System

Abstract

An object of a system according to an embodiment is to provide advice on a meal and exercise for the next day on the basis of calendar information, health information, and meal information of a user.SOLUTION: A system includes a calendar analysis part, a health information collection part, a meal information collection part, and an advice generation part. The calendar analysis unit analyzes calendar information of a user. The health information collection unit collects health information of the day from several days ago. The meal information collection unit analyzes the picture of the meal. The advice generation unit generates advice on a meal and exercise for the next day on the basis of the information collected by the calendar analysis unit, the health information collection unit, and the meal information collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to provide individualized health management and dietary and exercise advice to users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide advice on diet and exercise for the next day based on the user's calendar information, health information, and diet information. [Means for solving the problem]

[0006] The system according to the embodiment includes a calendar analysis unit, a health information collection unit, a dietary information collection unit, and an advice generation unit. The calendar analysis unit analyzes the user's calendar information. The health information collection unit collects health information for the current day from several days before. The dietary information collection unit analyzes photos of meals. The advice generation unit generates dietary and exercise advice for the next day based on the information collected by the calendar analysis unit, health information collection unit, and dietary information collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide advice on diet and exercise for the next day based on the user's calendar information, health information, and diet information. [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 analyzes a user's calendar information, automatically generates a schedule for the next day, and automatically provides health management advice using a generation AI based on health and diet information from several days before to the current day. This allows the health management system to understand the user's schedule for the next day while providing health management advice.

[0029] A health management system according to an embodiment includes a calendar analysis unit, a health information collection unit, a dietary information collection unit, and an advice generation unit. The calendar analysis unit analyzes a user's calendar information. For example, if the calendar contains events such as "9:00 meeting," "12:00 lunch," and "15:00 presentation," the calendar analysis unit automatically generates the next day's schedule based on this information. The health information collection unit collects health information for the current day from several days in advance. For example, the health information collection unit evaluates the user's exercise volume and health status based on data acquired from a smartwatch or fitness tracker. For example, the health information collection unit collects step count data and analyzes the user's exercise volume. It can also collect heart rate data and evaluate the user's health status. It can also collect sleep data and analyze the user's sleep quality. The dietary information collection unit analyzes photos of meals. For example, the user takes photos of the food they eat and inputs them into the dietary information collection unit, which analyzes the nutrients and calories of the food. The dietary information collection unit uses image recognition technology to analyze the photos of the food and estimate nutrients. The system can also calculate calories and evaluate the user's diet. It can also identify ingredients from photos of meals and analyze nutritional balance. The advice generation unit generates diet and exercise advice for the next day based on the information collected by the calendar analysis unit, health information collection unit, and diet information collection unit. For example, the advice generation unit may provide advice such as, "Since today's exercise volume was low, we recommend walking for 30 minutes tomorrow." It can also provide advice such as, "Since today's diet is lacking in vitamin C, it would be good to eat an orange tomorrow." The advice generation unit can also suggest appropriate exercise and diet based on the user's health condition. This allows the health management system according to the embodiment to receive health management advice while understanding the user's schedule for the next day. For example, by incorporating appropriate diet and exercise into a busy day, health can be maintained. Furthermore, because the generation AI automatically performs analysis and advice, users can manage their health effortlessly.

[0030] The calendar analysis unit learns the user's past behavioral patterns and can generate a more accurate story for the next day. For example, the generation AI in the calendar analysis unit analyzes the user's past calendar information and learns their behavioral patterns. For example, if a user has a habit of going to the gym every Monday, the system can automatically add a gym appointment to the next day's schedule based on that information. The system also learns the user's past behavioral patterns and understands the frequency of specific events and tasks. For example, if there is a task that must be performed at the end of the month, the system can reflect that pattern in the next day's schedule. The generation AI also learns the user's past behavioral patterns and improves prediction accuracy. For example, it can take into account events and activities that occur in specific seasons and add appropriate appointments to the next day's schedule. This allows the system to learn the user's past behavioral patterns and improve the accuracy of the story for the next day.

