system

Abstract

To provide a system that enables a user to efficiently find an expert who meets desired conditions, smoothly receive lessons, and enables the expert to receive appropriate remuneration for providing lessons. [Solution] A system including a means for a user to input conditions regarding the qualifications desired, a means for registering information on qualified experts, a means for saving information on registered experts in a database, a means for searching for the most suitable expert based on the conditions input by the user, a means for providing the search results to the user, a means for the user to check the schedule of the expert selected by the user and book a lesson, a means for notifying the expert of the lesson booking information, a means for managing the progress of the lesson and recording a performance log, a means for calculating the fee to the expert based on the recorded lesson log, and a means for paying the fee to the expert.

Description

[Technical Field]

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

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

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

[0004] In recent years, the number of people aiming to obtain various qualifications has been increasing, but it is not easy to find experts to provide appropriate guidance. It is also a lot of work to contact experts directly, arrange schedules, and negotiate fees. There is a need for a system that can solve these problems and efficiently match users aiming to obtain qualifications with experts. [Means for solving the problem]

[0005] This invention is a system including means for a user to input conditions related to desired qualifications, means for registering information on qualified experts, means for saving information on registered experts in a database, means for searching for the most suitable expert based on the conditions input by the user, means for providing the search results to the user, means for the user to check the schedule of the expert selected by the user and book a lesson, means for notifying the expert of the lesson reservation information, means for managing the progress of the lesson and recording an implementation log, means for calculating the fee to the expert based on the recorded lesson log, and means for paying the fee to the expert. This system enables users to efficiently find experts that meet their desired conditions and receive lessons smoothly, and enables experts to receive appropriate fees for providing lessons.

[0006] "User" refers to an individual or organization who wishes to use this system to take lessons to obtain a qualification.

[0007] "Expert" refers to an individual or organization that holds a specific qualification and is able to provide education and training related to that qualification.

[0008] "Qualification" means a formal certificate or accreditation attesting to a particular competency or skill.

[0009] "Lesson" means an education or training session provided by an expert to a user.

[0010] "Registration" refers to the act of entering expert and user information into the system and storing it in the database.

[0011] "Database" refers to a system for systematically storing and managing information on experts and users.

[0012] "Search" refers to the act of finding a suitable expert from a database based on user input criteria.

[0013] "Schedule" means a timetable showing the availability of an Expert.

[0014] "Reservation" refers to the act of a User reserving a lesson within the Expert's schedule.

[0015] "Notification" refers to the act of informing users and experts of information from the system.

[0016] "Log" refers to data that records the content and progress of a lesson.

[0017] "Remuneration" means the monetary compensation received by a Professional in consideration for providing a Lesson. [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] This invention is a system for efficiently matching users with qualified experts and providing effective lessons. This system consists of three main elements: a server, a terminal, and a user, and is operated in the following steps:

[0040] User and Expert Registration

[0041] First, users and experts who wish to use the system must register their respective information. Using a terminal, users enter information such as their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications held, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in a database.

[0042] Matching Process

[0043] The user inputs the desired qualifications and sends them via the terminal. The server searches the database for the most suitable experts based on the input information and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed to reserve a date and time for a lesson.

[0044] Conducting and logging lessons

[0045] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson using an online platform such as a video call. The server records the progress of the lesson and saves the progress log in a database.

[0046] Reward calculation and payment

[0047] When the lesson is completed, the server calculates the reward for the expert based on the lesson log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the process of paying the reward.

[0048] Specific examples

[0049] For example, a user may input a desire to "obtain an English Proficiency Test qualification" and specify the condition for lessons as "I would like to study twice a week at night." The server searches the database for "experts who hold the English Proficiency Test Grade 1 and are available at night." As a result, an expert named Mr. Suzuki is found and suggested to the user. The user selects Mr. Suzuki and books a lesson for the following Monday at 7 p.m.

[0050] On the day of the lesson, the user and Suzuki sensei will actively conduct the lesson via video call on the designated online platform. The server records the situation and creates a lesson log. After the lesson is completed, the reward is calculated based on this log and paid to Suzuki sensei.

[0051] This system allows users to easily find suitable experts, and experts can efficiently provide lessons and receive compensation, making the qualification process go more smoothly.

[0052] The processing flow will be explained below.

[0053] Step 1: User Registration

[0054] Terminal: Presents a form where the user enters their name, email address, password, and desired credentials.

[0055] User: Enter the required information and click the submit button.

[0056] Terminal: Sends the entered information to the server.

[0057] Step 2: Professional Registration

[0058] Device: Displays a form where the expert can enter their name, email address, password, certifications, years of experience, and pricing information.

[0059] Expert: Enter the required information and click the submit button.

[0060] Terminal: Sends the entered information to the server.

[0061] Step 3: Verify and save the information

[0062] Server: Validates the received user and expert information, ensuring all required fields are filled in and that the email address is formatted correctly.

[0063] Server: Stores the verified information in a database.

[0064] Step 4: Enter criteria and search

[0065] Terminal: Displays a form for the user to enter the lesson conditions they desire (e.g. time slot, number of times per week, fee, etc.).

[0066] User: Enter the required information and click the submit button.

[0067] Terminal: Sends the entered conditions to the server.

[0068] Server: Based on the input criteria, it searches the database for the most suitable experts and generates a list of search results.

[0069] Step 5: View and select search results

[0070] Server: Sends the search result list to the device.

[0071] Terminal: Display search results to the user in a list format.

[0072] User: Select the desired expert from the list of suggested experts.

[0073] Step 6: Book a lesson

[0074] Terminal: View the selected expert's schedule.

[0075] User: Select the desired lesson date and time and click the submit button.

[0076] Device: Send the selected date and time to the server.

[0077] Server: Stores lesson reservation information in a database and sends reservation notifications to experts.

[0078] Step 7: Reminders

[0079] Server: Send reminder notifications to users and experts before the lesson starts.

[0080] Step 8: Lesson implementation

[0081] User: Follow the reminder notification and launch the video calling platform to connect with the expert.

[0082] Experts: Launch a video chat platform during lesson time to connect with users.

[0083] Server: Monitors the progress of lessons and records progress logs.

[0084] Step 9: Complete the lesson and calculate your rewards

[0085] Server: After the lesson is completed, calculate the reward for the expert based on the recorded log.

[0086] Server: Generates reward statements and sends them to experts.

[0087] Terminal: Shows the expert a fee statement.

[0088] Step 10: Payment

[0089] Expert: Review and approve the fee statement.

[0090] Server: After receiving the expert's approval, the server executes the payment process.

[0091] Server: Notifies the expert that payment has been made.

[0092] Terminal: Shows the expert a notification that payment has been completed.

[0093] Example 1

[0094] 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."

[0095] In conventional qualification support systems, it was difficult to match users with the appropriate experts, and the process of conducting lessons and paying fees was cumbersome. This resulted in problems such as users being unable to take lessons efficiently and experts being unable to provide their services effectively. In addition, lesson progress management and fee calculation and payment were often done manually, which could be a burden.

[0096] 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.

[0097] In this invention, the server includes: means for a user to input conditions related to desired qualifications; means for registering information on qualified experts; means for saving information on registered experts in a database; means for searching for the most suitable expert based on the conditions input by the user; means for providing the search results to the user; means for the user to check the schedule of the expert selected by the user and book a lesson; means for notifying the expert of the lesson reservation information; means for the user and the expert to conduct the lesson on the designated online platform on the day of the lesson reservation; means for managing the progress of the lesson and recording the implementation log; means for calculating the fee to the expert based on the recorded lesson log; and means for paying the fee to the expert. This enables effective matching of users and experts, efficient lesson implementation, progress management, and fee calculation and payment to be automated, thereby reducing the overall workload.

[0098] "User" means an individual or organization who wishes to obtain a qualification and uses the system to take lessons.

[0099] An "expert" is an individual or organization that holds a specific qualification and has the knowledge and experience related to that qualification.

[0100] "Conditions" refers to the requirements and schedule for obtaining the qualifications the user desires, as well as preferences such as the frequency and time of lessons.

[0101] "Database" refers to a system that systematically stores and manages data such as user and expert registration information, lesson progress, and remuneration calculations.

[0102] "Schedule" refers to a list or timetable of dates and times when an Expert is available to conduct Lessons.

[0103] "Online Platform" means an internet-based service that provides video calling and messaging capabilities to enable users and experts to communicate in real time.

[0104] "Progress" is information that indicates the implementation status and progress of the lesson.

[0105] An "implementation log" is data that records details such as the start time, end time, and content of the lesson.

[0106] "Remuneration" means the amount of money a Professional receives in exchange for providing a Lesson.

[0107] "Notifications" are messages or alerts that communicate important information to users and experts, such as lesson booking information and reminders.

[0108] This invention is a system for efficiently matching users with qualified experts and providing effective lessons. This system consists of three main elements: a server, a terminal, and a user. Specifically, users input their desired qualifications and register information about qualified experts.

[0109] When users and experts enter information such as their name, email address, password, desired qualifications, and current qualifications from their respective devices, the server receives this information. At this time, the server verifies the format, such as the validity of the email address and the strength of the password. The necessary hardware includes a database server (using, for example, MySQL (registered trademark)) and a front-end system for displaying the information (using web technologies such as HTML and JavaScript (registered trademark)).

[0110] Once verified, the information is stored in a database. When a user enters the desired qualifications and conditions, for example, "I want to study for the English Proficiency Test twice a week at night," the server uses this information to search the database for the most suitable expert. Specific software could use Python or SQL queries to implement the search algorithm. For example, let's say you want to search for "experts who hold the English Proficiency Test Grade 1 and are available at night."

[0111] The search results are provided to the user as a list, and the user selects the desired expert from the list. Based on this selection, the server checks the expert's schedule and executes the procedure for the user to reserve the lesson date and time. Once the reservation is complete, the server notifies the expert of the reservation information. Notifications are sent using an email system or push notification service (such as SMTP or Firebase Cloud Messaging).

[0112] On the day of the lesson, the user and expert will have a video call using an online platform such as Zoom or GOOGLE MEET (registered trademark). The server records the progress of the lesson in real time and saves the implementation log in a database. This progress management utilizes log collection services and APIs (e.g., Amazon CloudWatch and Custom API).

[0113] Once the lesson is completed, the server calculates the expert's reward based on the lesson log. The reward calculation logic can be implemented using Python or JavaScript, and the calculation results are processed by a dedicated reward calculation system. The calculated reward details are provided to the expert via the terminal, and after the expert has confirmed and approved them, the server executes the reward payment process. Payments can be made by bank transfer or via electronic money payment systems (such as PayPal or Stripe).

[0114] As a concrete example, if a user inputs their desire to "obtain an Eiken qualification" and specifies as a condition "I want to study twice a week at night," the server searches the database for "experts who hold the Eiken Grade 1 qualification and are available at night," and displays a list of experts named "Mr. Suzuki" as search results. The user selects Mr. Suzuki and books a lesson at 7 p.m. on the following Monday. On the day of the lesson, the user and Mr. Suzuki conduct the lesson via video call via Zoom, and the server records the situation. After the lesson is over, the server calculates Mr. Suzuki's remuneration based on the log, and the remuneration is paid to the expert.

[0115] Prompt Sentence Examples

[0116] "Please explain the steps for a user who has a Grade 1 English Proficiency Test and is looking for an expert who is available for evening lessons to book a lesson for next Monday."

[0117] This system will enable users to easily find suitable experts, and experts will be able to provide lessons efficiently and receive compensation, which is expected to make the qualification process go more smoothly.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1:

[0120] Enter information

[0121] The user uses the terminal to enter information such as their name, email address, password, and desired qualifications.

[0122] The professional also uses the device to enter information such as their name, email address, password, qualifications, years of experience, and pricing.

[0123] Input: User information (name, email address, password, desired qualifications), expert information (name, email address, password, qualifications held, years of experience, pricing)

[0124] Output: The entered information is sent to the server

[0125] Specific operation: A user enters the name "Taro Tanaka" and enters the email address "tanaka@example.com." An expert enters the name "Ichiro Suzuki" and enters the email address "suzuki@example.com."

[0126] Step 2:

[0127] Receiving and verifying information

[0128] A server receives information sent by users and experts.

[0129] The server validates the format and content of the information (e.g., whether an email address is formatted correctly or whether a password meets a certain level of complexity).

[0130] Input: User information, expert information

[0131] Output: Verification results

[0132] What happens: The server verifies that "tanaka@example.com" is a valid email address. It checks the password complexity (e.g., whether it is at least 8 characters long and contains letters and numbers).

[0133] Step 3:

[0134] Information storage

[0135] The server saves the verified information in a database, such as MySQL.

[0136] Input: Verified user information, expert information

[0137] Output: Saved information

[0138] Specific operation: The server saves Taro Tanaka's information in the "user_info" table and Ichiro Suzuki's information in the "expert_info" table.

[0139] Step 4:

[0140] Enter conditions and search

[0141] The user enters the desired qualifications and conditions (e.g., two nights a week) on the terminal and sends them to the server.

[0142] The server searches the database for the most suitable expert based on the criteria.

[0143] Input: User conditions (qualifications, lesson frequency, time zone)

[0144] Output: A list of the best experts

[0145] Specific operation: The user enters the criteria "English Proficiency Test, 2 times a week at night." The server searches the database for "experts who hold the English Proficiency Test Level 1 and are available at night" and finds "Ichiro Suzuki."

[0146] Step 5:

[0147] Providing search results and selecting experts

[0148] The server provides the search results as a list to the user's terminal.

[0149] The user selects the desired expert from the list.

[0150] Input: list of search results

[0151] Output: Selected experts

[0152] Specific operation: The server generates a list of information about "Ichiro Suzuki" and provides it to the user. The user selects Ichiro Suzuki.

[0153] Step 6:

[0154] Reservations and schedule confirmation

[0155] The server checks the schedule of the selected expert and assists the user in reserving a date and time for the lesson.

[0156] Input: Selected expert, user's desired date and time

[0157] Output: Reservation information

[0158] Specific behavior: A user books a lesson for next Monday at 7pm. The server checks Suzuki Ichiro's schedule and confirms the reservation.

[0159] Step 7:

[0160] notification

[0161] The server notifies the specialist of the reservation information.

[0162] Input: Reservation information

[0163] Output: Notification message

[0164] Specific operation: The server sends a lesson reservation notification to Suzuki Ichiro's email address.

[0165] Step 8:

[0166] Lesson implementation

[0167] The user and the expert will conduct the lesson at the appointed date and time using an online platform (Zoom or Google (registered trademark) Meet).

[0168] Input: reservation date and time, online platform link

[0169] Output: Lesson implementation status

[0170] Specific operation: The user and expert log in to Zoom and start a video call.

[0171] Step 9:

[0172] Lesson Recording

[0173] The server records the progress of the lesson in real time and stores the lesson log in a database.

[0174] Input: lesson start time, end time, progress

[0175] Output: Lesson log

[0176] Specific operation: The server records the start time, end time, and main topic of the lesson and stores them in a database.

[0177] Step 10:

[0178] Reward calculation and payment

[0179] The server calculates the reward for the expert based on the lesson log.

[0180] The server provides the reward details to the expert, and after the expert approves it, the reward is paid.

[0181] Input: Lesson Log

[0182] Output: Remuneration details, payment processing

[0183] Specific operation: The server calculates the reward as "Suzuki Ichiro: 3,000 yen / hour x 2 hours = 6,000 yen" and provides the reward details to Suzuki Ichiro. After Suzuki Ichiro approves it, the server executes the process to transfer 6,000 yen to his bank account.

[0184] (Application example 1)

[0185] 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."

[0186] Conventional user-expert matching systems are limited to providing specific professional services, such as lessons for qualifications. This makes it difficult to respond to demand for food delivery and other professional services. Furthermore, the lack of reservation information notifications and reminder functions creates issues with the efficiency and reliability of service provision. Therefore, the present invention aims to solve these issues and provide a system that efficiently provides a wider variety of professional services.

[0187] 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.

[0188] In this invention, the server includes: a means for inputting conditions related to the qualifications desired by the user; a means for registering information on qualified experts; a means for storing information on the registered experts in a database; a means for searching for the most suitable expert based on the user's input conditions; a means for providing the search results to the user; a means for checking the schedule of the expert selected by the user and reserving a lesson; a means for notifying the expert of the lesson reservation information; a means for managing the progress of the lesson and recording a lesson log; a means for calculating the fee to the expert based on the recorded lesson log; a means for paying the fee to the expert; a means for inputting conditions related to the service desired by the user; a means for registering information on the expert who will provide the service; a means for searching for the most suitable expert based on the user's desired conditions and reserving a schedule for the provided service; a means for notifying the expert of the reservation information and sending a reminder. This allows users to easily match with a wider variety of professional service providers and efficiently receive services that meet their specific conditions. Furthermore, the reminder function improves the reliability and certainty of service provision.

[0189] A "user" is an individual or organization that wishes to use the system and enters their requirements.

[0190] An "expert" is an individual or organization that has a particular qualification or skill and is registered.

[0191] A "qualification" is a formal certification that attests to a particular skill or knowledge.

