System

Abstract

A system is provided.SOLUTION: Means for setting a goal by a user and storing the goal information in a database, means for collecting action data of the user and generating specific advice for achieving the goal by using an AI model, means for transmitting the generated advice to a terminal and displaying the advice so that the user can receive the advice, means for reporting daily progress by the user and recording the information in the database, and means for inputting feedback on the provided advice or content by the user; The system includes a means for storing the feedback information in a database, a means for generating further improved advice using a AI model based on the feedback information, and a means for selecting learning content based on the user's interest and behavior and providing the learning content to the user.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] In recent years, there are many tools available to support goal achievement and self-development. However, these tools generally only provide uniform advice, making it difficult to provide personalized support to each user. It is also difficult to provide dynamic feedback and advice based on a user's progress and reports. This often leads to a decline in user motivation, resulting in insufficient persistence and results until the goal is achieved. The present invention aims to solve these issues by utilizing AI to provide personalized advice and content to each user and continuously improve them. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to set goals and store the goal information in a database. It also includes a means for collecting user behavioral data and using an AI model to generate specific advice for achieving the goals, and provides a means for transmitting the generated advice to a terminal and displaying it for the user to receive. It also includes a means for the user to report daily progress and record that information in a database. It also includes a means for the user to input feedback on the advice and content provided and store that feedback information in a database. It provides a means for generating further improved advice using an AI model based on the feedback information. The system also includes a means for selecting learning content based on the user's interests and behavioral data and providing it to the user. This allows the user to receive personalized support and effectively achieve their goals.

[0006] "User" refers to an individual who uses the System to set goals and develop themselves.

[0007] "Goal" refers to a specific purpose or plan that a user aims to achieve.

[0008] "Database" refers to the information storage system for managing and storing user information, goals, behavioral data, feedback, etc. within the system.

[0009] "AI model" refers to algorithms and machine learning systems that use artificial intelligence to analyze data and generate personalized advice and plans for users.

[0010] "Advice" refers to specific measures or suggestions for achieving the goals set by the user.

[0011] "Terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access the System.

[0012] "Behavioral Data" refers to information entered and reported by users about their daily activities and progress toward achieving goals.

[0013] "Feedback" refers to the user's evaluation or opinion of the advice or content provided.

[0014] "Learning Content" refers to educational materials and information (e.g., articles, videos, audio guides, etc.) provided to users to help them achieve their goals or develop themselves. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention relates to a self-development coaching system that helps users set goals and support their achievement. This system operates primarily on a server, a terminal, and the user, and utilizes AI to provide personalized advice and learning content. The configuration and specific operation of this system are described below.

[0037] User Registration and Login

[0038] First, the user installs the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message confirming registration.

[0039] Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against a database. If authentication is successful, the server generates an authentication token and sends it to the user.

[0040] goal setting

[0041] The user inputs a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also inputs a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database.

[0042] The server uses the AI ​​model to generate a specific plan for achieving the goal based on the saved goal information. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0043] Daily progress reports

[0044] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database.

[0045] The server periodically analyzes the saved activity information and generates advice for the next step using an AI model. The advice generated might be, for example, "Increase your walking time a little" or "Try a new route next time." The server then sends the advice to the device, which then displays it to the user.

[0046] Providing Feedback

[0047] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device then sends the feedback information to the server, which stores it in a database.

[0048] The server uses the updated feedback information to further improve the AI ​​model and generate new advice optimized for the user. In this way, the server continues to provide personalized assistance according to the user's progress.

[0049] Providing learning content

[0050] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the device and displayed to the user.

[0051] This allows the system to effectively support users throughout the entire process of achieving their goals and promote continuous self-improvement.

[0052] The processing flow will be explained below.

[0053] User Registration and Login

[0054] Step 1:

[0055] The user launches the application, goes to the registration screen, enters their name, email address, and password, and presses the submit button.

[0056] Step 2:

[0057] The terminal transmits the entered registration information to the server.

[0058] Step 3:

[0059] The server stores the received registration information in a database and sends a success message to the terminal.

[0060] Step 4:

[0061] The user is taken to the login screen and logs in by entering their email address and password.

[0062] Step 5:

[0063] The terminal sends the login information to the server.

[0064] Step 6:

[0065] The server compares the entered information with the database, and if authentication is successful, it generates an authentication token and sends it to the terminal along with an authentication success message.If authentication fails, it sends an error message to the terminal.

[0066] goal setting

[0067] Step 1:

[0068] The user enters a new goal (e.g., "lose 5 kg in one month") on the goal setting screen, sets a deadline and specific measures, and presses the submit button.

[0069] Step 2:

[0070] The terminal transmits the target information to the server.

[0071] Step 3:

[0072] The server stores the received target information in a database.

[0073] Step 4:

[0074] The server passes the saved goal information to the AI ​​model, which generates a specific plan for achieving the goal.

[0075] Step 5:

[0076] The server stores the plan generated by the AI ​​model (e.g., "walk 30 minutes every day") in a database and sends it to the device.

[0077] Step 6:

[0078] The terminal displays the received plan to the user.

[0079] Daily progress reports

[0080] Step 1:

[0081] The user enters their daily activity (e.g., "I walked for 30 minutes today") on the report screen and presses the send button.

[0082] Step 2:

[0083] The terminal transmits activity information to the server.

[0084] Step 3:

[0085] The server stores the received activity information in a database.

[0086] Step 4:

[0087] The server passes the saved activity information to an AI model to generate advice for next steps.

[0088] Step 5:

[0089] The server stores the generated advice (e.g., "increase your walking time a little" or "try a new route next time") in a database and sends it to the device.

[0090] Step 6:

[0091] The terminal displays the received advice to the user.

[0092] Providing Feedback

[0093] Step 1:

[0094] The user enters feedback about the advice and learning content provided on the feedback input screen and presses the send button.

[0095] Step 2:

[0096] The terminal transmits the feedback information to the server.

[0097] Step 3:

[0098] The server stores the received feedback in a database.

[0099] Step 4:

[0100] The server passes the stored feedback information to the AI ​​model to generate further improvement advice.

[0101] Step 5:

[0102] The server stores the generated new advice in a database and sends it to the terminal.

[0103] Step 6:

[0104] The terminal displays the received new advice to the user.

[0105] Providing learning content

[0106] Step 1:

[0107] The server passes the user's behavioral data and feedback information to the AI ​​model, which selects the most appropriate learning content.

[0108] Step 2:

[0109] The server stores the selected learning content (e.g., articles, videos, audio guides, etc.) in a database and sends it to the terminal.

[0110] Step 3:

[0111] The terminal displays the received learning content to the user.

[0112] Through these steps, the system provides users with personalized self-improvement support and effectively promotes goal achievement.

[0113] Example 1

[0114] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0115] Conventional self-improvement support systems have had the challenge of providing personalized support for individual users' goals and progress. Furthermore, they lacked the ability to optimize based on user feedback or provide continuous advice according to progress. This made it difficult for users to maintain their motivation to achieve their goals.

[0116] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0117] In this invention, the server includes means for allowing a user to set a goal and storing the goal information in a database, means for collecting user behavioral data and generating specific advice for achieving the goal using a generative artificial intelligence model, means for transmitting the generated advice to a terminal and displaying it so that the user can receive it, means for the user to report daily progress and record the information in the database, means for the user to input feedback on the advice and content provided and storing the feedback information in the database, means for generating further improved advice using the generative artificial intelligence model based on the feedback information, means for selecting learning content based on the user's interests and behavioral data and providing it to the user, and means for creating input prompts for the generative artificial intelligence model. This allows users to receive support optimized for their individual goals, making it easier for them to maintain their motivation to achieve their goals.

[0118] "User" refers to a person who uses this system to set goals and aim to achieve them.

[0119] "Goal information" refers to the specific goal set by the user, as well as the detailed plan and deadline for achieving it.

[0120] "Database" refers to an information management system for storing user goal information, behavioral data, feedback information, and the like.

[0121] "Behavioral Data" refers to data collected by users reporting their daily activities and progress.

[0122] "Generative artificial intelligence model" refers to AI technology used to help users achieve their goals, specifically models for generating text and plans.

[0123] "Advice" refers to specific guidelines for action to achieve a user's goals, generated using a generative artificial intelligence model.

[0124] "Terminal" refers to a device (such as a smartphone or PC) that a user uses to use an application.

[0125] "Progress reporting" refers to a user recording their daily activities and achievements and sending them to the system.

[0126] "Feedback information" refers to evaluations and opinions entered by users regarding the advice and content provided.

[0127] "Learning Content" refers to educational materials and information provided to help users achieve their goals and develop themselves.

[0128] An "input prompt" is an instruction entered into a generative artificial intelligence model, and refers to the text used to generate specific advice or plans.

[0129] MODE FOR CARRYING OUT THE INVENTION

[0130] The present invention relates to a self-improvement coaching system for supporting a user in setting and achieving a goal. Specific embodiments of the system will be described below.

[0131] User Registration and Login

[0132] First, a user installs the application using a device such as a smartphone or PC. After installation, the user creates an account by entering their name, email address, and password on the account creation screen. The device sends this user information to the server, which then stores the received information in a database. Once registration is complete, the server sends a registration completion message to the device, which the device displays to the user.

[0133] Next, the user enters their email address and password on the login screen to log in. The device sends this information to the server, which then authenticates them by checking it against the user information in its database. If authentication is successful, the server generates an authentication token and sends it to the device. The device receives the authentication token and uses it to maintain the user's logged-in state.

[0134] goal setting

[0135] The user inputs a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." They also input the deadline for the goal and an implementation plan. The device sends this information to the server, which then stores the goal information in a database.

[0136] Based on the saved goal information, the server uses a generative artificial intelligence model (e.g., GPT-4) to generate a specific action plan for achieving the goal. A specific prompt could be, "Generate a specific action plan for the user to lose 5 kg in one month." The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0137] Daily progress reports

[0138] The user reports their daily progress on the activity input screen. For example, they enter information such as "I walked for 30 minutes today" and press the send button. The device sends this activity information to the server, which then stores the received information in a database.

[0139] The server periodically analyzes the stored activity information and generates advice for the next step using a generative artificial intelligence model. A specific prompt could be, "Generate advice for the next step based on the user's activity information." The generated advice could be, for example, "Increase your walking time a little" or "Try a new route next time." The server sends this advice to the device, which then displays it to the user.

[0140] Providing Feedback

[0141] The user provides feedback on the advice and learning content provided. They enter their evaluation and opinion on the feedback input screen and press the send button. The device then sends the feedback information to the server, which then stores the received information in a database.

[0142] The server uses the feedback information to further improve the generative artificial intelligence model and generate new advice. A specific prompt could be, "Based on the user's feedback information, provide optimized advice for the next step." The server sends this new advice to the device, which then displays it to the user.

[0143] Providing learning content

[0144] The server uses a generative artificial intelligence model to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. A specific prompt could be, "Provide appropriate learning content based on the user's behavioral data and feedback." The generated learning content is sent from the server to the device and can be used by the user.

[0145] conclusion

[0146] This system effectively supports users through the entire process from goal setting to goal achievement, and can promote continuous self-improvement. The present invention provides personalized support for the individual needs of each user, helping them maintain motivation to achieve their goals.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Step 1: User Registration

[0149] Input: The user installs the application and enters their name, email address, and password.

[0150] Operation: The terminal receives input information and sends it to the server.

[0151] Data processing: The server processes the received information and stores the user information in a database.

[0152] Output: The server sends a registration completion message to the terminal, which displays it to the user.

[0153] Step 2: User Login

[0154] Input: The user enters their email address and password on the login screen.

[0155] Operation: The terminal receives input information and sends it to the server.

[0156] Data calculation: The server checks the user information against the database and performs authentication.

[0157] Output: The server generates an authentication token and sends it to the device. The device receives the authentication token and the user is logged in.

[0158] Step 3: Goal Setting

[0159] Input: The user inputs a new goal, deadline, and action plan on the goal setting screen.

[0160] Operation: The terminal receives target information and sends it to the server.

[0161] Data storage: The server stores the target information in a database.

[0162] Output: A message that the save is complete is sent to the terminal, which displays it to the user.

[0163] Step 4: Generate a goal achievement plan

[0164] Input: The server retrieves the goal information stored in the database and inputs a prompt to the generative AI model. Example: "Generate a specific action plan for the user to lose 5 kg in one month."

[0165] How it works: The generative AI model performs data calculations based on the input prompt sentence and generates a specific action plan.

[0166] Output: The server receives the generated plan and sends it to the device. The device displays the plan to the user. Examples: "Walk 30 minutes every day" or "Go to the gym three times a week."

[0167] Step 5: Daily progress reports

[0168] Input: The user enters their daily activities in the activity input screen. Example: "I walked for 30 minutes today."

[0169] Operation: The terminal receives input information and sends it to the server.

[0170] Data storage: The server stores the received activity information in a database.

[0171] Output: A save confirmation message is sent to the terminal, which displays it to the user.

[0172] Step 6: Providing advice

[0173] Input: The server retrieves the activity information stored in the database and inputs a prompt to the generative AI model. Example: "Based on the user's activity information, generate advice for the next step."

[0174] How it works: The generative AI model performs data calculations based on the input prompt and generates specific advice.

[0175] Output: The server receives the generated advice and sends it to the device. The device displays the advice to the user. Examples: "Try to walk a little longer" or "Try a new route next time."

[0176] Step 7: Provide feedback

[0177] Input: Users enter feedback on the advice and content provided.

[0178] Operation: The terminal receives input information and sends it to the server.

[0179] Data storage: The server stores the received feedback information in a database.

[0180] Output: A save confirmation message is sent to the terminal, which displays it to the user.

[0181] Step 8: Optimizing Advice

[0182] Input: The server retrieves the feedback information stored in the database and inputs a prompt to the generative AI model. For example, "Based on the user's feedback information, provide optimized advice for the next step."

[0183] How it works: The generative AI model performs data calculations based on the input prompt sentence and generates new advice.

[0184] Output: The server receives the generated new advice and sends it to the terminal, which displays the new advice to the user.

[0185] Step 9: Provide learning content

[0186] Input: The server inputs a prompt to the generative AI model based on the user's behavioral data and feedback. Example: "Provide appropriate learning content based on the user's behavioral data and feedback."

[0187] How it works: The generative AI model performs data calculations based on the input prompt and selects learning content.

[0188] Output: The server receives the generated learning content and sends it to the device. The device displays the learning content to the user. Examples: "Effective meal menu for users with weight loss goals," "Exercise video."

[0189] (Application example 1)

[0190] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0191] Conventional self-improvement coaching systems do not provide sufficient specific and personalized support for the goals set by users. Furthermore, it is difficult for physical facilities such as fitness gyms to provide appropriate advice to individual users in a timely manner. Furthermore, it is not possible to dynamically update content and advice based on progress, which makes it difficult to effectively support users in maintaining their motivation and achieving their goals.

[0192] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0193] In this invention, the server includes a means for allowing a user to set a goal and storing the goal information in a database, a means for collecting user behavior data and generating specific advice for achieving the goal using a generative AI model, and a means for transmitting the generated advice to a terminal and displaying it so that the user can receive it. This makes it possible to provide training advice and learning content personalized to each user and effectively support the user in achieving their goal.

[0194] "User" refers to an individual who uses the system to set goals and receive support in achieving them.

[0195] "Goal setting" refers to the act of a user determining a specific goal they want to achieve and inputting that information.

[0196] "Database" refers to a system that stores and manages goal information, behavioral data, and feedback information.

[0197] "Behavioral Data" refers to information recorded by a user's daily progress and activities.

[0198] A "generative AI model" refers to a system that uses artificial intelligence to generate specific advice and plans to help users achieve their goals.

[0199] "Advice" refers to specific guidelines or suggestions generated by an AI model to achieve a goal.

[0200] "Terminal" refers to a device such as a smartphone or tablet that a user uses to access an application.

[0201] "Progress reporting" refers to the act of a user entering their daily activities and results into the system and providing that information.

[0202] "Feedback" refers to the act of a user inputting an evaluation or opinion regarding the advice or content provided.

[0203] "Training Advice" refers to specific guidelines for fitness and exercise.

[0204] "Learning Content" refers to educational materials and information provided to help users achieve their goals.

[0205] "Dynamic updating" refers to the process of continually changing the advice and content provided based on the user's progress and behavioral data.

[0206] "Training goal" refers to a specific fitness or exercise goal that a user aims to achieve.

[0207] "Recording" refers to the act of a user entering goals and progress into the system and saving that information.

[0208] "Personalization" refers to the process of optimizing the content and advice provided to users based on their individual needs and behavior.

[0209] This invention is a self-improvement coaching system that helps users set goals and support their achievement. This system operates primarily on a server, a terminal, and the user, and utilizes AI to provide personalized advice and learning content. The specific process for implementing this system is described below.

[0210] User Registration and Login

[0211] First, a user installs an application on a device such as a smartphone or tablet and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server, which then stores the received registration information in a database. Next, the user logs in by entering their email address and password on the login screen. The server authenticates the user by checking the database, and if authentication is successful, generates an authentication token and sends it to the user.

[0212] goal setting

[0213] The user enters a new goal on the application's goal setting screen. For example, they can set a specific goal such as "gain 5 kg of muscle mass in three months." They also enter a deadline and a specific plan. The device sends this goal information to the server, which stores it in a database. The server then uses a generative AI model to generate a specific plan for achieving the goal based on the saved goal information. This plan includes detailed action steps such as gym training three times a week and daily protein intake. The server then sends this plan to the device and displays it to the user.

[0214] Daily progress reports

[0215] Users report their daily training and activities on the activity input screen. For example, if they did 30 minutes of strength training today, they enter that information and press the send button. The device then sends the activity information to the server, which stores it in a database.

[0216] Providing Feedback

[0217] The user provides feedback on the generated advice and learning content. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. Based on the updated feedback information, the server uses the generative AI model to make further improvements and generate new, personalized advice.

[0218] Providing learning content

[0219] The server uses a generative AI model to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective training videos and nutritional advice to a user with training goals. The generated learning content is sent from the server to the device and displayed to the user.

[0220] This allows the system to effectively support the user throughout the entire process of achieving their goal and promote continuous self-improvement. For example, a user can set a training goal of "gaining 5 kg of muscle mass in three months" and include a detailed plan that includes three gym sessions per week and daily protein intake. Furthermore, in the daily activity report, the user can enter "I did 30 minutes of strength training today" and receive advice on their next training session. An example of a prompt to input to the generative AI model might be, "Set a goal of gaining 5 kg of muscle mass in three months. Plan to train at the gym three times per week and consume protein daily."

[0221] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0222] Step 1:

[0223] User Registration and Login

[0224] Input: The user enters their name, email address, and password into the device.

[0225] Operation: The device sends this registration information to the server. The server stores the received registration information in a database and sends the user a message confirming registration. The user then logs in by entering their email address and password. The server authenticates the user against the database, and if authentication is successful, generates an authentication token and sends it to the user.

