System

Abstract

An object of a system according to an embodiment is to make it easier for a user who likes an animation character or a two-dimension to maintain motivation for a diet.SOLUTION: A system includes an avatar generation part, a meal management part, an exercise management part, a support part, a conversation part, and a feedback part. The avatar generation unit generates an avatar based on an instruction from a user. The meal management unit manages meal contents of the user. The exercise manager may manage an exercise menu of the user. The cheering unit sends a cheering message while the user is exercising. The conversation unit has a daily conversation with the user. The feedback unit performs feedback when the user fails in the diet.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to maintain motivation to diet, and there has been a lack of effective support, especially for people who like anime characters and 2D content.

[0005] The system according to the embodiment aims to make it easier for users who like anime characters and two-dimensional content to maintain their motivation to diet. [Means for solving the problem]

[0006] The system according to the embodiment includes an avatar generation unit, a diet management unit, an exercise management unit, a cheering unit, a conversation unit, and a feedback unit. The avatar generation unit generates an avatar based on the user's instructions. The diet management unit manages the user's dietary content. The exercise management unit manages the user's exercise menu. The cheering unit sends cheering messages to the user while they are exercising. The conversation unit has daily conversations with the user. The feedback unit provides feedback when the user fails in their diet. [Effects of the Invention]

[0007] The system according to the embodiment can help users who like anime characters and two-dimensional content to maintain their motivation to diet. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 diet consulting app service according to an embodiment of the present invention is a system that automatically reads answers written by students, summarizes them using a generation AI, calculates the similarity to the model answer, and scores them. As a result, the diet consulting app service allows users to create avatars of their favorite characters, and the avatars provide coaching, helping them maintain their motivation to succeed in their diet.

[0029] A diet consulting app service according to an embodiment includes an avatar generation unit, a diet management unit, an exercise management unit, a cheering unit, a conversation unit, and a feedback unit. The avatar generation unit generates an avatar based on a user's instructions. For example, the user inputs prompts such as "blue hair color, a lively and cheerful personality, and a high-pitched voice," and the generation AI generates an avatar that matches those conditions. The diet management unit manages the user's diet. For example, the user inputs their daily diet into the app, and the generation AI analyzes the data to evaluate nutritional balance and calorie intake. The exercise management unit manages the user's exercise menu. For example, the user inputs their exercise goals and current physical fitness level, and the generation AI creates an exercise menu based on that information. The cheering unit sends cheering messages to the user while they exercise. For example, when the user starts exercising, the avatar vocally conveys cheering messages such as "Good luck! You're almost there!" The conversation unit engages in everyday conversations with the user. For example, when the user opens the app, the avatar speaks to them in the form of "How was your day today?" The feedback unit provides feedback to the user when the user fails in dieting. For example, if the user skips exercise, the avatar provides feedback in the form of "You haven't been exercising recently, so you've gained a little weight." This allows the diet consulting app service according to the embodiment to comprehensively support the user's diet.

[0030] The avatar generation unit can analyze a user's past SNS posts and message history, automatically infer the user's preferred character attributes, and generate an avatar. For example, the avatar generation unit analyzes the user's SNS posts and message history to extract frequently mentioned characters and attributes. For example, it generates an avatar based on keywords such as specific anime characters, colors, and personality traits. The avatar generation unit also learns the user's preferences from past SNS posts and automatically suggests recommended character attributes. For example, it analyzes the images and comments frequently posted by the user and reflects them in the avatar design. The avatar generation unit also analyzes message history to extract the characteristics of characters the user prefers. For example, if a user tends to prefer a particular personality or tone of voice, it generates an avatar based on that information. This allows for the automatic generation of an avatar that matches the user's preferences.

[0031] The avatar generation unit can provide avatar costumes and accessories in collaboration with the user's favorite anime or game characters. The avatar generation unit, for example, provides costumes and accessories in collaboration with the user's favorite anime or game characters. For example, the avatar can be dressed in a costume of a specific anime character. The avatar generation unit also periodically updates the collaboration items so that the user can enjoy new costumes and accessories. For example, new collaboration items can be added each season. The avatar generation unit also provides a function that allows the user to customize the costumes and accessories of their favorite characters. For example, the user can change the color or design to create original collaboration items. This makes it possible to provide an avatar in collaboration with the user's favorite character.

[0032] The avatar generation unit can set the avatar's movements and gestures to imitate the movements of famous anime scenes or characters specified by the user. For example, the avatar generation unit causes the avatar to imitate the movements of famous anime scenes or characters specified by the user. For example, it sets the avatar to movements that recreate famous scenes from a particular anime. The avatar generation unit also provides a function for customizing the avatar's movements and gestures, allowing the user to recreate the movements of their favorite anime characters. For example, it sets specific poses and facial expressions. The avatar generation unit also learns the movements of anime scenes specified by the user and causes the avatar to imitate those movements. For example, it sets the avatar to movements that recreate anime battle scenes or dance scenes. This makes it possible to provide an avatar that imitates the movements of anime scenes or characters specified by the user.

[0033] The diet management unit can analyze the user's diet history and propose an optimal meal plan based on past eating patterns. The diet management unit, for example, analyzes the user's past eating history and evaluates nutritional balance and calorie intake. For example, it proposes an optimal meal plan based on past data. The diet management unit also automatically generates a meal plan that takes into account the user's preferences and allergy information based on the diet history. For example, it proposes a plan that avoids certain ingredients. The diet management unit also analyzes past eating patterns and proposes a meal plan that the user can easily continue. For example, it provides a plan that includes many of the user's favorite ingredients. This makes it possible to propose an optimal meal plan based on the user's past eating patterns.

[0034] The diet management unit can automatically input dietary details and evaluate nutritional balance by scanning the barcodes of ingredients. The diet management unit can build a system that automatically inputs dietary details and evaluates nutritional balance by scanning the barcodes of ingredients, for example. For example, it obtains calorie and nutritional information of ingredients from the barcode. The diet management unit also uses a barcode scanning function to enable users to easily input dietary details. For example, it can scan barcodes using a smartphone camera. The diet management unit also evaluates nutritional balance based on the scanned ingredient data and provides appropriate advice to the user. For example, it can suggest ingredients to supplement a specific nutrient if a specific nutrient is lacking. This allows dietary details to be automatically input and nutritional balance to be evaluated by scanning the barcodes of ingredients.

