System

Abstract

An object of a system according to an embodiment is to provide an individual health management plan based on the lifestyle and health data of a user.SOLUTION: A system includes an input unit, an analysis unit, and a proposal unit. The input unit inputs lifestyle data and health data of a user. The analysis unit analyzes the data input by the input unit and evaluates the health condition of the user. The proposal unit generates an individual training plan, a meal proposal and management, and a lifestyle improvement advice based on the evaluation result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately provided individual health management plans based on a user's lifestyle and health data, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an individual health management plan based on the user's lifestyle habits and health data. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit inputs lifestyle habit data and health data of a user. The analysis unit analyzes the data input by the input unit and evaluates the user's health condition. The suggestion unit generates an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an individual health management plan based on the user's lifestyle habits and health data. [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 health management system according to an embodiment of the present invention automatically analyzes a user's lifestyle and health data and generates a training plan, dietary suggestions and management, and lifestyle improvement advice tailored to the user's individual health condition. In the health management system, the user inputs lifestyle and health data, and a generation AI analyzes the data and evaluates the user's health condition. Based on the evaluation results, the generation AI generates an individual training plan, dietary suggestions and management, and lifestyle improvement advice. For example, in a health management system, the user records their daily steps and dietary intake via a smartphone app. This information is input into the generation AI. The generation AI then analyzes the input data and evaluates the user's health condition. For example, if the user's exercise volume is insufficient, the generation AI points this out and provides advice for improvement. Based on the evaluation results, the generation AI generates an individual training plan, dietary suggestions and management, and lifestyle improvement advice. For example, the generation AI proposes a specific training plan to increase the user's exercise volume. It also analyzes the user's diet and makes dietary suggestions that take nutritional balance into consideration. It also provides advice for improving the user's lifestyle. This allows the health management system to analyze the user's lifestyle and health data and generate suggestions tailored to the user's individual health condition. This allows the health management system to analyze the user's lifestyle and health data and generate suggestions tailored to the user's individual health condition. For example, the user can understand their health condition in detail and perform appropriate training and dietary management. The user can also learn specific areas for lifestyle improvement and work to improve their health.

[0029] A health management system according to an embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit inputs a user's lifestyle data and health data. The user's lifestyle data includes, for example, the amount of exercise, dietary content, sleep time, and stress level. The health data includes, for example, blood pressure, heart rate, weight, and blood sugar level. The input unit allows the user to manually input the number of steps taken each day and dietary content via a smartphone app. The input unit can also automatically acquire data from devices such as fitness trackers and smartwatches. The input unit can also record data using voice input. The analysis unit uses a generation AI to analyze the data input by the input unit and evaluate the user's health status. The analysis is performed using, for example, statistical analysis or a machine learning algorithm. For example, if the user's exercise volume is insufficient, the generation AI can point out this and provide advice for improvement. The suggestion unit uses the generation AI to generate an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results obtained by the analysis unit. For example, the generation AI can propose a specific training plan to increase the user's exercise volume. The suggestion unit also analyzes the user's diet and makes dietary suggestions that take nutritional balance into consideration. Furthermore, the suggestion unit provides advice for improving the user's lifestyle. This allows the health management system according to the embodiment to analyze the user's lifestyle and health data and generate suggestions tailored to the user's individual health condition.

[0030] The input unit can input data on the user's daily exercise amount, dietary content, sleep time, and stress level. The exercise amount includes, for example, the number of steps, calories burned, and exercise time. For example, the user manually inputs the number of steps taken each day through a smartphone app. The input unit can also automatically acquire step count data from a device using a fitness tracker. The input unit can also record the amount of daily exercise using voice input. The dietary content includes, for example, calorie intake, types of nutrients, and meal frequency. For example, the user manually inputs the daily dietary content through a smartphone app. The input unit can also automatically analyze the dietary content using image recognition technology by taking photos of meals. The input unit can also scan food barcodes using a barcode scanner to input the dietary content. The sleep time includes, for example, bedtime, wake-up time, and sleep quality. For example, the user manually inputs the daily sleep time through a smartphone app. The input unit can also automatically acquire sleep data from a device using a smartwatch. Furthermore, the input unit can record daily sleep duration using voice input. Stress levels include, for example, self-report, heart rate variability, cortisol levels, etc. The input unit allows the user to manually input the daily stress level through a smartphone app, for example. The input unit can also use a stress measurement device to automatically acquire stress data from the device. Furthermore, the input unit can record daily stress levels using voice input. This allows detailed input of the user's daily lifestyle habits.