[0226] Output: An authentication token is sent to the user.

[0227] Step 2:

[0228] goal setting

[0229] Input: The user enters a new goal (e.g., gain 5 kg of muscle mass in 3 months) on the goal setting screen, along with the deadline and specific plan (training at the gym three times a week, consuming protein daily).

[0230] How it works: The device sends these goal information to a server, which stores the goal information in a database and uses a generative AI model to generate a specific plan for achieving the goal.

[0231] Output: The generated plan (detailed action steps) is displayed on the terminal.

[0232] Step 3:

[0233] Daily progress reports

[0234] Input: The user enters their daily training progress in the activity input screen (e.g., I did 30 minutes of strength training today).

[0235] How it works: Your device sends activity information to a server, which stores it in a database and uses AI models to suggest next steps.

[0236] Output: Next training advice will be displayed on the device.

[0237] Step 4:

[0238] Providing Feedback

[0239] Input: The user enters their evaluation and opinion on the provided advice or content in the feedback input screen.

[0240] How it works: The device sends feedback information to the server, which stores it in a database and uses the generative AI model for further refinement.

[0241] Output: The new and improved advice is displayed on the terminal.

[0242] Step 5:

[0243] Providing learning content

[0244] Input: The server selects learning content based on user behavioral data and feedback.

[0245] How it works: The server uses a generative AI model to select learning content (e.g., training videos or nutritional advice) appropriate for the user.

[0246] Output: The selected learning content will be displayed on the device.

[0247] At each step, the specific actions are as follows:

[0248] Database operations (save, match, update)

[0249] Use of generative AI models (advice generation, plan generation)

[0250] User interface operation (input screen / display screen)

[0251] For example, an example of an input prompt for the generative AI model would be, "Set a goal of gaining 5 kg of muscle mass in three months. Plan to train at the gym three times a week and consume protein daily," which would result in personalized advice.

[0252] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0253] This invention relates to a self-improvement coaching system that helps users set goals and achieve them. The system includes a server, a terminal, a user, and an emotion engine. Utilizing AI technology, the system not only provides users with personalized advice and learning content, but also recognizes the user's emotions and provides further optimized support based on those emotions. The configuration and specific operation of the system are described below.

[0254] User Registration and Login

[0255] First, the user launches the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message confirming registration.

[0256] Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against a database. If authentication is successful, the server generates an authentication token and sends it to the user.

[0257] goal setting

[0258] The user enters a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also enters a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model to generate a specific plan for achieving the goal. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0259] Daily progress reports and emotion recognition

[0260] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database.

[0261] The server periodically analyzes the saved activity information and uses the AI ​​model to generate advice for the next step. In addition, an emotion engine collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. The generated advice might be, for example, "increase your walking time a little" or "try a new route next time." The server then sends the advice to the device, which displays it to the user.

[0262] Feedback provision and sentiment analysis

[0263] Users provide feedback on the advice and learning content provided. They enter their evaluation and opinion on the feedback input screen and press the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data generated at the time.

[0264] The server passes the saved feedback information and emotion data to the AI ​​model for further refinement and generates new advice optimized for the user. The server then sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized assistance according to the user's progress.

[0265] Providing learning content

[0266] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the device and displayed to the user.

[0267] This allows the system to effectively support the entire process of users achieving their goals and promote continuous self-improvement.The introduction of an emotion engine allows the system to provide advice and content optimized for the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[0268] The processing flow will be explained below.

[0269] User Registration and Login

[0270] Step 1:

[0271] The user launches the application, goes to the registration screen, enters their name, email address, and password, and presses the submit button.

[0272] Step 2:

[0273] The terminal transmits the entered registration information to the server.

[0274] Step 3:

[0275] The server stores the received registration information in a database and sends a success message to the terminal.

[0276] Step 4:

[0277] The user is taken to the login screen and logs in by entering their email address and password.

[0278] Step 5:

[0279] The terminal sends the login information to the server.

[0280] Step 6:

[0281] The server compares the entered information with the database, and if authentication is successful, it generates an authentication token and sends it to the terminal along with an authentication success message.If authentication fails, it sends an error message to the terminal.

[0282] goal setting

[0283] Step 1:

[0284] The user enters a new goal (e.g., "lose 5 kg in one month") on the goal setting screen, sets a deadline and specific measures, and presses the submit button.

[0285] Step 2:

[0286] The terminal transmits the target information to the server.

[0287] Step 3:

[0288] The server stores the received target information in a database.

[0289] Step 4:

[0290] The server passes the saved goal information to the AI ​​model, which generates a specific plan for achieving the goal.

[0291] Step 5:

[0292] The server stores the plan generated by the AI ​​model (e.g., "walk 30 minutes every day") in a database and sends it to the device.

[0293] Step 6:

[0294] The terminal displays the received plan to the user.

[0295] Daily progress reports and emotion recognition

[0296] Step 1:

[0297] The user enters their daily activity (e.g., "I walked for 30 minutes today") on the report screen and presses the send button along with emotional data (e.g., facial recognition or an emotional icon selected by the user).

[0298] Step 2:

[0299] The terminal transmits the activity information and emotion data to the server.

[0300] Step 3:

[0301] The server stores the received activity information and emotion data in a database.

[0302] Step 4:

[0303] The server passes the stored activity information and emotion data to the AI ​​model to generate advice for next steps.

[0304] Step 5:

[0305] The server stores the generated advice (e.g., "increase your walking time a little" or "try a new route next time") in a database and sends it to the device.

[0306] Step 6:

[0307] The terminal displays the received advice to the user.

[0308] Feedback provision and sentiment analysis

[0309] Step 1:

[0310] The user inputs feedback about the advice or learning content provided on the feedback input screen, includes emotional data (e.g., an emotional icon or text input when giving feedback), and presses the send button.

[0311] Step 2:

[0312] The terminal transmits the feedback information and emotion data to the server.

[0313] Step 3:

[0314] The server stores the received feedback information and emotion data in a database.

[0315] Step 4:

[0316] The server passes the stored feedback information and emotion data to the AI ​​model to generate further improvement advice.

[0317] Step 5:

[0318] The server stores the generated new advice in a database and sends it to the terminal.

[0319] Step 6:

[0320] The terminal displays the received new advice to the user.

[0321] Providing learning content

[0322] Step 1:

[0323] The server passes the user's behavioral and emotional data to the AI ​​model, which then selects the most appropriate learning content.

[0324] Step 2:

[0325] The server stores the selected learning content (e.g., articles, videos, audio guides, etc.) in a database and sends it to the terminal.

[0326] Step 3:

[0327] The terminal displays the received learning content to the user.

[0328] Through these steps, the system can provide users with personalized self-improvement support, providing advice and content optimized for their emotional state, thereby increasing their motivation and improving their success rate in achieving their goals.

[0329] Example 2

[0330] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0331] In modern society, it is important for individuals to work efficiently and effectively toward their set goals. However, conventional systems have had difficulty providing personalized advice that fully takes into account the user's behavioral history and emotional state. Furthermore, the learning content provided is often uniform and fails to fully meet the needs of individual users. This can lead to a decline in users' motivation to achieve their goals, ultimately making it difficult for them to achieve them at all.

[0332] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0333] In this invention, the server includes: means for allowing a user to set a goal and store the goal information in a database; means for collecting user behavior data and generating specific advice for achieving the goal using an AI model; means for transmitting the generated advice to a terminal and displaying it so that the user can receive it; means for the user to report daily progress and record the information in a database; means for the user to input feedback on the advice and content provided and storing the feedback information in a database; means for generating further improved advice using an AI model based on the feedback information; means for using an emotion engine that analyzes user emotion data and optimizes advice based on the results; and means for selecting learning content based on the user's interests and behavior data and providing it to the user. This enables personalized support according to the user's individual situation and emotions, maintaining user motivation and enabling more effective goal achievement.

[0334] "Goal information" refers to the content of the goal set by the user, as well as the deadline and specific plan for achieving it.

[0335] A "database" is an information system for managing and storing user goal information, behavioral data, progress data, feedback information, etc.

[0336] "Behavioral Data" refers to the history of activities and actions reported by users on a daily basis.

[0337] An "AI model" is an algorithm that uses artificial intelligence technology to generate specific advice and plans for achieving goals based on user behavioral data and goal information.

[0338] "Advice" refers to specific guidelines or suggestions for action that a user needs to take to achieve their goal.

[0339] "Terminal" refers to an electronic device that a user operates and displays to use the system.

[0340] "Feedback information" is information obtained by users inputting their evaluations and opinions regarding the advice and content provided.

[0341] The "emotion engine" is a system that collects and analyzes users' emotional data and optimizes advice based on the results.

[0342] "Learning Content" refers to educational materials and content that help users achieve their goals.

[0343] "Optimization" is the process of providing advice and learning content to users in the most optimal way based on their behavioral data, feedback information, and emotional data.

[0344] The present invention relates to a self-improvement coaching system that helps users set goals and achieve them. The system includes a server, a terminal, a user, and an emotion engine. Utilizing AI technology, the system not only provides users with personalized advice and learning content, but also recognizes the user's emotions and provides further optimized support based on those emotions.

[0345] User Registration and Login

[0346] First, the user launches the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message notifying them of successful registration. Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against the database. If authentication is successful, the server generates an authentication token and sends it to the user.

[0347] goal setting

[0348] The user enters a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also enters a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model to generate a specific plan for achieving the goal. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0349] Daily progress reports and emotion recognition

[0350] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device sends the activity information to the server, which stores it in a database. The server periodically analyzes the saved activity information and uses an AI model to generate advice for the next step. Furthermore, an emotion engine collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. The generated advice might be, for example, "increase your walking time a little" or "try a new route next time." The server sends the advice to the device, which displays it to the user.

[0351] Feedback provision and sentiment analysis

[0352] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data at the time. The server passes the stored feedback information and emotional data to the AI ​​model for further improvements and generates new advice optimized for the user. The server sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized assistance according to the user's progress.

[0353] Providing learning content

[0354] Based on the user's behavioral data and feedback, the server uses AI to select learning content appropriate for the user. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the user's device and displayed to the user. This allows the system to effectively support the entire process of users achieving their goals and promote continuous self-improvement. The introduction of an emotion engine allows the system to provide optimized advice and content that takes into account the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[0355] Specific examples

[0356] For example, if a user sets a goal of "losing 5 kg in one month," the server will generate a specific plan such as "walking for 30 minutes every day" and "going to the gym three times a week." Furthermore, based on the emotional data when the user reports that they "walked for 30 minutes," the server will provide advice such as "take a 10-minute break next time." In this way, by incorporating user feedback and constantly optimizing advice and learning content, users can steadily progress toward their goals.

[0357] Prompt Sentence Examples

[0358] "Please tell me a concrete action plan to lose 5 kg in one month."

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1: The user launches the application, enters their name, email address, and password on the account creation screen, and presses the register button. The device sends this information to the server. The server stores the received information in a database and sends a message to the device indicating that registration is complete. The input is the user's registration information, and the output is a message indicating that registration is complete. Specifically, the device sends the data in the input form to the server as an HTTP request, and the server executes an SQL query in the database to save the information.

[0361] Step 2: The user enters their email address and password on the login screen and presses the login button. The device sends the login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server generates an authentication token and sends it to the device. The input is the user's login information and the output is the authentication token. Specifically, the device sends the input data to the server via an HTTP request, and the server uses an SQL query to search and check the database and generate the authentication token.

[0362] Step 3: The user enters a new goal (for example, "lose 5 kg in one month") on the goal setting screen, along with a deadline and implementation plan. The device sends this goal information to the server. The server stores the goal information in a database and uses an AI model to generate a plan for achieving the goal. The generated plan is sent to the device and displayed to the user. The input is the user's goal information, and the output is a specific action plan. Specifically, the device sends the goal information to the server, and the server runs the AI ​​model to generate a plan and saves it in the database.

[0363] Step 4: The user enters their daily activity (e.g., "Walked for 30 minutes") on the activity input screen and presses the send button. The device sends the activity information to the server. The server stores the information in a database and periodically analyzes it. The emotion engine collects the user's emotion data, and the AI ​​model generates optimized advice based on that. The generated advice is sent to the device and displayed to the user. The input is the user's activity information and emotion data, and the output is optimized advice. Specifically, the device sends the activity data, and the server analyzes the data using multiple algorithms and generates the results.

[0364] Step 5: The user enters their evaluation and opinion on the provided advice and learning content on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects emotional data at the time of feedback. The server uses this information to make further improvements to the AI ​​model and generate new advice. The generated advice is sent to the device and displayed to the user. The input is feedback information and emotional data, and the output is improved advice. Specifically, the device sends the feedback data, and the server stores it in a database and analyzes it to generate new advice.

[0365] Step 6: The server uses an AI model to select appropriate learning content based on the user's behavioral data and feedback. The selected learning content is sent to the device and displayed to the user. The input is behavioral data and feedback information, and the output is learning content. Specifically, the server analyzes the behavioral data and feedback, selects the most appropriate learning content using an AI model, and sends it to the device.

[0366] (Application example 2)

[0367] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0368] Today, many users need effective support for self-improvement and goal achievement. However, conventional coaching systems and fitness applications only provide general advice and lack personalized support based on individual users' behavioral data and emotions. This makes it difficult for users to maintain their motivation and achieve their goals. Furthermore, the lack of optimization of advice that takes into account changes in the user's emotions results in lower user satisfaction and success rates.

[0369] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for allowing a user to set a goal and storing the goal information in a database; means for collecting user behavioral data and generating specific advice for achieving the goal using an AI model; means for transmitting the generated advice to a terminal and displaying it so that the user can receive it; means for the user to report daily progress and record the information in a database; means for the user to input feedback on the advice and content provided and storing the feedback information in a database; means for generating further improved advice using an AI model based on the feedback information; means for selecting learning content based on the user's interests and behavioral data and providing it to the user; and means for collecting user emotion data and optimizing the generated advice using an emotion engine. This enables personalized support based on individual user behavioral data and emotions, thereby maintaining motivation and improving the success rate of goal achievement.

[0370] "User" refers to a person who uses this system to support self-development and goal achievement.

[0371] "Goal" means a specific result or achievement criterion that the user wants to achieve.

[0372] "Goal information" refers to information including detailed data about the set goal, such as deadlines and specific action plans.

[0373] "Database" refers to the information management system used by the system to store user goal information, behavioral data, feedback, and other related data.

[0374] "Behavioral data" is a record of the user's daily activities, and includes information such as the type, duration, and frequency of exercise.

[0375] An "AI model" refers to artificial intelligence that uses machine learning algorithms to analyze data and generate personalized advice and plans for users.

[0376] "Advice" refers to specific guidelines and suggestions for achieving goals generated by this system.

[0377] "Terminal" refers to a device used by a user to access the system, such as a smartphone, tablet, or computer.

[0378] "Progress" refers to information that indicates the state and process of a user's progress toward a goal.

[0379] "Feedback" refers to the user's opinions and evaluations regarding the advice and learning content they receive, as well as the information they input.

[0380] "Emotion data" is information that indicates the user's emotional state, and includes, for example, emotional expressions extracted from text or speech.

[0381] An "emotion engine" refers to a system that analyzes a user's emotional data and optimizes advice based on the results.

[0382] "Learning Content" refers to information and educational materials, such as exercise videos and meal plans, provided to help users achieve their goals.

[0383] "Optimization" refers to the act of tailoring effective advice or plans based on the user's specific situation and emotions.

[0384] The present invention relates to a virtual fitness coaching system that helps users set and achieve their goals. This system includes a server, a terminal, a user, and an emotion engine. The detailed configuration and processing steps of this system are described below.

[0385] User Registration and Login

[0386] First, a user launches an application using a device (e.g., a smartphone or tablet) and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server, which stores the received registration information in a database. Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against the database. If authentication is successful, the server generates an authentication token and sends it to the user.

[0387] goal setting

[0388] The user opens the goal setting screen on the device and enters a new goal. For example, they can set a specific goal such as "lose 5 kg in one month." At this time, they also enter a deadline and implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model such as TensorFlow to generate a specific plan for achieving the goal. The generated plan includes detailed action steps such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which displays it to the user.

[0389] Daily progress reports and emotion recognition

[0390] Users report their daily activity on their device. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database. The server periodically analyzes the saved activity information and uses an AI model to generate advice for the next step. An emotion engine also collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. Examples of generated advice include "increase your walking time a little" or "try a new route next time." The server then sends the advice to the device, which displays it to the user.

[0391] Feedback provision and sentiment analysis

[0392] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data at the time. The server passes the stored feedback information and emotional data to the AI ​​model for further improvements and generates new advice optimized for the user. The server sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized support according to the user's progress.

[0393] Providing learning content

[0394] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the user's device and displayed to the user. This effectively supports the user throughout the entire process of achieving their goal. In addition, the introduction of an emotion engine allows the system to provide advice and content optimized for the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[0395] Specific examples

[0396] For example, if a user sets a goal of "losing 5 kg in one month," the user can report progress by walking 30 minutes every day. If the user gives feedback such as "It felt great," the emotion engine analyzes this information and detects positive emotions. Based on this, the server generates advice such as "Try extending your walking time a little" or "Try a new walking route" and provides it to the user.

[0397] Prompt Sentence Examples

[0398] "Q: User A has a goal of walking 30 minutes every day and says that they felt great today. What advice would you suggest for their next step?"

[0399] In this way, users always receive personalized assistance and are able to effectively progress towards achieving their goals.

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1: User Registration

[0402] A user creates an account by entering their name, email address, and password using a terminal. This input information is sent from the terminal to the server. The server stores the received information in a database, generates a message indicating registration is complete, and sends it to the terminal. The input here is the user's personal information, and the output is a message indicating that the information has been saved to the database and that registration is complete.

[0403] Step 2: Log in

[0404] The user enters their email address and password on the login screen of their device. This information is sent from the device to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates an authentication token and sends it to the device. The input here is login information, and the generated authentication token is the output.

[0405] Step 3: Goal Setting

[0406] The user enters a new goal on the device's goal setting screen. For example, detailed goal information such as "lose 5 kg in one month" is entered. This information is sent from the device to the server, which stores it in a database. The server then uses an AI model to generate a specific plan for achieving the goal and sends it to the device. The input here is the goal information, and the output is the completion of saving it to the database and the generation of a goal achievement plan.

[0407] Step 4: Daily progress reports

[0408] The user enters their daily activity information into the device and presses the send button. For example, they enter information such as "I walked for 30 minutes." This data is sent from the device to the server, which stores it in a database. The server periodically analyzes the stored activity information and generates advice for the next step. The input here is the activity information, and the output is stored in the database and advice is generated.

[0409] Step 5: Emotion Recognition

[0410] The user inputs emotional feedback along with the progress report. For example, they input an emotional expression such as "I felt great today." This feedback is sent from the device to the server, which then analyzes the emotional data using an emotion engine. The server then optimizes advice based on the emotional data and sends it to the user. The input here is emotional feedback, and the output is optimized advice.

