest activities to prevent isolation. It can also monitor the user's social network over the long term and adjust the care plan according to changes. This can help maintain the user's social connections and support their mental health.

[0063] The care plan creation assistance system can also include a unit that delves deeper into the user's hobbies and interests. For example, it can analyze in detail the hobbies the user had in the past and their current interests and reflect these in the care plan. Specifically, it can provide support for the user to resume hobbies they enjoyed in the past. It can also make suggestions for finding new hobbies to improve the user's quality of life. It can also monitor the user's hobbies and interests over the long term and adjust the care plan according to changes. This makes it possible to provide a personalized care plan based on the user's hobbies and interests.

[0064] The care plan creation assistance system can also include a unit that analyzes the user's living environment. For example, it can analyze the user's living environment, surrounding facilities, transportation, etc., and reflect these in the care plan. Specifically, it can check whether the user's home is barrier-free and propose modifications as necessary. It can also incorporate public transportation and facilities that are easy for the user to access into the care plan. Furthermore, it can monitor the user's living environment over the long term and adjust the care plan according to changes. This makes it possible to provide the optimal care plan based on the user's living environment.

[0065] The care plan creation assistance system can further include a unit that analyzes the user's nutritional status. For example, it can analyze the user's dietary content and nutritional balance and reflect this in the care plan. Specifically, it can check the balance of nutrients the user is consuming and propose a meal plan to supplement any missing nutrients. It can also provide a meal plan that takes into account the user's dietary preferences and allergy information. It can also monitor the user's nutritional status over the long term and adjust the care plan according to changes. This can help maintain the user's health and improve their quality of life.

[0066] The care plan creation assistance system can further include a unit that analyzes the user's exercise habits. For example, it can analyze the type of exercise the user does and how often they do it, and reflect this in the care plan. Specifically, it can propose an exercise plan that the user can enjoy. It can also provide an exercise plan that suits the user's physical strength and health condition. It can also monitor the user's exercise habits over the long term and adjust the care plan according to changes. This makes it possible to provide an optimal care plan based on the user's exercise habits.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The user information acquisition unit acquires user information. For example, it collects data such as the user's health condition, lifestyle habits, and past care history. The user information acquisition unit also collects the user's daily emotional data, and the generation AI analyzes that data to propose a care plan based on the user's emotions. Step 2: The care plan generation unit generates a care plan based on the user information acquired by the user information acquisition unit. For example, the generation AI can propose an optimal care plan based on the user's health condition and lifestyle habits, and can also generate a care plan by referring to the latest medical guidelines. Step 3: The feedback collection unit collects user feedback on the care plan generated by the care plan generation unit. For example, feedback from the user's family and friends is collected, and the generation AI proposes a personalized care plan based on that information. Step 4: The care plan adjustment unit adjusts the care plan based on the feedback collected by the feedback collection unit. For example, the care plan may be adjusted based on the user's emotional data to provide a care plan that meets the user's needs.

[0069] (Example 2) A care plan creation support system according to an embodiment of the present invention is a system that uses Google Gemini to generate an optimal care plan based on user information when a care manager creates a care plan, thereby reducing the workload and improving the quality of the care plan. As a result, the care plan creation support system can reduce the workload of the care manager, improve the quality of the care plan, promote information sharing among care staff, and meet the needs of the user.

[0070] A care plan creation assistance system according to an embodiment includes a user information acquisition unit, a care plan generation unit, a feedback collection unit, and a care plan adjustment unit. The user information acquisition unit acquires user information. For example, it collects data such as the user's health condition, lifestyle habits, and past care history. The user information acquisition unit can also collect the user's emotional data. For example, it collects the user's daily emotional data, and the generation AI analyzes the data to propose a care plan based on the emotions. The care plan generation unit generates a care plan based on the user information acquired by the user information acquisition unit. For example, the generation AI proposes an optimal care plan based on the user's health condition and lifestyle habits. The generation AI can also generate a care plan by referring to the latest medical guidelines. The feedback collection unit collects user feedback on the care plan generated by the care plan generation unit. For example, the generation AI collects feedback from the user's family and friends, and proposes a personalized care plan based on that information. The care plan adjustment unit adjusts the care plan based on the feedback collected by the feedback collection unit. For example, it adjusts the care plan based on the user's emotional data to provide a care plan that meets the user's needs. This allows the care plan creation assistance system to efficiently generate and adjust care plans. For example, the care plan creation assistance system analyzes the user's past care plans and their results, and the generation AI predicts the optimal care plan. The implementation results of past care plans are collected in real time, and the generation AI dynamically adjusts the next care plan based on that data. The user's past care plans and their results are analyzed over the long term, and the generation AI predicts the optimal care plan based on the user's needs and reactions based on that data.