[0192] "Conditions" refers to information such as specific requirements regarding the service or qualifications desired by the user, and the desired date and time.

[0193] A "means" is a method or device for achieving a specific function or purpose.

[0194] A "database" is an information management system that stores and searches registered information in an organized manner.

[0195] "Search" is the act of retrieving information that matches a condition from a database.

[0196] A "schedule" is a timetable showing the schedules of service providers and users.

[0197] "Reminders" is a feature that notifies you of specific actions or events.

[0198] "Remuneration" means the consideration received by a professional for services rendered.

[0199] A "log" is data that records the progress and history of a service.

[0200] "Notification" is the act of informing a user or expert of specific information.

[0201] "Reservation" means the act of setting and reserving a date and time for a service or lesson.

[0202] A "service" is a specific task or support provided by an expert to a user.

[0203] This invention is a system that efficiently matches users with experts and provides a variety of professional services. This system is composed of three main elements: a server, a terminal, and a user, and includes the following means:

[0204] 1. User and Professional Registration

[0205] First, the user and expert register their respective information using a device. User information includes name, email address, password, desired qualifications, and service conditions, while expert information includes name, email address, password, qualifications and skills held, years of experience, and pricing. The server receives this information, verifies it, and then stores it in a database. The device can be a standard smartphone or tablet.

[0206] 2. Matching Process

[0207] The user inputs the desired qualifications and service requirements from the terminal. The server uses this information to search for the most suitable specialist from its database and provides the search results to the user as a list. The user selects the desired specialist from the displayed list, and the server checks the specialist's schedule and reserves the service for the desired date and time.

[0208] 3. Lesson or service provision and progress management

[0209] As the reserved service date and time approaches, the server sends reminder notifications to the user and the expert. On the day of the service, the user and the expert perform the service using online platforms such as video calls or chat tools. The server records the progress in real time and saves the execution log in a database. Notifications are ensured by using SMS notification services such as the Twilio API.

[0210] 4. Reward Calculation and Payment

[0211] Once the service is completed, the server calculates the fee to the expert based on the saved execution log. The calculated fee is provided to the expert via the terminal, and once the expert confirms and approves it, the payment is processed. Payment is processed using electronic payment services such as Stripe and PayPal.

[0212] Specific examples

[0213] For example, if a user requests a chef who can provide healthy Japanese meals twice a week, the server searches the database for chefs who meet the criteria and provides the results to the user. The user then selects the most suitable chef and reserves delivery for meals every Monday and Thursday at 7 p.m. The system notifies the chef of the reservation information and also sends reminders, ensuring that the service is provided reliably. After the service is completed, the remuneration is automatically calculated and paid to the chef. This allows users to easily find the right expert and receive services efficiently.

[0214] Prompt Sentence Examples

[0215] "I would like to have healthy Japanese meals served twice a week. Please help me build a platform that can match chefs with food requests. Please provide an overview of the process for supply and demand analysis, ingredient sourcing, lesson implementation, and compensation calculation."

[0216] This system allows users to efficiently use various professional services, and experts can efficiently provide services and receive compensation. Specific infrastructure includes the use of the Django framework, notifications via the Twilio API, and electronic payments via Stripe and PayPal.

[0217] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0218] Step 1:

[0219] User and Expert Registration

[0220] Users and experts use terminals to enter information such as name, email address, password, desired services and qualifications, etc. The server receives this information, verifies its accuracy and completeness, and then stores it in a database.

[0221] Input: Registration information (username, email address, password, credentials and service details)

[0222] Data processing: Verify that the format and content of the entered data are correct

[0223] Output: Save to database (user information and expert information)

[0224] Specifically, we will use the Django framework to create API endpoints that provide user and expert registration forms and save them to a database.

[0225] Step 2:

[0226] User condition input

[0227] The user inputs the desired qualifications and conditions for the service from the terminal. For example, they input specific requirements such as "I would like to have healthy Japanese meals twice a week." The server receives the input conditions and stores them in a database.

[0228] Input: Desired conditions (specific service and qualification requirements)

[0229] Data processing: Verification of the form and content of the conditions

[0230] Output: Save to database (desired conditions)

[0231] Specifically, it creates an input form, sends data asynchronously using an AJAX request, and saves it in a database on the server side.

[0232] Step 3:

[0233] Expert Search

[0234] The server searches the database for the most suitable expert based on the criteria entered by the user. For example, it searches for "experts who are qualified in Japanese cuisine and can provide health-conscious dishes." The search results are provided to the user in a list format.

[0235] Input: Desired conditions (match with conditions in the database)

[0236] Data operations: Applying criteria-based filtering and search algorithms

[0237] Output: Search result list (list of relevant experts)

[0238] Specifically, it uses Django ORM to execute a database query to find experts that meet the criteria.

[0239] Step 4:

[0240] Check schedule and make reservations

[0241] The user selects the desired expert from the displayed search result list, checks the expert's schedule, and makes a reservation. The server saves the reservation information in a database and updates the expert's schedule.

[0242] Input: ID of selected expert and desired date and time

[0243] Data processing: Check and update schedule availability

[0244] Output: Save reservation information (update database)

[0245] Specifically, it uses a Python script to update the schedule information in the database and notify the user that the reservation has been confirmed.

[0246] Step 5:

[0247] Reservation notifications and reminders

[0248] The server notifies the expert and user of the appointment information and also sends reminder notifications when the appointment time approaches, using the Twilio API to send SMS.

[0249] Input: Reservation information

[0250] Data processing: Converting to SMS format using Twilio API

[0251] Output: Send notification message (SMS)

[0252] Specifically, it uses the Twilio library and uses API keys to send appointment information and reminders to experts and users via SMS.

[0253] Step 6:

[0254] Service progress management and logging

[0255] On the day of the service delivery, the user and the expert communicate via video call or chat. The server manages the progress of the service in real time and records it as a log.

[0256] Input: Service progress information (start time, end time, other meter data)

[0257] Data calculation: Real-time update and recording of progress information

[0258] Output: Save progress log (database)

[0259] Specifically, we will build a dashboard that displays progress information along with real-time database updates.

[0260] Step 7:

[0261] Reward calculation and payment

[0262] Once the service is completed, the server calculates the fee to the expert based on the recorded log. After the calculation, a fee statement is sent to the expert and payment is made. Electronic payments are made using Stripe or PayPal.

[0263] Input: Service log (time, service content, etc.)

[0264] Data calculation: Applying reward calculation algorithms

[0265] Output: Compensation statement generation and payment execution

[0266] Specifically, it uses the Stripe API or PayPal API to automate fee calculation and payment to experts.

[0267] Prompt Sentence Examples

[0268] "I would like to have healthy Japanese meals served twice a week. Please help me build a platform that can match chefs with food requests. Please provide an overview of the process for supply and demand analysis, ingredient sourcing, lesson implementation, and compensation calculation."

[0269] 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.

[0270] This invention is a system that effectively matches users seeking qualifications with qualified experts, and provides optimal lessons by recognizing and considering the user's emotions. This system consists of three main elements: a server, a terminal, and a user, and an emotion engine is built into each processing step.

[0271] User and Expert Registration

[0272] First, users and experts who wish to use the system must register their information. Using a terminal, users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in the database.

[0273] Matching Process

[0274] The user inputs the qualifications they desire and sends them from their terminal. The emotion engine recognizes the user's emotions during this input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed with reserving the date and time of the lesson.

[0275] Conducting and logging lessons

[0276] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[0277] Adjusting lesson content

[0278] If the emotion engine detects stress or confusion in the user during a lesson, it will notify the expert and prompt them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[0279] Reward calculation and payment

[0280] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[0281] Specific examples

[0282] For example, if a user inputs a desire to "obtain an English proficiency test" and the emotion engine recognizes the user's lack of confidence, the server will search for and recommend experts with a particularly gentle teaching style. As a result, an expert named Mr. Tanaka is found and suggested to the user. The user selects Mr. Tanaka and books a lesson at 7 p.m. next Monday.

[0283] On the day of the lesson, the user and Professor Tanaka will start a video call via the selected online platform. The emotion engine will monitor the user's reactions during the lesson. For example, if the user shows signs of confusion, the server will relay this information to Professor Tanaka. Professor Tanaka will then adjust his / her teaching method, providing easier-to-understand explanations and adjusting the pace.

[0284] This system allows users to learn efficiently from experts who are best suited to their emotions and situations, and experts can receive appropriate compensation for the lessons they provide, making the qualification process more effective and comfortable.

[0285] The processing flow will be explained below.

[0286] Step 1: Enter your user information

[0287] Terminal: Presents a form where the user enters their name, email address, password, and desired credentials.

[0288] User: Enter the required information and click the submit button.

[0289] Terminal: Sends input information to the emotion engine and recognizes the user's emotions.

[0290] Step 2: Recognizing and transmitting emotions

[0291] Emotion engine: Analyzes user input and behavior to recognize emotions.

[0292] Device: Sends the recognized emotion information to the server.

[0293] Server: Receives input information and emotion information and performs verification.

[0294] Step 3: Verify and save the information

[0295] Server: Validate the received user information and sentiment information, check that all required fields are filled in, and that the email address format is correct.

[0296] Server: Save the verified information in the database.

[0297] Step 4: Enter expert information

[0298] Device: Displays a form where the expert can enter their name, email address, password, certifications, years of experience, and pricing information.

[0299] Expert: Enter the required information and click the submit button.

[0300] Terminal: Sends the entered information to the server.

[0301] Step 5: Verify and save expert information

[0302] Server: Validates received expert information and checks for accuracy.

[0303] Server: Save the verified information in the database.

[0304] Step 6: Enter criteria and search

[0305] Terminal: Displays a form for the user to enter the lesson conditions they desire (e.g. time slot, number of times per week, fee, etc.).

[0306] User: Enter the required information and click the submit button.

[0307] Terminal: Sends the input conditions and the results of the emotion engine to the server.

[0308] Server: Searches the database for the most suitable experts based on the input conditions and emotional information, and generates a list of search results.

[0309] Step 7: View and select search results

[0310] Server: Sends the search result list to the device.

[0311] Terminal: Display search results to the user in a list format.

[0312] User: Select the desired expert from the list of suggested experts.

[0313] Step 8: Book a lesson

[0314] Terminal: View the selected expert's schedule.

[0315] User: Select the desired lesson date and time and click the submit button.

[0316] Device: Send the selected date and time to the server.

[0317] Server: Stores lesson reservation information in a database and sends reservation notifications to experts.

[0318] Step 9: Reminders

[0319] Server: Send reminder notifications to users and experts before the lesson starts.

[0320] Step 10: Lesson implementation

[0321] User: Follow the reminder notification and launch the video calling platform to connect with the expert.

[0322] Experts: Launch a video chat platform during lesson time to connect with users.

[0323] Server: Monitors the progress of lessons and records progress and emotion logs.

[0324] Step 11: Adjust the lesson content

[0325] Emotion Engine: Analyzes user emotions in real time during lessons.

[0326] Server: Sends the user's emotional information to the expert, prompting the expert to adjust the content and pace of the lesson according to the user's emotions.

[0327] Step 12: Complete the lesson and calculate your rewards

[0328] Server: After the lesson is completed, calculate the reward for the expert based on the recorded log and emotion log.

[0329] Server: Generates reward statements and sends them to experts.

[0330] Terminal: Shows the expert a fee statement.

[0331] Step 13: Payment

[0332] Expert: Review and approve the fee statement.

[0333] Server: After receiving the expert's approval, the server executes the payment process.

[0334] Server: Notifies the expert that payment has been made.

[0335] Terminal: Shows the expert a notification that payment has been completed.

[0336] Example 2

[0337] 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."

[0338] Conventional qualification support systems provide lessons without considering the user's emotional state, which can lead to stress and reduced learning effectiveness. Furthermore, the calculation of compensation for experts is based solely on lesson logs, which means that the experts' efforts and the quality of the lessons are not properly evaluated.

[0339] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a means for inputting conditions related to the qualifications desired by the user; a means for registering information on qualified experts; a means for saving information on the registered experts in a database; a means for searching for the most suitable expert based on the conditions input by the user; a means for providing the search results to the user; a means for checking the schedule of the expert selected by the user and reserving a lesson; a means for notifying the expert of the lesson reservation information; a means for recognizing the user's emotions in real time and recording the results in a database; a means for managing the progress of the lesson and recording a performance log and an emotion log; a means for calculating the fee to the expert based on the recorded lesson log and emotion log; and a means for paying the fee to the expert. This enables the provision of an optimal lesson that takes the user's emotional state into consideration, and also enables the expert's efforts and the quality of the lesson to be appropriately evaluated.

[0340] "User" means an individual or organization that uses this system to obtain qualifications.

[0341] An "expert" is a person who holds a specific qualification and is able to provide guidance and advice based on that qualification.

[0342] An "emotion engine" is a software or hardware component that analyzes and recognizes a user's emotional state in real time based on their facial expressions, voice, etc.

[0343] The "database" is a system for centrally storing and managing data such as user and expert information, lesson logs, and emotion logs.

[0344] "Input conditions" refers to the conditions and requirements desired by the user regarding qualification acquisition.

[0345] A "lesson" is a teaching and educational session conducted by an expert for a user.

[0346] "Lesson Log" refers to a record of the content, progress and various interactions of a lesson.

[0347] An "emotion log" refers to a record of a user's emotional state.

[0348] "Remuneration Statement" means a document or data summarizing the details of the compensation paid to the Expert after the Lesson is completed.

[0349] "Reminder notification" refers to alerts and notifications that notify users and experts in advance of lesson dates and times, etc.

[0350] "Schedule" refers to a list of an Expert's available dates, times, and booked slots.

[0351] MODE FOR CARRYING OUT THE INVENTION

[0352] This invention is a system that effectively matches users seeking qualifications with qualified experts and provides optimal lessons taking into account the user's emotions. This system is composed of three main elements: a server, a terminal, and a user, and an emotion engine is built into each processing step.

[0353] User and Expert Registration

[0354] First, users and experts who wish to use the system must register their information using a terminal. Users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications held, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in a database.

[0355] Matching Process

[0356] The user inputs the qualifications they desire and sends them from their terminal. At this time, the emotion engine recognizes the user's emotions during the input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and provides a procedure that allows the user to reserve a date and time for a lesson.

[0357] Conducting and logging lessons

[0358] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[0359] Adjusting lesson content

[0360] If the emotion engine detects stress or confusion in the user during a lesson, the server notifies the expert and prompts them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[0361] Reward calculation and payment

[0362] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[0363] Specific examples

[0364] For example, if a user inputs their desire to "obtain an English proficiency test" and the emotion engine recognizes the user's lack of confidence, the server will search for and recommend experts with a particularly gentle teaching style. As a result, a specific expert is found and suggested to the user. The user selects that expert and books a lesson for the following Monday at 7 p.m. On the day of the lesson, the user and the expert will initiate a video call on the specified online platform. The emotion engine will monitor the user's reactions during the lesson, and if the user shows signs of confusion, for example, the server will relay that information to the expert. The expert will then adjust their teaching method, providing easier-to-understand explanations and a more pacing-oriented lesson.

[0365] Prompt Sentence Examples

[0366] An example of a prompt that allows the user to enter their wishes into the system is:

[0367] If a user enters their desire to "obtain an English proficiency test" but feels unsure, please recommend the most suitable expert.

[0368] This system allows users to learn efficiently from experts who are best suited to their emotions and situations, and experts can receive appropriate compensation for the lessons they provide, making the qualification process more effective and comfortable.

[0369] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0370] Step 1:

[0371] User and Expert Registration

[0372] The user uses the terminal to enter information such as their name, email address, password, and desired qualification. Specifically, they enter their name "Yamada Taro," their email address "taro@example.com," their password "password123," and their desired qualification "TOEIC750."

[0373] The terminal transmits these input data to the server.

[0374] The server validates the data it receives, checking, for example, the format of the email address and the strength of the password, and if the data is correct, stores it in the database.

[0375] Similarly, experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. Specifically, they enter their name "Sato Hanako," email address "hanako@example.com," password "securePass456," qualifications "TOEIC 950," years of experience "5 years," and pricing "5,000 yen per hour."

[0376] The terminal sends the input data to the server, which verifies the received data and, if correct, stores it in a database.

[0377] Step 2:

[0378] User qualifications and emotion recognition

[0379] The user inputs the desired qualifications (e.g., "TOEIC 750") into the terminal.

[0380] The terminal transmits the input data to the server.

[0381] The emotion engine monitors the user's input process and analyzes facial expressions and tone of voice to recognize emotional states (e.g., "unconfident").

[0382] The server stores the user's conditions and the emotion engine results in a database.

[0383] Step 3:

[0384] Providing matching results and reservations

[0385] The server searches the database for the most suitable expert based on the user's input criteria and emotional state, using a search algorithm that takes into account the expert's teaching style and qualifications.

[0386] The server sends the search results (e.g., "experts with a gentle teaching style") in the form of a list to the user's terminal.

[0387] The user selects the desired expert from the list.

[0388] The server obtains the schedule information of the experts and presents the user with available reservation dates and times.