[0411] Step 6: Provide feedback

[0412] The user enters feedback on the advice or content provided and presses the send button. This data is sent from the device to the server, which stores it in a database. The emotion engine collects the feedback and emotional data generated at the time, and uses an AI model to generate further improved advice. The input here is the feedback information, and the output is database storage and the generation of improved advice.

[0413] Step 7: Provide learning content

[0414] The server uses a generative AI model to select learning content appropriate for the user based on the user's behavioral data and feedback, and sends it to the device. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The input here is behavioral data and feedback, and the output is the selection and provision of learning content.

[0415] 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.

[0416] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0417] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0418] [Second embodiment]

[0419] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0420] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0421] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0422] 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.

[0423] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0424] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0425] 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.

[0426] 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.

[0427] 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 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.

[0428] 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.

[0429] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0430] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0431] This invention relates to a self-development coaching system that helps users set goals and support their achievement. This system operates primarily on a server, a terminal, and the user, and utilizes AI to provide personalized advice and learning content. The configuration and specific operation of this system are described below.

[0432] User Registration and Login

[0433] First, the user installs the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message confirming registration.

[0434] Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against a database. If authentication is successful, the server generates an authentication token and sends it to the user.

[0435] goal setting

[0436] The user inputs a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also inputs a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database.

[0437] The server uses the AI ​​model to generate a specific plan for achieving the goal based on the saved goal information. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0438] Daily progress reports

[0439] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database.

[0440] The server periodically analyzes the saved activity information and generates advice for the next step using an AI model. The advice generated might be, for example, "Increase your walking time a little" or "Try a new route next time." The server then sends the advice to the device, which then displays it to the user.

[0441] Providing Feedback

[0442] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device then sends the feedback information to the server, which stores it in a database.

[0443] The server uses the updated feedback information to further improve the AI ​​model and generate new advice optimized for the user. In this way, the server continues to provide personalized assistance according to the user's progress.

[0444] Providing learning content

[0445] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the device and displayed to the user.

[0446] This allows the system to effectively support users throughout the entire process of achieving their goals and promote continuous self-improvement.

[0447] The processing flow will be explained below.

[0448] User Registration and Login

[0449] Step 1:

[0450] The user launches the application, goes to the registration screen, enters their name, email address, and password, and presses the submit button.

[0451] Step 2:

[0452] The terminal transmits the entered registration information to the server.

[0453] Step 3:

[0454] The server stores the received registration information in a database and sends a success message to the terminal.

[0455] Step 4:

[0456] The user is taken to the login screen and logs in by entering their email address and password.

[0457] Step 5:

[0458] The terminal sends the login information to the server.

[0459] Step 6:

[0460] The server compares the entered information with the database, and if authentication is successful, it generates an authentication token and sends it to the terminal along with an authentication success message.If authentication fails, it sends an error message to the terminal.

[0461] goal setting

[0462] Step 1:

[0463] The user enters a new goal (e.g., "lose 5 kg in one month") on the goal setting screen, sets a deadline and specific measures, and presses the submit button.

[0464] Step 2:

[0465] The terminal transmits the target information to the server.

[0466] Step 3:

[0467] The server stores the received target information in a database.

[0468] Step 4:

[0469] The server passes the saved goal information to the AI ​​model, which generates a specific plan for achieving the goal.

[0470] Step 5:

[0471] The server stores the plan generated by the AI ​​model (e.g., "walk 30 minutes every day") in a database and sends it to the device.

[0472] Step 6:

[0473] The terminal displays the received plan to the user.

[0474] Daily progress reports

[0475] Step 1:

[0476] The user enters their daily activity (e.g., "I walked for 30 minutes today") on the report screen and presses the send button.

[0477] Step 2:

[0478] The terminal transmits activity information to the server.

[0479] Step 3:

[0480] The server stores the received activity information in a database.

[0481] Step 4:

[0482] The server passes the saved activity information to an AI model to generate advice for next steps.

[0483] Step 5:

[0484] The server stores the generated advice (e.g., "increase your walking time a little" or "try a new route next time") in a database and sends it to the device.

[0485] Step 6:

[0486] The terminal displays the received advice to the user.

[0487] Providing Feedback

[0488] Step 1:

[0489] The user enters feedback about the advice and learning content provided on the feedback input screen and presses the send button.

[0490] Step 2:

[0491] The terminal transmits the feedback information to the server.

[0492] Step 3:

[0493] The server stores the received feedback in a database.

[0494] Step 4:

[0495] The server passes the stored feedback information to the AI ​​model to generate further improvement advice.

[0496] Step 5:

[0497] The server stores the generated new advice in a database and sends it to the terminal.

[0498] Step 6:

[0499] The terminal displays the received new advice to the user.

[0500] Providing learning content

[0501] Step 1:

[0502] The server passes the user's behavioral data and feedback information to the AI ​​model, which selects the most appropriate learning content.

[0503] Step 2:

[0504] The server stores the selected learning content (e.g., articles, videos, audio guides, etc.) in a database and sends it to the terminal.

[0505] Step 3:

[0506] The terminal displays the received learning content to the user.

[0507] Through these steps, the system provides users with personalized self-improvement support and effectively promotes goal achievement.

[0508] Example 1

[0509] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0510] Conventional self-improvement support systems have had the challenge of providing personalized support for individual users' goals and progress. Furthermore, they lacked the ability to optimize based on user feedback or provide continuous advice according to progress. This made it difficult for users to maintain their motivation to achieve their goals.

[0511] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0512] In this invention, the server includes means for allowing a user to set a goal and storing the goal information in a database, means for collecting user behavioral data and generating specific advice for achieving the goal using a generative artificial intelligence model, means for transmitting the generated advice to a terminal and displaying it so that the user can receive it, means for the user to report daily progress and record the information in the database, means for the user to input feedback on the advice and content provided and storing the feedback information in the database, means for generating further improved advice using the generative artificial intelligence model based on the feedback information, means for selecting learning content based on the user's interests and behavioral data and providing it to the user, and means for creating input prompts for the generative artificial intelligence model. This allows users to receive support optimized for their individual goals, making it easier for them to maintain their motivation to achieve their goals.

[0513] "User" refers to a person who uses this system to set goals and aim to achieve them.

[0514] "Goal information" refers to the specific goal set by the user, as well as the detailed plan and deadline for achieving it.

[0515] "Database" refers to an information management system for storing user goal information, behavioral data, feedback information, and the like.

[0516] "Behavioral Data" refers to data collected by users reporting their daily activities and progress.

[0517] "Generative artificial intelligence model" refers to AI technology used to help users achieve their goals, specifically models for generating text and plans.

[0518] "Advice" refers to specific guidelines for action to achieve a user's goals, generated using a generative artificial intelligence model.

[0519] "Terminal" refers to a device (such as a smartphone or PC) that a user uses to use an application.

[0520] "Progress reporting" refers to a user recording their daily activities and achievements and sending them to the system.

[0521] "Feedback information" refers to evaluations and opinions entered by users regarding the advice and content provided.

[0522] "Learning Content" refers to educational materials and information provided to help users achieve their goals and develop themselves.

[0523] An "input prompt" is an instruction entered into a generative artificial intelligence model, and refers to the text used to generate specific advice or plans.

[0524] MODE FOR CARRYING OUT THE INVENTION

[0525] The present invention relates to a self-improvement coaching system for supporting a user in setting and achieving a goal. Specific embodiments of the system will be described below.

[0526] User Registration and Login

[0527] First, a user installs the application using a device such as a smartphone or PC. After installation, the user creates an account by entering their name, email address, and password on the account creation screen. The device sends this user information to the server, which then stores the received information in a database. Once registration is complete, the server sends a registration completion message to the device, which the device displays to the user.

[0528] Next, the user enters their email address and password on the login screen to log in. The device sends this information to the server, which then authenticates them by checking it against the user information in its database. If authentication is successful, the server generates an authentication token and sends it to the device. The device receives the authentication token and uses it to maintain the user's logged-in state.

[0529] goal setting

[0530] The user inputs a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." They also input the deadline for the goal and an implementation plan. The device sends this information to the server, which then stores the goal information in a database.

[0531] Based on the saved goal information, the server uses a generative artificial intelligence model (e.g., GPT-4) to generate a specific action plan for achieving the goal. A specific prompt could be, "Generate a specific action plan for the user to lose 5 kg in one month." The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0532] Daily progress reports

[0533] The user reports their daily progress on the activity input screen. For example, they enter information such as "I walked for 30 minutes today" and press the send button. The device sends this activity information to the server, which then stores the received information in a database.

[0534] The server periodically analyzes the stored activity information and generates advice for the next step using a generative artificial intelligence model. A specific prompt could be, "Generate advice for the next step based on the user's activity information." The generated advice could be, for example, "Increase your walking time a little" or "Try a new route next time." The server sends this advice to the device, which then displays it to the user.

[0535] Providing Feedback

[0536] The user provides feedback on the advice and learning content provided. They enter their evaluation and opinion on the feedback input screen and press the send button. The device then sends the feedback information to the server, which then stores the received information in a database.

[0537] The server uses the feedback information to further improve the generative artificial intelligence model and generate new advice. A specific prompt could be, "Based on the user's feedback information, provide optimized advice for the next step." The server sends this new advice to the device, which then displays it to the user.

[0538] Providing learning content

[0539] The server uses a generative artificial intelligence model to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. A specific prompt could be, "Provide appropriate learning content based on the user's behavioral data and feedback." The generated learning content is sent from the server to the device and can be used by the user.

[0540] conclusion

[0541] This system effectively supports users through the entire process from goal setting to goal achievement, and can promote continuous self-improvement. The present invention provides personalized support for the individual needs of each user, helping them maintain motivation to achieve their goals.

[0542] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0543] Step 1: User Registration

[0544] Input: The user installs the application and enters their name, email address, and password.

[0545] Operation: The terminal receives input information and sends it to the server.

[0546] Data processing: The server processes the received information and stores the user information in a database.

[0547] Output: The server sends a registration completion message to the terminal, which displays it to the user.

[0548] Step 2: User Login

[0549] Input: The user enters their email address and password on the login screen.

[0550] Operation: The terminal receives input information and sends it to the server.

[0551] Data calculation: The server checks the user information against the database and performs authentication.

[0552] Output: The server generates an authentication token and sends it to the device. The device receives the authentication token and the user is logged in.

[0553] Step 3: Goal Setting

[0554] Input: The user inputs a new goal, deadline, and action plan on the goal setting screen.

[0555] Operation: The terminal receives target information and sends it to the server.

[0556] Data storage: The server stores the target information in a database.

[0557] Output: A message that the save is complete is sent to the terminal, which displays it to the user.

[0558] Step 4: Generate a goal achievement plan

[0559] Input: The server retrieves the goal information stored in the database and inputs a prompt to the generative AI model. Example: "Generate a specific action plan for the user to lose 5 kg in one month."

[0560] How it works: The generative AI model performs data calculations based on the input prompt sentence and generates a specific action plan.

[0561] Output: The server receives the generated plan and sends it to the device. The device displays the plan to the user. Examples: "Walk 30 minutes every day" or "Go to the gym three times a week."

[0562] Step 5: Daily progress reports

[0563] Input: The user enters their daily activities in the activity input screen. Example: "I walked for 30 minutes today."

[0564] Operation: The terminal receives input information and sends it to the server.

[0565] Data storage: The server stores the received activity information in a database.

[0566] Output: A save confirmation message is sent to the terminal, which displays it to the user.

[0567] Step 6: Providing advice

[0568] Input: The server retrieves the activity information stored in the database and inputs a prompt to the generative AI model. Example: "Based on the user's activity information, generate advice for the next step."

[0569] How it works: The generative AI model performs data calculations based on the input prompt and generates specific advice.

[0570] Output: The server receives the generated advice and sends it to the device. The device displays the advice to the user. Examples: "Try to walk a little longer" or "Try a new route next time."

[0571] Step 7: Provide feedback

[0572] Input: Users enter feedback on the advice and content provided.

[0573] Operation: The terminal receives input information and sends it to the server.

[0574] Data storage: The server stores the received feedback information in a database.

[0575] Output: A save confirmation message is sent to the terminal, which displays it to the user.

[0576] Step 8: Optimizing Advice

[0577] Input: The server retrieves the feedback information stored in the database and inputs a prompt to the generative AI model. For example, "Based on the user's feedback information, provide optimized advice for the next step."

[0578] How it works: The generative AI model performs data calculations based on the input prompt sentence and generates new advice.

[0579] Output: The server receives the generated new advice and sends it to the terminal, which displays the new advice to the user.

[0580] Step 9: Provide learning content

[0581] Input: The server inputs a prompt to the generative AI model based on the user's behavioral data and feedback. Example: "Provide appropriate learning content based on the user's behavioral data and feedback."

[0582] How it works: The generative AI model performs data calculations based on the input prompt and selects learning content.

[0583] Output: The server receives the generated learning content and sends it to the device. The device displays the learning content to the user. Examples: "Effective meal menu for users with weight loss goals," "Exercise video."

[0584] (Application example 1)

[0585] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0586] Conventional self-improvement coaching systems do not provide sufficient specific and personalized support for the goals set by users. Furthermore, it is difficult for physical facilities such as fitness gyms to provide appropriate advice to individual users in a timely manner. Furthermore, it is not possible to dynamically update content and advice based on progress, which makes it difficult to effectively support users in maintaining their motivation and achieving their goals.

[0587] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0588] In this invention, the server includes a means for allowing a user to set a goal and storing the goal information in a database, a means for collecting user behavior data and generating specific advice for achieving the goal using a generative AI model, and a means for transmitting the generated advice to a terminal and displaying it so that the user can receive it. This makes it possible to provide training advice and learning content personalized to each user and effectively support the user in achieving their goal.

[0589] "User" refers to an individual who uses the system to set goals and receive support in achieving them.

[0590] "Goal setting" refers to the act of a user determining a specific goal they want to achieve and inputting that information.

[0591] "Database" refers to a system that stores and manages goal information, behavioral data, and feedback information.

[0592] "Behavioral Data" refers to information recorded by a user's daily progress and activities.

[0593] A "generative AI model" refers to a system that uses artificial intelligence to generate specific advice and plans to help users achieve their goals.

[0594] "Advice" refers to specific guidelines or suggestions generated by an AI model to achieve a goal.

[0595] "Terminal" refers to a device such as a smartphone or tablet that a user uses to access an application.

[0596] "Progress reporting" refers to the act of a user entering their daily activities and results into the system and providing that information.

[0597] "Feedback" refers to the act of a user inputting an evaluation or opinion regarding the advice or content provided.

[0598] "Training Advice" refers to specific guidelines for fitness and exercise.

[0599] "Learning Content" refers to educational materials and information provided to help users achieve their goals.

[0600] "Dynamic updating" refers to the process of continually changing the advice and content provided based on the user's progress and behavioral data.

[0601] "Training goal" refers to a specific fitness or exercise goal that a user aims to achieve.

[0602] "Recording" refers to the act of a user entering goals and progress into the system and saving that information.

[0603] "Personalization" refers to the process of optimizing the content and advice provided to users based on their individual needs and behavior.

[0604] This invention is a self-improvement coaching system that helps users set goals and support their achievement. This system operates primarily on a server, a terminal, and the user, and utilizes AI to provide personalized advice and learning content. The specific process for implementing this system is described below.

[0605] User Registration and Login

[0606] First, a user installs an application on a device such as a smartphone or tablet and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server, which then stores the received registration information in a database. Next, the user logs in by entering their email address and password on the login screen. The server authenticates the user by checking the database, and if authentication is successful, generates an authentication token and sends it to the user.

[0607] goal setting

[0608] The user enters a new goal on the application's goal setting screen. For example, they can set a specific goal such as "gain 5 kg of muscle mass in three months." They also enter a deadline and a specific plan. The device sends this goal information to the server, which stores it in a database. The server then uses a generative AI model to generate a specific plan for achieving the goal based on the saved goal information. This plan includes detailed action steps such as gym training three times a week and daily protein intake. The server then sends this plan to the device and displays it to the user.

[0609] Daily progress reports

[0610] Users report their daily training and activities on the activity input screen. For example, if they did 30 minutes of strength training today, they enter that information and press the send button. The device then sends the activity information to the server, which stores it in a database.

[0611] Providing Feedback

[0612] The user provides feedback on the generated advice and learning content. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. Based on the updated feedback information, the server uses the generative AI model to make further improvements and generate new, personalized advice.

[0613] Providing learning content

[0614] The server uses a generative AI model to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective training videos and nutritional advice to a user with training goals. The generated learning content is sent from the server to the device and displayed to the user.

[0615] This allows the system to effectively support the user throughout the entire process of achieving their goal and promote continuous self-improvement. For example, a user can set a training goal of "gaining 5 kg of muscle mass in three months" and include a detailed plan that includes three gym sessions per week and daily protein intake. Furthermore, in the daily activity report, the user can enter "I did 30 minutes of strength training today" and receive advice on their next training session. An example of a prompt to input to the generative AI model might be, "Set a goal of gaining 5 kg of muscle mass in three months. Plan to train at the gym three times per week and consume protein daily."

[0616] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0617] Step 1:

[0618] User Registration and Login

[0619] Input: The user enters their name, email address, and password into the device.

[0620] Operation: The device sends this registration information to the server. The server stores the received registration information in a database and sends the user a message confirming registration. The user then logs in by entering their email address and password. The server authenticates the user against the database, and if authentication is successful, generates an authentication token and sends it to the user.

[0621] Output: An authentication token is sent to the user.

[0622] Step 2:

[0623] goal setting

[0624] Input: The user enters a new goal (e.g., gain 5 kg of muscle mass in 3 months) on the goal setting screen, along with the deadline and specific plan (training at the gym three times a week, consuming protein daily).

[0625] How it works: The device sends these goal information to a server, which stores the goal information in a database and uses a generative AI model to generate a specific plan for achieving the goal.

[0626] Output: The generated plan (detailed action steps) is displayed on the terminal.

[0627] Step 3:

[0628] Daily progress reports

[0629] Input: The user enters their daily training progress in the activity input screen (e.g., I did 30 minutes of strength training today).

[0630] How it works: Your device sends activity information to a server, which stores it in a database and uses AI models to suggest next steps.

[0631] Output: Next training advice will be displayed on the device.

[0632] Step 4:

[0633] Providing Feedback

[0634] Input: The user enters their evaluation and opinion on the provided advice or content in the feedback input screen.

[0635] How it works: The device sends feedback information to the server, which stores it in a database and uses the generative AI model for further refinement.

[0636] Output: The new and improved advice is displayed on the terminal.

[0637] Step 5:

[0638] Providing learning content

[0639] Input: The server selects learning content based on user behavioral data and feedback.

[0640] How it works: The server uses a generative AI model to select learning content (e.g., training videos or nutritional advice) appropriate for the user.

[0641] Output: The selected learning content will be displayed on the device.

