System

Abstract

An object of the system according to the embodiment is to provide progress management and feedback for a user to achieve a goal.SOLUTION: A system according to an embodiment includes an objective setting section, a progress tracking section, an advice providing section, and a reward giving section. The goal setting unit uses the generated AI to find out what the user wants to achieve or improve, and sets an appropriate goal and deadline. The progress tracker tracks the user's progress and provides real-time feedback. The advice providing unit provides advice based on the progress status. The reward giving section gives a reward when the objective is achieved.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately manage progress or provide feedback to help users achieve their goals, and there is room for improvement.

[0005] The system according to the embodiment aims to provide progress management and feedback to help users achieve their goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal setting unit, a progress tracking unit, an advice providing unit, and a reward granting unit. The goal setting unit uses a generation AI to find out what goals the user wants to achieve and what they want to improve, and sets appropriate goals and deadlines. The progress tracking unit tracks the user's progress and provides feedback in real time. The advice providing unit provides advice based on the progress. The reward granting unit grants a reward when the goal is achieved. [Effects of the Invention]

[0007] The system according to the embodiment can provide progress management and feedback to help users achieve their goals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The goal achievement reward service according to an embodiment of the present invention is a system in which a generation AI finds out the goals a user wants to achieve or improve, sets appropriate goals and deadlines, tracks progress, provides advice, and awards rewards when the goals are achieved. In this way, the goal achievement reward service can help users achieve their goals.

[0029] A goal achievement reward service according to an embodiment includes a goal setting unit, a progress tracking unit, an advice providing unit, and a reward granting unit. The goal setting unit uses a generation AI to find out the goals a user wants to achieve or improve, and sets appropriate goals and deadlines. For example, if a user says, "I want to lose weight," the generation AI responds, "First, please tell us your current weight, body fat percentage, etc. Then, let's decide on a goal weight and deadline." The progress tracking unit tracks the user's progress and provides feedback in real time. For example, if the user records their daily weight, diet, and exercise, the generation AI analyzes the data to understand their progress. The advice providing unit provides advice based on their progress. For example, if a user is trying to lose weight, the generation AI may provide specific advice such as, "Today's meal was balanced, but you should add more vegetables." The reward granting unit grants a reward when a goal is achieved. For example, if a user achieves their diet goal, points from an electronic payment service may be awarded. This allows the goal achievement reward service according to an embodiment to help users achieve their goals.

[0030] The goal setting unit can analyze the user's past behavioral history and health data and propose individually optimized goals and deadlines. For example, the goal setting unit uses a generation AI to analyze the user's past weight fluctuations and diet history and propose realistic and achievable diet goals and deadlines. For example, it calculates the average weight loss rate from past data and sets goals based on that. The goal setting unit also analyzes the user's exercise history and activity data and proposes appropriate exercise goals and deadlines. For example, it calculates the amount of exercise the user can comfortably maintain from past exercise data and sets goals based on that. The goal setting unit also analyzes health data (e.g., blood pressure and blood sugar levels) and proposes specific goals and deadlines for improving health. For example, it sets target values ​​and deadlines based on past health checkup data. This makes it possible to propose optimal goals and deadlines for the user.

[0031] The goal setting unit can set realistic and achievable goals by taking into account the user's lifestyle habits and daily schedule. For example, the goal setting unit uses a generation AI to analyze the user's lifestyle habits (e.g., meal times and sleep patterns) and set realistic diet goals based on the results. For example, for a user who has the habit of eating late at night, the goal setting unit suggests a goal of eating dinner earlier. The goal setting unit also takes into account the user's daily schedule (e.g., work and family plans) to set reasonable exercise goals. For example, it suggests short exercise sessions on busy weekdays and longer exercise sessions on weekends. The goal setting unit also sets health improvement goals that the user can reasonably continue based on lifestyle habit data. For example, it suggests goals to increase the number of steps taken each day or the amount of water intake. This makes it possible to set goals that are tailored to the user's lifestyle.

[0032] The goal setting unit can link with the goals of the user's friends and family and set goals to be achieved together. For example, the goal setting unit uses a generation AI to collect goals of the user's friends and family and set goals to be achieved together. For example, the goal setting unit may set a goal for the whole family to continue eating healthy. The goal setting unit may also link with friends and family to set a goal for exercising together. For example, the goal setting unit may set a goal for walking together on weekends. The goal setting unit may also link with the goals of friends and family and set goals to be achieved by encouraging each other. For example, the goal may be set to go on a diet together with a friend. This allows goals to be achieved together with friends and family.

[0033] The goal setting unit can also handle goal setting in different fields and perform comprehensive goal management. For example, the generation AI sets the user's learning goals and performs comprehensive goal management in conjunction with health goals. For example, it sets a balanced amount of daily study time and exercise time. The goal setting unit also sets career goals and performs comprehensive goal management in conjunction with health goals. For example, it sets an exercise goal to reduce work stress. The goal setting unit also integrates goals in different fields and sets a comprehensive goal that the user can achieve without difficulty. For example, it sets a goal that takes into account the balance between learning, career, and health. This allows for comprehensive management of goals in different fields.

[0034] The progress tracking unit can analyze the user's progress data in real time and provide a dashboard that visualizes detailed progress. In the progress tracking unit, for example, the generation AI analyzes the user's weight, diet, and exercise data in real time and displays it on the dashboard. For example, it displays daily weight fluctuations and calorie intake in graphs. The progress tracking unit also analyzes the user's progress data and displays on the dashboard the progress toward achieving the goal. For example, it displays the weight remaining until the target weight is reached. The progress tracking unit also analyzes the progress data in real time and displays on the dashboard how close the user is to achieving the goal. For example, it displays the calories burned through exercise and the calorie intake through food. This allows the user to understand the progress in detail.

