System

Abstract

A system is provided.SOLUTION: A system comprising: means for creating a profile for inputting a goal, a problem, and an interest of a user; means for generating personalized advice from profile information input by using a generation AI; means for displaying the advice; means for generating an improvement suggestion by using the generation AI; and means for displaying the improvement suggestion.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional person-to-person mentoring requires a lot of time and resources, placing a heavy burden on the mentor. There is also the issue that the quality and content of the mentoring is not standardized, making personalized mentoring difficult. The present invention aims to solve these problems and provide personalized mentoring for each user more efficiently and effectively. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a profile creation means for inputting a user's goals, challenges, and interests, an advice generation means for generating personalized advice from the input profile information using a generation AI, a means for displaying the advice, a progress data input means for inputting the user's progress data, an improvement proposal generation means for generating improvement proposals using a generation AI based on the progress data, and a means for displaying the improvement proposals. The system further includes a means for saving the profile information created by the profile creation means in a database, and a means for notifying the user of the generated advice and improvement proposals, thereby achieving more continuous and effective mentoring.

[0006] 1. "User" means an individual or organizational member who uses the System to receive mentoring.

[0007] 2. "Objective" refers to a specific purpose or goal that a user wishes to achieve.

[0008] 3. "Challenges" refer to the difficulties or problems users face in achieving their goals.

[0009] 4. "Interests" refers to areas or topics in which you are particularly interested.

[0010] 5. "Profile creation means" means a means that provides the ability to input and organize a user's goals, issues, and interests.

[0011] 6. "Generative AI" refers to technology that uses artificial intelligence to analyze user profile information and generate personalized advice and improvement suggestions.

[0012] 7. "Advice generation means" means a means for providing a function for generating appropriate advice from a user's profile information using generation AI.

[0013] 8. "Display means" means a device or software that visually presents generated advice or improvement suggestions to the user.

[0014] 9. "Progress Data" means a record of the results or actions a User has achieved within a specific period of time.

[0015] 10. "Progress data input means" means a means that provides a function for users to input their own progress status into the system.

[0016] 11. "Improvement proposal generation means" means a means that provides a function for generating specific improvement proposals based on progress data using generation AI.

[0017] 12. "Database" means a system for efficiently managing and storing user profile information and progress data. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, and a database to provide personalized mentoring based on the user's goals, challenges, and interests.

[0040] Profile Creation Method

[0041] Terminal

[0042] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented, for example, as a web application or a mobile application.

[0043] server

[0044] The server receives the information entered by the user on the device and creates a profile, which is then stored in a database.

[0045] Advice Generation Method

[0046] server

[0047] The server generates personalized advice from the profile information stored in the database using a generation AI, which analyzes the profile data based on a specific algorithm and generates appropriate advice.

[0048] Terminal

[0049] The device receives the advice sent from the server and displays it to the user. For example, the advice may be, "First, introduce a time management tool and break down and plan your daily tasks."

[0050] Progress data input method

[0051] Terminal

[0052] The user periodically inputs progress data into the terminal, which records the results achieved and actions taken by the user.

[0053] server

[0054] The server receives the progress data sent from the terminal and stores it in a database.

[0055] Improvement proposal generation means

[0056] server

[0057] The server generates improvement proposals using a generation AI based on the progress data stored in the database. This generation AI analyzes the progress data and generates specific proposals to promote the user's growth.

[0058] Terminal

[0059] The device receives the improvement suggestions sent from the server and displays them to the user. For example, a suggestion such as "When debugging, try running unit tests first" may be displayed.

[0060] Specific examples

[0061] Case of user "Yamada Taro"

[0062] User "Yamada Taro" uses this system to improve himself. Yamada enters the following information into the terminal:

[0063] Goals: Acquire advanced programming skills and improve project management

[0064] Challenges: time management issues, complex debugging challenges

[0065] Interests: Machine learning, agile methods

[0066] The server stores this information in a database as a profile. The AI ​​then analyzes this profile and generates advice such as, "First, introduce a time management tool and break down and plan your daily tasks." This advice is displayed on Yamada's device, and he puts it into practice.

[0067] After a certain period of time, Yamada enters his progress data into his terminal. "I used a new time management tool this week, but there are still too many tasks." This data is sent to the server and stored in a database. The generation AI analyzes this progress data and generates improvement suggestions, such as: "When debugging, try running unit tests first." This suggestion is also displayed on his terminal, and Yamada plans his next steps.

[0068] In this way, the present invention efficiently provides personalized mentoring that meets the needs of each individual user, and supports the user's growth.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] User

[0072] Users use the device interface to input their goals, challenges, and interests, such as "acquiring advanced programming skills," "time management issues," or "machine learning."

[0073] Step 2:

[0074] Terminal

[0075] The device formats the user's input of goals, challenges, and interests and sends it to the server, where the necessary data is ready for processing.

[0076] Step 3:

[0077] server

[0078] The server receives the profile information sent from the terminal and stores it in a database using a profile creation means. The database centrally manages the profile information for each user.

[0079] Step 4:

[0080] server

[0081] The server uses generative AI to generate personalized advice based on the profile information stored in the database. A specific algorithm is used to generate advice. For example, the server might generate advice such as, "First, introduce time management tools and break down and plan your daily tasks."

[0082] Step 5:

[0083] Terminal

[0084] The terminal receives the advice sent from the server and displays it to the user, allowing the user to create an action plan based on the generated advice.

[0085] Step 6:

[0086] User

[0087] The user inputs progress data into the terminal at regular intervals, for example, recording progress such as "I used a new time management tool this week, but there are still too many tasks."

[0088] Step 7:

[0089] Terminal

[0090] The device formats the entered progress data and sends it to the server, which then analyzes the progress data.

[0091] Step 8:

[0092] server

[0093] The server stores the received progress data in a database, which manages all of the user's progress data.

[0094] Step 9:

[0095] server

[0096] The server uses generative AI to generate improvement suggestions based on progress data, such as "When debugging, try running unit tests first."

[0097] Step 10:

[0098] Terminal

[0099] The terminal receives the improvement suggestions sent from the server and displays them to the user, allowing the user to take specific improvement actions as the next step.

[0100] Through the above steps, this system is able to provide efficient and personalized mentoring and support the user's growth.

[0101] Example 1

[0102] 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."

[0103] Conventional mentoring systems have difficulty providing appropriate advice and improvement suggestions based on a user's individual goals, challenges, and interests. Furthermore, they lack the functionality to properly evaluate a user's progress and provide personalized advice and improvement suggestions in a timely manner. This has led to issues such as ineffective self-improvement and problem-solving for users.

[0104] 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.

[0105] In this invention, the server

[0106] a profile creation means for inputting the user's goals, challenges, and interests;

[0107] an advice generation means for generating personalized advice from input profile information using a generation AI;

[0108] and means for storing the information input by the profile creation means in a database and for storing progress data in the database.

[0109] This allows for optimal personalized mentoring tailored to each user's individual goals and challenges, and makes it possible to provide timely advice and suggestions for improvement based on the user's progress.

[0110] A "profile creation means" is a means that provides an interface for a user to input their goals, issues, and interests.

[0111] An "advice generation means" is a means for generating personalized advice from profile information using a generation AI.

[0112] The "progress data input means" is a means for providing an interface for the user to input his / her own progress data.

[0113] The "improvement proposal generation means" is a means for generating improvement proposals using a generation AI based on progress data.

[0114] The "means for storing in a database" refers to a means for storing profile information and progress data in a database.

[0115] The "means for notifying" is a means for notifying the user of the generated advice and improvement suggestions.

[0116] This invention is a system for providing personalized mentoring based on a user's goals, challenges, and interests. The system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, an improvement suggestion generation means using a generation AI, and a database.

[0117] Profile Creation Method

[0118] Terminal

[0119] The device provides an interface for users to input their goals, challenges, and interests. This interface is implemented as a web or mobile application. Specific software examples include web forms using HTML, CSS, JavaScript, etc.

[0120] server

[0121] The server receives the information entered by the user on the terminal and creates a profile. This profile information is stored in a database. A relational database management system (RDBMS) is used as the database.

[0122] Advice Generation Method

[0123] server

[0124] The server uses a generative AI to generate personalized advice from the profile information stored in the database. The generative AI uses an advanced natural language processing model, such as GPT-4. This generative AI analyzes the profile data and generates optimal advice.

[0125] Terminal

[0126] The device receives the advice sent from the server and displays it to the user via a web browser or mobile app screen.

[0127] Progress data input method

[0128] Terminal

[0129] The user inputs progress data at regular intervals into the terminal. This interface, like the profile creation means, is implemented as a web application or mobile application.

[0130] server

[0131] The server receives the progress data sent from the device and stores it in a database, thereby recording the user's progress.

[0132] Improvement proposal generation means

[0133] server

[0134] The server uses a generation AI to generate improvement suggestions based on the progress data stored in the database. This generation AI analyzes the progress data and generates specific suggestions to promote the user's growth.

[0135] Terminal

[0136] The device receives the improvement suggestions sent from the server and displays them to the user via a web browser or mobile app screen.

[0137] Specific examples

[0138] Case of user "A"

[0139] User "A" uses this system to improve himself. He enters the following information into the terminal:

[0140] Goal: Acquire advanced programming skills

[0141] Challenge: Time management issues

[0142] Interests: Machine Learning

[0143] The server stores this information in a database as a profile. The AI ​​then analyzes this profile and generates advice such as, "First, introduce a time management tool and break down and plan your daily tasks." This advice is displayed on the device, and Mr. A puts it into practice.

[0144] After a certain period of time, Person A enters progress data into the terminal. "I used the new time management tool this week, but there are still too many tasks." This data is sent to the server and stored in a database. The generation AI analyzes this progress data and generates improvement suggestions such as: "When debugging, try running unit tests first." This suggestion is also displayed on the terminal, allowing Person A to plan his next steps.

[0145] Examples of prompt statements

[0146] Here are some example prompts to enter into a generative AI model:

[0147] User "A"'s goal is "to acquire advanced programming skills," his challenge is "time management issues," and his interest is "machine learning." Based on this information, please generate appropriate advice.

[0148] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0149] Program processing flow

[0150] Step 1: Create a profile

[0151] Terminal

[0152] 1. The terminal provides the user with an interface for inputting goals, issues, and interests.

[0153] Input: Information about the user's goals, challenges, and interests

[0154] Output: Data that sends the input information to the server

[0155] Specific operation: The user inputs information such as "learning advanced programming skills, time management problems, machine learning," and the device sends this information to the server in JSON format.

[0156] server

[0157] 2. The server receives the information sent from the device and creates a profile.

[0158] Input: User information received from the device

[0159] Output: Data to save the created profile information to the database

[0160] Specific operation: The server analyzes the received information, creates a profile for user "A" and saves it in the database.

[0161] Step 2: Advice Generation

[0162] server

[0163] 1. The server retrieves profile information from the database and generates advice using generative AI.

[0164] Input: Profile information stored in the database

[0165] Output: Generated advice

[0166] Specific operation: The server retrieves the profile information of "Mr. A" from the database, inputs "Mr. A"'s information as a prompt into GPT-4, and generates appropriate advice.

[0167] 2. The server sends the generated advice to the terminal.

[0168] Input: Generated advice

[0169] Output: Data to send to the terminal

[0170] Specific operation: The server sends advice to the device saying, "First, introduce a time management tool and break down and plan your daily tasks."

[0171] Terminal

[0172] 3. The terminal receives the advice sent from the server and displays it to the user.

[0173] Input: Advice received from the server

[0174] Output: Advice displayed on screen

[0175] Specific operation: The device displays the received advice on the screen and the user confirms it.

[0176] Step 3: Enter progress data

[0177] Terminal

[0178] 1. The terminal provides an interface for the user to input progress data.

[0179] Input: User progress data

[0180] Output: Data to send the entered progress data to the server

[0181] Specific operation: The user enters progress data such as "I used the new time management tool this week, but I still have too many tasks," and the device sends this to the server.

[0182] server

[0183] 2. The server receives the progress data sent from the device and stores it in a database.

[0184] Input: Progress data received from the device

[0185] Output: Progress data stored in a database

[0186] Specific operation: The server analyzes the received progress data and stores it in the database.

[0187] Step 4: Generate improvement suggestions

[0188] server

[0189] 1. The server retrieves progress data from the database and generates improvement suggestions using generative AI.

[0190] Input: Progress data stored in the database

[0191] Output: Generated improvement suggestions

[0192] Specific operation: The server retrieves "Person A's" progress data from the database and inputs this information as prompts into GPT-4 to generate appropriate improvement suggestions.

[0193] 2. The server sends the generated improvement proposal to the terminal.

[0194] Input: Generated improvement suggestions

[0195] Output: Data to send to the terminal

[0196] Specific operation: The server sends an improvement suggestion to the terminal saying, "When debugging, try running unit tests first."

[0197] Terminal

[0198] 3. The device receives the improvement suggestions sent from the server and displays them to the user.

[0199] Input: Improvement suggestions received from the server

[0200] Output: Improvement suggestions displayed on the screen

[0201] Specific operation: The device displays the improvement suggestions it receives on the screen and the user confirms them.

[0202] (Application example 1)

[0203] 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."

[0204] This invention relates to a system that enables users to efficiently advance their self-development and learning. Conventional systems have the problem of being unable to provide specific advice or improvement suggestions based on the user's individual needs and progress, and can only provide general suggestions. Furthermore, they lack usability because they are not compatible with easy operation using smartphones.

[0205] 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.

[0206] In this invention, the server includes a profile creation means, a means for generating personalized advice from input profile information using a generation AI, a means for displaying the advice, a means for inputting user progress data, a means for generating improvement suggestions using a generation AI based on the progress data, a means for displaying the improvement suggestions, a means for implementing the system as a smartphone app, and a means for generating advice and improvement suggestions in the form of prompt sentences using the generation AI. This enables users to easily receive specific advice and improvement suggestions tailored to their individual needs via their smartphones.

[0207] "User" refers to a person who uses the system.

[0208] "Objective" refers to the specific purpose or goal that the user is trying to achieve.

[0209] "Challenges" refer to problems or difficulties faced by users.

[0210] "Interests" refers to areas or topics in which a user has particular interest or concern.

[0211] "Profile Creation Tool" refers to a tool for inputting a user's goals, challenges, and interests.

[0212] "Generative AI" refers to algorithms and models that use artificial intelligence techniques to analyze data and generate advice and improvement suggestions.

[0213] "Advice generation means" refers to a means for generating personalized advice from input profile information using a generation AI.

[0214] "Display means" refers to a means for visually presenting the generated advice and improvement suggestions to the user.

[0215] "Progress data input means" refers to a means for a user to input progress data.

[0216] "Improvement proposal generation means" refers to a means for generating specific improvement proposals using generation AI based on user progress data.

[0217] "Smartphone app" refers to an application that runs on a smartphone.

[0218] "Prompt sentence format" refers to the document format used as input to the generative AI.

[0219] A specific embodiment of a system for supporting self-improvement based on this invention is described below. The system provides users with personalized advice and improvement suggestions via a smartphone app. This system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, and an improvement suggestion generation means using a generation AI.

[0220] 1. Create a profile

[0221] Terminal

[0222] Users input their goals, challenges, and interests through the smartphone app interface, such as "acquiring advanced programming skills" or "interest in machine learning."

[0223] server

[0224] The server receives the profile information entered by the user and stores it in a database, which includes detailed data about the user's goals, challenges, and interests.

[0225] 2. Advice Generation

[0226] server

[0227] The server uses the stored profile information to generate personalized advice using a generative AI model, such as "First, introduce time management tools and break down and plan your daily tasks."

[0228] Terminal

[0229] The terminal receives the generated advice and displays it to the user, allowing the user to obtain specific guidelines for action.

[0230] 3. Progress data entry

[0231] Terminal

[0232] Users periodically enter progress data, including accomplishments achieved and actions taken, such as "I've been using the new time management tool, but I still have too many tasks."

[0233] server

[0234] The server receives the entered progress data and stores it in a database, which is used to generate improvement suggestions for the next step.

[0235] 4. Improvement proposal generation

[0236] server

[0237] The server uses a generative AI model to generate improvement suggestions based on the progress data, such as "When debugging, try running unit tests first."

[0238] Terminal

[0239] The device receives the generated improvement suggestions and displays them to the user, allowing the user to know the specific action to take next.

[0240] Specific examples

[0241] Here are some example prompts to input to a generative AI model:

[0242] Prompts for advice generation based on profile information:

[0243] User goal: Acquire advanced programming skills, challenges: Time management issues, interest: Machine learning. Provide tailored advice.

[0244] Prompts for generating improvement suggestions based on progress data:

[0245] User progress: I used the new time management tool, but I still have too many tasks. Provide improvement suggestions.

[0246] Using these prompts, the generative AI model can generate appropriate advice and improvement suggestions and provide them to the user. The server is built using Python's Flask, and the database uses SQLite. The generative AI model uses the OpenAI API.

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] Profile Creation

[0250] Input: Users input their goals, challenges, and interests through a smartphone app interface, such as "acquiring advanced programming skills" or "interested in machine learning."

[0251] Processing: The device collects this input data and sends it to the server, which analyzes it and stores it in a database as profile information.

[0252] Output: Profile information is saved to the database.

[0253] Step 2:

[0254] Advice Generation

[0255] Input: Saved profile information.

[0256] Processing: The server retrieves the profile information and inputs it to the generative AI model in the form of a prompt. Example prompt: "User goal: Acquire advanced programming skills, challenges: Time management issues, interest: Machine learning. Provide tailored advice."

[0257] Output: The generation AI generates advice based on the input profile information, and the server receives this advice and sends it to the device.

[0258] How it works: The generative AI responds with personalized advice based on the prompt it receives. The server sends this advice to the device, which then displays it to the user. For example, the advice might be, "First, introduce a time management tool and break down and plan your daily tasks."

[0259] Step 3:

[0260] Progress data entry

[0261] Input: Users periodically enter progress data through the smartphone app interface, such as "I used the new time management tool, but I still have too many tasks."

[0262] Processing: The device collects progress data and sends it to the server, which stores it in a database.

[0263] Output: Progress data is saved to the database.

[0264] How it works: The progress data entered by the user details the accomplishments and actions they have taken. The device sends this data in real time to the server, which stores it in a database.

[0265] Step 4:

[0266] Improvement proposal generation

[0267] Input: Progress data stored in the database.

[0268] Processing: The server retrieves the progress data and inputs it to the generative AI model in the form of a prompt. Example prompt: "User progress: I used the new time management tool, but there are still too many tasks. Provide improvement suggestions."

[0269] Output: The generation AI generates improvement suggestions based on the progress data, and the server receives these suggestions and sends them to the device.

[0270] Specific operation: Based on the received prompt, the generation AI returns a specific improvement suggestion to solve the user's problem. For example, it might generate a suggestion such as, "When debugging, try running unit tests first." The server sends this suggestion to the device, which then displays it to the user.

[0271] Step 5:

[0272] Notifications and Feedback

[0273] Input: Generated advice and improvement suggestions.

[0274] Processing: The server notifies the terminal of the generated advice and improvement suggestions.

[0275] Output: The consumer receives advice and suggestions for improvement through notifications.

[0276] Specific operation: The notification function allows users to receive advice and suggestions for improvement. The device receives these and displays them to the user, making them easy to access.

[0277] Through these processing steps, the system can use the generative AI model to provide personalized advice and improvement suggestions, enabling users to receive assistance tailored to their specific and individual needs.

[0278] 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.

[0279] The system provides personalized mentoring based on the user's goals, challenges, and interests, and further achieves a higher level of personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, an emotion engine, and a database.

[0280] Profile Creation Method

[0281] Terminal

[0282] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented as a web or mobile application.

[0283] server

[0284] The server receives the profile information sent from the terminal and stores it in a database.

[0285] Advice Generation Method

[0286] server

[0287] The server uses generative AI to generate personalized advice based on the profile information stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[0288] Terminal

[0289] The terminal receives the advice sent from the server and displays it to the user.

[0290] Progress data input method

[0291] Terminal

[0292] The user periodically inputs progress data into the terminal. For example, the user may record progress such as, "I've used a new time management tool, but there are still too many tasks."

[0293] server

[0294] The server receives the progress data sent from the terminal and stores it in a database.

[0295] Improvement proposal generation means

[0296] server

[0297] The server uses generative AI to generate improvement suggestions based on the progress data stored in the database, such as "When debugging, try running unit tests first."