[0642] At each step, the specific actions are as follows:

[0643] Database operations (save, match, update)

[0644] Use of generative AI models (advice generation, plan generation)

[0645] User interface operation (input screen / display screen)

[0646] For example, an example of an input prompt for the generative AI model would be, "Set a goal of gaining 5 kg of muscle mass in three months. Plan to train at the gym three times a week and consume protein daily," which would result in personalized advice.

[0647] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0648] This invention relates to a self-improvement coaching system that helps users set goals and achieve them. The system includes a server, a terminal, a user, and an emotion engine. Utilizing AI technology, the system not only provides users with personalized advice and learning content, but also recognizes the user's emotions and provides further optimized support based on those emotions. The configuration and specific operation of the system are described below.

[0649] User Registration and Login

[0650] First, the user launches the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message confirming registration.

[0651] Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against a database. If authentication is successful, the server generates an authentication token and sends it to the user.

[0652] goal setting

[0653] The user enters a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also enters a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model to generate a specific plan for achieving the goal. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0654] Daily progress reports and emotion recognition

[0655] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database.

[0656] The server periodically analyzes the saved activity information and uses the AI ​​model to generate advice for the next step. In addition, an emotion engine collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. The generated advice might be, for example, "increase your walking time a little" or "try a new route next time." The server then sends the advice to the device, which displays it to the user.

[0657] Feedback provision and sentiment analysis

[0658] Users provide feedback on the advice and learning content provided. They enter their evaluation and opinion on the feedback input screen and press the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data generated at the time.

[0659] The server passes the saved feedback information and emotion data to the AI ​​model for further refinement and generates new advice optimized for the user. The server then sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized assistance according to the user's progress.

[0660] Providing learning content

[0661] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the device and displayed to the user.

[0662] This allows the system to effectively support the entire process of users achieving their goals and promote continuous self-improvement.The introduction of an emotion engine allows the system to provide advice and content optimized for the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[0663] The processing flow will be explained below.

[0664] User Registration and Login

[0665] Step 1:

[0666] The user launches the application, goes to the registration screen, enters their name, email address, and password, and presses the submit button.

[0667] Step 2:

[0668] The terminal transmits the entered registration information to the server.

[0669] Step 3:

[0670] The server stores the received registration information in a database and sends a success message to the terminal.

[0671] Step 4:

[0672] The user is taken to the login screen and logs in by entering their email address and password.

[0673] Step 5:

[0674] The terminal sends the login information to the server.

[0675] Step 6:

[0676] The server compares the entered information with the database, and if authentication is successful, it generates an authentication token and sends it to the terminal along with an authentication success message.If authentication fails, it sends an error message to the terminal.

[0677] goal setting

[0678] Step 1:

[0679] The user enters a new goal (e.g., "lose 5 kg in one month") on the goal setting screen, sets a deadline and specific measures, and presses the submit button.

[0680] Step 2:

[0681] The terminal transmits the target information to the server.

[0682] Step 3:

[0683] The server stores the received target information in a database.

[0684] Step 4:

[0685] The server passes the saved goal information to the AI ​​model, which generates a specific plan for achieving the goal.

[0686] Step 5:

[0687] The server stores the plan generated by the AI ​​model (e.g., "walk 30 minutes every day") in a database and sends it to the device.

[0688] Step 6:

[0689] The terminal displays the received plan to the user.

[0690] Daily progress reports and emotion recognition

[0691] Step 1:

[0692] The user enters their daily activity (e.g., "I walked for 30 minutes today") on the report screen and presses the send button along with emotional data (e.g., facial recognition or an emotional icon selected by the user).

[0693] Step 2:

[0694] The terminal transmits the activity information and emotion data to the server.

[0695] Step 3:

[0696] The server stores the received activity information and emotion data in a database.

[0697] Step 4:

[0698] The server passes the stored activity information and emotion data to the AI ​​model to generate advice for next steps.

[0699] Step 5:

[0700] The server stores the generated advice (e.g., "increase your walking time a little" or "try a new route next time") in a database and sends it to the device.

[0701] Step 6:

[0702] The terminal displays the received advice to the user.

[0703] Feedback provision and sentiment analysis

[0704] Step 1:

[0705] The user inputs feedback about the advice or learning content provided on the feedback input screen, includes emotional data (e.g., an emotional icon or text input when giving feedback), and presses the send button.

[0706] Step 2:

[0707] The terminal transmits the feedback information and emotion data to the server.

[0708] Step 3:

[0709] The server stores the received feedback information and emotion data in a database.

[0710] Step 4:

[0711] The server passes the stored feedback information and emotion data to the AI ​​model to generate further improvement advice.

[0712] Step 5:

[0713] The server stores the generated new advice in a database and sends it to the terminal.

[0714] Step 6:

[0715] The terminal displays the received new advice to the user.

[0716] Providing learning content

[0717] Step 1:

[0718] The server passes the user's behavioral and emotional data to the AI ​​model, which then selects the most appropriate learning content.

[0719] Step 2:

[0720] The server stores the selected learning content (e.g., articles, videos, audio guides, etc.) in a database and sends it to the terminal.

[0721] Step 3:

[0722] The terminal displays the received learning content to the user.

[0723] Through these steps, the system can provide users with personalized self-improvement support, providing advice and content optimized for their emotional state, thereby increasing their motivation and improving their success rate in achieving their goals.

[0724] Example 2

[0725] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0726] In modern society, it is important for individuals to work efficiently and effectively toward their set goals. However, conventional systems have had difficulty providing personalized advice that fully takes into account the user's behavioral history and emotional state. Furthermore, the learning content provided is often uniform and fails to fully meet the needs of individual users. This can lead to a decline in users' motivation to achieve their goals, ultimately making it difficult for them to achieve them at all.

[0727] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0728] In this invention, the server includes: means for allowing a user to set a goal and store the goal information in a database; means for collecting user behavior data and generating specific advice for achieving the goal using an AI model; means for transmitting the generated advice to a terminal and displaying it so that the user can receive it; means for the user to report daily progress and record the information in a database; means for the user to input feedback on the advice and content provided and storing the feedback information in a database; means for generating further improved advice using an AI model based on the feedback information; means for using an emotion engine that analyzes user emotion data and optimizes advice based on the results; and means for selecting learning content based on the user's interests and behavior data and providing it to the user. This enables personalized support according to the user's individual situation and emotions, maintaining user motivation and enabling more effective goal achievement.

[0729] "Goal information" refers to the content of the goal set by the user, as well as the deadline and specific plan for achieving it.

[0730] A "database" is an information system for managing and storing user goal information, behavioral data, progress data, feedback information, etc.

[0731] "Behavioral Data" refers to the history of activities and actions reported by users on a daily basis.

[0732] An "AI model" is an algorithm that uses artificial intelligence technology to generate specific advice and plans for achieving goals based on user behavioral data and goal information.

[0733] "Advice" refers to specific guidelines or suggestions for action that a user needs to take to achieve their goal.

[0734] "Terminal" refers to an electronic device that a user operates and displays to use the system.

[0735] "Feedback information" is information obtained by users inputting their evaluations and opinions regarding the advice and content provided.

[0736] The "emotion engine" is a system that collects and analyzes users' emotional data and optimizes advice based on the results.

[0737] "Learning Content" refers to educational materials and content that help users achieve their goals.

[0738] "Optimization" is the process of providing advice and learning content to users in the most optimal way based on their behavioral data, feedback information, and emotional data.

[0739] The present invention relates to a self-improvement coaching system that helps users set goals and achieve them. The system includes a server, a terminal, a user, and an emotion engine. Utilizing AI technology, the system not only provides users with personalized advice and learning content, but also recognizes the user's emotions and provides further optimized support based on those emotions.

[0740] User Registration and Login

[0741] First, the user launches the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message notifying them of successful registration. Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against the database. If authentication is successful, the server generates an authentication token and sends it to the user.

[0742] goal setting

[0743] The user enters a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also enters a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model to generate a specific plan for achieving the goal. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0744] Daily progress reports and emotion recognition

[0745] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device sends the activity information to the server, which stores it in a database. The server periodically analyzes the saved activity information and uses an AI model to generate advice for the next step. Furthermore, an emotion engine collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. The generated advice might be, for example, "increase your walking time a little" or "try a new route next time." The server sends the advice to the device, which displays it to the user.

[0746] Feedback provision and sentiment analysis

[0747] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data at the time. The server passes the stored feedback information and emotional data to the AI ​​model for further improvements and generates new advice optimized for the user. The server sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized assistance according to the user's progress.

[0748] Providing learning content

[0749] Based on the user's behavioral data and feedback, the server uses AI to select learning content appropriate for the user. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the user's device and displayed to the user. This allows the system to effectively support the entire process of users achieving their goals and promote continuous self-improvement. The introduction of an emotion engine allows the system to provide optimized advice and content that takes into account the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[0750] Specific examples

[0751] For example, if a user sets a goal of "losing 5 kg in one month," the server will generate a specific plan such as "walking for 30 minutes every day" and "going to the gym three times a week." Furthermore, based on the emotional data when the user reports that they "walked for 30 minutes," the server will provide advice such as "take a 10-minute break next time." In this way, by incorporating user feedback and constantly optimizing advice and learning content, users can steadily progress toward their goals.

[0752] Prompt Sentence Examples

[0753] "Please tell me a concrete action plan to lose 5 kg in one month."

[0754] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0755] Step 1: The user launches the application, enters their name, email address, and password on the account creation screen, and presses the register button. The device sends this information to the server. The server stores the received information in a database and sends a message to the device indicating that registration is complete. The input is the user's registration information, and the output is a message indicating that registration is complete. Specifically, the device sends the data in the input form to the server as an HTTP request, and the server executes an SQL query in the database to save the information.

[0756] Step 2: The user enters their email address and password on the login screen and presses the login button. The device sends the login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server generates an authentication token and sends it to the device. The input is the user's login information and the output is the authentication token. Specifically, the device sends the input data to the server via an HTTP request, and the server uses an SQL query to search and check the database and generate the authentication token.

[0757] Step 3: The user enters a new goal (for example, "lose 5 kg in one month") on the goal setting screen, along with a deadline and implementation plan. The device sends this goal information to the server. The server stores the goal information in a database and uses an AI model to generate a plan for achieving the goal. The generated plan is sent to the device and displayed to the user. The input is the user's goal information, and the output is a specific action plan. Specifically, the device sends the goal information to the server, and the server runs the AI ​​model to generate a plan and saves it in the database.

[0758] Step 4: The user enters their daily activity (e.g., "Walked for 30 minutes") on the activity input screen and presses the send button. The device sends the activity information to the server. The server stores the information in a database and periodically analyzes it. The emotion engine collects the user's emotion data, and the AI ​​model generates optimized advice based on that. The generated advice is sent to the device and displayed to the user. The input is the user's activity information and emotion data, and the output is optimized advice. Specifically, the device sends the activity data, and the server analyzes the data using multiple algorithms and generates the results.

[0759] Step 5: The user enters their evaluation and opinion on the provided advice and learning content on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects emotional data at the time of feedback. The server uses this information to make further improvements to the AI ​​model and generate new advice. The generated advice is sent to the device and displayed to the user. The input is feedback information and emotional data, and the output is improved advice. Specifically, the device sends the feedback data, and the server stores it in a database and analyzes it to generate new advice.

[0760] Step 6: The server uses an AI model to select appropriate learning content based on the user's behavioral data and feedback. The selected learning content is sent to the device and displayed to the user. The input is behavioral data and feedback information, and the output is learning content. Specifically, the server analyzes the behavioral data and feedback, selects the most appropriate learning content using an AI model, and sends it to the device.

[0761] (Application example 2)

[0762] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0763] Today, many users need effective support for self-improvement and goal achievement. However, conventional coaching systems and fitness applications only provide general advice and lack personalized support based on individual users' behavioral data and emotions. This makes it difficult for users to maintain their motivation and achieve their goals. Furthermore, the lack of optimization of advice that takes into account changes in the user's emotions results in lower user satisfaction and success rates.

[0764] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for allowing a user to set a goal and storing the goal information in a database; means for collecting user behavioral data and generating specific advice for achieving the goal using an AI model; means for transmitting the generated advice to a terminal and displaying it so that the user can receive it; means for the user to report daily progress and record the information in a database; means for the user to input feedback on the advice and content provided and storing the feedback information in a database; means for generating further improved advice using an AI model based on the feedback information; means for selecting learning content based on the user's interests and behavioral data and providing it to the user; and means for collecting user emotion data and optimizing the generated advice using an emotion engine. This enables personalized support based on individual user behavioral data and emotions, thereby maintaining motivation and improving the success rate of goal achievement.

[0765] "User" refers to a person who uses this system to support self-development and goal achievement.

[0766] "Goal" means a specific result or achievement criterion that the user wants to achieve.

[0767] "Goal information" refers to information including detailed data about the set goal, such as deadlines and specific action plans.

[0768] "Database" refers to the information management system used by the system to store user goal information, behavioral data, feedback, and other related data.

[0769] "Behavioral data" is a record of the user's daily activities, and includes information such as the type, duration, and frequency of exercise.

[0770] An "AI model" refers to artificial intelligence that uses machine learning algorithms to analyze data and generate personalized advice and plans for users.

[0771] "Advice" refers to specific guidelines and suggestions for achieving goals generated by this system.

[0772] "Terminal" refers to a device used by a user to access the system, such as a smartphone, tablet, or computer.

[0773] "Progress" refers to information that indicates the state and process of a user's progress toward a goal.

[0774] "Feedback" refers to the user's opinions and evaluations regarding the advice and learning content they receive, as well as the information they input.

[0775] "Emotion data" is information that indicates the user's emotional state, and includes, for example, emotional expressions extracted from text or speech.

[0776] An "emotion engine" refers to a system that analyzes a user's emotional data and optimizes advice based on the results.

[0777] "Learning Content" refers to information and educational materials, such as exercise videos and meal plans, provided to help users achieve their goals.

[0778] "Optimization" refers to the act of tailoring effective advice or plans based on the user's specific situation and emotions.

[0779] The present invention relates to a virtual fitness coaching system that helps users set and achieve their goals. This system includes a server, a terminal, a user, and an emotion engine. The detailed configuration and processing steps of this system are described below.

[0780] User Registration and Login

[0781] First, a user launches an application using a device (e.g., a smartphone or tablet) and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server, which stores the received registration information in a database. Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against the database. If authentication is successful, the server generates an authentication token and sends it to the user.

[0782] goal setting

[0783] The user opens the goal setting screen on the device and enters a new goal. For example, they can set a specific goal such as "lose 5 kg in one month." At this time, they also enter a deadline and implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model such as TensorFlow to generate a specific plan for achieving the goal. The generated plan includes detailed action steps such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which displays it to the user.

[0784] Daily progress reports and emotion recognition

[0785] Users report their daily activity on their device. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database. The server periodically analyzes the saved activity information and uses an AI model to generate advice for the next step. An emotion engine also collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. Examples of generated advice include "increase your walking time a little" or "try a new route next time." The server then sends the advice to the device, which displays it to the user.

[0786] Feedback provision and sentiment analysis

[0787] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data at the time. The server passes the stored feedback information and emotional data to the AI ​​model for further improvements and generates new advice optimized for the user. The server sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized support according to the user's progress.

[0788] Providing learning content

[0789] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the user's device and displayed to the user. This effectively supports the user throughout the entire process of achieving their goal. In addition, the introduction of an emotion engine allows the system to provide advice and content optimized for the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[0790] Specific examples

[0791] For example, if a user sets a goal of "losing 5 kg in one month," the user can report progress by walking 30 minutes every day. If the user gives feedback such as "It felt great," the emotion engine analyzes this information and detects positive emotions. Based on this, the server generates advice such as "Try extending your walking time a little" or "Try a new walking route" and provides it to the user.

[0792] Prompt Sentence Examples

[0793] "Q: User A has a goal of walking 30 minutes every day and says that they felt great today. What advice would you suggest for their next step?"

[0794] In this way, users always receive personalized assistance and are able to effectively progress towards achieving their goals.

[0795] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0796] Step 1: User Registration

[0797] A user creates an account by entering their name, email address, and password using a terminal. This input information is sent from the terminal to the server. The server stores the received information in a database, generates a message indicating registration is complete, and sends it to the terminal. The input here is the user's personal information, and the output is a message indicating that the information has been saved to the database and that registration is complete.

[0798] Step 2: Log in

[0799] The user enters their email address and password on the login screen of their device. This information is sent from the device to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates an authentication token and sends it to the device. The input here is login information, and the generated authentication token is the output.

[0800] Step 3: Goal Setting

[0801] The user enters a new goal on the device's goal setting screen. For example, detailed goal information such as "lose 5 kg in one month" is entered. This information is sent from the device to the server, which stores it in a database. The server then uses an AI model to generate a specific plan for achieving the goal and sends it to the device. The input here is the goal information, and the output is the completion of saving it to the database and the generation of a goal achievement plan.

[0802] Step 4: Daily progress reports

[0803] The user enters their daily activity information into the device and presses the send button. For example, they enter information such as "I walked for 30 minutes." This data is sent from the device to the server, which stores it in a database. The server periodically analyzes the stored activity information and generates advice for the next step. The input here is the activity information, and the output is stored in the database and advice is generated.

[0804] Step 5: Emotion Recognition

[0805] The user inputs emotional feedback along with the progress report. For example, they input an emotional expression such as "I felt great today." This feedback is sent from the device to the server, which then analyzes the emotional data using an emotion engine. The server then optimizes advice based on the emotional data and sends it to the user. The input here is emotional feedback, and the output is optimized advice.

[0806] Step 6: Provide feedback

[0807] The user enters feedback on the advice or content provided and presses the send button. This data is sent from the device to the server, which stores it in a database. The emotion engine collects the feedback and emotional data generated at the time, and uses an AI model to generate further improved advice. The input here is the feedback information, and the output is database storage and the generation of improved advice.

[0808] Step 7: Provide learning content

[0809] The server uses a generative AI model to select learning content appropriate for the user based on the user's behavioral data and feedback, and sends it to the device. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The input here is behavioral data and feedback, and the output is the selection and provision of learning content.

[0810] 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.

[0811] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0812] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0813] [Third embodiment]

[0814] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0815] 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.

[0816] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0817] 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.

[0818] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0819] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0820] 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.

[0821] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0822] 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 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.

[0823] 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.

[0824] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0825] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0826] This invention relates to a self-development coaching system that helps users set goals and support their achievement. This system operates primarily on a server, a terminal, and the user, and utilizes AI to provide personalized advice and learning content. The configuration and specific operation of this system are described below.

[0827] User Registration and Login

[0828] First, the user installs the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message confirming registration.

[0829] Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against a database. If authentication is successful, the server generates an authentication token and sends it to the user.

[0830] goal setting

[0831] The user inputs a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also inputs a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database.

[0832] The server uses the AI ​​model to generate a specific plan for achieving the goal based on the saved goal information. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0833] Daily progress reports

[0834] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database.