[0035] The diet management unit can add a function that allows a user to share their dietary information with other users and receive feedback within the community. The diet management unit, for example, adds a function that allows a user to share their dietary information with other users and builds a system that receives feedback within the community. For example, by posting photos of meals and recipes. The diet management unit also provides a function that improves the user's diet plan based on feedback within the community. For example, it takes into account advice and comments from other users. The diet management unit also develops a system that allows users to encourage each other while dieting through the dietary information sharing function. For example, it displays meal ratings and rankings. This allows a user to share their dietary information with other users and receive feedback within the community.

[0036] The diet management unit can propose a meal plan based on the menu eaten by the user's favorite anime character. The diet management unit, for example, builds a system that proposes a meal plan based on the menu eaten by the user's favorite anime character. For example, menus are extracted from anime scenes. The diet management unit also provides recipes based on the anime character's meal menu, allowing the user to diet while having fun. For example, it proposes recipes that recreate the character's favorite foods. The diet management unit also proposes a meal plan that takes nutritional balance into consideration based on eating scenes of the user's favorite anime character. For example, it arranges the character's meal contents to be healthier. In this way, a meal plan can be proposed based on the menu eaten by the user's favorite anime character.

[0037] The exercise management unit can analyze the user's exercise history and suggest an optimal exercise menu based on past performance. The exercise management unit, for example, analyzes the user's past exercise history and builds a system that suggests an optimal exercise menu based on performance data. For example, it adjusts a training plan based on past exercise data. The exercise management unit also automatically generates an exercise menu based on the user's exercise history according to the user's physical fitness level and goals. For example, it adjusts exercise intensity taking into account the user's progress. The exercise management unit also analyzes the user's exercise history and customizes the exercise menu based on past performance data. For example, if a particular exercise is effective, it incorporates that exercise into the menu. This makes it possible to suggest an optimal exercise menu based on the user's past performance.

[0038] The exercise management unit can analyze data from the wearable device in real time and provide guidance on form and posture during exercise. The exercise management unit, for example, analyzes data from the wearable device in real time and builds a system that provides guidance on form and posture during exercise. For example, it uses data from a smartwatch or fitness tracker. The exercise management unit also analyzes data in real time, evaluates the user's exercise form and posture, and provides guidance on areas for improvement. For example, it analyzes posture while running and suggests appropriate form. The exercise management unit also monitors form and posture during exercise in real time based on data from the wearable device and provides feedback to the user. For example, it provides guidance on how to perform yoga poses correctly. This allows the data from the wearable device to be analyzed in real time and provide guidance on form and posture during exercise.

[0039] The exercise management unit can provide a simulation in which the user exercises together with a favorite anime character, thereby increasing enjoyment. The exercise management unit, for example, builds a system that provides a simulation in which the user exercises together with a favorite anime character. For example, it displays a video of an avatar exercising together with the user. The exercise management unit also enables the user to enjoy exercising through the simulation in which the user exercises together with the anime character. For example, the character provides exercise instructions. The exercise management unit also provides a simulation in which the user exercises together with a favorite anime character, thereby increasing motivation to exercise. For example, the character sends a message of encouragement. This allows the user to enjoy the simulation in which the user exercises together with a favorite anime character.

[0040] The exercise management unit can customize the exercise menu based on episodes or scenes from the user's favorite anime. For example, the exercise management unit builds a system that customizes the exercise menu based on episodes or scenes from the user's favorite anime. For example, it recreates exercises that appear in specific episodes. The exercise management unit also provides exercise menus based on anime scenes, allowing the user to exercise while having fun. For example, it suggests training that recreates anime battle scenes. The exercise management unit also customizes the exercise menu based on episodes or scenes from the user's favorite anime. For example, it incorporates training methods from anime characters. This allows the exercise menu to be customized based on episodes or scenes from the user's favorite anime.

[0041] The cheering unit can analyze the user's exercise data in real time and send cheering messages at the optimal timing. The cheering unit, for example, builds a system that analyzes the user's exercise data in real time and sends cheering messages at the optimal timing. For example, cheering messages are sent at the peak of exercise. The cheering unit also performs real-time data analysis and automatically generates cheering messages according to the user's exercise status. For example, an encouraging message is sent when the user is tired. The cheering unit also develops a system that sends cheering messages at the optimal timing based on the user's exercise data. For example, cheering messages are sent at the start and end of exercise. This makes it possible to analyze the user's exercise data in real time and send cheering messages at the optimal timing.

[0042] The cheering unit can provide cheering messages from the voice actors of the user's favorite anime characters. The cheering unit, for example, builds a system that provides cheering messages from the voice actors of the user's favorite anime characters. For example, it plays cheering messages recorded by a specific voice actor. The cheering unit also motivates the user through cheering messages from the voice actors of the anime characters. For example, it sends words of encouragement in the character's voice. The cheering unit also provides a function that allows the user to select a cheering message from their favorite voice actor. For example, it customizes the cheering message by selecting from multiple voice actors. This allows the user to provide a cheering message from the voice actor of their favorite anime character.

[0043] The cheering unit can customize cheering messages based on famous scenes and lines from the user's favorite anime. For example, the cheering unit builds a system that customizes cheering messages based on famous scenes and lines from the user's favorite anime. For example, famous lines from a particular anime are used as cheering messages. The cheering unit also provides cheering messages that recreate famous scenes from anime to increase the user's motivation. For example, lines from battle scenes are played as cheering messages. The cheering unit also customizes cheering messages based on lines from the user's favorite anime. For example, famous quotes from characters are used as cheering messages. This allows cheering messages to be customized based on famous scenes and lines from the user's favorite anime.

[0044] The daily conversation unit can analyze the user's past conversation history and generate individually customized conversation content. The daily conversation unit, for example, analyzes the user's past conversation history and builds a system that generates individually customized conversation content. For example, the system reflects the user's preferences and interests based on past conversation data. The daily conversation unit also automatically generates topics and topics that interest the user based on the conversation history. For example, the system customizes the conversation content based on themes that the user often talks about. The daily conversation unit also analyzes the user's past conversation history and provides individually customized conversation content. For example, if the user is interested in a particular topic, the system generates a conversation related to that topic. In this way, the user's past conversation history can be analyzed and individually customized conversation content can be generated.

