system

Abstract

The system according to this embodiment aims to maintain the motivation of the user to continue taking action toward their ideal self. [Solution] The system according to the embodiment comprises a reception unit, a subdivision unit, a presentation unit, a visualization unit, and a gamification unit. The reception unit receives the user's ideal image. The subdivision unit subdivides tasks based on the ideal image received by the reception unit. The presentation unit presents a plan based on the tasks subdivided by the subdivision unit. The visualization unit visualizes the progress based on the plan presented by the presentation unit. The gamification unit performs gamification based on the progress visualized by the visualization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to maintain motivation to continue actions towards their ideal image.

[0005] The system according to the embodiment aims to maintain motivation for a user to continue actions towards their ideal image.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a subdivision unit, a presentation unit, a visualization unit, and a gamification unit. The reception unit receives the user's ideal vision. The subdivision unit subdivides tasks based on the ideal vision received by the reception unit. The presentation unit presents a plan based on the tasks subdivided by the subdivision unit. The visualization unit visualizes the progress based on the plan presented by the presentation unit. The gamification unit performs gamification based on the progress visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can maintain the motivation for users to continue taking action toward their ideal self. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The agent AI system according to an embodiment of the present invention is a system that breaks down tasks to the action level based on a user's rough description of their ideal self, and incorporates visualization and gamification to maintain and manage motivation for continuation. This agent AI system, based on the user's description of their ideal self, breaks down tasks to the action level and presents a plan. Furthermore, it incorporates visualization and gamification to maintain and manage motivation for continuation. This allows the user to more clearly understand their current position and their future goals. For example, the agent AI system breaks down the user's current situation and creates a future using AI models of various experts (e.g., nutritionist, personal trainer, coach, financial expert, etc.). The user provides progress data from their smartphone, and the current situation is presented as an image. For example, it shows the future if the user slacks off, the future if they continue their current actions, and the future if they improve their actions. This allows the user to recognize their current urgency and understand that they can change their future through effort. This mechanism makes it easier for the user to maintain motivation to continue taking action towards their desired self. Furthermore, through visualization and gamification, users can visually track their progress, clearly identifying areas for improvement. For example, a nutritionist AI might suggest improvements to their diet, and a personal trainer AI might suggest improvements to their exercise routine. This allows users to move closer to their ideal self with comprehensive support. This enables the agent AI system to efficiently break down the user's ideal self, present plans, visualize progress, and implement gamification.

[0029] The agent AI system according to this embodiment comprises a reception unit, a subdivision unit, a presentation unit, a visualization unit, and a gamification unit. The reception unit receives the user's ideal image. The user's ideal image includes, but is not limited to, career goals, health status, and lifestyle. The reception unit allows the user to communicate their ideal image using, for example, voice input or text input. The subdivision unit subdivides tasks based on the ideal image received by the reception unit. Task subdivision is performed based on, for example, the granularity and prioritization method of the tasks, but is not limited to, such examples. For example, the subdivision unit subdivides the user's ideal image to a specific action level and divides it into achievable tasks. The presentation unit presents a plan based on the tasks subdivided by the subdivision unit. The presentation of the plan is performed based on, for example, the format and timing of the information presented, but is not limited to, such examples. For example, the presentation unit presents the user with the progress of the tasks and the next tasks to be performed. The visualization unit visualizes progress based on the plan presented by the presentation unit. Progress visualization is performed based on, for example, the type of graph used or the range of data displayed, but is not limited to such examples. For example, the visualization unit visually displays the user's progress, clearly indicating the degree of achievement and areas for improvement. The gamification unit performs gamification based on the progress visualized by the visualization unit. Gamification is performed based on, for example, a point system or type of reward, but is not limited to such examples. For example, the gamification unit awards points and provides rewards each time the user completes a task. This enables the agent AI system according to the embodiment to efficiently subdivide the user's ideal state, present a plan, visualize progress, and perform gamification.

[0030] The reception desk receives the user's ideal vision. This ideal vision includes, but is not limited to, career goals, health status, and lifestyle. The reception desk allows users to communicate their ideal vision using voice or text input. Specifically, with voice input, the user speaks their ideal vision through a microphone, and the system uses speech recognition technology to convert it into text data. With text input, the user enters their ideal vision using a keyboard or touchscreen. The reception desk analyzes this input data and utilizes natural language processing (NLP) technology to accurately understand the user's intentions and desires. For example, if a user enters "I want to become a manager within three years," the system extracts the keywords "within three years" and "manager" and uses this to understand the user's career goal. Furthermore, the reception desk can refer to the user's past input history and profile information to provide more personalized responses. For example, if a user has previously set health-related goals, the system takes that information into account when accepting a new ideal vision. This allows the reception desk to flexibly respond to the diverse needs and desires of users and collect accurate information.

[0031] The subdivision department breaks down tasks based on the ideal vision received by the reception department. Task subdivision is done based on, for example, the granularity and prioritization method of the tasks, but is not limited to such examples. Specifically, the subdivision department analyzes the user's ideal vision and breaks it down into specific actions and steps necessary to achieve it. For example, if a user sets the goal of "becoming a manager within three years," the subdivision department will break this down into specific tasks such as "improving leadership skills," "gaining project management experience," and "obtaining relevant qualifications." Furthermore, the subdivision department sets priorities for each task, clarifying which tasks the user should tackle first. Prioritization takes into account the importance and urgency of the tasks, as well as the user's current situation and resources. For example, if a user has many opportunities to improve their leadership skills in their current job, that task will be set as a priority. The subdivision department also monitors the progress of the tasks and readjusts or adds tasks as needed. In this way, the subdivision department can transform the user's ideal vision into concrete and actionable tasks, supporting efficient goal achievement.

[0032] The presentation unit presents a plan based on tasks subdivided by the subdivision unit. The plan presentation is based on, for example, the format and timing of the information presented, but is not limited to these examples. Specifically, the presentation unit provides the user with information on task progress and the next steps to take. For example, if a user is working on the task of "improving leadership skills," the presentation unit will present specific action items and resources related to that task. This may include recommendations for online leadership courses or suggestions for practical training. Furthermore, the presentation unit presents tasks at the optimal time, tailored to the user's schedule and lifestyle. For example, if a user has free time on weekday evenings, tasks will be presented at that time. In addition, the presentation unit collects user feedback and continuously improves the way and content of the plan presentation. This allows the presentation unit to provide users with effective and timely information and support them in achieving their goals.

[0033] The visualization unit visualizes progress based on the plan presented by the presentation unit. Progress visualization is performed based on, for example, the type of graph used and the range of data displayed, but is not limited to these examples. Specifically, the visualization unit visually displays the user's progress, clearly indicating achievements and areas for improvement. For example, it displays the progress of tasks the user is working on using bar graphs or line graphs, allowing users to grasp the achievement level of each task at a glance. The visualization unit also displays the user's past data and trends, providing information for evaluating long-term progress. This allows users to visually confirm the results of their efforts and maintain motivation. Furthermore, based on user feedback, the visualization unit improves the visualization methods and content to provide more effective information. This allows the visualization unit to clearly show the user's progress and increase their motivation towards achieving their goals.

[0034] The Gamification Department implements gamification based on progress visualized by the Visualization Department. Gamification is carried out based on, for example, point systems and types of rewards, but is not limited to these examples. Specifically, the Gamification Department awards points and provides rewards each time a user completes a task. For example, when a user completes a specific task, points are awarded according to the degree of completion, and a reward is provided when a certain number of points are accumulated. Rewards include badges, trophies, and perks, and are designed to increase user motivation. The Gamification Department also provides features to promote competition and cooperation among users. For example, users can earn additional points and rewards by comparing their progress with other users or by completing tasks together. In this way, the Gamification Department provides an environment where users can increase their motivation and enjoy working towards achieving their goals. Furthermore, the Gamification Department continuously improves the gamification elements and reward system based on user feedback to further enhance motivation. This allows the gamification department to support users in enjoying themselves while striving to achieve their goals, maximizing the overall effectiveness of the system.

[0035] The subdivision unit can subdivide tasks using AI models of various experts. For example, the subdivision unit can subdivide areas for dietary improvement using a nutritionist AI. For instance, the nutritionist AI can analyze the user's diet, evaluate nutritional balance, and suggest specific areas for improvement. The subdivision unit can also subdivide areas for exercise improvement using a personal trainer AI. For example, the personal trainer AI can analyze the user's exercise habits and create an exercise plan or provide exercise guidance. Furthermore, the subdivision unit can subdivide areas for behavioral improvement using a coaching AI. For example, the coaching AI can analyze the user's behavioral patterns and provide advice on behavioral improvement and motivation maintenance. Finally, the subdivision unit can subdivide areas for financial improvement using a financial AI. For example, the financial AI can analyze the user's financial situation and provide financial analysis and investment advice. This improves the accuracy of task subdivision by using AI models of various experts.