[0835] The server periodically analyzes the saved activity information and generates advice for the next step using an AI model. The advice generated might be, for example, "Increase your walking time a little" or "Try a new route next time." The server then sends the advice to the device, which then displays it to the user.

[0836] Providing Feedback

[0837] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device then sends the feedback information to the server, which stores it in a database.

[0838] The server uses the updated feedback information to further improve the AI ​​model and generate new advice optimized for the user. In this way, the server continues to provide personalized assistance according to the user's progress.

[0839] Providing learning content

[0840] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the device and displayed to the user.

[0841] This allows the system to effectively support users throughout the entire process of achieving their goals and promote continuous self-improvement.

[0842] The processing flow will be explained below.

[0843] User Registration and Login

[0844] Step 1:

[0845] The user launches the application, goes to the registration screen, enters their name, email address, and password, and presses the submit button.

[0846] Step 2:

[0847] The terminal transmits the entered registration information to the server.

[0848] Step 3:

[0849] The server stores the received registration information in a database and sends a success message to the terminal.

[0850] Step 4:

[0851] The user is taken to the login screen and logs in by entering their email address and password.

[0852] Step 5:

[0853] The terminal sends the login information to the server.

[0854] Step 6:

[0855] The server compares the entered information with the database, and if authentication is successful, it generates an authentication token and sends it to the terminal along with an authentication success message.If authentication fails, it sends an error message to the terminal.

[0856] goal setting

[0857] Step 1:

[0858] The user enters a new goal (e.g., "lose 5 kg in one month") on the goal setting screen, sets a deadline and specific measures, and presses the submit button.

[0859] Step 2:

[0860] The terminal transmits the target information to the server.

[0861] Step 3:

[0862] The server stores the received target information in a database.

[0863] Step 4:

[0864] The server passes the saved goal information to the AI ​​model, which generates a specific plan for achieving the goal.

[0865] Step 5:

[0866] The server stores the plan generated by the AI ​​model (e.g., "walk 30 minutes every day") in a database and sends it to the device.

[0867] Step 6:

[0868] The terminal displays the received plan to the user.

[0869] Daily progress reports

[0870] Step 1:

[0871] The user enters their daily activity (e.g., "I walked for 30 minutes today") on the report screen and presses the send button.

[0872] Step 2:

[0873] The terminal transmits activity information to the server.

[0874] Step 3:

[0875] The server stores the received activity information in a database.

[0876] Step 4:

[0877] The server passes the saved activity information to an AI model to generate advice for next steps.

[0878] Step 5:

[0879] The server stores the generated advice (e.g., "increase your walking time a little" or "try a new route next time") in a database and sends it to the device.

[0880] Step 6:

[0881] The terminal displays the received advice to the user.

[0882] Providing Feedback

[0883] Step 1:

[0884] The user enters feedback about the advice and learning content provided on the feedback input screen and presses the send button.

[0885] Step 2:

[0886] The terminal transmits the feedback information to the server.

[0887] Step 3:

[0888] The server stores the received feedback in a database.

[0889] Step 4:

[0890] The server passes the stored feedback information to the AI ​​model to generate further improvement advice.

[0891] Step 5:

[0892] The server stores the generated new advice in a database and sends it to the terminal.

[0893] Step 6:

[0894] The terminal displays the received new advice to the user.

[0895] Providing learning content

[0896] Step 1:

[0897] The server passes the user's behavioral data and feedback information to the AI ​​model, which selects the most appropriate learning content.

[0898] Step 2:

[0899] The server stores the selected learning content (e.g., articles, videos, audio guides, etc.) in a database and sends it to the terminal.

[0900] Step 3:

[0901] The terminal displays the received learning content to the user.

[0902] Through these steps, the system provides users with personalized self-improvement support and effectively promotes goal achievement.

[0903] Example 1

[0904] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0905] Conventional self-improvement support systems have had the challenge of providing personalized support for individual users' goals and progress. Furthermore, they lacked the ability to optimize based on user feedback or provide continuous advice according to progress. This made it difficult for users to maintain their motivation to achieve their goals.

[0906] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0907] In this invention, the server includes means for allowing a user to set a goal and storing the goal information in a database, means for collecting user behavioral data and generating specific advice for achieving the goal using a generative artificial intelligence model, means for transmitting the generated advice to a terminal and displaying it so that the user can receive it, means for the user to report daily progress and record the information in the database, means for the user to input feedback on the advice and content provided and storing the feedback information in the database, means for generating further improved advice using the generative artificial intelligence model based on the feedback information, means for selecting learning content based on the user's interests and behavioral data and providing it to the user, and means for creating input prompts for the generative artificial intelligence model. This allows users to receive support optimized for their individual goals, making it easier for them to maintain their motivation to achieve their goals.

[0908] "User" refers to a person who uses this system to set goals and aim to achieve them.

[0909] "Goal information" refers to the specific goal set by the user, as well as the detailed plan and deadline for achieving it.

[0910] "Database" refers to an information management system for storing user goal information, behavioral data, feedback information, and the like.

[0911] "Behavioral Data" refers to data collected by users reporting their daily activities and progress.

[0912] "Generative artificial intelligence model" refers to AI technology used to help users achieve their goals, specifically models for generating text and plans.

[0913] "Advice" refers to specific guidelines for action to achieve a user's goals, generated using a generative artificial intelligence model.

[0914] "Terminal" refers to a device (such as a smartphone or PC) that a user uses to use an application.

[0915] "Progress reporting" refers to a user recording their daily activities and achievements and sending them to the system.

[0916] "Feedback information" refers to evaluations and opinions entered by users regarding the advice and content provided.

[0917] "Learning Content" refers to educational materials and information provided to help users achieve their goals and develop themselves.

[0918] An "input prompt" is an instruction entered into a generative artificial intelligence model, and refers to the text used to generate specific advice or plans.

[0919] MODE FOR CARRYING OUT THE INVENTION

[0920] The present invention relates to a self-improvement coaching system for supporting a user in setting and achieving a goal. Specific embodiments of the system will be described below.

[0921] User Registration and Login

[0922] First, a user installs the application using a device such as a smartphone or PC. After installation, the user creates an account by entering their name, email address, and password on the account creation screen. The device sends this user information to the server, which then stores the received information in a database. Once registration is complete, the server sends a registration completion message to the device, which the device displays to the user.

[0923] Next, the user enters their email address and password on the login screen to log in. The device sends this information to the server, which then authenticates them by checking it against the user information in its database. If authentication is successful, the server generates an authentication token and sends it to the device. The device receives the authentication token and uses it to maintain the user's logged-in state.

[0924] goal setting

[0925] The user inputs a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." They also input the deadline for the goal and an implementation plan. The device sends this information to the server, which then stores the goal information in a database.

[0926] Based on the saved goal information, the server uses a generative artificial intelligence model (e.g., GPT-4) to generate a specific action plan for achieving the goal. A specific prompt could be, "Generate a specific action plan for the user to lose 5 kg in one month." The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[0927] Daily progress reports

[0928] The user reports their daily progress on the activity input screen. For example, they enter information such as "I walked for 30 minutes today" and press the send button. The device sends this activity information to the server, which then stores the received information in a database.

[0929] The server periodically analyzes the stored activity information and generates advice for the next step using a generative artificial intelligence model. A specific prompt could be, "Generate advice for the next step based on the user's activity information." The generated advice could be, for example, "Increase your walking time a little" or "Try a new route next time." The server sends this advice to the device, which then displays it to the user.

[0930] Providing Feedback

[0931] The user provides feedback on the advice and learning content provided. They enter their evaluation and opinion on the feedback input screen and press the send button. The device then sends the feedback information to the server, which then stores the received information in a database.

[0932] The server uses the feedback information to further improve the generative artificial intelligence model and generate new advice. A specific prompt could be, "Based on the user's feedback information, provide optimized advice for the next step." The server sends this new advice to the device, which then displays it to the user.

[0933] Providing learning content

[0934] The server uses a generative artificial intelligence model to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. A specific prompt could be, "Provide appropriate learning content based on the user's behavioral data and feedback." The generated learning content is sent from the server to the device and can be used by the user.

[0935] conclusion

[0936] This system effectively supports users through the entire process from goal setting to goal achievement, and can promote continuous self-improvement. The present invention provides personalized support for the individual needs of each user, helping them maintain motivation to achieve their goals.

[0937] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0938] Step 1: User Registration

[0939] Input: The user installs the application and enters their name, email address, and password.

[0940] Operation: The terminal receives input information and sends it to the server.

[0941] Data processing: The server processes the received information and stores the user information in a database.

[0942] Output: The server sends a registration completion message to the terminal, which displays it to the user.

[0943] Step 2: User Login

[0944] Input: The user enters their email address and password on the login screen.

[0945] Operation: The terminal receives input information and sends it to the server.

[0946] Data calculation: The server checks the user information against the database and performs authentication.

[0947] Output: The server generates an authentication token and sends it to the device. The device receives the authentication token and the user is logged in.

[0948] Step 3: Goal Setting

[0949] Input: The user inputs a new goal, deadline, and action plan on the goal setting screen.

[0950] Operation: The terminal receives target information and sends it to the server.

[0951] Data storage: The server stores the target information in a database.

[0952] Output: A message that the save is complete is sent to the terminal, which displays it to the user.

[0953] Step 4: Generate a goal achievement plan

[0954] Input: The server retrieves the goal information stored in the database and inputs a prompt to the generative AI model. Example: "Generate a specific action plan for the user to lose 5 kg in one month."

[0955] How it works: The generative AI model performs data calculations based on the input prompt sentence and generates a specific action plan.

[0956] Output: The server receives the generated plan and sends it to the device. The device displays the plan to the user. Examples: "Walk 30 minutes every day" or "Go to the gym three times a week."

[0957] Step 5: Daily progress reports

[0958] Input: The user enters their daily activities in the activity input screen. Example: "I walked for 30 minutes today."

[0959] Operation: The terminal receives input information and sends it to the server.

[0960] Data storage: The server stores the received activity information in a database.

[0961] Output: A save confirmation message is sent to the terminal, which displays it to the user.

[0962] Step 6: Providing advice

[0963] Input: The server retrieves the activity information stored in the database and inputs a prompt to the generative AI model. Example: "Based on the user's activity information, generate advice for the next step."

[0964] How it works: The generative AI model performs data calculations based on the input prompt and generates specific advice.

[0965] Output: The server receives the generated advice and sends it to the device. The device displays the advice to the user. Examples: "Try to walk a little longer" or "Try a new route next time."

[0966] Step 7: Provide feedback

[0967] Input: Users enter feedback on the advice and content provided.

[0968] Operation: The terminal receives input information and sends it to the server.

[0969] Data storage: The server stores the received feedback information in a database.

[0970] Output: A save confirmation message is sent to the terminal, which displays it to the user.

[0971] Step 8: Optimizing Advice

[0972] Input: The server retrieves the feedback information stored in the database and inputs a prompt to the generative AI model. For example, "Based on the user's feedback information, provide optimized advice for the next step."

[0973] How it works: The generative AI model performs data calculations based on the input prompt sentence and generates new advice.

[0974] Output: The server receives the generated new advice and sends it to the terminal, which displays the new advice to the user.

[0975] Step 9: Provide learning content

[0976] Input: The server inputs a prompt to the generative AI model based on the user's behavioral data and feedback. Example: "Provide appropriate learning content based on the user's behavioral data and feedback."

[0977] How it works: The generative AI model performs data calculations based on the input prompt and selects learning content.

[0978] Output: The server receives the generated learning content and sends it to the device. The device displays the learning content to the user. Examples: "Effective meal menu for users with weight loss goals," "Exercise video."

[0979] (Application example 1)

[0980] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0981] Conventional self-improvement coaching systems do not provide sufficient specific and personalized support for the goals set by users. Furthermore, it is difficult for physical facilities such as fitness gyms to provide appropriate advice to individual users in a timely manner. Furthermore, it is not possible to dynamically update content and advice based on progress, which makes it difficult to effectively support users in maintaining their motivation and achieving their goals.

[0982] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0983] In this invention, the server includes a means for allowing a user to set a goal and storing the goal information in a database, a means for collecting user behavior data and generating specific advice for achieving the goal using a generative AI model, and a means for transmitting the generated advice to a terminal and displaying it so that the user can receive it. This makes it possible to provide training advice and learning content personalized to each user and effectively support the user in achieving their goal.

[0984] "User" refers to an individual who uses the system to set goals and receive support in achieving them.

[0985] "Goal setting" refers to the act of a user determining a specific goal they want to achieve and inputting that information.

[0986] "Database" refers to a system that stores and manages goal information, behavioral data, and feedback information.

[0987] "Behavioral Data" refers to information recorded by a user's daily progress and activities.

[0988] A "generative AI model" refers to a system that uses artificial intelligence to generate specific advice and plans to help users achieve their goals.

[0989] "Advice" refers to specific guidelines or suggestions generated by an AI model to achieve a goal.

[0990] "Terminal" refers to a device such as a smartphone or tablet that a user uses to access an application.

[0991] "Progress reporting" refers to the act of a user entering their daily activities and results into the system and providing that information.

[0992] "Feedback" refers to the act of a user inputting an evaluation or opinion regarding the advice or content provided.

[0993] "Training Advice" refers to specific guidelines for fitness and exercise.

[0994] "Learning Content" refers to educational materials and information provided to help users achieve their goals.

[0995] "Dynamic updating" refers to the process of continually changing the advice and content provided based on the user's progress and behavioral data.

[0996] "Training goal" refers to a specific fitness or exercise goal that a user aims to achieve.

[0997] "Recording" refers to the act of a user entering goals and progress into the system and saving that information.

[0998] "Personalization" refers to the process of optimizing the content and advice provided to users based on their individual needs and behavior.

[0999] This invention is a self-improvement coaching system that helps users set goals and support their achievement. This system operates primarily on a server, a terminal, and the user, and utilizes AI to provide personalized advice and learning content. The specific process for implementing this system is described below.

[1000] User Registration and Login

[1001] First, a user installs an application on a device such as a smartphone or tablet and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server, which then stores the received registration information in a database. Next, the user logs in by entering their email address and password on the login screen. The server authenticates the user by checking the database, and if authentication is successful, generates an authentication token and sends it to the user.

[1002] goal setting

[1003] The user enters a new goal on the application's goal setting screen. For example, they can set a specific goal such as "gain 5 kg of muscle mass in three months." They also enter a deadline and a specific plan. The device sends this goal information to the server, which stores it in a database. The server then uses a generative AI model to generate a specific plan for achieving the goal based on the saved goal information. This plan includes detailed action steps such as gym training three times a week and daily protein intake. The server then sends this plan to the device and displays it to the user.

[1004] Daily progress reports

[1005] Users report their daily training and activities on the activity input screen. For example, if they did 30 minutes of strength training today, they enter that information and press the send button. The device then sends the activity information to the server, which stores it in a database.

[1006] Providing Feedback

[1007] The user provides feedback on the generated advice and learning content. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. Based on the updated feedback information, the server uses the generative AI model to make further improvements and generate new, personalized advice.

[1008] Providing learning content

[1009] The server uses a generative AI model to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective training videos and nutritional advice to a user with training goals. The generated learning content is sent from the server to the device and displayed to the user.

[1010] This allows the system to effectively support the user throughout the entire process of achieving their goal and promote continuous self-improvement. For example, a user can set a training goal of "gaining 5 kg of muscle mass in three months" and include a detailed plan that includes three gym sessions per week and daily protein intake. Furthermore, in the daily activity report, the user can enter "I did 30 minutes of strength training today" and receive advice on their next training session. An example of a prompt to input to the generative AI model might be, "Set a goal of gaining 5 kg of muscle mass in three months. Plan to train at the gym three times per week and consume protein daily."

[1011] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1012] Step 1:

[1013] User Registration and Login

[1014] Input: The user enters their name, email address, and password into the device.

[1015] Operation: The device sends this registration information to the server. The server stores the received registration information in a database and sends the user a message confirming registration. The user then logs in by entering their email address and password. The server authenticates the user against the database, and if authentication is successful, generates an authentication token and sends it to the user.

[1016] Output: An authentication token is sent to the user.

[1017] Step 2:

[1018] goal setting

[1019] Input: The user enters a new goal (e.g., gain 5 kg of muscle mass in 3 months) on the goal setting screen, along with the deadline and specific plan (training at the gym three times a week, consuming protein daily).

[1020] How it works: The device sends these goal information to a server, which stores the goal information in a database and uses a generative AI model to generate a specific plan for achieving the goal.

[1021] Output: The generated plan (detailed action steps) is displayed on the terminal.

[1022] Step 3:

[1023] Daily progress reports

[1024] Input: The user enters their daily training progress in the activity input screen (e.g., I did 30 minutes of strength training today).

[1025] How it works: Your device sends activity information to a server, which stores it in a database and uses AI models to suggest next steps.

[1026] Output: Next training advice will be displayed on the device.

[1027] Step 4:

[1028] Providing Feedback

[1029] Input: The user enters their evaluation and opinion on the provided advice or content in the feedback input screen.

[1030] How it works: The device sends feedback information to the server, which stores it in a database and uses the generative AI model for further refinement.

[1031] Output: The new and improved advice is displayed on the terminal.

[1032] Step 5:

[1033] Providing learning content

[1034] Input: The server selects learning content based on user behavioral data and feedback.

[1035] How it works: The server uses a generative AI model to select learning content (e.g., training videos or nutritional advice) appropriate for the user.

[1036] Output: The selected learning content will be displayed on the device.

[1037] At each step, the specific actions are as follows:

[1038] Database operations (save, match, update)

[1039] Use of generative AI models (advice generation, plan generation)

[1040] User interface operation (input screen / display screen)

[1041] For example, an example of an input prompt for the generative AI model would be, "Set a goal of gaining 5 kg of muscle mass in three months. Plan to train at the gym three times a week and consume protein daily," which would result in personalized advice.

[1042] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1043] This invention relates to a self-improvement coaching system that helps users set goals and achieve them. The system includes a server, a terminal, a user, and an emotion engine. Utilizing AI technology, the system not only provides users with personalized advice and learning content, but also recognizes the user's emotions and provides further optimized support based on those emotions. The configuration and specific operation of the system are described below.

[1044] User Registration and Login

[1045] First, the user launches the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message confirming registration.

[1046] Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against a database. If authentication is successful, the server generates an authentication token and sends it to the user.

[1047] goal setting

[1048] The user enters a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also enters a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model to generate a specific plan for achieving the goal. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[1049] Daily progress reports and emotion recognition

[1050] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database.

[1051] The server periodically analyzes the saved activity information and uses the AI ​​model to generate advice for the next step. In addition, an emotion engine collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. The generated advice might be, for example, "increase your walking time a little" or "try a new route next time." The server then sends the advice to the device, which displays it to the user.

[1052] Feedback provision and sentiment analysis

[1053] Users provide feedback on the advice and learning content provided. They enter their evaluation and opinion on the feedback input screen and press the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data generated at the time.