[0035] The progress tracking unit can compare the user's progress data with other users and provide feedback on the relative progress. For example, the generation AI in the progress tracking unit compares the user's progress data with other users and provides feedback on the relative progress. For example, the progress is evaluated in comparison with other users who have the same goal. The progress tracking unit also analyzes the user's progress data and provides feedback on how much progress the user has made compared with other users. For example, it compares the amount of weight lost over the same period. The progress tracking unit also compares the progress data with other users and displays the relative progress on a dashboard. For example, it displays the average progress of users who have the same goal. This allows the user to understand the user's progress in comparison with other users.

[0036] The progress tracking unit can link the progress to social media and share the user's progress with friends and followers. In the progress tracking unit, for example, the generation AI links the user's progress data to social media and shares it with friends and followers. For example, the progress of a diet is shared on Facebook or Twitter. The progress tracking unit also links the user's progress data to social media to receive encouragement and advice from friends and followers. For example, exercise results are shared on Instagram. The progress tracking unit also links the progress data to social media to compete with friends and followers. For example, a diet challenge is held with friends. This allows the user's progress to be shared on social media.

[0037] The progress tracking unit can link progress data with different devices and perform comprehensive data analysis. For example, the generation AI in the progress tracking unit links with a smartwatch or fitness tracker to comprehensively analyze the user's progress data. For example, it analyzes heart rate and step count data and evaluates the effectiveness of exercise. The progress tracking unit also integrates data collected from different devices to comprehensively analyze the user's progress. For example, it integrates heart rate data from a smartwatch and step count data from a fitness tracker. The progress tracking unit also links progress data with different devices to build a system that performs comprehensive data analysis. For example, it analyzes data from a smartwatch or fitness tracker in real time. This allows data from different devices to be integrated and analyzed.

[0038] The advice providing unit can provide individually customized advice based on the user's progress data and past success stories. For example, the generation AI analyzes the user's progress data and past success stories and provides individually customized advice. For example, advice is given based on diet methods that have been successful in the past. The advice providing unit also analyzes the user's progress data and provides specific advice by comparing it with past success stories. For example, it refers to data from other users who have achieved the same goal. The advice providing unit also provides customized advice according to the user's progress status based on the generation AI's past success stories. For example, it suggests an exercise plan that has been successful in the past. This allows the user to receive the most appropriate advice.

[0039] The advice providing unit can provide specific and actionable advice by taking into account the user's living environment and dietary preferences. For example, the generation AI of the advice providing unit provides actionable advice by taking into account the user's living environment (e.g., the area where they live and their home situation). For example, it may suggest exercises that can be done in a nearby park. The advice providing unit also analyzes the user's dietary preferences and provides specific dietary advice based on that. For example, it may suggest healthy recipes using the user's favorite ingredients. The advice providing unit also takes into account the living environment and dietary preferences and provides advice that the user can continue without difficulty. For example, it may suggest simple exercises and meals that fit into busy daily lives. This makes it possible to provide advice that is tailored to the user's living environment and preferences.

[0040] The advice providing unit can link the user's advice with the voice assistant and provide advice via voice. For example, the generation AI in the advice providing unit analyzes the user's progress data and provides advice via the voice assistant. For example, it suggests exercise through a smart speaker. The advice providing unit also takes into account the user's living environment and food preferences and provides specific advice via the voice assistant. For example, it suggests healthy recipes via voice. The advice providing unit also analyzes the user's emotional state using the generation AI and provides advice according to the emotion via the voice assistant. For example, it suggests relaxing music. This allows advice to be provided via the voice assistant.

[0041] The advice providing unit can visualize advice for the user and provide it in a visually easy-to-understand format. For example, the generation AI in the advice providing unit analyzes the user's progress data and visualizes it to provide advice. For example, it visually displays the effects of exercise using graphs and charts. The advice providing unit also takes into account the user's living environment and dietary preferences and provides specific advice through visualization. For example, it displays the nutritional balance of a meal in a graph. The advice providing unit also analyzes the user's emotional state and visualizes it to provide advice according to the emotion. For example, it displays changes in emotion in a graph and suggests relaxation methods. This makes it possible to provide advice in a visually easy-to-understand format.

[0042] The reward granting unit can use an electronic payment service to implement a system that grants rewards in stages according to the user's level of achievement. For example, the reward granting unit implements a system in which the electronic payment service grants rewards in stages according to the user's level of achievement. For example, a small reward is granted when 50% of the goal is achieved, and a large reward is granted when 100% is achieved. The reward granting unit also analyzes the user's progress data and grants rewards in stages according to the user's level of achievement. For example, points are granted each time a daily exercise goal is achieved. The reward granting unit also implements a system in which the electronic payment service grants rewards in stages according to the user's level of achievement, thereby maintaining the user's motivation. For example, a reward is granted according to the achievement of a weekly goal. This allows rewards to be granted in stages according to the user's level of achievement.

[0043] The reward granting unit can use an electronic payment service to analyze a user's reward history and suggest the most effective reward. For example, the reward granting unit uses an electronic payment service to analyze a user's reward history and suggest the most effective reward. For example, the reward granting unit suggests new rewards based on rewards that have been appreciated in the past. The reward granting unit also builds a system that analyzes a user's reward history and suggests effective rewards. For example, the reward granting unit suggests rewards that will please the user based on past reward history. The reward granting unit also uses an electronic payment service to analyze a user's reward history and suggest the most effective reward. For example, the reward granting unit suggests rewards that will please the user based on past reward history. This makes it possible to suggest the most suitable reward for the user.