[0298] Terminal

[0299] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[0300] Emotion Engine

[0301] Terminal

[0302] The device is equipped with an emotion recognition function and acquires emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[0303] server

[0304] The server analyzes the acquired emotional data and provides appropriate personalization. The emotional data is stored in a database along with profile information and progress data.

[0305] Program processing

[0306] Profile Creation

[0307] The server receives the user's input of goals, challenges, and interests and creates a profile, which is then stored in a database.

[0308] Advice Generation

[0309] The server uses a generation AI to generate personalized advice based on the profile information, which is then sent to the device and displayed.

[0310] Progress data entry and saving

[0311] The user periodically inputs progress data into the terminal, which then sends it to the server, which receives the progress data and stores it in a database.

[0312] Improvement proposal generation

[0313] The server uses a generation AI to generate improvement suggestions based on the progress data and emotion data, and these improvement suggestions are sent to the device and displayed.

[0314] emotion recognition

[0315] The device uses a camera and microphone to capture the user's emotional data, which the server analyzes and combines with profile information and progress data to provide optimal advice and suggestions for improvement.

[0316] Specific examples

[0317] Case of user "Taro Tanaka"

[0318] User "Taro Tanaka" uses the system to improve himself. Tanaka enters the following information into the terminal.

[0319] Goal: Acquire advanced programming skills

[0320] Challenge: Time management issues

[0321] Interests: Machine Learning

[0322] The server stores this information in a database as a profile. The AI ​​then analyzes the profile and generates advice such as "introduce time management tools and break down and plan your daily tasks." The advice is displayed on Tanaka's device, and he puts it into practice.

[0323] After a certain period of time, Tanaka enters his progress data into his terminal, recording, "I used the new time management tool, but there are still too many tasks." The server receives this and stores it in a database.

[0324] The server uses generative AI to analyze the progress data and the emotion data obtained from the emotion engine, and generates improvement suggestions such as, "When debugging, try running unit tests first." The suggestions are displayed on the device, and Tanaka plans his next steps.

[0325] The emotion engine provides suggestions and advice that take Tanaka's emotions into consideration, allowing him to take more appropriate actions. Through the above process, the present invention efficiently and effectively supports the growth of users.

[0326] The processing flow will be explained below.

[0327] The system provides personalized mentoring based on the user's goals, challenges, and interests, and further achieves a higher level of personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, an emotion engine, and a database.

[0328] Profile Creation Method

[0329] Terminal

[0330] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented as a web or mobile application.

[0331] server

[0332] The server receives the profile information sent from the terminal and stores it in a database.

[0333] Advice Generation Method

[0334] server

[0335] The server uses generative AI to generate personalized advice based on the profile information stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[0336] Terminal

[0337] The terminal receives the advice sent from the server and displays it to the user.

[0338] Progress data input method

[0339] Terminal

[0340] The user periodically inputs progress data into the terminal. For example, the user may record progress such as, "I've used a new time management tool, but there are still too many tasks."

[0341] server

[0342] The server receives the progress data sent from the terminal and stores it in a database.

[0343] Improvement proposal generation means

[0344] server

[0345] The server uses generative AI to generate improvement suggestions based on the progress data stored in the database, such as "When debugging, try running unit tests first."

[0346] Terminal

[0347] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[0348] Emotion Engine

[0349] Terminal

[0350] The device is equipped with an emotion recognition function and acquires emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[0351] server

[0352] The server analyzes the acquired emotional data and provides appropriate personalization. The emotional data is stored in a database along with profile information and progress data.

[0353] Program processing

[0354] Step 1:

[0355] User

[0356] Users use the device interface to input their goals, challenges, and interests, such as "acquiring advanced programming skills," "time management issues," or "machine learning."

[0357] Step 2:

[0358] Terminal

[0359] The device formats the user's input of goals, challenges, and interests and sends it to the server, where the necessary data is ready for processing.

[0360] Step 3:

[0361] server

[0362] The server receives the profile information sent from the terminal and stores it in a database using a profile creation means. The database centrally manages the profile information for each user.

[0363] Step 4:

[0364] server

[0365] The server uses generative AI to generate personalized advice based on the profile information stored in the database. A specific algorithm is used to generate advice. For example, the server might generate advice such as, "First, introduce time management tools and break down and plan your daily tasks."

[0366] Step 5:

[0367] Terminal

[0368] The terminal receives the advice sent from the server and displays it to the user, allowing the user to create an action plan based on the generated advice.

[0369] Step 6:

[0370] User

[0371] The user inputs progress data into the terminal at regular intervals, for example, recording progress such as "I used a new time management tool this week, but there are still too many tasks."

[0372] Step 7:

[0373] Terminal

[0374] The device formats the entered progress data and sends it to the server, which then analyzes the progress data.

[0375] Step 8:

[0376] server

[0377] The server stores the received progress data in a database, which manages all of the user's progress data.

[0378] Step 9:

[0379] Terminal

[0380] The device uses an emotion engine to acquire emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[0381] Step 10:

[0382] server

[0383] The server analyzes the received emotion data and stores it in a database, where it is integrated with profile information and progress data.

[0384] Step 11:

[0385] server

[0386] The server uses generative AI to generate improvement suggestions based on progress and emotion data, such as "When debugging, try running unit tests first."

[0387] Step 12:

[0388] Terminal

[0389] The terminal receives the improvement suggestions sent from the server and displays them to the user, allowing the user to take specific improvement actions as the next step.

[0390] Through the above steps, this system is able to provide efficient and personalized mentoring and support the user's growth.

[0391] Example 2

[0392] 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."

[0393] Conventional mentoring systems lacked personalization based on the user's goals and challenges, limiting the accuracy and appropriateness of the advice they provided. Furthermore, they did not take the user's emotions into account, making it difficult to respond flexibly based on their emotional state. As a result, there were problems with users' growth and problem-solving not progressing effectively.

[0394] 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.

[0395] In this invention, the server includes: a profile creation means for inputting a user's goals, challenges, and interests; an advice generation means for generating personalized advice from the input profile information using a generation AI; a means for displaying the advice; a progress data input means for inputting the user's progress data; an improvement proposal generation means for generating improvement proposals using a generation AI based on the progress data; a means for displaying the improvement proposals; an emotion recognition means for recognizing the user's emotions and acquiring emotion data; and a means for analyzing the emotion data and combining it with the profile information and progress data to provide optimal advice and improvement proposals. This enables highly personalized advice and improvement proposals that take emotions into account in relation to the user's goals and challenges.

[0396] A "profile creation tool" is an interface through which a user can input and collect information about their goals, challenges, and interests.

[0397] An "advice generation means" is a means for generating personalized advice from profile information using a generation AI.

[0398] The "means for displaying the advice" refers to an interface or device for displaying the generated advice to the user.

[0399] The "progress data input means" is an interface for users to input their own progress status.

[0400] The "improvement proposal generation means" is a means for generating improvement proposals using a generation AI based on progress data.

[0401] The "means for displaying the improvement proposal" refers to an interface or device for displaying the generated improvement proposal to the user.

[0402] The "emotion recognition means" refers to a camera, microphone, and software for recognizing the user's emotions and acquiring emotion data.

[0403] The "means for analyzing the emotion data and combining it with the profile information and progress data to provide optimal advice and improvement suggestions" refers to means for analyzing emotion data and integrating it with profile information and progress data to generate optimized advice and improvement suggestions.

[0404] This invention is a system that provides personalized mentoring based on a user's goals, challenges, and interests, and further achieves more advanced personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, an improvement suggestion generation means using a generation AI, an emotion engine, and a database.

[0405] Hardware and Software Configuration

[0406] Profile Creation Method

[0407] The device provides an interface for users to input their goals, challenges, and interests, which may include web or mobile applications. For example, a user may input a goal of "acquiring advanced programming skills."

[0408] Advice generation means and improvement proposal generation means

[0409] The server uses the generative AI model to generate personalized advice and improvement suggestions based on the profile information and progress data stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[0410] Display means

[0411] The device receives the advice and improvement suggestions sent from the server and displays them to the user as in-app messages or notifications.

[0412] Progress data input method

[0413] The user periodically enters their progress data into the device, for example, recording progress such as "I've used a new time management tool, but there are still too many tasks."

[0414] emotion recognition means

[0415] The device is equipped with an emotion recognition function and uses a camera and microphone to obtain emotional data from the user's facial expressions and voice. For example, it can analyze the user's facial expression to determine whether they are "highly stressed."

[0416] Specific examples

[0417] Below is a concrete example of how user "Taro Tanaka" uses this system:

[0418] Profile Creation:

[0419] User "Taro Tanaka" enters the following information into his terminal:

[0420] Goal: Acquire advanced programming skills

[0421] Challenge: Time management issues

[0422] Interests: Machine Learning

[0423] The server receives this information and stores it in a database as profile information.

[0424] Advice Generation:

[0425] The server uses a generative AI model to generate advice such as "introduce time management tools and break down and plan your daily tasks."

[0426] Progress Data Entry:

[0427] After a certain period of time, Taro Tanaka enters and submits progress data into his terminal, stating, "I used the new time management tool, but there are still too many tasks."

[0428] Improvement suggestion generation:

[0429] The server receives this progress data and uses a generative AI model to generate improvement suggestions such as, "When debugging, try running unit tests first."

[0430] Emotion recognition:

[0431] The device's camera recognizes Taro Tanaka's facial expressions and captures emotional data indicating that he is "highly stressed." The server analyzes this emotional data and combines it with his profile information and progress data to provide optimal improvement suggestions.

[0432] Prompt Sentence Examples

[0433] "If I want to learn advanced programming skills but have trouble managing my time, what advice would you give me?"

[0434] This enables the system to provide highly personalized advice and improvement suggestions that take into account the user's goals and challenges, even taking their emotions into account.

[0435] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0436] Step 1: Fill in and submit your profile information

[0437] User

[0438] Users input their goals, challenges, and interests into the terminal. For example, a user may input "advanced programming skills" as a goal.

[0439] input

[0440] Goal: Acquire advanced programming skills

[0441] Challenge: Time management issues

[0442] Interests: Machine Learning

[0443] Terminal

[0444] The terminal receives the information entered by the user and transmits it to the server.

[0445] output

[0446] Profile information (goals, challenges, interests) is sent to a server.

[0447] Specific actions

[0448] The terminal interface is provided with a text box and an input button. When a user enters information into the text box and presses the send button, the information is sent to the server.

[0449] Step 2: Save your profile information

[0450] server

[0451] The server receives the profile information sent from the terminal and stores it in a database.

[0452] input

[0453] Profile information sent from the device

[0454] output

[0455] Profile information stored in a database

[0456] Specific actions

[0457] The server parses the received profile information and stores each item (goals, challenges, interests) in the appropriate field in a database.

[0458] Step 3: Generate Advice

[0459] server

[0460] The server uses a generative AI model to analyze the profile information and generate personalized advice.

[0461] input

[0462] Profile information stored in a database

[0463] output

[0464] Generated Advice

[0465] Specific actions

[0466] The server calls the generative AI model, passing the profile information as input, and the model analyzes it to generate advice such as "introduce time management tools and break down and plan your daily tasks."

[0467] Step 4: Submitting and viewing advice

[0468] server

[0469] The generated advice is sent to the device.

[0470] input

[0471] Generated Advice

[0472] output

[0473] Advice sent to device

[0474] Terminal

[0475] The terminal receives the advice sent from the server and displays it to the user.

[0476] Specific actions

[0477] Advice will be displayed as notifications on the device or in-app messages, for example, in the notification bar on your smartphone.

[0478] Step 5: Enter and submit progress data

[0479] User

[0480] The user periodically inputs their progress data into the terminal, for example, "I used a new time management tool, but there are still too many tasks."

[0481] input

[0482] Progress Data

[0483] Terminal

[0484] The terminal transmits the progress data entered by the user to the server.

[0485] output

[0486] Progress data sent to the server

[0487] Specific actions

[0488] A text box for inputting progress data is placed on the terminal interface, and the user inputs the data and presses the send button.

[0489] Step 6: Save your progress

[0490] server

[0491] The server receives the progress data sent from the terminal and stores it in a database.

[0492] input

[0493] Progress data sent from the device

[0494] output

[0495] Progress data stored in a database

[0496] Specific actions

[0497] The server parses the progress data and stores it in the appropriate fields.

[0498] Step 7: Generate improvement suggestions

[0499] server

[0500] The server uses a generative AI model to generate improvement suggestions based on the progress data stored in the database.

[0501] input

[0502] Progress data stored in a database

[0503] output

[0504] Generated improvement suggestions

[0505] Specific actions

[0506] The server inputs progress data into the generative AI model, which analyzes it and generates a suggestion such as, "When debugging, try running unit tests first."

[0507] Step 8: Submit and view improvement suggestions

[0508] server

[0509] The generated improvement proposal is sent to the terminal.

[0510] input

[0511] Generated improvement suggestions

[0512] output

[0513] Improvement suggestions sent to the device

[0514] Terminal

[0515] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[0516] Specific actions

[0517] Improvement suggestions will be displayed as notifications on the device or in-app messages.

[0518] Step 9: Obtaining and Sending Emotion Data

[0519] Terminal

[0520] The device uses a camera and microphone to capture user emotional data, for example, analyzing emotions from facial expressions and voice in real time.

[0521] input

[0522] User's facial expressions and voice

[0523] output

[0524] Acquired emotion data

[0525] server

[0526] Emotion data transmitted from the terminal is received.

[0527] Specific actions

[0528] The device uses a camera and microphone to recognize the user's face and analyze the tone of their voice, and then sends emotional data to the server.

[0529] Step 10: Analyze and integrate sentiment data

[0530] server

[0531] The server analyzes the received emotional data and integrates it with profile information and progress data to generate optimal advice and improvement suggestions.

[0532] input

[0533] Emotional Data

[0534] Profile Information

[0535] Progress Data

[0536] output

[0537] Best advice and improvement suggestions

[0538] Specific actions

[0539] The server analyzes the emotional data and evaluates the user's stress level and emotional state along with their profile information and progress data, and uses a generative AI model to generate optimal advice and improvement suggestions to provide appropriate support to the user.

[0540] (Application example 2)

[0541] 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."

[0542] To help drivers improve their driving skills and reduce stress in autonomous vehicles, it is necessary to provide personalized advice and improvement suggestions to each driver. However, current systems lack the means to properly grasp the driver's emotional state and progress, making it difficult to provide appropriate feedback based on this. In addition, generating advice based on real-time emotion recognition is difficult, resulting in issues that prevent sufficient improvement of the driving experience.

[0543] 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 a profile creation means for inputting a user's goals, challenges, and interests, an advice generation means for generating personalized advice from the input profile information using a generation AI, a means for displaying advice, a progress data input means for inputting the user's progress data, an improvement proposal generation means for generating improvement proposals using a generation AI based on the progress data, a means for displaying the improvement proposals, an emotion recognition means for detecting the user's emotions, and a means for using the emotion data in combination with the profile information and progress data to provide optimal advice and improvement proposals. This makes it possible to provide appropriate feedback in real time based on the driver's emotional state and progress, thereby improving driving skills and reducing stress.

[0544] "Profile creation means" is a function that provides an interface for users to input their goals, challenges, and interests.

[0545] The "advice generation means" is a function that uses generation AI to generate personalized advice from the input profile information.

[0546] The "means for displaying advice" is a function for displaying the generated advice to the user.

[0547] The "progress data input means" is a function for inputting the user's progress data.

[0548] The "improvement proposal generation means" is a function that generates improvement proposals using a generation AI based on progress data.

[0549] The "means for displaying improvement proposals" is a function for displaying the generated improvement proposals to the user.

[0550] "Emotion recognition means" is a function that detects the user's emotions.

[0551] "Means for using emotional data in combination with profile information and progress data to provide optimal advice and improvement suggestions" is a function that analyzes emotional data in combination with profile information and progress data, and generates and provides optimal advice and improvement suggestions.

[0552] This invention is a system that provides personalized advice and feedback to drivers to improve their driving skills and reduce their stress. The system includes a user terminal, a server, a generative AI model, an emotion recognition means, and software for linking these components.

[0553] System configuration

[0554] Terminal

[0555] The terminal provides an interface where users can input their goals, challenges, and interests. Users input information through devices such as smartphone applications or tablets. This interface is intuitive and easy to operate, and the user's input data is quickly transmitted to the server.

[0556] server

[0557] The server has several main functions. First, it stores profile information in a database. Second, it uses a generative AI model to generate personalized advice from the profile information. Third, it collects progress data, based on which the generative AI model generates improvement suggestions. Furthermore, the server analyzes emotion data obtained from the emotion recognition means and combines it with the profile information and progress data to generate optimal advice and improvement suggestions.

[0558] emotion recognition means

[0559] The emotion recognition unit uses the device's camera and microphone to detect emotions from the user's facial expressions and voice. This data is sent to the server in real time and analyzed immediately according to the user's situation.

[0560] Specific examples

[0561] Consider a scenario where a user is using an autonomous vehicle. First, the user inputs their goals (e.g., improving driving skills), challenges (e.g., stress caused by traffic jams), and interests (e.g., eco-driving) into their device. This information is immediately sent to the server and stored in a database as a profile.

[0562] Next, the generative AI model generates advice based on the profile information. For example, for a user with the profile information "Goal: Improve driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving," the generative AI model would provide advice such as "Take deep breaths to relax and try not to pay attention to following vehicles." This advice is displayed on the device for the user to confirm.

[0563] While driving, the device's camera and microphone record the user's facial expressions and voice. If the user feels stressed, the emotion recognition function sends the data to the server. The server analyzes this emotion data and immediately provides appropriate feedback (e.g., "You are feeling stressed. We recommend that you take a short break.").

[0564] After completing a drive, the user inputs progress data into the device. Based on the progress data (e.g., "I felt stressed during this drive") and emotion data, the generative AI model generates improvement suggestions for the next step (e.g., "Try some relaxation techniques while driving"). These suggestions are also displayed on the device, allowing the user to use them for their next drive.

[0565] Hardware and software used

[0566] Hardware: Camera (for facial expression recognition), microphone (for voice recognition)

[0567] Software: Python, OpenCV (camera operation), sounddevice (microphone operation), HuggingFace Transformers library (generative AI model)

[0568] Prompt Sentence Examples

[0569] "Goal: Improving driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving, Emotion: Advice on stress"

[0570] In this way, the system can help drivers improve their driving skills and reduce stress.

[0571] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0572] Step 1:

[0573] The user inputs their goals, challenges, and interests. The user inputs their goals (e.g., improving driving skills), challenges (e.g., stress caused by traffic jams), and interests (e.g., eco-driving) into the device's input interface. The input data is sent to the server and stored in a database as profile information.

[0574] Step 2:

[0575] The server receives the profile information and generates personalized advice using a generative AI model. From the received profile information, a prompt sentence is generated to generate advice for "Goal: Improve driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving" and input into the generative AI model. The generated advice is sent to the device and displayed. For example, advice such as "Take deep breaths to relax and try not to pay attention to following vehicles" is displayed.

[0576] Step 3:

[0577] The device uses a camera and microphone to record the user's facial expressions and voice in real time. The data acquired by the camera and microphone is sent to an emotion recognition means to detect the user's emotional state (e.g., stress). The detected emotion data is then sent to the server.

[0578] Step 4:

[0579] The server analyzes the emotional data and combines it with the profile information to generate optimal feedback. The server generates prompts based on the emotional data and profile information and inputs them into the generative AI model. For example, feedback such as "You're feeling stressed. We recommend you take a short break" is generated and sent to the device. The feedback is displayed in real time.

[0580] Step 5:

[0581] After the user finishes driving, they input their progress data into the terminal. The progress data (e.g., "I felt stressed during this drive") is sent to the server and stored in a database.

[0582] Step 6:

[0583] The server analyzes the progress data and emotion data and generates improvement suggestions using a generative AI model. A prompt sentence is generated based on the progress data and emotion data and input into the generative AI model. For example, an improvement suggestion such as "Try some relaxation techniques the next time you drive" is generated and sent to the device. The improvement suggestion is displayed on the device so that the user can use it for their next drive.

[0584] In this way, the system can perform appropriate data processing and calculations based on the data obtained at each step, and provide optimal feedback and improvement suggestions to users.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] [Second embodiment]

[0589] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0590] 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.

[0591] 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).