[0031] The calendar analysis unit can incorporate weather forecasts and traffic information to generate a more realistic schedule. For example, the generation AI in the calendar analysis unit collects weather forecast data and combines it with calendar information to adjust the next day's schedule. For example, it can suggest indoor activities on rainy days. It also collects traffic information in real time and analyzes it in combination with calendar information. For example, if traffic congestion is expected, it can adjust the schedule taking travel time into account. It also optimizes the next day's schedule based on weather forecasts and traffic information. For example, it can suggest outdoor activities on sunny days and adjust travel time according to traffic conditions. This makes it possible to generate a realistic schedule that takes weather forecasts and traffic information into account.

[0032] The health information collection unit can refer to the user's past health data and grasp long-term health trends. In the health information collection unit, for example, the generation AI collects the user's past health data and analyzes long-term health trends. For example, it can grasp changes in exercise volume based on step count and heart rate data from the past few months. It can also predict health trends based on the user's past health data. For example, it can analyze weight gain or loss from past data and evaluate future health risks. The generation AI can also refer to the user's past health data to grasp long-term health trends. For example, it can analyze sleep patterns from past data and evaluate sleep quality. This makes it possible to refer to the user's past health data and grasp long-term health trends.

[0033] The health information collection unit can incorporate the user's sleep data and evaluate the overall health condition. In the health information collection unit, for example, the generation AI collects the user's sleep data and evaluates the overall health condition. For example, the quality of sleep can be analyzed based on the sleep data obtained from the smartwatch. The health condition can also be comprehensively evaluated based on the user's sleep data. For example, the sleep time and depth of sleep can be analyzed to evaluate the health condition. The generation AI can also incorporate the user's sleep data and evaluate the overall health condition. For example, the sleep data can be combined with exercise data and analyzed to evaluate the health condition. In this way, the user's sleep data can be incorporated and the overall health condition can be evaluated.

[0034] The meal information collection unit can refer to the user's past meal history and understand meal patterns. In the meal information collection unit, for example, the generation AI collects the user's past meal history and analyzes meal patterns. For example, it can understand the user's preferred ingredients and dishes from past data. It can also understand meal patterns based on the user's past meal history. For example, it can analyze the habit of eating specific dishes on specific days of the week and reflect this in meal suggestions for the next day. It can also refer to the user's past meal history and understand meal patterns. For example, it can analyze nutritional balance from past data and reflect this in meal suggestions for the next day. In this way, it can refer to the user's past meal history and understand meal patterns.

[0035] The dietary information collection unit can provide appropriate dietary advice by taking into account the user's allergy information and dietary restrictions. In the dietary information collection unit, for example, the generation AI collects the user's allergy information and provides dietary advice. For example, it can make meal suggestions that avoid ingredients that cause allergies. It also takes into account the user's dietary restrictions and provides appropriate dietary advice. For example, it can suggest low-calorie meals to a user who is on a diet. It also takes into account the user's allergy information and dietary restrictions and provides appropriate dietary advice. For example, it can suggest ingredients that are high in specific nutrients. This makes it possible to provide appropriate dietary advice by taking into account the user's allergy information and dietary restrictions.

[0036] The advice generation unit can refer to the user's past advice history and provide more personalized advice. For example, the generation AI of the advice generation unit collects the user's past advice history and provides diet and exercise advice for the next day. For example, if a previously suggested exercise was effective, a similar exercise can be suggested. Also, more personalized advice is provided based on the user's past advice history. For example, if past diet advice was well received, a similar diet can be suggested. Also, the generation AI can refer to the user's past advice history and provide diet and exercise advice for the next day. For example, it can analyze the user's preferences and effects from past data and provide optimal advice. This makes it possible to refer to the user's past advice history and provide more personalized advice.