[0031] The analysis unit can evaluate the user's health status based on the user's lifestyle habits and health data. Lifestyle habits include, for example, exercise habits, eating habits, and sleeping habits. The analysis unit can, for example, use a generating AI to analyze the user's exercise habits and point out any insufficient exercise. The analysis unit can also analyze the user's eating habits and point out any nutritional imbalance. The analysis unit can also analyze the user's sleeping habits and point out any poor sleep quality. The health status includes, for example, a health score, risk assessment, medical data, and the like. The analysis unit can, for example, use a generating AI to calculate the user's health score and perform a risk assessment. The analysis unit can also analyze the user's medical data and evaluate the health status. This allows the health status to be evaluated based on the user's lifestyle habits and health data.

[0032] The suggestion unit can generate a specific training plan for increasing the user's amount of exercise. The specific training plan for increasing the amount of exercise includes, for example, aerobic exercise, strength training, stretching, etc. The suggestion unit, for example, uses a generation AI to suggest a specific training plan for increasing the user's amount of exercise. For example, the generation AI analyzes the user's exercise habits and suggests a plan for increasing aerobic exercise. The suggestion unit can also suggest a plan that incorporates strength training. Furthermore, the suggestion unit can also suggest a plan that incorporates stretching. This makes it possible to provide a specific training plan for increasing the user's amount of exercise.

[0033] The suggestion unit can analyze the user's dietary content and generate dietary suggestions based on nutritional balance. Dietary suggestions based on nutritional balance include, for example, calorie restriction, nutritional balance, and recommendations of specific ingredients. The suggestion unit, for example, uses a generation AI to analyze the user's dietary content and make dietary suggestions based on nutritional balance. For example, the generation AI analyzes the user's dietary content and suggests calorie restriction. The suggestion unit can also make dietary suggestions that take nutritional balance into consideration. Furthermore, the suggestion unit can make dietary suggestions that recommend specific ingredients. In this way, the user's dietary content can be analyzed and dietary suggestions that take nutritional balance into consideration can be provided.

[0034] The suggestion unit can generate advice for improving the user's lifestyle habits. Advice for improving lifestyle habits includes, for example, improving sleep quality, stress management, and time management. The suggestion unit provides advice for improving the user's lifestyle habits, for example, using a generation AI. For example, the generation AI analyzes the user's sleep habits and provides advice for improving sleep quality. The suggestion unit can also provide advice for stress management. Furthermore, the suggestion unit can also provide advice for time management. This makes it possible to provide advice for improving the user's lifestyle habits.

[0035] The input unit can input the amount of daily exercise of the user. The amount of exercise includes, for example, the number of steps, calories burned, and exercise time. For example, the user manually inputs the number of steps taken each day through a smartphone app. The input unit can also automatically acquire step count data from a fitness tracker. Furthermore, the input unit can record the amount of daily exercise using voice input. This allows the user to input the amount of daily exercise in detail.

[0036] The input unit can input the user's dietary details. The dietary details include, for example, calorie intake, types of nutrients, meal frequency, etc. The input unit allows the user to manually input the daily dietary details via a smartphone app, for example. The input unit can also take photos of meals and automatically analyze the dietary details using image recognition technology. Furthermore, the input unit can also use a barcode scanner to scan food barcodes and input the dietary details. This allows the user to input detailed dietary details.

[0037] The input unit can input the user's sleep time. The sleep time includes, for example, bedtime, wake-up time, and sleep quality. For example, the user manually inputs the daily sleep time through a smartphone app. The input unit can also automatically acquire sleep data from a smartwatch. Furthermore, the input unit can record the daily sleep time using voice input. This allows the user to input the sleep time in detail.

[0038] The input unit can input the user's stress level. Examples of stress levels include self-reporting, heart rate variability, and cortisol levels. For example, the user manually inputs the daily stress level through a smartphone app. The input unit can also automatically acquire stress data from a stress measurement device. Furthermore, the input unit can record the daily stress level using voice input. This allows the user to input the stress level in detail.

[0039] The input unit can analyze the user's past input history and suggest the optimal input method. Optimal input methods include, for example, voice input, touch input, and template input. The input unit can, for example, automatically display data that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest data that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0040] The input unit can suggest data input based on a schedule by referring to the user's calendar information. The calendar information includes, for example, the type of schedule, the frequency of the schedule, and the importance of the schedule. The input unit, for example, can suggest related data input by referring to the schedule registered in the user's calendar. The input unit can also suggest data input related to a specific event from the user's calendar information. Furthermore, the input unit can suggest optimal data input based on the schedule based on the user's calendar information. This makes it possible to suggest optimal data input based on the user's calendar information.