[0036] The presentation unit can use a nutritionist AI to suggest areas for improvement in the user's diet. For example, the nutritionist AI can analyze the user's diet, evaluate its nutritional balance, and suggest specific areas for improvement. For instance, based on the user's diet, the nutritionist AI can identify nutrient deficiencies or excesses and propose a balanced meal plan. The nutritionist AI can also provide dietary improvement advice tailored to the user's goals. For example, it can suggest calorie restriction and adjustments to nutritional balance for users aiming to lose weight, and advise increasing protein intake for users aiming to build muscle. In this way, using the nutritionist AI allows for the effective presentation of areas for improvement in the user's diet.

[0037] The display unit can use a personal trainer AI to suggest areas for improvement in exercise. For example, the personal trainer AI can analyze the user's exercise habits and create an exercise plan and provide exercise guidance. For instance, the personal trainer AI can create an individualized exercise plan based on the user's exercise history and fitness level. The personal trainer AI can also provide advice on improving exercise according to the user's goals. For example, it can advise users aiming for weight loss to adjust the frequency and intensity of aerobic exercise, and suggest strength training menus for users aiming for muscle gain. In this way, using a personal trainer AI allows for the effective presentation of areas for improvement in exercise.

[0038] The presentation unit can use coaching AI to suggest areas for improvement in behavior. For example, the coaching AI can analyze the user's behavior patterns and provide advice for behavioral improvement and motivation maintenance. For instance, the coaching AI can create an action plan for the user to achieve their goals and present specific steps. The coaching AI can also provide advice to maintain the user's motivation. For example, it can provide encouraging messages to help the user continue their efforts towards their goals and provide feedback according to their progress. In this way, using coaching AI makes it possible to effectively suggest areas for improvement in behavior.

[0039] The presentation unit can use financial AI to suggest areas for financial improvement. For example, the presentation unit uses financial AI to analyze the user's financial situation and provide financial analysis and investment advice. For instance, based on the user's income and expenditure data, the financial AI can suggest areas for improvement in the balance of income and expenses. Furthermore, the financial AI can provide financial improvement advice tailored to the user's goals. For example, it can suggest saving methods to increase savings or investment plans that consider investment risks and returns. Thus, by using financial AI, areas for financial improvement can be effectively presented.

[0040] The visualization section can present the user's current appearance as an image. For example, the visualization section visually displays the user's current appearance based on data such as weight, body fat percentage, and health status. For instance, the visualization section can display changes in the user's weight and body fat percentage in a graph, clearly identifying areas for improvement in their health. Furthermore, the visualization section can present the user's current appearance as an image. For example, it can display the user's body shape and posture as an image, allowing for a visual understanding of areas for improvement. This allows users to visually grasp their current situation by presenting their current appearance as an image.

[0041] The visualization section can present future scenarios if the user slacks off, if they continue their current behavior, and if they improve their behavior. For example, the visualization section can create future scenarios based on the user's current behavior data and present them visually. For instance, it can simulate and visually display the user's future health and lifestyle if they slack off. The visualization section can also present future scenarios if the user continues their current behavior. For example, it can predict and visually display the user's health and lifestyle if they continue their current behavior. Furthermore, the visualization section can also present future scenarios if the user improves their behavior. For example, it can simulate and visually display the health and lifestyle that can be achieved through behavioral improvement. By presenting these future scenarios, the system can raise the user's awareness of their own behavior.

[0042] The reception desk can analyze the user's past ideal image history and select the optimal reception method. For example, the reception desk can suggest similar ideal images based on the ideal image the user has entered in the past. For instance, the reception desk can analyze the user's past ideal image history and suggest goals similar to those previously achieved. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has previously used voice input to communicate their ideal image, it will prioritize suggesting voice input. Furthermore, the reception desk can suggest the most suitable reception method for a specific time period based on the user's past ideal image history. For example, based on the user's past history of entering an ideal image at a specific time period, the reception desk will suggest the most suitable reception method for that time period. In this way, the optimal reception method can be selected by analyzing the past ideal image history.

[0043] The reception desk can filter the ideal lifestyle submitted by the user based on their current lifestyle and areas of interest. For example, it can suggest relevant ideal lifestyles based on the user's current occupation and hobbies. For instance, it can analyze the user's occupation and hobby data and filter and suggest relevant ideal lifestyles. The reception desk can also suggest feasible ideal lifestyles that align with the user's lifestyle. For example, it can filter and suggest feasible ideal lifestyles based on the user's lifestyle data. Furthermore, the reception desk can suggest ideal lifestyles that are likely to interest the user based on their areas of interest. For example, it can analyze the user's areas of interest data and filter and suggest ideal lifestyles that are likely to interest the user. By filtering based on the user's lifestyle and areas of interest, the reception desk can suggest more relevant ideal lifestyles.

[0044] The reception desk can prioritize accepting ideal lifestyles that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user lives in an urban area, the reception desk will prioritize accepting ideal lifestyles related to urban life. For example, the reception desk will analyze the user's geographical location information and prioritize accepting ideal lifestyles related to urban life. Also, if the user lives in a rural area, the reception desk can prioritize accepting ideal lifestyles related to the natural environment. For example, the reception desk will use the user's geographical location information to prioritize accepting ideal lifestyles related to the natural environment. Furthermore, if the user is traveling, the reception desk can prioritize accepting ideal lifestyles related to their travel destination. For example, the reception desk will analyze the user's geographical location information and prioritize accepting ideal lifestyles related to their travel destination. In this way, by considering the user's geographical location information, the reception desk can prioritize accepting ideal lifestyles that are highly relevant.

[0045] The reception desk can analyze a user's social media activity when receiving an ideal image request and then accept relevant ideal images. For example, the reception desk can suggest relevant ideal images based on the content a user frequently posts on social media. For example, the reception desk can analyze a user's social media activity and suggest ideal images related to frequently posted content. The reception desk can also suggest relevant ideal images by referring to ideal images shared by a user's followers and friends. For example, the reception desk can analyze the content posted by a user's followers and friends and suggest relevant ideal images. Furthermore, the reception desk can suggest relevant ideal images based on the trends of the online communities a user participates in. For example, the reception desk can analyze the trends of the online communities a user participates in and suggest relevant ideal images. In this way, relevant ideal images can be accepted by analyzing social media activity.

[0046] The subdivision unit can select the optimal subdivision method by referring to the past data of each expert's AI model during the subdivision process. For example, the subdivision unit can subdivide dietary improvement tasks based on the past data of a nutritionist AI. For example, the subdivision unit analyzes the nutritionist AI's past data and presents specific tasks for dietary improvement. The subdivision unit can also subdivide exercise improvement tasks based on the past data of a personal trainer AI. For example, the subdivision unit analyzes the personal trainer AI's past data and presents specific tasks for exercise improvement. Furthermore, the subdivision unit can subdivide behavior improvement tasks based on the coaching AI's past data. For example, the subdivision unit analyzes the coaching AI's past data and presents specific tasks for behavior improvement. This allows the system to select the optimal subdivision method by referring to the past data of each expert's AI model.

[0047] The task subdivision unit can customize the task subdivision method based on the user's current lifestyle. For example, if the user is busy, the subdivision unit can suggest tasks that can be completed in a short time. For example, the subdivision unit analyzes the user's lifestyle data and presents tasks that can be completed in a short time. The subdivision unit can also suggest tasks that require more time if the user has more free time. For example, the subdivision unit presents tasks that require more time based on the user's lifestyle data. Furthermore, if the user possesses specific skills, the subdivision unit can suggest tasks that utilize those skills. For example, the subdivision unit analyzes the user's skill data and presents tasks that utilize those skills. In this way, by customizing the task subdivision method based on the user's lifestyle, more appropriate tasks can be presented.

[0048] The task subdivision unit can select the optimal task subdivision method by considering the user's geographical location information during the subdivision process. For example, if the user lives in an urban area, the subdivision unit can subdivide tasks related to urban life. For example, the subdivision unit analyzes the user's geographical location information and subdivides tasks related to urban life. The subdivision unit can also subdivide tasks related to the natural environment if the user lives in a rural area. For example, the subdivision unit subdivides tasks related to the natural environment based on the user's geographical location information. Furthermore, if the user is traveling, the subdivision unit can subdivide tasks related to their travel destination. For example, the subdivision unit analyzes the user's geographical location information and subdivides tasks related to their travel destination. In this way, the optimal task subdivision method can be selected by considering the user's geographical location information.

[0049] The subdivision unit can analyze the user's social media activity during the subdivision process and propose methods for subdividing tasks. For example, the subdivision unit can subdivide relevant tasks based on the content the user frequently posts on social media. For example, the subdivision unit analyzes the user's social media activity and subdivides tasks related to frequently posted content. The subdivision unit can also subdivide relevant tasks by referring to tasks shared by the user's followers and friends. For example, the subdivision unit analyzes the content posted by the user's followers and friends and subdivides relevant tasks. Furthermore, the subdivision unit can subdivide relevant tasks based on the trends of the online communities the user participates in. For example, the subdivision unit analyzes the trends of the online communities the user participates in and subdivides relevant tasks. In this way, relevant tasks can be subdivided by analyzing social media activity.