[0045] The everyday conversation unit can analyze a user's schedule and plans and provide reminders and advice at the appropriate time. For example, the everyday conversation unit can build a system that analyzes a user's schedule and plans and provides reminders and advice at the appropriate time. For example, it can work with a calendar app to send reminders. The everyday conversation unit can also provide the information and advice the user needs at the appropriate time based on schedule data. For example, it can send advice on how to relax before a meeting. The everyday conversation unit can also analyze a user's plans and automatically generate reminders for important events and tasks. For example, it can send a reminder to check the progress of a task before a deadline. This makes it possible to analyze a user's schedule and plans and provide reminders and advice at the appropriate time.

[0046] The daily conversation unit provides a conversation simulation with a user's favorite anime character, thereby increasing the enjoyment. The daily conversation unit, for example, builds a system that provides a conversation simulation with a user's favorite anime character. For example, an avatar speaks in the character's voice. The daily conversation unit also allows the user to enjoy conversations through conversation simulations with anime characters. For example, the character answers the user's questions. The daily conversation unit also provides a conversation simulation with a user's favorite anime character, allowing the user to enjoy everyday conversations. For example, the character talks about the user's day. This provides a conversation simulation with a user's favorite anime character, thereby increasing the enjoyment.

[0047] The everyday conversation unit can customize the conversation content based on episodes and scenes from the user's favorite anime. The everyday conversation unit, for example, builds a system that customizes the conversation content based on episodes and scenes from the user's favorite anime. For example, it provides topics related to specific episodes. The everyday conversation unit also provides conversation content based on anime scenes, allowing the user to enjoy conversation. For example, it provides conversations that recreate famous scenes from anime. The everyday conversation unit also customizes the conversation content based on episodes and scenes from the user's favorite anime. For example, it incorporates lines from characters into the conversation. This allows the conversation content to be customized based on episodes and scenes from the user's favorite anime.

[0048] The feedback unit can analyze the user's diet history, identify the cause of failure, and propose specific improvement measures. For example, the feedback unit builds a system that analyzes the user's diet history, identifies the cause of failure, and proposes specific improvement measures. For example, it proposes improvements to diet and exercise based on past data. The feedback unit also identifies patterns in which the user is likely to fail based on the diet history, and automatically generates improvement measures. For example, if failure is caused by a specific diet or exercise, it proposes countermeasures. The feedback unit also analyzes the user's diet history, identifies the cause of failure, and provides specific improvement measures. For example, it makes suggestions to adjust the balance of meals or the frequency of exercise. In this way, it is possible to analyze the user's diet history, identify the cause of failure, and propose specific improvement measures.

[0049] The feedback unit can customize the feedback content based on the story of a user's favorite anime character. For example, the feedback unit builds a system that customizes the feedback content based on the story of a user's favorite anime character. For example, the feedback unit provides feedback that references the character's episodes. The feedback unit also provides feedback based on the anime character's story so that the user can enjoy and accept it. For example, the feedback unit incorporates the character's growth story. The feedback unit also customizes the feedback based on the story of a user's favorite anime character. For example, the feedback unit provides feedback that quotes the character's famous scenes. This allows the feedback content to be customized based on the story of a user's favorite anime character.

[0050] The feedback unit can provide feedback based on famous scenes and lines from the user's favorite anime. For example, the feedback unit builds a system that provides feedback based on famous scenes and lines from the user's favorite anime. For example, famous lines from a particular anime are used as feedback. The feedback unit also provides feedback that recreates famous scenes from anime, allowing the user to enjoy and accept it. For example, lines from a battle scene are played as feedback. The feedback unit also customizes feedback based on lines from the user's favorite anime. For example, famous quotes from characters are used as feedback. This makes it possible to provide feedback based on famous scenes and lines from the user's favorite anime.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The avatar generation unit can monitor the user's health condition and reflect it in the avatar's appearance. For example, if the user is tired, the avatar will also have a tired expression. The avatar generation unit also dynamically changes the avatar's appearance based on the user's health data. For example, if the user continues to exercise, the avatar will have a healthy appearance. The avatar generation unit also customizes the avatar's appearance according to the user's health condition. For example, if the user is feeling stressed, the avatar will have a relaxed expression. This makes it possible to provide an avatar that reflects the user's health condition.

[0053] The diet management unit can automatically generate an ingredient shopping list based on the user's dietary details. For example, it analyzes the dietary details entered by the user and lists the necessary ingredients. The diet management unit also suggests ingredients to purchase regularly based on the user's diet history. For example, it adds ingredients that the user frequently uses to the list. The diet management unit also provides a function to purchase ingredients in cooperation with online shopping sites based on the ingredient shopping list. For example, it automatically places orders based on the list. This makes it possible to automatically generate an ingredient shopping list based on the user's dietary details.

[0054] The exercise management unit can propose a post-exercise recovery plan based on the user's exercise data. For example, it can suggest post-exercise stretching and massage methods. The exercise management unit also customizes the recovery plan based on the user's exercise history. For example, it can suggest methods to focus on caring for specific muscles. The exercise management unit also provides music and videos that help the user relax based on the recovery plan. For example, it can play relaxing music during recovery. This makes it possible to propose a post-exercise recovery plan for the user.

[0055] The cheering club can suggest reflection time after exercise based on the user's exercise data. For example, it can provide time for the user to reflect on their performance after exercise. The cheering club can also customize the content of the reflection time based on the user's exercise history. For example, it can suggest ways to reflect on exercise successes and areas for improvement. The cheering club can also support the user in setting their next goal based on the reflection time. For example, it can provide advice on setting their next exercise goal. This allows it to suggest reflection time after exercise for the user.

[0056] The daily conversation section can suggest topics that interest the user based on the user's past conversation history. For example, the conversation content is customized based on the themes that the user often talks about. The daily conversation section also creates a conversation flow based on topics that interest the user. For example, the user can talk about their favorite movies or music. The daily conversation section also provides related information and news based on the user's interests. For example, it provides the latest information on areas that interest the user. This makes it possible to suggest topics that interest the user.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The avatar generation unit generates an avatar based on the user's instructions. For example, the user can input instructions such as "hair color should be blue, personality should be lively and cheerful, and voice should be high-pitched" as a prompt, and the generation AI will generate an avatar that matches those conditions. Step 2: The dietary management unit manages the user's dietary information. For example, the user enters their daily dietary information into the app, and the AI ​​analyzes the data to evaluate nutritional balance and calorie intake. Step 3: The exercise management unit manages the user's exercise menu. For example, the user inputs their exercise goals and current physical fitness level, and the generation AI creates an exercise menu based on that. Step 4: The cheering section sends cheering messages to the user while they are exercising. For example, when the user starts exercising, an avatar will voice a cheering message such as "Do your best! You're almost there!" Step 5: The conversation part engages in everyday conversations with the user. For example, when a user opens the app, the avatar speaks to them in the form of, "How was your day?" Step 6: The feedback unit provides feedback to the user if the user fails to diet. For example, if the user neglects exercise, the avatar provides feedback such as, "You haven't been exercising recently, so you've gained a little weight."