[0389] The user selects the desired date and time and confirms the reservation through the terminal.

[0390] The server stores the reservation data in a database and notifies the specialist.

[0391] Step 4:

[0392] Lesson implementation and logging

[0393] The server sends reminders to the user and the expert for the day of the lesson.

[0394] The user and the expert initiate a video call on a designated online platform (e.g., Zoom).

[0395] The emotion engine analyzes the user's facial expressions and voice during the lesson to recognize their emotional state in real time, for example, analyzing a "confused expression."

[0396] The server records the progress of the lesson, the dialogue content, and the emotion log in a database.

[0397] Step 5:

[0398] Adjusting lesson content

[0399] When the emotion engine recognizes a change in the user's emotion (e.g., "high stress"), it sends that information to the server.

[0400] The server notifies the expert of the information on the emotional changes.

[0401] The expert will use the information to adjust the content and pace of the lesson, for example by providing more detailed explanations or slowing down the pace.

[0402] Step 6:

[0403] Reward calculation and payment

[0404] After the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. Specifically, it calculates the lesson time and evaluation items based on a formula.

[0405] The server sends the calculated remuneration details to the expert's terminal.

[0406] The expert checks and approves the compensation details on the terminal.

[0407] The server processes the approved payment by transferring it to the expert's bank account.

[0408] These steps result in a system that takes into account the user's emotional state, provides optimal lessons, and appropriately rewards the experts.

[0409] (Application example 2)

[0410] 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."

[0411] Conventional qualification support systems have the problem that lessons proceed without taking into account the user's emotions or current skill state, which makes it difficult to alleviate the stress and confusion felt by users. Furthermore, when it comes to improving the skills of employees in a company's factory, there is a lack of a way to grasp the emotional state of each employee in real time and provide optimal guidance. This makes effective learning difficult.

[0412] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input conditions related to desired qualifications, means for registering information on qualified experts, means for saving information on registered experts in a database, means for searching for the most suitable expert based on the conditions input by the user, means for providing the search results to the user, means for checking the schedule of the expert selected by the user and reserving a lesson, means for notifying the expert of the lesson reservation information, means for managing the progress of the lesson and recording a lesson log, means for calculating the fee to the expert based on the recorded lesson log, means for paying the fee to the expert, and means for recognizing the user's emotions and adjusting the lesson content in real time. This makes it possible to provide effective lessons that take the user's emotional state into consideration.

[0413] The "means for users to input conditions related to the qualifications they desire" is a function that allows users who wish to obtain qualifications to input their desired qualifications and related conditions into the system.

[0414] The "means for registering information on qualified professionals" is a function that allows professionals who hold specific qualifications to register their information in the system.

[0415] The "means for storing information on registered experts in a database" is a function that uses a database to stably store and manage information on registered experts.

[0416] "Means for searching for the most suitable expert based on the conditions entered by the user" refers to a function that has algorithms and logic for searching for the most suitable expert from within the database based on the conditions entered by the user.

[0417] The "means for providing search results to the user" is a function for displaying or providing information on the searched experts to the user.

[0418] The "means for checking the schedule of the expert selected by the user and reserving a lesson" is a function for checking the schedule of the expert selected by the user and reserving a lesson based on that schedule.

[0419] The "means for notifying the expert of lesson reservation information" is a function for notifying the expert of details of the reserved lesson.

[0420] The "means for managing the progress of a lesson and recording an implementation log" is a function for managing the progress of a lesson in progress and recording the details as a log.

[0421] The "means for calculating the remuneration to the expert based on the recorded lesson log" is a function for calculating the remuneration to the expert based on the lesson implementation log.

[0422] The "means for paying the remuneration to the expert" is a function for actually paying the calculated remuneration to the expert.

[0423] "Means for recognizing the user's emotions and adjusting the lesson content in real time" is a function that uses emotion recognition technology to recognize the user's emotions and adjusts the optimal instruction in real time according to the user's situation during the lesson.

[0424] This invention is a system that effectively matches users seeking qualifications with qualified professionals, and provides optimal lessons by recognizing and considering the user's emotions. It can also be applied to improving the skills of employees in factories. This system consists of three main elements: a server, a terminal, and a user.

[0425] User and Expert Registration

[0426] First, users and experts who wish to use the system must register their information. Using a terminal, users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. The server receives this information, verifies it, and then stores it in a database.

[0427] Matching Process

[0428] The user inputs the qualifications they desire and sends them from their terminal. The emotion engine recognizes the user's emotions during this input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the search results to the user as a list. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed to reserve a date and time for a lesson.

[0429] Conducting and logging lessons

[0430] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[0431] Adjusting lesson content

[0432] If the emotion engine detects stress or confusion in the user during a lesson, it will notify the expert and prompt them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[0433] Reward calculation and payment

[0434] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[0435] Factory Applications

[0436] A "skill improvement support robot" can be installed for factory workers. This robot will monitor each employee's work skills and emotional state in real time and provide appropriate training and guidance. It will capture facial images of employees via webcam and analyze their emotions using an emotion recognition model. Based on the employee's emotional data, the robot will display appropriate messages and adjust the work content and guidance methods.

[0437] For example, if employee A shows the emotion of "anger," the robot can display the message, "A, you seem to be feeling angry right now. Please stay calm and continue working." If employee A shows the emotion of "joy," the robot can display the message, "A, you're doing well. Keep going and do your best."

[0438] Specific prompt examples

[0439] "Build a response system that takes employees' facial and text data as input, recognizes their emotions, and suggests optimal coaching methods."

[0440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0441] Step 1:

[0442] User and Expert Registration

[0443] Input: The user enters their name, email address, password, and desired qualification information into the terminal. Similarly, the expert enters their name, email address, password, qualifications, years of experience, pricing, and other information into the terminal.

[0444] Processing: The device sends this information to the server, which then verifies the received information, checking the authenticity of the credentials, the format of the email address, etc.

[0445] Output: After the verification is complete, the server saves the information in the database. The user and the expert receive a notification that their registration is complete.

[0446] Step 2:

[0447] User condition input and emotion recognition

[0448] Input: The user inputs the desired qualifications and conditions into the terminal. At this time, the user's facial expression is captured through the terminal's camera and emotion recognition is performed.

[0449] Processing: The device sends the user's input conditions and emotion recognition data to the server. Emotion recognition is performed using an emotion engine that analyzes the user's facial image.

[0450] Output: The server receives the user's input conditions and emotional data. Based on the emotional data, the server outputs the user's state (e.g., stress, confusion, etc.) as a numerical value or label.

[0451] Step 3:

[0452] Expert Matching

[0453] Input: The server searches the database based on the user's input conditions and emotion data.

[0454] Processing: Using a search algorithm, we list the experts best suited to the user's situation. The search takes into account not only the user's conditions but also their emotional state.

[0455] Output: The list of experts obtained as a search result is provided to the user. The search results are displayed on the user's terminal.

[0456] Step 4:

[0457] Book a lesson

[0458] Input: The user selects the desired expert from the list of experts presented and specifies the date and time of the lesson on the terminal.

[0459] Processing: The terminal sends the reservation information to the server, which checks the expert's schedule and confirms the reservation.

[0460] Output: The reservation confirmation information is notified to the expert and the user. The expert's schedule is updated.

[0461] Step 5:

[0462] Conducting and recording lessons

[0463] Input: On the day of the lesson, the user and the expert initiate a video call on the designated online platform.

[0464] Processing: The emotion engine monitors the user's emotions during the lesson in real time and sends the data to the server.

[0465] Output: The server records the lesson progress and execution log and saves it together with the emotion log. The real-time emotion data is notified to the expert.

[0466] Step 6:

[0467] Adjusting lesson content

[0468] Input: If the emotion engine detects that the user is stressed or confused, it sends that information to the server.

[0469] Processing: The server notifies the expert of the user's emotional state and prompts them to adjust their teaching methods.

[0470] Output: The expert adjusts the teaching method and content appropriately based on the user's emotional state.

[0471] Step 7:

[0472] Reward calculation and payment

[0473] Input: After the lesson is completed, the server calculates the reward for the expert based on the recorded lesson log and emotion log.

[0474] Processing: A calculation algorithm is used to calculate the required fee. The expert is provided with a fee breakdown.

[0475] Output: The expert confirms and approves the reward amount. The server executes the reward payment process and the reward is paid to the expert.

[0476] In this way, a system that takes into account the user's emotions is realized through specific processing steps to provide an effective lesson environment.

[0477] 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.

[0478] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0479] 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.

[0480] [Second embodiment]

[0481] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0482] 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.

[0483] 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).

[0484] 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.

[0485] 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.

[0486] 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).

[0487] 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. 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.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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."

[0493] This invention is a system for efficiently matching users with qualified experts and providing effective lessons. This system consists of three main elements: a server, a terminal, and a user, and is operated in the following steps:

[0494] User and Expert Registration

[0495] First, users and experts who wish to use the system must register their respective information. Using a terminal, users enter information such as their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications held, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in a database.

[0496] Matching Process

[0497] The user inputs the desired qualifications and sends them via the terminal. The server searches the database for the most suitable experts based on the input information and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed to reserve a date and time for a lesson.

[0498] Conducting and logging lessons

[0499] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson using an online platform such as a video call. The server records the progress of the lesson and saves the progress log in a database.

[0500] Reward calculation and payment

[0501] When the lesson is completed, the server calculates the reward for the expert based on the lesson log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the process of paying the reward.

[0502] Specific examples

[0503] For example, a user may input a desire to "obtain an English Proficiency Test qualification" and specify the condition for lessons as "I would like to study twice a week at night." The server searches the database for "experts who hold the English Proficiency Test Grade 1 and are available at night." As a result, an expert named Mr. Suzuki is found and suggested to the user. The user selects Mr. Suzuki and books a lesson for the following Monday at 7 p.m.

[0504] On the day of the lesson, the user and Suzuki sensei will actively conduct the lesson via video call on the designated online platform. The server records the situation and creates a lesson log. After the lesson is completed, the reward is calculated based on this log and paid to Suzuki sensei.

[0505] This system allows users to easily find suitable experts, and experts can efficiently provide lessons and receive compensation, making the qualification process go more smoothly.

[0506] The processing flow will be explained below.

[0507] Step 1: User Registration

[0508] Terminal: Presents a form where the user enters their name, email address, password, and desired credentials.

[0509] User: Enter the required information and click the submit button.

[0510] Terminal: Sends the entered information to the server.

[0511] Step 2: Professional Registration

[0512] Device: Displays a form where the expert can enter their name, email address, password, certifications, years of experience, and pricing information.

[0513] Expert: Enter the required information and click the submit button.

[0514] Terminal: Sends the entered information to the server.

[0515] Step 3: Verify and save the information

[0516] Server: Validates the received user and expert information, ensuring all required fields are filled in and that the email address is formatted correctly.

[0517] Server: Stores the verified information in a database.

[0518] Step 4: Enter criteria and search

[0519] Terminal: Displays a form for the user to enter the lesson conditions they desire (e.g. time slot, number of times per week, fee, etc.).

[0520] User: Enter the required information and click the submit button.

[0521] Terminal: Sends the entered conditions to the server.

[0522] Server: Based on the input criteria, it searches the database for the most suitable experts and generates a list of search results.

[0523] Step 5: View and select search results

[0524] Server: Sends the search result list to the device.

[0525] Terminal: Display search results to the user in a list format.

[0526] User: Select the desired expert from the list of suggested experts.

[0527] Step 6: Book a lesson

[0528] Terminal: View the selected expert's schedule.

[0529] User: Select the desired lesson date and time and click the submit button.

[0530] Device: Send the selected date and time to the server.

[0531] Server: Stores lesson reservation information in a database and sends reservation notifications to experts.

[0532] Step 7: Reminders

[0533] Server: Send reminder notifications to users and experts before the lesson starts.

[0534] Step 8: Lesson implementation

[0535] User: Follow the reminder notification and launch the video calling platform to connect with the expert.

[0536] Experts: Launch a video chat platform during lesson time to connect with users.

[0537] Server: Monitors the progress of lessons and records progress logs.

[0538] Step 9: Complete the lesson and calculate your rewards

[0539] Server: After the lesson is completed, calculate the reward for the expert based on the recorded log.

[0540] Server: Generates reward statements and sends them to experts.

[0541] Terminal: Shows the expert a fee statement.

[0542] Step 10: Payment

[0543] Expert: Review and approve the fee statement.

[0544] Server: After receiving the expert's approval, the server executes the payment process.

[0545] Server: Notifies the expert that payment has been made.

[0546] Terminal: Shows the expert a notification that payment has been completed.

[0547] Example 1

[0548] 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."

[0549] In conventional qualification support systems, it was difficult to match users with the appropriate experts, and the process of conducting lessons and paying fees was cumbersome. This resulted in problems such as users being unable to take lessons efficiently and experts being unable to provide their services effectively. In addition, lesson progress management and fee calculation and payment were often done manually, which could be a burden.

[0550] 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.

[0551] In this invention, the server includes: means for a user to input conditions related to desired qualifications; means for registering information on qualified experts; means for saving information on registered experts in a database; means for searching for the most suitable expert based on the conditions input by the user; means for providing the search results to the user; means for the user to check the schedule of the expert selected by the user and book a lesson; means for notifying the expert of the lesson reservation information; means for the user and the expert to conduct the lesson on the designated online platform on the day of the lesson reservation; means for managing the progress of the lesson and recording the implementation log; means for calculating the fee to the expert based on the recorded lesson log; and means for paying the fee to the expert. This enables effective matching of users and experts, efficient lesson implementation, progress management, and fee calculation and payment to be automated, thereby reducing the overall workload.

[0552] "User" means an individual or organization who wishes to obtain a qualification and uses the system to take lessons.

[0553] An "expert" is an individual or organization that holds a specific qualification and has the knowledge and experience related to that qualification.

[0554] "Conditions" refers to the requirements and schedule for obtaining the qualifications the user desires, as well as preferences such as the frequency and time of lessons.

[0555] "Database" refers to a system that systematically stores and manages data such as user and expert registration information, lesson progress, and remuneration calculations.

[0556] "Schedule" refers to a list or timetable of dates and times when an Expert is available to conduct Lessons.

[0557] "Online Platform" means an internet-based service that provides video calling and messaging capabilities to enable users and experts to communicate in real time.

[0558] "Progress" is information that indicates the implementation status and progress of the lesson.

[0559] An "implementation log" is data that records details such as the start time, end time, and content of the lesson.

[0560] "Remuneration" means the amount of money a Professional receives in exchange for providing a Lesson.

[0561] "Notifications" are messages or alerts that communicate important information to users and experts, such as lesson booking information and reminders.

[0562] This invention is a system for efficiently matching users with qualified experts and providing effective lessons. This system consists of three main elements: a server, a terminal, and a user. Specifically, users input their desired qualifications and register information about qualified experts.

[0563] When users and experts enter information such as their name, email address, password, desired qualifications, and current qualifications from their respective devices, the server receives this information. At this time, the server verifies the format, such as the validity of the email address and the strength of the password. The necessary hardware includes a database server (using MySQL, for example) and a front-end system for displaying the information (web technologies such as HTML and JavaScript).

[0564] Once verified, the information is stored in a database. When a user enters the desired qualifications and conditions, for example, "I want to study for the English Proficiency Test twice a week at night," the server uses this information to search the database for the most suitable expert. Specific software could use Python or SQL queries to implement the search algorithm. For example, let's say you want to search for "experts who hold the English Proficiency Test Grade 1 and are available at night."

[0565] The search results are provided to the user as a list, and the user selects the desired expert from the list. Based on this selection, the server checks the expert's schedule and executes the procedure for the user to reserve the lesson date and time. Once the reservation is complete, the server notifies the expert of the reservation information. Notifications are sent using an email system or push notification service (such as SMTP or Firebase Cloud Messaging).

[0566] On the day of the lesson, the user and the expert will have a video call using an online platform such as Zoom or Google Meet. The server records the progress of the lesson in real time and saves the implementation log in a database. This progress management is done using log collection services and APIs (e.g., Amazon CloudWatch and Custom API).

[0567] Once the lesson is completed, the server calculates the expert's reward based on the lesson log. The reward calculation logic can be implemented using Python or JavaScript, and the calculation results are processed by a dedicated reward calculation system. The calculated reward details are provided to the expert via the terminal, and after the expert has confirmed and approved them, the server executes the reward payment process. Payments can be made by bank transfer or via electronic money payment systems (such as PayPal or Stripe).

[0568] As a concrete example, if a user inputs their desire to "obtain an Eiken qualification" and specifies as a condition "I want to study twice a week at night," the server searches the database for "experts who hold the Eiken Grade 1 qualification and are available at night," and displays a list of experts named "Mr. Suzuki" as search results. The user selects Mr. Suzuki and books a lesson at 7 p.m. on the following Monday. On the day of the lesson, the user and Mr. Suzuki conduct the lesson via video call via Zoom, and the server records the situation. After the lesson is over, the server calculates Mr. Suzuki's remuneration based on the log, and the remuneration is paid to the expert.