[0071] The user information acquisition unit can acquire the user's health condition, lifestyle habits, and past care history. For example, the user information acquisition unit collects the user's daily emotional data, and the generation AI analyzes that data to propose a care plan based on the user's emotions. For example, if the user is feeling stressed, a plan including relaxation activities is proposed. The user's emotional data is also collected in real time, and the generation AI dynamically adjusts the care plan based on the user's emotions based on that data. For example, if the user is feeling anxious, activities that provide a sense of security are added. The user's emotional data is also collected over the long term, and the generation AI analyzes emotional patterns based on that data to propose an optimal care plan. For example, if the user tends to feel depressed during certain times of the day, fun activities are incorporated into those times. This allows detailed information about the user to be acquired.

[0072] The care plan generation unit can generate a care plan by referring to the latest medical guidelines. For example, the care plan generation unit collects feedback from the user's family and friends, and the generation AI proposes a personalized care plan based on that information. For example, family members provide the user's preferences and habits. The care plan generation unit also collects feedback from family and friends in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if a family member reports a change in the user's health condition, the plan can be immediately changed. The care plan generation unit also collects feedback from family and friends over the long term, and the generation AI proposes an optimal care plan based on the user's needs and preferences based on that data. For example, the generation AI incorporates the user's favorite activities into the plan based on information provided by family members. This makes it possible to provide a care plan that reflects the latest medical information.

[0073] The feedback collection unit collects the user's emotional data, and the care plan adjustment unit can adjust the care plan based on the emotional data. The feedback collection unit, for example, stores the user's past care plans and their results in a database, and the generation AI analyzes the data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. The results of past care plans are also collected in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. The generation AI also analyzes the user's past care plans and their results over the long term, and based on that data, it predicts the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activity that the user found most satisfying based on past data. This makes it possible to adjust the care plan according to the user's emotions.

[0074] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0075] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0076] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0077] The care plan generation unit can generate a care plan according to the user's emotional state. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan including relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan according to the user's emotional state.

[0078] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0079] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0080] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0081] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0082] The care plan generation unit can generate a care plan according to the user's emotional state. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan including relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan according to the user's emotional state.

[0083] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0084] The care plan generation unit can generate a care plan tailored to the user's emotions based on the emotion data. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, the care plan generation unit proposes a plan that includes relaxation activities. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI uses that data to propose a care plan aimed at emotional stability. For example, if the user tends to feel depressed during a certain time of day, the generation AI can incorporate fun activities into that time period. The care plan can also be dynamically adjusted based on the user's emotional state using the emotion estimation function. For example, if the user is feeling anxious, activities that provide a sense of security can be added. This allows the generation of summaries that capture emotional nuances, thereby reflecting emotional factors in the evaluation.

[0085] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0086] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0087] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0088] The care plan generation unit can generate a care plan according to the user's emotional state. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan including relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan according to the user's emotional state.

[0089] The care plan generation unit can generate a care plan tailored to the user's emotions based on the emotion data. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, the care plan generation unit proposes a plan that includes relaxation activities. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI uses that data to propose a care plan aimed at emotional stability. For example, if the user tends to feel depressed during a certain time of day, the generation AI can incorporate fun activities into that time period. The care plan can also be dynamically adjusted based on the user's emotional state using the emotion estimation function. For example, if the user is feeling anxious, activities that provide a sense of security can be added. This allows the generation of summaries that capture emotional nuances, thereby reflecting emotional factors in the evaluation.

[0090] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0091] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0092] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0093] The care plan generation unit can generate a care plan according to the user's emotional state. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan including relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan according to the user's emotional state.