[0059] (Example 2) The diet consulting app service according to an embodiment of the present invention is a system that automatically reads answers written by students, summarizes them using a generation AI, calculates the similarity to the model answer, and scores them. As a result, the diet consulting app service allows users to create avatars of their favorite characters, and the avatars provide coaching, helping them maintain their motivation to succeed in their diet.

[0060] A diet consulting app service according to an embodiment includes an avatar generation unit, a diet management unit, an exercise management unit, a cheering unit, a conversation unit, and a feedback unit. The avatar generation unit generates an avatar based on a user's instructions. For example, the user inputs prompts such as "blue hair color, a lively and cheerful personality, and a high-pitched voice," and the generation AI generates an avatar that matches those conditions. The diet management unit manages the user's diet. For example, the user inputs their daily diet into the app, and the generation AI analyzes the data to evaluate nutritional balance and calorie intake. The exercise management unit manages the user's exercise menu. For example, the user inputs their exercise goals and current physical fitness level, and the generation AI creates an exercise menu based on that information. The cheering unit sends cheering messages to the user while they exercise. For example, when the user starts exercising, the avatar vocally conveys cheering messages such as "Good luck! You're almost there!" The conversation unit engages in everyday conversations with the user. For example, when the user opens the app, the avatar speaks to them in the form of "How was your day today?" The feedback unit provides feedback to the user when the user fails in dieting. For example, if the user skips exercise, the avatar provides feedback in the form of "You haven't been exercising recently, so you've gained a little weight." This allows the diet consulting app service according to the embodiment to comprehensively support the user's diet.

[0061] The avatar generation unit can analyze a user's past SNS posts and message history, automatically infer the user's preferred character attributes, and generate an avatar. For example, the avatar generation unit analyzes the user's SNS posts and message history to extract frequently mentioned characters and attributes. For example, it generates an avatar based on keywords such as specific anime characters, colors, and personality traits. The avatar generation unit also learns the user's preferences from past SNS posts and automatically suggests recommended character attributes. For example, it analyzes the images and comments frequently posted by the user and reflects them in the avatar design. The avatar generation unit also analyzes message history to extract the characteristics of characters the user prefers. For example, if a user tends to prefer a particular personality or tone of voice, it generates an avatar based on that information. This allows for the automatic generation of an avatar that matches the user's preferences.

[0062] The avatar generation unit analyzes the user's real-time facial expression and tone of voice and can dynamically change the avatar's facial expression and voice to match the user's mood at that time. The avatar generation unit, for example, uses the user's camera and microphone to analyze the user's real-time facial expression and tone of voice and dynamically change the avatar's facial expression and voice. For example, if the user is smiling, the avatar also smiles. The avatar generation unit also performs real-time facial expression analysis and changes the avatar's facial expression to match the user's mood. For example, if the user is tired, the avatar shows an encouraging expression. The avatar generation unit also analyzes the tone of voice and changes the avatar's voice according to the user's emotions. For example, if the user is excited, the avatar's voice is adjusted to sound more uplifting. This allows the avatar's facial expression and voice to dynamically change to match the user's mood.

[0063] The avatar generation unit can use the emotion estimation function to learn the avatar characteristics that evoke the most positive emotions in the user and reflect them in the avatar design. For example, the avatar generation unit uses the emotion estimation function to learn the characteristics that evoke positive emotions in the user toward the avatar. For example, if the user has a positive reaction to a particular hairstyle or clothing, the avatar generation unit reflects those characteristics in the avatar. The avatar generation unit also collects user emotion data and automatically generates an avatar design that elicits positive emotions. For example, the avatar generation unit learns the avatar expressions and poses that make the user smile. The avatar generation unit also uses the emotion estimation function to identify the avatar characteristics that evoke the most positive emotions in the user and customizes the avatar based on that information. For example, if the user has a positive reaction to a particular color or style, the avatar generation unit incorporates those elements. This allows the generation of an avatar that evokes the most positive emotions in the user.

[0064] The avatar generation unit can provide avatar costumes and accessories in collaboration with the user's favorite anime or game characters. The avatar generation unit, for example, provides costumes and accessories in collaboration with the user's favorite anime or game characters. For example, the avatar can be dressed in a costume of a specific anime character. The avatar generation unit also periodically updates the collaboration items so that the user can enjoy new costumes and accessories. For example, new collaboration items can be added each season. The avatar generation unit also provides a function that allows the user to customize the costumes and accessories of their favorite characters. For example, the user can change the color or design to create original collaboration items. This makes it possible to provide an avatar in collaboration with the user's favorite character.

[0065] The avatar generation unit can set the avatar's movements and gestures to imitate the movements of famous anime scenes or characters specified by the user. For example, the avatar generation unit causes the avatar to imitate the movements of famous anime scenes or characters specified by the user. For example, it sets the avatar to movements that recreate famous scenes from a particular anime. The avatar generation unit also provides a function for customizing the avatar's movements and gestures, allowing the user to recreate the movements of their favorite anime characters. For example, it sets specific poses and facial expressions. The avatar generation unit also learns the movements of anime scenes specified by the user and causes the avatar to imitate those movements. For example, it sets the avatar to movements that recreate anime battle scenes or dance scenes. This makes it possible to provide an avatar that imitates the movements of anime scenes or characters specified by the user.

[0066] The avatar generation unit uses the emotion estimation function to analyze the emotions of the user when interacting with the avatar in real time, and can optimize the avatar's reactions. The avatar generation unit, for example, uses the emotion estimation function to analyze the emotions of the user when interacting with the avatar in real time, and optimize the avatar's reactions. For example, if the user is happy, the avatar also shows a happy expression. The avatar generation unit also builds a system that dynamically changes the avatar's reactions based on the user's emotion data. For example, if the user is sad, the avatar will offer words of encouragement. The avatar generation unit also uses the emotion estimation function to automatically generate the avatar's reactions according to the user's emotions. For example, if the user is excited, the avatar will respond in an excited voice. This makes it possible to optimize the avatar's reactions according to the user's emotions.