[0044] The reward granting unit can use the electronic payment service to link rewards with other services and provide a wide range of options. For example, the electronic payment service can link rewards with other services to provide a wide range of options. For example, rewards for online shopping or travel can be provided. The reward granting unit can also analyze the user's reward history and link with other services to provide effective rewards. For example, rewards for online shopping or travel can be provided. The reward granting unit can also use the electronic payment service to link rewards with other services to provide a wide range of options. For example, rewards for online shopping or travel can be provided. This can expand the reward options.

[0045] The reward granting unit can use the electronic payment service to introduce a mechanism that allows rewards to be shared with friends and family, thereby promoting joint goal achievement. For example, the reward granting unit introduces a mechanism that allows the electronic payment service to share rewards with friends and family, thereby promoting joint goal achievement. For example, the whole family can set a goal of continuing to eat healthy and share a reward. The reward granting unit can also analyze the user's reward history and suggest rewards that can be shared with friends and family. For example, a reward for going on a trip with a friend can be offered. The reward granting unit can also introduce a mechanism that allows the electronic payment service to share rewards with friends and family, thereby promoting joint goal achievement. For example, the whole family can set a goal of continuing to eat healthy and share a reward. In this way, sharing rewards can promote joint goal achievement.

[0046] The food and exercise recording section can analyze the user's food and exercise data in detail to evaluate nutritional balance and exercise effects. For example, the generation AI in the food and exercise recording section analyzes the user's food data in detail to evaluate nutritional balance. For example, it analyzes the calorie intake and nutrient intake of meals to suggest a balanced diet. The food and exercise recording section also analyzes the user's exercise data in detail to evaluate exercise effects. For example, it analyzes the calories burned and heart rate during exercise to suggest an effective exercise plan. The food and exercise recording section also analyzes the food and exercise data in detail to comprehensively evaluate the user's health condition. For example, it suggests a health plan that takes into account the balance between food and exercise. This allows for a detailed evaluation of the effects of the user's food and exercise.

[0047] The food and exercise recording section can integrate the user's food and exercise data with other health data to perform a comprehensive health assessment. For example, the generation AI can integrate the user's food data and sleep data to perform a comprehensive health assessment. For example, it can analyze the nutritional balance of meals and the quality of sleep and suggest ways to improve health. The food and exercise recording section can also integrate the user's exercise data with other health data (e.g., stress level) to perform a comprehensive health assessment. For example, it can analyze the effects of exercise and stress level and suggest ways to reduce stress. The food and exercise recording section can also integrate the food and exercise data with other health data to perform a comprehensive assessment of the user's health status. For example, it can suggest a health plan that takes into account the balance of food, exercise, and sleep. This allows for a comprehensive assessment of the user's health status.

[0048] The food and exercise recording unit shares the user's food and exercise data with other users, thereby promoting encouragement and advice within the community. For example, the generation AI of the food and exercise recording unit shares the user's food data with other users, thereby promoting encouragement and advice within the community. For example, users with the same goals share food advice. The food and exercise recording unit also shares the user's exercise data with other users, thereby promoting encouragement and advice within the community. For example, users share exercise results and encourage each other. The food and exercise recording unit also shares food and exercise data with other users, building a system that promotes encouragement and advice within the community. For example, users with the same goals share information. This can promote encouragement and advice within the community.

[0049] The food and exercise recording section can visualize the user's food and exercise data and provide it in a form that is visually easy to understand. For example, the food and exercise recording section can use a generation AI to visualize the user's food data and provide it in a form that is visually easy to understand. For example, it can display the nutritional balance of meals in a graph. The food and exercise recording section can also visualize the user's exercise data and provide it in a form that is visually easy to understand. For example, it can display the calories burned during exercise in a chart. A system can also be built where the food and exercise recording section visualizes the food and exercise data and provides it in a form that the user can intuitively understand. For example, it can display the balance between food and exercise in a graph. This makes it possible to provide the user's food and exercise data in a form that is visually easy to understand.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The goal setting unit can take into consideration the user's hobbies and interests and set goals that can be achieved while having fun. For example, if the user likes music, it can suggest exercises that can be done while listening to music. If the user likes cooking, it can set the goal to make healthy recipes. Furthermore, if the user likes outdoor activities, it can set hiking or camping as a goal. This makes it possible to set goals that match the user's hobbies and interests.

[0052] The goal setting unit can take into account the user's past failures and set goals that will prevent the user from failing again. For example, a user who has failed at dieting in the past can be set a small, reasonable goal. For a user who has not been able to continue exercising in the past, it can also suggest exercises that can be done in a short amount of time. Furthermore, for a user who has not been able to achieve a goal in the past due to stress, it can set a goal that incorporates relaxation techniques to reduce stress. This makes it possible to set realistic goals that take past failures into account.

[0053] The goal setting unit can set balanced goals taking into account the user's social roles and responsibilities. For example, for a user who aims to balance work and family life, goals can be set that take into account the balance between work and family life. Also, for a student, goals can be set that take into account the balance between academics and health. Furthermore, for a user who is caring for elderly relatives or raising children, health goals can be set within a reasonable range. This makes it possible to set goals that are in line with the user's social roles and responsibilities.

[0054] The goal setting unit can set individually optimized goals taking into account the user's cultural background and values. For example, for a user with a specific food culture, healthy eating goals that suit that culture can be set. Also, for a user with specific religious values, goals can be set that take those values ​​into consideration. Furthermore, for a user with a specific lifestyle, goals that suit that lifestyle can be set. This makes it possible to set goals that match the user's cultural background and values.

