System

Abstract

An object of the system according to the embodiment is to eliminate anxiety about work and a decrease in motivation after the beginning of the next week and cut off a lively start.SOLUTION: A system according to an embodiment includes a user information input unit, an information analysis unit, a message generation unit, an objective setting unit, a relaxation providing unit, and a successful experience designing unit. The user information input unit inputs user information. The information analysis unit analyzes the user information input by the user information input unit. The message generation unit generates a personalized message based on the result analyzed by the information analysis unit. The goal setting unit sets a short-term goal on the basis of the result analyzed by the information analysis unit. The relaxation provider may provide content for relaxation or concentration improvement based on a result of the analysis by the information analyzer. The successful experience design unit designs a successful experience based on the result analyzed by the information analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not providing sufficient means to effectively alleviate anxiety and loss of motivation about work at the start of the new week.

[0005] The system according to the embodiment aims to eliminate anxiety and loss of motivation about work at the start of the new week, and to get off to a lively start. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, an information analysis unit, a message generation unit, a goal setting unit, a relaxation provision unit, and a successful experience design unit. The user information input unit inputs user information. The information analysis unit analyzes the user information input by the user information input unit. The message generation unit generates a personalized message based on the results of the analysis by the information analysis unit. The goal setting unit sets short-term goals based on the results of the analysis by the information analysis unit. The relaxation provision unit provides content for relaxation and improving concentration based on the results of the analysis by the information analysis unit. The successful experience design unit designs successful experiences based on the results of the analysis by the information analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment eliminates anxiety and loss of motivation about work at the start of the new week, allowing employees to get off to a lively start. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The Monday Motivator AI according to an embodiment of the present invention is a system in which a user inputs their mood, goals, and work-related anxieties into an app over the weekend, and a generation AI analyzes this information to provide personalized motivation-boosting messages, suggestions for short-term goal setting, music for relaxation and improving concentration, and meditation guides, and designs small success experiences to coincide with the start of the week.In this way, the Monday Motivator AI helps users eliminate anxiety and lack of motivation about work at the start of the week, allowing them to start Monday with energy.

[0029] The Monday Motivator AI according to the embodiment includes a user information input unit, an information analysis unit, a message generation unit, a goal setting unit, a relaxation provision unit, and a success experience design unit. The user information input unit inputs information such as their mood, goals, and anxiety about work over the weekend. For example, the user inputs specific feelings and goals such as "I'm anxious because there's a lot of work this week" or "I'm looking forward to starting a new project." The information analysis unit analyzes the user information input by the user information input unit. For example, the generation AI analyzes data based on the user's mood, goals, and anxiety. The message generation unit generates personalized messages based on the results of the analysis by the information analysis unit. For example, the generation AI generates messages such as "You can achieve great results this week!" or "Taking on a new project is hard, but I'm sure you'll succeed." The goal setting unit sets short-term goals based on the results of the analysis by the information analysis unit. For example, the generation AI suggests specific goals such as "Your goal this week is to work intensively for one hour every day" or "Complete the initial stages of a new project." The relaxation provider provides content for relaxation and improved concentration based on the results of the analysis by the information analyzer. For example, it makes suggestions such as, "Listen to this music when you want to relax" or "Try guided meditation to improve your concentration." The success experience designer designs small success experiences based on the results of the analysis by the information analyzer. For example, it makes suggestions such as, "Completing a simple task on Monday morning will give you a sense of accomplishment." In this way, the Monday Motivator AI of the embodiment helps users eliminate anxiety and lack of motivation about work at the start of the week, allowing them to start Monday with energy. For example, personalized messages and goal setting help users work with confidence, and music and guided meditations for relaxation and improved concentration help users work efficiently while reducing stress. Furthermore, small success experiences help users start the week with a positive attitude.

[0030] In the user information input unit, the generation AI provides real-time feedback on the user information entered by the user, allowing the input content to be optimized. For example, as the user enters information, the generation AI suggests grammar and expression corrections in real time. For example, if the user enters, "I'm worried because there's a lot of work this week," the generation AI will ask, "Which part of the work are you worried about specifically?" to elicit more detailed information. In addition, in the user information input unit, the generation AI checks the consistency of the input content and provides appropriate feedback. For example, if the user enters, "I'm looking forward to working on a new project," the generation AI will ask, "Which part are you looking forward to specifically?" to elicit more detailed information. In this way, by providing real-time feedback on the information entered by the user and optimizing the input content, personalized messages and goal setting can be created based on more accurate information.

[0031] The user information input unit analyzes the user's past input data, learns input trends and patterns, and predicts and suggests the next input. For example, the user information input unit displays predictive candidates based on the user's past input data the next time the user inputs something. For example, if a user previously inputs "I have a lot of meetings on Mondays," the unit predicts and displays similar content the next time. The user information input unit also uses a generation AI to analyze past input data and learn input trends and patterns. For example, if a user frequently inputs "The project is behind schedule," the generation AI learns this tendency and suggests "How is the project progressing?" the next time the user inputs something. Furthermore, the user information input unit predicts the user's input content based on past input data and provides appropriate feedback. For example, if a user inputs "What are your goals this week?", the generation AI would suggest "Complete the initial phase of the new project" based on past data. This analysis of the user's past input data and prediction and suggestions for the next input streamlines the user's input work and provides more accurate information.

[0032] The user information input unit allows users to provide information using voice or image input, and can also collect data other than text. For example, the user information input unit allows users to provide information using voice input, and the generation AI analyzes the voice data and converts it into text. For example, if a user inputs "What are your goals this week?" by voice, the generation AI converts it into text and saves it. The user information input unit also allows users to provide information using image input, and the generation AI analyzes image data and converts it into text. For example, if a user uploads handwritten notes as images, the generation AI analyzes their contents and converts them into text. Furthermore, the user information input unit allows the generation AI to analyze voice data and image data and provide appropriate feedback. For example, if a user inputs "What are your goals this week?" by voice, the generation AI might suggest, "Complete the initial phase of a new project." This allows users to provide information using voice or image input, and collects data other than text, enabling personalized messages and goal setting based on a wider variety of information.

[0033] The user information input unit allows a user to share information input by the user with other users and obtain community-based feedback. The user information input unit adds a function for sharing information input by the user with other users and obtaining community-based feedback, for example. For example, when the user inputs "What are your goals for this week?", the user receives advice and encouraging comments from other users. The user information input unit also allows the generation AI to analyze the community-based feedback and provide appropriate feedback. For example, when the user inputs "I'm looking forward to working on a new project," the user receives advice and encouraging comments from other users. The user information input unit also allows the generation AI to analyze the community-based feedback and provide appropriate feedback. For example, when the user inputs "What are your goals for this week?", the user receives advice and encouraging comments from other users. This allows the user to share the information input by the user with other users and obtain community-based feedback, thereby receiving advice and encouragement from a wider variety of perspectives.