[0037] The advice generation unit can provide actionable advice by taking into account the user's lifestyle and work schedule. For example, the generation AI of the advice generation unit analyzes the user's lifestyle and work schedule and provides actionable diet and exercise advice. For example, it can suggest short, effective exercises that fit into the schedule of busy days. It also provides actionable diet and exercise advice based on the user's lifestyle. For example, it can suggest morning exercises for a user who is in the habit of exercising early in the morning. It also provides actionable advice by taking into account the user's work schedule. For example, it can suggest stretches that can be done in between meetings. In this way, it is possible to provide actionable advice by taking into account the user's lifestyle and work schedule.

[0038] The advice generation unit can suggest activities to do together with the user's friends and family. For example, the generation AI analyzes the schedules of the user's friends and family and suggests activities to do together. For example, it can suggest walking for the whole family. It can also suggest activities to do together with the user's friends and family. For example, it can suggest sports to do together with friends. It can also consider the schedules of the user's friends and family and suggest activities to do together. For example, it can suggest outdoor activities that the whole family can enjoy. In this way, it can suggest activities to do together with the user's friends and family.

[0039] The advice generation unit takes into account the user's hobbies and interests and can provide advice that can be followed while having fun. For example, the advice generation unit uses a generation AI to analyze the user's hobbies and interests and provide advice about diet and exercise that can be followed while having fun. For example, dance exercises can be suggested to a user who likes dancing. Advice that can be followed while having fun is also provided based on the user's hobbies and interests. For example, new recipes can be suggested to a user who likes cooking. Advice that can be followed while having fun is also provided by the generation AI, taking into account the user's hobbies and interests. For example, hiking can be suggested to a user who likes the outdoors. In this way, advice that can be followed while having fun can be provided, taking into account the user's hobbies and interests.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The health management system can further include an activity suggestion unit that takes into account the user's hobbies and interests. For example, if the user likes outdoor activities, weekend hiking or camping can be suggested. If the user enjoys music, exercises to go with the music can be suggested. Furthermore, if the user enjoys cooking, new recipes can be suggested, providing a way to enjoy healthy meals. This allows activities to be suggested that take into account the user's hobbies and interests, making health management fun.

[0042] The health management system can further include a suggestion unit for improving the user's sleep environment. For example, it can suggest optimal bedding and bedroom temperature settings based on the user's sleep data. It can also suggest relaxing music and aromas to improve the quality of sleep. It can also analyze the user's sleep patterns and suggest appropriate bedtimes and wake-up times. This can improve the user's sleep environment and overall health.

[0043] The health management system may further include a suggestion unit for promoting the user's social activities. For example, the system may synchronize the user's schedule with friends and family and suggest activities to do together. The system may also suggest local events and community activities that the user can participate in. Furthermore, the system may suggest activities for making new friends based on the user's hobbies and interests. This may promote the user's social activities and improve their mental health.

[0044] The health management system can further include a motivation providing unit to help users develop exercise habits. For example, it can provide real-time feedback on the degree of achievement of exercise goals set by the user. It can also provide incentives to encourage users to continue exercising. Furthermore, it can propose appropriate exercise plans based on the user's exercise data and provide advice to maintain motivation. This helps users develop exercise habits and support health management.

[0045] The health management system may further include a goal setting unit for achieving the user's health goals. For example, it may propose a specific action plan for the health goals set by the user. It may also monitor the user's progress in real time and provide feedback for goal achievement. Furthermore, it may provide incentives according to the user's health goals and support the user in maintaining motivation. This provides support for the user in achieving their health goals and allows for effective health management.