[0592] 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.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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."

[0601] The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, and a database to provide personalized mentoring based on the user's goals, challenges, and interests.

[0602] Profile Creation Method

[0603] Terminal

[0604] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented, for example, as a web application or a mobile application.

[0605] server

[0606] The server receives the information entered by the user on the device and creates a profile, which is then stored in a database.

[0607] Advice Generation Method

[0608] server

[0609] The server generates personalized advice from the profile information stored in the database using a generation AI, which analyzes the profile data based on a specific algorithm and generates appropriate advice.

[0610] Terminal

[0611] The device receives the advice sent from the server and displays it to the user. For example, the advice may be, "First, introduce a time management tool and break down and plan your daily tasks."

[0612] Progress data input method

[0613] Terminal

[0614] The user periodically inputs progress data into the terminal, which records the results achieved and actions taken by the user.

[0615] server

[0616] The server receives the progress data sent from the terminal and stores it in a database.

[0617] Improvement proposal generation means

[0618] server

[0619] The server generates improvement proposals using a generation AI based on the progress data stored in the database. This generation AI analyzes the progress data and generates specific proposals to promote the user's growth.

[0620] Terminal

[0621] The device receives the improvement suggestions sent from the server and displays them to the user. For example, a suggestion such as "When debugging, try running unit tests first" may be displayed.

[0622] Specific examples

[0623] Case of user "Yamada Taro"

[0624] User "Yamada Taro" uses this system to improve himself. Yamada enters the following information into the terminal:

[0625] Goals: Acquire advanced programming skills and improve project management

[0626] Challenges: time management issues, complex debugging challenges

[0627] Interests: Machine learning, agile methods

[0628] The server stores this information in a database as a profile. The AI ​​then analyzes this profile and generates advice such as, "First, introduce a time management tool and break down and plan your daily tasks." This advice is displayed on Yamada's device, and he puts it into practice.

[0629] After a certain period of time, Yamada enters his progress data into his terminal. "I used a new time management tool this week, but there are still too many tasks." This data is sent to the server and stored in a database. The generation AI analyzes this progress data and generates improvement suggestions, such as: "When debugging, try running unit tests first." This suggestion is also displayed on his terminal, and Yamada plans his next steps.

[0630] In this way, the present invention efficiently provides personalized mentoring that meets the needs of each individual user, and supports the user's growth.

[0631] The processing flow will be explained below.

[0632] Step 1:

[0633] User

[0634] Users use the device interface to input their goals, challenges, and interests, such as "acquiring advanced programming skills," "time management issues," or "machine learning."

[0635] Step 2:

[0636] Terminal

[0637] The device formats the user's input of goals, challenges, and interests and sends it to the server, where the necessary data is ready for processing.

[0638] Step 3:

[0639] server

[0640] The server receives the profile information sent from the terminal and stores it in a database using a profile creation means. The database centrally manages the profile information for each user.

[0641] Step 4:

[0642] server

[0643] The server uses generative AI to generate personalized advice based on the profile information stored in the database. A specific algorithm is used to generate advice. For example, the server might generate advice such as, "First, introduce time management tools and break down and plan your daily tasks."

[0644] Step 5:

[0645] Terminal

[0646] The terminal receives the advice sent from the server and displays it to the user, allowing the user to create an action plan based on the generated advice.

[0647] Step 6:

[0648] User

[0649] The user inputs progress data into the terminal at regular intervals, for example, recording progress such as "I used a new time management tool this week, but there are still too many tasks."

[0650] Step 7:

[0651] Terminal

[0652] The device formats the entered progress data and sends it to the server, which then analyzes the progress data.

[0653] Step 8:

[0654] server

[0655] The server stores the received progress data in a database, which manages all of the user's progress data.

[0656] Step 9:

[0657] server

[0658] The server uses generative AI to generate improvement suggestions based on progress data, such as "When debugging, try running unit tests first."

[0659] Step 10:

[0660] Terminal

[0661] The terminal receives the improvement suggestions sent from the server and displays them to the user, allowing the user to take specific improvement actions as the next step.

[0662] Through the above steps, this system is able to provide efficient and personalized mentoring and support the user's growth.

[0663] Example 1

[0664] 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."

[0665] Conventional mentoring systems have difficulty providing appropriate advice and improvement suggestions based on a user's individual goals, challenges, and interests. Furthermore, they lack the functionality to properly evaluate a user's progress and provide personalized advice and improvement suggestions in a timely manner. This has led to issues such as ineffective self-improvement and problem-solving for users.

[0666] 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.

[0667] In this invention, the server

[0668] a profile creation means for inputting the user's goals, challenges, and interests;

[0669] an advice generation means for generating personalized advice from input profile information using a generation AI;

[0670] and means for storing the information input by the profile creation means in a database and for storing progress data in the database.

[0671] This allows for optimal personalized mentoring tailored to each user's individual goals and challenges, and makes it possible to provide timely advice and suggestions for improvement based on the user's progress.

[0672] A "profile creation means" is a means that provides an interface for a user to input their goals, issues, and interests.

[0673] An "advice generation means" is a means for generating personalized advice from profile information using a generation AI.

[0674] The "progress data input means" is a means for providing an interface for the user to input his / her own progress data.

[0675] The "improvement proposal generation means" is a means for generating improvement proposals using a generation AI based on progress data.

[0676] The "means for storing in a database" refers to a means for storing profile information and progress data in a database.

[0677] The "means for notifying" is a means for notifying the user of the generated advice and improvement suggestions.

[0678] This invention is a system for providing personalized mentoring based on a user's goals, challenges, and interests. The system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, an improvement suggestion generation means using a generation AI, and a database.

[0679] Profile Creation Method

[0680] Terminal

[0681] The device provides an interface for users to input their goals, challenges, and interests. This interface is implemented as a web or mobile application. Specific software examples include web forms using HTML, CSS, JavaScript, etc.

[0682] server

[0683] The server receives the information entered by the user on the terminal and creates a profile. This profile information is stored in a database. A relational database management system (RDBMS) is used as the database.

[0684] Advice Generation Method

[0685] server

[0686] The server uses a generative AI to generate personalized advice from the profile information stored in the database. The generative AI uses an advanced natural language processing model, such as GPT-4. This generative AI analyzes the profile data and generates optimal advice.

[0687] Terminal

[0688] The device receives the advice sent from the server and displays it to the user via a web browser or mobile app screen.

[0689] Progress data input method

[0690] Terminal

[0691] The user inputs progress data at regular intervals into the terminal. This interface, like the profile creation means, is implemented as a web application or mobile application.

[0692] server

[0693] The server receives the progress data sent from the device and stores it in a database, thereby recording the user's progress.

[0694] Improvement proposal generation means

[0695] server

[0696] The server uses a generation AI to generate improvement suggestions based on the progress data stored in the database. This generation AI analyzes the progress data and generates specific suggestions to promote the user's growth.

[0697] Terminal

[0698] The device receives the improvement suggestions sent from the server and displays them to the user via a web browser or mobile app screen.

[0699] Specific examples

[0700] Case of user "A"

[0701] User "A" uses this system to improve himself. He enters the following information into the terminal:

[0702] Goal: Acquire advanced programming skills

[0703] Challenge: Time management issues

[0704] Interests: Machine Learning

[0705] The server stores this information in a database as a profile. The AI ​​then analyzes this profile and generates advice such as, "First, introduce a time management tool and break down and plan your daily tasks." This advice is displayed on the device, and Mr. A puts it into practice.

[0706] After a certain period of time, Person A enters progress data into the terminal. "I used the new time management tool this week, but there are still too many tasks." This data is sent to the server and stored in a database. The generation AI analyzes this progress data and generates improvement suggestions such as: "When debugging, try running unit tests first." This suggestion is also displayed on the terminal, allowing Person A to plan his next steps.

[0707] Examples of prompt statements

[0708] Here are some example prompts to enter into a generative AI model:

[0709] User "A"'s goal is "to acquire advanced programming skills," his challenge is "time management issues," and his interest is "machine learning." Based on this information, please generate appropriate advice.

[0710] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0711] Program processing flow

[0712] Step 1: Create a profile

[0713] Terminal

[0714] 1. The terminal provides the user with an interface for inputting goals, issues, and interests.

[0715] Input: Information about the user's goals, challenges, and interests

[0716] Output: Data that sends the input information to the server

[0717] Specific operation: The user inputs information such as "learning advanced programming skills, time management problems, machine learning," and the device sends this information to the server in JSON format.

[0718] server

[0719] 2. The server receives the information sent from the device and creates a profile.

[0720] Input: User information received from the device

[0721] Output: Data to save the created profile information to the database

[0722] Specific operation: The server analyzes the received information, creates a profile for user "A" and saves it in the database.

[0723] Step 2: Advice Generation

[0724] server

[0725] 1. The server retrieves profile information from the database and generates advice using generative AI.

[0726] Input: Profile information stored in the database

[0727] Output: Generated advice

[0728] Specific operation: The server retrieves the profile information of "Mr. A" from the database, inputs "Mr. A"'s information as a prompt into GPT-4, and generates appropriate advice.

[0729] 2. The server sends the generated advice to the terminal.

[0730] Input: Generated advice

[0731] Output: Data to send to the terminal

[0732] Specific operation: The server sends advice to the device saying, "First, introduce a time management tool and break down and plan your daily tasks."

[0733] Terminal

[0734] 3. The terminal receives the advice sent from the server and displays it to the user.

[0735] Input: Advice received from the server

[0736] Output: Advice displayed on screen

[0737] Specific operation: The device displays the received advice on the screen and the user confirms it.

[0738] Step 3: Enter progress data

[0739] Terminal

[0740] 1. The terminal provides an interface for the user to input progress data.

[0741] Input: User progress data

[0742] Output: Data to send the entered progress data to the server

[0743] Specific operation: The user enters progress data such as "I used the new time management tool this week, but I still have too many tasks," and the device sends this to the server.

[0744] server

[0745] 2. The server receives the progress data sent from the device and stores it in a database.

[0746] Input: Progress data received from the device

[0747] Output: Progress data stored in a database

[0748] Specific operation: The server analyzes the received progress data and stores it in the database.

[0749] Step 4: Generate improvement suggestions

[0750] server

[0751] 1. The server retrieves progress data from the database and generates improvement suggestions using generative AI.

[0752] Input: Progress data stored in the database

[0753] Output: Generated improvement suggestions

[0754] Specific operation: The server retrieves "Person A's" progress data from the database and inputs this information as prompts into GPT-4 to generate appropriate improvement suggestions.

[0755] 2. The server sends the generated improvement proposal to the terminal.

[0756] Input: Generated improvement suggestions

[0757] Output: Data to send to the terminal

[0758] Specific operation: The server sends an improvement suggestion to the terminal saying, "When debugging, try running unit tests first."

[0759] Terminal

[0760] 3. The device receives the improvement suggestions sent from the server and displays them to the user.

[0761] Input: Improvement suggestions received from the server

[0762] Output: Improvement suggestions displayed on the screen

[0763] Specific operation: The device displays the improvement suggestions it receives on the screen and the user confirms them.

[0764] (Application example 1)

[0765] 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."

[0766] This invention relates to a system that enables users to efficiently advance their self-development and learning. Conventional systems have the problem of being unable to provide specific advice or improvement suggestions based on the user's individual needs and progress, and can only provide general suggestions. Furthermore, they lack usability because they are not compatible with easy operation using smartphones.

[0767] 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.

[0768] In this invention, the server includes a profile creation means, a means for generating personalized advice from input profile information using a generation AI, a means for displaying the advice, a means for inputting user progress data, a means for generating improvement suggestions using a generation AI based on the progress data, a means for displaying the improvement suggestions, a means for implementing the system as a smartphone app, and a means for generating advice and improvement suggestions in the form of prompt sentences using the generation AI. This enables users to easily receive specific advice and improvement suggestions tailored to their individual needs via their smartphones.

[0769] "User" refers to a person who uses the system.

[0770] "Objective" refers to the specific purpose or goal that the user is trying to achieve.

[0771] "Challenges" refer to problems or difficulties faced by users.

[0772] "Interests" refers to areas or topics in which a user has particular interest or concern.

[0773] "Profile Creation Tool" refers to a tool for inputting a user's goals, challenges, and interests.

[0774] "Generative AI" refers to algorithms and models that use artificial intelligence techniques to analyze data and generate advice and improvement suggestions.

[0775] "Advice generation means" refers to a means for generating personalized advice from input profile information using a generation AI.

[0776] "Display means" refers to a means for visually presenting the generated advice and improvement suggestions to the user.

[0777] "Progress data input means" refers to a means for a user to input progress data.

[0778] "Improvement proposal generation means" refers to a means for generating specific improvement proposals using generation AI based on user progress data.

[0779] "Smartphone app" refers to an application that runs on a smartphone.

[0780] "Prompt sentence format" refers to the document format used as input to the generative AI.

[0781] A specific embodiment of a system for supporting self-improvement based on this invention is described below. The system provides users with personalized advice and improvement suggestions via a smartphone app. This system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, and an improvement suggestion generation means using a generation AI.

[0782] 1. Create a profile

[0783] Terminal

[0784] Users input their goals, challenges, and interests through the smartphone app interface, such as "acquiring advanced programming skills" or "interest in machine learning."

[0785] server

[0786] The server receives the profile information entered by the user and stores it in a database, which includes detailed data about the user's goals, challenges, and interests.

[0787] 2. Advice Generation

[0788] server

[0789] The server uses the stored profile information to generate personalized advice using a generative AI model, such as "First, introduce time management tools and break down and plan your daily tasks."

[0790] Terminal

[0791] The terminal receives the generated advice and displays it to the user, allowing the user to obtain specific guidelines for action.

[0792] 3. Progress data entry

[0793] Terminal

[0794] Users periodically enter progress data, including accomplishments achieved and actions taken, such as "I've been using the new time management tool, but I still have too many tasks."

[0795] server

[0796] The server receives the entered progress data and stores it in a database, which is used to generate improvement suggestions for the next step.

[0797] 4. Improvement proposal generation

[0798] server

[0799] The server uses a generative AI model to generate improvement suggestions based on the progress data, such as "When debugging, try running unit tests first."

[0800] Terminal

[0801] The device receives the generated improvement suggestions and displays them to the user, allowing the user to know the specific action to take next.

[0802] Specific examples

[0803] Here are some example prompts to input to a generative AI model:

[0804] Prompts for advice generation based on profile information:

[0805] User goal: Acquire advanced programming skills, challenges: Time management issues, interest: Machine learning. Provide tailored advice.

[0806] Prompts for generating improvement suggestions based on progress data:

[0807] User progress: I used the new time management tool, but I still have too many tasks. Provide improvement suggestions.

[0808] Using these prompts, the generative AI model can generate appropriate advice and improvement suggestions and provide them to the user. The server is built using Python's Flask, and the database uses SQLite. The generative AI model uses the OpenAI API.

[0809] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0810] Step 1:

[0811] Profile Creation

[0812] Input: Users input their goals, challenges, and interests through a smartphone app interface, such as "acquiring advanced programming skills" or "interested in machine learning."

[0813] Processing: The device collects this input data and sends it to the server, which analyzes it and stores it in a database as profile information.

[0814] Output: Profile information is saved to the database.

[0815] Step 2:

[0816] Advice Generation

[0817] Input: Saved profile information.

[0818] Processing: The server retrieves the profile information and inputs it to the generative AI model in the form of a prompt. Example prompt: "User goal: Acquire advanced programming skills, challenges: Time management issues, interest: Machine learning. Provide tailored advice."

[0819] Output: The generation AI generates advice based on the input profile information, and the server receives this advice and sends it to the device.

[0820] How it works: The generative AI responds with personalized advice based on the prompt it receives. The server sends this advice to the device, which then displays it to the user. For example, the advice might be, "First, introduce a time management tool and break down and plan your daily tasks."

[0821] Step 3:

[0822] Progress data entry

[0823] Input: Users periodically enter progress data through the smartphone app interface, such as "I used the new time management tool, but I still have too many tasks."

[0824] Processing: The device collects progress data and sends it to the server, which stores it in a database.

[0825] Output: Progress data is saved to the database.

[0826] How it works: The progress data entered by the user details the accomplishments and actions they have taken. The device sends this data in real time to the server, which stores it in a database.

[0827] Step 4:

[0828] Improvement proposal generation

[0829] Input: Progress data stored in the database.

[0830] Processing: The server retrieves the progress data and inputs it to the generative AI model in the form of a prompt. Example prompt: "User progress: I used the new time management tool, but there are still too many tasks. Provide improvement suggestions."

[0831] Output: The generation AI generates improvement suggestions based on the progress data, and the server receives these suggestions and sends them to the device.

[0832] Specific operation: Based on the received prompt, the generation AI returns a specific improvement suggestion to solve the user's problem. For example, it might generate a suggestion such as, "When debugging, try running unit tests first." The server sends this suggestion to the device, which then displays it to the user.

[0833] Step 5:

[0834] Notifications and Feedback

[0835] Input: Generated advice and improvement suggestions.

[0836] Processing: The server notifies the terminal of the generated advice and improvement suggestions.

[0837] Output: The consumer receives advice and suggestions for improvement through notifications.

[0838] Specific operation: The notification function allows users to receive advice and suggestions for improvement. The device receives these and displays them to the user, making them easy to access.

[0839] Through these processing steps, the system can use the generative AI model to provide personalized advice and improvement suggestions, enabling users to receive assistance tailored to their specific and individual needs.

[0840] 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.

[0841] The system provides personalized mentoring based on the user's goals, challenges, and interests, and further achieves a higher level of personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, an emotion engine, and a database.

[0842] Profile Creation Method

[0843] Terminal

[0844] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented as a web or mobile application.

[0845] server

[0846] The server receives the profile information sent from the terminal and stores it in a database.

[0847] Advice Generation Method

[0848] server

[0849] The server uses generative AI to generate personalized advice based on the profile information stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[0850] Terminal

[0851] The terminal receives the advice sent from the server and displays it to the user.

[0852] Progress data input method

[0853] Terminal

[0854] The user periodically inputs progress data into the terminal. For example, the user may record progress such as, "I've used a new time management tool, but there are still too many tasks."

[0855] server

[0856] The server receives the progress data sent from the terminal and stores it in a database.

[0857] Improvement proposal generation means

[0858] server

[0859] The server uses generative AI to generate improvement suggestions based on the progress data stored in the database, such as "When debugging, try running unit tests first."

[0860] Terminal

[0861] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[0862] Emotion Engine

[0863] Terminal

[0864] The device is equipped with an emotion recognition function and acquires emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[0865] server

[0866] The server analyzes the acquired emotional data and provides appropriate personalization. The emotional data is stored in a database along with profile information and progress data.

[0867] Program processing

[0868] Profile Creation

[0869] The server receives the user's input of goals, challenges, and interests and creates a profile, which is then stored in a database.

[0870] Advice Generation

[0871] The server uses a generation AI to generate personalized advice based on the profile information, which is then sent to the device and displayed.

[0872] Progress data entry and saving

[0873] The user periodically inputs progress data into the terminal, which then sends it to the server, which receives the progress data and stores it in a database.

[0874] Improvement proposal generation

[0875] The server uses a generation AI to generate improvement suggestions based on the progress data and emotion data, and these improvement suggestions are sent to the device and displayed.

[0876] emotion recognition

[0877] The device uses a camera and microphone to capture the user's emotional data, which the server analyzes and combines with profile information and progress data to provide optimal advice and suggestions for improvement.

[0878] Specific examples

[0879] Case of user "Taro Tanaka"

[0880] User "Taro Tanaka" uses the system to improve himself. Tanaka enters the following information into the terminal.

[0881] Goal: Acquire advanced programming skills

[0882] Challenge: Time management issues

[0883] Interests: Machine Learning

[0884] The server stores this information in a database as a profile. The AI ​​then analyzes the profile and generates advice such as "introduce time management tools and break down and plan your daily tasks." The advice is displayed on Tanaka's device, and he puts it into practice.

[0885] After a certain period of time, Tanaka enters his progress data into his terminal, recording, "I used the new time management tool, but there are still too many tasks." The server receives this and stores it in a database.

[0886] The server uses generative AI to analyze the progress data and the emotion data obtained from the emotion engine, and generates improvement suggestions such as, "When debugging, try running unit tests first." The suggestions are displayed on the device, and Tanaka plans his next steps.