[0569] Prompt Sentence Examples

[0570] "Please explain the steps for a user who has a Grade 1 English Proficiency Test and is looking for an expert who is available for evening lessons to book a lesson for next Monday."

[0571] This system will enable users to easily find suitable experts, and experts will be able to provide lessons efficiently and receive compensation, which is expected to make the qualification process go more smoothly.

[0572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0573] Step 1:

[0574] Enter information

[0575] The user uses the terminal to enter information such as their name, email address, password, and desired qualifications.

[0576] The professional also uses the device to enter information such as their name, email address, password, qualifications, years of experience, and pricing.

[0577] Input: User information (name, email address, password, desired qualifications), expert information (name, email address, password, qualifications held, years of experience, pricing)

[0578] Output: The entered information is sent to the server

[0579] Specific operation: A user enters the name "Taro Tanaka" and enters the email address "tanaka@example.com." An expert enters the name "Ichiro Suzuki" and enters the email address "suzuki@example.com."

[0580] Step 2:

[0581] Receiving and verifying information

[0582] A server receives information sent by users and experts.

[0583] The server validates the format and content of the information (e.g., whether an email address is formatted correctly or whether a password meets a certain level of complexity).

[0584] Input: User information, expert information

[0585] Output: Verification results

[0586] What happens: The server verifies that "tanaka@example.com" is a valid email address. It checks the password complexity (e.g., whether it is at least 8 characters long and contains letters and numbers).

[0587] Step 3:

[0588] Information storage

[0589] The server saves the verified information in a database, such as MySQL.

[0590] Input: Verified user information, expert information

[0591] Output: Saved information

[0592] Specific operation: The server saves Taro Tanaka's information in the "user_info" table and Ichiro Suzuki's information in the "expert_info" table.

[0593] Step 4:

[0594] Enter conditions and search

[0595] The user enters the desired qualifications and conditions (e.g., two nights a week) on the terminal and sends them to the server.

[0596] The server searches the database for the most suitable expert based on the criteria.

[0597] Input: User conditions (qualifications, lesson frequency, time zone)

[0598] Output: A list of the best experts

[0599] Specific operation: The user enters the criteria "English Proficiency Test, 2 times a week at night." The server searches the database for "experts who hold the English Proficiency Test Level 1 and are available at night" and finds "Ichiro Suzuki."

[0600] Step 5:

[0601] Providing search results and selecting experts

[0602] The server provides the search results as a list to the user's terminal.

[0603] The user selects the desired expert from the list.

[0604] Input: list of search results

[0605] Output: Selected experts

[0606] Specific operation: The server generates a list of information about "Ichiro Suzuki" and provides it to the user. The user selects Ichiro Suzuki.

[0607] Step 6:

[0608] Reservations and schedule confirmation

[0609] The server checks the schedule of the selected expert and assists the user in reserving a date and time for the lesson.

[0610] Input: Selected expert, user's desired date and time

[0611] Output: Reservation information

[0612] Specific behavior: A user books a lesson for next Monday at 7pm. The server checks Suzuki Ichiro's schedule and confirms the reservation.

[0613] Step 7:

[0614] notification

[0615] The server notifies the specialist of the reservation information.

[0616] Input: Reservation information

[0617] Output: Notification message

[0618] Specific operation: The server sends a lesson reservation notification to Suzuki Ichiro's email address.

[0619] Step 8:

[0620] Lesson implementation

[0621] The user and the expert will conduct the lesson at the appointed date and time using an online platform (Zoom or Google Meet).

[0622] Input: reservation date and time, online platform link

[0623] Output: Lesson implementation status

[0624] Specific operation: The user and expert log in to Zoom and start a video call.

[0625] Step 9:

[0626] Lesson Recording

[0627] The server records the progress of the lesson in real time and stores the lesson log in a database.

[0628] Input: lesson start time, end time, progress

[0629] Output: Lesson log

[0630] Specific operation: The server records the start time, end time, and main topic of the lesson and stores them in a database.

[0631] Step 10:

[0632] Reward calculation and payment

[0633] The server calculates the reward for the expert based on the lesson log.

[0634] The server provides the reward details to the expert, and after the expert approves it, the reward is paid.

[0635] Input: Lesson Log

[0636] Output: Remuneration details, payment processing

[0637] Specific operation: The server calculates the reward as "Suzuki Ichiro: 3,000 yen / hour x 2 hours = 6,000 yen" and provides the reward details to Suzuki Ichiro. After Suzuki Ichiro approves it, the server executes the process to transfer 6,000 yen to his bank account.

[0638] (Application example 1)

[0639] 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."

[0640] Conventional user-expert matching systems are limited to providing specific professional services, such as lessons for qualifications. This makes it difficult to respond to demand for food delivery and other professional services. Furthermore, the lack of reservation information notifications and reminder functions creates issues with the efficiency and reliability of service provision. Therefore, the present invention aims to solve these issues and provide a system that efficiently provides a wider variety of professional services.

[0641] 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.

[0642] In this invention, the server includes: a means for inputting conditions related to the qualifications desired by the user; a means for registering information on qualified experts; a means for storing information on the registered experts in a database; a means for searching for the most suitable expert based on the user's input conditions; a means for providing the search results to the user; a means for checking the schedule of the expert selected by the user and reserving a lesson; a means for notifying the expert of the lesson reservation information; a means for managing the progress of the lesson and recording a lesson log; a means for calculating the fee to the expert based on the recorded lesson log; a means for paying the fee to the expert; a means for inputting conditions related to the service desired by the user; a means for registering information on the expert who will provide the service; a means for searching for the most suitable expert based on the user's desired conditions and reserving a schedule for the provided service; a means for notifying the expert of the reservation information and sending a reminder. This allows users to easily match with a wider variety of professional service providers and efficiently receive services that meet their specific conditions. Furthermore, the reminder function improves the reliability and certainty of service provision.

[0643] A "user" is an individual or organization that wishes to use the system and enters their requirements.

[0644] An "expert" is an individual or organization that has a particular qualification or skill and is registered.

[0645] A "qualification" is a formal certification that attests to a particular skill or knowledge.

[0646] "Conditions" refers to information such as specific requirements regarding the service or qualifications desired by the user, and the desired date and time.

[0647] A "means" is a method or device for achieving a specific function or purpose.

[0648] A "database" is an information management system that stores and searches registered information in an organized manner.

[0649] "Search" is the act of retrieving information that matches a condition from a database.

[0650] A "schedule" is a timetable showing the schedules of service providers and users.

[0651] "Reminders" is a feature that notifies you of specific actions or events.

[0652] "Remuneration" means the consideration received by a professional for services rendered.

[0653] A "log" is data that records the progress and history of a service.

[0654] "Notification" is the act of informing a user or expert of specific information.

[0655] "Reservation" means the act of setting and reserving a date and time for a service or lesson.

[0656] A "service" is a specific task or support provided by an expert to a user.

[0657] This invention is a system that efficiently matches users with experts and provides a variety of professional services. This system is composed of three main elements: a server, a terminal, and a user, and includes the following means:

[0658] 1. User and Professional Registration

[0659] First, the user and expert register their respective information using a device. User information includes name, email address, password, desired qualifications, and service conditions, while expert information includes name, email address, password, qualifications and skills held, years of experience, and pricing. The server receives this information, verifies it, and then stores it in a database. The device can be a standard smartphone or tablet.

[0660] 2. Matching Process

[0661] The user inputs the desired qualifications and service requirements from the terminal. The server uses this information to search for the most suitable specialist from its database and provides the search results to the user as a list. The user selects the desired specialist from the displayed list, and the server checks the specialist's schedule and reserves the service for the desired date and time.

[0662] 3. Lesson or service provision and progress management

[0663] As the reserved service date and time approaches, the server sends reminder notifications to the user and the expert. On the day of the service, the user and the expert perform the service using online platforms such as video calls or chat tools. The server records the progress in real time and saves the execution log in a database. Notifications are ensured by using SMS notification services such as the Twilio API.

[0664] 4. Reward Calculation and Payment

[0665] Once the service is completed, the server calculates the fee to the expert based on the saved execution log. The calculated fee is provided to the expert via the terminal, and once the expert confirms and approves it, the payment is processed. Payment is processed using electronic payment services such as Stripe and PayPal.

[0666] Specific examples

[0667] For example, if a user requests a chef who can provide healthy Japanese meals twice a week, the server searches the database for chefs who meet the criteria and provides the results to the user. The user then selects the most suitable chef and reserves delivery for meals every Monday and Thursday at 7 p.m. The system notifies the chef of the reservation information and also sends reminders, ensuring that the service is provided reliably. After the service is completed, the remuneration is automatically calculated and paid to the chef. This allows users to easily find the right expert and receive services efficiently.

[0668] Prompt Sentence Examples

[0669] "I would like to have healthy Japanese meals served twice a week. Please help me build a platform that can match chefs with food requests. Please provide an overview of the process for supply and demand analysis, ingredient sourcing, lesson implementation, and compensation calculation."

[0670] This system allows users to efficiently use various professional services, and experts can efficiently provide services and receive compensation. Specific infrastructure includes the use of the Django framework, notifications via the Twilio API, and electronic payments via Stripe and PayPal.

[0671] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0672] Step 1:

[0673] User and Expert Registration

[0674] Users and experts use terminals to enter information such as name, email address, password, desired services and qualifications, etc. The server receives this information, verifies its accuracy and completeness, and then stores it in a database.

[0675] Input: Registration information (username, email address, password, credentials and service details)

[0676] Data processing: Verify that the format and content of the entered data are correct

[0677] Output: Save to database (user information and expert information)

[0678] Specifically, we will use the Django framework to create API endpoints that provide user and expert registration forms and save them to a database.

[0679] Step 2:

[0680] User condition input

[0681] The user inputs the desired qualifications and conditions for the service from the terminal. For example, they input specific requirements such as "I would like to have healthy Japanese meals twice a week." The server receives the input conditions and stores them in a database.

[0682] Input: Desired conditions (specific service and qualification requirements)

[0683] Data processing: Verification of the form and content of the conditions

[0684] Output: Save to database (desired conditions)

[0685] Specifically, it creates an input form, sends data asynchronously using an AJAX request, and saves it in a database on the server side.

[0686] Step 3:

[0687] Expert Search

[0688] The server searches the database for the most suitable expert based on the criteria entered by the user. For example, it searches for "experts who are qualified in Japanese cuisine and can provide health-conscious dishes." The search results are provided to the user in a list format.

[0689] Input: Desired conditions (match with conditions in the database)

[0690] Data operations: Applying criteria-based filtering and search algorithms

[0691] Output: Search result list (list of relevant experts)

[0692] Specifically, it uses Django ORM to execute a database query to find experts that meet the criteria.

[0693] Step 4:

[0694] Check schedule and make reservations

[0695] The user selects the desired expert from the displayed search result list, checks the expert's schedule, and makes a reservation. The server saves the reservation information in a database and updates the expert's schedule.

[0696] Input: ID of selected expert and desired date and time

[0697] Data processing: Check and update schedule availability

[0698] Output: Save reservation information (update database)

[0699] Specifically, it uses a Python script to update the schedule information in the database and notify the user that the reservation has been confirmed.

[0700] Step 5:

[0701] Reservation notifications and reminders

[0702] The server notifies the expert and user of the appointment information and also sends reminder notifications when the appointment time approaches, using the Twilio API to send SMS.

[0703] Input: Reservation information

[0704] Data processing: Converting to SMS format using Twilio API

[0705] Output: Send notification message (SMS)

[0706] Specifically, it uses the Twilio library and uses API keys to send appointment information and reminders to experts and users via SMS.

[0707] Step 6:

[0708] Service progress management and logging

[0709] On the day of the service delivery, the user and the expert communicate via video call or chat. The server manages the progress of the service in real time and records it as a log.

[0710] Input: Service progress information (start time, end time, other meter data)

[0711] Data calculation: Real-time update and recording of progress information

[0712] Output: Save progress log (database)

[0713] Specifically, we will build a dashboard that displays progress information along with real-time database updates.

[0714] Step 7:

[0715] Reward calculation and payment

[0716] Once the service is completed, the server calculates the fee to the expert based on the recorded log. After the calculation, a fee statement is sent to the expert and payment is made. Electronic payments are made using Stripe or PayPal.

[0717] Input: Service log (time, service content, etc.)

[0718] Data calculation: Applying reward calculation algorithms

[0719] Output: Compensation statement generation and payment execution

[0720] Specifically, it uses the Stripe API or PayPal API to automate fee calculation and payment to experts.

[0721] Prompt Sentence Examples

[0722] "I would like to have healthy Japanese meals served twice a week. Please help me build a platform that can match chefs with food requests. Please provide an overview of the process for supply and demand analysis, ingredient sourcing, lesson implementation, and compensation calculation."

[0723] 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.

[0724] This invention is a system that effectively matches users seeking qualifications with qualified experts, and provides optimal lessons by recognizing and considering the user's emotions. This system consists of three main elements: a server, a terminal, and a user, and an emotion engine is built into each processing step.

[0725] User and Expert Registration

[0726] First, users and experts who wish to use the system must register their information. Using a terminal, users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in the database.

[0727] Matching Process

[0728] The user inputs the qualifications they desire and sends them from their terminal. The emotion engine recognizes the user's emotions during this input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed with reserving the date and time of the lesson.

[0729] Conducting and logging lessons

[0730] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[0731] Adjusting lesson content

[0732] If the emotion engine detects stress or confusion in the user during a lesson, it will notify the expert and prompt them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[0733] Reward calculation and payment

[0734] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[0735] Specific examples

[0736] For example, if a user inputs a desire to "obtain an English proficiency test" and the emotion engine recognizes the user's lack of confidence, the server will search for and recommend experts with a particularly gentle teaching style. As a result, an expert named Mr. Tanaka is found and suggested to the user. The user selects Mr. Tanaka and books a lesson at 7 p.m. next Monday.

[0737] On the day of the lesson, the user and Professor Tanaka will start a video call via the selected online platform. The emotion engine will monitor the user's reactions during the lesson. For example, if the user shows signs of confusion, the server will relay this information to Professor Tanaka. Professor Tanaka will then adjust his / her teaching method, providing easier-to-understand explanations and adjusting the pace.

[0738] This system allows users to learn efficiently from experts who are best suited to their emotions and situations, and experts can receive appropriate compensation for the lessons they provide, making the qualification process more effective and comfortable.

[0739] The processing flow will be explained below.

[0740] Step 1: Enter your user information

[0741] Terminal: Presents a form where the user enters their name, email address, password, and desired credentials.

[0742] User: Enter the required information and click the submit button.

[0743] Terminal: Sends input information to the emotion engine and recognizes the user's emotions.

[0744] Step 2: Recognizing and transmitting emotions

[0745] Emotion engine: Analyzes user input and behavior to recognize emotions.

[0746] Device: Sends the recognized emotion information to the server.

[0747] Server: Receives input information and emotion information and performs verification.

[0748] Step 3: Verify and save the information

[0749] Server: Validate the received user information and sentiment information, check that all required fields are filled in, and that the email address format is correct.

[0750] Server: Save the verified information in the database.

[0751] Step 4: Enter expert information

[0752] Device: Displays a form where the expert can enter their name, email address, password, certifications, years of experience, and pricing information.

[0753] Expert: Enter the required information and click the submit button.

[0754] Terminal: Sends the entered information to the server.

[0755] Step 5: Verify and save expert information

[0756] Server: Validates received expert information and checks for accuracy.

[0757] Server: Save the verified information in the database.

[0758] Step 6: Enter criteria and search

[0759] Terminal: Displays a form for the user to enter the lesson conditions they desire (e.g. time slot, number of times per week, fee, etc.).

[0760] User: Enter the required information and click the submit button.

[0761] Terminal: Sends the input conditions and the results of the emotion engine to the server.

[0762] Server: Searches the database for the most suitable experts based on the input conditions and emotional information, and generates a list of search results.

[0763] Step 7: View and select search results

[0764] Server: Sends the search result list to the device.

[0765] Terminal: Display search results to the user in a list format.

[0766] User: Select the desired expert from the list of suggested experts.

[0767] Step 8: Book a lesson

[0768] Terminal: View the selected expert's schedule.

[0769] User: Select the desired lesson date and time and click the submit button.

[0770] Device: Send the selected date and time to the server.

[0771] Server: Stores lesson reservation information in a database and sends reservation notifications to experts.

[0772] Step 9: Reminders

[0773] Server: Send reminder notifications to users and experts before the lesson starts.

[0774] Step 10: Lesson implementation

[0775] User: Follow the reminder notification and launch the video calling platform to connect with the expert.

[0776] Experts: Launch a video chat platform during lesson time to connect with users.

[0777] Server: Monitors the progress of lessons and records progress and emotion logs.

[0778] Step 11: Adjust the lesson content

[0779] Emotion Engine: Analyzes user emotions in real time during lessons.

[0780] Server: Sends the user's emotional information to the expert, prompting the expert to adjust the content and pace of the lesson according to the user's emotions.

[0781] Step 12: Complete the lesson and calculate your rewards

[0782] Server: After the lesson is completed, calculate the reward for the expert based on the recorded log and emotion log.

[0783] Server: Generates reward statements and sends them to experts.