[0046] The health management system can further include a preventive medical care suggestion unit based on the user's health data. For example, the system can analyze the user's health data and predict future health risks. It can also suggest appropriate preventive measures for predicted health risks. Furthermore, it can suggest schedules for regular health checks and examinations based on the user's health data. This allows the user's health risks to be detected early and appropriate preventive measures to be taken.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The calendar analysis unit analyzes the user's calendar information. For example, if the calendar contains events such as "9:00 meeting," "12:00 lunch," and "15:00 presentation," the calendar analysis unit automatically generates the next day's schedule based on this information. Step 2: The health information collection unit collects health information from several days before to the current day. For example, the health information collection unit evaluates the user's exercise volume and health condition based on data obtained from a smartwatch or fitness tracker. The health information collection unit, for example, collects step count data and analyzes the user's exercise volume. It can also collect heart rate data and evaluate the user's health condition. It can also collect sleep data and analyze the user's sleep quality. Step 3: The meal information collection unit analyzes the meal photos. For example, the user takes a photo of the food they ate and inputs it into the meal information collection unit, which then analyzes the nutrients and calories of the food. The meal information collection unit uses, for example, image recognition technology to analyze the meal photos and estimate the nutrients. It can also calculate calories and evaluate the user's meal content. It can also identify ingredients from the meal photos and analyze the nutritional balance. Step 4: The advice generation unit generates diet and exercise advice for the next day based on the information collected by the calendar analysis unit, health information collection unit, and diet information collection unit. For example, the advice generation unit may provide advice such as, "Since today's exercise amount was low, we recommend walking for 30 minutes tomorrow." It may also provide advice such as, "Since today's diet is lacking in vitamin C, it would be good to eat an orange tomorrow." The advice generation unit may also suggest appropriate exercise and diet depending on the user's health condition.

[0049] (Example 2) A health management system according to an embodiment of the present invention analyzes a user's calendar information, automatically generates a schedule for the next day, and automatically provides health management advice using a generation AI based on health and diet information from several days before to the current day. This allows the health management system to understand the user's schedule for the next day while providing health management advice.

[0050] A health management system according to an embodiment includes a calendar analysis unit, a health information collection unit, a dietary information collection unit, and an advice generation unit. The calendar analysis unit analyzes a user's calendar information. For example, if the calendar contains events such as "9:00 meeting," "12:00 lunch," and "15:00 presentation," the calendar analysis unit automatically generates the next day's schedule based on this information. The health information collection unit collects health information for the current day from several days in advance. For example, the health information collection unit evaluates the user's exercise volume and health status based on data acquired from a smartwatch or fitness tracker. For example, the health information collection unit collects step count data and analyzes the user's exercise volume. It can also collect heart rate data and evaluate the user's health status. It can also collect sleep data and analyze the user's sleep quality. The dietary information collection unit analyzes photos of meals. For example, the user takes photos of the food they eat and inputs them into the dietary information collection unit, which analyzes the nutrients and calories of the food. The dietary information collection unit uses image recognition technology to analyze the photos of the food and estimate nutrients. The system can also calculate calories and evaluate the user's diet. It can also identify ingredients from photos of meals and analyze nutritional balance. The advice generation unit generates diet and exercise advice for the next day based on the information collected by the calendar analysis unit, health information collection unit, and diet information collection unit. For example, the advice generation unit may provide advice such as, "Since today's exercise volume was low, we recommend walking for 30 minutes tomorrow." It can also provide advice such as, "Since today's diet is lacking in vitamin C, it would be good to eat an orange tomorrow." The advice generation unit can also suggest appropriate exercise and diet based on the user's health condition. This allows the health management system according to the embodiment to receive health management advice while understanding the user's schedule for the next day. For example, by incorporating appropriate diet and exercise into a busy day, health can be maintained. Furthermore, because the generation AI automatically performs analysis and advice, users can manage their health effortlessly.