[0041] The input unit can analyze the user's social media activity to suggest related data inputs. Social media activity includes, for example, the content of posts, the number of likes, the content of comments, etc. The input unit can suggest, for example, data inputs related to places where the user has checked in on social media. The input unit can also analyze the content of the user's posts on social media to suggest related data inputs. Furthermore, the input unit can also suggest related data inputs based on the activities of the user's friends on social media. In this way, related data inputs can be suggested based on the user's social media activity.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, for example, the frequency of the data, the impact of the data, and the reliability of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data.

[0043] The analysis unit can apply different analysis algorithms depending on the data category. Data categories include, for example, health data, lifestyle habit data, and emotion data. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. The analysis unit can also apply a nutrition analysis algorithm to dietary data. Furthermore, the analysis unit can also apply a sleep analysis algorithm to sleep data. This makes it possible to apply the optimal analysis algorithm depending on the data category.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, past health scores, past risk assessments, past trends, etc. The analysis unit, for example, corrects the current analysis results based on the user's past analysis results. The analysis unit can also extract trends from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can learn the user's past analysis results and improve the accuracy of the analysis algorithm. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0045] The analysis unit can determine the priority of analysis based on the time of data submission. The time of data submission includes, for example, the frequency of submission, the timing of submission, and the importance of submission. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission. This makes it possible to determine the priority of analysis based on the time of data submission.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data. The relevance of the data includes, for example, correlation analysis, causal relationships, co-occurrence relationships, etc. The analysis unit, for example, prioritizes the analysis of highly related data. The analysis unit can also postpone the analysis of less related data. Furthermore, the analysis unit can also determine the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data.

[0047] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The level of expertise includes, for example, whether or not the user has a qualification, years of experience, and self-evaluation. For example, the analysis unit can explain the analysis results in simple terms to a user with little technical expertise. The analysis unit can also explain the analysis results using detailed technical terms to a user with technical expertise. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows the analysis results to be provided appropriately according to the user's level of expertise.

[0048] The suggestion unit can adjust the level of detail of the suggestion based on the user's health condition. The health condition includes, for example, a health score, a risk assessment, medical data, etc. The suggestion unit can provide detailed suggestions to a user in good health, for example. The suggestion unit can also provide concise suggestions to a user in poor health. Furthermore, the suggestion unit can determine the priority of the suggestions based on the user's health condition. This makes it possible to adjust the level of detail of the suggestion according to the user's health condition.

[0049] The suggestion unit can apply different suggestion algorithms depending on the lifestyle habits of the user. Lifestyle habits include, for example, exercise habits, eating habits, and sleeping habits. For example, the suggestion unit applies an exercise suggestion algorithm to a user with exercise habits. The suggestion unit can also apply a meal suggestion algorithm to a user with eating habits. Furthermore, the suggestion unit can also apply a sleep suggestion algorithm to a user with sleeping habits. This makes it possible to apply the optimal suggestion algorithm depending on the lifestyle habits of the user.

[0050] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. Past suggestion results include, for example, the success rate of past suggestions, past feedback, past trends, etc. The suggestion unit corrects current suggestions, for example, based on the user's past suggestion results. The suggestion unit can also extract trends from the user's past suggestion results and reflect them in current suggestions. Furthermore, the suggestion unit can learn the user's past suggestion results and improve the accuracy of the suggestion algorithm. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results.

[0051] The suggestion unit can determine the priority of suggestions based on the user's lifestyle. Examples of lifestyle include work status, family status, and health status. For example, when the user is busy, the suggestion unit can provide the most important suggestions with priority. Furthermore, when the user has time, the suggestion unit can provide detailed suggestions. Furthermore, the suggestion unit can adjust the schedule of suggestions based on the user's lifestyle. This allows the priority of suggestions to be determined according to the user's lifestyle.

[0052] The suggestion unit can adjust the order of suggestions based on the user's health data. The health data includes, for example, blood pressure, heart rate, weight, blood sugar level, etc. For example, if the health data is important, the suggestion unit can provide the most important suggestions preferentially. Also, if the health data is less important, the suggestion unit can suggest the other health data later. Furthermore, the suggestion unit can determine the order of suggestions based on the health data. This allows the order of suggestions to be adjusted based on the user's health data.

[0053] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. The level of expertise includes, for example, whether or not the user has a qualification, years of experience, and self-evaluation. For example, the suggestion unit can explain the suggestions in simple terms to a user with little expertise. The suggestion unit can also explain the suggestions using detailed technical terms to a user with expertise. Furthermore, the suggestion unit can adjust the way the suggestions are expressed depending on the user's level of expertise. This makes it possible to provide appropriate suggestions depending on the user's level of expertise.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The health management system can further analyze the user's social media activity and customize training plans and meal suggestions based on the user's interests. For example, if the user expresses interest in a particular sport on social media, the suggestion unit can suggest training plans related to that sport. Also, if the user is interested in a particular ingredient or dish, the suggestion unit can make meal suggestions based on that. Furthermore, suggestions can be made based on the training and meal plans of the user's friends. This makes it possible to provide suggestions tailored to the user's interests.