[0784] Terminal: Shows the expert a fee statement.

[0785] Step 13: Payment

[0786] Expert: Review and approve the fee statement.

[0787] Server: After receiving the expert's approval, the server executes the payment process.

[0788] Server: Notifies the expert that payment has been made.

[0789] Terminal: Shows the expert a notification that payment has been completed.

[0790] Example 2

[0791] 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."

[0792] Conventional qualification support systems provide lessons without considering the user's emotional state, which can lead to stress and reduced learning effectiveness. Furthermore, the calculation of compensation for experts is based solely on lesson logs, which means that the experts' efforts and the quality of the lessons are not properly evaluated.

[0793] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a means for inputting conditions related to the qualifications desired by the user; a means for registering information on qualified experts; a means for saving information on the registered experts in a database; a means for searching for the most suitable expert based on the conditions input by the user; a means for providing the search results to the user; a means for checking the schedule of the expert selected by the user and reserving a lesson; a means for notifying the expert of the lesson reservation information; a means for recognizing the user's emotions in real time and recording the results in a database; a means for managing the progress of the lesson and recording a performance log and an emotion log; a means for calculating the fee to the expert based on the recorded lesson log and emotion log; and a means for paying the fee to the expert. This enables the provision of an optimal lesson that takes the user's emotional state into consideration, and also enables the expert's efforts and the quality of the lesson to be appropriately evaluated.

[0794] "User" means an individual or organization that uses this system to obtain qualifications.

[0795] An "expert" is a person who holds a specific qualification and is able to provide guidance and advice based on that qualification.

[0796] An "emotion engine" is a software or hardware component that analyzes and recognizes a user's emotional state in real time based on their facial expressions, voice, etc.

[0797] The "database" is a system for centrally storing and managing data such as user and expert information, lesson logs, and emotion logs.

[0798] "Input conditions" refers to the conditions and requirements desired by the user regarding qualification acquisition.

[0799] A "lesson" is a teaching and educational session conducted by an expert for a user.

[0800] "Lesson Log" refers to a record of the content, progress and various interactions of a lesson.

[0801] An "emotion log" refers to a record of a user's emotional state.

[0802] "Remuneration Statement" means a document or data summarizing the details of the compensation paid to the Expert after the Lesson is completed.

[0803] "Reminder notification" refers to alerts and notifications that notify users and experts in advance of lesson dates and times, etc.

[0804] "Schedule" refers to a list of an Expert's available dates, times, and booked slots.

[0805] MODE FOR CARRYING OUT THE INVENTION

[0806] This invention is a system that effectively matches users seeking qualifications with qualified experts and provides optimal lessons taking into account the user's emotions. This system is composed of three main elements: a server, a terminal, and a user, and an emotion engine is built into each processing step.

[0807] User and Expert Registration

[0808] First, users and experts who wish to use the system must register their information using a terminal. Users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications held, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in a database.

[0809] Matching Process

[0810] The user inputs the qualifications they desire and sends them from their terminal. At this time, the emotion engine recognizes the user's emotions during the input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and provides a procedure that allows the user to reserve a date and time for a lesson.

[0811] Conducting and logging lessons

[0812] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[0813] Adjusting lesson content

[0814] If the emotion engine detects stress or confusion in the user during a lesson, the server notifies the expert and prompts them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[0815] Reward calculation and payment

[0816] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[0817] Specific examples

[0818] For example, if a user inputs their desire to "obtain an English proficiency test" and the emotion engine recognizes the user's lack of confidence, the server will search for and recommend experts with a particularly gentle teaching style. As a result, a specific expert is found and suggested to the user. The user selects that expert and books a lesson for the following Monday at 7 p.m. On the day of the lesson, the user and the expert will initiate a video call on the specified online platform. The emotion engine will monitor the user's reactions during the lesson, and if the user shows signs of confusion, for example, the server will relay that information to the expert. The expert will then adjust their teaching method, providing easier-to-understand explanations and a more pacing-oriented lesson.

[0819] Prompt Sentence Examples

[0820] An example of a prompt that allows the user to enter their wishes into the system is:

[0821] If a user enters their desire to "obtain an English proficiency test" but feels unsure, please recommend the most suitable expert.

[0822] This system allows users to learn efficiently from experts who are best suited to their emotions and situations, and experts can receive appropriate compensation for the lessons they provide, making the qualification process more effective and comfortable.

[0823] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0824] Step 1:

[0825] User and Expert Registration

[0826] The user uses the terminal to enter information such as their name, email address, password, and desired qualification. Specifically, they enter their name "Yamada Taro," their email address "taro@example.com," their password "password123," and their desired qualification "TOEIC750."

[0827] The terminal transmits these input data to the server.

[0828] The server validates the data it receives, checking, for example, the format of the email address and the strength of the password, and if the data is correct, stores it in the database.

[0829] Similarly, experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. Specifically, they enter their name "Sato Hanako," email address "hanako@example.com," password "securePass456," qualifications "TOEIC 950," years of experience "5 years," and pricing "5,000 yen per hour."

[0830] The terminal sends the input data to the server, which verifies the received data and, if correct, stores it in a database.

[0831] Step 2:

[0832] User qualifications and emotion recognition

[0833] The user inputs the desired qualifications (e.g., "TOEIC 750") into the terminal.

[0834] The terminal transmits the input data to the server.

[0835] The emotion engine monitors the user's input process and analyzes facial expressions and tone of voice to recognize emotional states (e.g., "unconfident").

[0836] The server stores the user's conditions and the emotion engine results in a database.

[0837] Step 3:

[0838] Providing matching results and reservations

[0839] The server searches the database for the most suitable expert based on the user's input criteria and emotional state, using a search algorithm that takes into account the expert's teaching style and qualifications.

[0840] The server sends the search results (e.g., "experts with a gentle teaching style") in the form of a list to the user's terminal.

[0841] The user selects the desired expert from the list.

[0842] The server obtains the schedule information of the experts and presents the user with available reservation dates and times.

[0843] The user selects the desired date and time and confirms the reservation through the terminal.

[0844] The server stores the reservation data in a database and notifies the specialist.

[0845] Step 4:

[0846] Lesson implementation and logging

[0847] The server sends reminders to the user and the expert for the day of the lesson.

[0848] The user and the expert initiate a video call on a designated online platform (e.g., Zoom).

[0849] The emotion engine analyzes the user's facial expressions and voice during the lesson to recognize their emotional state in real time, for example, analyzing a "confused expression."

[0850] The server records the progress of the lesson, the dialogue content, and the emotion log in a database.

[0851] Step 5:

[0852] Adjusting lesson content

[0853] When the emotion engine recognizes a change in the user's emotion (e.g., "high stress"), it sends that information to the server.

[0854] The server notifies the expert of the information on the emotional changes.

[0855] The expert will use the information to adjust the content and pace of the lesson, for example by providing more detailed explanations or slowing down the pace.

[0856] Step 6:

[0857] Reward calculation and payment

[0858] After the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. Specifically, it calculates the lesson time and evaluation items based on a formula.

[0859] The server sends the calculated remuneration details to the expert's terminal.

[0860] The expert checks and approves the compensation details on the terminal.

[0861] The server processes the approved payment by transferring it to the expert's bank account.

[0862] These steps result in a system that takes into account the user's emotional state, provides optimal lessons, and appropriately rewards the experts.

[0863] (Application example 2)

[0864] 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."

[0865] Conventional qualification support systems have the problem that lessons proceed without taking into account the user's emotions or current skill state, which makes it difficult to alleviate the stress and confusion felt by users. Furthermore, when it comes to improving the skills of employees in a company's factory, there is a lack of a way to grasp the emotional state of each employee in real time and provide optimal guidance. This makes effective learning difficult.

[0866] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input conditions related to desired qualifications, means for registering information on qualified experts, means for saving information on registered experts in a database, means for searching for the most suitable expert based on the conditions input by the user, means for providing the search results to the user, means for checking the schedule of the expert selected by the user and reserving a lesson, means for notifying the expert of the lesson reservation information, means for managing the progress of the lesson and recording a lesson log, means for calculating the fee to the expert based on the recorded lesson log, means for paying the fee to the expert, and means for recognizing the user's emotions and adjusting the lesson content in real time. This makes it possible to provide effective lessons that take the user's emotional state into consideration.

[0867] The "means for users to input conditions related to the qualifications they desire" is a function that allows users who wish to obtain qualifications to input their desired qualifications and related conditions into the system.

[0868] The "means for registering information on qualified professionals" is a function that allows professionals who hold specific qualifications to register their information in the system.

[0869] The "means for storing information on registered experts in a database" is a function that uses a database to stably store and manage information on registered experts.

[0870] "Means for searching for the most suitable expert based on the conditions entered by the user" refers to a function that has algorithms and logic for searching for the most suitable expert from within the database based on the conditions entered by the user.

[0871] The "means for providing search results to the user" is a function for displaying or providing information on the searched experts to the user.

[0872] The "means for checking the schedule of the expert selected by the user and reserving a lesson" is a function for checking the schedule of the expert selected by the user and reserving a lesson based on that schedule.

[0873] The "means for notifying the expert of lesson reservation information" is a function for notifying the expert of details of the reserved lesson.

[0874] The "means for managing the progress of a lesson and recording an implementation log" is a function for managing the progress of a lesson in progress and recording the details as a log.

[0875] The "means for calculating the remuneration to the expert based on the recorded lesson log" is a function for calculating the remuneration to the expert based on the lesson implementation log.

[0876] The "means for paying the remuneration to the expert" is a function for actually paying the calculated remuneration to the expert.

[0877] "Means for recognizing the user's emotions and adjusting the lesson content in real time" is a function that uses emotion recognition technology to recognize the user's emotions and adjusts the optimal instruction in real time according to the user's situation during the lesson.

[0878] This invention is a system that effectively matches users seeking qualifications with qualified professionals, and provides optimal lessons by recognizing and considering the user's emotions. It can also be applied to improving the skills of employees in factories. This system consists of three main elements: a server, a terminal, and a user.

[0879] User and Expert Registration

[0880] First, users and experts who wish to use the system must register their information. Using a terminal, users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. The server receives this information, verifies it, and then stores it in a database.

[0881] Matching Process

[0882] The user inputs the qualifications they desire and sends them from their terminal. The emotion engine recognizes the user's emotions during this input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the search results to the user as a list. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed to reserve a date and time for a lesson.

[0883] Conducting and logging lessons

[0884] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[0885] Adjusting lesson content

[0886] If the emotion engine detects stress or confusion in the user during a lesson, it will notify the expert and prompt them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[0887] Reward calculation and payment

[0888] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[0889] Factory Applications

[0890] A "skill improvement support robot" can be installed for factory workers. This robot will monitor each employee's work skills and emotional state in real time and provide appropriate training and guidance. It will capture facial images of employees via webcam and analyze their emotions using an emotion recognition model. Based on the employee's emotional data, the robot will display appropriate messages and adjust the work content and guidance methods.

[0891] For example, if employee A shows the emotion of "anger," the robot can display the message, "A, you seem to be feeling angry right now. Please stay calm and continue working." If employee A shows the emotion of "joy," the robot can display the message, "A, you're doing well. Keep going and do your best."

[0892] Specific prompt examples

[0893] "Build a response system that takes employees' facial and text data as input, recognizes their emotions, and suggests optimal coaching methods."

[0894] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0895] Step 1:

[0896] User and Expert Registration

[0897] Input: The user enters their name, email address, password, and desired qualification information into the terminal. Similarly, the expert enters their name, email address, password, qualifications, years of experience, pricing, and other information into the terminal.

[0898] Processing: The device sends this information to the server, which then verifies the received information, checking the authenticity of the credentials, the format of the email address, etc.

[0899] Output: After the verification is complete, the server saves the information in the database. The user and the expert receive a notification that their registration is complete.

[0900] Step 2:

[0901] User condition input and emotion recognition

[0902] Input: The user inputs the desired qualifications and conditions into the terminal. At this time, the user's facial expression is captured through the terminal's camera and emotion recognition is performed.

[0903] Processing: The device sends the user's input conditions and emotion recognition data to the server. Emotion recognition is performed using an emotion engine that analyzes the user's facial image.

[0904] Output: The server receives the user's input conditions and emotional data. Based on the emotional data, the server outputs the user's state (e.g., stress, confusion, etc.) as a numerical value or label.

[0905] Step 3:

[0906] Expert Matching

[0907] Input: The server searches the database based on the user's input conditions and emotion data.

[0908] Processing: Using a search algorithm, we list the experts best suited to the user's situation. The search takes into account not only the user's conditions but also their emotional state.

[0909] Output: The list of experts obtained as a search result is provided to the user. The search results are displayed on the user's terminal.

[0910] Step 4:

[0911] Book a lesson

[0912] Input: The user selects the desired expert from the list of experts presented and specifies the date and time of the lesson on the terminal.

[0913] Processing: The terminal sends the reservation information to the server, which checks the expert's schedule and confirms the reservation.

[0914] Output: The reservation confirmation information is notified to the expert and the user. The expert's schedule is updated.

[0915] Step 5:

[0916] Conducting and recording lessons

[0917] Input: On the day of the lesson, the user and the expert initiate a video call on the designated online platform.

[0918] Processing: The emotion engine monitors the user's emotions during the lesson in real time and sends the data to the server.

[0919] Output: The server records the lesson progress and execution log and saves it together with the emotion log. The real-time emotion data is notified to the expert.

[0920] Step 6:

[0921] Adjusting lesson content

[0922] Input: If the emotion engine detects that the user is stressed or confused, it sends that information to the server.

[0923] Processing: The server notifies the expert of the user's emotional state and prompts them to adjust their teaching methods.

[0924] Output: The expert adjusts the teaching method and content appropriately based on the user's emotional state.

[0925] Step 7:

[0926] Reward calculation and payment

[0927] Input: After the lesson is completed, the server calculates the reward for the expert based on the recorded lesson log and emotion log.

[0928] Processing: A calculation algorithm is used to calculate the required fee. The expert is provided with a fee breakdown.

[0929] Output: The expert confirms and approves the reward amount. The server executes the reward payment process and the reward is paid to the expert.

[0930] In this way, a system that takes into account the user's emotions is realized through specific processing steps to provide an effective lesson environment.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] [Third embodiment]

[0935] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0936] 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.

[0937] 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).

[0938] 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.

[0939] 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.

[0940] 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).

[0941] 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. 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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."

[0947] This invention is a system for efficiently matching users with qualified experts and providing effective lessons. This system consists of three main elements: a server, a terminal, and a user, and is operated in the following steps:

[0948] User and Expert Registration

[0949] First, users and experts who wish to use the system must register their respective information. Using a terminal, users enter information such as their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications held, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in a database.

[0950] Matching Process

[0951] The user inputs the desired qualifications and sends them via the terminal. The server searches the database for the most suitable experts based on the input information and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed to reserve a date and time for a lesson.

[0952] Conducting and logging lessons

[0953] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson using an online platform such as a video call. The server records the progress of the lesson and saves the progress log in a database.

[0954] Reward calculation and payment

[0955] When the lesson is completed, the server calculates the reward for the expert based on the lesson log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the process of paying the reward.

[0956] Specific examples

[0957] For example, a user may input a desire to "obtain an English Proficiency Test qualification" and specify the condition for lessons as "I would like to study twice a week at night." The server searches the database for "experts who hold the English Proficiency Test Grade 1 and are available at night." As a result, an expert named Mr. Suzuki is found and suggested to the user. The user selects Mr. Suzuki and books a lesson for the following Monday at 7 p.m.

[0958] On the day of the lesson, the user and Suzuki sensei will actively conduct the lesson via video call on the designated online platform. The server records the situation and creates a lesson log. After the lesson is completed, the reward is calculated based on this log and paid to Suzuki sensei.

[0959] This system allows users to easily find suitable experts, and experts can efficiently provide lessons and receive compensation, making the qualification process go more smoothly.

[0960] The processing flow will be explained below.

[0961] Step 1: User Registration

[0962] Terminal: Presents a form where the user enters their name, email address, password, and desired credentials.

[0963] User: Enter the required information and click the submit button.

[0964] Terminal: Sends the entered information to the server.

[0965] Step 2: Professional Registration

[0966] Device: Displays a form where the expert can enter their name, email address, password, certifications, years of experience, and pricing information.

[0967] Expert: Enter the required information and click the submit button.

[0968] Terminal: Sends the entered information to the server.

[0969] Step 3: Verify and save the information

[0970] Server: Validates the received user and expert information, ensuring all required fields are filled in and that the email address is formatted correctly.

[0971] Server: Stores the verified information in a database.

[0972] Step 4: Enter criteria and search

[0973] Terminal: Displays a form for the user to enter the lesson conditions they desire (e.g. time slot, number of times per week, fee, etc.).

[0974] User: Enter the required information and click the submit button.

[0975] Terminal: Sends the entered conditions to the server.

[0976] Server: Based on the input criteria, it searches the database for the most suitable experts and generates a list of search results.

[0977] Step 5: View and select search results

[0978] Server: Sends the search result list to the device.

[0979] Terminal: Display search results to the user in a list format.