[0051] The calendar analysis unit learns the user's past behavioral patterns and can generate a more accurate story for the next day. For example, the generation AI in the calendar analysis unit analyzes the user's past calendar information and learns their behavioral patterns. For example, if a user has a habit of going to the gym every Monday, the system can automatically add a gym appointment to the next day's schedule based on that information. The system also learns the user's past behavioral patterns and understands the frequency of specific events and tasks. For example, if there is a task that must be performed at the end of the month, the system can reflect that pattern in the next day's schedule. The generation AI also learns the user's past behavioral patterns and improves prediction accuracy. For example, it can take into account events and activities that occur in specific seasons and add appropriate appointments to the next day's schedule. This allows the system to learn the user's past behavioral patterns and improve the accuracy of the story for the next day.

[0052] The calendar analysis unit can estimate the user's mood and emotions and adjust the content of the story based on that. For example, the generation AI analyzes the user's past calendar information and emotional data to estimate their mood and emotions. For example, if past data shows that a particular event causes stress to the user, the generation AI can adjust the next day's schedule based on that information. The generation AI also collects the user's emotional data in real time and analyzes it in combination with the calendar information. For example, if the user is feeling stressed, it can add relaxing activities to the next day's schedule. The generation AI also adjusts the next day's story based on the user's emotional data. For example, if the user is feeling positive, it can add challenging tasks to the schedule. This allows the generation AI to adjust the content of the story based on the user's mood and emotions.

[0053] The calendar analysis unit can incorporate weather forecasts and traffic information to generate a more realistic schedule. For example, the generation AI in the calendar analysis unit collects weather forecast data and combines it with calendar information to adjust the next day's schedule. For example, it can suggest indoor activities on rainy days. It also collects traffic information in real time and analyzes it in combination with calendar information. For example, if traffic congestion is expected, it can adjust the schedule taking travel time into account. It also optimizes the next day's schedule based on weather forecasts and traffic information. For example, it can suggest outdoor activities on sunny days and adjust travel time according to traffic conditions. This makes it possible to generate a realistic schedule that takes weather forecasts and traffic information into account.

[0054] The health information collection unit can refer to the user's past health data and grasp long-term health trends. In the health information collection unit, for example, the generation AI collects the user's past health data and analyzes long-term health trends. For example, it can grasp changes in exercise volume based on step count and heart rate data from the past few months. It can also predict health trends based on the user's past health data. For example, it can analyze weight gain or loss from past data and evaluate future health risks. The generation AI can also refer to the user's past health data to grasp long-term health trends. For example, it can analyze sleep patterns from past data and evaluate sleep quality. This makes it possible to refer to the user's past health data and grasp long-term health trends.

[0055] The health information collection unit can incorporate the user's sleep data and evaluate the overall health condition. In the health information collection unit, for example, the generation AI collects the user's sleep data and evaluates the overall health condition. For example, the quality of sleep can be analyzed based on the sleep data obtained from the smartwatch. The health condition can also be comprehensively evaluated based on the user's sleep data. For example, the sleep time and depth of sleep can be analyzed to evaluate the health condition. The generation AI can also incorporate the user's sleep data and evaluate the overall health condition. For example, the sleep data can be combined with exercise data and analyzed to evaluate the health condition. In this way, the user's sleep data can be incorporated and the overall health condition can be evaluated.

[0056] The health information collection unit can estimate the user's emotional state based on the analysis results of the health information and provide stress management advice. In the health information collection unit, for example, the generation AI estimates the user's emotional state based on the analysis results of the health information. For example, if the amount of exercise is low, it can determine that stress may be building up. The generation AI can also estimate the user's emotional state based on the analysis results of the health information and provide stress management advice. For example, if stress is building up, it can suggest activities that will help you relax. The generation AI can also estimate the user's emotional state based on the analysis results of the health information and provide stress management advice. For example, if the heart rate is high, it can determine that stress is building up and suggest ways to relax. In this way, the generation AI can estimate the user's emotional state based on the analysis results of the health information and provide stress management advice.