[0055] The progress tracking unit can analyze the user's progress data and provide incentives according to the level of achievement. For example, a small incentive can be provided when 50% of the goal is achieved, and a large incentive can be provided when 100% is achieved. The progress tracking unit can also analyze the user's progress data and provide incentives in stages according to the level of achievement. Furthermore, the progress tracking unit can also build a system that analyzes the user's progress data and provides incentives according to the level of achievement. This makes it possible to provide incentives according to the level of achievement of the user.

[0056] The progress tracking unit can analyze the user's progress data and provide feedback according to the level of achievement. For example, an encouraging message can be sent when 50% of a goal is achieved. The progress tracking unit can also analyze the user's progress data and provide feedback in stages according to the level of achievement. Furthermore, the progress tracking unit can also build a system that analyzes the user's progress data and provides feedback according to the level of achievement. This makes it possible to provide feedback according to the level of achievement of the user.

[0057] The progress tracking unit can analyze the user's progress data and provide advice according to the level of achievement. For example, advice on the next step can be provided when 50% of the goal is achieved. The progress tracking unit can also analyze the user's progress data and provide advice in stages according to the level of achievement. Furthermore, the progress tracking unit can also build a system that analyzes the user's progress data and provides advice according to the level of achievement. This makes it possible to provide advice according to the user's level of achievement.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The goal setting unit uses the generation AI to find out what the user wants to achieve and improve, and sets appropriate goals and deadlines. For example, if the user says, "I want to go on a diet," the generation AI will respond by saying, "First, please tell us your current weight and body fat percentage. Then we can decide on your target weight and deadline." Step 2: The progress tracking unit tracks the user's progress and provides real-time feedback. For example, the user can record their daily weight, diet, and exercise, and the generating AI will analyze that data to understand their progress. Step 3: The advice provider provides advice based on the user's progress. For example, if the user is trying to lose weight, the AI ​​generator can provide specific advice such as, "Today's meal was balanced, but you should eat a few more vegetables." Step 4: The reward granting unit grants a reward when the goal is achieved. For example, when the user achieves a diet goal, points for an electronic payment service are granted.

[0060] (Example 2) The goal achievement reward service according to an embodiment of the present invention is a system in which a generation AI finds out the goals a user wants to achieve or improve, sets appropriate goals and deadlines, tracks progress, provides advice, and awards rewards when the goals are achieved. In this way, the goal achievement reward service can help users achieve their goals.

[0061] A goal achievement reward service according to an embodiment includes a goal setting unit, a progress tracking unit, an advice providing unit, and a reward granting unit. The goal setting unit uses a generation AI to find out the goals a user wants to achieve or improve, and sets appropriate goals and deadlines. For example, if a user says, "I want to lose weight," the generation AI responds, "First, please tell us your current weight, body fat percentage, etc. Then, let's decide on a goal weight and deadline." The progress tracking unit tracks the user's progress and provides feedback in real time. For example, if the user records their daily weight, diet, and exercise, the generation AI analyzes the data to understand their progress. The advice providing unit provides advice based on their progress. For example, if a user is trying to lose weight, the generation AI may provide specific advice such as, "Today's meal was balanced, but you should add more vegetables." The reward granting unit grants a reward when a goal is achieved. For example, if a user achieves their diet goal, points from an electronic payment service may be awarded. This allows the goal achievement reward service according to an embodiment to help users achieve their goals.

[0062] The goal setting unit can analyze the user's past behavioral history and health data and propose individually optimized goals and deadlines. For example, the goal setting unit uses a generation AI to analyze the user's past weight fluctuations and diet history and propose realistic and achievable diet goals and deadlines. For example, it calculates the average weight loss rate from past data and sets goals based on that. The goal setting unit also analyzes the user's exercise history and activity data and proposes appropriate exercise goals and deadlines. For example, it calculates the amount of exercise the user can comfortably maintain from past exercise data and sets goals based on that. The goal setting unit also analyzes health data (e.g., blood pressure and blood sugar levels) and proposes specific goals and deadlines for improving health. For example, it sets target values ​​and deadlines based on past health checkup data. This makes it possible to propose optimal goals and deadlines for the user.

[0063] The goal setting unit can set realistic and achievable goals by taking into account the user's lifestyle habits and daily schedule. For example, the goal setting unit uses a generation AI to analyze the user's lifestyle habits (e.g., meal times and sleep patterns) and set realistic diet goals based on the results. For example, for a user who has the habit of eating late at night, the goal setting unit suggests a goal of eating dinner earlier. The goal setting unit also takes into account the user's daily schedule (e.g., work and family plans) to set reasonable exercise goals. For example, it suggests short exercise sessions on busy weekdays and longer exercise sessions on weekends. The goal setting unit also sets health improvement goals that the user can reasonably continue based on lifestyle habit data. For example, it suggests goals to increase the number of steps taken each day or the amount of water intake. This makes it possible to set goals that are tailored to the user's lifestyle.

[0064] The goal setting unit can use the emotion estimation function to analyze the user's current emotional state and set goals to increase motivation. For example, the goal setting unit can use the emotion estimation function to suggest relaxation techniques to reduce the anxiety and stress the user feels when setting goals. For example, it can suggest relaxing music. The goal setting unit can also analyze the user's emotional state in real time and set goals when the user is feeling strongly positive. For example, it can set goals when the user is relaxed. The goal setting unit can also set goals that are likely to increase the user's motivation based on the emotion estimation data. For example, it can set small goals that are likely to give the user a sense of accomplishment. This allows goal setting to be performed based on the user's emotional state.