[0980] User: Select the desired expert from the list of suggested experts.

[0981] Step 6: Book a lesson

[0982] Terminal: View the selected expert's schedule.

[0983] User: Select the desired lesson date and time and click the submit button.

[0984] Device: Send the selected date and time to the server.

[0985] Server: Stores lesson reservation information in a database and sends reservation notifications to experts.

[0986] Step 7: Reminders

[0987] Server: Send reminder notifications to users and experts before the lesson starts.

[0988] Step 8: Lesson implementation

[0989] User: Follow the reminder notification and launch the video calling platform to connect with the expert.

[0990] Experts: Launch a video chat platform during lesson time to connect with users.

[0991] Server: Monitors the progress of lessons and records progress logs.

[0992] Step 9: Complete the lesson and calculate your rewards

[0993] Server: After the lesson is completed, calculate the reward for the expert based on the recorded log.

[0994] Server: Generates reward statements and sends them to experts.

[0995] Terminal: Shows the expert a fee statement.

[0996] Step 10: Payment

[0997] Expert: Review and approve the fee statement.

[0998] Server: After receiving the expert's approval, the server executes the payment process.

[0999] Server: Notifies the expert that payment has been made.

[1000] Terminal: Shows the expert a notification that payment has been completed.

[1001] Example 1

[1002] 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."

[1003] In conventional qualification support systems, it was difficult to match users with the appropriate experts, and the process of conducting lessons and paying fees was cumbersome. This resulted in problems such as users being unable to take lessons efficiently and experts being unable to provide their services effectively. In addition, lesson progress management and fee calculation and payment were often done manually, which could be a burden.

[1004] 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.

[1005] In this invention, the server includes: means for a user to input conditions related to desired qualifications; means for registering information on qualified experts; means for saving information on registered experts in a database; means for searching for the most suitable expert based on the conditions input by the user; means for providing the search results to the user; means for the user to check the schedule of the expert selected by the user and book a lesson; means for notifying the expert of the lesson reservation information; means for the user and the expert to conduct the lesson on the designated online platform on the day of the lesson reservation; means for managing the progress of the lesson and recording the implementation log; means for calculating the fee to the expert based on the recorded lesson log; and means for paying the fee to the expert. This enables effective matching of users and experts, efficient lesson implementation, progress management, and fee calculation and payment to be automated, thereby reducing the overall workload.

[1006] "User" means an individual or organization who wishes to obtain a qualification and uses the system to take lessons.

[1007] An "expert" is an individual or organization that holds a specific qualification and has the knowledge and experience related to that qualification.

[1008] "Conditions" refers to the requirements and schedule for obtaining the qualifications the user desires, as well as preferences such as the frequency and time of lessons.

[1009] "Database" refers to a system that systematically stores and manages data such as user and expert registration information, lesson progress, and remuneration calculations.

[1010] "Schedule" refers to a list or timetable of dates and times when an Expert is available to conduct Lessons.

[1011] "Online Platform" means an internet-based service that provides video calling and messaging capabilities to enable users and experts to communicate in real time.

[1012] "Progress" is information that indicates the implementation status and progress of the lesson.

[1013] An "implementation log" is data that records details such as the start time, end time, and content of the lesson.

[1014] "Remuneration" means the amount of money a Professional receives in exchange for providing a Lesson.

[1015] "Notifications" are messages or alerts that communicate important information to users and experts, such as lesson booking information and reminders.

[1016] This invention is a system for efficiently matching users with qualified experts and providing effective lessons. This system consists of three main elements: a server, a terminal, and a user. Specifically, users input their desired qualifications and register information about qualified experts.

[1017] When users and experts enter information such as their name, email address, password, desired qualifications, and current qualifications from their respective devices, the server receives this information. At this time, the server verifies the format, such as the validity of the email address and the strength of the password. The necessary hardware includes a database server (using MySQL, for example) and a front-end system for displaying the information (web technologies such as HTML and JavaScript).

[1018] Once verified, the information is stored in a database. When a user enters the desired qualifications and conditions, for example, "I want to study for the English Proficiency Test twice a week at night," the server uses this information to search the database for the most suitable expert. Specific software could use Python or SQL queries to implement the search algorithm. For example, let's say you want to search for "experts who hold the English Proficiency Test Grade 1 and are available at night."

[1019] The search results are provided to the user as a list, and the user selects the desired expert from the list. Based on this selection, the server checks the expert's schedule and executes the procedure for the user to reserve the lesson date and time. Once the reservation is complete, the server notifies the expert of the reservation information. Notifications are sent using an email system or push notification service (such as SMTP or Firebase Cloud Messaging).

[1020] On the day of the lesson, the user and the expert will have a video call using an online platform such as Zoom or Google Meet. The server records the progress of the lesson in real time and saves the implementation log in a database. This progress management is done using log collection services and APIs (e.g., Amazon CloudWatch and Custom API).

[1021] Once the lesson is completed, the server calculates the expert's reward based on the lesson log. The reward calculation logic can be implemented using Python or JavaScript, and the calculation results are processed by a dedicated reward calculation system. The calculated reward details are provided to the expert via the terminal, and after the expert has confirmed and approved them, the server executes the reward payment process. Payments can be made by bank transfer or via electronic money payment systems (such as PayPal or Stripe).

[1022] As a concrete example, if a user inputs their desire to "obtain an Eiken qualification" and specifies as a condition "I want to study twice a week at night," the server searches the database for "experts who hold the Eiken Grade 1 qualification and are available at night," and displays a list of experts named "Mr. Suzuki" as search results. The user selects Mr. Suzuki and books a lesson at 7 p.m. on the following Monday. On the day of the lesson, the user and Mr. Suzuki conduct the lesson via video call via Zoom, and the server records the situation. After the lesson is over, the server calculates Mr. Suzuki's remuneration based on the log, and the remuneration is paid to the expert.

[1023] Prompt Sentence Examples

[1024] "Please explain the steps for a user who has a Grade 1 English Proficiency Test and is looking for an expert who is available for evening lessons to book a lesson for next Monday."

[1025] This system will enable users to easily find suitable experts, and experts will be able to provide lessons efficiently and receive compensation, which is expected to make the qualification process go more smoothly.

[1026] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1027] Step 1:

[1028] Enter information

[1029] The user uses the terminal to enter information such as their name, email address, password, and desired qualifications.

[1030] The professional also uses the device to enter information such as their name, email address, password, qualifications, years of experience, and pricing.

[1031] Input: User information (name, email address, password, desired qualifications), expert information (name, email address, password, qualifications held, years of experience, pricing)

[1032] Output: The entered information is sent to the server

[1033] Specific operation: A user enters the name "Taro Tanaka" and enters the email address "tanaka@example.com." An expert enters the name "Ichiro Suzuki" and enters the email address "suzuki@example.com."

[1034] Step 2:

[1035] Receiving and verifying information

[1036] A server receives information sent by users and experts.

[1037] The server validates the format and content of the information (e.g., whether an email address is formatted correctly or whether a password meets a certain level of complexity).

[1038] Input: User information, expert information

[1039] Output: Verification results

[1040] What happens: The server verifies that "tanaka@example.com" is a valid email address. It checks the password complexity (e.g., whether it is at least 8 characters long and contains letters and numbers).

[1041] Step 3:

[1042] Information storage

[1043] The server saves the verified information in a database, such as MySQL.

[1044] Input: Verified user information, expert information

[1045] Output: Saved information

[1046] Specific operation: The server saves Taro Tanaka's information in the "user_info" table and Ichiro Suzuki's information in the "expert_info" table.

[1047] Step 4:

[1048] Enter conditions and search

[1049] The user enters the desired qualifications and conditions (e.g., two nights a week) on the terminal and sends them to the server.

[1050] The server searches the database for the most suitable expert based on the criteria.

[1051] Input: User conditions (qualifications, lesson frequency, time zone)

[1052] Output: A list of the best experts

[1053] Specific operation: The user enters the criteria "English Proficiency Test, 2 times a week at night." The server searches the database for "experts who hold the English Proficiency Test Level 1 and are available at night" and finds "Ichiro Suzuki."

[1054] Step 5:

[1055] Providing search results and selecting experts

[1056] The server provides the search results as a list to the user's terminal.

[1057] The user selects the desired expert from the list.

[1058] Input: list of search results

[1059] Output: Selected experts

[1060] Specific operation: The server generates a list of information about "Ichiro Suzuki" and provides it to the user. The user selects Ichiro Suzuki.

[1061] Step 6:

[1062] Reservations and schedule confirmation

[1063] The server checks the schedule of the selected expert and assists the user in reserving a date and time for the lesson.

[1064] Input: Selected expert, user's desired date and time

[1065] Output: Reservation information

[1066] Specific behavior: A user books a lesson for next Monday at 7pm. The server checks Suzuki Ichiro's schedule and confirms the reservation.

[1067] Step 7:

[1068] notification

[1069] The server notifies the specialist of the reservation information.

[1070] Input: Reservation information

[1071] Output: Notification message

[1072] Specific operation: The server sends a lesson reservation notification to Suzuki Ichiro's email address.

[1073] Step 8:

[1074] Lesson implementation

[1075] The user and the expert will conduct the lesson at the appointed date and time using an online platform (Zoom or Google Meet).

[1076] Input: reservation date and time, online platform link

[1077] Output: Lesson implementation status

[1078] Specific operation: The user and expert log in to Zoom and start a video call.

[1079] Step 9:

[1080] Lesson Recording

[1081] The server records the progress of the lesson in real time and stores the lesson log in a database.

[1082] Input: lesson start time, end time, progress

[1083] Output: Lesson log

[1084] Specific operation: The server records the start time, end time, and main topic of the lesson and stores them in a database.

[1085] Step 10:

[1086] Reward calculation and payment

[1087] The server calculates the reward for the expert based on the lesson log.

[1088] The server provides the reward details to the expert, and after the expert approves it, the reward is paid.

[1089] Input: Lesson Log

[1090] Output: Remuneration details, payment processing

[1091] Specific operation: The server calculates the reward as "Suzuki Ichiro: 3,000 yen / hour x 2 hours = 6,000 yen" and provides the reward details to Suzuki Ichiro. After Suzuki Ichiro approves it, the server executes the process to transfer 6,000 yen to his bank account.

[1092] (Application example 1)

[1093] 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."

[1094] Conventional user-expert matching systems are limited to providing specific professional services, such as lessons for qualifications. This makes it difficult to respond to demand for food delivery and other professional services. Furthermore, the lack of reservation information notifications and reminder functions creates issues with the efficiency and reliability of service provision. Therefore, the present invention aims to solve these issues and provide a system that efficiently provides a wider variety of professional services.

[1095] 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.

[1096] In this invention, the server includes: a means for inputting conditions related to the qualifications desired by the user; a means for registering information on qualified experts; a means for storing information on the registered experts in a database; a means for searching for the most suitable expert based on the user's input conditions; a means for providing the search results to the user; a means for checking the schedule of the expert selected by the user and reserving a lesson; a means for notifying the expert of the lesson reservation information; a means for managing the progress of the lesson and recording a lesson log; a means for calculating the fee to the expert based on the recorded lesson log; a means for paying the fee to the expert; a means for inputting conditions related to the service desired by the user; a means for registering information on the expert who will provide the service; a means for searching for the most suitable expert based on the user's desired conditions and reserving a schedule for the provided service; a means for notifying the expert of the reservation information and sending a reminder. This allows users to easily match with a wider variety of professional service providers and efficiently receive services that meet their specific conditions. Furthermore, the reminder function improves the reliability and certainty of service provision.

[1097] A "user" is an individual or organization that wishes to use the system and enters their requirements.

[1098] An "expert" is an individual or organization that has a particular qualification or skill and is registered.

[1099] A "qualification" is a formal certification that attests to a particular skill or knowledge.

[1100] "Conditions" refers to information such as specific requirements regarding the service or qualifications desired by the user, and the desired date and time.

[1101] A "means" is a method or device for achieving a specific function or purpose.

[1102] A "database" is an information management system that stores and searches registered information in an organized manner.

[1103] "Search" is the act of retrieving information that matches a condition from a database.

[1104] A "schedule" is a timetable showing the schedules of service providers and users.

[1105] "Reminders" is a feature that notifies you of specific actions or events.

[1106] "Remuneration" means the consideration received by a professional for services rendered.

[1107] A "log" is data that records the progress and history of a service.

[1108] "Notification" is the act of informing a user or expert of specific information.

[1109] "Reservation" means the act of setting and reserving a date and time for a service or lesson.

[1110] A "service" is a specific task or support provided by an expert to a user.

[1111] This invention is a system that efficiently matches users with experts and provides a variety of professional services. This system is composed of three main elements: a server, a terminal, and a user, and includes the following means:

[1112] 1. User and Professional Registration

[1113] First, the user and expert register their respective information using a device. User information includes name, email address, password, desired qualifications, and service conditions, while expert information includes name, email address, password, qualifications and skills held, years of experience, and pricing. The server receives this information, verifies it, and then stores it in a database. The device can be a standard smartphone or tablet.

[1114] 2. Matching Process

[1115] The user inputs the desired qualifications and service requirements from the terminal. The server uses this information to search for the most suitable specialist from its database and provides the search results to the user as a list. The user selects the desired specialist from the displayed list, and the server checks the specialist's schedule and reserves the service for the desired date and time.

[1116] 3. Lesson or service provision and progress management

[1117] As the reserved service date and time approaches, the server sends reminder notifications to the user and the expert. On the day of the service, the user and the expert perform the service using online platforms such as video calls or chat tools. The server records the progress in real time and saves the execution log in a database. Notifications are ensured by using SMS notification services such as the Twilio API.

[1118] 4. Reward Calculation and Payment

[1119] Once the service is completed, the server calculates the fee to the expert based on the saved execution log. The calculated fee is provided to the expert via the terminal, and once the expert confirms and approves it, the payment is processed. Payment is processed using electronic payment services such as Stripe and PayPal.

[1120] Specific examples

[1121] For example, if a user requests a chef who can provide healthy Japanese meals twice a week, the server searches the database for chefs who meet the criteria and provides the results to the user. The user then selects the most suitable chef and reserves delivery for meals every Monday and Thursday at 7 p.m. The system notifies the chef of the reservation information and also sends reminders, ensuring that the service is provided reliably. After the service is completed, the remuneration is automatically calculated and paid to the chef. This allows users to easily find the right expert and receive services efficiently.

[1122] Prompt Sentence Examples

[1123] "I would like to have healthy Japanese meals served twice a week. Please help me build a platform that can match chefs with food requests. Please provide an overview of the process for supply and demand analysis, ingredient sourcing, lesson implementation, and compensation calculation."

[1124] This system allows users to efficiently use various professional services, and experts can efficiently provide services and receive compensation. Specific infrastructure includes the use of the Django framework, notifications via the Twilio API, and electronic payments via Stripe and PayPal.

[1125] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1126] Step 1:

[1127] User and Expert Registration

[1128] Users and experts use terminals to enter information such as name, email address, password, desired services and qualifications, etc. The server receives this information, verifies its accuracy and completeness, and then stores it in a database.

[1129] Input: Registration information (username, email address, password, credentials and service details)

[1130] Data processing: Verify that the format and content of the entered data are correct

[1131] Output: Save to database (user information and expert information)

[1132] Specifically, we will use the Django framework to create API endpoints that provide user and expert registration forms and save them to a database.

[1133] Step 2:

[1134] User condition input

[1135] The user inputs the desired qualifications and conditions for the service from the terminal. For example, they input specific requirements such as "I would like to have healthy Japanese meals twice a week." The server receives the input conditions and stores them in a database.

[1136] Input: Desired conditions (specific service and qualification requirements)

[1137] Data processing: Verification of the form and content of the conditions

[1138] Output: Save to database (desired conditions)

[1139] Specifically, it creates an input form, sends data asynchronously using an AJAX request, and saves it in a database on the server side.

[1140] Step 3:

[1141] Expert Search

[1142] The server searches the database for the most suitable expert based on the criteria entered by the user. For example, it searches for "experts who are qualified in Japanese cuisine and can provide health-conscious dishes." The search results are provided to the user in a list format.

[1143] Input: Desired conditions (match with conditions in the database)

[1144] Data operations: Applying criteria-based filtering and search algorithms

[1145] Output: Search result list (list of relevant experts)

[1146] Specifically, it uses Django ORM to execute a database query to find experts that meet the criteria.

[1147] Step 4:

[1148] Check schedule and make reservations

[1149] The user selects the desired expert from the displayed search result list, checks the expert's schedule, and makes a reservation. The server saves the reservation information in a database and updates the expert's schedule.

[1150] Input: ID of selected expert and desired date and time

[1151] Data processing: Check and update schedule availability

[1152] Output: Save reservation information (update database)

[1153] Specifically, it uses a Python script to update the schedule information in the database and notify the user that the reservation has been confirmed.

[1154] Step 5:

[1155] Reservation notifications and reminders

[1156] The server notifies the expert and user of the appointment information and also sends reminder notifications when the appointment time approaches, using the Twilio API to send SMS.