[0034] The information analysis unit can integrate the user's input information with other data sources (e.g., social media and health data) to perform a comprehensive analysis. For example, the information analysis unit integrates the user's input information with social media data, and the generation AI performs a comprehensive analysis. For example, the information analysis unit analyzes the user's social media posts and generates personalized messages. The information analysis unit also integrates the user's health data and performs a comprehensive analysis. For example, the generation AI analyzes the user's fitness and sleep data and provides appropriate feedback. Furthermore, the information analysis unit also integrates the user's input information with other data sources to perform a comprehensive analysis. For example, if the user inputs, "What are your goals this week?", the generation AI might suggest, "Complete the initial phase of a new project" based on social media and health data. By integrating the user's input information with other data sources and performing a comprehensive analysis, optimal support can be provided to the user from a more multifaceted perspective.

[0035] The information analysis unit allows the generation AI to simulate different scenarios based on the user's input information and propose an optimal personalized message. For example, when the user inputs, "What are your goals this week?", the generation AI simulates multiple scenarios and proposes the optimal goal. The information analysis unit also allows the generation AI to simulate different scenarios based on the user's input information and provide appropriate feedback. For example, when the user inputs, "I'm looking forward to working on a new project," the generation AI simulates multiple scenarios and proposes the optimal goal. The information analysis unit also allows the generation AI to simulate different scenarios based on the user's input information and propose the optimal personalized message. For example, when the user inputs, "What are your goals this week?", the generation AI simulates multiple scenarios and proposes the optimal goal. In this way, by simulating different scenarios and proposing the optimal personalized message, it is possible to provide the user with an optimal action plan or relaxation method.

[0036] The information analysis unit allows the generation AI to generate personalized messages tailored to the user's lifestyle and hobbies based on the user's input information. The information analysis unit allows the generation AI to generate personalized messages tailored to the user's lifestyle based on the user's input information. For example, if the user inputs, "What are your goals this week?", the generation AI will suggest goals tailored to the user's lifestyle. The information analysis unit also allows the generation AI to generate personalized messages tailored to the user's hobbies. For example, if the user inputs, "I like music," the generation AI will suggest, "Your goal this week is to listen to music for 30 minutes every day." The information analysis unit also allows the generation AI to generate personalized messages tailored to the user's lifestyle and hobbies. For example, if the user inputs, "What are your goals this week?", the generation AI will suggest goals tailored to the user's lifestyle and hobbies. This allows the generation AI to generate personalized messages tailored to the user's lifestyle and hobbies, thereby providing more friendly support to the user.

[0037] The goal setting unit can analyze the user's past goal achievement data and suggest achievable short-term goals. For example, if a user previously entered "Complete five tasks in one week," the goal setting unit can suggest "Complete five tasks this week as well." The goal setting unit also uses the generation AI to analyze the user's past goal achievement data and provide appropriate feedback. For example, if a user enters "I'm looking forward to working on a new project," the generation AI can ask "Which parts are you looking forward to specifically?" to elicit more details. Furthermore, the goal setting unit can suggest achievable short-term goals based on the user's past goal achievement data. For example, if the user enters "What is your goal this week?" the generation AI can suggest "Complete the initial stages of a new project." This makes it easier for users to set realistic goals by analyzing the user's past goal achievement data and suggesting achievable short-term goals.

[0038] The goal setting unit can set realistic short-term goals by referencing the user's schedule and task management data. For example, if the user's schedule states, "I have a lot of meetings," the unit might suggest, "Your goal this week is to spend one hour working intensively between meetings." The goal setting unit also analyzes the user's schedule and task management data and provides appropriate feedback. For example, if the user enters, "I'm looking forward to working on a new project," the unit might ask, "Which part specifically are you looking forward to?" to elicit more details. Furthermore, the goal setting unit sets realistic short-term goals based on the user's schedule and task management data. For example, if the user enters, "What is your goal this week?" the unit might suggest, "Complete the initial phase of the new project." This allows the user to achieve their goals more easily by setting realistic short-term goals by referencing the user's schedule and task management data.

[0039] The goal setting unit can generate a step-by-step guide for achieving the user's short-term goals and provide a specific action plan. For example, when the user inputs, "What are your goals this week?", the system suggests specific steps for achieving the goal. In addition, the goal setting unit uses the generation AI to analyze the user's short-term goals and provide appropriate feedback. For example, when the user inputs, "I'm looking forward to working on a new project," the system asks, "Which parts are you looking forward to specifically?" to elicit more details. In addition, the goal setting unit uses the generation AI to generate a step-by-step guide based on the user's short-term goals and provide a specific action plan. For example, when the user inputs, "What are your goals this week?", the system suggests specific steps for achieving the goal. This makes it easier for the user to achieve their goals by generating a step-by-step guide for achieving the user's short-term goals and providing a specific action plan.

[0040] The relaxation providing unit can analyze the user's past data on relaxation and concentration improvement and suggest optimal music and guided meditations. For example, the relaxation providing unit analyzes the user's past relaxation data and suggests optimal music. For example, if a user previously entered "Relax with classical music," the unit might suggest, "Listen to classical music and relax this week." The relaxation providing unit also uses the generation AI to analyze the user's past data on concentration improvement and provide appropriate feedback. For example, if a user enters, "I'm looking forward to working on a new project," the unit might ask, "Which part specifically are you looking forward to?" to elicit more details. Furthermore, the relaxation providing unit uses the generation AI to suggest optimal music and guided meditations based on the user's past relaxation and concentration improvement data. For example, if the user enters, "What is your goal this week?" the unit might suggest, "Complete the initial stages of the new project." This allows the user to relax more effectively and improve their concentration by analyzing the user's past data on relaxation and concentration improvement and suggesting optimal music and guided meditations.

[0041] The relaxation provider can monitor the user's physiological data (e.g., heart rate and stress level) in real time and provide music and meditation guides accordingly. For example, the relaxation provider can monitor the user's heart rate in real time and provide relaxation music accordingly. For example, if the heart rate is high, it can suggest "relaxing classical music." The relaxation provider's generation AI can also analyze the user's stress level and provide appropriate feedback. For example, if the user inputs, "I'm looking forward to working on a new project," it can ask, "Which part are you looking forward to specifically?" to elicit more details. Furthermore, the relaxation provider's generation AI can provide optimal music and meditation guides based on the user's physiological data. For example, if the user inputs, "What is your goal this week?" it can suggest, "Complete the initial phase of the new project." This allows the user to relax more effectively and improve their concentration by monitoring the user's physiological data in real time and providing music and meditation guides accordingly.