[0057] The meal information collection unit can refer to the user's past meal history and understand meal patterns. In the meal information collection unit, for example, the generation AI collects the user's past meal history and analyzes meal patterns. For example, it can understand the user's preferred ingredients and dishes from past data. It can also understand meal patterns based on the user's past meal history. For example, it can analyze the habit of eating specific dishes on specific days of the week and reflect this in meal suggestions for the next day. It can also refer to the user's past meal history and understand meal patterns. For example, it can analyze nutritional balance from past data and reflect this in meal suggestions for the next day. In this way, it can refer to the user's past meal history and understand meal patterns.

[0058] The dietary information collection unit can provide appropriate dietary advice by taking into account the user's allergy information and dietary restrictions. In the dietary information collection unit, for example, the generation AI collects the user's allergy information and provides dietary advice. For example, it can make meal suggestions that avoid ingredients that cause allergies. It also takes into account the user's dietary restrictions and provides appropriate dietary advice. For example, it can suggest low-calorie meals to a user who is on a diet. It also takes into account the user's allergy information and dietary restrictions and provides appropriate dietary advice. For example, it can suggest ingredients that are high in specific nutrients. This makes it possible to provide appropriate dietary advice by taking into account the user's allergy information and dietary restrictions.

[0059] The meal information collection unit can estimate the user's emotional state based on the analysis results of the meal information and provide advice for managing emotional changes caused by meals. In the meal information collection unit, for example, the generation AI estimates the user's emotional state based on the analysis results of the meal information. For example, it can analyze the effect of specific ingredients on the user's mood. Furthermore, based on the analysis results of the meal information, it can estimate the user's emotional state and provide advice for managing emotional changes caused by meals. For example, it can suggest ingredients that reduce stress. Furthermore, based on the analysis results of the meal information, the generation AI can estimate the user's emotional state and provide advice for managing emotional changes caused by meals. For example, it can suggest ingredients that will improve your mood. In this way, it can estimate the user's emotional state based on the analysis results of the meal information and provide advice for managing emotional changes caused by meals.

[0060] The advice generation unit can refer to the user's past advice history and provide more personalized advice. For example, the generation AI of the advice generation unit collects the user's past advice history and provides diet and exercise advice for the next day. For example, if a previously suggested exercise was effective, a similar exercise can be suggested. Also, more personalized advice is provided based on the user's past advice history. For example, if past diet advice was well received, a similar diet can be suggested. Also, the generation AI can refer to the user's past advice history and provide diet and exercise advice for the next day. For example, it can analyze the user's preferences and effects from past data and provide optimal advice. This makes it possible to refer to the user's past advice history and provide more personalized advice.

[0061] The advice generation unit can take the user's emotional state into consideration and provide advice to keep emotions positive. For example, the generation AI analyzes the user's emotional state and provides diet and exercise advice to keep emotions positive. For example, if the user is feeling stressed, it can suggest exercise that will help them relax. Furthermore, based on the user's emotional state, it can provide diet and exercise advice to keep emotions positive. For example, it can suggest meals that the user will enjoy. Furthermore, the generation AI can take the user's emotional state into consideration and provide advice to keep emotions positive. For example, it can suggest meals and exercise that will help the user relax. In this way, it is possible to take the user's emotional state into consideration and provide advice to keep emotions positive.

[0062] The advice generation unit can provide actionable advice by taking into account the user's lifestyle and work schedule. For example, the generation AI of the advice generation unit analyzes the user's lifestyle and work schedule and provides actionable diet and exercise advice. For example, it can suggest short, effective exercises that fit into the schedule of busy days. It also provides actionable diet and exercise advice based on the user's lifestyle. For example, it can suggest morning exercises for a user who is in the habit of exercising early in the morning. It also provides actionable advice by taking into account the user's work schedule. For example, it can suggest stretches that can be done in between meetings. In this way, it is possible to provide actionable advice by taking into account the user's lifestyle and work schedule.