[0050] The presentation unit can present the optimal plan by referencing past data from each expert's AI model. For example, the presentation unit can present a diet improvement plan based on past data from a nutritionist AI. For example, the presentation unit analyzes the nutritionist AI's past data and presents a specific diet improvement plan. The presentation unit can also present an exercise improvement plan based on past data from a personal trainer AI. For example, the presentation unit analyzes the personal trainer AI's past data and presents a specific exercise improvement plan. The presentation unit can also present a behavior improvement plan based on past data from a coaching AI. For example, the presentation unit analyzes the coaching AI's past data and presents a specific behavior improvement plan. In this way, by referencing past data from each expert's AI model, the optimal plan can be presented.

[0051] The presentation unit can customize the method of presenting plans based on the user's current living situation. For example, if the user is busy, the presentation unit can present plans that can be completed in a short time. For example, the presentation unit can analyze the user's living situation data and present plans that can be completed in a short time. Also, if the user has time, the presentation unit can present plans that require more time. For example, the presentation unit can present plans that require more time based on the user's living situation data. Furthermore, if the user possesses specific skills, the presentation unit can present plans that utilize those skills. For example, the presentation unit can analyze the user's skill data and present plans that utilize those skills. In this way, by customizing the method of presenting plans based on the user's living situation, more appropriate plans can be presented.

[0052] The presentation unit can present the most suitable plan by considering the user's geographical location information. For example, if the user lives in an urban area, the presentation unit can present a plan related to urban life. For example, the presentation unit analyzes the user's geographical location information and presents a plan related to urban life. Also, if the user lives in a rural area, the presentation unit can present a plan related to the natural environment. For example, the presentation unit presents a plan related to the natural environment based on the user's geographical location information. Furthermore, if the user is traveling, the presentation unit can present a plan related to their travel destination. For example, the presentation unit analyzes the user's geographical location information and presents a plan related to their travel destination. In this way, the optimal plan can be presented by considering the user's geographical location information.

[0053] The presentation unit can analyze the user's social media activity and propose a method for presenting plans. For example, the presentation unit can present relevant plans based on the content the user frequently posts on social media. For example, the presentation unit analyzes the user's social media activity and presents plans related to the content that is frequently posted. The presentation unit can also present relevant plans by referring to plans shared by the user's followers and friends. For example, the presentation unit analyzes the content posted by the user's followers and friends and presents relevant plans. The presentation unit can also present relevant plans based on the trends of the online communities the user participates in. For example, the presentation unit analyzes the trends of the online communities the user participates in and presents relevant plans. In this way, relevant plans can be presented by analyzing social media activity.

[0054] The visualization unit can select the optimal visualization method by referring to past visualization data during the visualization process. For example, the visualization unit can propose the optimal method based on visualization methods that users have preferred to use in the past. For instance, the visualization unit can analyze past visualization data and select the method that users found most effective in understanding the data. The visualization unit can also select the method that most effectively shows the user's progress from past visualization data. For example, the visualization unit can analyze past visualization data and select the method that most effectively shows the user's progress. In this way, the optimal visualization method can be selected by referring to past visualization data.

[0055] The visualization unit can customize the visualization method based on the user's current lifestyle during the visualization process. For example, if the user is busy, the visualization unit can provide a visualization method that allows them to check progress quickly. For example, the visualization unit can analyze the user's lifestyle data and provide a visualization method that allows them to check progress quickly. The visualization unit can also provide a visualization method that allows the user to check detailed progress if they have more time. For example, the visualization unit can provide a visualization method that allows them to check detailed progress based on the user's lifestyle data. Furthermore, if the user possesses specific skills, the visualization unit can provide a visualization method that utilizes those skills. For example, the visualization unit can analyze the user's skill data and provide a visualization method that utilizes those skills. By customizing the visualization method based on the user's lifestyle, more appropriate visualization becomes possible.

[0056] The visualization unit can select the optimal visualization method by considering the user's geographical location information during the visualization process. For example, if the user lives in an urban area, the visualization unit can visualize progress related to urban life. For example, the visualization unit analyzes the user's geographical location information and visualizes progress related to urban life. Also, if the user lives in a rural area, the visualization unit can visualize progress related to the natural environment. For example, the visualization unit visualizes progress related to the natural environment based on the user's geographical location information. Furthermore, if the user is traveling, the visualization unit can visualize progress related to the travel destination. For example, the visualization unit analyzes the user's geographical location information and visualizes progress related to the travel destination. In this way, the optimal visualization method can be selected by considering the user's geographical location information.

[0057] The Visualization Department can analyze a user's social media activity and propose visualization methods during the visualization process. For example, the Visualization Department can visualize relevant progress based on the content a user frequently posts on social media. For example, the Visualization Department analyzes a user's social media activity and visualizes progress related to frequently posted content. The Visualization Department can also visualize relevant progress by referring to progress shared by a user's followers and friends. For example, the Visualization Department analyzes the content posted by a user's followers and friends and visualizes relevant progress. Furthermore, the Visualization Department can visualize relevant progress based on trends in online communities in which a user participates. For example, the Visualization Department analyzes the trends in online communities in which a user participates and visualizes relevant progress. In this way, relevant progress can be visualized by analyzing social media activity.

[0058] The gamification department can select the optimal method during gamification by referring to past gamification data. For example, the gamification department can propose the optimal method based on game elements that users have previously enjoyed using. For example, the gamification department can analyze past gamification data and select the method that users enjoyed the most. The gamification department can also select the method that best increases user motivation from past gamification data. For example, the gamification department can analyze past gamification data and select the method that best increases user motivation. In this way, the optimal method can be selected by referring to past gamification data.

[0059] The gamification unit can customize the gamification methods based on the user's current lifestyle during the gamification process. For example, if the user is busy, the gamification unit can provide game elements that can be enjoyed in a short amount of time. For example, the gamification unit can analyze the user's lifestyle data and provide game elements that can be enjoyed in a short amount of time. The gamification unit can also provide game elements that can be enjoyed over a longer period of time if the user has more free time. For example, the gamification unit can provide game elements that can be enjoyed over a longer period of time based on the user's lifestyle data. Furthermore, if the user possesses a specific skill, the gamification unit can provide game elements that utilize that skill. For example, the gamification unit can analyze the user's skill data and provide game elements that utilize that skill. By customizing the gamification methods based on the user's lifestyle, more appropriate gamification becomes possible.

[0060] The gamification unit can select the optimal method during gamification by considering the user's geographical location information. For example, if the user lives in an urban area, the gamification unit can provide game elements related to urban life. For example, the gamification unit analyzes the user's geographical location information and provides game elements related to urban life. The gamification unit can also provide game elements related to the natural environment if the user lives in a rural area. For example, the gamification unit provides game elements related to the natural environment based on the user's geographical location information. Furthermore, if the user is traveling, the gamification unit can provide game elements related to the travel destination. For example, the gamification unit analyzes the user's geographical location information and provides game elements related to the travel destination. In this way, the optimal method can be selected by considering the user's geographical location information.

[0061] The Gamification Department can analyze users' social media activity during gamification and propose gamification methods. For example, the Gamification Department can provide relevant game elements based on the content users frequently post on social media. For example, the Gamification Department can analyze users' social media activity and provide game elements related to frequently posted content. The Gamification Department can also provide relevant game elements by referring to game elements shared by users' followers and friends. For example, the Gamification Department can analyze the content posted by users' followers and friends and provide relevant game elements. The Gamification Department can also provide relevant game elements based on the trends of online communities in which users participate. For example, the Gamification Department can analyze the trends of online communities in which users participate and provide relevant game elements. In this way, relevant game elements can be provided by analyzing social media activity.

[0062] The gamification unit can provide optimal game elements during gamification, taking into account the user's health condition. For example, if the user is tired, the gamification unit can provide relaxing game elements. For instance, the gamification unit analyzes the user's health data and provides relaxing game elements to reduce fatigue. The gamification unit can also provide active game elements if the user is seeking healthy exercise. For example, based on the user's health data, the gamification unit provides active game elements that promote healthy exercise. Furthermore, if the user is feeling unwell, the gamification unit can provide game elements that include rest. For example, the gamification unit analyzes the user's health data and provides game elements that include rest to improve their condition. In this way, by considering the user's health condition, the optimal game elements can be provided.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The reception desk can analyze the user's past ideal image history and select the optimal reception method. For example, it can suggest similar ideal images based on the user's past input. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can suggest the most suitable reception method for a specific time of day based on the user's past ideal image history. In this way, the optimal reception method can be selected by analyzing the user's past ideal image history.

[0065] The subdivision section can select the optimal subdivision method by referring to the past data of each expert's AI model. For example, it can subdivide tasks related to dietary improvement based on the past data of a nutritionist AI. It can also subdivide tasks related to exercise improvement based on the past data of a personal trainer AI. Furthermore, it can subdivide tasks related to behavioral improvement based on the past data of a coaching AI. In this way, the optimal subdivision method can be selected by referring to the past data of each expert's AI model.