[0067] The diet management unit can analyze the user's diet history and propose an optimal meal plan based on past eating patterns. The diet management unit, for example, analyzes the user's past eating history and evaluates nutritional balance and calorie intake. For example, it proposes an optimal meal plan based on past data. The diet management unit also automatically generates a meal plan that takes into account the user's preferences and allergy information based on the diet history. For example, it proposes a plan that avoids certain ingredients. The diet management unit also analyzes past eating patterns and proposes a meal plan that the user can easily continue. For example, it provides a plan that includes many of the user's favorite ingredients. This makes it possible to propose an optimal meal plan based on the user's past eating patterns.

[0068] The diet management unit can automatically input dietary details and evaluate nutritional balance by scanning the barcodes of ingredients. The diet management unit can build a system that automatically inputs dietary details and evaluates nutritional balance by scanning the barcodes of ingredients, for example. For example, it obtains calorie and nutritional information of ingredients from the barcode. The diet management unit also uses a barcode scanning function to enable users to easily input dietary details. For example, it can scan barcodes using a smartphone camera. The diet management unit also evaluates nutritional balance based on the scanned ingredient data and provides appropriate advice to the user. For example, it can suggest ingredients to supplement a specific nutrient if a specific nutrient is lacking. This allows dietary details to be automatically input and nutritional balance to be evaluated by scanning the barcodes of ingredients.

[0069] The diet management unit can add a function that allows a user to share their dietary information with other users and receive feedback within the community. The diet management unit, for example, adds a function that allows a user to share their dietary information with other users and builds a system that receives feedback within the community. For example, by posting photos of meals and recipes. The diet management unit also provides a function that improves the user's diet plan based on feedback within the community. For example, it takes into account advice and comments from other users. The diet management unit also develops a system that allows users to encourage each other while dieting through the dietary information sharing function. For example, it displays meal ratings and rankings. This allows a user to share their dietary information with other users and receive feedback within the community.

[0070] The diet management unit can propose a meal plan based on the menu eaten by the user's favorite anime character. The diet management unit, for example, builds a system that proposes a meal plan based on the menu eaten by the user's favorite anime character. For example, menus are extracted from anime scenes. The diet management unit also provides recipes based on the anime character's meal menu, allowing the user to diet while having fun. For example, it proposes recipes that recreate the character's favorite foods. The diet management unit also proposes a meal plan that takes nutritional balance into consideration based on eating scenes of the user's favorite anime character. For example, it arranges the character's meal contents to be healthier. In this way, a meal plan can be proposed based on the menu eaten by the user's favorite anime character.

[0071] The meal management unit uses the emotion estimation function to analyze in real time whether the user is enjoying a meal and make suggestions to increase the enjoyment. The meal management unit, for example, uses the emotion estimation function to build a system that analyzes in real time whether the user is enjoying a meal and makes suggestions to increase the enjoyment. For example, it analyzes the user's facial expressions and tone of voice. The meal management unit also automatically generates suggestions to increase the enjoyment of a meal based on the user's emotion data. For example, it suggests ingredients and menus that the user can enjoy. The meal management unit also uses the emotion estimation function to monitor whether the user is enjoying a meal and provides advice to increase the enjoyment. For example, it suggests music and videos that can help the user relax while eating. In this way, it is possible to analyze in real time whether the user is enjoying a meal and make suggestions to increase the enjoyment.

[0072] The exercise management unit can analyze the user's exercise history and suggest an optimal exercise menu based on past performance. The exercise management unit, for example, analyzes the user's past exercise history and builds a system that suggests an optimal exercise menu based on performance data. For example, it adjusts a training plan based on past exercise data. The exercise management unit also automatically generates an exercise menu based on the user's exercise history according to the user's physical fitness level and goals. For example, it adjusts exercise intensity taking into account the user's progress. The exercise management unit also analyzes the user's exercise history and customizes the exercise menu based on past performance data. For example, if a particular exercise is effective, it incorporates that exercise into the menu. This makes it possible to suggest an optimal exercise menu based on the user's past performance.

[0073] The exercise management unit can analyze data from the wearable device in real time and provide guidance on form and posture during exercise. The exercise management unit, for example, analyzes data from the wearable device in real time and builds a system that provides guidance on form and posture during exercise. For example, it uses data from a smartwatch or fitness tracker. The exercise management unit also analyzes data in real time, evaluates the user's exercise form and posture, and provides guidance on areas for improvement. For example, it analyzes posture while running and suggests appropriate form. The exercise management unit also monitors form and posture during exercise in real time based on data from the wearable device and provides feedback to the user. For example, it provides guidance on how to perform yoga poses correctly. This allows the data from the wearable device to be analyzed in real time and provide guidance on form and posture during exercise.

[0074] The exercise management unit can use the emotion estimation function to analyze the stress and fatigue felt by the user during exercise and suggest appropriate rest timing. The exercise management unit, for example, uses the emotion estimation function to analyze the stress and fatigue felt by the user during exercise and build a system that suggests appropriate rest timing. For example, it analyzes the user's facial expressions and heart rate. The exercise management unit also monitors stress and fatigue during exercise based on the user's emotion data and suggests appropriate rest timing. For example, it encourages the user to take a break when they are tired. The exercise management unit also uses the emotion estimation function to analyze the stress and fatigue felt by the user during exercise in real time and dynamically adjust the rest timing. For example, it suggests breaks according to changes in the user's emotions. This makes it possible to analyze the stress and fatigue felt by the user during exercise and suggest appropriate rest timing.

[0075] The exercise management unit can provide a simulation in which the user exercises together with a favorite anime character, thereby increasing enjoyment. The exercise management unit, for example, builds a system that provides a simulation in which the user exercises together with a favorite anime character. For example, it displays a video of an avatar exercising together with the user. The exercise management unit also enables the user to enjoy exercising through the simulation in which the user exercises together with the anime character. For example, the character provides exercise instructions. The exercise management unit also provides a simulation in which the user exercises together with a favorite anime character, thereby increasing motivation to exercise. For example, the character sends a message of encouragement. This allows the user to enjoy the simulation in which the user exercises together with a favorite anime character.