[0094] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0095] The care plan generation unit can generate a care plan tailored to the user's emotions based on the emotion data. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, the care plan generation unit proposes a plan that includes relaxation activities. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI uses that data to propose a care plan aimed at emotional stability. For example, if the user tends to feel depressed during a certain time of day, the generation AI can incorporate fun activities into that time period. The care plan can also be dynamically adjusted based on the user's emotional state using the emotion estimation function. For example, if the user is feeling anxious, activities that provide a sense of security can be added. This allows the generation of summaries that capture emotional nuances, thereby reflecting emotional factors in the evaluation.

[0096] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0097] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0098] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0099] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0100] The care plan generation unit can generate a care plan tailored to the user's emotions based on the emotion data. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, the care plan generation unit proposes a plan that includes relaxation activities. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI uses that data to propose a care plan aimed at emotional stability. For example, if the user tends to feel depressed during a certain time of day, the generation AI can incorporate fun activities into that time period. The care plan can also be dynamically adjusted based on the user's emotional state using the emotion estimation function. For example, if the user is feeling anxious, activities that provide a sense of security can be added. This allows the generation of summaries that capture emotional nuances, thereby reflecting emotional factors in the evaluation.

[0101] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0102] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0103] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0104] The care plan generation unit can generate a care plan according to the user's emotional state. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan including relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan according to the user's emotional state.

[0105] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0106] The care plan generation unit can generate a care plan tailored to the user's emotions based on the emotion data. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, the care plan generation unit proposes a plan that includes relaxation activities. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI uses that data to propose a care plan aimed at emotional stability. For example, if the user tends to feel depressed during a certain time of day, the generation AI can incorporate fun activities into that time period. The care plan can also be dynamically adjusted based on the user's emotional state using the emotion estimation function. For example, if the user is feeling anxious, activities that provide a sense of security can be added. This allows the generation of summaries that capture emotional nuances, thereby reflecting emotional factors in the evaluation.

[0107] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0108] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0109] The care plan generation unit can generate a care plan according to the user's emotional state. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan including relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan according to the user's emotional state.

[0110] The care plan generation unit can analyze past care plans and their results to generate an optimal care plan. For example, the care plan generation unit stores the user's past care plans and their results in a database, and the generation AI analyzes that data to predict the optimal care plan. For example, it proposes a new plan based on past success stories. It also collects the implementation results of past care plans in real time, and the generation AI dynamically adjusts the next care plan based on that data. For example, if a past plan was ineffective, it proposes a different approach. It also analyzes the user's past care plans and their results over the long term, and the generation AI uses that data to predict the optimal care plan based on the user's needs and reactions. For example, it re-suggests the activities that the user was most satisfied with based on past data. This makes it possible to provide an optimal care plan based on past care plans and their results.

[0111] The care plan generation unit can generate a care plan tailored to the user's emotions based on the emotion data. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, the care plan generation unit proposes a plan that includes relaxation activities. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI uses that data to propose a care plan aimed at emotional stability. For example, if the user tends to feel depressed during a certain time of day, the generation AI can incorporate fun activities into that time period. The care plan can also be dynamically adjusted based on the user's emotional state using the emotion estimation function. For example, if the user is feeling anxious, activities that provide a sense of security can be added. This allows the generation of summaries that capture emotional nuances, thereby reflecting emotional factors in the evaluation.

[0112] The care plan generation unit can generate a care plan by incorporating feedback from family or friends. For example, the care plan generation unit registers the user's hobbies and interests in a database, and the generation AI proposes a care plan based on that information. For example, if the user likes music, music therapy can be incorporated into the plan. The care plan generation unit also collects the user's hobbies and interests in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new hobby, that activity can be added to the plan. The care plan generation unit also collects the user's hobbies and interests over the long term, and the generation AI proposes a care plan that increases psychological satisfaction based on that data. For example, if the user shows a strong interest in a particular hobby, activities related to that hobby can be increased. This makes it possible to provide a personalized care plan that reflects the opinions of family and friends.