[0887] The emotion engine provides suggestions and advice that take Tanaka's emotions into consideration, allowing him to take more appropriate actions. Through the above process, the present invention efficiently and effectively supports the growth of users.

[0888] The processing flow will be explained below.

[0889] The system provides personalized mentoring based on the user's goals, challenges, and interests, and further achieves a higher level of personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, an emotion engine, and a database.

[0890] Profile Creation Method

[0891] Terminal

[0892] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented as a web or mobile application.

[0893] server

[0894] The server receives the profile information sent from the terminal and stores it in a database.

[0895] Advice Generation Method

[0896] server

[0897] The server uses generative AI to generate personalized advice based on the profile information stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[0898] Terminal

[0899] The terminal receives the advice sent from the server and displays it to the user.

[0900] Progress data input method

[0901] Terminal

[0902] The user periodically inputs progress data into the terminal. For example, the user may record progress such as, "I've used a new time management tool, but there are still too many tasks."

[0903] server

[0904] The server receives the progress data sent from the terminal and stores it in a database.

[0905] Improvement proposal generation means

[0906] server

[0907] The server uses generative AI to generate improvement suggestions based on the progress data stored in the database, such as "When debugging, try running unit tests first."

[0908] Terminal

[0909] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[0910] Emotion Engine

[0911] Terminal

[0912] The device is equipped with an emotion recognition function and acquires emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[0913] server

[0914] The server analyzes the acquired emotional data and provides appropriate personalization. The emotional data is stored in a database along with profile information and progress data.

[0915] Program processing

[0916] Step 1:

[0917] User

[0918] Users use the device interface to input their goals, challenges, and interests, such as "acquiring advanced programming skills," "time management issues," or "machine learning."

[0919] Step 2:

[0920] Terminal

[0921] The device formats the user's input of goals, challenges, and interests and sends it to the server, where the necessary data is ready for processing.

[0922] Step 3:

[0923] server

[0924] The server receives the profile information sent from the terminal and stores it in a database using a profile creation means. The database centrally manages the profile information for each user.

[0925] Step 4:

[0926] server

[0927] The server uses generative AI to generate personalized advice based on the profile information stored in the database. A specific algorithm is used to generate advice. For example, the server might generate advice such as, "First, introduce time management tools and break down and plan your daily tasks."

[0928] Step 5:

[0929] Terminal

[0930] The terminal receives the advice sent from the server and displays it to the user, allowing the user to create an action plan based on the generated advice.

[0931] Step 6:

[0932] User

[0933] The user inputs progress data into the terminal at regular intervals, for example, recording progress such as "I used a new time management tool this week, but there are still too many tasks."

[0934] Step 7:

[0935] Terminal

[0936] The device formats the entered progress data and sends it to the server, which then analyzes the progress data.

[0937] Step 8:

[0938] server

[0939] The server stores the received progress data in a database, which manages all of the user's progress data.

[0940] Step 9:

[0941] Terminal

[0942] The device uses an emotion engine to acquire emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[0943] Step 10:

[0944] server

[0945] The server analyzes the received emotion data and stores it in a database, where it is integrated with profile information and progress data.

[0946] Step 11:

[0947] server

[0948] The server uses generative AI to generate improvement suggestions based on progress and emotion data, such as "When debugging, try running unit tests first."

[0949] Step 12:

[0950] Terminal

[0951] The terminal receives the improvement suggestions sent from the server and displays them to the user, allowing the user to take specific improvement actions as the next step.

[0952] Through the above steps, this system is able to provide efficient and personalized mentoring and support the user's growth.

[0953] Example 2

[0954] 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."

[0955] Conventional mentoring systems lacked personalization based on the user's goals and challenges, limiting the accuracy and appropriateness of the advice they provided. Furthermore, they did not take the user's emotions into account, making it difficult to respond flexibly based on their emotional state. As a result, there were problems with users' growth and problem-solving not progressing effectively.

[0956] 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.

[0957] In this invention, the server includes: a profile creation means for inputting a user's goals, challenges, and interests; an advice generation means for generating personalized advice from the input profile information using a generation AI; a means for displaying the advice; a progress data input means for inputting the user's progress data; an improvement proposal generation means for generating improvement proposals using a generation AI based on the progress data; a means for displaying the improvement proposals; an emotion recognition means for recognizing the user's emotions and acquiring emotion data; and a means for analyzing the emotion data and combining it with the profile information and progress data to provide optimal advice and improvement proposals. This enables highly personalized advice and improvement proposals that take emotions into account in relation to the user's goals and challenges.

[0958] A "profile creation tool" is an interface through which a user can input and collect information about their goals, challenges, and interests.

[0959] An "advice generation means" is a means for generating personalized advice from profile information using a generation AI.

[0960] The "means for displaying the advice" refers to an interface or device for displaying the generated advice to the user.

[0961] The "progress data input means" is an interface for users to input their own progress status.

[0962] The "improvement proposal generation means" is a means for generating improvement proposals using a generation AI based on progress data.

[0963] The "means for displaying the improvement proposal" refers to an interface or device for displaying the generated improvement proposal to the user.

[0964] The "emotion recognition means" refers to a camera, microphone, and software for recognizing the user's emotions and acquiring emotion data.

[0965] The "means for analyzing the emotion data and combining it with the profile information and progress data to provide optimal advice and improvement suggestions" refers to means for analyzing emotion data and integrating it with profile information and progress data to generate optimized advice and improvement suggestions.

[0966] This invention is a system that provides personalized mentoring based on a user's goals, challenges, and interests, and further achieves more advanced personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, an improvement suggestion generation means using a generation AI, an emotion engine, and a database.

[0967] Hardware and Software Configuration

[0968] Profile Creation Method

[0969] The device provides an interface for users to input their goals, challenges, and interests, which may include web or mobile applications. For example, a user may input a goal of "acquiring advanced programming skills."

[0970] Advice generation means and improvement proposal generation means

[0971] The server uses the generative AI model to generate personalized advice and improvement suggestions based on the profile information and progress data stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[0972] Display means

[0973] The device receives the advice and improvement suggestions sent from the server and displays them to the user as in-app messages or notifications.

[0974] Progress data input method

[0975] The user periodically enters their progress data into the device, for example, recording progress such as "I've used a new time management tool, but there are still too many tasks."

[0976] emotion recognition means

[0977] The device is equipped with an emotion recognition function and uses a camera and microphone to obtain emotional data from the user's facial expressions and voice. For example, it can analyze the user's facial expression to determine whether they are "highly stressed."

[0978] Specific examples

[0979] Below is a concrete example of how user "Taro Tanaka" uses this system:

[0980] Profile Creation:

[0981] User "Taro Tanaka" enters the following information into his terminal:

[0982] Goal: Acquire advanced programming skills

[0983] Challenge: Time management issues

[0984] Interests: Machine Learning

[0985] The server receives this information and stores it in a database as profile information.

[0986] Advice Generation:

[0987] The server uses a generative AI model to generate advice such as "introduce time management tools and break down and plan your daily tasks."

[0988] Progress Data Entry:

[0989] After a certain period of time, Taro Tanaka enters and submits progress data into his terminal, stating, "I used the new time management tool, but there are still too many tasks."

[0990] Improvement suggestion generation:

[0991] The server receives this progress data and uses a generative AI model to generate improvement suggestions such as, "When debugging, try running unit tests first."

[0992] Emotion recognition:

[0993] The device's camera recognizes Taro Tanaka's facial expressions and captures emotional data indicating that he is "highly stressed." The server analyzes this emotional data and combines it with his profile information and progress data to provide optimal improvement suggestions.

[0994] Prompt Sentence Examples

[0995] "If I want to learn advanced programming skills but have trouble managing my time, what advice would you give me?"

[0996] This enables the system to provide highly personalized advice and improvement suggestions that take into account the user's goals and challenges, even taking their emotions into account.

[0997] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0998] Step 1: Fill in and submit your profile information

[0999] User

[1000] Users input their goals, challenges, and interests into the terminal. For example, a user may input "advanced programming skills" as a goal.

[1001] input

[1002] Goal: Acquire advanced programming skills

[1003] Challenge: Time management issues

[1004] Interests: Machine Learning

[1005] Terminal

[1006] The terminal receives the information entered by the user and transmits it to the server.

[1007] output

[1008] Profile information (goals, challenges, interests) is sent to a server.

[1009] Specific actions

[1010] The terminal interface is provided with a text box and an input button. When a user enters information into the text box and presses the send button, the information is sent to the server.

[1011] Step 2: Save your profile information

[1012] server

[1013] The server receives the profile information sent from the terminal and stores it in a database.

[1014] input

[1015] Profile information sent from the device

[1016] output

[1017] Profile information stored in a database

[1018] Specific actions

[1019] The server parses the received profile information and stores each item (goals, challenges, interests) in the appropriate field in a database.

[1020] Step 3: Generate Advice

[1021] server

[1022] The server uses a generative AI model to analyze the profile information and generate personalized advice.

[1023] input

[1024] Profile information stored in a database

[1025] output

[1026] Generated Advice

[1027] Specific actions

[1028] The server calls the generative AI model, passing the profile information as input, and the model analyzes it to generate advice such as "introduce time management tools and break down and plan your daily tasks."

[1029] Step 4: Submitting and viewing advice

[1030] server

[1031] The generated advice is sent to the device.

[1032] input

[1033] Generated Advice

[1034] output

[1035] Advice sent to device

[1036] Terminal

[1037] The terminal receives the advice sent from the server and displays it to the user.

[1038] Specific actions

[1039] Advice will be displayed as notifications on the device or in-app messages, for example, in the notification bar on your smartphone.

[1040] Step 5: Enter and submit progress data

[1041] User

[1042] The user periodically inputs their progress data into the terminal, for example, "I used a new time management tool, but there are still too many tasks."

[1043] input

[1044] Progress Data

[1045] Terminal

[1046] The terminal transmits the progress data entered by the user to the server.

[1047] output

[1048] Progress data sent to the server

[1049] Specific actions

[1050] A text box for inputting progress data is placed on the terminal interface, and the user inputs the data and presses the send button.

[1051] Step 6: Save your progress

[1052] server

[1053] The server receives the progress data sent from the terminal and stores it in a database.

[1054] input

[1055] Progress data sent from the device

[1056] output

[1057] Progress data stored in a database

[1058] Specific actions

[1059] The server parses the progress data and stores it in the appropriate fields.

[1060] Step 7: Generate improvement suggestions

[1061] server

[1062] The server uses a generative AI model to generate improvement suggestions based on the progress data stored in the database.

[1063] input

[1064] Progress data stored in a database

[1065] output

[1066] Generated improvement suggestions

[1067] Specific actions

[1068] The server inputs progress data into the generative AI model, which analyzes it and generates a suggestion such as, "When debugging, try running unit tests first."

[1069] Step 8: Submit and view improvement suggestions

[1070] server

[1071] The generated improvement proposal is sent to the terminal.

[1072] input

[1073] Generated improvement suggestions

[1074] output

[1075] Improvement suggestions sent to the device

[1076] Terminal

[1077] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[1078] Specific actions

[1079] Improvement suggestions will be displayed as notifications on the device or in-app messages.

[1080] Step 9: Obtaining and Sending Emotion Data

[1081] Terminal

[1082] The device uses a camera and microphone to capture user emotional data, for example, analyzing emotions from facial expressions and voice in real time.

[1083] input

[1084] User's facial expressions and voice

[1085] output

[1086] Acquired emotion data

[1087] server

[1088] Emotion data transmitted from the terminal is received.

[1089] Specific actions

[1090] The device uses a camera and microphone to recognize the user's face and analyze the tone of their voice, and then sends emotional data to the server.

[1091] Step 10: Analyze and integrate sentiment data

[1092] server

[1093] The server analyzes the received emotional data and integrates it with profile information and progress data to generate optimal advice and improvement suggestions.

[1094] input

[1095] Emotional Data

[1096] Profile Information

[1097] Progress Data

[1098] output

[1099] Best advice and improvement suggestions

[1100] Specific actions

[1101] The server analyzes the emotional data and evaluates the user's stress level and emotional state along with their profile information and progress data, and uses a generative AI model to generate optimal advice and improvement suggestions to provide appropriate support to the user.

[1102] (Application example 2)

[1103] 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."

[1104] To help drivers improve their driving skills and reduce stress in autonomous vehicles, it is necessary to provide personalized advice and improvement suggestions to each driver. However, current systems lack the means to properly grasp the driver's emotional state and progress, making it difficult to provide appropriate feedback based on this. In addition, generating advice based on real-time emotion recognition is difficult, resulting in issues that prevent sufficient improvement of the driving experience.

[1105] 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 a profile creation means for inputting a user's goals, challenges, and interests, an advice generation means for generating personalized advice from the input profile information using a generation AI, a means for displaying advice, a progress data input means for inputting the user's progress data, an improvement proposal generation means for generating improvement proposals using a generation AI based on the progress data, a means for displaying the improvement proposals, an emotion recognition means for detecting the user's emotions, and a means for using the emotion data in combination with the profile information and progress data to provide optimal advice and improvement proposals. This makes it possible to provide appropriate feedback in real time based on the driver's emotional state and progress, thereby improving driving skills and reducing stress.

[1106] "Profile creation means" is a function that provides an interface for users to input their goals, challenges, and interests.

[1107] The "advice generation means" is a function that uses generation AI to generate personalized advice from the input profile information.

[1108] The "means for displaying advice" is a function for displaying the generated advice to the user.

[1109] The "progress data input means" is a function for inputting the user's progress data.

[1110] The "improvement proposal generation means" is a function that generates improvement proposals using a generation AI based on progress data.

[1111] The "means for displaying improvement proposals" is a function for displaying the generated improvement proposals to the user.

[1112] "Emotion recognition means" is a function that detects the user's emotions.

[1113] "Means for using emotional data in combination with profile information and progress data to provide optimal advice and improvement suggestions" is a function that analyzes emotional data in combination with profile information and progress data, and generates and provides optimal advice and improvement suggestions.

[1114] This invention is a system that provides personalized advice and feedback to drivers to improve their driving skills and reduce their stress. The system includes a user terminal, a server, a generative AI model, an emotion recognition means, and software for linking these components.

[1115] System configuration

[1116] Terminal

[1117] The terminal provides an interface where users can input their goals, challenges, and interests. Users input information through devices such as smartphone applications or tablets. This interface is intuitive and easy to operate, and the user's input data is quickly transmitted to the server.

[1118] server

[1119] The server has several main functions. First, it stores profile information in a database. Second, it uses a generative AI model to generate personalized advice from the profile information. Third, it collects progress data, based on which the generative AI model generates improvement suggestions. Furthermore, the server analyzes emotion data obtained from the emotion recognition means and combines it with the profile information and progress data to generate optimal advice and improvement suggestions.

[1120] emotion recognition means

[1121] The emotion recognition unit uses the device's camera and microphone to detect emotions from the user's facial expressions and voice. This data is sent to the server in real time and analyzed immediately according to the user's situation.

[1122] Specific examples

[1123] Consider a scenario where a user is using an autonomous vehicle. First, the user inputs their goals (e.g., improving driving skills), challenges (e.g., stress caused by traffic jams), and interests (e.g., eco-driving) into their device. This information is immediately sent to the server and stored in a database as a profile.

[1124] Next, the generative AI model generates advice based on the profile information. For example, for a user with the profile information "Goal: Improve driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving," the generative AI model would provide advice such as "Take deep breaths to relax and try not to pay attention to following vehicles." This advice is displayed on the device for the user to confirm.

[1125] While driving, the device's camera and microphone record the user's facial expressions and voice. If the user feels stressed, the emotion recognition function sends the data to the server. The server analyzes this emotion data and immediately provides appropriate feedback (e.g., "You are feeling stressed. We recommend that you take a short break.").

[1126] After completing a drive, the user inputs progress data into the device. Based on the progress data (e.g., "I felt stressed during this drive") and emotion data, the generative AI model generates improvement suggestions for the next step (e.g., "Try some relaxation techniques while driving"). These suggestions are also displayed on the device, allowing the user to use them for their next drive.

[1127] Hardware and software used

[1128] Hardware: Camera (for facial expression recognition), microphone (for voice recognition)

[1129] Software: Python, OpenCV (camera operation), sounddevice (microphone operation), HuggingFace Transformers library (generative AI model)

[1130] Prompt Sentence Examples

[1131] "Goal: Improving driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving, Emotion: Advice on stress"

[1132] In this way, the system can help drivers improve their driving skills and reduce stress.

[1133] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1134] Step 1:

[1135] The user inputs their goals, challenges, and interests. The user inputs their goals (e.g., improving driving skills), challenges (e.g., stress caused by traffic jams), and interests (e.g., eco-driving) into the device's input interface. The input data is sent to the server and stored in a database as profile information.

[1136] Step 2:

[1137] The server receives the profile information and generates personalized advice using a generative AI model. From the received profile information, a prompt sentence is generated to generate advice for "Goal: Improve driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving" and input into the generative AI model. The generated advice is sent to the device and displayed. For example, advice such as "Take deep breaths to relax and try not to pay attention to following vehicles" is displayed.

[1138] Step 3:

[1139] The device uses a camera and microphone to record the user's facial expressions and voice in real time. The data acquired by the camera and microphone is sent to an emotion recognition means to detect the user's emotional state (e.g., stress). The detected emotion data is then sent to the server.

[1140] Step 4:

[1141] The server analyzes the emotional data and combines it with the profile information to generate optimal feedback. The server generates prompts based on the emotional data and profile information and inputs them into the generative AI model. For example, feedback such as "You're feeling stressed. We recommend you take a short break" is generated and sent to the device. The feedback is displayed in real time.

[1142] Step 5:

[1143] After the user finishes driving, they input their progress data into the terminal. The progress data (e.g., "I felt stressed during this drive") is sent to the server and stored in a database.

[1144] Step 6:

[1145] The server analyzes the progress data and emotion data and generates improvement suggestions using a generative AI model. A prompt sentence is generated based on the progress data and emotion data and input into the generative AI model. For example, an improvement suggestion such as "Try some relaxation techniques the next time you drive" is generated and sent to the device. The improvement suggestion is displayed on the device so that the user can use it for their next drive.

[1146] In this way, the system can perform appropriate data processing and calculations based on the data obtained at each step, and provide optimal feedback and improvement suggestions to users.

[1147] 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.

[1148] 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.

[1149] 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.

[1150] [Third embodiment]

[1151] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1152] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1153] 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).

[1154] 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.

[1155] 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.

[1156] 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).

[1157] 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.

[1158] 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.

[1159] 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.

[1160] 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.

[1161] 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.

[1162] 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."

[1163] The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, and a database to provide personalized mentoring based on the user's goals, challenges, and interests.

[1164] Profile Creation Method

[1165] Terminal

[1166] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented, for example, as a web application or a mobile application.

[1167] server

[1168] The server receives the information entered by the user on the device and creates a profile, which is then stored in a database.

[1169] Advice Generation Method

[1170] server

[1171] The server generates personalized advice from the profile information stored in the database using a generation AI, which analyzes the profile data based on a specific algorithm and generates appropriate advice.

[1172] Terminal

[1173] The device receives the advice sent from the server and displays it to the user. For example, the advice may be, "First, introduce a time management tool and break down and plan your daily tasks."

[1174] Progress data input method

[1175] Terminal

[1176] The user periodically inputs progress data into the terminal, which records the results achieved and actions taken by the user.

[1177] server

[1178] The server receives the progress data sent from the terminal and stores it in a database.

[1179] Improvement proposal generation means

[1180] server

[1181] The server generates improvement proposals using a generation AI based on the progress data stored in the database. This generation AI analyzes the progress data and generates specific proposals to promote the user's growth.

[1182] Terminal

[1183] The device receives the improvement suggestions sent from the server and displays them to the user. For example, a suggestion such as "When debugging, try running unit tests first" may be displayed.

[1184] Specific examples

[1185] Case of user "Yamada Taro"

[1186] User "Yamada Taro" uses this system to improve himself. Yamada enters the following information into the terminal:

[1187] Goals: Acquire advanced programming skills and improve project management

[1188] Challenges: time management issues, complex debugging challenges

[1189] Interests: Machine learning, agile methods

[1190] The server stores this information in a database as a profile. The AI ​​then analyzes this profile and generates advice such as, "First, introduce a time management tool and break down and plan your daily tasks." This advice is displayed on Yamada's device, and he puts it into practice.