[0042] The relaxation provider can upload music and guided meditations selected by the user to the generation AI and generate personalized content based on them. For example, the relaxation provider can upload music selected by the user to the generation AI and generate personalized relaxation music based on that music. For example, when a user uploads their favorite music, the generation AI creates a relaxing playlist based on that music. The relaxation provider can also analyze the meditation guides uploaded by the user and provide appropriate feedback. For example, if the user inputs, "I'm looking forward to working on a new project," the generation AI can ask, "Which parts are you looking forward to specifically?" to elicit more details. Furthermore, the relaxation provider can generate personalized content based on the music and guided meditations uploaded by the user. For example, if the user inputs, "What is your goal this week?" the generation AI can suggest, "Complete the initial stages of the new project." This allows the user to relax more effectively and improve their concentration by generating personalized content based on the music and guided meditations selected by the user.

[0043] The relaxation providing unit allows users to share content for relaxation or improving concentration with other users and receive community-based feedback. The relaxation providing unit adds, for example, a function that allows users to share content for relaxation or improving concentration with other users and receive community-based feedback. For example, a user can share a playlist they created and receive ratings and comments from other users. The relaxation providing unit also allows the generation AI to analyze the community-based feedback and provide appropriate feedback. For example, if a user inputs, "I'm looking forward to working on a new project," the AI ​​asks, "What specifically are you looking forward to?" to elicit more details. The relaxation providing unit also allows the generation AI to analyze the community-based feedback and provide appropriate feedback. For example, if a user inputs, "What are your goals for this week?" the AI ​​receives advice and encouraging comments from other users. This allows users to share content for relaxation or improving concentration with other users and receive community-based feedback, thereby enabling them to relax more effectively and improve their concentration.

[0044] The success experience design unit can design realistic, small success experiences by referencing the user's schedule and task management data. For example, if the user's schedule states, "I have a lot of meetings," the unit might suggest, "Your goal this week is to complete one task between meetings." The success experience design unit's generation AI also analyzes the user's schedule and task management data to provide appropriate feedback. For example, if the user enters, "I'm looking forward to working on a new project," the unit might ask, "What specifically are you looking forward to?" to elicit more details. Furthermore, the success experience design unit's generation AI designs realistic, small success experiences based on the user's schedule and task management data. For example, if the user enters, "What is your goal this week?" the unit might suggest, "Complete the initial stages of the new project." This makes it easier for users to feel a sense of accomplishment by designing realistic, small success experiences by referencing the user's schedule and task management data.

[0045] The success experience design unit allows users to share their small successes with other users and work together to achieve successes. The success experience design unit adds a function that allows users to share their small successes with other users and work together to achieve successes. For example, by entering "What are your goals this week?", users can team up with users who have the same goals and work together to achieve them. Furthermore, the success experience design unit's generation AI analyzes the user's small successes and provides appropriate feedback. For example, if a user enters "I'm looking forward to working on a new project," the AI ​​will ask "What specifically are you looking forward to?" to elicit more details. Furthermore, the success experience design unit allows users to share their small successes with other users and work together to achieve successes. For example, by entering "What are your goals this week?", users can team up with users who have the same goals and work together to achieve them. This allows users to share their small successes with other users and work together to achieve successes, making it easier for them to feel a sense of accomplishment.

[0046] The success experience design unit can generate a step-by-step guide for the user to achieve small successes and provide a specific action plan. For example, the success experience design unit generates a step-by-step guide for the user to achieve small successes and provides a specific action plan. For example, when the user inputs, "What are your goals this week?", the unit suggests specific steps for achieving the goal. In addition, the success experience design unit uses the generation AI to analyze the user's small successes and provide appropriate feedback. For example, when the user inputs, "I'm looking forward to working on a new project," the unit asks, "What specifically are you looking forward to?" to elicit more details. In addition, the success experience design unit uses the generation AI to generate a step-by-step guide based on the user's small successes and provide a specific action plan. For example, when the user inputs, "What are your goals this week?", the unit suggests specific steps for achieving the goal. This generates a step-by-step guide for the user to achieve small successes and provides a specific action plan, making it easier for the user to feel a sense of accomplishment.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] In the user information input section, the generation AI provides real-time feedback on the information entered by the user, allowing the input content to be optimized. For example, if a user enters, "I'm worried because there's a lot of work this week," the generation AI will ask, "Which specific work are you worried about?" to extract more detailed information. The generation AI also checks the consistency of the input content and provides appropriate feedback. This provides real-time feedback on the information entered by the user and optimizes the input content, enabling personalized messages and goal setting based on more accurate information.

[0049] The user information input unit analyzes the user's past input data, learns input trends and patterns, and can predict and suggest the next input. For example, if a user has previously input "I have a lot of meetings on Mondays," the unit will predict and display similar content the next time. In addition, the generation AI analyzes past input data and learns input trends and patterns, displaying predictive candidates the next time the user inputs information. This makes input work more efficient for users and provides more accurate information.

[0050] The user information input unit allows users to provide information using voice input or image input, and can also collect data other than text. For example, users can provide information using voice input, and the generation AI analyzes the voice data and converts it into text. Alternatively, users can provide information using image input, and the generation AI analyzes the image data and converts it into text. This makes it possible to create personalized messages and set goals based on a wider variety of information.

[0051] The user information input unit allows users to share information they have entered with other users and receive community-based feedback. For example, users can share information they have entered with other users and receive advice and encouraging comments from other users. In addition, the generation AI analyzes the community-based feedback and provides appropriate feedback, allowing users to receive advice and encouragement from a wider variety of perspectives.

[0052] The information analysis unit can integrate the user's input information with other data sources (e.g., social media and health data) and perform comprehensive analysis. For example, it can analyze the user's social media posts and generate personalized messages. In addition, the generation AI can integrate the user's health data and perform comprehensive analysis, allowing it to provide optimal support to the user from a more multifaceted perspective.