[0113] The care plan generation unit can generate a care plan taking into account hobbies or interests. For example, the care plan generation unit registers the user's food and exercise preferences in a database, and the generation AI proposes a care plan based on that information. For example, a meal plan using the user's favorite ingredients is proposed. The care plan generation unit also collects the user's food and exercise preferences in real time, and the generation AI dynamically adjusts the care plan based on that information. For example, if the user starts a new exercise, that exercise is added to the plan. The care plan generation unit also collects the user's food and exercise preferences over the long term, and the generation AI proposes an optimal care plan based on that data. For example, if the user has a strong preference for a particular meal, that meal is incorporated into the plan. This makes it possible to provide a care plan based on the user's hobbies and interests.

[0114] The care plan generation unit can generate a care plan that reflects dietary or exercise preferences. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan that includes relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan based on the user's dietary and exercise preferences.

[0115] The care plan generation unit can generate a care plan according to the user's emotional state. For example, the care plan generation unit uses an emotion estimation function to analyze the user's emotional state in real time and propose a care plan based on that data. For example, if the user is feeling stressed, a plan including relaxation activities is proposed. The care plan generation unit also collects the user's emotional state over the long term, and the generation AI proposes a care plan aimed at emotional stability based on that data. For example, if the user tends to feel depressed during a certain time of day, fun activities are incorporated into that time period. The emotion estimation function is also used to dynamically adjust the care plan according to the user's emotional state. For example, if the user is feeling anxious, activities that provide a sense of security are added. This makes it possible to provide a care plan according to the user's emotional state.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The care plan creation assistance system can also include a unit that analyzes the user's social network. For example, it can analyze the type of community the user belongs to and the type of friendships they have, and reflect this in the care plan. Specifically, it can incorporate local club activities and volunteer activities that the user participates in into the care plan. It can also analyze the frequency of the user's interactions with friends and family and suggest activities to prevent isolation. It can also monitor the user's social network over the long term and adjust the care plan according to changes. This can help maintain the user's social connections and support their mental health.

[0118] The care plan creation assistance system can also include a unit that delves deeper into the user's hobbies and interests. For example, it can analyze in detail the hobbies the user had in the past and their current interests and reflect these in the care plan. Specifically, it can provide support for the user to resume hobbies they enjoyed in the past. It can also make suggestions for finding new hobbies to improve the user's quality of life. It can also monitor the user's hobbies and interests over the long term and adjust the care plan according to changes. This makes it possible to provide a personalized care plan based on the user's hobbies and interests.

[0119] The care plan creation assistance system can also include a unit that analyzes the user's living environment. For example, it can analyze the user's living environment, surrounding facilities, transportation, etc., and reflect these in the care plan. Specifically, it can check whether the user's home is barrier-free and propose modifications as necessary. It can also incorporate public transportation and facilities that are easy for the user to access into the care plan. Furthermore, it can monitor the user's living environment over the long term and adjust the care plan according to changes. This makes it possible to provide the optimal care plan based on the user's living environment.

[0120] The care plan creation assistance system can further include a unit that analyzes the user's nutritional status. For example, it can analyze the user's dietary content and nutritional balance and reflect this in the care plan. Specifically, it can check the balance of nutrients the user is consuming and propose a meal plan to supplement any missing nutrients. It can also provide a meal plan that takes into account the user's dietary preferences and allergy information. It can also monitor the user's nutritional status over the long term and adjust the care plan according to changes. This can help maintain the user's health and improve their quality of life.

[0121] The care plan creation assistance system can further include a unit that analyzes the user's exercise habits. For example, it can analyze the type of exercise the user does and how often they do it, and reflect this in the care plan. Specifically, it can propose an exercise plan that the user can enjoy. It can also provide an exercise plan that suits the user's physical strength and health condition. It can also monitor the user's exercise habits over the long term and adjust the care plan according to changes. This makes it possible to provide an optimal care plan based on the user's exercise habits.

[0122] The care plan creation assistance system can further include a unit that analyzes the user's emotional data. For example, the system can collect the user's daily emotional data, analyze the data, and propose a care plan based on the user's emotions. Specifically, if the user is feeling stressed, a plan that includes relaxation activities can be proposed. The system can also collect the user's emotional data in real time, and the generation AI can dynamically adjust the care plan based on the user's emotions based on the data. Furthermore, the system can collect the user's emotional data over the long term, and the generation AI can analyze the user's emotional patterns based on the data to propose an optimal care plan. This makes it possible to provide a personalized care plan based on the user's emotions.