[0063] The advice generation unit can suggest activities to do together with the user's friends and family. For example, the generation AI analyzes the schedules of the user's friends and family and suggests activities to do together. For example, it can suggest walking for the whole family. It can also suggest activities to do together with the user's friends and family. For example, it can suggest sports to do together with friends. It can also consider the schedules of the user's friends and family and suggest activities to do together. For example, it can suggest outdoor activities that the whole family can enjoy. In this way, it can suggest activities to do together with the user's friends and family.

[0064] The advice generation unit takes into account the user's hobbies and interests and can provide advice that can be followed while having fun. For example, the advice generation unit uses a generation AI to analyze the user's hobbies and interests and provide advice about diet and exercise that can be followed while having fun. For example, dance exercises can be suggested to a user who likes dancing. Advice that can be followed while having fun is also provided based on the user's hobbies and interests. For example, new recipes can be suggested to a user who likes cooking. Advice that can be followed while having fun is also provided by the generation AI, taking into account the user's hobbies and interests. For example, hiking can be suggested to a user who likes the outdoors. In this way, advice that can be followed while having fun can be provided, taking into account the user's hobbies and interests.

[0065] The advice generation unit can use the emotion estimation function to analyze the user's emotional response to the advice and adjust the advice to elicit positive emotions. For example, the advice generation unit uses a generation AI to collect the user's emotional response to the advice in real time and adjust the advice to elicit positive emotions. For example, it can suggest exercises that the user can enjoy. Furthermore, it can use the emotion estimation function to analyze the user's emotional response to the advice and adjust the advice to elicit positive emotions. For example, it can suggest dishes using the user's favorite ingredients. Furthermore, the generation AI can analyze the user's emotional response to the advice and adjust the advice to elicit positive emotions. For example, it can suggest activities that will help the user relax. In this way, it is possible to use the emotion estimation function to analyze the user's emotional response to the advice and adjust the advice to elicit positive emotions.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The health management system can further include an activity suggestion unit that takes into account the user's hobbies and interests. For example, if the user likes outdoor activities, weekend hiking or camping can be suggested. If the user enjoys music, exercises to go with the music can be suggested. Furthermore, if the user enjoys cooking, new recipes can be suggested, providing a way to enjoy healthy meals. This allows activities to be suggested that take into account the user's hobbies and interests, making health management fun.

[0068] The health management system can further include a suggestion unit for improving the user's sleep environment. For example, it can suggest optimal bedding and bedroom temperature settings based on the user's sleep data. It can also suggest relaxing music and aromas to improve the quality of sleep. It can also analyze the user's sleep patterns and suggest appropriate bedtimes and wake-up times. This can improve the user's sleep environment and overall health.

[0069] The health management system may further include a stress monitoring unit that monitors the user's stress level in real time. For example, the system may measure heart rate and electrodermal activity to evaluate the user's stress level. If the user's stress level is high, the system may suggest relaxing activities or breathing techniques. The system may also monitor the user's stress level over the long term and provide advice for stress management. This allows the user to understand their stress level in real time and take appropriate measures.

[0070] The health management system may further include a suggestion unit for promoting the user's social activities. For example, the system may synchronize the user's schedule with friends and family and suggest activities to do together. The system may also suggest local events and community activities that the user can participate in. Furthermore, the system may suggest activities for making new friends based on the user's hobbies and interests. This may promote the user's social activities and improve their mental health.

[0071] The health management system can further include a meal suggestion unit that takes into account the user's emotional state. For example, if the user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. Also, if the user is feeling positive, it can suggest recipes using ingredients that increase energy. Furthermore, it can monitor the user's emotional state in real time and suggest appropriate meals. This makes it possible to suggest meals according to the user's emotional state and support health management.

[0072] The health management system can further include a motivation providing unit to help users develop exercise habits. For example, it can provide real-time feedback on the degree of achievement of exercise goals set by the user. It can also provide incentives to encourage users to continue exercising. Furthermore, it can propose appropriate exercise plans based on the user's exercise data and provide advice to maintain motivation. This helps users develop exercise habits and support health management.