[0056] The health management system can also refer to the user's calendar information and provide training plans and meal suggestions based on the user's schedule. For example, if the user is busy on a certain day, a short and effective training plan can be suggested. Also, if the user plans to participate in a specific event, special training and meal suggestions can be made in preparation for the event. Furthermore, if the user is on vacation, training and meal suggestions that will have a relaxing effect can be made. In this way, suggestions can be provided that suit the user's schedule.

[0057] The health management system can also analyze the user's past input history and suggest the optimal input method. For example, it can automatically display data that the user has frequently input in the past as candidates. It can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest data that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0058] The health management system can further adjust the way in which the analysis results are presented depending on the user's level of expertise. For example, the analysis results can be explained in simple terms to a user with little expertise. On the other hand, the analysis results can be explained using detailed technical terms to a user with expertise. Furthermore, the way in which the analysis results are presented can be adjusted depending on the user's level of expertise. This allows the analysis results to be provided appropriately depending on the user's level of expertise.

[0059] The health management system can further determine the priority of suggestions based on the user's lifestyle. For example, if the user is busy, the most important suggestions can be provided first. Also, if the user has time, detailed suggestions can be provided. Furthermore, the schedule of suggestions can be adjusted based on the user's lifestyle. In this way, the priority of suggestions can be determined according to the user's lifestyle.

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

[0061] Step 1: The input unit inputs the user's lifestyle and health data. Lifestyle data includes exercise amount, dietary content, sleep time, stress level, etc., while health data includes blood pressure, heart rate, weight, blood sugar level, etc. The input unit includes methods for the user to manually input data through a smartphone app, methods for automatically acquiring data from devices such as fitness trackers and smartwatches, and methods for recording data using voice input. Step 2: The analysis unit uses the generation AI to analyze the data entered by the input unit and evaluate the user's health condition. The analysis is performed using statistical analysis and machine learning algorithms. For example, if the user's exercise volume is insufficient, the analysis unit will point this out and provide advice for improvement. Step 3: The proposal unit uses the generation AI to generate an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results obtained by the analysis unit. For example, it may propose a specific training plan to increase the user's exercise, analyze the user's diet to suggest meals that take nutritional balance into consideration, and provide advice on improving lifestyle habits.

[0062] (Example 2) A health management system according to an embodiment of the present invention automatically analyzes a user's lifestyle and health data and generates a training plan, dietary suggestions and management, and lifestyle improvement advice tailored to the user's individual health condition. In the health management system, the user inputs lifestyle and health data, and a generation AI analyzes the data and evaluates the user's health condition. Based on the evaluation results, the generation AI generates an individual training plan, dietary suggestions and management, and lifestyle improvement advice. For example, in a health management system, the user records their daily steps and dietary intake via a smartphone app. This information is input into the generation AI. The generation AI then analyzes the input data and evaluates the user's health condition. For example, if the user's exercise volume is insufficient, the generation AI points this out and provides advice for improvement. Based on the evaluation results, the generation AI generates an individual training plan, dietary suggestions and management, and lifestyle improvement advice. For example, the generation AI proposes a specific training plan to increase the user's exercise volume. It also analyzes the user's diet and makes dietary suggestions that take nutritional balance into consideration. It also provides advice for improving the user's lifestyle. This allows the health management system to analyze the user's lifestyle and health data and generate suggestions tailored to the user's individual health condition. This allows the health management system to analyze the user's lifestyle and health data and generate suggestions tailored to the user's individual health condition. For example, the user can understand their health condition in detail and perform appropriate training and dietary management. The user can also learn specific areas for lifestyle improvement and work to improve their health.

[0063] A health management system according to an embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit inputs a user's lifestyle data and health data. The user's lifestyle data includes, for example, the amount of exercise, dietary content, sleep time, and stress level. The health data includes, for example, blood pressure, heart rate, weight, and blood sugar level. The input unit allows the user to manually input the number of steps taken each day and dietary content via a smartphone app. The input unit can also automatically acquire data from devices such as fitness trackers and smartwatches. The input unit can also record data using voice input. The analysis unit uses a generation AI to analyze the data input by the input unit and evaluate the user's health status. The analysis is performed using, for example, statistical analysis or a machine learning algorithm. For example, if the user's exercise volume is insufficient, the generation AI can point out this and provide advice for improvement. The suggestion unit uses the generation AI to generate an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results obtained by the analysis unit. For example, the generation AI can propose a specific training plan to increase the user's exercise volume. The suggestion unit also analyzes the user's diet and makes dietary suggestions that take nutritional balance into consideration. Furthermore, the suggestion unit provides advice for improving the user's lifestyle. This allows the health management system according to the embodiment to analyze the user's lifestyle and health data and generate suggestions tailored to the user's individual health condition.