[0053] The information analysis unit allows the generation AI to simulate different scenarios based on the user's input information and propose the optimal personalized message. For example, if a user inputs "What are your goals for this week?", the generation AI will simulate multiple scenarios and propose the optimal goal. This allows the system to simulate different scenarios and propose the optimal personalized message, thereby providing the user with the optimal action plan or relaxation method.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: In the user information input section, the user inputs information such as their mood, goals, and anxiety about work over the weekend. For example, the user inputs specific feelings and goals such as "I'm anxious because there's a lot of work this week" or "I'm looking forward to working on a new project." Step 2: The information analysis unit analyzes the user information entered by the user information input unit. For example, the generation AI analyzes the data based on the user's mood, goals, and anxieties. Step 3: The message generator generates a personalized message based on the results of the analysis by the information analyzer. For example, the generator might generate messages such as, "You can achieve great results this week!" or "Taking on a new project is hard, but I'm sure you'll succeed." Step 4: The goal setting department sets short-term goals based on the results of the analysis by the information analysis department. For example, they suggest specific goals such as "This week's goal is to do one hour of focused work every day" or "Complete the initial phase of a new project." Step 5: The relaxation provider provides content for relaxation and improving concentration based on the results of the analysis by the information analyzer. For example, it makes suggestions such as "Listen to this music when you want to relax" or "Try guided meditation to improve your concentration." Step 6: The Success Experience Design Department designs small success experiences based on the results of the analysis by the Information Analysis Department. For example, they may suggest, "Completing a simple task on Monday morning will give you a sense of accomplishment."

[0056] (Example 2) The Monday Motivator AI according to an embodiment of the present invention is a system in which a user inputs their mood, goals, and work-related anxieties into an app over the weekend, and a generation AI analyzes this information to provide personalized motivation-boosting messages, suggestions for short-term goal setting, music for relaxation and improving concentration, and meditation guides, and designs small success experiences to coincide with the start of the week.In this way, the Monday Motivator AI helps users eliminate anxiety and lack of motivation about work at the start of the week, allowing them to start Monday with energy.

[0057] The Monday Motivator AI according to the embodiment includes a user information input unit, an information analysis unit, a message generation unit, a goal setting unit, a relaxation provision unit, and a success experience design unit. The user information input unit inputs information such as their mood, goals, and anxiety about work over the weekend. For example, the user inputs specific feelings and goals such as "I'm anxious because there's a lot of work this week" or "I'm looking forward to starting a new project." The information analysis unit analyzes the user information input by the user information input unit. For example, the generation AI analyzes data based on the user's mood, goals, and anxiety. The message generation unit generates personalized messages based on the results of the analysis by the information analysis unit. For example, the generation AI generates messages such as "You can achieve great results this week!" or "Taking on a new project is hard, but I'm sure you'll succeed." The goal setting unit sets short-term goals based on the results of the analysis by the information analysis unit. For example, the generation AI suggests specific goals such as "Your goal this week is to work intensively for one hour every day" or "Complete the initial stages of a new project." The relaxation provider provides content for relaxation and improved concentration based on the results of the analysis by the information analyzer. For example, it makes suggestions such as, "Listen to this music when you want to relax" or "Try guided meditation to improve your concentration." The success experience designer designs small success experiences based on the results of the analysis by the information analyzer. For example, it makes suggestions such as, "Completing a simple task on Monday morning will give you a sense of accomplishment." In this way, the Monday Motivator AI of the embodiment helps users eliminate anxiety and lack of motivation about work at the start of the week, allowing them to start Monday with energy. For example, personalized messages and goal setting help users work with confidence, and music and guided meditations for relaxation and improved concentration help users work efficiently while reducing stress. Furthermore, small success experiences help users start the week with a positive attitude.

[0058] In the user information input unit, the generation AI provides real-time feedback on the user information entered by the user, allowing the input content to be optimized. For example, as the user enters information, the generation AI suggests grammar and expression corrections in real time. For example, if the user enters, "I'm worried because there's a lot of work this week," the generation AI will ask, "Which part of the work are you worried about specifically?" to elicit more detailed information. In addition, in the user information input unit, the generation AI checks the consistency of the input content and provides appropriate feedback. For example, if the user enters, "I'm looking forward to working on a new project," the generation AI will ask, "Which part are you looking forward to specifically?" to elicit more detailed information. In this way, by providing real-time feedback on the information entered by the user and optimizing the input content, personalized messages and goal setting can be created based on more accurate information.

[0059] The user information input unit analyzes the user's past input data, learns input trends and patterns, and predicts and suggests the next input. For example, the user information input unit displays predictive candidates based on the user's past input data the next time the user inputs something. For example, if a user previously inputs "I have a lot of meetings on Mondays," the unit predicts and displays similar content the next time. The user information input unit also uses a generation AI to analyze past input data and learn input trends and patterns. For example, if a user frequently inputs "The project is behind schedule," the generation AI learns this tendency and suggests "How is the project progressing?" the next time the user inputs something. Furthermore, the user information input unit predicts the user's input content based on past input data and provides appropriate feedback. For example, if a user inputs "What are your goals this week?", the generation AI would suggest "Complete the initial phase of the new project" based on past data. This analysis of the user's past input data and prediction and suggestions for the next input streamlines the user's input work and provides more accurate information.

[0060] The user information input unit can use the emotion estimation function to estimate the user's emotion in real time when the user inputs text and provide an input guide to elicit positive emotions. For example, when the user inputs text, the user information input unit uses the emotion estimation function to analyze the user's emotion in real time and display a guide to elicit positive emotions. For example, when the user inputs text, "I'm anxious," the emotion estimation function asks, "What exactly are you anxious about?" to elicit more details. The user information input unit also uses the emotion estimation function to analyze the user's emotion and provide appropriate feedback. For example, when the user inputs text, "I'm looking forward to working on a new project," the emotion estimation function asks, "What exactly are you looking forward to?" to elicit more details. The user information input unit also uses the emotion estimation function to analyze the user's emotion and provide a guide to elicit positive emotions. For example, when the user inputs text, "What is your goal this week?" the emotion estimation function suggests, "Completing the initial phase of the new project." This improves the user's motivation by estimating the user's emotion in real time when the user inputs text and providing an input guide to elicit positive emotions.

[0061] The user information input unit allows users to provide information using voice or image input, and can also collect data other than text. For example, the user information input unit allows users to provide information using voice input, and the generation AI analyzes the voice data and converts it into text. For example, if a user inputs "What are your goals this week?" by voice, the generation AI converts it into text and saves it. The user information input unit also allows users to provide information using image input, and the generation AI analyzes image data and converts it into text. For example, if a user uploads handwritten notes as images, the generation AI analyzes their contents and converts them into text. Furthermore, the user information input unit allows the generation AI to analyze voice data and image data and provide appropriate feedback. For example, if a user inputs "What are your goals this week?" by voice, the generation AI might suggest, "Complete the initial phase of a new project." This allows users to provide information using voice or image input, and collects data other than text, enabling personalized messages and goal setting based on a wider variety of information.