[0065] The goal setting unit can link with the goals of the user's friends and family and set goals to be achieved together. For example, the goal setting unit uses a generation AI to collect goals of the user's friends and family and set goals to be achieved together. For example, the goal setting unit may set a goal for the whole family to continue eating healthy. The goal setting unit may also link with friends and family to set a goal for exercising together. For example, the goal setting unit may set a goal for walking together on weekends. The goal setting unit may also link with the goals of friends and family and set goals to be achieved by encouraging each other. For example, the goal may be set to go on a diet together with a friend. This allows goals to be achieved together with friends and family.

[0066] The goal setting unit can also handle goal setting in different fields and perform comprehensive goal management. For example, the generation AI sets the user's learning goals and performs comprehensive goal management in conjunction with health goals. For example, it sets a balanced amount of daily study time and exercise time. The goal setting unit also sets career goals and performs comprehensive goal management in conjunction with health goals. For example, it sets an exercise goal to reduce work stress. The goal setting unit also integrates goals in different fields and sets a comprehensive goal that the user can achieve without difficulty. For example, it sets a goal that takes into account the balance between learning, career, and health. This allows for comprehensive management of goals in different fields.

[0067] The progress tracking unit can analyze the user's progress data in real time and provide a dashboard that visualizes detailed progress. In the progress tracking unit, for example, the generation AI analyzes the user's weight, diet, and exercise data in real time and displays it on the dashboard. For example, it displays daily weight fluctuations and calorie intake in graphs. The progress tracking unit also analyzes the user's progress data and displays on the dashboard the progress toward achieving the goal. For example, it displays the weight remaining until the target weight is reached. The progress tracking unit also analyzes the progress data in real time and displays on the dashboard how close the user is to achieving the goal. For example, it displays the calories burned through exercise and the calorie intake through food. This allows the user to understand the progress in detail.

[0068] The progress tracking unit can compare the user's progress data with other users and provide feedback on the relative progress. For example, the generation AI in the progress tracking unit compares the user's progress data with other users and provides feedback on the relative progress. For example, the progress is evaluated in comparison with other users who have the same goal. The progress tracking unit also analyzes the user's progress data and provides feedback on how much progress the user has made compared with other users. For example, it compares the amount of weight lost over the same period. The progress tracking unit also compares the progress data with other users and displays the relative progress on a dashboard. For example, it displays the average progress of users who have the same goal. This allows the user to understand the user's progress in comparison with other users.

[0069] The progress tracking unit can use the emotion estimation function to analyze the user's emotional response to progress and provide feedback to maintain motivation. The progress tracking unit, for example, uses the emotion estimation function to analyze the user's emotional response to progress and provide feedback to maintain motivation. For example, an encouraging message is sent when positive emotions are strong. The progress tracking unit also analyzes the user's emotional response in real time and provides feedback according to the progress status. For example, an encouraging message is sent when negative emotions are strong. The progress tracking unit also analyzes the user's emotional response to progress based on the emotion estimation data and provides specific advice to maintain motivation. For example, the next goal is suggested when positive emotions are strong. In this way, feedback can be provided based on the user's emotional response.

[0070] The progress tracking unit can link the progress to social media and share the user's progress with friends and followers. In the progress tracking unit, for example, the generation AI links the user's progress data to social media and shares it with friends and followers. For example, the progress of a diet is shared on Facebook or Twitter. The progress tracking unit also links the user's progress data to social media to receive encouragement and advice from friends and followers. For example, exercise results are shared on Instagram. The progress tracking unit also links the progress data to social media to compete with friends and followers. For example, a diet challenge is held with friends. This allows the user's progress to be shared on social media.

[0071] The progress tracking unit can link progress data with different devices and perform comprehensive data analysis. For example, the generation AI in the progress tracking unit links with a smartwatch or fitness tracker to comprehensively analyze the user's progress data. For example, it analyzes heart rate and step count data and evaluates the effectiveness of exercise. The progress tracking unit also integrates data collected from different devices to comprehensively analyze the user's progress. For example, it integrates heart rate data from a smartwatch and step count data from a fitness tracker. The progress tracking unit also links progress data with different devices to build a system that performs comprehensive data analysis. For example, it analyzes data from a smartwatch or fitness tracker in real time. This allows data from different devices to be integrated and analyzed.

[0072] The progress tracking unit can use the emotion estimation function to track changes in the user's emotions relative to their progress and provide encouragement and advice according to their emotions. The progress tracking unit, for example, uses the emotion estimation function to track changes in the user's emotions relative to their progress and provide encouragement and advice according to their emotions. For example, when positive emotions are strong, the progress tracking unit suggests the next goal. The progress tracking unit also analyzes changes in the user's emotions in real time and provides feedback according to the progress status. For example, when negative emotions are strong, the progress tracking unit sends an encouraging message. The progress tracking unit also tracks changes in the user's emotions relative to their progress based on the emotion estimation data and provides specific advice to maintain motivation. For example, when positive emotions are strong, the progress tracking unit suggests the next goal. This makes it possible to provide encouragement and advice according to the user's emotions.

[0073] The advice providing unit can provide individually customized advice based on the user's progress data and past success stories. For example, the generation AI analyzes the user's progress data and past success stories and provides individually customized advice. For example, advice is given based on diet methods that have been successful in the past. The advice providing unit also analyzes the user's progress data and provides specific advice by comparing it with past success stories. For example, it refers to data from other users who have achieved the same goal. The advice providing unit also provides customized advice according to the user's progress status based on the generation AI's past success stories. For example, it suggests an exercise plan that has been successful in the past. This allows the user to receive the most appropriate advice.