[1054] The server passes the saved feedback information and emotion data to the AI ​​model for further refinement and generates new advice optimized for the user. The server then sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized assistance according to the user's progress.

[1055] Providing learning content

[1056] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the device and displayed to the user.

[1057] This allows the system to effectively support the entire process of users achieving their goals and promote continuous self-improvement.The introduction of an emotion engine allows the system to provide advice and content optimized for the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[1058] The processing flow will be explained below.

[1059] User Registration and Login

[1060] Step 1:

[1061] The user launches the application, goes to the registration screen, enters their name, email address, and password, and presses the submit button.

[1062] Step 2:

[1063] The terminal transmits the entered registration information to the server.

[1064] Step 3:

[1065] The server stores the received registration information in a database and sends a success message to the terminal.

[1066] Step 4:

[1067] The user is taken to the login screen and logs in by entering their email address and password.

[1068] Step 5:

[1069] The terminal sends the login information to the server.

[1070] Step 6:

[1071] The server compares the entered information with the database, and if authentication is successful, it generates an authentication token and sends it to the terminal along with an authentication success message.If authentication fails, it sends an error message to the terminal.

[1072] goal setting

[1073] Step 1:

[1074] The user enters a new goal (e.g., "lose 5 kg in one month") on the goal setting screen, sets a deadline and specific measures, and presses the submit button.

[1075] Step 2:

[1076] The terminal transmits the target information to the server.

[1077] Step 3:

[1078] The server stores the received target information in a database.

[1079] Step 4:

[1080] The server passes the saved goal information to the AI ​​model, which generates a specific plan for achieving the goal.

[1081] Step 5:

[1082] The server stores the plan generated by the AI ​​model (e.g., "walk 30 minutes every day") in a database and sends it to the device.

[1083] Step 6:

[1084] The terminal displays the received plan to the user.

[1085] Daily progress reports and emotion recognition

[1086] Step 1:

[1087] The user enters their daily activity (e.g., "I walked for 30 minutes today") on the report screen and presses the send button along with emotional data (e.g., facial recognition or an emotional icon selected by the user).

[1088] Step 2:

[1089] The terminal transmits the activity information and emotion data to the server.

[1090] Step 3:

[1091] The server stores the received activity information and emotion data in a database.

[1092] Step 4:

[1093] The server passes the stored activity information and emotion data to the AI ​​model to generate advice for next steps.

[1094] Step 5:

[1095] The server stores the generated advice (e.g., "increase your walking time a little" or "try a new route next time") in a database and sends it to the device.

[1096] Step 6:

[1097] The terminal displays the received advice to the user.

[1098] Feedback provision and sentiment analysis

[1099] Step 1:

[1100] The user inputs feedback about the advice or learning content provided on the feedback input screen, includes emotional data (e.g., an emotional icon or text input when giving feedback), and presses the send button.

[1101] Step 2:

[1102] The terminal transmits the feedback information and emotion data to the server.

[1103] Step 3:

[1104] The server stores the received feedback information and emotion data in a database.

[1105] Step 4:

[1106] The server passes the stored feedback information and emotion data to the AI ​​model to generate further improvement advice.

[1107] Step 5:

[1108] The server stores the generated new advice in a database and sends it to the terminal.

[1109] Step 6:

[1110] The terminal displays the received new advice to the user.

[1111] Providing learning content

[1112] Step 1:

[1113] The server passes the user's behavioral and emotional data to the AI ​​model, which then selects the most appropriate learning content.

[1114] Step 2:

[1115] The server stores the selected learning content (e.g., articles, videos, audio guides, etc.) in a database and sends it to the terminal.

[1116] Step 3:

[1117] The terminal displays the received learning content to the user.

[1118] Through these steps, the system can provide users with personalized self-improvement support, providing advice and content optimized for their emotional state, thereby increasing their motivation and improving their success rate in achieving their goals.

[1119] Example 2

[1120] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1121] In modern society, it is important for individuals to work efficiently and effectively toward their set goals. However, conventional systems have had difficulty providing personalized advice that fully takes into account the user's behavioral history and emotional state. Furthermore, the learning content provided is often uniform and fails to fully meet the needs of individual users. This can lead to a decline in users' motivation to achieve their goals, ultimately making it difficult for them to achieve them at all.

[1122] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1123] In this invention, the server includes: means for allowing a user to set a goal and store the goal information in a database; means for collecting user behavior data and generating specific advice for achieving the goal using an AI model; means for transmitting the generated advice to a terminal and displaying it so that the user can receive it; means for the user to report daily progress and record the information in a database; means for the user to input feedback on the advice and content provided and storing the feedback information in a database; means for generating further improved advice using an AI model based on the feedback information; means for using an emotion engine that analyzes user emotion data and optimizes advice based on the results; and means for selecting learning content based on the user's interests and behavior data and providing it to the user. This enables personalized support according to the user's individual situation and emotions, maintaining user motivation and enabling more effective goal achievement.

[1124] "Goal information" refers to the content of the goal set by the user, as well as the deadline and specific plan for achieving it.

[1125] A "database" is an information system for managing and storing user goal information, behavioral data, progress data, feedback information, etc.

[1126] "Behavioral Data" refers to the history of activities and actions reported by users on a daily basis.

[1127] An "AI model" is an algorithm that uses artificial intelligence technology to generate specific advice and plans for achieving goals based on user behavioral data and goal information.

[1128] "Advice" refers to specific guidelines or suggestions for action that a user needs to take to achieve their goal.

[1129] "Terminal" refers to an electronic device that a user operates and displays to use the system.

[1130] "Feedback information" is information obtained by users inputting their evaluations and opinions regarding the advice and content provided.

[1131] The "emotion engine" is a system that collects and analyzes users' emotional data and optimizes advice based on the results.

[1132] "Learning Content" refers to educational materials and content that help users achieve their goals.

[1133] "Optimization" is the process of providing advice and learning content to users in the most optimal way based on their behavioral data, feedback information, and emotional data.

[1134] The present invention relates to a self-improvement coaching system that helps users set goals and achieve them. The system includes a server, a terminal, a user, and an emotion engine. Utilizing AI technology, the system not only provides users with personalized advice and learning content, but also recognizes the user's emotions and provides further optimized support based on those emotions.

[1135] User Registration and Login

[1136] First, the user launches the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message notifying them of successful registration. Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against the database. If authentication is successful, the server generates an authentication token and sends it to the user.

[1137] goal setting

[1138] The user enters a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also enters a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model to generate a specific plan for achieving the goal. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[1139] Daily progress reports and emotion recognition

[1140] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device sends the activity information to the server, which stores it in a database. The server periodically analyzes the saved activity information and uses an AI model to generate advice for the next step. Furthermore, an emotion engine collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. The generated advice might be, for example, "increase your walking time a little" or "try a new route next time." The server sends the advice to the device, which displays it to the user.

[1141] Feedback provision and sentiment analysis

[1142] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data at the time. The server passes the stored feedback information and emotional data to the AI ​​model for further improvements and generates new advice optimized for the user. The server sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized assistance according to the user's progress.

[1143] Providing learning content

[1144] Based on the user's behavioral data and feedback, the server uses AI to select learning content appropriate for the user. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the user's device and displayed to the user. This allows the system to effectively support the entire process of users achieving their goals and promote continuous self-improvement. The introduction of an emotion engine allows the system to provide optimized advice and content that takes into account the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[1145] Specific examples

[1146] For example, if a user sets a goal of "losing 5 kg in one month," the server will generate a specific plan such as "walking for 30 minutes every day" and "going to the gym three times a week." Furthermore, based on the emotional data when the user reports that they "walked for 30 minutes," the server will provide advice such as "take a 10-minute break next time." In this way, by incorporating user feedback and constantly optimizing advice and learning content, users can steadily progress toward their goals.

[1147] Prompt Sentence Examples

[1148] "Please tell me a concrete action plan to lose 5 kg in one month."

[1149] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1150] Step 1: The user launches the application, enters their name, email address, and password on the account creation screen, and presses the register button. The device sends this information to the server. The server stores the received information in a database and sends a message to the device indicating that registration is complete. The input is the user's registration information, and the output is a message indicating that registration is complete. Specifically, the device sends the data in the input form to the server as an HTTP request, and the server executes an SQL query in the database to save the information.

[1151] Step 2: The user enters their email address and password on the login screen and presses the login button. The device sends the login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server generates an authentication token and sends it to the device. The input is the user's login information and the output is the authentication token. Specifically, the device sends the input data to the server via an HTTP request, and the server uses an SQL query to search and check the database and generate the authentication token.

[1152] Step 3: The user enters a new goal (for example, "lose 5 kg in one month") on the goal setting screen, along with a deadline and implementation plan. The device sends this goal information to the server. The server stores the goal information in a database and uses an AI model to generate a plan for achieving the goal. The generated plan is sent to the device and displayed to the user. The input is the user's goal information, and the output is a specific action plan. Specifically, the device sends the goal information to the server, and the server runs the AI ​​model to generate a plan and saves it in the database.

[1153] Step 4: The user enters their daily activity (e.g., "Walked for 30 minutes") on the activity input screen and presses the send button. The device sends the activity information to the server. The server stores the information in a database and periodically analyzes it. The emotion engine collects the user's emotion data, and the AI ​​model generates optimized advice based on that. The generated advice is sent to the device and displayed to the user. The input is the user's activity information and emotion data, and the output is optimized advice. Specifically, the device sends the activity data, and the server analyzes the data using multiple algorithms and generates the results.

[1154] Step 5: The user enters their evaluation and opinion on the provided advice and learning content on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects emotional data at the time of feedback. The server uses this information to make further improvements to the AI ​​model and generate new advice. The generated advice is sent to the device and displayed to the user. The input is feedback information and emotional data, and the output is improved advice. Specifically, the device sends the feedback data, and the server stores it in a database and analyzes it to generate new advice.

[1155] Step 6: The server uses an AI model to select appropriate learning content based on the user's behavioral data and feedback. The selected learning content is sent to the device and displayed to the user. The input is behavioral data and feedback information, and the output is learning content. Specifically, the server analyzes the behavioral data and feedback, selects the most appropriate learning content using an AI model, and sends it to the device.

[1156] (Application example 2)

[1157] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1158] Today, many users need effective support for self-improvement and goal achievement. However, conventional coaching systems and fitness applications only provide general advice and lack personalized support based on individual users' behavioral data and emotions. This makes it difficult for users to maintain their motivation and achieve their goals. Furthermore, the lack of optimization of advice that takes into account changes in the user's emotions results in lower user satisfaction and success rates.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for allowing a user to set a goal and storing the goal information in a database; means for collecting user behavioral data and generating specific advice for achieving the goal using an AI model; means for transmitting the generated advice to a terminal and displaying it so that the user can receive it; means for the user to report daily progress and record the information in a database; means for the user to input feedback on the advice and content provided and storing the feedback information in a database; means for generating further improved advice using an AI model based on the feedback information; means for selecting learning content based on the user's interests and behavioral data and providing it to the user; and means for collecting user emotion data and optimizing the generated advice using an emotion engine. This enables personalized support based on individual user behavioral data and emotions, thereby maintaining motivation and improving the success rate of goal achievement.

[1160] "User" refers to a person who uses this system to support self-development and goal achievement.

[1161] "Goal" means a specific result or achievement criterion that the user wants to achieve.

[1162] "Goal information" refers to information including detailed data about the set goal, such as deadlines and specific action plans.

[1163] "Database" refers to the information management system used by the system to store user goal information, behavioral data, feedback, and other related data.

[1164] "Behavioral data" is a record of the user's daily activities, and includes information such as the type, duration, and frequency of exercise.

[1165] An "AI model" refers to artificial intelligence that uses machine learning algorithms to analyze data and generate personalized advice and plans for users.

[1166] "Advice" refers to specific guidelines and suggestions for achieving goals generated by this system.

[1167] "Terminal" refers to a device used by a user to access the system, such as a smartphone, tablet, or computer.

[1168] "Progress" refers to information that indicates the state and process of a user's progress toward a goal.

[1169] "Feedback" refers to the user's opinions and evaluations regarding the advice and learning content they receive, as well as the information they input.

[1170] "Emotion data" is information that indicates the user's emotional state, and includes, for example, emotional expressions extracted from text or speech.

[1171] An "emotion engine" refers to a system that analyzes a user's emotional data and optimizes advice based on the results.

[1172] "Learning Content" refers to information and educational materials, such as exercise videos and meal plans, provided to help users achieve their goals.

[1173] "Optimization" refers to the act of tailoring effective advice or plans based on the user's specific situation and emotions.

[1174] The present invention relates to a virtual fitness coaching system that helps users set and achieve their goals. This system includes a server, a terminal, a user, and an emotion engine. The detailed configuration and processing steps of this system are described below.

[1175] User Registration and Login

[1176] First, a user launches an application using a device (e.g., a smartphone or tablet) and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server, which stores the received registration information in a database. Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against the database. If authentication is successful, the server generates an authentication token and sends it to the user.

[1177] goal setting

[1178] The user opens the goal setting screen on the device and enters a new goal. For example, they can set a specific goal such as "lose 5 kg in one month." At this time, they also enter a deadline and implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model such as TensorFlow to generate a specific plan for achieving the goal. The generated plan includes detailed action steps such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which displays it to the user.

[1179] Daily progress reports and emotion recognition

[1180] Users report their daily activity on their device. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database. The server periodically analyzes the saved activity information and uses an AI model to generate advice for the next step. An emotion engine also collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. Examples of generated advice include "increase your walking time a little" or "try a new route next time." The server then sends the advice to the device, which displays it to the user.

[1181] Feedback provision and sentiment analysis

[1182] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data at the time. The server passes the stored feedback information and emotional data to the AI ​​model for further improvements and generates new advice optimized for the user. The server sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized support according to the user's progress.

[1183] Providing learning content

[1184] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the user's device and displayed to the user. This effectively supports the user throughout the entire process of achieving their goal. In addition, the introduction of an emotion engine allows the system to provide advice and content optimized for the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[1185] Specific examples

[1186] For example, if a user sets a goal of "losing 5 kg in one month," the user can report progress by walking 30 minutes every day. If the user gives feedback such as "It felt great," the emotion engine analyzes this information and detects positive emotions. Based on this, the server generates advice such as "Try extending your walking time a little" or "Try a new walking route" and provides it to the user.

[1187] Prompt Sentence Examples

[1188] "Q: User A has a goal of walking 30 minutes every day and says that they felt great today. What advice would you suggest for their next step?"

[1189] In this way, users always receive personalized assistance and are able to effectively progress towards achieving their goals.

[1190] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1191] Step 1: User Registration

[1192] A user creates an account by entering their name, email address, and password using a terminal. This input information is sent from the terminal to the server. The server stores the received information in a database, generates a message indicating registration is complete, and sends it to the terminal. The input here is the user's personal information, and the output is a message indicating that the information has been saved to the database and that registration is complete.

[1193] Step 2: Log in

[1194] The user enters their email address and password on the login screen of their device. This information is sent from the device to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates an authentication token and sends it to the device. The input here is login information, and the generated authentication token is the output.

[1195] Step 3: Goal Setting

[1196] The user enters a new goal on the device's goal setting screen. For example, detailed goal information such as "lose 5 kg in one month" is entered. This information is sent from the device to the server, which stores it in a database. The server then uses an AI model to generate a specific plan for achieving the goal and sends it to the device. The input here is the goal information, and the output is the completion of saving it to the database and the generation of a goal achievement plan.

[1197] Step 4: Daily progress reports

[1198] The user enters their daily activity information into the device and presses the send button. For example, they enter information such as "I walked for 30 minutes." This data is sent from the device to the server, which stores it in a database. The server periodically analyzes the stored activity information and generates advice for the next step. The input here is the activity information, and the output is stored in the database and advice is generated.

[1199] Step 5: Emotion Recognition

[1200] The user inputs emotional feedback along with the progress report. For example, they input an emotional expression such as "I felt great today." This feedback is sent from the device to the server, which then analyzes the emotional data using an emotion engine. The server then optimizes advice based on the emotional data and sends it to the user. The input here is emotional feedback, and the output is optimized advice.

[1201] Step 6: Provide feedback

[1202] The user enters feedback on the advice or content provided and presses the send button. This data is sent from the device to the server, which stores it in a database. The emotion engine collects the feedback and emotional data generated at the time, and uses an AI model to generate further improved advice. The input here is the feedback information, and the output is database storage and the generation of improved advice.

[1203] Step 7: Provide learning content

[1204] The server uses a generative AI model to select learning content appropriate for the user based on the user's behavioral data and feedback, and sends it to the device. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The input here is behavioral data and feedback, and the output is the selection and provision of learning content.

[1205] 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.

[1206] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1207] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1208] [Fourth embodiment]

[1209] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1210] 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.

[1211] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1212] 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.

[1213] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1214] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1215] 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.

[1216] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1217] 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.

[1218] 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 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.

[1219] 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.

[1220] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1221] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1222] This invention relates to a self-development coaching system that helps users set goals and support their achievement. This system operates primarily on a server, a terminal, and the user, and utilizes AI to provide personalized advice and learning content. The configuration and specific operation of this system are described below.

[1223] User Registration and Login

[1224] First, the user installs the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message confirming registration.

[1225] Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against a database. If authentication is successful, the server generates an authentication token and sends it to the user.

[1226] goal setting

[1227] The user inputs a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also inputs a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database.

[1228] The server uses the AI ​​model to generate a specific plan for achieving the goal based on the saved goal information. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[1229] Daily progress reports

[1230] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database.

[1231] The server periodically analyzes the saved activity information and generates advice for the next step using an AI model. The advice generated might be, for example, "Increase your walking time a little" or "Try a new route next time." The server then sends the advice to the device, which then displays it to the user.

[1232] Providing Feedback

[1233] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device then sends the feedback information to the server, which stores it in a database.

[1234] The server uses the updated feedback information to further improve the AI ​​model and generate new advice optimized for the user. In this way, the server continues to provide personalized assistance according to the user's progress.

[1235] Providing learning content

[1236] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the device and displayed to the user.

[1237] This allows the system to effectively support users throughout the entire process of achieving their goals and promote continuous self-improvement.

[1238] The processing flow will be explained below.

[1239] User Registration and Login

[1240] Step 1:

[1241] The user launches the application, goes to the registration screen, enters their name, email address, and password, and presses the submit button.

[1242] Step 2:

[1243] The terminal transmits the entered registration information to the server.

[1244] Step 3:

[1245] The server stores the received registration information in a database and sends a success message to the terminal.

[1246] Step 4:

[1247] The user is taken to the login screen and logs in by entering their email address and password.

[1248] Step 5:

[1249] The terminal sends the login information to the server.

[1250] Step 6:

[1251] The server compares the entered information with the database, and if authentication is successful, it generates an authentication token and sends it to the terminal along with an authentication success message.If authentication fails, it sends an error message to the terminal.