[0062] The user information input unit allows a user to share information input by the user with other users and obtain community-based feedback. The user information input unit adds a function for sharing information input by the user with other users and obtaining community-based feedback, for example. For example, when the user inputs "What are your goals for this week?", the user receives advice and encouraging comments from other users. The user information input unit also allows the generation AI to analyze the community-based feedback and provide appropriate feedback. For example, when the user inputs "I'm looking forward to working on a new project," the user receives advice and encouraging comments from other users. The user information input unit also allows the generation AI to analyze the community-based feedback and provide appropriate feedback. For example, when the user inputs "What are your goals for this week?", the user receives advice and encouraging comments from other users. This allows the user to share the information input by the user with other users and obtain community-based feedback, thereby receiving advice and encouragement from a wider variety of perspectives.

[0063] The user information input unit can use the emotion estimation function to analyze the emotions of the user when entering information and provide emotional support according to the input content. For example, the user information input unit uses the emotion estimation function to analyze the emotions of the user when entering information in real time and provide emotional support according to the input content. For example, if the user enters "I'm anxious," the unit suggests, "Try a meditation guide to help you relax." The user information input unit also uses the emotion estimation function to analyze the user's emotions and provide appropriate feedback. For example, if the user enters, "I'm looking forward to working on a new project," the unit asks, "What exactly are you looking forward to?" to elicit more details. The user information input unit also uses the emotion estimation function to analyze the user's emotions and provide emotional support. For example, if the user enters, "What is your goal this week?" the unit suggests, "Complete the initial stages of the new project." This allows the system to analyze the user's emotions when entering information and provide emotional support according to the input content, thereby enabling support that is tailored to the user's emotions.

[0064] The information analysis unit can integrate the user's input information with other data sources (e.g., social media and health data) to perform a comprehensive analysis. For example, the information analysis unit integrates the user's input information with social media data, and the generation AI performs a comprehensive analysis. For example, the information analysis unit analyzes the user's social media posts and generates personalized messages. The information analysis unit also integrates the user's health data and performs a comprehensive analysis. For example, the generation AI analyzes the user's fitness and sleep data and provides appropriate feedback. Furthermore, the information analysis unit also integrates the user's input information with other data sources to perform a comprehensive analysis. For example, if the user inputs, "What are your goals this week?", the generation AI might suggest, "Complete the initial phase of a new project" based on social media and health data. By integrating the user's input information with other data sources and performing a comprehensive analysis, optimal support can be provided to the user from a more multifaceted perspective.

[0065] The information analysis unit can use the emotion estimation function to analyze the user's emotional state and generate a personalized message based on the emotion. For example, the information analysis unit uses the emotion estimation function to analyze the user's emotional state in real time and generate a personalized message based on the emotion. For example, if the user inputs "I'm anxious," the information analysis unit generates a message such as "Try a guided meditation to relax." The information analysis unit also uses the emotion estimation function to analyze the user's emotions and provide appropriate feedback. For example, if the user inputs "I'm looking forward to working on a new project," the information analysis unit asks "What exactly are you looking forward to?" to elicit more details. The information analysis unit also uses the emotion estimation function to analyze the user's emotions and generate a personalized message based on the emotion. For example, if the user inputs "What is your goal this week?" the information analysis unit suggests "Completing the initial phase of the new project." This allows the information analysis unit to analyze the user's emotional state and generate a personalized message based on the emotion, thereby providing support that is tailored to the user's emotions.

[0066] The information analysis unit allows the generation AI to simulate different scenarios based on the user's input information and propose an optimal personalized message. For example, when the user inputs, "What are your goals this week?", the generation AI simulates multiple scenarios and proposes the optimal goal. The information analysis unit also allows the generation AI to simulate different scenarios based on the user's input information and provide appropriate feedback. For example, when the user inputs, "I'm looking forward to working on a new project," the generation AI simulates multiple scenarios and proposes the optimal goal. The information analysis unit also allows the generation AI to simulate different scenarios based on the user's input information and propose the optimal personalized message. For example, when the user inputs, "What are your goals this week?", the generation AI simulates multiple scenarios and proposes the optimal goal. In this way, by simulating different scenarios and proposing the optimal personalized message, it is possible to provide the user with an optimal action plan or relaxation method.

[0067] The information analysis unit allows the generation AI to generate personalized messages tailored to the user's lifestyle and hobbies based on the user's input information. The information analysis unit allows the generation AI to generate personalized messages tailored to the user's lifestyle based on the user's input information. For example, if the user inputs, "What are your goals this week?", the generation AI will suggest goals tailored to the user's lifestyle. The information analysis unit also allows the generation AI to generate personalized messages tailored to the user's hobbies. For example, if the user inputs, "I like music," the generation AI will suggest, "Your goal this week is to listen to music for 30 minutes every day." The information analysis unit also allows the generation AI to generate personalized messages tailored to the user's lifestyle and hobbies. For example, if the user inputs, "What are your goals this week?", the generation AI will suggest goals tailored to the user's lifestyle and hobbies. This allows the generation AI to generate personalized messages tailored to the user's lifestyle and hobbies, thereby providing more friendly support to the user.

[0068] The information analysis unit can use the emotion estimation function to monitor the user's emotional state in real time and provide a personalized message according to the emotion. For example, the information analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and provide a personalized message according to the emotion. For example, if the user inputs "I'm anxious," the information analysis unit provides a message such as "Try a guided meditation to relax." The information analysis unit also uses the emotion estimation function to analyze the user's emotions and provide appropriate feedback. For example, if the user inputs "I'm looking forward to working on a new project," the information analysis unit asks "What exactly are you looking forward to?" to elicit more details. The information analysis unit also uses the emotion estimation function to analyze the user's emotions and provide a personalized message according to the emotion. For example, if the user inputs "What is your goal this week?" the information analysis unit suggests "Completing the initial phase of the new project." This makes it possible to monitor the user's emotional state in real time and provide a personalized message according to the emotion, thereby providing support that is tailored to the user's emotions.