[0066] The presentation unit can refer to past data from each expert's AI model to present the optimal plan. For example, it can present a diet improvement plan based on past data from a nutritionist AI. It can also present an exercise improvement plan based on past data from a personal trainer AI. Furthermore, it can present a behavior improvement plan based on past data from a coaching AI. In this way, by referring to past data from each expert's AI model, the optimal plan can be presented.

[0067] The visualization unit can select the optimal visualization method by referring to past visualization data. For example, it can suggest the optimal method based on visualization methods that users have preferred to use in the past. It can also select the method that most effectively shows the user's progress from past visualization data. Furthermore, it can analyze past visualization data to select the method that most effectively shows the user's progress. In this way, the optimal visualization method can be selected by referring to past visualization data.

[0068] The gamification department can select the optimal method by referring to past gamification data. For example, it can propose the optimal method based on game elements that users have preferred to use in the past. It can also select the method that best increases user motivation from past gamification data. Furthermore, it can analyze past gamification data to select the method that best increases user motivation. In this way, the optimal method can be selected by referring to past gamification data.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk receives the user's ideal profile. This profile includes career goals, health status, lifestyle, etc. The reception desk allows the user to communicate their ideal profile using voice input or text input. Step 2: The subdivision department subdivides the tasks based on the ideal image received by the reception department. Task subdivision is carried out based on the granularity and prioritization method of the tasks. For example, the ideal user image is subdivided to the level of specific actions and divided into achievable tasks. Step 3: The presentation unit presents a plan based on the tasks subdivided by the subdivision unit. The plan is presented based on the format and timing of the information to be presented. For example, it may present the user with the progress of a task and the next task to be performed. Step 4: The visualization unit visualizes the progress based on the plan presented by the presentation unit. The visualization of progress is performed based on the type of graph used and the range of data to be displayed. For example, the user's progress is visually displayed to clarify the degree of achievement and areas for improvement. Step 5: The Gamification Department implements gamification based on the progress visualized by the Visualization Department. Gamification is carried out based on a point system and types of rewards. For example, points are awarded and rewards are provided each time a user completes a task.

[0071] (Example of form 2) The agent AI system according to an embodiment of the present invention is a system that breaks down tasks to the action level based on a user's rough description of their ideal self, and incorporates visualization and gamification to maintain and manage motivation for continuation. This agent AI system, based on the user's description of their ideal self, breaks down tasks to the action level and presents a plan. Furthermore, it incorporates visualization and gamification to maintain and manage motivation for continuation. This allows the user to more clearly understand their current position and their future goals. For example, the agent AI system breaks down the user's current situation and creates a future using AI models of various experts (e.g., nutritionist, personal trainer, coach, financial expert, etc.). The user provides progress data from their smartphone, and the current situation is presented as an image. For example, it shows the future if the user slacks off, the future if they continue their current actions, and the future if they improve their actions. This allows the user to recognize their current urgency and understand that they can change their future through effort. This mechanism makes it easier for the user to maintain motivation to continue taking action towards their desired self. Furthermore, through visualization and gamification, users can visually track their progress, clearly identifying areas for improvement. For example, a nutritionist AI might suggest improvements to their diet, and a personal trainer AI might suggest improvements to their exercise routine. This allows users to move closer to their ideal self with comprehensive support. This enables the agent AI system to efficiently break down the user's ideal self, present plans, visualize progress, and implement gamification.

[0072] The agent AI system according to this embodiment comprises a reception unit, a subdivision unit, a presentation unit, a visualization unit, and a gamification unit. The reception unit receives the user's ideal image. The user's ideal image includes, but is not limited to, career goals, health status, and lifestyle. The reception unit allows the user to communicate their ideal image using, for example, voice input or text input. The subdivision unit subdivides tasks based on the ideal image received by the reception unit. Task subdivision is performed based on, for example, the granularity and prioritization method of the tasks, but is not limited to, such examples. For example, the subdivision unit subdivides the user's ideal image to a specific action level and divides it into achievable tasks. The presentation unit presents a plan based on the tasks subdivided by the subdivision unit. The presentation of the plan is performed based on, for example, the format and timing of the information presented, but is not limited to, such examples. For example, the presentation unit presents the user with the progress of the tasks and the next tasks to be performed. The visualization unit visualizes progress based on the plan presented by the presentation unit. Progress visualization is performed based on, for example, the type of graph used or the range of data displayed, but is not limited to such examples. For example, the visualization unit visually displays the user's progress, clearly indicating the degree of achievement and areas for improvement. The gamification unit performs gamification based on the progress visualized by the visualization unit. Gamification is performed based on, for example, a point system or type of reward, but is not limited to such examples. For example, the gamification unit awards points and provides rewards each time the user completes a task. This enables the agent AI system according to the embodiment to efficiently subdivide the user's ideal state, present a plan, visualize progress, and perform gamification.

[0073] The reception desk receives the user's ideal vision. This ideal vision includes, but is not limited to, career goals, health status, and lifestyle. The reception desk allows users to communicate their ideal vision using voice or text input. Specifically, with voice input, the user speaks their ideal vision through a microphone, and the system uses speech recognition technology to convert it into text data. With text input, the user enters their ideal vision using a keyboard or touchscreen. The reception desk analyzes this input data and utilizes natural language processing (NLP) technology to accurately understand the user's intentions and desires. For example, if a user enters "I want to become a manager within three years," the system extracts the keywords "within three years" and "manager" and uses this to understand the user's career goal. Furthermore, the reception desk can refer to the user's past input history and profile information to provide more personalized responses. For example, if a user has previously set health-related goals, the system takes that information into account when accepting a new ideal vision. This allows the reception desk to flexibly respond to the diverse needs and desires of users and collect accurate information.

[0074] The subdivision department breaks down tasks based on the ideal vision received by the reception department. Task subdivision is done based on, for example, the granularity and prioritization method of the tasks, but is not limited to such examples. Specifically, the subdivision department analyzes the user's ideal vision and breaks it down into specific actions and steps necessary to achieve it. For example, if a user sets the goal of "becoming a manager within three years," the subdivision department will break this down into specific tasks such as "improving leadership skills," "gaining project management experience," and "obtaining relevant qualifications." Furthermore, the subdivision department sets priorities for each task, clarifying which tasks the user should tackle first. Prioritization takes into account the importance and urgency of the tasks, as well as the user's current situation and resources. For example, if a user has many opportunities to improve their leadership skills in their current job, that task will be set as a priority. The subdivision department also monitors the progress of the tasks and readjusts or adds tasks as needed. In this way, the subdivision department can transform the user's ideal vision into concrete and actionable tasks, supporting efficient goal achievement.

[0075] The presentation unit presents a plan based on tasks subdivided by the subdivision unit. The plan presentation is based on, for example, the format and timing of the information presented, but is not limited to these examples. Specifically, the presentation unit provides the user with information on task progress and the next steps to take. For example, if a user is working on the task of "improving leadership skills," the presentation unit will present specific action items and resources related to that task. This may include recommendations for online leadership courses or suggestions for practical training. Furthermore, the presentation unit presents tasks at the optimal time, tailored to the user's schedule and lifestyle. For example, if a user has free time on weekday evenings, tasks will be presented at that time. In addition, the presentation unit collects user feedback and continuously improves the way and content of the plan presentation. This allows the presentation unit to provide users with effective and timely information and support them in achieving their goals.

[0076] The visualization unit visualizes progress based on the plan presented by the presentation unit. Progress visualization is performed based on, for example, the type of graph used and the range of data displayed, but is not limited to these examples. Specifically, the visualization unit visually displays the user's progress, clearly indicating achievements and areas for improvement. For example, it displays the progress of tasks the user is working on using bar graphs or line graphs, allowing users to grasp the achievement level of each task at a glance. The visualization unit also displays the user's past data and trends, providing information for evaluating long-term progress. This allows users to visually confirm the results of their efforts and maintain motivation. Furthermore, based on user feedback, the visualization unit improves the visualization methods and content to provide more effective information. This allows the visualization unit to clearly show the user's progress and increase their motivation towards achieving their goals.

[0077] The Gamification Department implements gamification based on progress visualized by the Visualization Department. Gamification is carried out based on, for example, point systems and types of rewards, but is not limited to these examples. Specifically, the Gamification Department awards points and provides rewards each time a user completes a task. For example, when a user completes a specific task, points are awarded according to the degree of completion, and a reward is provided when a certain number of points are accumulated. Rewards include badges, trophies, and perks, and are designed to increase user motivation. The Gamification Department also provides features to promote competition and cooperation among users. For example, users can earn additional points and rewards by comparing their progress with other users or by completing tasks together. In this way, the Gamification Department provides an environment where users can increase their motivation and enjoy working towards achieving their goals. Furthermore, the Gamification Department continuously improves the gamification elements and reward system based on user feedback to further enhance motivation. This allows the gamification department to support users in enjoying themselves while striving to achieve their goals, maximizing the overall effectiveness of the system.