[0064] The input unit can input data on the user's daily exercise amount, dietary content, sleep time, and stress level. The exercise amount includes, for example, the number of steps, calories burned, and exercise time. For example, the user manually inputs the number of steps taken each day through a smartphone app. The input unit can also automatically acquire step count data from a device using a fitness tracker. The input unit can also record the amount of daily exercise using voice input. The dietary content includes, for example, calorie intake, types of nutrients, and meal frequency. For example, the user manually inputs the daily dietary content through a smartphone app. The input unit can also automatically analyze the dietary content using image recognition technology by taking photos of meals. The input unit can also scan food barcodes using a barcode scanner to input the dietary content. The sleep time includes, for example, bedtime, wake-up time, and sleep quality. For example, the user manually inputs the daily sleep time through a smartphone app. The input unit can also automatically acquire sleep data from a device using a smartwatch. Furthermore, the input unit can record daily sleep duration using voice input. Stress levels include, for example, self-report, heart rate variability, cortisol levels, etc. The input unit allows the user to manually input the daily stress level through a smartphone app, for example. The input unit can also use a stress measurement device to automatically acquire stress data from the device. Furthermore, the input unit can record daily stress levels using voice input. This allows detailed input of the user's daily lifestyle habits.

[0065] The analysis unit can evaluate the user's health status based on the user's lifestyle habits and health data. Lifestyle habits include, for example, exercise habits, eating habits, and sleeping habits. The analysis unit can, for example, use a generating AI to analyze the user's exercise habits and point out any insufficient exercise. The analysis unit can also analyze the user's eating habits and point out any nutritional imbalance. The analysis unit can also analyze the user's sleeping habits and point out any poor sleep quality. The health status includes, for example, a health score, risk assessment, medical data, and the like. The analysis unit can, for example, use a generating AI to calculate the user's health score and perform a risk assessment. The analysis unit can also analyze the user's medical data and evaluate the health status. This allows the health status to be evaluated based on the user's lifestyle habits and health data.

[0066] The suggestion unit can generate a specific training plan for increasing the user's amount of exercise. The specific training plan for increasing the amount of exercise includes, for example, aerobic exercise, strength training, stretching, etc. The suggestion unit, for example, uses a generation AI to suggest a specific training plan for increasing the user's amount of exercise. For example, the generation AI analyzes the user's exercise habits and suggests a plan for increasing aerobic exercise. The suggestion unit can also suggest a plan that incorporates strength training. Furthermore, the suggestion unit can also suggest a plan that incorporates stretching. This makes it possible to provide a specific training plan for increasing the user's amount of exercise.

[0067] The suggestion unit can analyze the user's dietary content and generate dietary suggestions based on nutritional balance. Dietary suggestions based on nutritional balance include, for example, calorie restriction, nutritional balance, and recommendations of specific ingredients. The suggestion unit, for example, uses a generation AI to analyze the user's dietary content and make dietary suggestions based on nutritional balance. For example, the generation AI analyzes the user's dietary content and suggests calorie restriction. The suggestion unit can also make dietary suggestions that take nutritional balance into consideration. Furthermore, the suggestion unit can make dietary suggestions that recommend specific ingredients. In this way, the user's dietary content can be analyzed and dietary suggestions that take nutritional balance into consideration can be provided.

[0068] The suggestion unit can generate advice for improving the user's lifestyle habits. Advice for improving lifestyle habits includes, for example, improving sleep quality, stress management, and time management. The suggestion unit provides advice for improving the user's lifestyle habits, for example, using a generation AI. For example, the generation AI analyzes the user's sleep habits and provides advice for improving sleep quality. The suggestion unit can also provide advice for stress management. Furthermore, the suggestion unit can also provide advice for time management. This makes it possible to provide advice for improving the user's lifestyle habits.

[0069] The input unit can input the amount of daily exercise of the user. The amount of exercise includes, for example, the number of steps, calories burned, and exercise time. For example, the user manually inputs the number of steps taken each day through a smartphone app. The input unit can also automatically acquire step count data from a fitness tracker. Furthermore, the input unit can record the amount of daily exercise using voice input. This allows the user to input the amount of daily exercise in detail.