[0069] The goal setting unit can analyze the user's past goal achievement data and suggest achievable short-term goals. For example, if a user previously entered "Complete five tasks in one week," the goal setting unit can suggest "Complete five tasks this week as well." The goal setting unit also uses the generation AI to analyze the user's past goal achievement data and provide appropriate feedback. For example, if a user enters "I'm looking forward to working on a new project," the generation AI can ask "Which parts are you looking forward to specifically?" to elicit more details. Furthermore, the goal setting unit can suggest achievable short-term goals based on the user's past goal achievement data. For example, if the user enters "What is your goal this week?" the generation AI can suggest "Complete the initial stages of a new project." This makes it easier for users to set realistic goals by analyzing the user's past goal achievement data and suggesting achievable short-term goals.

[0070] The goal setting unit can set realistic short-term goals by referencing the user's schedule and task management data. For example, if the user's schedule states, "I have a lot of meetings," the unit might suggest, "Your goal this week is to spend one hour working intensively between meetings." The goal setting unit also analyzes the user's schedule and task management data and provides appropriate feedback. For example, if the user enters, "I'm looking forward to working on a new project," the unit might ask, "Which part specifically are you looking forward to?" to elicit more details. Furthermore, the goal setting unit sets realistic short-term goals based on the user's schedule and task management data. For example, if the user enters, "What is your goal this week?" the unit might suggest, "Complete the initial phase of the new project." This allows the user to achieve their goals more easily by setting realistic short-term goals by referencing the user's schedule and task management data.

[0071] The goal setting unit uses the emotion estimation function to suggest short-term goals that take into account the user's emotional state, thereby maintaining motivation. For example, the goal setting unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest short-term goals based on the emotion. For example, if the user inputs "I'm anxious," the goal setting unit suggests "My goal this week is to meditate every day to relax." The goal setting unit also uses the emotion estimation function to analyze the user's emotions and provide appropriate feedback. For example, if the user inputs "I'm looking forward to working on a new project," the goal setting unit asks "Which part are you looking forward to specifically?" to elicit more details. Furthermore, the goal setting unit uses the emotion estimation function to analyze the user's emotions and suggest short-term goals based on the emotions. For example, if the user inputs "What is my goal this week?" the goal setting unit suggests "Complete the initial stages of the new project." This allows the user to achieve their goals more effectively by suggesting short-term goals that take into account the user's emotional state and maintaining motivation.

[0072] The goal setting unit can generate a step-by-step guide for achieving the user's short-term goals and provide a specific action plan. For example, when the user inputs, "What are your goals this week?", the system suggests specific steps for achieving the goal. In addition, the goal setting unit uses the generation AI to analyze the user's short-term goals and provide appropriate feedback. For example, when the user inputs, "I'm looking forward to working on a new project," the system asks, "Which parts are you looking forward to specifically?" to elicit more details. In addition, the goal setting unit uses the generation AI to generate a step-by-step guide based on the user's short-term goals and provide a specific action plan. For example, when the user inputs, "What are your goals this week?", the system suggests specific steps for achieving the goal. This makes it easier for the user to achieve their goals by generating a step-by-step guide for achieving the user's short-term goals and providing a specific action plan.

[0073] The goal setting unit can analyze the user's emotional state using the emotion estimation function and suggest short-term goals according to the emotion. For example, the goal setting unit uses the emotion estimation function to analyze the user's emotional state in real time and suggest short-term goals according to the emotion. For example, if the user inputs "I'm anxious," the goal setting unit suggests "My goal this week is to meditate every day to relax." The goal setting unit also uses the emotion estimation function to analyze the user's emotions and provide appropriate feedback. For example, if the user inputs "I'm looking forward to working on a new project," the goal setting unit asks "Which part are you looking forward to specifically?" to elicit more details. The goal setting unit also uses the emotion estimation function to analyze the user's emotions and suggest short-term goals according to the emotion. For example, if the user inputs "What is your goal this week?" the goal setting unit suggests "Completing the initial stages of the new project." This allows the user to achieve their goals more effectively by analyzing the user's emotional state and suggesting short-term goals according to their emotions.

[0074] The relaxation providing unit can analyze the user's past data on relaxation and concentration improvement and suggest optimal music and guided meditations. For example, the relaxation providing unit analyzes the user's past relaxation data and suggests optimal music. For example, if a user previously entered "Relax with classical music," the unit might suggest, "Listen to classical music and relax this week." The relaxation providing unit also uses the generation AI to analyze the user's past data on concentration improvement and provide appropriate feedback. For example, if a user enters, "I'm looking forward to working on a new project," the unit might ask, "Which part specifically are you looking forward to?" to elicit more details. Furthermore, the relaxation providing unit uses the generation AI to suggest optimal music and guided meditations based on the user's past relaxation and concentration improvement data. For example, if the user enters, "What is your goal this week?" the unit might suggest, "Complete the initial stages of the new project." This allows the user to relax more effectively and improve their concentration by analyzing the user's past data on relaxation and concentration improvement and suggesting optimal music and guided meditations.

[0075] The relaxation provider can monitor the user's physiological data (e.g., heart rate and stress level) in real time and provide music and meditation guides accordingly. For example, the relaxation provider can monitor the user's heart rate in real time and provide relaxation music accordingly. For example, if the heart rate is high, it can suggest "relaxing classical music." The relaxation provider's generation AI can also analyze the user's stress level and provide appropriate feedback. For example, if the user inputs, "I'm looking forward to working on a new project," it can ask, "Which part are you looking forward to specifically?" to elicit more details. Furthermore, the relaxation provider's generation AI can provide optimal music and meditation guides based on the user's physiological data. For example, if the user inputs, "What is your goal this week?" it can suggest, "Complete the initial phase of the new project." This allows the user to relax more effectively and improve their concentration by monitoring the user's physiological data in real time and providing music and meditation guides accordingly.

[0076] The relaxation providing unit can analyze the user's emotional state using the emotion estimation function and provide content for relaxation or improving concentration according to the emotion. For example, the relaxation providing unit can use the emotion estimation function to analyze the user's emotional state in real time and provide relaxation music according to the emotion. For example, if the user inputs "I'm anxious," the relaxation providing unit can suggest "classical music for relaxation." The relaxation providing unit can also use the emotion estimation function to analyze the user's emotion and provide appropriate feedback. For example, if the user inputs "I'm looking forward to working on a new project," the relaxation providing unit can ask "Which part are you looking forward to specifically?" to elicit more details. The relaxation providing unit can also analyze the user's emotion using the emotion estimation function and provide content for relaxation or improving concentration according to the emotion. For example, if the user inputs "What is your goal this week?" the relaxation providing unit can suggest "Completing the initial stage of the new project." This allows the user to more effectively relax and improve their concentration by analyzing the user's emotional state and providing content for relaxation or improving concentration according to the emotion.