[1191] After a certain period of time, Yamada enters his progress data into his terminal. "I used a new time management tool this week, but there are still too many tasks." This data is sent to the server and stored in a database. The generation AI analyzes this progress data and generates improvement suggestions, such as: "When debugging, try running unit tests first." This suggestion is also displayed on his terminal, and Yamada plans his next steps.

[1192] In this way, the present invention efficiently provides personalized mentoring that meets the needs of each individual user, and supports the user's growth.

[1193] The processing flow will be explained below.

[1194] Step 1:

[1195] User

[1196] Users use the device interface to input their goals, challenges, and interests, such as "acquiring advanced programming skills," "time management issues," or "machine learning."

[1197] Step 2:

[1198] Terminal

[1199] The device formats the user's input of goals, challenges, and interests and sends it to the server, where the necessary data is ready for processing.

[1200] Step 3:

[1201] server

[1202] The server receives the profile information sent from the terminal and stores it in a database using a profile creation means. The database centrally manages the profile information for each user.

[1203] Step 4:

[1204] server

[1205] The server uses generative AI to generate personalized advice based on the profile information stored in the database. A specific algorithm is used to generate advice. For example, the server might generate advice such as, "First, introduce time management tools and break down and plan your daily tasks."

[1206] Step 5:

[1207] Terminal

[1208] The terminal receives the advice sent from the server and displays it to the user, allowing the user to create an action plan based on the generated advice.

[1209] Step 6:

[1210] User

[1211] The user inputs progress data into the terminal at regular intervals, for example, recording progress such as "I used a new time management tool this week, but there are still too many tasks."

[1212] Step 7:

[1213] Terminal

[1214] The device formats the entered progress data and sends it to the server, which then analyzes the progress data.

[1215] Step 8:

[1216] server

[1217] The server stores the received progress data in a database, which manages all of the user's progress data.

[1218] Step 9:

[1219] server

[1220] The server uses generative AI to generate improvement suggestions based on progress data, such as "When debugging, try running unit tests first."

[1221] Step 10:

[1222] Terminal

[1223] The terminal receives the improvement suggestions sent from the server and displays them to the user, allowing the user to take specific improvement actions as the next step.

[1224] Through the above steps, this system is able to provide efficient and personalized mentoring and support the user's growth.

[1225] Example 1

[1226] 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."

[1227] Conventional mentoring systems have difficulty providing appropriate advice and improvement suggestions based on a user's individual goals, challenges, and interests. Furthermore, they lack the functionality to properly evaluate a user's progress and provide personalized advice and improvement suggestions in a timely manner. This has led to issues such as ineffective self-improvement and problem-solving for users.

[1228] 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.

[1229] In this invention, the server

[1230] a profile creation means for inputting the user's goals, challenges, and interests;

[1231] an advice generation means for generating personalized advice from input profile information using a generation AI;

[1232] and means for storing the information input by the profile creation means in a database and for storing progress data in the database.

[1233] This allows for optimal personalized mentoring tailored to each user's individual goals and challenges, and makes it possible to provide timely advice and suggestions for improvement based on the user's progress.

[1234] A "profile creation means" is a means that provides an interface for a user to input their goals, issues, and interests.

[1235] An "advice generation means" is a means for generating personalized advice from profile information using a generation AI.

[1236] The "progress data input means" is a means for providing an interface for the user to input his / her own progress data.

[1237] The "improvement proposal generation means" is a means for generating improvement proposals using a generation AI based on progress data.

[1238] The "means for storing in a database" refers to a means for storing profile information and progress data in a database.

[1239] The "means for notifying" is a means for notifying the user of the generated advice and improvement suggestions.

[1240] This invention is a system for providing personalized mentoring based on a user's goals, challenges, and interests. The system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, an improvement suggestion generation means using a generation AI, and a database.

[1241] Profile Creation Method

[1242] Terminal

[1243] The device provides an interface for users to input their goals, challenges, and interests. This interface is implemented as a web or mobile application. Specific software examples include web forms using HTML, CSS, JavaScript, etc.

[1244] server

[1245] The server receives the information entered by the user on the terminal and creates a profile. This profile information is stored in a database. A relational database management system (RDBMS) is used as the database.

[1246] Advice Generation Method

[1247] server

[1248] The server uses a generative AI to generate personalized advice from the profile information stored in the database. The generative AI uses an advanced natural language processing model, such as GPT-4. This generative AI analyzes the profile data and generates optimal advice.

[1249] Terminal

[1250] The device receives the advice sent from the server and displays it to the user via a web browser or mobile app screen.

[1251] Progress data input method

[1252] Terminal

[1253] The user inputs progress data at regular intervals into the terminal. This interface, like the profile creation means, is implemented as a web application or mobile application.

[1254] server

[1255] The server receives the progress data sent from the device and stores it in a database, thereby recording the user's progress.

[1256] Improvement proposal generation means

[1257] server

[1258] The server uses a generation AI to generate improvement proposals based on the progress data stored in the database. This generation AI analyzes the progress data and generates specific proposals to promote the user's growth.

[1259] Terminal

[1260] The device receives the improvement suggestions sent from the server and displays them to the user via a web browser or mobile app screen.

[1261] Specific examples

[1262] Case of user "A"

[1263] User "A" uses this system to improve himself. He enters the following information into the terminal:

[1264] Goal: Acquire advanced programming skills

[1265] Challenge: Time management issues

[1266] Interests: Machine Learning

[1267] The server stores this information in a database as a profile. The AI ​​then analyzes this profile and generates advice such as, "First, introduce a time management tool and break down and plan your daily tasks." This advice is displayed on the device, and Mr. A puts it into practice.

[1268] After a certain period of time, Person A enters progress data into the terminal. "I used the new time management tool this week, but there are still too many tasks." This data is sent to the server and stored in a database. The generation AI analyzes this progress data and generates improvement suggestions such as: "When debugging, try running unit tests first." This suggestion is also displayed on the terminal, allowing Person A to plan his next steps.

[1269] Examples of prompt statements

[1270] Here are some example prompts to enter into a generative AI model:

[1271] User "A"'s goal is "to acquire advanced programming skills," his challenge is "time management issues," and his interest is "machine learning." Based on this information, please generate appropriate advice.

[1272] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1273] Program processing flow

[1274] Step 1: Create a profile

[1275] Terminal

[1276] 1. The terminal provides the user with an interface for inputting goals, issues, and interests.

[1277] Input: Information about the user's goals, challenges, and interests

[1278] Output: Data that sends the input information to the server

[1279] Specific operation: The user inputs information such as "learning advanced programming skills, time management problems, machine learning," and the device sends this information to the server in JSON format.

[1280] server

[1281] 2. The server receives the information sent from the device and creates a profile.

[1282] Input: User information received from the device

[1283] Output: Data to save the created profile information to the database

[1284] Specific operation: The server analyzes the received information, creates a profile for user "A" and saves it in the database.

[1285] Step 2: Advice Generation

[1286] server

[1287] 1. The server retrieves profile information from the database and generates advice using generative AI.

[1288] Input: Profile information stored in the database

[1289] Output: Generated advice

[1290] Specific operation: The server retrieves the profile information of "Mr. A" from the database, inputs "Mr. A"'s information as a prompt into GPT-4, and generates appropriate advice.

[1291] 2. The server sends the generated advice to the terminal.

[1292] Input: Generated advice

[1293] Output: Data to send to the terminal

[1294] Specific operation: The server sends advice to the device saying, "First, introduce a time management tool and break down and plan your daily tasks."

[1295] Terminal

[1296] 3. The terminal receives the advice sent from the server and displays it to the user.

[1297] Input: Advice received from the server

[1298] Output: Advice displayed on screen

[1299] Specific operation: The device displays the received advice on the screen and the user confirms it.

[1300] Step 3: Enter progress data

[1301] Terminal

[1302] 1. The terminal provides an interface for the user to input progress data.

[1303] Input: User progress data

[1304] Output: Data to send the entered progress data to the server

[1305] Specific operation: The user enters progress data such as "I used the new time management tool this week, but I still have too many tasks," and the device sends this to the server.

[1306] server

[1307] 2. The server receives the progress data sent from the device and stores it in a database.

[1308] Input: Progress data received from the device

[1309] Output: Progress data stored in a database

[1310] Specific operation: The server analyzes the received progress data and stores it in the database.

[1311] Step 4: Generate improvement suggestions

[1312] server

[1313] 1. The server retrieves progress data from the database and generates improvement suggestions using generative AI.

[1314] Input: Progress data stored in the database

[1315] Output: Generated improvement suggestions

[1316] Specific operation: The server retrieves "Person A's" progress data from the database and inputs this information as prompts into GPT-4 to generate appropriate improvement suggestions.

[1317] 2. The server sends the generated improvement proposal to the terminal.

[1318] Input: Generated improvement suggestions

[1319] Output: Data to send to the terminal

[1320] Specific operation: The server sends an improvement suggestion to the terminal saying, "When debugging, try running unit tests first."

[1321] Terminal

[1322] 3. The device receives the improvement suggestions sent from the server and displays them to the user.

[1323] Input: Improvement suggestions received from the server

[1324] Output: Improvement suggestions displayed on the screen

[1325] Specific operation: The device displays the improvement suggestions it receives on the screen and the user confirms them.

[1326] (Application example 1)

[1327] 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."

[1328] This invention relates to a system that enables users to efficiently advance their self-development and learning. Conventional systems have the problem of being unable to provide specific advice or improvement suggestions based on the user's individual needs and progress, and can only provide general suggestions. Furthermore, they lack usability because they are not compatible with easy operation using smartphones.

[1329] 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.

[1330] In this invention, the server includes a profile creation means, a means for generating personalized advice from input profile information using a generation AI, a means for displaying the advice, a means for inputting user progress data, a means for generating improvement suggestions using a generation AI based on the progress data, a means for displaying the improvement suggestions, a means for implementing the system as a smartphone app, and a means for generating advice and improvement suggestions in the form of prompt sentences using the generation AI. This enables users to easily receive specific advice and improvement suggestions tailored to their individual needs via their smartphones.

[1331] "User" refers to a person who uses the system.

[1332] "Objective" refers to the specific purpose or goal that the user is trying to achieve.

[1333] "Challenges" refer to problems or difficulties faced by users.

[1334] "Interests" refers to areas or topics in which a user has particular interest or concern.

[1335] "Profile Creation Tool" refers to a tool for inputting a user's goals, challenges, and interests.

[1336] "Generative AI" refers to algorithms and models that use artificial intelligence techniques to analyze data and generate advice and improvement suggestions.

[1337] "Advice generation means" refers to a means for generating personalized advice from input profile information using a generation AI.

[1338] "Display means" refers to a means for visually presenting the generated advice and improvement suggestions to the user.

[1339] "Progress data input means" refers to a means for a user to input progress data.

[1340] "Improvement proposal generation means" refers to a means for generating specific improvement proposals using generation AI based on user progress data.

[1341] "Smartphone app" refers to an application that runs on a smartphone.

[1342] "Prompt sentence format" refers to the document format used as input to the generative AI.

[1343] A specific embodiment of a system for supporting self-improvement based on this invention is described below. The system provides users with personalized advice and improvement suggestions via a smartphone app. This system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, and an improvement suggestion generation means using a generation AI.

[1344] 1. Create a profile

[1345] Terminal

[1346] Users input their goals, challenges, and interests through the smartphone app interface, such as "acquiring advanced programming skills" or "interest in machine learning."

[1347] server

[1348] The server receives the profile information entered by the user and stores it in a database, which includes detailed data about the user's goals, challenges, and interests.

[1349] 2. Advice Generation

[1350] server

[1351] The server uses the stored profile information to generate personalized advice using a generative AI model, such as "First, introduce time management tools and break down and plan your daily tasks."

[1352] Terminal

[1353] The terminal receives the generated advice and displays it to the user, allowing the user to obtain specific guidelines for action.

[1354] 3. Progress data entry

[1355] Terminal

[1356] Users periodically enter progress data, including accomplishments achieved and actions taken, such as "I've been using the new time management tool, but I still have too many tasks."

[1357] server

[1358] The server receives the entered progress data and stores it in a database, which is used to generate improvement suggestions for the next step.

[1359] 4. Improvement proposal generation

[1360] server

[1361] The server uses a generative AI model to generate improvement suggestions based on the progress data, such as "When debugging, try running unit tests first."

[1362] Terminal

[1363] The device receives the generated improvement suggestions and displays them to the user, allowing the user to know the specific action to take next.

[1364] Specific examples

[1365] Here are some example prompts to input to a generative AI model:

[1366] Prompts for advice generation based on profile information:

[1367] User goal: Acquire advanced programming skills, challenges: Time management issues, interest: Machine learning. Provide tailored advice.

[1368] Prompts for generating improvement suggestions based on progress data:

[1369] User progress: I used the new time management tool, but I still have too many tasks. Provide improvement suggestions.

[1370] Using these prompts, the generative AI model can generate appropriate advice and improvement suggestions and provide them to the user. The server is built using Python's Flask, and the database uses SQLite. The generative AI model uses the OpenAI API.

[1371] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1372] Step 1:

[1373] Profile Creation

[1374] Input: Users input their goals, challenges, and interests through a smartphone app interface, such as "acquiring advanced programming skills" or "interested in machine learning."

[1375] Processing: The device collects this input data and sends it to the server, which analyzes it and stores it in a database as profile information.

[1376] Output: Profile information is saved to the database.

[1377] Step 2:

[1378] Advice Generation

[1379] Input: Saved profile information.

[1380] Processing: The server retrieves the profile information and inputs it to the generative AI model in the form of a prompt. Example prompt: "User goal: Acquire advanced programming skills, challenges: Time management issues, interest: Machine learning. Provide tailored advice."

[1381] Output: The generation AI generates advice based on the input profile information, and the server receives this advice and sends it to the device.

[1382] How it works: The generative AI responds with personalized advice based on the prompt it receives. The server sends this advice to the device, which then displays it to the user. For example, the advice might be, "First, introduce a time management tool and break down and plan your daily tasks."

[1383] Step 3:

[1384] Progress data entry

[1385] Input: Users periodically enter progress data through the smartphone app interface, such as "I used the new time management tool, but I still have too many tasks."

[1386] Processing: The device collects progress data and sends it to the server, which stores it in a database.

[1387] Output: Progress data is saved to the database.

[1388] How it works: The progress data entered by the user details the accomplishments and actions they have taken. The device sends this data in real time to the server, which stores it in a database.

[1389] Step 4:

[1390] Improvement proposal generation

[1391] Input: Progress data stored in the database.

[1392] Processing: The server retrieves the progress data and inputs it to the generative AI model in the form of a prompt. Example prompt: "User progress: I used the new time management tool, but there are still too many tasks. Provide improvement suggestions."

[1393] Output: The generation AI generates improvement suggestions based on the progress data, and the server receives these suggestions and sends them to the device.

[1394] Specific operation: Based on the received prompt, the generation AI returns a specific improvement suggestion to solve the user's problem. For example, it might generate a suggestion such as, "When debugging, try running unit tests first." The server sends this suggestion to the device, which then displays it to the user.

[1395] Step 5:

[1396] Notifications and Feedback

[1397] Input: Generated advice and improvement suggestions.

[1398] Processing: The server notifies the terminal of the generated advice and improvement suggestions.

[1399] Output: The consumer receives advice and suggestions for improvement through notifications.

[1400] Specific operation: The notification function allows users to receive advice and suggestions for improvement. The device receives these and displays them to the user, making them easy to access.

[1401] Through these processing steps, the system can use the generative AI model to provide personalized advice and improvement suggestions, enabling users to receive assistance tailored to their specific and individual needs.

[1402] 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.

[1403] The system provides personalized mentoring based on the user's goals, challenges, and interests, and further achieves a higher level of personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, an emotion engine, and a database.

[1404] Profile Creation Method

[1405] Terminal

[1406] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented as a web or mobile application.

[1407] server

[1408] The server receives the profile information sent from the terminal and stores it in a database.

[1409] Advice Generation Method

[1410] server

[1411] The server uses generative AI to generate personalized advice based on the profile information stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[1412] Terminal

[1413] The terminal receives the advice sent from the server and displays it to the user.

[1414] Progress data input method

[1415] Terminal

[1416] The user periodically inputs progress data into the terminal. For example, the user may record progress such as, "I've used a new time management tool, but there are still too many tasks."

[1417] server

[1418] The server receives the progress data sent from the terminal and stores it in a database.

[1419] Improvement proposal generation means

[1420] server

[1421] The server uses generative AI to generate improvement suggestions based on the progress data stored in the database, such as "When debugging, try running unit tests first."

[1422] Terminal

[1423] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[1424] Emotion Engine

[1425] Terminal

[1426] The device is equipped with an emotion recognition function and acquires emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[1427] server

[1428] The server analyzes the acquired emotional data and provides appropriate personalization. The emotional data is stored in a database along with profile information and progress data.

[1429] Program processing

[1430] Profile Creation

[1431] The server receives the user's input of goals, challenges, and interests and creates a profile, which is then stored in a database.

[1432] Advice Generation

[1433] The server uses a generation AI to generate personalized advice based on the profile information, which is then sent to the device and displayed.

[1434] Progress data entry and saving

[1435] The user periodically inputs progress data into the terminal, which then sends it to the server, which receives the progress data and stores it in a database.

[1436] Improvement proposal generation

[1437] The server uses a generation AI to generate improvement suggestions based on the progress data and emotion data, and these improvement suggestions are sent to the device and displayed.

[1438] emotion recognition

[1439] The device uses a camera and microphone to capture the user's emotional data, which the server analyzes and combines with profile information and progress data to provide optimal advice and suggestions for improvement.

[1440] Specific examples

[1441] Case of user "Taro Tanaka"

[1442] User "Taro Tanaka" uses the system to improve himself. Tanaka enters the following information into the terminal.

[1443] Goal: Acquire advanced programming skills

[1444] Challenge: Time management issues

[1445] Interests: Machine Learning

[1446] The server stores this information in a database as a profile. The AI ​​then analyzes the profile and generates advice such as "introduce time management tools and break down and plan your daily tasks." The advice is displayed on Tanaka's device, and he puts it into practice.

[1447] After a certain period of time, Tanaka enters his progress data into his terminal, recording, "I used the new time management tool, but there are still too many tasks." The server receives this and stores it in a database.

[1448] The server uses generative AI to analyze the progress data and the emotion data obtained from the emotion engine, and generates improvement suggestions such as, "When debugging, try running unit tests first." The suggestions are displayed on the device, and Tanaka plans his next steps.

[1449] The emotion engine provides suggestions and advice that take Tanaka's emotions into consideration, allowing him to take more appropriate actions. Through the above process, the present invention efficiently and effectively supports the growth of users.

[1450] The processing flow will be explained below.

[1451] The system provides personalized mentoring based on the user's goals, challenges, and interests, and further achieves a higher level of personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, an emotion engine, and a database.

[1452] Profile Creation Method

[1453] Terminal

[1454] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented as a web or mobile application.

[1455] server

[1456] The server receives the profile information sent from the terminal and stores it in a database.

[1457] Advice Generation Method

[1458] server

[1459] The server uses generative AI to generate personalized advice based on the profile information stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[1460] Terminal

[1461] The terminal receives the advice sent from the server and displays it to the user.

[1462] Progress data input method

[1463] Terminal

[1464] The user periodically inputs progress data into the terminal. For example, the user may record progress such as, "I've used a new time management tool, but there are still too many tasks."

[1465] server

[1466] The server receives the progress data sent from the terminal and stores it in a database.

[1467] Improvement proposal generation means

[1468] server

[1469] The server uses generative AI to generate improvement suggestions based on the progress data stored in the database, such as "When debugging, try running unit tests first."

[1470] Terminal

[1471] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[1472] Emotion Engine

[1473] Terminal

[1474] The device is equipped with an emotion recognition function and acquires emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[1475] server

[1476] The server analyzes the acquired emotional data and provides appropriate personalization. The emotional data is stored in a database along with profile information and progress data.

[1477] Program processing

[1478] Step 1:

[1479] User

[1480] Users use the device interface to input their goals, challenges, and interests, such as "acquiring advanced programming skills," "time management issues," or "machine learning."

[1481] Step 2:

[1482] Terminal

[1483] The device formats the user's input of goals, challenges, and interests and sends it to the server, where the necessary data is ready for processing.