[0070] The input unit can input the user's dietary details. The dietary details include, for example, calorie intake, types of nutrients, meal frequency, etc. The input unit allows the user to manually input the daily dietary details via a smartphone app, for example. The input unit can also take photos of meals and automatically analyze the dietary details using image recognition technology. Furthermore, the input unit can also use a barcode scanner to scan food barcodes and input the dietary details. This allows the user to input detailed dietary details.

[0071] The input unit can input the user's sleep time. The sleep time includes, for example, bedtime, wake-up time, and sleep quality. For example, the user manually inputs the daily sleep time through a smartphone app. The input unit can also automatically acquire sleep data from a smartwatch. Furthermore, the input unit can record the daily sleep time using voice input. This allows the user to input the sleep time in detail.

[0072] The input unit can input the user's stress level. Examples of stress levels include self-reporting, heart rate variability, and cortisol levels. For example, the user manually inputs the daily stress level through a smartphone app. The input unit can also automatically acquire stress data from a stress measurement device. Furthermore, the input unit can record the daily stress level using voice input. This allows the user to input the stress level in detail.

[0073] The input unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. Methods for estimating emotions include, for example, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the input unit can provide an interface with calm colors to reduce visual stress. Furthermore, if the user is having fun, the input unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the input unit can provide a simple, highly visible interface to make input work easier. This makes it possible to adjust the design of the input interface according to the user's emotions.

[0074] The input unit can analyze the user's past input history and suggest the optimal input method. Optimal input methods include, for example, voice input, touch input, and template input. The input unit can, for example, automatically display data that the user has frequently input in the past as candidates. The input unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest data that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0075] The input unit can suggest data input based on a schedule by referring to the user's calendar information. The calendar information includes, for example, the type of schedule, the frequency of the schedule, and the importance of the schedule. The input unit, for example, can suggest related data input by referring to the schedule registered in the user's calendar. The input unit can also suggest data input related to a specific event from the user's calendar information. Furthermore, the input unit can suggest optimal data input based on the schedule based on the user's calendar information. This makes it possible to suggest optimal data input based on the user's calendar information.

[0076] The input unit can analyze the user's social media activity to suggest related data inputs. Social media activity includes, for example, the content of posts, the number of likes, the content of comments, etc. The input unit can suggest, for example, data inputs related to places where the user has checked in on social media. The input unit can also analyze the content of the user's posts on social media to suggest related data inputs. Furthermore, the input unit can also suggest related data inputs based on the activities of the user's friends on social media. In this way, related data inputs can be suggested based on the user's social media activity.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. Methods for estimating emotions include, for example, facial expression recognition, voice analysis, and self-reporting. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide analysis results with visually stimulating effects when the user is excited. This makes it possible to adjust the way the analysis is presented depending on the user's emotions.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. The importance of the data includes, for example, the frequency of the data, the impact of the data, and the reliability of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a concise analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. This makes it possible to adjust the level of detail of the analysis according to the importance of the data.

[0079] The analysis unit can apply different analysis algorithms depending on the data category. Data categories include, for example, health data, lifestyle habit data, and emotion data. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. The analysis unit can also apply a nutrition analysis algorithm to dietary data. Furthermore, the analysis unit can also apply a sleep analysis algorithm to sleep data. This makes it possible to apply the optimal analysis algorithm depending on the data category.

[0080] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, for example, past health scores, past risk assessments, past trends, etc. The analysis unit, for example, corrects the current analysis results based on the user's past analysis results. The analysis unit can also extract trends from the user's past analysis results and reflect them in the current analysis. Furthermore, the analysis unit can learn the user's past analysis results and improve the accuracy of the analysis algorithm. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Methods for estimating emotions include, for example, facial expression recognition, voice analysis, and self-reporting. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows the length of the analysis to be adjusted according to the user's emotions.

[0082] The analysis unit can determine the priority of analysis based on the time of data submission. The time of data submission includes, for example, the frequency of submission, the timing of submission, and the importance of submission. The analysis unit, for example, prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can adjust the analysis schedule based on the time of submission. This makes it possible to determine the priority of analysis based on the time of data submission.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data. The relevance of the data includes, for example, correlation analysis, causal relationships, co-occurrence relationships, etc. The analysis unit, for example, prioritizes the analysis of highly related data. The analysis unit can also postpone the analysis of less related data. Furthermore, the analysis unit can also determine the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data.

[0084] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The level of expertise includes, for example, whether or not the user has a qualification, years of experience, and self-evaluation. For example, the analysis unit can explain the analysis results in simple terms to a user with little technical expertise. The analysis unit can also explain the analysis results using detailed technical terms to a user with technical expertise. Furthermore, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This allows the analysis results to be provided appropriately according to the user's level of expertise.