[1157] Input: Reservation information

[1158] Data processing: Converting to SMS format using Twilio API

[1159] Output: Send notification message (SMS)

[1160] Specifically, it uses the Twilio library and uses API keys to send appointment information and reminders to experts and users via SMS.

[1161] Step 6:

[1162] Service progress management and logging

[1163] On the day of the service delivery, the user and the expert communicate via video call or chat. The server manages the progress of the service in real time and records it as a log.

[1164] Input: Service progress information (start time, end time, other meter data)

[1165] Data calculation: Real-time update and recording of progress information

[1166] Output: Save progress log (database)

[1167] Specifically, we will build a dashboard that displays progress information along with real-time database updates.

[1168] Step 7:

[1169] Reward calculation and payment

[1170] Once the service is completed, the server calculates the fee to the expert based on the recorded log. After the calculation, a fee statement is sent to the expert and payment is made. Electronic payments are made using Stripe or PayPal.

[1171] Input: Service log (time, service content, etc.)

[1172] Data calculation: Applying reward calculation algorithms

[1173] Output: Compensation statement generation and payment execution

[1174] Specifically, it uses the Stripe API or PayPal API to automate fee calculation and payment to experts.

[1175] Prompt Sentence Examples

[1176] "I would like to have healthy Japanese meals served twice a week. Please help me build a platform that can match chefs with food requests. Please provide an overview of the process for supply and demand analysis, ingredient sourcing, lesson implementation, and compensation calculation."

[1177] 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.

[1178] This invention is a system that effectively matches users seeking qualifications with qualified experts, and provides optimal lessons by recognizing and considering the user's emotions. This system consists of three main elements: a server, a terminal, and a user, and an emotion engine is built into each processing step.

[1179] User and Expert Registration

[1180] First, users and experts who wish to use the system must register their information. Using a terminal, users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in the database.

[1181] Matching Process

[1182] The user inputs the qualifications they desire and sends them from their terminal. The emotion engine recognizes the user's emotions during this input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed with reserving the date and time of the lesson.

[1183] Conducting and logging lessons

[1184] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[1185] Adjusting lesson content

[1186] If the emotion engine detects stress or confusion in the user during a lesson, it will notify the expert and prompt them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[1187] Reward calculation and payment

[1188] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[1189] Specific examples

[1190] For example, if a user inputs a desire to "obtain an English proficiency test" and the emotion engine recognizes the user's lack of confidence, the server will search for and recommend experts with a particularly gentle teaching style. As a result, an expert named Mr. Tanaka is found and suggested to the user. The user selects Mr. Tanaka and books a lesson at 7 p.m. next Monday.

[1191] On the day of the lesson, the user and Professor Tanaka will start a video call via the selected online platform. The emotion engine will monitor the user's reactions during the lesson. For example, if the user shows signs of confusion, the server will relay this information to Professor Tanaka. Professor Tanaka will then adjust his / her teaching method, providing easier-to-understand explanations and adjusting the pace.

[1192] This system allows users to learn efficiently from experts who are best suited to their emotions and situations, and experts can receive appropriate compensation for the lessons they provide, making the qualification process more effective and comfortable.

[1193] The processing flow will be explained below.

[1194] Step 1: Enter your user information

[1195] Terminal: Presents a form where the user enters their name, email address, password, and desired credentials.

[1196] User: Enter the required information and click the submit button.

[1197] Terminal: Sends input information to the emotion engine and recognizes the user's emotions.

[1198] Step 2: Recognizing and transmitting emotions

[1199] Emotion engine: Analyzes user input and behavior to recognize emotions.

[1200] Device: Sends the recognized emotion information to the server.

[1201] Server: Receives input information and emotion information and performs verification.

[1202] Step 3: Verify and save the information

[1203] Server: Validate the received user information and sentiment information, check that all required fields are filled in, and that the email address format is correct.

[1204] Server: Save the verified information in the database.

[1205] Step 4: Enter expert information

[1206] Device: Displays a form where the expert can enter their name, email address, password, certifications, years of experience, and pricing information.

[1207] Expert: Enter the required information and click the submit button.

[1208] Terminal: Sends the entered information to the server.

[1209] Step 5: Verify and save expert information

[1210] Server: Validates received expert information and checks for accuracy.

[1211] Server: Save the verified information in the database.

[1212] Step 6: Enter criteria and search

[1213] Terminal: Displays a form for the user to enter the lesson conditions they desire (e.g. time slot, number of times per week, fee, etc.).

[1214] User: Enter the required information and click the submit button.

[1215] Terminal: Sends the input conditions and the results of the emotion engine to the server.

[1216] Server: Searches the database for the most suitable experts based on the input conditions and emotional information, and generates a list of search results.

[1217] Step 7: View and select search results

[1218] Server: Sends the search result list to the device.

[1219] Terminal: Display search results to the user in a list format.

[1220] User: Select the desired expert from the list of suggested experts.

[1221] Step 8: Book a lesson

[1222] Terminal: View the selected expert's schedule.

[1223] User: Select the desired lesson date and time and click the submit button.

[1224] Device: Send the selected date and time to the server.

[1225] Server: Stores lesson reservation information in a database and sends reservation notifications to experts.

[1226] Step 9: Reminders

[1227] Server: Send reminder notifications to users and experts before the lesson starts.

[1228] Step 10: Lesson implementation

[1229] User: Follow the reminder notification and launch the video calling platform to connect with the expert.

[1230] Experts: Launch a video chat platform during lesson time to connect with users.

[1231] Server: Monitors the progress of lessons and records progress and emotion logs.

[1232] Step 11: Adjust the lesson content

[1233] Emotion Engine: Analyzes user emotions in real time during lessons.

[1234] Server: Sends the user's emotional information to the expert, prompting the expert to adjust the content and pace of the lesson according to the user's emotions.

[1235] Step 12: Complete the lesson and calculate your rewards

[1236] Server: After the lesson is completed, calculate the reward for the expert based on the recorded log and emotion log.

[1237] Server: Generates reward statements and sends them to experts.

[1238] Terminal: Shows the expert a fee statement.

[1239] Step 13: Payment

[1240] Expert: Review and approve the fee statement.

[1241] Server: After receiving the expert's approval, the server executes the payment process.

[1242] Server: Notifies the expert that payment has been made.

[1243] Terminal: Shows the expert a notification that payment has been completed.

[1244] Example 2

[1245] 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."

[1246] Conventional qualification support systems provide lessons without considering the user's emotional state, which can lead to stress and reduced learning effectiveness. Furthermore, the calculation of compensation for experts is based solely on lesson logs, which means that the experts' efforts and the quality of the lessons are not properly evaluated.

[1247] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a means for inputting conditions related to the qualifications desired by the user; a means for registering information on qualified experts; a means for saving information on the registered experts in a database; a means for searching for the most suitable expert based on the conditions input by the user; a means for providing the search results to the user; a means for checking the schedule of the expert selected by the user and reserving a lesson; a means for notifying the expert of the lesson reservation information; a means for recognizing the user's emotions in real time and recording the results in a database; a means for managing the progress of the lesson and recording a performance log and an emotion log; a means for calculating the fee to the expert based on the recorded lesson log and emotion log; and a means for paying the fee to the expert. This enables the provision of an optimal lesson that takes the user's emotional state into consideration, and also enables the expert's efforts and the quality of the lesson to be appropriately evaluated.

[1248] "User" means an individual or organization that uses this system to obtain qualifications.

[1249] An "expert" is a person who holds a specific qualification and is able to provide guidance and advice based on that qualification.

[1250] An "emotion engine" is a software or hardware component that analyzes and recognizes a user's emotional state in real time based on their facial expressions, voice, etc.

[1251] The "database" is a system for centrally storing and managing data such as user and expert information, lesson logs, and emotion logs.

[1252] "Input conditions" refers to the conditions and requirements desired by the user regarding qualification acquisition.

[1253] A "lesson" is a teaching and educational session conducted by an expert for a user.

[1254] "Lesson Log" refers to a record of the content, progress and various interactions of a lesson.

[1255] An "emotion log" refers to a record of a user's emotional state.

[1256] "Remuneration Statement" means a document or data summarizing the details of the compensation paid to the Expert after the Lesson is completed.

[1257] "Reminder notification" refers to alerts and notifications that notify users and experts in advance of lesson dates and times, etc.

[1258] "Schedule" refers to a list of an Expert's available dates, times, and booked slots.

[1259] MODE FOR CARRYING OUT THE INVENTION

[1260] This invention is a system that effectively matches users seeking qualifications with qualified experts and provides optimal lessons taking into account the user's emotions. This system is composed of three main elements: a server, a terminal, and a user, and an emotion engine is built into each processing step.

[1261] User and Expert Registration

[1262] First, users and experts who wish to use the system must register their information using a terminal. Users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications held, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in a database.

[1263] Matching Process

[1264] The user inputs the qualifications they desire and sends them from their terminal. At this time, the emotion engine recognizes the user's emotions during the input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and provides a procedure that allows the user to reserve a date and time for a lesson.

[1265] Conducting and logging lessons

[1266] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[1267] Adjusting lesson content

[1268] If the emotion engine detects stress or confusion in the user during a lesson, the server notifies the expert and prompts them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[1269] Reward calculation and payment

[1270] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[1271] Specific examples

[1272] For example, if a user inputs their desire to "obtain an English proficiency test" and the emotion engine recognizes the user's lack of confidence, the server will search for and recommend experts with a particularly gentle teaching style. As a result, a specific expert is found and suggested to the user. The user selects that expert and books a lesson for the following Monday at 7 p.m. On the day of the lesson, the user and the expert will initiate a video call on the specified online platform. The emotion engine will monitor the user's reactions during the lesson, and if the user shows signs of confusion, for example, the server will relay that information to the expert. The expert will then adjust their teaching method, providing easier-to-understand explanations and a more pacing-oriented lesson.

[1273] Prompt Sentence Examples

[1274] An example of a prompt that allows the user to enter their wishes into the system is:

[1275] If a user enters their desire to "obtain an English proficiency test" but feels unsure, please recommend the most suitable expert.

[1276] This system allows users to learn efficiently from experts who are best suited to their emotions and situations, and experts can receive appropriate compensation for the lessons they provide, making the qualification process more effective and comfortable.

[1277] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1278] Step 1:

[1279] User and Expert Registration

[1280] The user uses the terminal to enter information such as their name, email address, password, and desired qualification. Specifically, they enter their name "Yamada Taro," their email address "taro@example.com," their password "password123," and their desired qualification "TOEIC750."

[1281] The terminal transmits these input data to the server.

[1282] The server validates the data it receives, checking, for example, the format of the email address and the strength of the password, and if the data is correct, stores it in the database.

[1283] Similarly, experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. Specifically, they enter their name "Sato Hanako," email address "hanako@example.com," password "securePass456," qualifications "TOEIC 950," years of experience "5 years," and pricing "5,000 yen per hour."

[1284] The terminal sends the input data to the server, which verifies the received data and, if correct, stores it in a database.

[1285] Step 2:

[1286] User qualifications and emotion recognition

[1287] The user inputs the desired qualifications (e.g., "TOEIC 750") into the terminal.

[1288] The terminal transmits the input data to the server.

[1289] The emotion engine monitors the user's input process and analyzes facial expressions and tone of voice to recognize emotional states (e.g., "unconfident").

[1290] The server stores the user's conditions and the emotion engine results in a database.

[1291] Step 3:

[1292] Providing matching results and reservations

[1293] The server searches the database for the most suitable expert based on the user's input criteria and emotional state, using a search algorithm that takes into account the expert's teaching style and qualifications.

[1294] The server sends the search results (e.g., "experts with a gentle teaching style") in the form of a list to the user's terminal.

[1295] The user selects the desired expert from the list.

[1296] The server obtains the schedule information of the experts and presents the user with available reservation dates and times.

[1297] The user selects the desired date and time and confirms the reservation through the terminal.

[1298] The server stores the reservation data in a database and notifies the specialist.

[1299] Step 4:

[1300] Lesson implementation and logging

[1301] The server sends reminders to the user and the expert for the day of the lesson.

[1302] The user and the expert initiate a video call on a designated online platform (e.g., Zoom).

[1303] The emotion engine analyzes the user's facial expressions and voice during the lesson to recognize their emotional state in real time, for example, analyzing a "confused expression."

[1304] The server records the progress of the lesson, the dialogue content, and the emotion log in a database.

[1305] Step 5:

[1306] Adjusting lesson content

[1307] When the emotion engine recognizes a change in the user's emotion (e.g., "high stress"), it sends that information to the server.

[1308] The server notifies the expert of the information on the emotional changes.

[1309] The expert will use the information to adjust the content and pace of the lesson, for example by providing more detailed explanations or slowing down the pace.

[1310] Step 6:

[1311] Reward calculation and payment

[1312] After the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. Specifically, it calculates the lesson time and evaluation items based on a formula.

[1313] The server sends the calculated remuneration details to the expert's terminal.

[1314] The expert checks and approves the compensation details on the terminal.

[1315] The server processes the approved payment by transferring it to the expert's bank account.

[1316] These steps result in a system that takes into account the user's emotional state, provides optimal lessons, and appropriately rewards the experts.

[1317] (Application example 2)

[1318] 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."

[1319] Conventional qualification support systems have the problem that lessons proceed without taking into account the user's emotions or current skill state, which makes it difficult to alleviate the stress and confusion felt by users. Furthermore, when it comes to improving the skills of employees in a company's factory, there is a lack of a way to grasp the emotional state of each employee in real time and provide optimal guidance. This makes effective learning difficult.

[1320] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input conditions related to desired qualifications, means for registering information on qualified experts, means for saving information on registered experts in a database, means for searching for the most suitable expert based on the conditions input by the user, means for providing the search results to the user, means for checking the schedule of the expert selected by the user and reserving a lesson, means for notifying the expert of the lesson reservation information, means for managing the progress of the lesson and recording a lesson log, means for calculating the fee to the expert based on the recorded lesson log, means for paying the fee to the expert, and means for recognizing the user's emotions and adjusting the lesson content in real time. This makes it possible to provide effective lessons that take the user's emotional state into consideration.

[1321] The "means for users to input conditions related to the qualifications they desire" is a function that allows users who wish to obtain qualifications to input their desired qualifications and related conditions into the system.

[1322] The "means for registering information on qualified professionals" is a function that allows professionals who hold specific qualifications to register their information in the system.

[1323] The "means for storing information on registered experts in a database" is a function that uses a database to stably store and manage information on registered experts.

[1324] "Means for searching for the most suitable expert based on the conditions entered by the user" refers to a function that has algorithms and logic for searching for the most suitable expert from within the database based on the conditions entered by the user.

[1325] The "means for providing search results to the user" is a function for displaying or providing information on the searched experts to the user.

[1326] The "means for checking the schedule of the expert selected by the user and reserving a lesson" is a function for checking the schedule of the expert selected by the user and reserving a lesson based on that schedule.

[1327] The "means for notifying the expert of lesson reservation information" is a function for notifying the expert of details of the reserved lesson.

[1328] The "means for managing the progress of a lesson and recording an implementation log" is a function for managing the progress of a lesson in progress and recording the details as a log.

[1329] The "means for calculating the remuneration to the expert based on the recorded lesson log" is a function for calculating the remuneration to the expert based on the lesson implementation log.

[1330] The "means for paying the remuneration to the expert" is a function for actually paying the calculated remuneration to the expert.

[1331] "Means for recognizing the user's emotions and adjusting the lesson content in real time" is a function that uses emotion recognition technology to recognize the user's emotions and adjusts the optimal instruction in real time according to the user's situation during the lesson.

[1332] This invention is a system that effectively matches users seeking qualifications with qualified professionals, and provides optimal lessons by recognizing and considering the user's emotions. It can also be applied to improving the skills of employees in factories. This system consists of three main elements: a server, a terminal, and a user.

[1333] User and Expert Registration

[1334] First, users and experts who wish to use the system must register their information. Using a terminal, users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. The server receives this information, verifies it, and then stores it in a database.

[1335] Matching Process

[1336] The user inputs the qualifications they desire and sends them from their terminal. The emotion engine recognizes the user's emotions during this input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the search results to the user as a list. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed to reserve a date and time for a lesson.

[1337] Conducting and logging lessons

[1338] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[1339] Adjusting lesson content

[1340] If the emotion engine detects stress or confusion in the user during a lesson, it will notify the expert and prompt them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[1341] Reward calculation and payment

[1342] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[1343] Factory Applications

[1344] A "skill improvement support robot" can be installed for factory workers. This robot will monitor each employee's work skills and emotional state in real time and provide appropriate training and guidance. It will capture facial images of employees via webcam and analyze their emotions using an emotion recognition model. Based on the employee's emotional data, the robot will display appropriate messages and adjust the work content and guidance methods.

[1345] For example, if employee A shows the emotion of "anger," the robot can display the message, "A, you seem to be feeling angry right now. Please stay calm and continue working." If employee A shows the emotion of "joy," the robot can display the message, "A, you're doing well. Keep going and do your best."

[1346] Specific prompt examples

[1347] "Build a response system that takes employees' facial and text data as input, recognizes their emotions, and suggests optimal coaching methods."