[1252] goal setting

[1253] Step 1:

[1254] The user enters a new goal (e.g., "lose 5 kg in one month") on the goal setting screen, sets a deadline and specific measures, and presses the submit button.

[1255] Step 2:

[1256] The terminal transmits the target information to the server.

[1257] Step 3:

[1258] The server stores the received target information in a database.

[1259] Step 4:

[1260] The server passes the saved goal information to the AI ​​model, which generates a specific plan for achieving the goal.

[1261] Step 5:

[1262] The server stores the plan generated by the AI ​​model (e.g., "walk 30 minutes every day") in a database and sends it to the device.

[1263] Step 6:

[1264] The terminal displays the received plan to the user.

[1265] Daily progress reports

[1266] Step 1:

[1267] The user enters their daily activity (e.g., "I walked for 30 minutes today") on the report screen and presses the send button.

[1268] Step 2:

[1269] The terminal transmits activity information to the server.

[1270] Step 3:

[1271] The server stores the received activity information in a database.

[1272] Step 4:

[1273] The server passes the saved activity information to an AI model to generate advice for next steps.

[1274] Step 5:

[1275] The server stores the generated advice (e.g., "increase your walking time a little" or "try a new route next time") in a database and sends it to the device.

[1276] Step 6:

[1277] The terminal displays the received advice to the user.

[1278] Providing Feedback

[1279] Step 1:

[1280] The user enters feedback about the advice and learning content provided on the feedback input screen and presses the send button.

[1281] Step 2:

[1282] The terminal transmits the feedback information to the server.

[1283] Step 3:

[1284] The server stores the received feedback in a database.

[1285] Step 4:

[1286] The server passes the stored feedback information to the AI ​​model to generate further improvement advice.

[1287] Step 5:

[1288] The server stores the generated new advice in a database and sends it to the terminal.

[1289] Step 6:

[1290] The terminal displays the received new advice to the user.

[1291] Providing learning content

[1292] Step 1:

[1293] The server passes the user's behavioral data and feedback information to the AI ​​model, which selects the most appropriate learning content.

[1294] Step 2:

[1295] The server stores the selected learning content (e.g., articles, videos, audio guides, etc.) in a database and sends it to the terminal.

[1296] Step 3:

[1297] The terminal displays the received learning content to the user.

[1298] Through these steps, the system provides users with personalized self-improvement support and effectively promotes goal achievement.

[1299] Example 1

[1300] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1301] Conventional self-improvement support systems have had the challenge of providing personalized support for individual users' goals and progress. Furthermore, they lacked the ability to optimize based on user feedback or provide continuous advice according to progress. This made it difficult for users to maintain their motivation to achieve their goals.

[1302] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1303] In this invention, the server includes means for allowing a user to set a goal and storing the goal information in a database, means for collecting user behavioral data and generating specific advice for achieving the goal using a generative artificial intelligence model, means for transmitting the generated advice to a terminal and displaying it so that the user can receive it, means for the user to report daily progress and record the information in the database, means for the user to input feedback on the advice and content provided and storing the feedback information in the database, means for generating further improved advice using the generative artificial intelligence model based on the feedback information, means for selecting learning content based on the user's interests and behavioral data and providing it to the user, and means for creating input prompts for the generative artificial intelligence model. This allows users to receive support optimized for their individual goals, making it easier for them to maintain their motivation to achieve their goals.

[1304] "User" refers to a person who uses this system to set goals and aim to achieve them.

[1305] "Goal information" refers to the specific goal set by the user, as well as the detailed plan and deadline for achieving it.

[1306] "Database" refers to an information management system for storing user goal information, behavioral data, feedback information, and the like.

[1307] "Behavioral Data" refers to data collected by users reporting their daily activities and progress.

[1308] "Generative artificial intelligence model" refers to AI technology used to help users achieve their goals, specifically models for generating text and plans.

[1309] "Advice" refers to specific guidelines for action to achieve a user's goals, generated using a generative artificial intelligence model.

[1310] "Terminal" refers to a device (such as a smartphone or PC) that a user uses to use an application.

[1311] "Progress reporting" refers to a user recording their daily activities and achievements and sending them to the system.

[1312] "Feedback information" refers to evaluations and opinions entered by users regarding the advice and content provided.

[1313] "Learning Content" refers to educational materials and information provided to help users achieve their goals and develop themselves.

[1314] An "input prompt" is an instruction entered into a generative artificial intelligence model, and refers to the text used to generate specific advice or plans.

[1315] MODE FOR CARRYING OUT THE INVENTION

[1316] The present invention relates to a self-improvement coaching system for supporting a user in setting and achieving a goal. Specific embodiments of the system will be described below.

[1317] User Registration and Login

[1318] First, a user installs the application using a device such as a smartphone or PC. After installation, the user creates an account by entering their name, email address, and password on the account creation screen. The device sends this user information to the server, which then stores the received information in a database. Once registration is complete, the server sends a registration completion message to the device, which the device displays to the user.

[1319] Next, the user enters their email address and password on the login screen to log in. The device sends this information to the server, which then authenticates them by checking it against the user information in its database. If authentication is successful, the server generates an authentication token and sends it to the device. The device receives the authentication token and uses it to maintain the user's logged-in state.

[1320] goal setting

[1321] The user inputs a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." They also input the deadline for the goal and an implementation plan. The device sends this information to the server, which then stores the goal information in a database.

[1322] Based on the saved goal information, the server uses a generative artificial intelligence model (e.g., GPT-4) to generate a specific action plan for achieving the goal. A specific prompt could be, "Generate a specific action plan for the user to lose 5 kg in one month." The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[1323] Daily progress reports

[1324] The user reports their daily progress on the activity input screen. For example, they enter information such as "I walked for 30 minutes today" and press the send button. The device sends this activity information to the server, which then stores the received information in a database.

[1325] The server periodically analyzes the stored activity information and generates advice for the next step using a generative artificial intelligence model. A specific prompt could be, "Generate advice for the next step based on the user's activity information." The generated advice could be, for example, "Increase your walking time a little" or "Try a new route next time." The server sends this advice to the device, which then displays it to the user.

[1326] Providing Feedback

[1327] The user provides feedback on the advice and learning content provided. They enter their evaluation and opinion on the feedback input screen and press the send button. The device then sends the feedback information to the server, which then stores the received information in a database.

[1328] The server uses the feedback information to further improve the generative artificial intelligence model and generate new advice. A specific prompt could be, "Based on the user's feedback information, provide optimized advice for the next step." The server sends this new advice to the device, which then displays it to the user.

[1329] Providing learning content

[1330] The server uses a generative artificial intelligence model to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. A specific prompt could be, "Provide appropriate learning content based on the user's behavioral data and feedback." The generated learning content is sent from the server to the device and can be used by the user.

[1331] conclusion

[1332] This system effectively supports users through the entire process from goal setting to goal achievement, and can promote continuous self-improvement. The present invention provides personalized support for the individual needs of each user, helping them maintain motivation to achieve their goals.

[1333] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1334] Step 1: User Registration

[1335] Input: The user installs the application and enters their name, email address, and password.

[1336] Operation: The terminal receives input information and sends it to the server.

[1337] Data processing: The server processes the received information and stores the user information in a database.

[1338] Output: The server sends a registration completion message to the terminal, which displays it to the user.

[1339] Step 2: User Login

[1340] Input: The user enters their email address and password on the login screen.

[1341] Operation: The terminal receives input information and sends it to the server.

[1342] Data calculation: The server checks the user information against the database and performs authentication.

[1343] Output: The server generates an authentication token and sends it to the device. The device receives the authentication token and the user is logged in.

[1344] Step 3: Goal Setting

[1345] Input: The user inputs a new goal, deadline, and action plan on the goal setting screen.

[1346] Operation: The terminal receives target information and sends it to the server.

[1347] Data storage: The server stores the target information in a database.

[1348] Output: A message that the save is complete is sent to the terminal, which displays it to the user.

[1349] Step 4: Generate a goal achievement plan

[1350] Input: The server retrieves the goal information stored in the database and inputs a prompt to the generative AI model. Example: "Generate a specific action plan for the user to lose 5 kg in one month."

[1351] How it works: The generative AI model performs data calculations based on the input prompt sentence and generates a specific action plan.

[1352] Output: The server receives the generated plan and sends it to the device. The device displays the plan to the user. Examples: "Walk 30 minutes every day" or "Go to the gym three times a week."

[1353] Step 5: Daily progress reports

[1354] Input: The user enters their daily activities in the activity input screen. Example: "I walked for 30 minutes today."

[1355] Operation: The terminal receives input information and sends it to the server.

[1356] Data storage: The server stores the received activity information in a database.

[1357] Output: A save confirmation message is sent to the terminal, which displays it to the user.

[1358] Step 6: Providing advice

[1359] Input: The server retrieves the activity information stored in the database and inputs a prompt to the generative AI model. Example: "Based on the user's activity information, generate advice for the next step."

[1360] How it works: The generative AI model performs data calculations based on the input prompt and generates specific advice.

[1361] Output: The server receives the generated advice and sends it to the device. The device displays the advice to the user. Examples: "Try to walk a little longer" or "Try a new route next time."

[1362] Step 7: Provide feedback

[1363] Input: Users enter feedback on the advice and content provided.

[1364] Operation: The terminal receives input information and sends it to the server.

[1365] Data storage: The server stores the received feedback information in a database.

[1366] Output: A save confirmation message is sent to the terminal, which displays it to the user.

[1367] Step 8: Optimizing Advice

[1368] Input: The server retrieves the feedback information stored in the database and inputs a prompt to the generative AI model. For example, "Based on the user's feedback information, provide optimized advice for the next step."

[1369] How it works: The generative AI model performs data calculations based on the input prompt sentence and generates new advice.

[1370] Output: The server receives the generated new advice and sends it to the terminal, which displays the new advice to the user.

[1371] Step 9: Provide learning content

[1372] Input: The server inputs a prompt to the generative AI model based on the user's behavioral data and feedback. Example: "Provide appropriate learning content based on the user's behavioral data and feedback."

[1373] How it works: The generative AI model performs data calculations based on the input prompt and selects learning content.

[1374] Output: The server receives the generated learning content and sends it to the device. The device displays the learning content to the user. Examples: "Effective meal menu for users with weight loss goals," "Exercise video."

[1375] (Application example 1)

[1376] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1377] Conventional self-improvement coaching systems do not provide sufficient specific and personalized support for the goals set by users. Furthermore, it is difficult for physical facilities such as fitness gyms to provide appropriate advice to individual users in a timely manner. Furthermore, it is not possible to dynamically update content and advice based on progress, which makes it difficult to effectively support users in maintaining their motivation and achieving their goals.

[1378] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1379] In this invention, the server includes a means for allowing a user to set a goal and storing the goal information in a database, a means for collecting user behavior data and generating specific advice for achieving the goal using a generative AI model, and a means for transmitting the generated advice to a terminal and displaying it so that the user can receive it. This makes it possible to provide training advice and learning content personalized to each user and effectively support the user in achieving their goal.

[1380] "User" refers to an individual who uses the system to set goals and receive support in achieving them.

[1381] "Goal setting" refers to the act of a user determining a specific goal they want to achieve and inputting that information.

[1382] "Database" refers to a system that stores and manages goal information, behavioral data, and feedback information.

[1383] "Behavioral Data" refers to information recorded by a user's daily progress and activities.

[1384] A "generative AI model" refers to a system that uses artificial intelligence to generate specific advice and plans to help users achieve their goals.

[1385] "Advice" refers to specific guidelines or suggestions generated by an AI model to achieve a goal.

[1386] "Terminal" refers to a device such as a smartphone or tablet that a user uses to access an application.

[1387] "Progress reporting" refers to the act of a user entering their daily activities and results into the system and providing that information.

[1388] "Feedback" refers to the act of a user inputting an evaluation or opinion regarding the advice or content provided.

[1389] "Training Advice" refers to specific guidelines for fitness and exercise.

[1390] "Learning Content" refers to educational materials and information provided to help users achieve their goals.

[1391] "Dynamic updating" refers to the process of continually changing the advice and content provided based on the user's progress and behavioral data.

[1392] "Training goal" refers to a specific fitness or exercise goal that a user aims to achieve.

[1393] "Recording" refers to the act of a user entering goals and progress into the system and saving that information.

[1394] "Personalization" refers to the process of optimizing the content and advice provided to users based on their individual needs and behavior.

[1395] This invention is a self-improvement coaching system that helps users set goals and support their achievement. This system operates primarily on a server, a terminal, and the user, and utilizes AI to provide personalized advice and learning content. The specific process for implementing this system is described below.

[1396] User Registration and Login

[1397] First, a user installs an application on a device such as a smartphone or tablet and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server, which then stores the received registration information in a database. Next, the user logs in by entering their email address and password on the login screen. The server authenticates the user by checking the database, and if authentication is successful, generates an authentication token and sends it to the user.

[1398] goal setting

[1399] The user enters a new goal on the application's goal setting screen. For example, they can set a specific goal such as "gain 5 kg of muscle mass in three months." They also enter a deadline and a specific plan. The device sends this goal information to the server, which stores it in a database. The server then uses a generative AI model to generate a specific plan for achieving the goal based on the saved goal information. This plan includes detailed action steps such as gym training three times a week and daily protein intake. The server then sends this plan to the device and displays it to the user.

[1400] Daily progress reports

[1401] Users report their daily training and activities on the activity input screen. For example, if they did 30 minutes of strength training today, they enter that information and press the send button. The device then sends the activity information to the server, which stores it in a database.

[1402] Providing Feedback

[1403] The user provides feedback on the generated advice and learning content. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. Based on the updated feedback information, the server uses the generative AI model to make further improvements and generate new, personalized advice.

[1404] Providing learning content

[1405] The server uses a generative AI model to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective training videos and nutritional advice to a user with training goals. The generated learning content is sent from the server to the device and displayed to the user.

[1406] This allows the system to effectively support the user throughout the entire process of achieving their goal and promote continuous self-improvement. For example, a user can set a training goal of "gaining 5 kg of muscle mass in three months" and include a detailed plan that includes three gym sessions per week and daily protein intake. Furthermore, in the daily activity report, the user can enter "I did 30 minutes of strength training today" and receive advice on their next training session. An example of a prompt to input to the generative AI model might be, "Set a goal of gaining 5 kg of muscle mass in three months. Plan to train at the gym three times per week and consume protein daily."

[1407] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1408] Step 1:

[1409] User Registration and Login

[1410] Input: The user enters their name, email address, and password into the device.

[1411] Operation: The device sends this registration information to the server. The server stores the received registration information in a database and sends the user a message confirming registration. The user then logs in by entering their email address and password. The server authenticates the user against the database, and if authentication is successful, generates an authentication token and sends it to the user.

[1412] Output: An authentication token is sent to the user.

[1413] Step 2:

[1414] goal setting

[1415] Input: The user enters a new goal (e.g., gain 5 kg of muscle mass in 3 months) on the goal setting screen, along with the deadline and specific plan (training at the gym three times a week, consuming protein daily).

[1416] How it works: The device sends these goal information to a server, which stores the goal information in a database and uses a generative AI model to generate a specific plan for achieving the goal.

[1417] Output: The generated plan (detailed action steps) is displayed on the terminal.

[1418] Step 3:

[1419] Daily progress reports

[1420] Input: The user enters their daily training progress in the activity input screen (e.g., I did 30 minutes of strength training today).

[1421] How it works: Your device sends activity information to a server, which stores it in a database and uses AI models to suggest next steps.

[1422] Output: Next training advice will be displayed on the device.

[1423] Step 4:

[1424] Providing Feedback

[1425] Input: The user enters their evaluation and opinion on the provided advice or content in the feedback input screen.

[1426] How it works: The device sends feedback information to the server, which stores it in a database and uses the generative AI model for further refinement.

[1427] Output: The new and improved advice is displayed on the terminal.

[1428] Step 5:

[1429] Providing learning content

[1430] Input: The server selects learning content based on user behavioral data and feedback.

[1431] How it works: The server uses a generative AI model to select learning content (e.g., training videos or nutritional advice) appropriate for the user.

[1432] Output: The selected learning content will be displayed on the device.

[1433] At each step, the specific actions are as follows:

[1434] Database operations (save, match, update)

[1435] Use of generative AI models (advice generation, plan generation)

[1436] User interface operation (input screen / display screen)

[1437] For example, an example of an input prompt for the generative AI model would be, "Set a goal of gaining 5 kg of muscle mass in three months. Plan to train at the gym three times a week and consume protein daily," which would result in personalized advice.

[1438] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1439] This invention relates to a self-improvement coaching system that helps users set goals and achieve them. The system includes a server, a terminal, a user, and an emotion engine. Utilizing AI technology, the system not only provides users with personalized advice and learning content, but also recognizes the user's emotions and provides further optimized support based on those emotions. The configuration and specific operation of the system are described below.

[1440] User Registration and Login

[1441] First, the user launches the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message confirming registration.

[1442] Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against a database. If authentication is successful, the server generates an authentication token and sends it to the user.

[1443] goal setting

[1444] The user enters a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also enters a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model to generate a specific plan for achieving the goal. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[1445] Daily progress reports and emotion recognition

[1446] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database.

[1447] The server periodically analyzes the saved activity information and uses the AI ​​model to generate advice for the next step. In addition, an emotion engine collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. The generated advice might be, for example, "increase your walking time a little" or "try a new route next time." The server then sends the advice to the device, which displays it to the user.

[1448] Feedback provision and sentiment analysis

[1449] Users provide feedback on the advice and learning content provided. They enter their evaluation and opinion on the feedback input screen and press the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data generated at the time.

[1450] The server passes the saved feedback information and emotion data to the AI ​​model for further refinement and generates new advice optimized for the user. The server then sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized assistance according to the user's progress.

[1451] Providing learning content

[1452] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the device and displayed to the user.

[1453] This allows the system to effectively support the entire process of users achieving their goals and promote continuous self-improvement.The introduction of an emotion engine allows the system to provide advice and content optimized for the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[1454] The processing flow will be explained below.

[1455] User Registration and Login

[1456] Step 1:

[1457] The user launches the application, goes to the registration screen, enters their name, email address, and password, and presses the submit button.

[1458] Step 2:

[1459] The terminal transmits the entered registration information to the server.

[1460] Step 3:

[1461] The server stores the received registration information in a database and sends a success message to the terminal.

[1462] Step 4:

[1463] The user is taken to the login screen and logs in by entering their email address and password.

[1464] Step 5:

[1465] The terminal sends the login information to the server.

[1466] Step 6:

[1467] The server compares the entered information with the database, and if authentication is successful, it generates an authentication token and sends it to the terminal along with an authentication success message.If authentication fails, it sends an error message to the terminal.

[1468] goal setting

[1469] Step 1:

[1470] The user enters a new goal (e.g., "lose 5 kg in one month") on the goal setting screen, sets a deadline and specific measures, and presses the submit button.

[1471] Step 2:

[1472] The terminal transmits the target information to the server.