[0076] The exercise management unit can customize the exercise menu based on episodes or scenes from the user's favorite anime. For example, the exercise management unit builds a system that customizes the exercise menu based on episodes or scenes from the user's favorite anime. For example, it recreates exercises that appear in specific episodes. The exercise management unit also provides exercise menus based on anime scenes, allowing the user to exercise while having fun. For example, it suggests training that recreates anime battle scenes. The exercise management unit also customizes the exercise menu based on episodes or scenes from the user's favorite anime. For example, it incorporates training methods from anime characters. This allows the exercise menu to be customized based on episodes or scenes from the user's favorite anime.

[0077] The exercise management unit can use the emotion estimation function to suggest music and videos to amplify the positive emotions felt by the user while exercising. The exercise management unit, for example, uses the emotion estimation function to build a system that suggests music and videos to amplify the positive emotions felt by the user while exercising. For example, music is selected based on the user's emotion data. The exercise management unit also suggests music and videos that elicit positive emotions while exercising based on the user's emotion data. For example, music that helps the user relax is provided. The exercise management unit also uses the emotion estimation function to suggest music and videos in real time to amplify the positive emotions felt by the user while exercising. For example, the music is changed according to changes in the user's emotion. This makes it possible to suggest music and videos to amplify the positive emotions felt by the user while exercising.

[0078] The cheering unit can analyze the user's exercise data in real time and send cheering messages at the optimal timing. The cheering unit, for example, builds a system that analyzes the user's exercise data in real time and sends cheering messages at the optimal timing. For example, cheering messages are sent at the peak of exercise. The cheering unit also performs real-time data analysis and automatically generates cheering messages according to the user's exercise status. For example, an encouraging message is sent when the user is tired. The cheering unit also develops a system that sends cheering messages at the optimal timing based on the user's exercise data. For example, cheering messages are sent at the start and end of exercise. This makes it possible to analyze the user's exercise data in real time and send cheering messages at the optimal timing.

[0079] The cheering unit can use the emotion estimation function to learn and apply the cheering message that most motivates the user. For example, the cheering unit uses the emotion estimation function to build a system that learns and applies the cheering message that most motivates the user. For example, the cheering message is customized based on the user's emotion data. The cheering unit also automatically generates the cheering message that most motivates the user based on the user's emotion data. For example, the cheering unit sends a message that makes the user feel positive. The cheering unit also uses the emotion estimation function to provide the cheering message that most motivates the user in real time. For example, the cheering message is adjusted according to changes in the user's emotion. This allows the cheering unit to learn and apply the cheering message that most motivates the user.

[0080] The cheering unit can provide cheering messages from the voice actors of the user's favorite anime characters. The cheering unit, for example, builds a system that provides cheering messages from the voice actors of the user's favorite anime characters. For example, it plays cheering messages recorded by a specific voice actor. The cheering unit also motivates the user through cheering messages from the voice actors of the anime characters. For example, it sends words of encouragement in the character's voice. The cheering unit also provides a function that allows the user to select a cheering message from their favorite voice actor. For example, it customizes the cheering message by selecting from multiple voice actors. This allows the user to provide a cheering message from the voice actor of their favorite anime character.

[0081] The cheering unit can customize cheering messages based on famous scenes and lines from the user's favorite anime. For example, the cheering unit builds a system that customizes cheering messages based on famous scenes and lines from the user's favorite anime. For example, famous lines from a particular anime are used as cheering messages. The cheering unit also provides cheering messages that recreate famous scenes from anime to increase the user's motivation. For example, lines from battle scenes are played as cheering messages. The cheering unit also customizes cheering messages based on lines from the user's favorite anime. For example, famous quotes from characters are used as cheering messages. This allows cheering messages to be customized based on famous scenes and lines from the user's favorite anime.

[0082] The cheering unit uses the emotion estimation function to analyze the emotions felt by the user while exercising in real time and send the optimal cheering message. The cheering unit, for example, uses the emotion estimation function to build a system that analyzes the emotions felt by the user while exercising in real time and sends the optimal cheering message. For example, it analyzes the user's facial expressions and tone of voice. The cheering unit also automatically generates the optimal cheering message while exercising based on the user's emotion data. For example, it sends an encouraging message when the user is tired. The cheering unit also uses the emotion estimation function to monitor the emotions felt by the user while exercising in real time and dynamically adjust the cheering message. For example, it changes the cheering message according to changes in the user's emotions. This makes it possible to analyze the emotions felt by the user while exercising in real time and send the optimal cheering message.

[0083] The daily conversation unit can analyze the user's past conversation history and generate individually customized conversation content. The daily conversation unit, for example, analyzes the user's past conversation history and builds a system that generates individually customized conversation content. For example, the system reflects the user's preferences and interests based on past conversation data. The daily conversation unit also automatically generates topics and topics that interest the user based on the conversation history. For example, the system customizes the conversation content based on themes that the user often talks about. The daily conversation unit also analyzes the user's past conversation history and provides individually customized conversation content. For example, if the user is interested in a particular topic, the system generates a conversation related to that topic. In this way, the user's past conversation history can be analyzed and individually customized conversation content can be generated.

[0084] The everyday conversation unit can analyze a user's schedule and plans and provide reminders and advice at the appropriate time. For example, the everyday conversation unit can build a system that analyzes a user's schedule and plans and provides reminders and advice at the appropriate time. For example, it can work with a calendar app to send reminders. The everyday conversation unit can also provide the information and advice the user needs at the appropriate time based on schedule data. For example, it can send advice on how to relax before a meeting. The everyday conversation unit can also analyze a user's plans and automatically generate reminders for important events and tasks. For example, it can send a reminder to check the progress of a task before a deadline. This makes it possible to analyze a user's schedule and plans and provide reminders and advice at the appropriate time.

[0085] The daily conversation unit can use the emotion estimation function to learn and apply conversation content that is most relaxing for the user. The daily conversation unit, for example, uses the emotion estimation function to build a system that learns and applies conversation content that is most relaxing for the user. For example, it selects relaxing topics based on the user's emotion data. The daily conversation unit also automatically generates relaxing conversation content based on the user's emotion data. For example, it provides topics related to music or hobbies that are relaxing for the user. The daily conversation unit also uses the emotion estimation function to provide conversation content that is most relaxing for the user in real time. For example, it adjusts the conversation content according to changes in the user's emotion. In this way, it can learn and apply conversation content that is most relaxing for the user.