[0073] The health management system can further include an exercise suggestion unit that takes into account the user's emotional state. For example, if the user is feeling stressed, it can suggest yoga or stretching, which have a relaxing effect. Or, if the user is feeling positive, it can suggest running or dancing, which will increase energy. Furthermore, it can monitor the user's emotional state in real time and suggest appropriate exercises. This makes it possible to suggest exercises according to the user's emotional state and support health management.

[0074] The health management system may further include a goal setting unit for achieving the user's health goals. For example, it may propose a specific action plan for the health goals set by the user. It may also monitor the user's progress in real time and provide feedback for goal achievement. Furthermore, it may provide incentives according to the user's health goals and support the user in maintaining motivation. This provides support for the user in achieving their health goals and allows for effective health management.

[0075] The health management system can further include a sleep improvement suggestion unit that takes into account the user's emotional state. For example, if the user is feeling stressed, it can suggest relaxing music or aromas. Also, if the user is feeling positive, it can make suggestions for creating a comfortable sleeping environment. Furthermore, it can monitor the user's emotional state in real time and make appropriate sleep improvement suggestions. This makes it possible to make sleep improvement suggestions according to the user's emotional state and support health management.

[0076] The health management system can further include a preventive medical care suggestion unit based on the user's health data. For example, the system can analyze the user's health data and predict future health risks. It can also suggest appropriate preventive measures for predicted health risks. Furthermore, it can suggest schedules for regular health checks and examinations based on the user's health data. This allows the user's health risks to be detected early and appropriate preventive measures to be taken.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The calendar analysis unit analyzes the user's calendar information. For example, if the calendar contains events such as "9:00 meeting," "12:00 lunch," and "15:00 presentation," the calendar analysis unit automatically generates the next day's schedule based on this information. Step 2: The health information collection unit collects health information from several days before to the current day. For example, the health information collection unit evaluates the user's exercise volume and health condition based on data obtained from a smartwatch or fitness tracker. The health information collection unit, for example, collects step count data and analyzes the user's exercise volume. It can also collect heart rate data and evaluate the user's health condition. It can also collect sleep data and analyze the user's sleep quality. Step 3: The meal information collection unit analyzes the meal photos. For example, the user takes a photo of the food they ate and inputs it into the meal information collection unit, which then analyzes the nutrients and calories of the food. The meal information collection unit uses, for example, image recognition technology to analyze the meal photos and estimate the nutrients. It can also calculate calories and evaluate the user's meal content. It can also identify ingredients from the meal photos and analyze the nutritional balance. Step 4: The advice generation unit generates diet and exercise advice for the next day based on the information collected by the calendar analysis unit, health information collection unit, and diet information collection unit. For example, the advice generation unit may provide advice such as, "Since today's exercise amount was low, we recommend walking for 30 minutes tomorrow." It may also provide advice such as, "Since today's diet is lacking in vitamin C, it would be good to eat an orange tomorrow." The advice generation unit may also suggest appropriate exercise and diet depending on the user's health condition.

[0079] 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.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] 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.

[0106] 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.

[0107] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0145] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a calendar analysis unit that analyzes the user's calendar information; Health Information Collection Department, which collects health information from several days before the day of the event, a food information collection unit that analyzes food photos; an advice generation unit that generates advice on diet and exercise for the next day based on the information collected by the calendar analysis unit, the health information collection unit, and the diet information collection unit. A system characterized by:

2. The calendar analysis unit Learn the user's past behavioral patterns and generate a more accurate story for the next day 2. The system of claim 1.

3. The calendar analysis unit Inferring the user's mood or emotion and adjusting the content of the story based on that.

2. The system of claim 1.

4. The calendar analysis unit Incorporating weather and traffic information to generate more realistic schedules 2. The system of claim 1.

5. The health information collection unit: Viewing the user's past health data to understand long-term health trends 2. The system of claim 1.

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