[0078] The subdivision unit can subdivide tasks using AI models of various experts. For example, the subdivision unit can subdivide areas for dietary improvement using a nutritionist AI. For instance, the nutritionist AI can analyze the user's diet, evaluate nutritional balance, and suggest specific areas for improvement. The subdivision unit can also subdivide areas for exercise improvement using a personal trainer AI. For example, the personal trainer AI can analyze the user's exercise habits and create an exercise plan or provide exercise guidance. Furthermore, the subdivision unit can subdivide areas for behavioral improvement using a coaching AI. For example, the coaching AI can analyze the user's behavioral patterns and provide advice on behavioral improvement and motivation maintenance. Finally, the subdivision unit can subdivide areas for financial improvement using a financial AI. For example, the financial AI can analyze the user's financial situation and provide financial analysis and investment advice. This improves the accuracy of task subdivision by using AI models of various experts.

[0079] The presentation unit can use a nutritionist AI to suggest areas for improvement in the user's diet. For example, the nutritionist AI can analyze the user's diet, evaluate its nutritional balance, and suggest specific areas for improvement. For instance, based on the user's diet, the nutritionist AI can identify nutrient deficiencies or excesses and propose a balanced meal plan. The nutritionist AI can also provide dietary improvement advice tailored to the user's goals. For example, it can suggest calorie restriction and adjustments to nutritional balance for users aiming to lose weight, and advise increasing protein intake for users aiming to build muscle. In this way, using the nutritionist AI allows for the effective presentation of areas for improvement in the user's diet.

[0080] The display unit can use a personal trainer AI to suggest areas for improvement in exercise. For example, the personal trainer AI can analyze the user's exercise habits and create an exercise plan and provide exercise guidance. For instance, the personal trainer AI can create an individualized exercise plan based on the user's exercise history and fitness level. The personal trainer AI can also provide advice on improving exercise according to the user's goals. For example, it can advise users aiming for weight loss to adjust the frequency and intensity of aerobic exercise, and suggest strength training menus for users aiming for muscle gain. In this way, using a personal trainer AI allows for the effective presentation of areas for improvement in exercise.

[0081] The presentation unit can use coaching AI to suggest areas for improvement in behavior. For example, the coaching AI can analyze the user's behavior patterns and provide advice for behavioral improvement and motivation maintenance. For instance, the coaching AI can create an action plan for the user to achieve their goals and present specific steps. The coaching AI can also provide advice to maintain the user's motivation. For example, it can provide encouraging messages to help the user continue their efforts towards their goals and provide feedback according to their progress. In this way, using coaching AI makes it possible to effectively suggest areas for improvement in behavior.

[0082] The presentation unit can use financial AI to suggest areas for financial improvement. For example, the presentation unit uses financial AI to analyze the user's financial situation and provide financial analysis and investment advice. For instance, based on the user's income and expenditure data, the financial AI can suggest areas for improvement in the balance of income and expenses. Furthermore, the financial AI can provide financial improvement advice tailored to the user's goals. For example, it can suggest saving methods to increase savings or investment plans that consider investment risks and returns. Thus, by using financial AI, areas for financial improvement can be effectively presented.

[0083] The visualization section can present the user's current appearance as an image. For example, the visualization section visually displays the user's current appearance based on data such as weight, body fat percentage, and health status. For instance, the visualization section can display changes in the user's weight and body fat percentage in a graph, clearly identifying areas for improvement in their health. Furthermore, the visualization section can present the user's current appearance as an image. For example, it can display the user's body shape and posture as an image, allowing for a visual understanding of areas for improvement. This allows users to visually grasp their current situation by presenting their current appearance as an image.

[0084] The visualization section can present future scenarios if the user slacks off, if they continue their current behavior, and if they improve their behavior. For example, the visualization section can create future scenarios based on the user's current behavior data and present them visually. For instance, it can simulate and visually display the user's future health and lifestyle if they slack off. The visualization section can also present future scenarios if the user continues their current behavior. For example, it can predict and visually display the user's health and lifestyle if they continue their current behavior. Furthermore, the visualization section can also present future scenarios if the user improves their behavior. For example, it can simulate and visually display the health and lifestyle that can be achieved through behavioral improvement. By presenting these future scenarios, the system can raise the user's awareness of their own behavior.

[0085] The reception system can estimate the user's emotions and adjust the timing of the ideal image presentation based on those emotions. For example, if the user is stressed, the reception system will present the ideal image at a time when the user is relaxed. For example, the reception system can estimate the user's emotions using facial recognition or voice analysis and present the ideal image when the user's stress has decreased. The reception system can also present the ideal image immediately if the user is highly motivated, making it easier for them to take action. For example, the reception system can monitor the user's emotional state in real time and present the ideal image when their motivation has increased. The reception system can also present the ideal image after the user has rested if they are tired. For example, the reception system can analyze the user's biometric data and present the ideal image when the user has recovered from fatigue. By adjusting the timing of the ideal image presentation according to the user's emotions, the ideal image can be presented at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0086] The reception desk can analyze the user's past ideal image history and select the optimal reception method. For example, the reception desk can suggest similar ideal images based on the ideal image the user has entered in the past. For instance, the reception desk can analyze the user's past ideal image history and suggest goals similar to those previously achieved. The reception desk can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has previously used voice input to communicate their ideal image, it will prioritize suggesting voice input. Furthermore, the reception desk can suggest the most suitable reception method for a specific time period based on the user's past ideal image history. For example, based on the user's past history of entering an ideal image at a specific time period, the reception desk will suggest the most suitable reception method for that time period. In this way, the optimal reception method can be selected by analyzing the past ideal image history.

[0087] The reception desk can filter the ideal lifestyle submitted by the user based on their current lifestyle and areas of interest. For example, it can suggest relevant ideal lifestyles based on the user's current occupation and hobbies. For instance, it can analyze the user's occupation and hobby data and filter and suggest relevant ideal lifestyles. The reception desk can also suggest feasible ideal lifestyles that align with the user's lifestyle. For example, it can filter and suggest feasible ideal lifestyles based on the user's lifestyle data. Furthermore, the reception desk can suggest ideal lifestyles that are likely to interest the user based on their areas of interest. For example, it can analyze the user's areas of interest data and filter and suggest ideal lifestyles that are likely to interest the user. By filtering based on the user's lifestyle and areas of interest, the reception desk can suggest more relevant ideal lifestyles.

[0088] The reception desk can estimate the user's emotions and determine the priority of ideal images to accept based on the estimated emotions. For example, if the user is excited, the reception desk will prioritize challenging ideal images. For example, the reception desk can estimate the user's emotions using facial recognition or voice analysis and prioritize challenging ideal images if the user is excited. The reception desk can also prioritize realistic ideal images if the user is calm. For example, the reception desk can monitor the user's emotional state in real time and prioritize realistic ideal images if the user is calm. The reception desk can also prioritize reassuring ideal images if the user is feeling anxious. For example, the reception desk can analyze the user's biometric data and prioritize reassuring ideal images if the user is feeling anxious. In this way, by determining the priority of ideal images according to the user's emotions, a more appropriate ideal image can be accepted. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0089] The reception desk can prioritize accepting ideal lifestyles that are highly relevant to the user, taking into account the user's geographical location information. For example, if the user lives in an urban area, the reception desk will prioritize accepting ideal lifestyles related to urban life. For example, the reception desk will analyze the user's geographical location information and prioritize accepting ideal lifestyles related to urban life. Also, if the user lives in a rural area, the reception desk can prioritize accepting ideal lifestyles related to the natural environment. For example, the reception desk will use the user's geographical location information to prioritize accepting ideal lifestyles related to the natural environment. Furthermore, if the user is traveling, the reception desk can prioritize accepting ideal lifestyles related to their travel destination. For example, the reception desk will analyze the user's geographical location information and prioritize accepting ideal lifestyles related to their travel destination. In this way, by considering the user's geographical location information, the reception desk can prioritize accepting ideal lifestyles that are highly relevant.

[0090] The reception desk can analyze a user's social media activity when receiving an ideal image request and then accept relevant ideal images. For example, the reception desk can suggest relevant ideal images based on the content a user frequently posts on social media. For example, the reception desk can analyze a user's social media activity and suggest ideal images related to frequently posted content. The reception desk can also suggest relevant ideal images by referring to ideal images shared by a user's followers and friends. For example, the reception desk can analyze the content posted by a user's followers and friends and suggest relevant ideal images. Furthermore, the reception desk can suggest relevant ideal images based on the trends of the online communities a user participates in. For example, the reception desk can analyze the trends of the online communities a user participates in and suggest relevant ideal images. In this way, relevant ideal images can be accepted by analyzing social media activity.