[0077] The relaxation provider can upload music and guided meditations selected by the user to the generation AI and generate personalized content based on them. For example, the relaxation provider can upload music selected by the user to the generation AI and generate personalized relaxation music based on that music. For example, when a user uploads their favorite music, the generation AI creates a relaxing playlist based on that music. The relaxation provider can also analyze the meditation guides uploaded by the user and provide appropriate feedback. For example, if the user inputs, "I'm looking forward to working on a new project," the generation AI can ask, "Which parts are you looking forward to specifically?" to elicit more details. Furthermore, the relaxation provider can generate personalized content based on the music and guided meditations uploaded by the user. For example, if the user inputs, "What is your goal this week?" the generation AI can suggest, "Complete the initial stages of the new project." This allows the user to relax more effectively and improve their concentration by generating personalized content based on the music and guided meditations selected by the user.

[0078] The relaxation providing unit allows users to share content for relaxation or improving concentration with other users and receive community-based feedback. The relaxation providing unit adds, for example, a function that allows users to share content for relaxation or improving concentration with other users and receive community-based feedback. For example, a user can share a playlist they created and receive ratings and comments from other users. The relaxation providing unit also allows the generation AI to analyze the community-based feedback and provide appropriate feedback. For example, if a user inputs, "I'm looking forward to working on a new project," the AI ​​asks, "What specifically are you looking forward to?" to elicit more details. The relaxation providing unit also allows the generation AI to analyze the community-based feedback and provide appropriate feedback. For example, if a user inputs, "What are your goals for this week?" the AI ​​receives advice and encouraging comments from other users. This allows users to share content for relaxation or improving concentration with other users and receive community-based feedback, thereby enabling them to relax more effectively and improve their concentration.

[0079] The relaxation providing unit can use the emotion estimation function to monitor the user's emotional state in real time and provide content for relaxation or improving concentration according to the emotion. For example, the relaxation providing unit can use the emotion estimation function to monitor the user's emotional state in real time and provide relaxation music according to the emotion. For example, if the user inputs "I'm anxious," the relaxation providing unit can suggest "classical music for relaxation." The relaxation providing unit can also use the emotion estimation function to analyze the user's emotion and provide appropriate feedback. For example, if the user inputs "I'm looking forward to working on a new project," the relaxation providing unit can ask "Which part are you looking forward to specifically?" to elicit more details. The relaxation providing unit can also use the emotion estimation function to analyze the user's emotion and provide content for relaxation or improving concentration according to the emotion. For example, if the user inputs "What is your goal this week?" the relaxation providing unit can suggest "Completing the initial stage of the new project." This allows the user to more effectively relax and improve their concentration by monitoring the user's emotional state in real time and providing content for relaxation or improving concentration according to the emotion.

[0080] The success experience design unit can design realistic, small success experiences by referencing the user's schedule and task management data. For example, if the user's schedule states, "I have a lot of meetings," the unit might suggest, "Your goal this week is to complete one task between meetings." The success experience design unit's generation AI also analyzes the user's schedule and task management data to provide appropriate feedback. For example, if the user enters, "I'm looking forward to working on a new project," the unit might ask, "What specifically are you looking forward to?" to elicit more details. Furthermore, the success experience design unit's generation AI designs realistic, small success experiences based on the user's schedule and task management data. For example, if the user enters, "What is your goal this week?" the unit might suggest, "Complete the initial stages of the new project." This makes it easier for users to feel a sense of accomplishment by designing realistic, small success experiences by referencing the user's schedule and task management data.

[0081] The success experience design unit uses the emotion estimation function to design small success experiences that take into account the user's emotional state, thereby maintaining motivation. For example, the success experience design unit uses the emotion estimation function to analyze the user's emotional state in real time and design small success experiences that correspond to the user's emotions. For example, if the user inputs "I'm anxious," the unit suggests, "My goal this week is to meditate every day to relax." The success experience design unit also uses the emotion estimation function to analyze the user's emotions and provide appropriate feedback. For example, if the user inputs, "I'm looking forward to working on a new project," the unit asks, "Which part are you looking forward to specifically?" to elicit more details. Furthermore, the success experience design unit uses the emotion estimation function to analyze the user's emotions and design small success experiences that correspond to the user's emotions. For example, if the user inputs, "What is your goal this week?" the unit suggests, "Completing the initial phase of the new project." This allows the unit to design small success experiences that take into account the user's emotional state and maintain motivation, making it easier for the user to feel a sense of accomplishment.

[0082] The success experience design unit allows users to share their small successes with other users and work together to achieve successes. The success experience design unit adds a function that allows users to share their small successes with other users and work together to achieve successes. For example, by entering "What are your goals this week?", users can team up with users who have the same goals and work together to achieve them. Furthermore, the success experience design unit's generation AI analyzes the user's small successes and provides appropriate feedback. For example, if a user enters "I'm looking forward to working on a new project," the AI ​​will ask "What specifically are you looking forward to?" to elicit more details. Furthermore, the success experience design unit allows users to share their small successes with other users and work together to achieve successes. For example, by entering "What are your goals this week?", users can team up with users who have the same goals and work together to achieve them. This allows users to share their small successes with other users and work together to achieve successes, making it easier for them to feel a sense of accomplishment.

[0083] The success experience design unit can generate a step-by-step guide for the user to achieve small successes and provide a specific action plan. For example, the success experience design unit generates a step-by-step guide for the user to achieve small successes and provides a specific action plan. For example, when the user inputs, "What are your goals this week?", the unit suggests specific steps for achieving the goal. In addition, the success experience design unit uses the generation AI to analyze the user's small successes and provide appropriate feedback. For example, when the user inputs, "I'm looking forward to working on a new project," the unit asks, "What specifically are you looking forward to?" to elicit more details. In addition, the success experience design unit uses the generation AI to generate a step-by-step guide based on the user's small successes and provide a specific action plan. For example, when the user inputs, "What are your goals this week?", the unit suggests specific steps for achieving the goal. This generates a step-by-step guide for the user to achieve small successes and provides a specific action plan, making it easier for the user to feel a sense of accomplishment.