[1484] Step 3:

[1485] server

[1486] The server receives the profile information sent from the terminal and stores it in a database using a profile creation means. The database centrally manages the profile information for each user.

[1487] Step 4:

[1488] server

[1489] The server uses generative AI to generate personalized advice based on the profile information stored in the database. A specific algorithm is used to generate advice. For example, the server might generate advice such as, "First, introduce time management tools and break down and plan your daily tasks."

[1490] Step 5:

[1491] Terminal

[1492] The terminal receives the advice sent from the server and displays it to the user, allowing the user to create an action plan based on the generated advice.

[1493] Step 6:

[1494] User

[1495] The user inputs progress data into the terminal at regular intervals, for example, recording progress such as "I used a new time management tool this week, but there are still too many tasks."

[1496] Step 7:

[1497] Terminal

[1498] The device formats the entered progress data and sends it to the server, which then analyzes the progress data.

[1499] Step 8:

[1500] server

[1501] The server stores the received progress data in a database, which manages all of the user's progress data.

[1502] Step 9:

[1503] Terminal

[1504] The device uses an emotion engine to acquire emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[1505] Step 10:

[1506] server

[1507] The server analyzes the received emotion data and stores it in a database, where it is integrated with profile information and progress data.

[1508] Step 11:

[1509] server

[1510] The server uses generative AI to generate improvement suggestions based on progress and emotion data, such as "When debugging, try running unit tests first."

[1511] Step 12:

[1512] Terminal

[1513] The terminal receives the improvement suggestions sent from the server and displays them to the user, allowing the user to take specific improvement actions as the next step.

[1514] Through the above steps, this system is able to provide efficient and personalized mentoring and support the user's growth.

[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 headset type terminal 314 will be referred to as a "terminal."

[1517] Conventional mentoring systems lacked personalization based on the user's goals and challenges, limiting the accuracy and appropriateness of the advice they provided. Furthermore, they did not take the user's emotions into account, making it difficult to respond flexibly based on their emotional state. As a result, there were problems with users' growth and problem-solving not progressing effectively.

[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: a profile creation means for inputting a user's goals, challenges, and interests; an advice generation means for generating personalized advice from the input profile information using a generation AI; a means for displaying the advice; a progress data input means for inputting the user's progress data; an improvement proposal generation means for generating improvement proposals using a generation AI based on the progress data; a means for displaying the improvement proposals; an emotion recognition means for recognizing the user's emotions and acquiring emotion data; and a means for analyzing the emotion data and combining it with the profile information and progress data to provide optimal advice and improvement proposals. This enables highly personalized advice and improvement proposals that take emotions into account in relation to the user's goals and challenges.

[1520] A "profile creation tool" is an interface through which a user can input and collect information about their goals, challenges, and interests.

[1521] An "advice generation means" is a means for generating personalized advice from profile information using a generation AI.

[1522] The "means for displaying the advice" refers to an interface or device for displaying the generated advice to the user.

[1523] The "progress data input means" is an interface for users to input their own progress status.

[1524] The "improvement proposal generation means" is a means for generating improvement proposals using a generation AI based on progress data.

[1525] The "means for displaying the improvement proposal" refers to an interface or device for displaying the generated improvement proposal to the user.

[1526] The "emotion recognition means" refers to a camera, microphone, and software for recognizing the user's emotions and acquiring emotion data.

[1527] The "means for analyzing the emotion data and combining it with the profile information and progress data to provide optimal advice and improvement suggestions" refers to means for analyzing emotion data and integrating it with profile information and progress data to generate optimized advice and improvement suggestions.

[1528] This invention is a system that provides personalized mentoring based on a user's goals, challenges, and interests, and further achieves more advanced personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, an improvement suggestion generation means using a generation AI, an emotion engine, and a database.

[1529] Hardware and Software Configuration

[1530] Profile Creation Method

[1531] The device provides an interface for users to input their goals, challenges, and interests, which may include web or mobile applications. For example, a user may input a goal of "acquiring advanced programming skills."

[1532] Advice generation means and improvement proposal generation means

[1533] The server uses the generative AI model to generate personalized advice and improvement suggestions based on the profile information and progress data stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[1534] Display means

[1535] The device receives the advice and improvement suggestions sent from the server and displays them to the user as in-app messages or notifications.

[1536] Progress data input method

[1537] The user periodically enters their progress data into the device, for example, recording progress such as "I've used a new time management tool, but there are still too many tasks."

[1538] emotion recognition means

[1539] The device is equipped with an emotion recognition function and uses a camera and microphone to obtain emotional data from the user's facial expressions and voice. For example, it can analyze the user's facial expression to determine whether they are "highly stressed."

[1540] Specific examples

[1541] Below is a concrete example of how user "Taro Tanaka" uses this system:

[1542] Profile Creation:

[1543] User "Taro Tanaka" enters the following information into his terminal:

[1544] Goal: Acquire advanced programming skills

[1545] Challenge: Time management issues

[1546] Interests: Machine Learning

[1547] The server receives this information and stores it in a database as profile information.

[1548] Advice Generation:

[1549] The server uses a generative AI model to generate advice such as "introduce time management tools and break down and plan your daily tasks."

[1550] Progress Data Entry:

[1551] After a certain period of time, Taro Tanaka enters and submits progress data into his terminal, stating, "I used the new time management tool, but there are still too many tasks."

[1552] Improvement suggestion generation:

[1553] The server receives this progress data and uses a generative AI model to generate improvement suggestions such as, "When debugging, try running unit tests first."

[1554] Emotion recognition:

[1555] The device's camera recognizes Taro Tanaka's facial expressions and captures emotional data indicating that he is "highly stressed." The server analyzes this emotional data and combines it with his profile information and progress data to provide optimal improvement suggestions.

[1556] Prompt Sentence Examples

[1557] "If I want to learn advanced programming skills but have trouble managing my time, what advice would you give me?"

[1558] This enables the system to provide highly personalized advice and improvement suggestions that take into account the user's goals and challenges, even taking their emotions into account.

[1559] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1560] Step 1: Fill in and submit your profile information

[1561] User

[1562] Users input their goals, challenges, and interests into the terminal. For example, a user may input "advanced programming skills" as a goal.

[1563] input

[1564] Goal: Acquire advanced programming skills

[1565] Challenge: Time management issues

[1566] Interests: Machine Learning

[1567] Terminal

[1568] The terminal receives the information entered by the user and transmits it to the server.

[1569] output

[1570] Profile information (goals, challenges, interests) is sent to a server.

[1571] Specific actions

[1572] The terminal interface is provided with a text box and an input button. When a user enters information into the text box and presses the send button, the information is sent to the server.

[1573] Step 2: Save your profile information

[1574] server

[1575] The server receives the profile information sent from the terminal and stores it in a database.

[1576] input

[1577] Profile information sent from the device

[1578] output

[1579] Profile information stored in a database

[1580] Specific actions

[1581] The server parses the received profile information and stores each item (goals, challenges, interests) in the appropriate field in a database.

[1582] Step 3: Generate Advice

[1583] server

[1584] The server uses a generative AI model to analyze the profile information and generate personalized advice.

[1585] input

[1586] Profile information stored in a database

[1587] output

[1588] Generated Advice

[1589] Specific actions

[1590] The server calls the generative AI model, passing the profile information as input, and the model analyzes it to generate advice such as "introduce time management tools and break down and plan your daily tasks."

[1591] Step 4: Submitting and viewing advice

[1592] server

[1593] The generated advice is sent to the device.

[1594] input

[1595] Generated Advice

[1596] output

[1597] Advice sent to device

[1598] Terminal

[1599] The terminal receives the advice sent from the server and displays it to the user.

[1600] Specific actions

[1601] Advice will be displayed as notifications on the device or in-app messages, for example, in the notification bar on your smartphone.

[1602] Step 5: Enter and submit progress data

[1603] User

[1604] The user periodically inputs their progress data into the terminal, for example, "I used a new time management tool, but there are still too many tasks."

[1605] input

[1606] Progress Data

[1607] Terminal

[1608] The terminal transmits the progress data entered by the user to the server.

[1609] output

[1610] Progress data sent to the server

[1611] Specific actions

[1612] A text box for inputting progress data is placed on the terminal interface, and the user inputs the data and presses the send button.

[1613] Step 6: Save your progress

[1614] server

[1615] The server receives the progress data sent from the terminal and stores it in a database.

[1616] input

[1617] Progress data sent from the device

[1618] output

[1619] Progress data stored in a database

[1620] Specific actions

[1621] The server parses the progress data and stores it in the appropriate fields.

[1622] Step 7: Generate improvement suggestions

[1623] server

[1624] The server uses a generative AI model to generate improvement suggestions based on the progress data stored in the database.

[1625] input

[1626] Progress data stored in a database

[1627] output

[1628] Generated improvement suggestions

[1629] Specific actions

[1630] The server inputs progress data into the generative AI model, which analyzes it and generates a suggestion such as, "When debugging, try running unit tests first."

[1631] Step 8: Submit and view improvement suggestions

[1632] server

[1633] The generated improvement proposal is sent to the terminal.

[1634] input

[1635] Generated improvement suggestions

[1636] output

[1637] Improvement suggestions sent to the device

[1638] Terminal

[1639] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[1640] Specific actions

[1641] Improvement suggestions will be displayed as notifications on the device or in-app messages.

[1642] Step 9: Obtaining and Sending Emotion Data

[1643] Terminal

[1644] The device uses a camera and microphone to capture user emotional data, for example, analyzing emotions from facial expressions and voice in real time.

[1645] input

[1646] User's facial expressions and voice

[1647] output

[1648] Acquired emotion data

[1649] server

[1650] Emotion data transmitted from the terminal is received.

[1651] Specific actions

[1652] The device uses a camera and microphone to recognize the user's face and analyze the tone of their voice, and then sends emotional data to the server.

[1653] Step 10: Analyze and integrate sentiment data

[1654] server

[1655] The server analyzes the received emotional data and integrates it with profile information and progress data to generate optimal advice and improvement suggestions.

[1656] input

[1657] Emotional Data

[1658] Profile Information

[1659] Progress Data

[1660] output

[1661] Best advice and improvement suggestions

[1662] Specific actions

[1663] The server analyzes the emotional data and evaluates the user's stress level and emotional state along with their profile information and progress data, and uses a generative AI model to generate optimal advice and improvement suggestions to provide appropriate support to the user.

[1664] (Application example 2)

[1665] 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."

[1666] To help drivers improve their driving skills and reduce stress in autonomous vehicles, it is necessary to provide personalized advice and improvement suggestions to each driver. However, current systems lack the means to properly grasp the driver's emotional state and progress, making it difficult to provide appropriate feedback based on this. In addition, generating advice based on real-time emotion recognition is difficult, resulting in issues that prevent sufficient improvement of the driving experience.

[1667] 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 a profile creation means for inputting a user's goals, challenges, and interests, an advice generation means for generating personalized advice from the input profile information using a generation AI, a means for displaying advice, a progress data input means for inputting the user's progress data, an improvement proposal generation means for generating improvement proposals using a generation AI based on the progress data, a means for displaying the improvement proposals, an emotion recognition means for detecting the user's emotions, and a means for using the emotion data in combination with the profile information and progress data to provide optimal advice and improvement proposals. This makes it possible to provide appropriate feedback in real time based on the driver's emotional state and progress, thereby improving driving skills and reducing stress.

[1668] "Profile creation means" is a function that provides an interface for users to input their goals, challenges, and interests.

[1669] The "advice generation means" is a function that uses generation AI to generate personalized advice from the input profile information.

[1670] The "means for displaying advice" is a function for displaying the generated advice to the user.

[1671] The "progress data input means" is a function for inputting the user's progress data.

[1672] The "improvement proposal generation means" is a function that generates improvement proposals using a generation AI based on progress data.

[1673] The "means for displaying improvement proposals" is a function for displaying the generated improvement proposals to the user.

[1674] "Emotion recognition means" is a function that detects the user's emotions.

[1675] "Means for using emotional data in combination with profile information and progress data to provide optimal advice and improvement suggestions" is a function that analyzes emotional data in combination with profile information and progress data, and generates and provides optimal advice and improvement suggestions.

[1676] This invention is a system that provides personalized advice and feedback to drivers to improve their driving skills and reduce their stress. The system includes a user terminal, a server, a generative AI model, an emotion recognition means, and software for linking these components.

[1677] System configuration

[1678] Terminal

[1679] The terminal provides an interface where users can input their goals, challenges, and interests. Users input information through devices such as smartphone applications or tablets. This interface is intuitive and easy to operate, and the user's input data is quickly transmitted to the server.

[1680] server

[1681] The server has several main functions. First, it stores profile information in a database. Second, it uses a generative AI model to generate personalized advice from the profile information. Third, it collects progress data, based on which the generative AI model generates improvement suggestions. Furthermore, the server analyzes emotion data obtained from the emotion recognition means and combines it with the profile information and progress data to generate optimal advice and improvement suggestions.

[1682] emotion recognition means

[1683] The emotion recognition unit uses the device's camera and microphone to detect emotions from the user's facial expressions and voice. This data is sent to the server in real time and analyzed immediately according to the user's situation.

[1684] Specific examples

[1685] Consider a scenario where a user is using an autonomous vehicle. First, the user inputs their goals (e.g., improving driving skills), challenges (e.g., stress caused by traffic jams), and interests (e.g., eco-driving) into their device. This information is immediately sent to the server and stored in a database as a profile.

[1686] Next, the generative AI model generates advice based on the profile information. For example, for a user with the profile information "Goal: Improve driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving," the generative AI model would provide advice such as "Take deep breaths to relax and try not to pay attention to following vehicles." This advice is displayed on the device for the user to confirm.

[1687] While driving, the device's camera and microphone record the user's facial expressions and voice. If the user feels stressed, the emotion recognition function sends the data to the server. The server analyzes this emotion data and immediately provides appropriate feedback (e.g., "You are feeling stressed. We recommend that you take a short break.").

[1688] After completing a drive, the user inputs progress data into the device. Based on the progress data (e.g., "I felt stressed during this drive") and emotion data, the generative AI model generates improvement suggestions for the next step (e.g., "Try some relaxation techniques while driving"). These suggestions are also displayed on the device, allowing the user to use them for their next drive.

[1689] Hardware and software used

[1690] Hardware: Camera (for facial expression recognition), microphone (for voice recognition)

[1691] Software: Python, OpenCV (camera operation), sounddevice (microphone operation), HuggingFace Transformers library (generative AI model)

[1692] Prompt Sentence Examples

[1693] "Goal: Improving driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving, Emotion: Advice on stress"

[1694] In this way, the system can help drivers improve their driving skills and reduce stress.

[1695] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1696] Step 1:

[1697] The user inputs their goals, challenges, and interests. The user inputs their goals (e.g., improving driving skills), challenges (e.g., stress caused by traffic jams), and interests (e.g., eco-driving) into the device's input interface. The input data is sent to the server and stored in a database as profile information.

[1698] Step 2:

[1699] The server receives the profile information and generates personalized advice using a generative AI model. From the received profile information, a prompt sentence is generated to generate advice for "Goal: Improve driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving" and input into the generative AI model. The generated advice is sent to the device and displayed. For example, advice such as "Take deep breaths to relax and try not to pay attention to following vehicles" is displayed.

[1700] Step 3:

[1701] The device uses a camera and microphone to record the user's facial expressions and voice in real time. The data acquired by the camera and microphone is sent to an emotion recognition means to detect the user's emotional state (e.g., stress). The detected emotion data is then sent to the server.

[1702] Step 4:

[1703] The server analyzes the emotional data and combines it with the profile information to generate optimal feedback. The server generates prompts based on the emotional data and profile information and inputs them into the generative AI model. For example, feedback such as "You're feeling stressed. We recommend you take a short break" is generated and sent to the device. The feedback is displayed in real time.

[1704] Step 5:

[1705] After the user finishes driving, they input their progress data into the terminal. The progress data (e.g., "I felt stressed during this drive") is sent to the server and stored in a database.

[1706] Step 6:

[1707] The server analyzes the progress data and emotion data and generates improvement suggestions using a generative AI model. A prompt sentence is generated based on the progress data and emotion data and input into the generative AI model. For example, an improvement suggestion such as "Try some relaxation techniques the next time you drive" is generated and sent to the device. The improvement suggestion is displayed on the device so that the user can use it for their next drive.

[1708] In this way, the system can perform appropriate data processing and calculations based on the data obtained at each step, and provide optimal feedback and improvement suggestions to users.

[1709] 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.

[1710] 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.

[1711] 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.

[1712] [Fourth embodiment]

[1713] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1714] 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.

[1715] 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).

[1716] 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.

[1717] 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.

[1718] 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).

[1719] 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.

[1720] 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.

[1721] 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.

[1722] 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.

[1723] 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.

[1724] 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.

[1725] 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."

[1726] The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, and a database to provide personalized mentoring based on the user's goals, challenges, and interests.

[1727] Profile Creation Method

[1728] Terminal

[1729] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented, for example, as a web application or a mobile application.

[1730] server

[1731] The server receives the information entered by the user on the device and creates a profile, which is then stored in a database.

[1732] Advice Generation Method

[1733] server

[1734] The server generates personalized advice from the profile information stored in the database using a generation AI, which analyzes the profile data based on a specific algorithm and generates appropriate advice.

[1735] Terminal

[1736] The device receives the advice sent from the server and displays it to the user. For example, the advice may be, "First, introduce a time management tool and break down and plan your daily tasks."

[1737] Progress data input method

[1738] Terminal

[1739] The user periodically inputs progress data into the terminal, which records the results achieved and actions taken by the user.

[1740] server

[1741] The server receives the progress data sent from the terminal and stores it in a database.

[1742] Improvement proposal generation means

[1743] server

[1744] The server generates improvement proposals using a generation AI based on the progress data stored in the database. This generation AI analyzes the progress data and generates specific proposals to promote the user's growth.

[1745] Terminal

[1746] The device receives the improvement suggestions sent from the server and displays them to the user. For example, a suggestion such as "When debugging, try running unit tests first" may be displayed.

[1747] Specific examples

[1748] Case of user "Yamada Taro"

[1749] User "Yamada Taro" uses this system to improve himself. Yamada enters the following information into the terminal:

[1750] Goals: Acquire advanced programming skills and improve project management

[1751] Challenges: time management issues, complex debugging challenges

[1752] Interests: Machine learning, agile methods

[1753] The server stores this information in a database as a profile. The AI ​​then analyzes this profile and generates advice such as, "First, introduce a time management tool and break down and plan your daily tasks." This advice is displayed on Yamada's device, and he puts it into practice.

[1754] After a certain period of time, Yamada enters his progress data into his terminal. "I used a new time management tool this week, but there are still too many tasks." This data is sent to the server and stored in a database. The generation AI analyzes this progress data and generates improvement suggestions, such as: "When debugging, try running unit tests first." This suggestion is also displayed on his terminal, and Yamada plans his next steps.

[1755] In this way, the present invention efficiently provides personalized mentoring that meets the needs of each individual user, and supports the user's growth.

[1756] The processing flow will be explained below.

[1757] Step 1:

[1758] User

[1759] Users use the device interface to input their goals, challenges, and interests, such as "acquiring advanced programming skills," "time management issues," or "machine learning."

[1760] Step 2:

[1761] Terminal

[1762] The device formats the user's input of goals, challenges, and interests and sends it to the server, where the necessary data is ready for processing.

[1763] Step 3:

[1764] server

[1765] The server receives the profile information sent from the terminal and stores it in a database using a profile creation means. The database centrally manages the profile information for each user.

[1766] Step 4:

[1767] server

[1768] The server uses generative AI to generate personalized advice based on the profile information stored in the database. A specific algorithm is used to generate advice. For example, the server might generate advice such as, "First, introduce time management tools and break down and plan your daily tasks."

[1769] Step 5:

[1770] Terminal

[1771] The terminal receives the advice sent from the server and displays it to the user, allowing the user to create an action plan based on the generated advice.

[1772] Step 6:

[1773] User

[1774] The user inputs progress data into the terminal at regular intervals, for example, recording progress such as "I used a new time management tool this week, but there are still too many tasks."

[1775] Step 7:

[1776] Terminal

[1777] The device formats the entered progress data and sends it to the server, which then analyzes the progress data.

[1778] Step 8:

[1779] server

[1780] The server stores the received progress data in a database, which manages all of the user's progress data.