[0091] The task segmentation unit can estimate the user's emotions and adjust the task segmentation method based on the estimated emotions. For example, if the user is stressed, the segmentation unit can break down the task into smaller parts to make it easier for the user to feel a sense of accomplishment. For example, the segmentation unit can estimate emotions using facial recognition or voice analysis and break down tasks into smaller parts to reduce stress. The segmentation unit can also present larger tasks and set challenging goals if the user is highly motivated. For example, the segmentation unit can monitor the user's emotional state in real time and present larger tasks when motivation is high. The segmentation unit can also suggest tasks that include rest if the user is tired. For example, the segmentation unit can analyze the user's biometric data and suggest tasks that include rest to help the user recover from fatigue. In this way, by adjusting the task segmentation method according to the user's emotions, more appropriate tasks can be presented. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The subdivision unit can select the optimal subdivision method by referring to the past data of each expert's AI model during the subdivision process. For example, the subdivision unit can subdivide dietary improvement tasks based on the past data of a nutritionist AI. For example, the subdivision unit analyzes the nutritionist AI's past data and presents specific tasks for dietary improvement. The subdivision unit can also subdivide exercise improvement tasks based on the past data of a personal trainer AI. For example, the subdivision unit analyzes the personal trainer AI's past data and presents specific tasks for exercise improvement. Furthermore, the subdivision unit can subdivide behavior improvement tasks based on the coaching AI's past data. For example, the subdivision unit analyzes the coaching AI's past data and presents specific tasks for behavior improvement. This allows the system to select the optimal subdivision method by referring to the past data of each expert's AI model.

[0093] The task subdivision unit can customize the task subdivision method based on the user's current lifestyle. For example, if the user is busy, the subdivision unit can suggest tasks that can be completed in a short time. For example, the subdivision unit analyzes the user's lifestyle data and presents tasks that can be completed in a short time. The subdivision unit can also suggest tasks that require more time if the user has more free time. For example, the subdivision unit presents tasks that require more time based on the user's lifestyle data. Furthermore, if the user possesses specific skills, the subdivision unit can suggest tasks that utilize those skills. For example, the subdivision unit analyzes the user's skill data and presents tasks that utilize those skills. In this way, by customizing the task subdivision method based on the user's lifestyle, more appropriate tasks can be presented.

[0094] The task segmentation unit can estimate the user's emotions and determine the priority of tasks to segment based on the estimated emotions. For example, if the user is excited, the segmentation unit will prioritize subdividing challenging tasks. For example, the segmentation unit can estimate emotions using facial recognition or voice analysis and prioritize subdividing challenging tasks if the user is excited. The segmentation unit can also prioritize subdividing realistic tasks if the user is calm. For example, the segmentation unit can monitor the user's emotional state in real time and prioritize subdividing realistic tasks if the user is calm. The segmentation unit can also prioritize subdividing tasks that provide a sense of security if the user is feeling anxious. For example, the segmentation unit can analyze the user's biometric data and prioritize subdividing tasks that provide a sense of security if the user is feeling anxious. By determining the priority of tasks according to the user's emotions, more appropriate tasks can be presented. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0095] The task subdivision unit can select the optimal task subdivision method by considering the user's geographical location information during the subdivision process. For example, if the user lives in an urban area, the subdivision unit can subdivide tasks related to urban life. For example, the subdivision unit analyzes the user's geographical location information and subdivides tasks related to urban life. The subdivision unit can also subdivide tasks related to the natural environment if the user lives in a rural area. For example, the subdivision unit subdivides tasks related to the natural environment based on the user's geographical location information. Furthermore, if the user is traveling, the subdivision unit can subdivide tasks related to their travel destination. For example, the subdivision unit analyzes the user's geographical location information and subdivides tasks related to their travel destination. In this way, the optimal task subdivision method can be selected by considering the user's geographical location information.

[0096] The subdivision unit can analyze the user's social media activity during the subdivision process and propose methods for subdividing tasks. For example, the subdivision unit can subdivide relevant tasks based on the content the user frequently posts on social media. For example, the subdivision unit analyzes the user's social media activity and subdivides tasks related to frequently posted content. The subdivision unit can also subdivide relevant tasks by referring to tasks shared by the user's followers and friends. For example, the subdivision unit analyzes the content posted by the user's followers and friends and subdivides relevant tasks. Furthermore, the subdivision unit can subdivide relevant tasks based on the trends of the online communities the user participates in. For example, the subdivision unit analyzes the trends of the online communities the user participates in and subdivides relevant tasks. In this way, relevant tasks can be subdivided by analyzing social media activity.

[0097] The presentation unit can estimate the user's emotions and adjust the way plans are presented based on the estimated emotions. For example, if the user is stressed, the presentation unit will present a simple and easy-to-understand plan. For example, the presentation unit may estimate the user's emotions using facial recognition or voice analysis and present a simple and easy-to-understand plan to reduce stress. The presentation unit can also present a detailed plan if the user is highly motivated. For example, the presentation unit may monitor the user's emotional state in real time and present a detailed plan when their motivation increases. The presentation unit can also present a plan that includes rest if the user is tired. For example, the presentation unit may analyze the user's biometric data and present a plan that includes rest to help them recover from fatigue. By adjusting the way plans are presented according to the user's emotions, a more appropriate plan can be presented. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The presentation unit can present the optimal plan by referencing past data from each expert's AI model. For example, the presentation unit can present a diet improvement plan based on past data from a nutritionist AI. For example, the presentation unit analyzes the nutritionist AI's past data and presents a specific diet improvement plan. The presentation unit can also present an exercise improvement plan based on past data from a personal trainer AI. For example, the presentation unit analyzes the personal trainer AI's past data and presents a specific exercise improvement plan. The presentation unit can also present a behavior improvement plan based on past data from a coaching AI. For example, the presentation unit analyzes the coaching AI's past data and presents a specific behavior improvement plan. In this way, by referencing past data from each expert's AI model, the optimal plan can be presented.

[0099] The presentation unit can customize the method of presenting plans based on the user's current living situation. For example, if the user is busy, the presentation unit can present plans that can be completed in a short time. For example, the presentation unit can analyze the user's living situation data and present plans that can be completed in a short time. Also, if the user has time, the presentation unit can present plans that require more time. For example, the presentation unit can present plans that require more time based on the user's living situation data. Furthermore, if the user possesses specific skills, the presentation unit can present plans that utilize those skills. For example, the presentation unit can analyze the user's skill data and present plans that utilize those skills. In this way, by customizing the method of presenting plans based on the user's living situation, more appropriate plans can be presented.

[0100] The presentation unit can estimate the user's emotions and prioritize plans based on those emotions. For example, if the user is excited, the presentation unit will prioritize challenging plans. For example, the presentation unit can estimate emotions using facial recognition or voice analysis and prioritize challenging plans if the user is excited. The presentation unit can also prioritize realistic plans if the user is calm. For example, the presentation unit can monitor the user's emotional state in real time and prioritize realistic plans if the user is calm. The presentation unit can also prioritize reassuring plans if the user is feeling anxious. For example, the presentation unit can analyze the user's biometric data and prioritize reassuring plans if the user is feeling anxious. By prioritizing plans according to the user's emotions, a more appropriate plan can be presented. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The presentation unit can present the most suitable plan by considering the user's geographical location information. For example, if the user lives in an urban area, the presentation unit can present a plan related to urban life. For example, the presentation unit analyzes the user's geographical location information and presents a plan related to urban life. Also, if the user lives in a rural area, the presentation unit can present a plan related to the natural environment. For example, the presentation unit presents a plan related to the natural environment based on the user's geographical location information. Furthermore, if the user is traveling, the presentation unit can present a plan related to their travel destination. For example, the presentation unit analyzes the user's geographical location information and presents a plan related to their travel destination. In this way, the optimal plan can be presented by considering the user's geographical location information.

[0102] The presentation unit can analyze the user's social media activity and propose a method for presenting plans. For example, the presentation unit can present relevant plans based on the content the user frequently posts on social media. For example, the presentation unit analyzes the user's social media activity and presents plans related to the content that is frequently posted. The presentation unit can also present relevant plans by referring to plans shared by the user's followers and friends. For example, the presentation unit analyzes the content posted by the user's followers and friends and presents relevant plans. The presentation unit can also present relevant plans based on the trends of the online communities the user participates in. For example, the presentation unit analyzes the trends of the online communities the user participates in and presents relevant plans. In this way, relevant plans can be presented by analyzing social media activity.

[0103] The visualization unit can estimate the user's emotions and adjust the progress visualization method based on the estimated emotions. For example, if the user is stressed, the visualization unit can provide a simple and highly visual visualization method. For instance, it can estimate emotions using facial recognition or voice analysis and provide a simple and highly visual visualization method to reduce stress. The visualization unit can also provide a detailed visualization method if the user is highly motivated. For example, it can monitor the user's emotional state in real time and provide a detailed visualization method when motivation increases. Furthermore, if the user is tired, the visualization unit can provide a visualization method that includes rest. For example, it can analyze the user's biometric data and provide a visualization method that includes rest to help the user recover from fatigue. This allows for more appropriate visualization by adjusting the progress visualization method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The visualization unit can select the optimal visualization method by referring to past visualization data during the visualization process. For example, the visualization unit can propose the optimal method based on visualization methods that users have preferred to use in the past. For instance, the visualization unit can analyze past visualization data and select the method that users found most effective in understanding the data. The visualization unit can also select the method that most effectively shows the user's progress from past visualization data. For example, the visualization unit can analyze past visualization data and select the method that most effectively shows the user's progress. In this way, the optimal visualization method can be selected by referring to past visualization data.