[0084] The success experience design unit can use the emotion estimation function to analyze the user's emotional state and design small success experiences that correspond to the user's emotions. For example, the success experience design unit uses the emotion estimation function to analyze the user's emotional state in real time and design small success experiences that correspond to the user's emotions. For example, if the user inputs "I'm anxious," the unit suggests, "My goal this week is to meditate every day to relax." The success experience design unit also uses the emotion estimation function to analyze the user's emotions and provide appropriate feedback. For example, if the user inputs, "I'm looking forward to working on a new project," the unit asks, "What specifically are you looking forward to?" to elicit more details. The success experience design unit also uses the emotion estimation function to analyze the user's emotions and design small success experiences that correspond to the user's emotions. For example, if the user inputs, "What is your goal this week?" the unit suggests, "To complete the initial stage of the new project." In this way, by analyzing the user's emotional state and designing small success experiences that correspond to the user's emotions, the user can more easily feel a sense of accomplishment.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] In the user information input section, the generation AI provides real-time feedback on the information entered by the user, allowing the input content to be optimized. For example, if a user enters, "I'm worried because there's a lot of work this week," the generation AI will ask, "Which specific work are you worried about?" to extract more detailed information. The generation AI also checks the consistency of the input content and provides appropriate feedback. This provides real-time feedback on the information entered by the user and optimizes the input content, enabling personalized messages and goal setting based on more accurate information.

[0087] The user information input unit analyzes the user's past input data, learns input trends and patterns, and can predict and suggest the next input. For example, if a user has previously input "I have a lot of meetings on Mondays," the unit will predict and display similar content the next time. In addition, the generation AI analyzes past input data and learns input trends and patterns, displaying predictive candidates the next time the user inputs information. This makes input work more efficient for users and provides more accurate information.

[0088] The user information input unit uses the emotion estimation function to estimate the user's emotions in real time when entering information, and can provide input guidance to elicit positive emotions. For example, if the user enters "I'm anxious," the emotion estimation function will ask "What specifically are you anxious about?" to elicit more details. The emotion estimation function can also be used to analyze the user's emotions and display guidance to elicit positive emotions, thereby improving the user's motivation.

[0089] The user information input unit allows users to provide information using voice input or image input, and can also collect data other than text. For example, users can provide information using voice input, and the generation AI analyzes the voice data and converts it into text. Alternatively, users can provide information using image input, and the generation AI analyzes the image data and converts it into text. This makes it possible to create personalized messages and set goals based on a wider variety of information.

[0090] The user information input unit allows users to share information they have entered with other users and receive community-based feedback. For example, users can share information they have entered with other users and receive advice and encouraging comments from other users. In addition, the generation AI analyzes the community-based feedback and provides appropriate feedback, allowing users to receive advice and encouragement from a wider variety of perspectives.

[0091] The information analysis unit can integrate the user's input information with other data sources (e.g., social media and health data) and perform comprehensive analysis. For example, it can analyze the user's social media posts and generate personalized messages. In addition, the generation AI can integrate the user's health data and perform comprehensive analysis, allowing it to provide optimal support to the user from a more multifaceted perspective.

[0092] The information analysis unit uses the emotion estimation function to analyze the user's emotional state and generate personalized messages based on the emotion. For example, if the user enters "I'm anxious," the system generates the message "Try guided meditation to help you relax." Furthermore, by analyzing the user's emotions using the emotion estimation function and providing appropriate feedback, the system can provide support tailored to the user's emotions.

[0093] The information analysis unit allows the generation AI to simulate different scenarios based on the user's input information and propose the optimal personalized message. For example, if a user inputs "What are your goals for this week?", the generation AI will simulate multiple scenarios and propose the optimal goal. This allows the system to simulate different scenarios and propose the optimal personalized message, thereby providing the user with the optimal action plan or relaxation method.

[0094] The information analysis unit uses the emotion estimation function to monitor the user's emotional state in real time and provide personalized messages based on the emotion. For example, if the user enters "I'm anxious," the system will provide a message such as "Try a guided meditation to help you relax." Furthermore, by analyzing the user's emotions using the emotion estimation function and providing appropriate feedback, it is possible to provide support that is sensitive to the user's emotions.

[0095] The goal setting unit uses the emotion estimation function to suggest short-term goals that take the user's emotional state into account, helping to maintain motivation. For example, if the user inputs "I'm anxious," the unit will suggest, "Your goal this week is to meditate every day to relax." The emotion estimation function can also be used to analyze the user's emotions and provide appropriate feedback, helping the user achieve their goals more effectively.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: In the user information input section, the user inputs information such as their mood, goals, and anxiety about work over the weekend. For example, the user inputs specific feelings and goals such as "I'm anxious because there's a lot of work this week" or "I'm looking forward to working on a new project." Step 2: The information analysis unit analyzes the user information entered by the user information input unit. For example, the generation AI analyzes the data based on the user's mood, goals, and anxieties. Step 3: The message generator generates a personalized message based on the results of the analysis by the information analyzer. For example, the generator might generate messages such as, "You can achieve great results this week!" or "Taking on a new project is hard, but I'm sure you'll succeed." Step 4: The goal setting department sets short-term goals based on the results of the analysis by the information analysis department. For example, they suggest specific goals such as "This week's goal is to do one hour of focused work every day" or "Complete the initial phase of a new project." Step 5: The relaxation provider provides content for relaxation and improving concentration based on the results of the analysis by the information analyzer. For example, it makes suggestions such as "Listen to this music when you want to relax" or "Try guided meditation to improve your concentration." Step 6: The Success Experience Design Department designs small success experiences based on the results of the analysis by the Information Analysis Department. For example, they may suggest, "Completing a simple task on Monday morning will give you a sense of accomplishment."

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information input unit for inputting user information; an information analysis unit that analyzes the user information input by the user information input unit; a message generation unit that generates a personalized message based on the analysis result by the information analysis unit; a goal setting unit that sets short-term goals based on the results of the analysis by the information analysis unit; a relaxation providing unit that provides content for relaxation and improving concentration based on the results of the analysis by the information analyzing unit; and a successful experience design unit that designs a successful experience based on the results of the analysis by the information analysis unit. A system characterized by:

2. The user information input unit Allow users to provide information using voice or visual input, and collect non-textual data The system of claim 1 .

3. The information analysis unit Based on the user's input information, the generation AI refers to the user's past behavioral history and performance data to perform more accurate personalization. The system of claim 1 .

4. The goal setting unit Analyzes the user's past goal achievement data and suggests achievable short-term goals The system of claim 1 .

5. The relaxation providing unit Analyzes the user's past relaxation and concentration data to suggest optimal music and meditation guides The system of claim 1 .

6. The success experience design department Analyze the user's past success experience data and design the optimal small success experience. The system of claim 1 .

7. The user information input unit The emotion estimation function estimates the user's emotions in real time when they input text, and provides input guidance to elicit positive emotions. The system of claim 1 .

Images