[0086] The daily conversation unit provides a conversation simulation with a user's favorite anime character, thereby increasing the enjoyment. The daily conversation unit, for example, builds a system that provides a conversation simulation with a user's favorite anime character. For example, an avatar speaks in the character's voice. The daily conversation unit also allows the user to enjoy conversations through conversation simulations with anime characters. For example, the character answers the user's questions. The daily conversation unit also provides a conversation simulation with a user's favorite anime character, allowing the user to enjoy everyday conversations. For example, the character talks about the user's day. This provides a conversation simulation with a user's favorite anime character, thereby increasing the enjoyment.

[0087] The everyday conversation unit can customize the conversation content based on episodes and scenes from the user's favorite anime. The everyday conversation unit, for example, builds a system that customizes the conversation content based on episodes and scenes from the user's favorite anime. For example, it provides topics related to specific episodes. The everyday conversation unit also provides conversation content based on anime scenes, allowing the user to enjoy conversation. For example, it provides conversations that recreate famous scenes from anime. The everyday conversation unit also customizes the conversation content based on episodes and scenes from the user's favorite anime. For example, it incorporates lines from characters into the conversation. This allows the conversation content to be customized based on episodes and scenes from the user's favorite anime.

[0088] The daily conversation unit uses the emotion estimation function to analyze the emotions felt by the user during a conversation in real time and provide optimal conversation content. The daily conversation unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during a conversation in real time and build a system that provides optimal conversation content. For example, it analyzes the user's facial expressions and tone of voice. The daily conversation unit also automatically generates optimal topics and content for the conversation based on the user's emotion data. For example, it provides topics that allow the user to relax. The daily conversation unit also uses the emotion estimation function to monitor the emotions felt by the user during a conversation in real time and dynamically adjust the conversation content. For example, it changes the topic in response to changes in the user's emotion. In this way, the emotions felt by the user during a conversation can be analyzed in real time and optimal conversation content can be provided.

[0089] The feedback unit can analyze the user's diet history, identify the cause of failure, and propose specific improvement measures. For example, the feedback unit builds a system that analyzes the user's diet history, identifies the cause of failure, and proposes specific improvement measures. For example, it proposes improvements to diet and exercise based on past data. The feedback unit also identifies patterns in which the user is likely to fail based on the diet history, and automatically generates improvement measures. For example, if failure is caused by a specific diet or exercise, it proposes countermeasures. The feedback unit also analyzes the user's diet history, identifies the cause of failure, and provides specific improvement measures. For example, it makes suggestions to adjust the balance of meals or the frequency of exercise. In this way, it is possible to analyze the user's diet history, identify the cause of failure, and propose specific improvement measures.

[0090] The feedback unit can use the emotion estimation function to provide feedback in a form that is easy for the user to accept. The feedback unit, for example, uses the emotion estimation function to build a system that provides feedback in a form that is easy for the user to accept. For example, the feedback format is adjusted based on the user's emotion data. The feedback unit also customizes the feedback content based on the user's emotion data and provides it in a form that is easy for the user to accept. For example, feedback that elicits positive emotions is provided. The feedback unit also uses the emotion estimation function to provide feedback in a form that is easy for the user to accept. For example, the tone and content of the feedback are adjusted according to changes in the user's emotion. This makes it possible to provide feedback in a form that is easy for the user to accept.

[0091] The feedback unit can customize the feedback content based on the story of a user's favorite anime character. For example, the feedback unit builds a system that customizes the feedback content based on the story of a user's favorite anime character. For example, the feedback unit provides feedback that references the character's episodes. The feedback unit also provides feedback based on the anime character's story so that the user can enjoy and accept it. For example, the feedback unit incorporates the character's growth story. The feedback unit also customizes the feedback based on the story of a user's favorite anime character. For example, the feedback unit provides feedback that quotes the character's famous scenes. This allows the feedback content to be customized based on the story of a user's favorite anime character.

[0092] The feedback unit can provide feedback based on famous scenes and lines from the user's favorite anime. For example, the feedback unit builds a system that provides feedback based on famous scenes and lines from the user's favorite anime. For example, famous lines from a particular anime are used as feedback. The feedback unit also provides feedback that recreates famous scenes from anime, allowing the user to enjoy and accept it. For example, lines from a battle scene are played as feedback. The feedback unit also customizes feedback based on lines from the user's favorite anime. For example, famous quotes from characters are used as feedback. This makes it possible to provide feedback based on famous scenes and lines from the user's favorite anime.

[0093] The feedback unit uses the emotion estimation function to analyze the emotions of the user when receiving feedback in real time and provide optimal feedback. The feedback unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving feedback in real time and builds a system that provides optimal feedback. For example, the feedback unit analyzes the user's facial expressions and tone of voice. The feedback unit also customizes the feedback content based on the user's emotion data and provides it in a form that is easy to accept. For example, it provides feedback that elicits positive emotions. The feedback unit also uses the emotion estimation function to monitor the emotions of the user when receiving feedback in real time and dynamically adjust the feedback content. For example, it changes the tone and content of the feedback according to changes in the user's emotions. In this way, the emotions of the user when receiving feedback can be analyzed in real time and optimal feedback can be provided.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The avatar generation unit can monitor the user's health condition and reflect it in the avatar's appearance. For example, if the user is tired, the avatar will also have a tired expression. The avatar generation unit also dynamically changes the avatar's appearance based on the user's health data. For example, if the user continues to exercise, the avatar will have a healthy appearance. The avatar generation unit also customizes the avatar's appearance according to the user's health condition. For example, if the user is feeling stressed, the avatar will have a relaxed expression. This makes it possible to provide an avatar that reflects the user's health condition.

[0096] The diet management unit can automatically generate an ingredient shopping list based on the user's dietary details. For example, it analyzes the dietary details entered by the user and lists the necessary ingredients. The diet management unit also suggests ingredients to purchase regularly based on the user's diet history. For example, it adds ingredients that the user frequently uses to the list. The diet management unit also provides a function to purchase ingredients in cooperation with online shopping sites based on the ingredient shopping list. For example, it automatically places orders based on the list. This makes it possible to automatically generate an ingredient shopping list based on the user's dietary details.

[0097] The exercise management unit can propose a post-exercise recovery plan based on the user's exercise data. For example, it can suggest post-exercise stretching and massage methods. The exercise management unit also customizes the recovery plan based on the user's exercise history. For example, it can suggest methods to focus on caring for specific muscles. The exercise management unit also provides music and videos that help the user relax based on the recovery plan. For example, it can play relaxing music during recovery. This makes it possible to propose a post-exercise recovery plan for the user.