[0105] The visualization unit can customize the visualization method based on the user's current lifestyle during the visualization process. For example, if the user is busy, the visualization unit can provide a visualization method that allows them to check progress quickly. For example, the visualization unit can analyze the user's lifestyle data and provide a visualization method that allows them to check progress quickly. The visualization unit can also provide a visualization method that allows the user to check detailed progress if they have more time. For example, the visualization unit can provide a visualization method that allows them to check detailed progress based on the user's lifestyle data. Furthermore, if the user possesses specific skills, the visualization unit can provide a visualization method that utilizes those skills. For example, the visualization unit can analyze the user's skill data and provide a visualization method that utilizes those skills. By customizing the visualization method based on the user's lifestyle, more appropriate visualization becomes possible.

[0106] The visualization unit can estimate the user's emotions and determine visualization priorities based on those estimated emotions. For example, if the user is excited, the visualization unit will prioritize visualizing challenging progress. For instance, it can estimate emotions using facial recognition or voice analysis, and prioritize visualizing challenging progress if the user is excited. Furthermore, if the user is calm, the visualization unit can prioritize visualizing realistic progress. For example, it can monitor the user's emotional state in real time and prioritize visualizing realistic progress if the user is calm. Also, if the user is feeling anxious, the visualization unit can prioritize visualizing reassuring progress. For example, it can analyze the user's biometric data and prioritize visualizing reassuring progress if the user is feeling anxious. This allows for more appropriate visualizations by determining visualization priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The visualization unit can select the optimal visualization method by considering the user's geographical location information during the visualization process. For example, if the user lives in an urban area, the visualization unit can visualize progress related to urban life. For example, the visualization unit analyzes the user's geographical location information and visualizes progress related to urban life. Also, if the user lives in a rural area, the visualization unit can visualize progress related to the natural environment. For example, the visualization unit visualizes progress related to the natural environment based on the user's geographical location information. Furthermore, if the user is traveling, the visualization unit can visualize progress related to the travel destination. For example, the visualization unit analyzes the user's geographical location information and visualizes progress related to the travel destination. In this way, the optimal visualization method can be selected by considering the user's geographical location information.

[0108] The Visualization Department can analyze a user's social media activity and propose visualization methods during the visualization process. For example, the Visualization Department can visualize relevant progress based on the content a user frequently posts on social media. For example, the Visualization Department analyzes a user's social media activity and visualizes progress related to frequently posted content. The Visualization Department can also visualize relevant progress by referring to progress shared by a user's followers and friends. For example, the Visualization Department analyzes the content posted by a user's followers and friends and visualizes relevant progress. Furthermore, the Visualization Department can visualize relevant progress based on trends in online communities in which a user participates. For example, the Visualization Department analyzes the trends in online communities in which a user participates and visualizes relevant progress. In this way, relevant progress can be visualized by analyzing social media activity.

[0109] The gamification unit can estimate the user's emotions and adjust the gamification method based on the estimated emotions. For example, if the user is feeling stressed, the gamification unit can provide relaxing game elements. For example, the gamification unit can estimate emotions using facial recognition or voice analysis and provide relaxing game elements to reduce stress. The gamification unit can also provide challenging game elements when the user is highly motivated. For example, the gamification unit can monitor the user's emotional state in real time and provide challenging game elements when motivation is high. The gamification unit can also provide game elements that include rest when the user is tired. For example, the gamification unit can analyze the user's biometric data and provide game elements that include rest to help them recover from fatigue. This allows for more appropriate gamification by adjusting the gamification method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0110] The gamification department can select the optimal method during gamification by referring to past gamification data. For example, the gamification department can propose the optimal method based on game elements that users have previously enjoyed using. For example, the gamification department can analyze past gamification data and select the method that users enjoyed the most. The gamification department can also select the method that best increases user motivation from past gamification data. For example, the gamification department can analyze past gamification data and select the method that best increases user motivation. In this way, the optimal method can be selected by referring to past gamification data.

[0111] The gamification unit can customize the gamification methods based on the user's current lifestyle during the gamification process. For example, if the user is busy, the gamification unit can provide game elements that can be enjoyed in a short amount of time. For example, the gamification unit can analyze the user's lifestyle data and provide game elements that can be enjoyed in a short amount of time. The gamification unit can also provide game elements that can be enjoyed over a longer period of time if the user has more free time. For example, the gamification unit can provide game elements that can be enjoyed over a longer period of time based on the user's lifestyle data. Furthermore, if the user possesses a specific skill, the gamification unit can provide game elements that utilize that skill. For example, the gamification unit can analyze the user's skill data and provide game elements that utilize that skill. By customizing the gamification methods based on the user's lifestyle, more appropriate gamification becomes possible.

[0112] The gamification unit can estimate the user's emotions and determine the priority of gamification based on those emotions. For example, if the user is excited, the gamification unit will prioritize providing challenging game elements. For instance, the gamification unit can estimate emotions using facial recognition or voice analysis and prioritize challenging game elements if the user is excited. The gamification unit can also prioritize realistic game elements if the user is calm. For example, the gamification unit can monitor the user's emotional state in real time and prioritize realistic game elements if the user is calm. Furthermore, the gamification unit can prioritize reassuring game elements if the user is feeling anxious. For example, the gamification unit can analyze the user's biometric data and prioritize reassuring game elements if the user is feeling anxious. This allows for more appropriate gamification by determining the priority of gamification according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0113] The gamification unit can select the optimal method during gamification by considering the user's geographical location information. For example, if the user lives in an urban area, the gamification unit can provide game elements related to urban life. For example, the gamification unit analyzes the user's geographical location information and provides game elements related to urban life. The gamification unit can also provide game elements related to the natural environment if the user lives in a rural area. For example, the gamification unit provides game elements related to the natural environment based on the user's geographical location information. Furthermore, if the user is traveling, the gamification unit can provide game elements related to the travel destination. For example, the gamification unit analyzes the user's geographical location information and provides game elements related to the travel destination. In this way, the optimal method can be selected by considering the user's geographical location information.

[0114] The Gamification Department can analyze users' social media activity during gamification and propose gamification methods. For example, the Gamification Department can provide relevant game elements based on the content users frequently post on social media. For example, the Gamification Department can analyze users' social media activity and provide game elements related to frequently posted content. The Gamification Department can also provide relevant game elements by referring to game elements shared by users' followers and friends. For example, the Gamification Department can analyze the content posted by users' followers and friends and provide relevant game elements. The Gamification Department can also provide relevant game elements based on the trends of online communities in which users participate. For example, the Gamification Department can analyze the trends of online communities in which users participate and provide relevant game elements. In this way, relevant game elements can be provided by analyzing social media activity.

[0115] The gamification unit can provide optimal game elements during gamification, taking into account the user's health condition. For example, if the user is tired, the gamification unit can provide relaxing game elements. For instance, the gamification unit analyzes the user's health data and provides relaxing game elements to reduce fatigue. The gamification unit can also provide active game elements if the user is seeking healthy exercise. For example, based on the user's health data, the gamification unit provides active game elements that promote healthy exercise. Furthermore, if the user is feeling unwell, the gamification unit can provide game elements that include rest. For example, the gamification unit analyzes the user's health data and provides game elements that include rest to improve their condition. In this way, by considering the user's health condition, the optimal game elements can be provided.

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

[0117] The reception system can estimate the user's emotions and adjust the ideal image reception process based on those estimates. For example, if a user is feeling stressed, they can receive their ideal image in a relaxing environment. If a user is highly motivated, they can receive their ideal image immediately, making it easier for them to take action. Furthermore, if a user is tired, they can receive their ideal image after resting. By adjusting the ideal image reception process according to the user's emotions, the system can ensure that the ideal image is received at a more appropriate time.

[0118] The task breakdown section can estimate the user's emotions and adjust the task breakdown method based on those emotions. For example, if the user is stressed, the task can be broken down into smaller parts to make it easier for them to feel a sense of accomplishment. Conversely, if the user is highly motivated, a larger task can be presented, setting a challenging goal. Furthermore, if the user is tired, a task including rest can be suggested. In this way, by adjusting the task breakdown method according to the user's emotions, more appropriate tasks can be presented.

[0119] The presentation unit can estimate the user's emotions and adjust the way the plan is presented based on those emotions. For example, if the user is feeling stressed, it can present a simple and easy-to-understand plan. If the user is highly motivated, it can present a more detailed plan. Furthermore, if the user is tired, it can present a plan that includes rest. By adjusting the way the plan is presented according to the user's emotions, a more appropriate plan can be presented.

[0120] The visualization unit can estimate the user's emotions and adjust the progress visualization method based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible visualization method. If the user is highly motivated, it can provide a more detailed visualization method. Furthermore, if the user is tired, it can provide a visualization method that includes rest. By adjusting the progress visualization method according to the user's emotions, more appropriate visualization becomes possible.

[0121] The gamification unit can estimate the user's emotions and adjust the gamification method based on those emotions. For example, if the user is stressed, it can provide relaxing game elements. If the user is highly motivated, it can provide challenging game elements. Furthermore, if the user is tired, it can provide game elements that include rest. By adjusting the gamification method according to the user's emotions, more appropriate gamification becomes possible.