[0123] The care plan creation assistance system can further include a unit that analyzes the user's sleep patterns. For example, it can analyze the user's sleep duration and quality and reflect this in the care plan. Specifically, if the user is not getting enough sleep, it can suggest improvements to the sleep environment or relaxation activities. It can also monitor the user's sleep patterns in real time, and the generation AI can dynamically adjust the care plan based on that data. Furthermore, it can collect the user's sleep patterns over the long term, and the generation AI can propose the optimal care plan based on that data. This makes it possible to provide the optimal care plan based on the user's sleep patterns.

[0124] The care plan creation assistance system can further include a unit that analyzes the user's stress level. For example, the system can analyze the user's stress level and reflect it in the care plan. Specifically, if the user is feeling high stress, it can suggest activities to reduce stress. The system can also monitor the user's stress level in real time, and the generation AI can dynamically adjust the care plan based on that data according to the stress level. Furthermore, the system can collect the user's stress level over the long term, and the generation AI can propose the optimal care plan based on that data. This makes it possible to provide the optimal care plan based on the user's stress level.

[0125] The care plan creation assistance system can further include a unit that analyzes the user's emotional data. For example, the system can collect the user's daily emotional data, analyze the data, and propose a care plan based on the user's emotions. Specifically, if the user is feeling stressed, a plan that includes relaxation activities can be proposed. The system can also collect the user's emotional data in real time, and the generation AI can dynamically adjust the care plan based on the user's emotions based on the data. Furthermore, the system can collect the user's emotional data over the long term, and the generation AI can analyze the user's emotional patterns based on the data to propose an optimal care plan. This makes it possible to provide a personalized care plan based on the user's emotions.

[0126] The care plan creation assistance system can further include a unit that analyzes the user's emotional data. For example, the system can collect the user's daily emotional data, analyze the data, and propose a care plan based on the user's emotions. Specifically, if the user is feeling stressed, a plan that includes relaxation activities can be proposed. The system can also collect the user's emotional data in real time, and the generation AI can dynamically adjust the care plan based on the user's emotions based on the data. Furthermore, the system can collect the user's emotional data over the long term, and the generation AI can analyze the user's emotional patterns based on the data to propose an optimal care plan. This makes it possible to provide a personalized care plan based on the user's emotions.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The user information acquisition unit acquires user information. For example, it collects data such as the user's health condition, lifestyle habits, and past care history. The user information acquisition unit also collects the user's daily emotional data, and the generation AI analyzes that data to propose a care plan based on the user's emotions. Step 2: The care plan generation unit generates a care plan based on the user information acquired by the user information acquisition unit. For example, the generation AI can propose an optimal care plan based on the user's health condition and lifestyle habits, and can also generate a care plan by referring to the latest medical guidelines. Step 3: The feedback collection unit collects user feedback on the care plan generated by the care plan generation unit. For example, feedback from the user's family and friends is collected, and the generation AI proposes a personalized care plan based on that information. Step 4: The care plan adjustment unit adjusts the care plan based on the feedback collected by the feedback collection unit. For example, the care plan may be adjusted based on the user's emotional data to provide a care plan that meets the user's needs.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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.

[0147] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0148] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] 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.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0174] 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.

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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]

[0196] 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 user information acquisition unit that acquires user information; a care plan generation unit that generates a care plan based on the user information acquired by the user information acquisition unit; a feedback collection unit that collects user feedback regarding the care plan generated by the care plan generation unit; a care plan adjustment unit that adjusts the care plan based on the feedback collected by the feedback collection unit. A system characterized by:

2. The user information acquisition unit Acquire the user's health condition, lifestyle habits, and past care history 2. The system of claim 1.

3. The care plan generation unit Generate care plans based on the latest medical guidelines 2. The system of claim 1.

4. The feedback collection unit: Collect user emotion data, The care plan adjustment unit adjust a care plan based on the emotional data 2. The system of claim 1.

5. The care plan generation unit Generate a care plan with feedback from family or friends 2. The system of claim 1.

Images