[0085] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are presented based on the estimated user's emotion. Methods for estimating emotion include, for example, facial expression recognition, voice analysis, and self-reporting. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can provide suggestions that include visually stimulating effects when the user is excited. This makes it possible to adjust the way in which suggestions are presented according to the user's emotion.

[0086] The suggestion unit can adjust the level of detail of the suggestion based on the user's health condition. The health condition includes, for example, a health score, a risk assessment, medical data, etc. The suggestion unit can provide detailed suggestions to a user in good health, for example. The suggestion unit can also provide concise suggestions to a user in poor health. Furthermore, the suggestion unit can determine the priority of the suggestions based on the user's health condition. This makes it possible to adjust the level of detail of the suggestion according to the user's health condition.

[0087] The suggestion unit can apply different suggestion algorithms depending on the lifestyle habits of the user. Lifestyle habits include, for example, exercise habits, eating habits, and sleeping habits. For example, the suggestion unit applies an exercise suggestion algorithm to a user with exercise habits. The suggestion unit can also apply a meal suggestion algorithm to a user with eating habits. Furthermore, the suggestion unit can also apply a sleep suggestion algorithm to a user with sleeping habits. This makes it possible to apply the optimal suggestion algorithm depending on the lifestyle habits of the user.

[0088] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. Past suggestion results include, for example, the success rate of past suggestions, past feedback, past trends, etc. The suggestion unit corrects current suggestions, for example, based on the user's past suggestion results. The suggestion unit can also extract trends from the user's past suggestion results and reflect them in current suggestions. Furthermore, the suggestion unit can learn the user's past suggestion results and improve the accuracy of the suggestion algorithm. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results.

[0089] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. Methods for estimating emotion include, for example, facial expression recognition, voice analysis, and self-reporting. For example, the suggestion unit can provide short and to-the-point suggestions when the user is in a hurry. The suggestion unit can also provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide suggestions with visually stimulating effects when the user is excited. This allows the length of the suggestion to be adjusted according to the user's emotion.

[0090] The suggestion unit can determine the priority of suggestions based on the user's lifestyle. Examples of lifestyle include work status, family status, and health status. For example, when the user is busy, the suggestion unit can provide the most important suggestions with priority. Furthermore, when the user has time, the suggestion unit can provide detailed suggestions. Furthermore, the suggestion unit can adjust the schedule of suggestions based on the user's lifestyle. This allows the priority of suggestions to be determined according to the user's lifestyle.

[0091] The suggestion unit can adjust the order of suggestions based on the user's health data. The health data includes, for example, blood pressure, heart rate, weight, blood sugar level, etc. For example, if the health data is important, the suggestion unit can provide the most important suggestions preferentially. Also, if the health data is less important, the suggestion unit can suggest the other health data later. Furthermore, the suggestion unit can determine the order of suggestions based on the health data. This allows the order of suggestions to be adjusted based on the user's health data.

[0092] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. The level of expertise includes, for example, whether or not the user has a qualification, years of experience, and self-evaluation. For example, the suggestion unit can explain the suggestions in simple terms to a user with little expertise. The suggestion unit can also explain the suggestions using detailed technical terms to a user with expertise. Furthermore, the suggestion unit can adjust the way the suggestions are expressed depending on the user's level of expertise. This makes it possible to provide appropriate suggestions depending on the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input the user's lifestyle data and health data using the reception device 38 or camera 42 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI to evaluate the user's health condition. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input the user's lifestyle habit data and health data using the microphone 238 or camera 42 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI to evaluate the user's health condition. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit can input the user's lifestyle habit data and health data using the microphone 238 or camera 42 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI to evaluate the user's health condition. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input the user's lifestyle habit data and health data using the microphone 238 or camera 42 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI to evaluate the user's health condition. For example, the suggestion unit is realized by the specific processing unit 290 of the data processing device 12, and generates an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The health management system can further estimate the user's emotions and adjust the difficulty of the training plan based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can suggest light exercise that has a relaxing effect. Also, if the user is highly motivated, the suggestion unit can suggest a challenging training plan. Furthermore, if the user is tired, the suggestion unit can suggest stretching or yoga to promote recovery. In this way, a training plan can be provided that matches the user's emotions.

[0095] The health management system can further analyze the user's social media activity and customize training plans and meal suggestions based on the user's interests. For example, if the user expresses interest in a particular sport on social media, the suggestion unit can suggest training plans related to that sport. Also, if the user is interested in a particular ingredient or dish, the suggestion unit can make meal suggestions based on that. Furthermore, suggestions can be made based on the training and meal plans of the user's friends. This makes it possible to provide suggestions tailored to the user's interests.