[1781] Step 9:

[1782] server

[1783] The server uses generative AI to generate improvement suggestions based on progress data, such as "When debugging, try running unit tests first."

[1784] Step 10:

[1785] Terminal

[1786] The terminal receives the improvement suggestions sent from the server and displays them to the user, allowing the user to take specific improvement actions as the next step.

[1787] Through the above steps, this system is able to provide efficient and personalized mentoring and support the user's growth.

[1788] Example 1

[1789] 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."

[1790] Conventional mentoring systems have difficulty providing appropriate advice and improvement suggestions based on a user's individual goals, challenges, and interests. Furthermore, they lack the functionality to properly evaluate a user's progress and provide personalized advice and improvement suggestions in a timely manner. This has led to issues such as ineffective self-improvement and problem-solving for users.

[1791] 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.

[1792] In this invention, the server

[1793] a profile creation means for inputting the user's goals, challenges, and interests;

[1794] an advice generation means for generating personalized advice from input profile information using a generation AI;

[1795] and means for storing the information input by the profile creation means in a database and for storing progress data in the database.

[1796] This allows for optimal personalized mentoring tailored to each user's individual goals and challenges, and makes it possible to provide timely advice and suggestions for improvement based on the user's progress.

[1797] A "profile creation means" is a means that provides an interface for a user to input their goals, issues, and interests.

[1798] An "advice generation means" is a means for generating personalized advice from profile information using a generation AI.

[1799] The "progress data input means" is a means for providing an interface for the user to input his / her own progress data.

[1800] The "improvement proposal generation means" is a means for generating improvement proposals using a generation AI based on progress data.

[1801] The "means for storing in a database" refers to a means for storing profile information and progress data in a database.

[1802] The "means for notifying" is a means for notifying the user of the generated advice and improvement suggestions.

[1803] This invention is a system for providing personalized mentoring based on a user's goals, challenges, and interests. The system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, an improvement suggestion generation means using a generation AI, and a database.

[1804] Profile Creation Method

[1805] Terminal

[1806] The device provides an interface for users to input their goals, challenges, and interests. This interface is implemented as a web or mobile application. Specific software examples include web forms using HTML, CSS, JavaScript, etc.

[1807] server

[1808] The server receives the information entered by the user on the terminal and creates a profile. This profile information is stored in a database. A relational database management system (RDBMS) is used as the database.

[1809] Advice Generation Method

[1810] server

[1811] The server uses a generative AI to generate personalized advice from the profile information stored in the database. The generative AI uses an advanced natural language processing model, such as GPT-4. This generative AI analyzes the profile data and generates optimal advice.

[1812] Terminal

[1813] The device receives the advice sent from the server and displays it to the user via a web browser or mobile app screen.

[1814] Progress data input method

[1815] Terminal

[1816] The user inputs progress data at regular intervals into the terminal. This interface, like the profile creation means, is implemented as a web application or mobile application.

[1817] server

[1818] The server receives the progress data sent from the device and stores it in a database, thereby recording the user's progress.

[1819] Improvement proposal generation means

[1820] server

[1821] The server uses a generation AI to generate improvement suggestions based on the progress data stored in the database. This generation AI analyzes the progress data and generates specific suggestions to promote the user's growth.

[1822] Terminal

[1823] The device receives the improvement suggestions sent from the server and displays them to the user via a web browser or mobile app screen.

[1824] Specific examples

[1825] Case of user "A"

[1826] User "A" uses this system to improve himself. He enters the following information into the terminal:

[1827] Goal: Acquire advanced programming skills

[1828] Challenge: Time management issues

[1829] Interests: Machine Learning

[1830] The server stores this information in a database as a profile. The AI ​​then analyzes this profile and generates advice such as, "First, introduce a time management tool and break down and plan your daily tasks." This advice is displayed on the device, and Mr. A puts it into practice.

[1831] After a certain period of time, Person A enters progress data into the terminal. "I used the new time management tool this week, but there are still too many tasks." This data is sent to the server and stored in a database. The generation AI analyzes this progress data and generates improvement suggestions such as: "When debugging, try running unit tests first." This suggestion is also displayed on the terminal, allowing Person A to plan his next steps.

[1832] Examples of prompt statements

[1833] Here are some example prompts to enter into a generative AI model:

[1834] User "A"'s goal is "to acquire advanced programming skills," his challenge is "time management issues," and his interest is "machine learning." Based on this information, please generate appropriate advice.

[1835] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1836] Program processing flow

[1837] Step 1: Create a profile

[1838] Terminal

[1839] 1. The terminal provides the user with an interface for inputting goals, issues, and interests.

[1840] Input: Information about the user's goals, challenges, and interests

[1841] Output: Data that sends the input information to the server

[1842] Specific operation: The user inputs information such as "learning advanced programming skills, time management problems, machine learning," and the device sends this information to the server in JSON format.

[1843] server

[1844] 2. The server receives the information sent from the device and creates a profile.

[1845] Input: User information received from the device

[1846] Output: Data to save the created profile information to the database

[1847] Specific operation: The server analyzes the received information, creates a profile for user "A" and saves it in the database.

[1848] Step 2: Advice Generation

[1849] server

[1850] 1. The server retrieves profile information from the database and generates advice using generative AI.

[1851] Input: Profile information stored in the database

[1852] Output: Generated advice

[1853] Specific operation: The server retrieves the profile information of "Mr. A" from the database, inputs "Mr. A"'s information as a prompt into GPT-4, and generates appropriate advice.

[1854] 2. The server sends the generated advice to the terminal.

[1855] Input: Generated advice

[1856] Output: Data to send to the terminal

[1857] Specific operation: The server sends advice to the device saying, "First, introduce a time management tool and break down and plan your daily tasks."

[1858] Terminal

[1859] 3. The terminal receives the advice sent from the server and displays it to the user.

[1860] Input: Advice received from the server

[1861] Output: Advice displayed on screen

[1862] Specific operation: The device displays the received advice on the screen and the user confirms it.

[1863] Step 3: Enter progress data

[1864] Terminal

[1865] 1. The terminal provides an interface for the user to input progress data.

[1866] Input: User progress data

[1867] Output: Data to send the entered progress data to the server

[1868] Specific operation: The user enters progress data such as "I used the new time management tool this week, but I still have too many tasks," and the device sends this to the server.

[1869] server

[1870] 2. The server receives the progress data sent from the device and stores it in a database.

[1871] Input: Progress data received from the device

[1872] Output: Progress data stored in a database

[1873] Specific operation: The server analyzes the received progress data and stores it in the database.

[1874] Step 4: Generate improvement suggestions

[1875] server

[1876] 1. The server retrieves progress data from the database and generates improvement suggestions using generative AI.

[1877] Input: Progress data stored in the database

[1878] Output: Generated improvement suggestions

[1879] Specific operation: The server retrieves "Person A's" progress data from the database and inputs this information as prompts into GPT-4 to generate appropriate improvement suggestions.

[1880] 2. The server sends the generated improvement proposal to the terminal.

[1881] Input: Generated improvement suggestions

[1882] Output: Data to send to the terminal

[1883] Specific operation: The server sends an improvement suggestion to the terminal saying, "When debugging, try running unit tests first."

[1884] Terminal

[1885] 3. The device receives the improvement suggestions sent from the server and displays them to the user.

[1886] Input: Improvement suggestions received from the server

[1887] Output: Improvement suggestions displayed on the screen

[1888] Specific operation: The device displays the improvement suggestions it receives on the screen and the user confirms them.

[1889] (Application example 1)

[1890] 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."

[1891] This invention relates to a system that enables users to efficiently advance their self-development and learning. Conventional systems have the problem of being unable to provide specific advice or improvement suggestions based on the user's individual needs and progress, and can only provide general suggestions. Furthermore, they lack usability because they are not compatible with easy operation using smartphones.

[1892] 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.

[1893] In this invention, the server includes a profile creation means, a means for generating personalized advice from input profile information using a generation AI, a means for displaying the advice, a means for inputting user progress data, a means for generating improvement suggestions using a generation AI based on the progress data, a means for displaying the improvement suggestions, a means for implementing the system as a smartphone app, and a means for generating advice and improvement suggestions in the form of prompt sentences using the generation AI. This enables users to easily receive specific advice and improvement suggestions tailored to their individual needs via their smartphones.

[1894] "User" refers to a person who uses the system.

[1895] "Objective" refers to the specific purpose or goal that the user is trying to achieve.

[1896] "Challenges" refer to problems or difficulties faced by users.

[1897] "Interests" refers to areas or topics in which a user has particular interest or concern.

[1898] "Profile Creation Tool" refers to a tool for inputting a user's goals, challenges, and interests.

[1899] "Generative AI" refers to algorithms and models that use artificial intelligence techniques to analyze data and generate advice and improvement suggestions.

[1900] "Advice generation means" refers to a means for generating personalized advice from input profile information using a generation AI.

[1901] "Display means" refers to a means for visually presenting the generated advice and improvement suggestions to the user.

[1902] "Progress data input means" refers to a means for a user to input progress data.

[1903] "Improvement proposal generation means" refers to a means for generating specific improvement proposals using generation AI based on user progress data.

[1904] "Smartphone app" refers to an application that runs on a smartphone.

[1905] "Prompt sentence format" refers to the document format used as input to the generative AI.

[1906] A specific embodiment of a system for supporting self-improvement based on this invention is described below. The system provides users with personalized advice and improvement suggestions via a smartphone app. This system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, and an improvement suggestion generation means using a generation AI.

[1907] 1. Create a profile

[1908] Terminal

[1909] Users input their goals, challenges, and interests through the smartphone app interface, such as "acquiring advanced programming skills" or "interest in machine learning."

[1910] server

[1911] The server receives the profile information entered by the user and stores it in a database, which includes detailed data about the user's goals, challenges, and interests.

[1912] 2. Advice Generation

[1913] server

[1914] The server uses the stored profile information to generate personalized advice using a generative AI model, such as "First, introduce time management tools and break down and plan your daily tasks."

[1915] Terminal

[1916] The terminal receives the generated advice and displays it to the user, allowing the user to obtain specific guidelines for action.

[1917] 3. Progress data entry

[1918] Terminal

[1919] Users periodically enter progress data, including accomplishments achieved and actions taken, such as "I've been using the new time management tool, but I still have too many tasks."

[1920] server

[1921] The server receives the entered progress data and stores it in a database, which is used to generate improvement suggestions for the next step.

[1922] 4. Improvement proposal generation

[1923] server

[1924] The server uses a generative AI model to generate improvement suggestions based on the progress data, such as "When debugging, try running unit tests first."

[1925] Terminal

[1926] The device receives the generated improvement suggestions and displays them to the user, allowing the user to know the specific action to take next.

[1927] Specific examples

[1928] Here are some example prompts to input to a generative AI model:

[1929] Prompts for advice generation based on profile information:

[1930] User goal: Acquire advanced programming skills, challenges: Time management issues, interest: Machine learning. Provide tailored advice.

[1931] Prompts for generating improvement suggestions based on progress data:

[1932] User progress: I used the new time management tool, but I still have too many tasks. Provide improvement suggestions.

[1933] Using these prompts, the generative AI model can generate appropriate advice and improvement suggestions and provide them to the user. The server is built using Python's Flask, and the database uses SQLite. The generative AI model uses the OpenAI API.

[1934] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1935] Step 1:

[1936] Profile Creation

[1937] Input: Users input their goals, challenges, and interests through a smartphone app interface, such as "acquiring advanced programming skills" or "interested in machine learning."

[1938] Processing: The device collects this input data and sends it to the server, which analyzes it and stores it in a database as profile information.

[1939] Output: Profile information is saved to the database.

[1940] Step 2:

[1941] Advice Generation

[1942] Input: Saved profile information.

[1943] Processing: The server retrieves the profile information and inputs it to the generative AI model in the form of a prompt. Example prompt: "User goal: Acquire advanced programming skills, challenges: Time management issues, interest: Machine learning. Provide tailored advice."

[1944] Output: The generation AI generates advice based on the input profile information, and the server receives this advice and sends it to the device.

[1945] How it works: The generative AI responds with personalized advice based on the prompt it receives. The server sends this advice to the device, which then displays it to the user. For example, the advice might be, "First, introduce a time management tool and break down and plan your daily tasks."

[1946] Step 3:

[1947] Progress data entry

[1948] Input: Users periodically enter progress data through the smartphone app interface, such as "I used the new time management tool, but I still have too many tasks."

[1949] Processing: The device collects progress data and sends it to the server, which stores it in a database.

[1950] Output: Progress data is saved to the database.

[1951] How it works: The progress data entered by the user details the accomplishments and actions they have taken. The device sends this data in real time to the server, which stores it in a database.

[1952] Step 4:

[1953] Improvement proposal generation

[1954] Input: Progress data stored in the database.

[1955] Processing: The server retrieves the progress data and inputs it to the generative AI model in the form of a prompt. Example prompt: "User progress: I used the new time management tool, but there are still too many tasks. Provide improvement suggestions."

[1956] Output: The generation AI generates improvement suggestions based on the progress data, and the server receives these suggestions and sends them to the device.

[1957] Specific operation: Based on the received prompt, the generation AI returns a specific improvement suggestion to solve the user's problem. For example, it might generate a suggestion such as, "When debugging, try running unit tests first." The server sends this suggestion to the device, which then displays it to the user.

[1958] Step 5:

[1959] Notifications and Feedback

[1960] Input: Generated advice and improvement suggestions.

[1961] Processing: The server notifies the terminal of the generated advice and improvement suggestions.

[1962] Output: The consumer receives advice and suggestions for improvement through notifications.

[1963] Specific operation: The notification function allows users to receive advice and suggestions for improvement. The device receives these and displays them to the user, making them easy to access.

[1964] Through these processing steps, the system can use the generative AI model to provide personalized advice and improvement suggestions, enabling users to receive assistance tailored to their specific and individual needs.

[1965] 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.

[1966] The system provides personalized mentoring based on the user's goals, challenges, and interests, and further achieves a higher level of personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, an emotion engine, and a database.

[1967] Profile Creation Method

[1968] Terminal

[1969] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented as a web or mobile application.

[1970] server

[1971] The server receives the profile information sent from the terminal and stores it in a database.

[1972] Advice Generation Method

[1973] server

[1974] The server uses generative AI to generate personalized advice based on the profile information stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[1975] Terminal

[1976] The terminal receives the advice sent from the server and displays it to the user.

[1977] Progress data input method

[1978] Terminal

[1979] The user periodically inputs progress data into the terminal. For example, the user may record progress such as, "I've used a new time management tool, but there are still too many tasks."

[1980] server

[1981] The server receives the progress data sent from the terminal and stores it in a database.

[1982] Improvement proposal generation means

[1983] server

[1984] The server uses generative AI to generate improvement suggestions based on the progress data stored in the database, such as "When debugging, try running unit tests first."

[1985] Terminal

[1986] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[1987] Emotion Engine

[1988] Terminal

[1989] The device is equipped with an emotion recognition function and acquires emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[1990] server

[1991] The server analyzes the acquired emotional data and provides appropriate personalization. The emotional data is stored in a database along with profile information and progress data.

[1992] Program processing

[1993] Profile Creation

[1994] The server receives the user's input of goals, challenges, and interests and creates a profile, which is then stored in a database.

[1995] Advice Generation

[1996] The server uses a generation AI to generate personalized advice based on the profile information, which is then sent to the device and displayed.

[1997] Progress data entry and saving

[1998] The user periodically inputs progress data into the terminal, which then sends it to the server, which receives the progress data and stores it in a database.

[1999] Improvement proposal generation

[2000] The server uses a generation AI to generate improvement suggestions based on the progress data and emotion data, and these improvement suggestions are sent to the device and displayed.

[2001] emotion recognition

[2002] The device uses a camera and microphone to capture the user's emotional data, which the server analyzes and combines with profile information and progress data to provide optimal advice and suggestions for improvement.

[2003] Specific examples

[2004] Case of user "Taro Tanaka"

[2005] User "Taro Tanaka" uses the system to improve himself. Tanaka enters the following information into the terminal.

[2006] Goal: Acquire advanced programming skills

[2007] Challenge: Time management issues

[2008] Interests: Machine Learning

[2009] The server stores this information in a database as a profile. The AI ​​then analyzes the profile and generates advice such as "introduce time management tools and break down and plan your daily tasks." The advice is displayed on Tanaka's device, and he puts it into practice.

[2010] After a certain period of time, Tanaka enters his progress data into his terminal, recording, "I used the new time management tool, but there are still too many tasks." The server receives this and stores it in a database.

[2011] The server uses generative AI to analyze the progress data and the emotion data obtained from the emotion engine, and generates improvement suggestions such as, "When debugging, try running unit tests first." The suggestions are displayed on the device, and Tanaka plans his next steps.

[2012] The emotion engine provides suggestions and advice that take Tanaka's emotions into consideration, allowing him to take more appropriate actions. Through the above process, the present invention efficiently and effectively supports the growth of users.

[2013] The processing flow will be explained below.

[2014] The system provides personalized mentoring based on the user's goals, challenges, and interests, and further achieves a higher level of personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using generative AI, a display means, a progress data input means, an improvement suggestion generation means using generative AI, an emotion engine, and a database.

[2015] Profile Creation Method

[2016] Terminal

[2017] The device provides an interface where users can input their goals, challenges, and interests. This interface can be implemented as a web or mobile application.

[2018] server

[2019] The server receives the profile information sent from the terminal and stores it in a database.

[2020] Advice Generation Method

[2021] server

[2022] The server uses generative AI to generate personalized advice based on the profile information stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[2023] Terminal

[2024] The terminal receives the advice sent from the server and displays it to the user.

[2025] Progress data input method

[2026] Terminal

[2027] The user periodically inputs progress data into the terminal. For example, the user may record progress such as, "I've used a new time management tool, but there are still too many tasks."

[2028] server

[2029] The server receives the progress data sent from the terminal and stores it in a database.

[2030] Improvement proposal generation means

[2031] server

[2032] The server uses generative AI to generate improvement suggestions based on the progress data stored in the database, such as "When debugging, try running unit tests first."

[2033] Terminal

[2034] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[2035] Emotion Engine

[2036] Terminal

[2037] The device is equipped with an emotion recognition function and acquires emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[2038] server

[2039] The server analyzes the acquired emotional data and provides appropriate personalization. The emotional data is stored in a database along with profile information and progress data.

[2040] Program processing

[2041] Step 1:

[2042] User

[2043] Users use the device interface to input their goals, challenges, and interests, such as "acquiring advanced programming skills," "time management issues," or "machine learning."

[2044] Step 2:

[2045] Terminal

[2046] The device formats the user's input of goals, challenges, and interests and sends it to the server, where the necessary data is ready for processing.

[2047] Step 3:

[2048] server

[2049] The server receives the profile information sent from the terminal and stores it in a database using a profile creation means. The database centrally manages the profile information for each user.

[2050] Step 4:

[2051] server

[2052] The server uses generative AI to generate personalized advice based on the profile information stored in the database. A specific algorithm is used to generate advice. For example, the server might generate advice such as, "First, introduce time management tools and break down and plan your daily tasks."

[2053] Step 5:

[2054] Terminal

[2055] The terminal receives the advice sent from the server and displays it to the user, allowing the user to create an action plan based on the generated advice.

[2056] Step 6:

[2057] User

[2058] The user inputs progress data into the terminal at regular intervals, for example, recording progress such as "I used a new time management tool this week, but there are still too many tasks."

[2059] Step 7:

[2060] Terminal

[2061] The device formats the entered progress data and sends it to the server, which then analyzes the progress data.

[2062] Step 8:

[2063] server

[2064] The server stores the received progress data in a database, which manages all of the user's progress data.

[2065] Step 9:

[2066] Terminal

[2067] The device uses an emotion engine to acquire emotion data from the user's facial expressions and voice, for example, by using a camera or microphone to recognize the user's emotions in real time.

[2068] Step 10:

[2069] server

[2070] The server analyzes the received emotion data and stores it in a database, where it is integrated with profile information and progress data.

[2071] Step 11:

[2072] server

[2073] The server uses generative AI to generate improvement suggestions based on progress and emotion data, such as "When debugging, try running unit tests first."

[2074] Step 12:

[2075] Terminal

[2076] The terminal receives the improvement suggestions sent from the server and displays them to the user, allowing the user to take specific improvement actions as the next step.

[2077] Through the above steps, this system is able to provide efficient and personalized mentoring and support the user's growth.

[2078] Example 2

[2079] 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."