[0074] The advice providing unit can provide specific and actionable advice by taking into account the user's living environment and dietary preferences. For example, the generation AI of the advice providing unit provides actionable advice by taking into account the user's living environment (e.g., the area where they live and their home situation). For example, it may suggest exercises that can be done in a nearby park. The advice providing unit also analyzes the user's dietary preferences and provides specific dietary advice based on that. For example, it may suggest healthy recipes using the user's favorite ingredients. The advice providing unit also takes into account the living environment and dietary preferences and provides advice that the user can continue without difficulty. For example, it may suggest simple exercises and meals that fit into busy daily lives. This makes it possible to provide advice that is tailored to the user's living environment and preferences.

[0075] The advice providing unit uses the emotion estimation function to provide advice according to the user's emotional state, thereby reducing stress. The advice providing unit, for example, uses the emotion estimation function to provide advice according to the user's emotional state, thereby reducing stress. For example, when the user is feeling stressed, the advice providing unit suggests a relaxation method. The advice providing unit also analyzes the user's emotional state in real time and suggests the next goal when the user is feeling strongly positive. For example, when the user is relaxed, the advice providing unit suggests a new exercise plan. The advice providing unit also provides specific advice according to the user's emotional state based on the emotion estimation data. For example, when the user is feeling strongly negative, the advice providing unit sends an encouraging message. In this way, advice according to the user's emotional state can be provided, thereby reducing stress.

[0076] The advice providing unit can link the user's advice with the voice assistant and provide advice via voice. For example, the generation AI in the advice providing unit analyzes the user's progress data and provides advice via the voice assistant. For example, it suggests exercise through a smart speaker. The advice providing unit also takes into account the user's living environment and food preferences and provides specific advice via the voice assistant. For example, it suggests healthy recipes via voice. The advice providing unit also analyzes the user's emotional state using the generation AI and provides advice according to the emotion via the voice assistant. For example, it suggests relaxing music. This allows advice to be provided via the voice assistant.

[0077] The advice providing unit can visualize advice for the user and provide it in a visually easy-to-understand format. For example, the generation AI in the advice providing unit analyzes the user's progress data and visualizes it to provide advice. For example, it visually displays the effects of exercise using graphs and charts. The advice providing unit also takes into account the user's living environment and dietary preferences and provides specific advice through visualization. For example, it displays the nutritional balance of a meal in a graph. The advice providing unit also analyzes the user's emotional state and visualizes it to provide advice according to the emotion. For example, it displays changes in emotion in a graph and suggests relaxation methods. This makes it possible to provide advice in a visually easy-to-understand format.

[0078] The advice providing unit can use the emotion estimation function to identify the timing when the user is likely to accept advice and provide advice at that timing. The advice providing unit, for example, uses the emotion estimation function to identify the timing when the user is likely to accept advice and provide advice at that timing. For example, it suggests exercise when the user is relaxed. The advice providing unit also analyzes the user's emotional state in real time and provides advice when positive emotions are strong. For example, it suggests a new meal plan when the user is relaxed. The advice providing unit also identifies the timing when the user is likely to accept advice based on the emotion estimation data and provides specific advice at that timing. For example, it suggests the next goal when positive emotions are strong. This makes it possible to provide advice at a timing when the user is likely to accept advice.

[0079] The reward granting unit can use an electronic payment service to implement a system that grants rewards in stages according to the user's level of achievement. For example, the reward granting unit implements a system in which the electronic payment service grants rewards in stages according to the user's level of achievement. For example, a small reward is granted when 50% of the goal is achieved, and a large reward is granted when 100% is achieved. The reward granting unit also analyzes the user's progress data and grants rewards in stages according to the user's level of achievement. For example, points are granted each time a daily exercise goal is achieved. The reward granting unit also implements a system in which the electronic payment service grants rewards in stages according to the user's level of achievement, thereby maintaining the user's motivation. For example, a reward is granted according to the achievement of a weekly goal. This allows rewards to be granted in stages according to the user's level of achievement.

[0080] The reward granting unit can use an electronic payment service to analyze a user's reward history and suggest the most effective reward. For example, the reward granting unit uses an electronic payment service to analyze a user's reward history and suggest the most effective reward. For example, the reward granting unit suggests new rewards based on rewards that have been appreciated in the past. The reward granting unit also builds a system that analyzes a user's reward history and suggests effective rewards. For example, the reward granting unit suggests rewards that will please the user based on past reward history. The reward granting unit also uses an electronic payment service to analyze a user's reward history and suggest the most effective reward. For example, the reward granting unit suggests rewards that will please the user based on past reward history. This makes it possible to suggest the most suitable reward for the user.

[0081] The reward granting unit can use the emotion estimation function to identify the reward that the user will be most pleased with and provide that reward. The reward granting unit can, for example, use the emotion estimation function to identify the reward that the user will be most pleased with and provide that reward. For example, the reward granting unit can analyze the user's emotional response and provide the reward that shows the most positive response. The reward granting unit can also analyze the user's emotional state in real time to identify the reward that the user will be most pleased with. For example, the reward granting unit can provide a reward when the user is relaxed. The reward granting unit can also identify the reward that the user will be most pleased with based on the emotion estimation data and provide that reward. For example, the reward can be provided when the user has a strong positive emotion. This makes it possible to provide the reward that the user will be most pleased with.

[0082] The reward granting unit can use the electronic payment service to link rewards with other services and provide a wide range of options. For example, the electronic payment service can link rewards with other services to provide a wide range of options. For example, rewards for online shopping or travel can be provided. The reward granting unit can also analyze the user's reward history and link with other services to provide effective rewards. For example, rewards for online shopping or travel can be provided. The reward granting unit can also use the electronic payment service to link rewards with other services to provide a wide range of options. For example, rewards for online shopping or travel can be provided. This can expand the reward options.