[0122] The reception desk can analyze the user's past ideal image history and select the optimal reception method. For example, it can suggest similar ideal images based on the user's past input. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can suggest the most suitable reception method for a specific time of day based on the user's past ideal image history. In this way, the optimal reception method can be selected by analyzing the user's past ideal image history.

[0123] The subdivision section can select the optimal subdivision method by referring to the past data of each expert's AI model. For example, it can subdivide tasks related to dietary improvement based on the past data of a nutritionist AI. It can also subdivide tasks related to exercise improvement based on the past data of a personal trainer AI. Furthermore, it can subdivide tasks related to behavioral improvement based on the past data of a coaching AI. In this way, the optimal subdivision method can be selected by referring to the past data of each expert's AI model.

[0124] The presentation unit can refer to past data from each expert's AI model to present the optimal plan. For example, it can present a diet improvement plan based on past data from a nutritionist AI. It can also present an exercise improvement plan based on past data from a personal trainer AI. Furthermore, it can present a behavior improvement plan based on past data from a coaching AI. In this way, by referring to past data from each expert's AI model, the optimal plan can be presented.

[0125] The visualization unit can select the optimal visualization method by referring to past visualization data. For example, it can suggest the optimal method based on visualization methods that users have preferred to use in the past. It can also select the method that most effectively shows the user's progress from past visualization data. Furthermore, it can analyze past visualization data to select the method that most effectively shows the user's progress. In this way, the optimal visualization method can be selected by referring to past visualization data.

[0126] The gamification department can select the optimal method by referring to past gamification data. For example, it can propose the optimal method based on game elements that users have preferred to use in the past. It can also select the method that best increases user motivation from past gamification data. Furthermore, it can analyze past gamification data to select the method that best increases user motivation. In this way, the optimal method can be selected by referring to past gamification data.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The reception desk receives the user's ideal profile. This profile includes career goals, health status, lifestyle, etc. The reception desk allows the user to communicate their ideal profile using voice input or text input. Step 2: The subdivision department subdivides the tasks based on the ideal image received by the reception department. Task subdivision is carried out based on the granularity and prioritization method of the tasks. For example, the ideal user image is subdivided to the level of specific actions and divided into achievable tasks. Step 3: The presentation unit presents a plan based on the tasks subdivided by the subdivision unit. The plan is presented based on the format and timing of the information to be presented. For example, it may present the user with the progress of a task and the next task to be performed. Step 4: The visualization unit visualizes the progress based on the plan presented by the presentation unit. The visualization of progress is performed based on the type of graph used and the range of data to be displayed. For example, the user's progress is visually displayed to clarify the degree of achievement and areas for improvement. Step 5: The Gamification Department implements gamification based on the progress visualized by the Visualization Department. Gamification is carried out based on a point system and types of rewards. For example, points are awarded and rewards are provided each time a user completes a task.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each element of the agent AI system described above is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's ideal image via voice input or text input. The subdivision unit is implemented by the identification processing unit 290 of the data processing device 12 and subdivides tasks based on the user's ideal image. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents a plan based on the subdivided tasks. The visualization unit is implemented by the control unit 46A of the smart device 14 and visually displays the progress. The gamification unit is implemented by the control unit 46A of the smart device 14 and performs gamification based on the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each element of the agent AI system described above is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the user's ideal image via voice input or text input. The subdivision unit is implemented by the identification processing unit 290 of the data processing device 12 and subdivides tasks based on the user's ideal image. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents a plan based on the subdivided tasks. The visualization unit is implemented by the control unit 46A of the smart glasses 214 and visually displays the progress. The gamification unit is implemented by the control unit 46A of the smart glasses 214 and performs gamification based on the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each element of the agent AI system described above is implemented in at least one of the following: the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the user's ideal image via voice input or text input. The subdivision unit is implemented by the identification processing unit 290 of the data processing device 12 and subdivides tasks based on the user's ideal image. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents a plan based on the subdivided tasks. The visualization unit is implemented by the control unit 46A of the headset terminal 314 and visually displays the progress. The gamification unit is implemented by the control unit 46A of the headset terminal 314 and performs gamification based on the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each element of the agent AI system described above is implemented in at least one of the following: the robot 414 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the user's ideal image via voice or text input. The subdivision unit is implemented by the identification processing unit 290 of the data processing device 12 and subdivides tasks based on the user's ideal image. The presentation unit is implemented by the control unit 46A of the robot 414 and presents a plan based on the subdivided tasks. The visualization unit is implemented by the control unit 46A of the robot 414 and visually displays the progress. The gamification unit is implemented by the control unit 46A of the robot 414 and performs gamification based on the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0182] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A reception desk that accepts the user's ideal image, A subdivision unit subdivides tasks based on the ideal image received by the reception unit, A presentation unit that presents a plan based on the tasks subdivided by the aforementioned subdivision unit, A visualization unit visualizes the progress based on the plan presented by the aforementioned presentation unit, The system includes a gamification unit that performs gamification based on the progress visualized by the visualization unit. A system characterized by the following features. (Note 2) The aforementioned subdivision unit is The task is broken down using AI models from various experts. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is, Use an AI-powered nutritionist to suggest areas for improvement in your diet. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is, Use a personal trainer AI to suggest areas for improvement in your exercise routine. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, Use coaching AI to suggest areas for improvement in behavior. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, Use financial AI to identify areas for financial improvement. The system described in Appendix 1, characterized by the features described herein. (Note 7) The visualization unit is, Present the user's current state as an image. The system described in Appendix 1, characterized by the features described herein. (Note 8) The visualization unit is, This presents the future if you slack off, the future if you continue acting as you are now, and the future if you improve your behavior. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of acceptance of the ideal image based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past ideal user history and select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving an ideal profile, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of ideal customer profiles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving an ideal image, the system prioritizes accepting ideal images that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving an ideal image, the system analyzes the user's social media activity and accepts relevant ideal images. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned subdivision unit is It estimates the user's emotions and adjusts how tasks are broken down based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned subdivision unit is During segmentation, the optimal segmentation method is selected by referring to past data from each expert's AI model. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned subdivision unit is When breaking down tasks, customize the method of task breakdown based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned subdivision unit is Estimate the user's emotions and determine the priority of tasks to be broken down based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned subdivision unit is When subdividing tasks, the optimal task subdivision method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned subdivision unit is During task segmentation, we analyze users' social media activity to propose methods for subdividing tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, It estimates the user's emotions and adjusts how the plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, When presenting a plan, the system references past data from each expert's AI model to suggest the optimal solution. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting the plan, customize the presentation method based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, It estimates user sentiment and determines plan priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, When presenting a plan, the system will take the user's geographical location into consideration to suggest the most suitable option. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting the plan, we analyze the user's social media activity and propose a method for presenting the plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The visualization unit is, It estimates the user's emotions and adjusts the progress visualization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The visualization unit is, During visualization, the system selects the optimal visualization method by referring to past visualization data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The visualization unit is, During visualization, the visualization method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The visualization unit is, The system estimates user emotions and determines visualization priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The visualization unit is, When visualizing data, the optimal visualization method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The visualization unit is, During the visualization process, we analyze users' social media activity and propose visualization methods. The system described in Appendix 1, characterized by the features described herein. (Note 33) The gamification unit is, It estimates the user's emotions and adjusts the gamification method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The gamification unit is, During gamification, the optimal method is selected by referring to past gamification data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The gamification unit is, During gamification, the gamification methods are customized based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 36) The gamification unit is, It estimates user emotions and prioritizes gamification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The gamification unit is, When gamifying, the optimal method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The gamification unit is, During gamification, we analyze users' social media activity and propose gamification methods. The system described in Appendix 1, characterized by the features described herein. (Note 39) The gamification unit is, When gamifying, provide optimal game elements while considering the user's health status. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts the user's ideal image, A subdivision unit subdivides tasks based on the ideal image received by the reception unit, A presentation unit that presents a plan based on the tasks subdivided by the aforementioned subdivision unit, A visualization unit visualizes the progress based on the plan presented by the aforementioned presentation unit, The system includes a gamification unit that performs gamification based on the progress visualized by the visualization unit. A system characterized by the following features.

2. The aforementioned subdivision unit is The task is broken down using AI models from various experts. The system according to feature 1.

3. The aforementioned display unit is, Use an AI nutritionist to suggest areas for improvement in your diet. The system according to feature 1.

4. The aforementioned display unit is, Use a personal trainer AI to suggest areas for improvement in your exercise routine. The system according to feature 1.

5. The aforementioned display unit is, Use coaching AI to suggest areas for improvement in behavior. The system according to feature 1.

6. The aforementioned display unit is, Use financial AI to identify areas for financial improvement. The system according to feature 1.

7. The visualization unit is, Present the user's current state as an image. The system according to feature 1.

8. The visualization unit is, This presents the future if you slack off, the future if you continue acting as you are now, and the future if you improve your behavior. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of acceptance of the ideal image based on the estimated user emotions. The system according to feature 1.

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