[0096] The health management system can also refer to the user's calendar information and provide training plans and meal suggestions based on the user's schedule. For example, if the user is busy on a certain day, a short and effective training plan can be suggested. Also, if the user plans to participate in a specific event, special training and meal suggestions can be made in preparation for the event. Furthermore, if the user is on vacation, training and meal suggestions that will have a relaxing effect can be made. In this way, suggestions can be provided that suit the user's schedule.

[0097] The health management system can further estimate the user's emotions and adjust the content of the meal suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can suggest meals using ingredients that have a relaxing effect. Also, if the user is enjoying themselves, the suggestion unit can suggest new recipes or challenging dishes. Furthermore, if the user is tired, the suggestion unit can suggest easy-to-prepare, nutritionally balanced meals. In this way, meal suggestions can be provided according to the user's emotions.

[0098] The health management system can also analyze the user's past input history and suggest the optimal input method. For example, it can automatically display data that the user has frequently input in the past as candidates. It can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest data that will be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history.

[0099] The health management system can further estimate the user's emotions and adjust the content of lifestyle improvement advice based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can provide specific advice for stress management. If the user is relaxed, the suggestion unit can also suggest a new hobby or relaxation method. Furthermore, if the user is highly motivated, the suggestion unit can suggest setting a challenging goal or introducing a new habit. In this way, lifestyle improvement advice can be provided according to the user's emotions.

[0100] The health management system can further adjust the way in which the analysis results are presented depending on the user's level of expertise. For example, the analysis results can be explained in simple terms to a user with little expertise. On the other hand, the analysis results can be explained using detailed technical terms to a user with expertise. Furthermore, the way in which the analysis results are presented can be adjusted depending on the user's level of expertise. This allows the analysis results to be provided appropriately depending on the user's level of expertise.

[0101] The health management system can further estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is in a hurry, concise analysis results that focus on the main points can be provided. Furthermore, if the user is excited, analysis results with visually stimulating effects can be provided. In this way, the way in which the analysis results are presented can be adjusted according to the user's emotions.

[0102] The health management system can further determine the priority of suggestions based on the user's lifestyle. For example, if the user is busy, the most important suggestions can be provided first. Also, if the user has time, detailed suggestions can be provided. Furthermore, the schedule of suggestions can be adjusted based on the user's lifestyle. In this way, the priority of suggestions can be determined according to the user's lifestyle.

[0103] The health management system can further estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, it can provide short and to-the-point suggestions. If the user is relaxed, it can provide detailed suggestions. If the user is excited, it can provide suggestions with visually stimulating effects. In this way, it is possible to adjust the length of the suggestions according to the user's emotions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The input unit inputs the user's lifestyle and health data. Lifestyle data includes exercise amount, dietary content, sleep time, stress level, etc., while health data includes blood pressure, heart rate, weight, blood sugar level, etc. The input unit includes methods for the user to manually input data through a smartphone app, methods for automatically acquiring data from devices such as fitness trackers and smartwatches, and methods for recording data using voice input. Step 2: The analysis unit uses the generation AI to analyze the data entered by the input unit and evaluate the user's health condition. The analysis is performed using statistical analysis and machine learning algorithms. For example, if the user's exercise volume is insufficient, the analysis unit will point this out and provide advice for improvement. Step 3: The proposal unit uses the generation AI to generate an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results obtained by the analysis unit. For example, it may propose a specific training plan to increase the user's exercise, analyze the user's diet to suggest meals that take nutritional balance into consideration, and provide advice on improving lifestyle habits.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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.

[0135] 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.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

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

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

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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, in order to avoid confusion and to 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.

[0176] 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.

[0177] [Explanation of symbols]

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

Claims

1. an input unit for inputting lifestyle habit data and health data of a user; an analysis unit that analyzes the data input by the input unit and evaluates the health condition of the user; a suggestion unit that generates an individual training plan, meal suggestions and management, and lifestyle improvement advice based on the evaluation results obtained by the analysis unit. A system characterized by:

2. The input unit Enter data on the user's daily exercise, diet, sleep, and stress levels 2. The system of claim 1.

3. The analysis unit Evaluate the user's health status based on their lifestyle and health data 2. The system of claim 1.

4. The proposal unit Generate specific training plans to help users increase their physical activity 2. The system of claim 1.

5. The proposal unit Analyzes the user's diet and generates meal suggestions based on nutritional balance 2. The system of claim 1.

6. The proposal unit Generate advice to improve the user's lifestyle 2. The system of claim 1.

7. The input unit Enter the user's daily exercise amount 2. The system of claim 1.

8. The input unit Enter the user's meal details 2. The system of claim 1.

Images