[0098] The cheering club can suggest reflection time after exercise based on the user's exercise data. For example, it can provide time for the user to reflect on their performance after exercise. The cheering club can also customize the content of the reflection time based on the user's exercise history. For example, it can suggest ways to reflect on exercise successes and areas for improvement. The cheering club can also support the user in setting their next goal based on the reflection time. For example, it can provide advice on setting their next exercise goal. This allows it to suggest reflection time after exercise for the user.

[0099] The daily conversation section can suggest topics that interest the user based on the user's past conversation history. For example, the conversation content is customized based on the themes that the user often talks about. The daily conversation section also creates a conversation flow based on topics that interest the user. For example, the user can talk about their favorite movies or music. The daily conversation section also provides related information and news based on the user's interests. For example, it provides the latest information on areas that interest the user. This makes it possible to suggest topics that interest the user.

[0100] The avatar generation unit can use the emotion estimation function to learn the avatar characteristics that make the user most relaxed and reflect them in the avatar design. For example, it can incorporate colors and designs that make the user feel relaxed into the avatar. The avatar generation unit can also learn facial expressions and poses that make the avatar feel relaxing based on the user's emotion data. For example, it can reflect facial expressions that make the user feel relaxed in the avatar. The avatar generation unit can also use the emotion estimation function to identify the avatar characteristics that make the user most relaxed and customize the avatar based on that information. For example, it can incorporate music and backgrounds that make the user feel relaxed into the avatar. This allows it to generate an avatar that makes the user feel most relaxed.

[0101] The meal management unit uses the emotion estimation function to analyze the emotions felt by the user while eating in real time and make suggestions to increase the enjoyment of the meal. For example, it analyzes the user's facial expressions and tone of voice. The meal management unit also automatically generates suggestions to increase the enjoyment of the meal based on the user's emotion data. For example, it suggests ingredients and menus that the user will enjoy. The meal management unit also uses the emotion estimation function to monitor the emotions felt by the user while eating and provides advice to increase the enjoyment. For example, it suggests music or videos that will help the user relax while eating. In this way, it is possible to analyze the emotions felt by the user while eating in real time and make suggestions to increase the enjoyment of the meal.

[0102] The exercise management unit can use the emotion estimation function to suggest music and videos that amplify the positive emotions felt by the user while exercising. For example, music is selected based on the user's emotion data. The exercise management unit also suggests music and videos that elicit positive emotions while exercising based on the user's emotion data. For example, it provides music that helps the user relax. The exercise management unit also uses the emotion estimation function to suggest music and videos in real time that amplify the positive emotions felt by the user while exercising. For example, it changes the music according to changes in the user's emotion. This makes it possible to suggest music and videos that amplify the positive emotions felt by the user while exercising.

[0103] The cheering unit can use the emotion estimation function to learn and apply the cheering message that most motivates the user. For example, the cheering message is customized based on the user's emotion data. The cheering unit can also automatically generate the cheering message that most motivates the user based on the user's emotion data. For example, the cheering unit can send a message that evokes positive emotions. The cheering unit can also use the emotion estimation function to provide the cheering message that most motivates the user in real time. For example, the cheering message can be adjusted according to changes in the user's emotions. This allows the cheering unit to learn and apply the cheering message that most motivates the user.

[0104] The feedback unit uses the emotion estimation function to analyze the emotions of the user when receiving feedback in real time and provide optimal feedback. For example, it analyzes the user's facial expressions and tone of voice. The feedback unit also customizes the feedback content based on the user's emotion data and provides it in a form that is easy to accept. For example, it provides feedback that elicits positive emotions. The feedback unit also uses the emotion estimation function to monitor the emotions of the user when receiving feedback in real time and dynamically adjust the feedback content. For example, it changes the tone and content of the feedback according to changes in the user's emotions. This allows the user's emotions when receiving feedback to be analyzed in real time and optimal feedback to be provided.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The avatar generation unit generates an avatar based on the user's instructions. For example, the user can input instructions such as "hair color should be blue, personality should be lively and cheerful, and voice should be high-pitched" as a prompt, and the generation AI will generate an avatar that matches those conditions. Step 2: The dietary management unit manages the user's dietary information. For example, the user enters their daily dietary information into the app, and the AI ​​analyzes the data to evaluate nutritional balance and calorie intake. Step 3: The exercise management unit manages the user's exercise menu. For example, the user inputs their exercise goals and current physical fitness level, and the generation AI creates an exercise menu based on that. Step 4: The cheering section sends cheering messages to the user while they are exercising. For example, when the user starts exercising, an avatar will voice a cheering message such as "Do your best! You're almost there!" Step 5: The conversation part engages in everyday conversations with the user. For example, when a user opens the app, the avatar speaks to them in the form of, "How was your day?" Step 6: The feedback unit provides feedback to the user if the user fails to diet. For example, if the user neglects exercise, the avatar provides feedback such as, "You haven't been exercising recently, so you've gained a little weight."

[0107] 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.

[0108] 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.

[0109] 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.

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

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

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0151] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an avatar generation unit that generates an avatar based on a user's instruction; a meal management unit that manages the meal contents of the user; an exercise management unit that manages an exercise menu for the user; a cheering unit that sends cheering messages to the user while the user is exercising; a conversation unit that conducts daily conversations with the user; a feedback unit that provides feedback when the user fails to diet; A system characterized by:

2. The avatar generation unit Analyzing the user's real-time facial expressions and tone of voice, and dynamically changing the avatar's facial expressions and voice to match the user's mood at that time.

2. The system of claim 1.

3. The dietary management unit Analyze the user's feelings about food and propose a meal plan that elicits positive emotions 2. The system of claim 1.

4. The exercise management unit Analyze the stress and fatigue felt by the user during exercise and suggest appropriate rest times 2. The system of claim 1.

5. The cheering section: Learn and apply the most motivating message to the user 2. The system of claim 1.

6. The daily conversation section includes: Learn and adapt the conversational content that the user finds most comfortable 2. The system of claim 1.

7. The feedback unit Provide feedback in a manner that is acceptable to the user 2. The system of claim 1.

8. The avatar generation unit The system learns the characteristics of the avatar that the user feels most positive about and reflects this in the design of the avatar.

2. The system of claim 1.

Images