[0083] The reward granting unit can use the electronic payment service to introduce a mechanism that allows rewards to be shared with friends and family, thereby promoting joint goal achievement. For example, the reward granting unit introduces a mechanism that allows the electronic payment service to share rewards with friends and family, thereby promoting joint goal achievement. For example, the whole family can set a goal of continuing to eat healthy and share a reward. The reward granting unit can also analyze the user's reward history and suggest rewards that can be shared with friends and family. For example, a reward for going on a trip with a friend can be offered. The reward granting unit can also introduce a mechanism that allows the electronic payment service to share rewards with friends and family, thereby promoting joint goal achievement. For example, the whole family can set a goal of continuing to eat healthy and share a reward. In this way, sharing rewards can promote joint goal achievement.

[0084] The reward granting unit uses the emotion estimation function to analyze the emotion the user feels when receiving a reward and can provide the reward at the optimal timing. The reward granting unit, for example, uses the emotion estimation function to analyze the emotion the user feels when receiving a reward and can provide the reward at the optimal timing. For example, a reward is provided when the user is relaxed. The reward granting unit also analyzes the user's emotional state in real time and can provide the reward at the optimal timing. For example, a reward is provided when the user feels a strong positive emotion. The reward granting unit also analyzes the emotion the user feels when receiving a reward based on the emotion estimation data and can provide the reward at the optimal timing. For example, a reward is provided when the user feels a strong positive emotion. This makes it possible to provide the reward at the timing when the user is most pleased.

[0085] The food and exercise recording section can analyze the user's food and exercise data in detail to evaluate nutritional balance and exercise effects. For example, the generation AI in the food and exercise recording section analyzes the user's food data in detail to evaluate nutritional balance. For example, it analyzes the calorie intake and nutrient intake of meals to suggest a balanced diet. The food and exercise recording section also analyzes the user's exercise data in detail to evaluate exercise effects. For example, it analyzes the calories burned and heart rate during exercise to suggest an effective exercise plan. The food and exercise recording section also analyzes the food and exercise data in detail to comprehensively evaluate the user's health condition. For example, it suggests a health plan that takes into account the balance between food and exercise. This allows for a detailed evaluation of the effects of the user's food and exercise.

[0086] The food and exercise recording section can integrate the user's food and exercise data with other health data to perform a comprehensive health assessment. For example, the generation AI can integrate the user's food data and sleep data to perform a comprehensive health assessment. For example, it can analyze the nutritional balance of meals and the quality of sleep and suggest ways to improve health. The food and exercise recording section can also integrate the user's exercise data with other health data (e.g., stress level) to perform a comprehensive health assessment. For example, it can analyze the effects of exercise and stress level and suggest ways to reduce stress. The food and exercise recording section can also integrate the food and exercise data with other health data to perform a comprehensive assessment of the user's health status. For example, it can suggest a health plan that takes into account the balance of food, exercise, and sleep. This allows for a comprehensive assessment of the user's health status.

[0087] The food and exercise recording unit can use the emotion estimation function to analyze the user's emotional response to food and exercise and make suggestions that will elicit positive emotions. The food and exercise recording unit, for example, uses the emotion estimation function to analyze the user's emotional response to food and make suggestions that will elicit positive emotions. For example, it can suggest recipes using the user's favorite ingredients. The food and exercise recording unit can also analyze the user's emotional response to exercise in real time and make suggestions that will elicit positive emotions. For example, it can suggest exercise plans that the user can enjoy. The food and exercise recording unit can also analyze the user's emotional response to food and exercise based on the emotion estimation data and make specific suggestions that will elicit positive emotions. For example, it can suggest meals and exercises that will help the user relax. This makes it possible to make suggestions that will elicit positive emotions from the user.

[0088] The food and exercise recording unit shares the user's food and exercise data with other users, thereby promoting encouragement and advice within the community. For example, the generation AI of the food and exercise recording unit shares the user's food data with other users, thereby promoting encouragement and advice within the community. For example, users with the same goals share food advice. The food and exercise recording unit also shares the user's exercise data with other users, thereby promoting encouragement and advice within the community. For example, users share exercise results and encourage each other. The food and exercise recording unit also shares food and exercise data with other users, building a system that promotes encouragement and advice within the community. For example, users with the same goals share information. This can promote encouragement and advice within the community.

[0089] The food and exercise recording section can visualize the user's food and exercise data and provide it in a form that is visually easy to understand. For example, the food and exercise recording section can use a generation AI to visualize the user's food data and provide it in a form that is visually easy to understand. For example, it can display the nutritional balance of meals in a graph. The food and exercise recording section can also visualize the user's exercise data and provide it in a form that is visually easy to understand. For example, it can display the calories burned during exercise in a chart. A system can also be built where the food and exercise recording section visualizes the food and exercise data and provides it in a form that the user can intuitively understand. For example, it can display the balance between food and exercise in a graph. This makes it possible to provide the user's food and exercise data in a form that is visually easy to understand.

[0090] The food and exercise recording unit can use the emotion estimation function to analyze the emotions a user feels when recording their food and exercise, and make suggestions to increase their motivation to record. The food and exercise recording unit, for example, uses the emotion estimation function to analyze the emotions a user feels when recording their food and exercise, and make suggestions to increase their motivation to record. For example, it encourages the user to record when they have strong positive emotions. The food and exercise recording unit also analyzes the user's emotional response to the exercise record in real time, and makes suggestions to increase their motivation to record. For example, it encourages the user to record when they are relaxed. The food and exercise recording unit also analyzes the emotions a user feels when recording their food and exercise, based on the emotion estimation data, and makes specific suggestions to increase their motivation to record. For example, it encourages the user to record when they have strong positive emotions. This makes it possible to make suggestions to increase the user's motivation to record.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The goal setting unit can take into consideration the user's hobbies and interests and set goals that can be achieved while having fun. For example, if the user likes music, it can suggest exercises that can be done while listening to music. If the user likes cooking, it can set the goal to make healthy recipes. Furthermore, if the user likes outdoor activities, it can set hiking or camping as a goal. This makes it possible to set goals that match the user's hobbies and interests.