[1473] Step 3:

[1474] The server stores the received target information in a database.

[1475] Step 4:

[1476] The server passes the saved goal information to the AI ​​model, which generates a specific plan for achieving the goal.

[1477] Step 5:

[1478] The server stores the plan generated by the AI ​​model (e.g., "walk 30 minutes every day") in a database and sends it to the device.

[1479] Step 6:

[1480] The terminal displays the received plan to the user.

[1481] Daily progress reports and emotion recognition

[1482] Step 1:

[1483] The user enters their daily activity (e.g., "I walked for 30 minutes today") on the report screen and presses the send button along with emotional data (e.g., facial recognition or an emotional icon selected by the user).

[1484] Step 2:

[1485] The terminal transmits the activity information and emotion data to the server.

[1486] Step 3:

[1487] The server stores the received activity information and emotion data in a database.

[1488] Step 4:

[1489] The server passes the stored activity information and emotion data to the AI ​​model to generate advice for next steps.

[1490] Step 5:

[1491] The server stores the generated advice (e.g., "increase your walking time a little" or "try a new route next time") in a database and sends it to the device.

[1492] Step 6:

[1493] The terminal displays the received advice to the user.

[1494] Feedback provision and sentiment analysis

[1495] Step 1:

[1496] The user inputs feedback about the advice or learning content provided on the feedback input screen, includes emotional data (e.g., an emotional icon or text input when giving feedback), and presses the send button.

[1497] Step 2:

[1498] The terminal transmits the feedback information and emotion data to the server.

[1499] Step 3:

[1500] The server stores the received feedback information and emotion data in a database.

[1501] Step 4:

[1502] The server passes the stored feedback information and emotion data to the AI ​​model to generate further improvement advice.

[1503] Step 5:

[1504] The server stores the generated new advice in a database and sends it to the terminal.

[1505] Step 6:

[1506] The terminal displays the received new advice to the user.

[1507] Providing learning content

[1508] Step 1:

[1509] The server passes the user's behavioral and emotional data to the AI ​​model, which then selects the most appropriate learning content.

[1510] Step 2:

[1511] The server stores the selected learning content (e.g., articles, videos, audio guides, etc.) in a database and sends it to the terminal.

[1512] Step 3:

[1513] The terminal displays the received learning content to the user.

[1514] Through these steps, the system can provide users with personalized self-improvement support, providing advice and content optimized for their emotional state, thereby increasing their motivation and improving their success rate in achieving their goals.

[1515] Example 2

[1516] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1517] In modern society, it is important for individuals to work efficiently and effectively toward their set goals. However, conventional systems have had difficulty providing personalized advice that fully takes into account the user's behavioral history and emotional state. Furthermore, the learning content provided is often uniform and fails to fully meet the needs of individual users. This can lead to a decline in users' motivation to achieve their goals, ultimately making it difficult for them to achieve them at all.

[1518] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1519] In this invention, the server includes: means for allowing a user to set a goal and store the goal information in a database; means for collecting user behavior data and generating specific advice for achieving the goal using an AI model; means for transmitting the generated advice to a terminal and displaying it so that the user can receive it; means for the user to report daily progress and record the information in a database; means for the user to input feedback on the advice and content provided and storing the feedback information in a database; means for generating further improved advice using an AI model based on the feedback information; means for using an emotion engine that analyzes user emotion data and optimizes advice based on the results; and means for selecting learning content based on the user's interests and behavior data and providing it to the user. This enables personalized support according to the user's individual situation and emotions, maintaining user motivation and enabling more effective goal achievement.

[1520] "Goal information" refers to the content of the goal set by the user, as well as the deadline and specific plan for achieving it.

[1521] A "database" is an information system for managing and storing user goal information, behavioral data, progress data, feedback information, etc.

[1522] "Behavioral Data" refers to the history of activities and actions reported by users on a daily basis.

[1523] An "AI model" is an algorithm that uses artificial intelligence technology to generate specific advice and plans for achieving goals based on user behavioral data and goal information.

[1524] "Advice" refers to specific guidelines or suggestions for action that a user needs to take to achieve their goal.

[1525] "Terminal" refers to an electronic device that a user operates and displays to use the system.

[1526] "Feedback information" is information obtained by users inputting their evaluations and opinions regarding the advice and content provided.

[1527] The "emotion engine" is a system that collects and analyzes users' emotional data and optimizes advice based on the results.

[1528] "Learning Content" refers to educational materials and content that help users achieve their goals.

[1529] "Optimization" is the process of providing advice and learning content to users in the most optimal way based on their behavioral data, feedback information, and emotional data.

[1530] The present invention relates to a self-improvement coaching system that helps users set goals and achieve them. The system includes a server, a terminal, a user, and an emotion engine. Utilizing AI technology, the system not only provides users with personalized advice and learning content, but also recognizes the user's emotions and provides further optimized support based on those emotions.

[1531] User Registration and Login

[1532] First, the user launches the application and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server. The server stores the received registration information in a database and sends the user a message notifying them of successful registration. Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against the database. If authentication is successful, the server generates an authentication token and sends it to the user.

[1533] goal setting

[1534] The user enters a new goal on the goal setting screen. For example, they can set a specific goal such as "lose 5 kg in one month." The user also enters a deadline and an implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model to generate a specific plan for achieving the goal. The generated plan includes detailed action steps, such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which then displays it to the user.

[1535] Daily progress reports and emotion recognition

[1536] The user reports their daily activity. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device sends the activity information to the server, which stores it in a database. The server periodically analyzes the saved activity information and uses an AI model to generate advice for the next step. Furthermore, an emotion engine collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. The generated advice might be, for example, "increase your walking time a little" or "try a new route next time." The server sends the advice to the device, which displays it to the user.

[1537] Feedback provision and sentiment analysis

[1538] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data at the time. The server passes the stored feedback information and emotional data to the AI ​​model for further improvements and generates new advice optimized for the user. The server sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized assistance according to the user's progress.

[1539] Providing learning content

[1540] Based on the user's behavioral data and feedback, the server uses AI to select learning content appropriate for the user. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the user's device and displayed to the user. This allows the system to effectively support the entire process of users achieving their goals and promote continuous self-improvement. The introduction of an emotion engine allows the system to provide optimized advice and content that takes into account the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[1541] Specific examples

[1542] For example, if a user sets a goal of "losing 5 kg in one month," the server will generate a specific plan such as "walking for 30 minutes every day" and "going to the gym three times a week." Furthermore, based on the emotional data when the user reports that they "walked for 30 minutes," the server will provide advice such as "take a 10-minute break next time." In this way, by incorporating user feedback and constantly optimizing advice and learning content, users can steadily progress toward their goals.

[1543] Prompt Sentence Examples

[1544] "Please tell me a concrete action plan to lose 5 kg in one month."

[1545] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1546] Step 1: The user launches the application, enters their name, email address, and password on the account creation screen, and presses the register button. The device sends this information to the server. The server stores the received information in a database and sends a message to the device indicating that registration is complete. The input is the user's registration information, and the output is a message indicating that registration is complete. Specifically, the device sends the data in the input form to the server as an HTTP request, and the server executes an SQL query in the database to save the information.

[1547] Step 2: The user enters their email address and password on the login screen and presses the login button. The device sends the login information to the server. The server compares it with the database and authenticates the user. If authentication is successful, the server generates an authentication token and sends it to the device. The input is the user's login information and the output is the authentication token. Specifically, the device sends the input data to the server via an HTTP request, and the server uses an SQL query to search and check the database and generate the authentication token.

[1548] Step 3: The user enters a new goal (for example, "lose 5 kg in one month") on the goal setting screen, along with a deadline and implementation plan. The device sends this goal information to the server. The server stores the goal information in a database and uses an AI model to generate a plan for achieving the goal. The generated plan is sent to the device and displayed to the user. The input is the user's goal information, and the output is a specific action plan. Specifically, the device sends the goal information to the server, and the server runs the AI ​​model to generate a plan and saves it in the database.

[1549] Step 4: The user enters their daily activity (e.g., "Walked for 30 minutes") on the activity input screen and presses the send button. The device sends the activity information to the server. The server stores the information in a database and periodically analyzes it. The emotion engine collects the user's emotion data, and the AI ​​model generates optimized advice based on that. The generated advice is sent to the device and displayed to the user. The input is the user's activity information and emotion data, and the output is optimized advice. Specifically, the device sends the activity data, and the server analyzes the data using multiple algorithms and generates the results.

[1550] Step 5: The user enters their evaluation and opinion on the provided advice and learning content on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects emotional data at the time of feedback. The server uses this information to make further improvements to the AI ​​model and generate new advice. The generated advice is sent to the device and displayed to the user. The input is feedback information and emotional data, and the output is improved advice. Specifically, the device sends the feedback data, and the server stores it in a database and analyzes it to generate new advice.

[1551] Step 6: The server uses an AI model to select appropriate learning content based on the user's behavioral data and feedback. The selected learning content is sent to the device and displayed to the user. The input is behavioral data and feedback information, and the output is learning content. Specifically, the server analyzes the behavioral data and feedback, selects the most appropriate learning content using an AI model, and sends it to the device.

[1552] (Application example 2)

[1553] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1554] Today, many users need effective support for self-improvement and goal achievement. However, conventional coaching systems and fitness applications only provide general advice and lack personalized support based on individual users' behavioral data and emotions. This makes it difficult for users to maintain their motivation and achieve their goals. Furthermore, the lack of optimization of advice that takes into account changes in the user's emotions results in lower user satisfaction and success rates.

[1555] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for allowing a user to set a goal and storing the goal information in a database; means for collecting user behavioral data and generating specific advice for achieving the goal using an AI model; means for transmitting the generated advice to a terminal and displaying it so that the user can receive it; means for the user to report daily progress and record the information in a database; means for the user to input feedback on the advice and content provided and storing the feedback information in a database; means for generating further improved advice using an AI model based on the feedback information; means for selecting learning content based on the user's interests and behavioral data and providing it to the user; and means for collecting user emotion data and optimizing the generated advice using an emotion engine. This enables personalized support based on individual user behavioral data and emotions, thereby maintaining motivation and improving the success rate of goal achievement.

[1556] "User" refers to a person who uses this system to support self-development and goal achievement.

[1557] "Goal" means a specific result or achievement criterion that the user wants to achieve.

[1558] "Goal information" refers to information including detailed data about the set goal, such as deadlines and specific action plans.

[1559] "Database" refers to the information management system used by the system to store user goal information, behavioral data, feedback, and other related data.

[1560] "Behavioral data" is a record of the user's daily activities, and includes information such as the type, duration, and frequency of exercise.

[1561] An "AI model" refers to artificial intelligence that uses machine learning algorithms to analyze data and generate personalized advice and plans for users.

[1562] "Advice" refers to specific guidelines and suggestions for achieving goals generated by this system.

[1563] "Terminal" refers to a device used by a user to access the system, such as a smartphone, tablet, or computer.

[1564] "Progress" refers to information that indicates the state and process of a user's progress toward a goal.

[1565] "Feedback" refers to the user's opinions and evaluations regarding the advice and learning content they receive, as well as the information they input.

[1566] "Emotion data" is information that indicates the user's emotional state, and includes, for example, emotional expressions extracted from text or speech.

[1567] An "emotion engine" refers to a system that analyzes a user's emotional data and optimizes advice based on the results.

[1568] "Learning Content" refers to information and educational materials, such as exercise videos and meal plans, provided to help users achieve their goals.

[1569] "Optimization" refers to the act of tailoring effective advice or plans based on the user's specific situation and emotions.

[1570] The present invention relates to a virtual fitness coaching system that helps users set and achieve their goals. This system includes a server, a terminal, a user, and an emotion engine. The detailed configuration and processing steps of this system are described below.

[1571] User Registration and Login

[1572] First, a user launches an application using a device (e.g., a smartphone or tablet) and creates an account. The user registers by entering their name, email address, and password. The device sends this information to the server, which stores the received registration information in a database. Next, the user logs in by entering their email address and password on the login screen. The device sends this information to the server, which then authenticates the user by checking it against the database. If authentication is successful, the server generates an authentication token and sends it to the user.

[1573] goal setting

[1574] The user opens the goal setting screen on the device and enters a new goal. For example, they can set a specific goal such as "lose 5 kg in one month." At this time, they also enter a deadline and implementation plan. The device sends this goal information to the server, which stores it in a database. Based on the saved goal information, the server uses an AI model such as TensorFlow to generate a specific plan for achieving the goal. The generated plan includes detailed action steps such as "walk 30 minutes every day" or "go to the gym three times a week." The server sends this plan to the device, which displays it to the user.

[1575] Daily progress reports and emotion recognition

[1576] Users report their daily activity on their device. For example, if they walked for 30 minutes today, they enter that information on the activity input screen and press the send button. The device then sends the activity information to the server, which stores it in a database. The server periodically analyzes the saved activity information and uses an AI model to generate advice for the next step. An emotion engine also collects the user's emotional data and uses that data to optimize the advice generated by the AI ​​model. Examples of generated advice include "increase your walking time a little" or "try a new route next time." The server then sends the advice to the device, which displays it to the user.

[1577] Feedback provision and sentiment analysis

[1578] The user provides feedback on the advice and learning content provided. The user enters their evaluation and opinion on the feedback input screen and presses the send button. The device sends the feedback information to the server, which stores it in a database. The emotion engine collects the user's feedback and the emotional data at the time. The server passes the stored feedback information and emotional data to the AI ​​model for further improvements and generates new advice optimized for the user. The server sends the advice to the device, which displays it to the user. This allows the server to continue providing personalized support according to the user's progress.

[1579] Providing learning content

[1580] The server uses AI to select learning content appropriate for the user based on the user's behavioral data and feedback. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The generated learning content is sent from the server to the user's device and displayed to the user. This effectively supports the user throughout the entire process of achieving their goal. In addition, the introduction of an emotion engine allows the system to provide advice and content optimized for the user's emotional state, thereby increasing the user's motivation and improving the success rate of goal achievement.

[1581] Specific examples

[1582] For example, if a user sets a goal of "losing 5 kg in one month," the user can report progress by walking 30 minutes every day. If the user gives feedback such as "It felt great," the emotion engine analyzes this information and detects positive emotions. Based on this, the server generates advice such as "Try extending your walking time a little" or "Try a new walking route" and provides it to the user.

[1583] Prompt Sentence Examples

[1584] "Q: User A has a goal of walking 30 minutes every day and says that they felt great today. What advice would you suggest for their next step?"

[1585] In this way, users always receive personalized assistance and are able to effectively progress towards achieving their goals.

[1586] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1587] Step 1: User Registration

[1588] A user creates an account by entering their name, email address, and password using a terminal. This input information is sent from the terminal to the server. The server stores the received information in a database, generates a message indicating registration is complete, and sends it to the terminal. The input here is the user's personal information, and the output is a message indicating that the information has been saved to the database and that registration is complete.

[1589] Step 2: Log in

[1590] The user enters their email address and password on the login screen of their device. This information is sent from the device to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates an authentication token and sends it to the device. The input here is login information, and the generated authentication token is the output.

[1591] Step 3: Goal Setting

[1592] The user enters a new goal on the device's goal setting screen. For example, detailed goal information such as "lose 5 kg in one month" is entered. This information is sent from the device to the server, which stores it in a database. The server then uses an AI model to generate a specific plan for achieving the goal and sends it to the device. The input here is the goal information, and the output is the completion of saving it to the database and the generation of a goal achievement plan.

[1593] Step 4: Daily progress reports

[1594] The user enters their daily activity information into the device and presses the send button. For example, they enter information such as "I walked for 30 minutes." This data is sent from the device to the server, which stores it in a database. The server periodically analyzes the stored activity information and generates advice for the next step. The input here is the activity information, and the output is stored in the database and advice is generated.

[1595] Step 5: Emotion Recognition

[1596] The user inputs emotional feedback along with the progress report. For example, they input an emotional expression such as "I felt great today." This feedback is sent from the device to the server, which then analyzes the emotional data using an emotion engine. The server then optimizes advice based on the emotional data and sends it to the user. The input here is emotional feedback, and the output is optimized advice.

[1597] Step 6: Provide feedback

[1598] The user enters feedback on the advice or content provided and presses the send button. This data is sent from the device to the server, which stores it in a database. The emotion engine collects the feedback and emotional data generated at the time, and uses an AI model to generate further improved advice. The input here is the feedback information, and the output is database storage and the generation of improved advice.

[1599] Step 7: Provide learning content

[1600] The server uses a generative AI model to select learning content appropriate for the user based on the user's behavioral data and feedback, and sends it to the device. For example, it can recommend effective meal plans and exercise videos to a user with a weight loss goal. The input here is behavioral data and feedback, and the output is the selection and provision of learning content.

[1601] 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.

[1602] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1603] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1604] 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.

[1605] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1606] 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.

[1607] 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).

[1608] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1609] 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."

[1610] 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.

[1611] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1612] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1613] 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.

[1614] 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.

[1615] 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.

[1616] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1617] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1618] 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.

[1619] 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.

[1620] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1621] 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.

[1622] The following is further disclosed regarding the above embodiment.

[1623] (Claim 1)

[1624] a means for a user to set goals and store the goal information in a database;

[1625] A means of collecting user behavioral data and using AI models to generate specific advice for achieving goals;

[1626] means for transmitting the generated advice to a terminal and displaying it so that the user can receive it;

[1627] a means for users to report their daily progress and record that information in a database;

[1628] a means for users to input feedback about the advice or content provided and store the feedback information in a database;

[1629] a means for generating further improved advice using an AI model based on the feedback information;

[1630] A means for selecting and providing learning content to users based on their interests and behavioral data;

[1631] A system including:

[1632] (Claim 2)

[1633] The system according to claim 1, further comprising a means for generating specific means and plans for achieving a goal using an AI model and providing the plan to the user when the user sets a goal.

[1634] (Claim 3)

[1635] 2. The system according to claim 1, further comprising means for periodically analyzing user behavior data and goal achievement progress data, and dynamically updating advice and learning content based on the results.

[1636] "Example 1"

[1637] (Claim 1)

[1638] a means for a user to set goals and store the goal information in a database;

[1639] A means f...

Claims

1. a means for a user to set goals and store the goal information in a database; A means of collecting user behavioral data and using AI models to generate specific advice for achieving goals; means for transmitting the generated advice to a terminal and displaying it so that the user can receive it; a means for users to report their daily progress and record that information in a database; a means for users to input feedback about the advice or content provided and store the feedback information in a database; a means for generating further improved advice using an AI model based on the feedback information; A means for selecting and providing learning content to users based on their interests and behavioral data; A system including:

2. The system according to claim 1, further comprising a means for generating specific means and plans for achieving a goal using an AI model and providing the plan to the user when the user sets a goal.

3. 2. The system according to claim 1, further comprising means for periodically analyzing user behavior data and goal achievement progress data, and dynamically updating advice and learning content based on the results of the analysis.

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