[2080] Conventional mentoring systems lacked personalization based on the user's goals and challenges, limiting the accuracy and appropriateness of the advice they provided. Furthermore, they did not take the user's emotions into account, making it difficult to respond flexibly based on their emotional state. As a result, there were problems with users' growth and problem-solving not progressing effectively.

[2081] 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.

[2082] In this invention, the server includes: a profile creation means for inputting a user's goals, challenges, and interests; an advice generation means for generating personalized advice from the input profile information using a generation AI; a means for displaying the advice; a progress data input means for inputting the user's progress data; an improvement proposal generation means for generating improvement proposals using a generation AI based on the progress data; a means for displaying the improvement proposals; an emotion recognition means for recognizing the user's emotions and acquiring emotion data; and a means for analyzing the emotion data and combining it with the profile information and progress data to provide optimal advice and improvement proposals. This enables highly personalized advice and improvement proposals that take emotions into account in relation to the user's goals and challenges.

[2083] A "profile creation tool" is an interface through which a user can input and collect information about their goals, challenges, and interests.

[2084] An "advice generation means" is a means for generating personalized advice from profile information using a generation AI.

[2085] The "means for displaying the advice" refers to an interface or device for displaying the generated advice to the user.

[2086] The "progress data input means" is an interface for users to input their own progress status.

[2087] The "improvement proposal generation means" is a means for generating improvement proposals using a generation AI based on progress data.

[2088] The "means for displaying the improvement proposal" refers to an interface or device for displaying the generated improvement proposal to the user.

[2089] The "emotion recognition means" refers to a camera, microphone, and software for recognizing the user's emotions and acquiring emotion data.

[2090] The "means for analyzing the emotion data and combining it with the profile information and progress data to provide optimal advice and improvement suggestions" refers to means for analyzing emotion data and integrating it with profile information and progress data to generate optimized advice and improvement suggestions.

[2091] This invention is a system that provides personalized mentoring based on a user's goals, challenges, and interests, and further achieves more advanced personalization by recognizing the user's emotions using an emotion engine. The system includes a profile creation means, an advice generation means using a generation AI, a display means, a progress data input means, an improvement suggestion generation means using a generation AI, an emotion engine, and a database.

[2092] Hardware and Software Configuration

[2093] Profile Creation Method

[2094] The device provides an interface for users to input their goals, challenges, and interests, which may include web or mobile applications. For example, a user may input a goal of "acquiring advanced programming skills."

[2095] Advice generation means and improvement proposal generation means

[2096] The server uses the generative AI model to generate personalized advice and improvement suggestions based on the profile information and progress data stored in the database, such as "introduce time management tools and break down and plan your daily tasks."

[2097] Display means

[2098] The device receives the advice and improvement suggestions sent from the server and displays them to the user as in-app messages or notifications.

[2099] Progress data input method

[2100] The user periodically enters their progress data into the device, for example, recording progress such as "I've used a new time management tool, but there are still too many tasks."

[2101] emotion recognition means

[2102] The device is equipped with an emotion recognition function and uses a camera and microphone to obtain emotional data from the user's facial expressions and voice. For example, it can analyze the user's facial expression to determine whether they are "highly stressed."

[2103] Specific examples

[2104] Below is a concrete example of how user "Taro Tanaka" uses this system:

[2105] Profile Creation:

[2106] User "Taro Tanaka" enters the following information into his terminal:

[2107] Goal: Acquire advanced programming skills

[2108] Challenge: Time management issues

[2109] Interests: Machine Learning

[2110] The server receives this information and stores it in a database as profile information.

[2111] Advice Generation:

[2112] The server uses a generative AI model to generate advice such as "introduce time management tools and break down and plan your daily tasks."

[2113] Progress Data Entry:

[2114] After a certain period of time, Taro Tanaka enters and submits progress data into his terminal, stating, "I used the new time management tool, but there are still too many tasks."

[2115] Improvement suggestion generation:

[2116] The server receives this progress data and uses a generative AI model to generate improvement suggestions such as, "When debugging, try running unit tests first."

[2117] Emotion recognition:

[2118] The device's camera recognizes Taro Tanaka's facial expressions and captures emotional data indicating that he is "highly stressed." The server analyzes this emotional data and combines it with his profile information and progress data to provide optimal improvement suggestions.

[2119] Prompt Sentence Examples

[2120] "If I want to learn advanced programming skills but have trouble managing my time, what advice would you give me?"

[2121] This enables the system to provide highly personalized advice and improvement suggestions that take into account the user's goals and challenges, even taking their emotions into account.

[2122] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2123] Step 1: Fill in and submit your profile information

[2124] User

[2125] Users input their goals, challenges, and interests into the terminal. For example, a user may input "advanced programming skills" as a goal.

[2126] input

[2127] Goal: Acquire advanced programming skills

[2128] Challenge: Time management issues

[2129] Interests: Machine Learning

[2130] Terminal

[2131] The terminal receives the information entered by the user and transmits it to the server.

[2132] output

[2133] Profile information (goals, challenges, interests) is sent to a server.

[2134] Specific actions

[2135] The terminal interface is provided with a text box and an input button. When a user enters information into the text box and presses the send button, the information is sent to the server.

[2136] Step 2: Save your profile information

[2137] server

[2138] The server receives the profile information sent from the terminal and stores it in a database.

[2139] input

[2140] Profile information sent from the device

[2141] output

[2142] Profile information stored in a database

[2143] Specific actions

[2144] The server parses the received profile information and stores each item (goals, challenges, interests) in the appropriate field in a database.

[2145] Step 3: Generate Advice

[2146] server

[2147] The server uses a generative AI model to analyze the profile information and generate personalized advice.

[2148] input

[2149] Profile information stored in a database

[2150] output

[2151] Generated Advice

[2152] Specific actions

[2153] The server calls the generative AI model, passing the profile information as input, and the model analyzes it to generate advice such as "introduce time management tools and break down and plan your daily tasks."

[2154] Step 4: Submitting and viewing advice

[2155] server

[2156] The generated advice is sent to the device.

[2157] input

[2158] Generated Advice

[2159] output

[2160] Advice sent to device

[2161] Terminal

[2162] The terminal receives the advice sent from the server and displays it to the user.

[2163] Specific actions

[2164] Advice will be displayed as notifications on the device or in-app messages, for example, in the notification bar on your smartphone.

[2165] Step 5: Enter and submit progress data

[2166] User

[2167] The user periodically inputs their progress data into the terminal, for example, "I used a new time management tool, but there are still too many tasks."

[2168] input

[2169] Progress Data

[2170] Terminal

[2171] The terminal transmits the progress data entered by the user to the server.

[2172] output

[2173] Progress data sent to the server

[2174] Specific actions

[2175] A text box for inputting progress data is placed on the terminal interface, and the user inputs the data and presses the send button.

[2176] Step 6: Save your progress

[2177] server

[2178] The server receives the progress data sent from the terminal and stores it in a database.

[2179] input

[2180] Progress data sent from the device

[2181] output

[2182] Progress data stored in a database

[2183] Specific actions

[2184] The server parses the progress data and stores it in the appropriate fields.

[2185] Step 7: Generate improvement suggestions

[2186] server

[2187] The server uses a generative AI model to generate improvement suggestions based on the progress data stored in the database.

[2188] input

[2189] Progress data stored in a database

[2190] output

[2191] Generated improvement suggestions

[2192] Specific actions

[2193] The server inputs progress data into the generative AI model, which analyzes it and generates a suggestion such as, "When debugging, try running unit tests first."

[2194] Step 8: Submit and view improvement suggestions

[2195] server

[2196] The generated improvement proposal is sent to the terminal.

[2197] input

[2198] Generated improvement suggestions

[2199] output

[2200] Improvement suggestions sent to the device

[2201] Terminal

[2202] The terminal receives the improvement suggestions sent from the server and displays them to the user.

[2203] Specific actions

[2204] Improvement suggestions will be displayed as notifications on the device or in-app messages.

[2205] Step 9: Obtaining and Sending Emotion Data

[2206] Terminal

[2207] The device uses a camera and microphone to capture user emotional data, for example, analyzing emotions from facial expressions and voice in real time.

[2208] input

[2209] User's facial expressions and voice

[2210] output

[2211] Acquired emotion data

[2212] server

[2213] Emotion data transmitted from the terminal is received.

[2214] Specific actions

[2215] The device uses a camera and microphone to recognize the user's face and analyze the tone of their voice, and then sends emotional data to the server.

[2216] Step 10: Analyze and integrate sentiment data

[2217] server

[2218] The server analyzes the received emotional data and integrates it with profile information and progress data to generate optimal advice and improvement suggestions.

[2219] input

[2220] Emotional Data

[2221] Profile Information

[2222] Progress Data

[2223] output

[2224] Best advice and improvement suggestions

[2225] Specific actions

[2226] The server analyzes the emotional data and evaluates the user's stress level and emotional state along with their profile information and progress data, and uses a generative AI model to generate optimal advice and improvement suggestions to provide appropriate support to the user.

[2227] (Application example 2)

[2228] 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."

[2229] To help drivers improve their driving skills and reduce stress in autonomous vehicles, it is necessary to provide personalized advice and improvement suggestions to each driver. However, current systems lack the means to properly grasp the driver's emotional state and progress, making it difficult to provide appropriate feedback based on this. In addition, generating advice based on real-time emotion recognition is difficult, resulting in issues that prevent sufficient improvement of the driving experience.

[2230] 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 a profile creation means for inputting a user's goals, challenges, and interests, an advice generation means for generating personalized advice from the input profile information using a generation AI, a means for displaying advice, a progress data input means for inputting the user's progress data, an improvement proposal generation means for generating improvement proposals using a generation AI based on the progress data, a means for displaying the improvement proposals, an emotion recognition means for detecting the user's emotions, and a means for using the emotion data in combination with the profile information and progress data to provide optimal advice and improvement proposals. This makes it possible to provide appropriate feedback in real time based on the driver's emotional state and progress, thereby improving driving skills and reducing stress.

[2231] "Profile creation means" is a function that provides an interface for users to input their goals, challenges, and interests.

[2232] The "advice generation means" is a function that uses generation AI to generate personalized advice from the input profile information.

[2233] The "means for displaying advice" is a function for displaying the generated advice to the user.

[2234] The "progress data input means" is a function for inputting the user's progress data.

[2235] The "improvement proposal generation means" is a function that generates improvement proposals using a generation AI based on progress data.

[2236] The "means for displaying improvement proposals" is a function for displaying the generated improvement proposals to the user.

[2237] "Emotion recognition means" is a function that detects the user's emotions.

[2238] "Means for using emotional data in combination with profile information and progress data to provide optimal advice and improvement suggestions" is a function that analyzes emotional data in combination with profile information and progress data, and generates and provides optimal advice and improvement suggestions.

[2239] This invention is a system that provides personalized advice and feedback to drivers to improve their driving skills and reduce their stress. The system includes a user terminal, a server, a generative AI model, an emotion recognition means, and software for linking these components.

[2240] System configuration

[2241] Terminal

[2242] The terminal provides an interface where users can input their goals, challenges, and interests. Users input information through devices such as smartphone applications or tablets. This interface is intuitive and easy to operate, and the user's input data is quickly transmitted to the server.

[2243] server

[2244] The server has several main functions. First, it stores profile information in a database. Second, it uses a generative AI model to generate personalized advice from the profile information. Third, it collects progress data, based on which the generative AI model generates improvement suggestions. Furthermore, the server analyzes emotion data obtained from the emotion recognition means and combines it with the profile information and progress data to generate optimal advice and improvement suggestions.

[2245] emotion recognition means

[2246] The emotion recognition unit uses the device's camera and microphone to detect emotions from the user's facial expressions and voice. This data is sent to the server in real time and analyzed immediately according to the user's situation.

[2247] Specific examples

[2248] Consider a scenario where a user is using an autonomous vehicle. First, the user inputs their goals (e.g., improving driving skills), challenges (e.g., stress caused by traffic jams), and interests (e.g., eco-driving) into their device. This information is immediately sent to the server and stored in a database as a profile.

[2249] Next, the generative AI model generates advice based on the profile information. For example, for a user with the profile information "Goal: Improve driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving," the generative AI model would provide advice such as "Take deep breaths to relax and try not to pay attention to following vehicles." This advice is displayed on the device for the user to confirm.

[2250] While driving, the device's camera and microphone record the user's facial expressions and voice. If the user feels stressed, the emotion recognition function sends the data to the server. The server analyzes this emotion data and immediately provides appropriate feedback (e.g., "You are feeling stressed. We recommend that you take a short break.").

[2251] After completing a drive, the user inputs progress data into the device. Based on the progress data (e.g., "I felt stressed during this drive") and emotion data, the generative AI model generates improvement suggestions for the next step (e.g., "Try some relaxation techniques while driving"). These suggestions are also displayed on the device, allowing the user to use them for their next drive.

[2252] Hardware and software used

[2253] Hardware: Camera (for facial expression recognition), microphone (for voice recognition)

[2254] Software: Python, OpenCV (camera operation), sounddevice (microphone operation), HuggingFace Transformers library (generative AI model)

[2255] Prompt Sentence Examples

[2256] "Goal: Improving driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving, Emotion: Advice on stress"

[2257] In this way, the system can help drivers improve their driving skills and reduce stress.

[2258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2259] Step 1:

[2260] The user inputs their goals, challenges, and interests. The user inputs their goals (e.g., improving driving skills), challenges (e.g., stress caused by traffic jams), and interests (e.g., eco-driving) into the device's input interface. The input data is sent to the server and stored in a database as profile information.

[2261] Step 2:

[2262] The server receives the profile information and generates personalized advice using a generative AI model. From the received profile information, a prompt sentence is generated to generate advice for "Goal: Improve driving skills, Challenge: Stress in traffic jams, Interest: Eco-driving" and input into the generative AI model. The generated advice is sent to the device and displayed. For example, advice such as "Take deep breaths to relax and try not to pay attention to following vehicles" is displayed.

[2263] Step 3:

[2264] The device uses a camera and microphone to record the user's facial expressions and voice in real time. The data acquired by the camera and microphone is sent to an emotion recognition means to detect the user's emotional state (e.g., stress). The detected emotion data is then sent to the server.

[2265] Step 4:

[2266] The server analyzes the emotional data and combines it with the profile information to generate optimal feedback. The server generates prompts based on the emotional data and profile information and inputs them into the generative AI model. For example, feedback such as "You're feeling stressed. We recommend you take a short break" is generated and sent to the device. The feedback is displayed in real time.

[2267] Step 5:

[2268] After the user finishes driving, they input their progress data into the terminal. The progress data (e.g., "I felt stressed during this drive") is sent to the server and stored in a database.

[2269] Step 6:

[2270] The server analyzes the progress data and emotion data and generates improvement suggestions using a generative AI model. A prompt sentence is generated based on the progress data and emotion data and input into the generative AI model. For example, an improvement suggestion such as "Try some relaxation techniques the next time you drive" is generated and sent to the device. The improvement suggestion is displayed on the device so that the user can use it for their next drive.

[2271] In this way, the system can perform appropriate data processing and calculations based on the data obtained at each step, and provide optimal feedback and improvement suggestions to users.

[2272] 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.

[2273] 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.

[2274] 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.

[2275] 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.

[2276] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion 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.

[2277] 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.

[2278] 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).

[2279] 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.

[2280] 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."

[2281] 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.

[2282] 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).

[2283] 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.

[2284] 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.

[2285] 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.

[2286] 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.

[2287] 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.

[2288] 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.

[2289] 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.

[2290] 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.

[2291] 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.

[2292] 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.

[2293] The following is further disclosed regarding the above embodiment.

[2294] (Claim 1)

[2295] a profile creation means for inputting the user's goals, challenges, and interests;

[2296] an advice generation means for generating personalized advice from input profile information using a generation AI;

[2297] means for displaying the advice;

[2298] a progress data input means for inputting progress data of a user;

[2299] an improvement proposal generation means for generating an improvement proposal using a generation AI based on the progress data;

[2300] means for displaying the improvement proposal;

[2301] A system including:

[2302] (Claim 2)

[2303] 10. The system of claim 1, further comprising means for storing the profile information created by the profile creating means in a database.

[2304] (Claim 3)

[2305] 10. The system of claim 1, further comprising means for notifying a user of the generated advice and improvement suggestions.

[2306] "Example 1"

[2307] (Claim 1)

[2308] a profile creation means for inputting the user's goals, challenges, and interests;

[2309] an advice generation means for generating personalized advice from input profile information using a generation AI;

[2310] means for displaying the advice;

[2311] a progress data input means for inputting progress data of a user;

[2312] an improvement proposal generation means for generating an improvement proposal using a generation AI based on the progress data;

[2313] means for displaying the improvement proposal;

[2314] a means for storing the information input by the profile creation means in a database and for storing progress data in the database;

[2315] a means for notifying the user of the generated advice and improvement suggestions;

[2316] A system including:

[2317] (Claim 2)

[2318] 10. The system of claim 1, further comprising means for storing the profile information created by the profile creating means in a database.

[2319] (Claim 3)

[2320] 10. The system of claim 1, further comprising means for notifying a user of the generated advice and improvement suggestions.

[2321] "Application Example 1"

[2322] (Claim 1)

[2323] a profile creation means for inputting the user's goals, challenges, and interests;

[2324] an advice generation means for generating personalized advice from input profile information using a generation AI;

[2325] means for displaying the advice;

[2326] a progress data input means for inputting progress data of a user;

[2327] an improvement proposal generation means for generating an improvement proposal using a generation AI based on the progress data;

[2328] means for displaying the improvement proposal;

[2329] A means for implementing the system as a smartphone application;

[2330] A means for generating advice and improvement suggestions in the form of prompt sentences using a generative AI;

[2331] A system including:

[2332] (Claim 2)

[2333] 10. The system of claim 1, further comprising means for storing the profile information created by the profile creating means in a database.

[2334] (Claim 3)

[2335] 10. The system of claim 1, further comprising means for notifying a user of the generated advice and improvement suggestions.

[2336] "Example 2: Combining Emotion Engines"

[2337] (Claim 1)

[2338] a profile creation means for inputting the user's goals, challenges, and interests;

[2339] an advice generation means for generating personalized advice from input profile information using a generation AI;

[2340] means for displaying the advice;

[2341] a progress data input means for inputting progress data of a user;

[2342] an improvement proposal generation means for generating an improvement proposal using a generation AI based on the progress data;

[2343] means for displaying the improvement proposal;

[2344] emotion recognition means for recognizing the emotion of a user and acquiring emotion data;

[2345] means for analyzing the emotion data and combining it with the profile information and progress data to provide optimal advice and improvement suggestions;

[2346] A system including:

[2347] (Claim 2)

[2348] 10. The system of claim 1, further comprising means for storing the profile information and emotion data created by the profile creating means in a database.

[2349] (Claim 3)

[2350] 10. The system of claim 1, further comprising means for notifying a user of the generated advice and improvement suggestions.

[2351] "Application example 2 when combining emotion engines"

[2352] (Claim 1)

[2353] a profile creation means for inputting the user's goals, challenges, and interests;

[2354] an advice generation means for generating personalized advice from input profile information using a generation AI;

[2355] means for displaying the advice;

[2356] a progress data input means for inputting progress data of a user;

[2357] an improvement proposal generation means for generating an improvement proposal using a generation AI based on the progress data;

[2358] means for displaying the improvement proposal;

[2359] emotion recognition means for detecting an emotion of a user;

[2360] means for providing optimal advice and suggestions for improvement using said emotion data in combination with profile information and progress data;

[2361] A system including:

[2362] (Claim 2)

[2363] 10. The system of claim 1, further comprising means for storing the profile information created by the profile creating means in a database.

[2364] (Claim 3)

[2365] 10. The system of claim 1, further comprising means for notifying a user of the generated advice and improvement suggestions. [Explanation of symbols]

[2366] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a profile creation means for inputting the user's goals, challenges, and interests; an advice generation means for generating personalized advice from input profile information using a generation AI; means for displaying the advice; a progress data input means for inputting progress data of a user; an improvement proposal generation means for generating an improvement proposal using a generation AI based on the progress data; means for displaying the improvement proposal; A system including:

2. 2. The system of claim 1, further comprising means for storing the profile information created by the profile creating means in a database.

3. The system of claim 1 further comprising means for notifying a user of the generated advice and improvement suggestions.

Images