[0093] The goal setting unit can take into account the user's past failures and set goals that will prevent the user from failing again. For example, a user who has failed at dieting in the past can be set a small, reasonable goal. For a user who has not been able to continue exercising in the past, it can also suggest exercises that can be done in a short amount of time. Furthermore, for a user who has not been able to achieve a goal in the past due to stress, it can set a goal that incorporates relaxation techniques to reduce stress. This makes it possible to set realistic goals that take past failures into account.

[0094] The goal setting unit can set balanced goals taking into account the user's social roles and responsibilities. For example, for a user who aims to balance work and family life, goals can be set that take into account the balance between work and family life. Also, for a student, goals can be set that take into account the balance between academics and health. Furthermore, for a user who is caring for elderly relatives or raising children, health goals can be set within a reasonable range. This makes it possible to set goals that are in line with the user's social roles and responsibilities.

[0095] The goal setting unit can use the emotion estimation function to adjust the difficulty of the goal based on the user's emotional state. For example, when the user is feeling stressed, an easy goal can be set. When the user is relaxed, a slightly more difficult goal can be set. Furthermore, when the user is feeling positive, a more challenging goal can be set. This allows for flexible goal setting according to the user's emotional state.

[0096] The goal setting unit can set individually optimized goals taking into account the user's cultural background and values. For example, for a user with a specific food culture, healthy eating goals that suit that culture can be set. Also, for a user with specific religious values, goals can be set that take those values ​​into consideration. Furthermore, for a user with a specific lifestyle, goals that suit that lifestyle can be set. This makes it possible to set goals that match the user's cultural background and values.

[0097] The progress tracking unit can analyze the user's progress data and provide incentives according to the level of achievement. For example, a small incentive can be provided when 50% of the goal is achieved, and a large incentive can be provided when 100% is achieved. The progress tracking unit can also analyze the user's progress data and provide incentives in stages according to the level of achievement. Furthermore, the progress tracking unit can also build a system that analyzes the user's progress data and provides incentives according to the level of achievement. This makes it possible to provide incentives according to the level of achievement of the user.

[0098] The progress tracking unit can analyze the user's progress data and provide feedback according to the level of achievement. For example, an encouraging message can be sent when 50% of a goal is achieved. The progress tracking unit can also analyze the user's progress data and provide feedback in stages according to the level of achievement. Furthermore, the progress tracking unit can also build a system that analyzes the user's progress data and provides feedback according to the level of achievement. This makes it possible to provide feedback according to the level of achievement of the user.

[0099] The progress tracking unit can use the emotion estimation function to provide feedback on the progress status based on the user's emotional state. For example, when the user has positive emotions, it can provide positive feedback according to the level of achievement. Also, when the user has negative emotions, it can send an encouraging message. Furthermore, it can provide real-time feedback on the progress status based on the user's emotional state. This makes it possible to provide feedback according to the user's emotional state.

[0100] The progress tracking unit can analyze the user's progress data and provide advice according to the level of achievement. For example, advice on the next step can be provided when 50% of the goal is achieved. The progress tracking unit can also analyze the user's progress data and provide advice in stages according to the level of achievement. Furthermore, the progress tracking unit can also build a system that analyzes the user's progress data and provides advice according to the level of achievement. This makes it possible to provide advice according to the user's level of achievement.

[0101] The progress tracking unit can use the emotion estimation function to visualize the progress status based on the user's emotional state. For example, when the user has positive emotions, a positive graph corresponding to the achievement level can be displayed. Also, when the user has negative emotions, an encouraging message can be displayed. Furthermore, the progress status can be visualized in real time based on the user's emotional state. This makes it possible to visualize the progress status according to the user's emotional state.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The goal setting unit uses the generation AI to find out what the user wants to achieve and improve, and sets appropriate goals and deadlines. For example, if the user says, "I want to go on a diet," the generation AI will respond by saying, "First, please tell us your current weight and body fat percentage. Then we can decide on your target weight and deadline." Step 2: The progress tracking unit tracks the user's progress and provides real-time feedback. For example, the user can record their daily weight, diet, and exercise, and the generating AI will analyze that data to understand their progress. Step 3: The advice provider provides advice based on the user's progress. For example, if the user is trying to lose weight, the AI ​​generator can provide specific advice such as, "Today's meal was balanced, but you should eat a few more vegetables." Step 4: The reward granting unit grants a reward when the goal is achieved. For example, when the user achieves a diet goal, points for an electronic payment service are granted.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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. Using generative AI, A goal setting unit that finds out the goals that the user wants to achieve and the improvements that they want to make, and sets appropriate goals and deadlines; a progress tracking unit that tracks the user's progress and provides real-time feedback; an advice providing unit that provides advice based on the progress status; a reward granting unit that grants a reward when the goal is achieved. A system characterized by:

2. The goal setting unit Analyzing the user's past behavioral history and health data, and proposing individually optimized goals and deadlines 2. The system of claim 1.

3. The goal setting unit Set realistic and achievable goals taking into account the user's lifestyle and daily schedule 2. The system of claim 1.

4. The goal setting unit Analyzing the user's current emotional state and setting goals to increase motivation 2. The system of claim 1.

5. The goal setting unit Collaborate with the goals of the user's friends and family to set goals to be achieved together 2. The system of claim 1.

Images