[1348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1349] Step 1:

[1350] User and Expert Registration

[1351] Input: The user enters their name, email address, password, and desired qualification information into the terminal. Similarly, the expert enters their name, email address, password, qualifications, years of experience, pricing, and other information into the terminal.

[1352] Processing: The device sends this information to the server, which then verifies the received information, checking the authenticity of the credentials, the format of the email address, etc.

[1353] Output: After the verification is complete, the server saves the information in the database. The user and the expert receive a notification that their registration is complete.

[1354] Step 2:

[1355] User condition input and emotion recognition

[1356] Input: The user inputs the desired qualifications and conditions into the terminal. At this time, the user's facial expression is captured through the terminal's camera and emotion recognition is performed.

[1357] Processing: The device sends the user's input conditions and emotion recognition data to the server. Emotion recognition is performed using an emotion engine that analyzes the user's facial image.

[1358] Output: The server receives the user's input conditions and emotional data. Based on the emotional data, the server outputs the user's state (e.g., stress, confusion, etc.) as a numerical value or label.

[1359] Step 3:

[1360] Expert Matching

[1361] Input: The server searches the database based on the user's input conditions and emotion data.

[1362] Processing: Using a search algorithm, we list the experts best suited to the user's situation. The search takes into account not only the user's conditions but also their emotional state.

[1363] Output: The list of experts obtained as a search result is provided to the user. The search results are displayed on the user's terminal.

[1364] Step 4:

[1365] Book a lesson

[1366] Input: The user selects the desired expert from the list of experts presented and specifies the date and time of the lesson on the terminal.

[1367] Processing: The terminal sends the reservation information to the server, which checks the expert's schedule and confirms the reservation.

[1368] Output: The reservation confirmation information is notified to the expert and the user. The expert's schedule is updated.

[1369] Step 5:

[1370] Conducting and recording lessons

[1371] Input: On the day of the lesson, the user and the expert initiate a video call on the designated online platform.

[1372] Processing: The emotion engine monitors the user's emotions during the lesson in real time and sends the data to the server.

[1373] Output: The server records the lesson progress and execution log and saves it together with the emotion log. The real-time emotion data is notified to the expert.

[1374] Step 6:

[1375] Adjusting lesson content

[1376] Input: If the emotion engine detects that the user is stressed or confused, it sends that information to the server.

[1377] Processing: The server notifies the expert of the user's emotional state and prompts them to adjust their teaching methods.

[1378] Output: The expert adjusts the teaching method and content appropriately based on the user's emotional state.

[1379] Step 7:

[1380] Reward calculation and payment

[1381] Input: After the lesson is completed, the server calculates the reward for the expert based on the recorded lesson log and emotion log.

[1382] Processing: A calculation algorithm is used to calculate the required fee. The expert is provided with a fee breakdown.

[1383] Output: The expert confirms and approves the reward amount. The server executes the reward payment process and the reward is paid to the expert.

[1384] In this way, a system that takes into account the user's emotions is realized through specific processing steps to provide an effective lesson environment.

[1385] 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.

[1386] 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.

[1387] 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.

[1388] [Fourth embodiment]

[1389] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1390] 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.

[1391] 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).

[1392] 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.

[1393] 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.

[1394] 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).

[1395] 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. 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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."

[1402] This invention is a system for efficiently matching users with qualified experts and providing effective lessons. This system consists of three main elements: a server, a terminal, and a user, and is operated in the following steps:

[1403] User and Expert Registration

[1404] First, users and experts who wish to use the system must register their respective information. Using a terminal, users enter information such as their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications held, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in a database.

[1405] Matching Process

[1406] The user inputs the desired qualifications and sends them via the terminal. The server searches the database for the most suitable experts based on the input information and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed to reserve a date and time for a lesson.

[1407] Conducting and logging lessons

[1408] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson using an online platform such as a video call. The server records the progress of the lesson and saves the progress log in a database.

[1409] Reward calculation and payment

[1410] When the lesson is completed, the server calculates the reward for the expert based on the lesson log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the process of paying the reward.

[1411] Specific examples

[1412] For example, a user may input a desire to "obtain an English Proficiency Test qualification" and specify the condition for lessons as "I would like to study twice a week at night." The server searches the database for "experts who hold the English Proficiency Test Grade 1 and are available at night." As a result, an expert named Mr. Suzuki is found and suggested to the user. The user selects Mr. Suzuki and books a lesson for the following Monday at 7 p.m.

[1413] On the day of the lesson, the user and Suzuki sensei will actively conduct the lesson via video call on the designated online platform. The server records the situation and creates a lesson log. After the lesson is completed, the reward is calculated based on this log and paid to Suzuki sensei.

[1414] This system allows users to easily find suitable experts, and experts can efficiently provide lessons and receive compensation, making the qualification process go more smoothly.

[1415] The processing flow will be explained below.

[1416] Step 1: User Registration

[1417] Terminal: Presents a form where the user enters their name, email address, password, and desired credentials.

[1418] User: Enter the required information and click the submit button.

[1419] Terminal: Sends the entered information to the server.

[1420] Step 2: Professional Registration

[1421] Device: Displays a form where the expert can enter their name, email address, password, certifications, years of experience, and pricing information.

[1422] Expert: Enter the required information and click the submit button.

[1423] Terminal: Sends the entered information to the server.

[1424] Step 3: Verify and save the information

[1425] Server: Validates the received user and expert information, ensuring all required fields are filled in and that the email address is formatted correctly.

[1426] Server: Stores the verified information in a database.

[1427] Step 4: Enter criteria and search

[1428] Terminal: Displays a form for the user to enter the lesson conditions they desire (e.g. time slot, number of times per week, fee, etc.).

[1429] User: Enter the required information and click the submit button.

[1430] Terminal: Sends the entered conditions to the server.

[1431] Server: Based on the input criteria, it searches the database for the most suitable experts and generates a list of search results.

[1432] Step 5: View and select search results

[1433] Server: Sends the search result list to the device.

[1434] Terminal: Display search results to the user in a list format.

[1435] User: Select the desired expert from the list of suggested experts.

[1436] Step 6: Book a lesson

[1437] Terminal: View the selected expert's schedule.

[1438] User: Select the desired lesson date and time and click the submit button.

[1439] Device: Send the selected date and time to the server.

[1440] Server: Stores lesson reservation information in a database and sends reservation notifications to experts.

[1441] Step 7: Reminders

[1442] Server: Send reminder notifications to users and experts before the lesson starts.

[1443] Step 8: Lesson implementation

[1444] User: Follow the reminder notification and launch the video calling platform to connect with the expert.

[1445] Experts: Launch a video chat platform during lesson time to connect with users.

[1446] Server: Monitors the progress of lessons and records progress logs.

[1447] Step 9: Complete the lesson and calculate your rewards

[1448] Server: After the lesson is completed, calculate the reward for the expert based on the recorded log.

[1449] Server: Generates reward statements and sends them to experts.

[1450] Terminal: Shows the expert a fee statement.

[1451] Step 10: Payment

[1452] Expert: Review and approve the fee statement.

[1453] Server: After receiving the expert's approval, the server executes the payment process.

[1454] Server: Notifies the expert that payment has been made.

[1455] Terminal: Shows the expert a notification that payment has been completed.

[1456] Example 1

[1457] 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."

[1458] In conventional qualification support systems, it was difficult to match users with the appropriate experts, and the process of conducting lessons and paying fees was cumbersome. This resulted in problems such as users being unable to take lessons efficiently and experts being unable to provide their services effectively. In addition, lesson progress management and fee calculation and payment were often done manually, which could be a burden.

[1459] 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.

[1460] In this invention, the server includes: means for a user to input conditions related to desired qualifications; means for registering information on qualified experts; means for saving information on registered experts in a database; means for searching for the most suitable expert based on the conditions input by the user; means for providing the search results to the user; means for the user to check the schedule of the expert selected by the user and book a lesson; means for notifying the expert of the lesson reservation information; means for the user and the expert to conduct the lesson on the designated online platform on the day of the lesson reservation; means for managing the progress of the lesson and recording the implementation log; means for calculating the fee to the expert based on the recorded lesson log; and means for paying the fee to the expert. This enables effective matching of users and experts, efficient lesson implementation, progress management, and fee calculation and payment to be automated, thereby reducing the overall workload.

[1461] "User" means an individual or organization who wishes to obtain a qualification and uses the system to take lessons.

[1462] An "expert" is an individual or organization that holds a specific qualification and has the knowledge and experience related to that qualification.

[1463] "Conditions" refers to the requirements and schedule for obtaining the qualifications the user desires, as well as preferences such as the frequency and time of lessons.

[1464] "Database" refers to a system that systematically stores and manages data such as user and expert registration information, lesson progress, and remuneration calculations.

[1465] "Schedule" refers to a list or timetable of dates and times when an Expert is available to conduct Lessons.

[1466] "Online Platform" means an internet-based service that provides video calling and messaging capabilities to enable users and experts to communicate in real time.

[1467] "Progress" is information that indicates the implementation status and progress of the lesson.

[1468] An "implementation log" is data that records details such as the start time, end time, and content of the lesson.

[1469] "Remuneration" means the amount of money a Professional receives in exchange for providing a Lesson.

[1470] "Notifications" are messages or alerts that communicate important information to users and experts, such as lesson booking information and reminders.

[1471] This invention is a system for efficiently matching users with qualified experts and providing effective lessons. This system consists of three main elements: a server, a terminal, and a user. Specifically, users input their desired qualifications and register information about qualified experts.

[1472] When users and experts enter information such as their name, email address, password, desired qualifications, and current qualifications from their respective devices, the server receives this information. At this time, the server verifies the format, such as the validity of the email address and the strength of the password. The necessary hardware includes a database server (using MySQL, for example) and a front-end system for displaying the information (web technologies such as HTML and JavaScript).

[1473] Once verified, the information is stored in a database. When a user enters the desired qualifications and conditions, for example, "I want to study for the English Proficiency Test twice a week at night," the server uses this information to search the database for the most suitable expert. Specific software could use Python or SQL queries to implement the search algorithm. For example, let's say you want to search for "experts who hold the English Proficiency Test Grade 1 and are available at night."

[1474] The search results are provided to the user as a list, and the user selects the desired expert from the list. Based on this selection, the server checks the expert's schedule and executes the procedure for the user to reserve the lesson date and time. Once the reservation is complete, the server notifies the expert of the reservation information. Notifications are sent using an email system or push notification service (such as SMTP or Firebase Cloud Messaging).

[1475] On the day of the lesson, the user and the expert will have a video call using an online platform such as Zoom or Google Meet. The server records the progress of the lesson in real time and saves the implementation log in a database. This progress management is done using log collection services and APIs (e.g., Amazon CloudWatch and Custom API).

[1476] Once the lesson is completed, the server calculates the expert's reward based on the lesson log. The reward calculation logic can be implemented using Python or JavaScript, and the calculation results are processed by a dedicated reward calculation system. The calculated reward details are provided to the expert via the terminal, and after the expert has confirmed and approved them, the server executes the reward payment process. Payments can be made by bank transfer or via electronic money payment systems (such as PayPal or Stripe).

[1477] As a concrete example, if a user inputs their desire to "obtain an Eiken qualification" and specifies as a condition "I want to study twice a week at night," the server searches the database for "experts who hold the Eiken Grade 1 qualification and are available at night," and displays a list of experts named "Mr. Suzuki" as search results. The user selects Mr. Suzuki and books a lesson at 7 p.m. on the following Monday. On the day of the lesson, the user and Mr. Suzuki conduct the lesson via video call via Zoom, and the server records the situation. After the lesson is over, the server calculates Mr. Suzuki's remuneration based on the log, and the remuneration is paid to the expert.

[1478] Prompt Sentence Examples

[1479] "Please explain the steps for a user who has a Grade 1 English Proficiency Test and is looking for an expert who is available for evening lessons to book a lesson for next Monday."

[1480] This system will enable users to easily find suitable experts, and experts will be able to provide lessons efficiently and receive compensation, which is expected to make the qualification process go more smoothly.

[1481] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1482] Step 1:

[1483] Enter information

[1484] The user uses the terminal to enter information such as their name, email address, password, and desired qualifications.

[1485] The professional also uses the device to enter information such as their name, email address, password, qualifications, years of experience, and pricing.

[1486] Input: User information (name, email address, password, desired qualifications), expert information (name, email address, password, qualifications held, years of experience, pricing)

[1487] Output: The entered information is sent to the server

[1488] Specific operation: A user enters the name "Taro Tanaka" and enters the email address "tanaka@example.com." An expert enters the name "Ichiro Suzuki" and enters the email address "suzuki@example.com."

[1489] Step 2:

[1490] Receiving and verifying information

[1491] A server receives information sent by users and experts.

[1492] The server validates the format and content of the information (e.g., whether an email address is formatted correctly or whether a password meets a certain level of complexity).

[1493] Input: User information, expert information

[1494] Output: Verification results

[1495] What happens: The server verifies that "tanaka@example.com" is a valid email address. It checks the password complexity (e.g., whether it is at least 8 characters long and contains letters and numbers).

[1496] Step 3:

[1497] Information storage

[1498] The server saves the verified information in a database, such as MySQL.

[1499] Input: Verified user information, expert information

[1500] Output: Saved information

[1501] Specific operation: The server saves Taro Tanaka's information in the "user_info" table and Ichiro Suzuki's information in the "expert_info" table.

[1502] Step 4:

[1503] Enter conditions and search

[1504] The user enters the desired qualifications and conditions (e.g., two nights a week) on the terminal and sends them to the server.

[1505] The server searches the database for the most suitable expert based on the criteria.

[1506] Input: User conditions (qualifications, lesson frequency, time zone)

[1507] Output: A list of the best experts

[1508] Specific operation: The user enters the criteria "English Proficiency Test, 2 times a week at night." The server searches the database for "experts who hold the English Proficiency Test Level 1 and are available at night" and finds "Ichiro Suzuki."

[1509] Step 5:

[1510] Providing search results and selecting experts

[1511] The server provides the search results as a list to the user's terminal.

[1512] The user selects the desired expert from the list.

[1513] Input: list of search results

[1514] Output: Selected experts

[1515] Specific operation: The server generates a list of information about "Ichiro Suzuki" and provides it to the user. The user selects Ichiro Suzuki.

[1516] Step 6:

[1517] Reservations and schedule confirmation

[1518] The server checks the schedule of the selected expert and assists the user in reserving a date and time for the lesson.

[1519] Input: Selected expert, user's desired date and time

[1520] Output: Reservation information

[1521] Specific behavior: A user books a lesson for next Monday at 7pm. The server checks Suzuki Ichiro's schedule and confirms the reservation.

[1522] Step 7:

[1523] notification

[1524] The server notifies the specialist of the reservation information.

[1525] Input: Reservation information

[1526] Output: Notification message

[1527] Specific operation: The server sends a lesson reservation notification to Suzuki Ichiro's email address.

[1528] Step 8:

[1529] Lesson implementation

[1530] The user and the expert will conduct the lesson at the appointed date and time using an online platform (Zoom or Google Meet).

[1531] Input: reservation date and time, online platform link

[1532] Output: Lesson implementation status

[1533] Specific operation: The user and expert log in to Zoom and start a video call.

[1534] Step 9:

[1535] Lesson Recording

[1536] The server records the progress of the lesson in real time and stores the lesson log in a database.

[1537] Input: lesson start time, end time, progress

[1538] Output: Lesson log

[1539] Specific operation: The server records the start time, end time, and main topic of the lesson and stores them in a database.

[1540] Step 10:

[1541] Reward calculation and payment

[1542] The server calculates the reward for the expert based on the lesson log.

[1543] The server provides the reward details to the expert, and after the expert approves it, the reward is paid.

[1544] Input: Lesson Log

[1545] Output: Remuneration details, payment processing

[1546] Specific operation: The server calculates the reward as "Suzuki Ichiro: 3,000 yen / hour x 2 hours = 6,000 yen" and provides the reward details to Suzuki Ichiro. After Suzuki Ichiro approves it, the server executes the process to transfer 6,000 yen to his bank account.

[1547] (Application example 1)

[1548] 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."

[1549] Conventional user-expert matching systems are limited to providing specific professional services, such as lessons for qualifications. This makes it difficult to respond to demand for food delivery and other professional services. Furthermore, the lack of reservation information notifications and reminder functions creates issues with the efficiency and reliability of service provision. Therefore, the present invention aims to solve these issues and provide a system that efficiently provides a wider variety of professional services.

[1550] 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.

[1551] In this invention, the server includes: a means for inputting conditions related to the qualifications desired by the user; a means for registering information on qualified experts; a means for storing information on the registered experts in a database; a means for searching for the most suitable expert based on the user's input conditions; a means for providing the search results to the user; a means for checking the schedule of the expert selected by the user and reserving a lesson; a means for notifying the expert of the lesson reservation information; a means for managing the progress of the lesson and recording a lesson log; a means for calculating the fee to the expert based on the recorded lesson log; a means for paying the fee to the expert; a means for inputting conditions related to the service desired by the user; a means for registering information on the expert who will provide the service; a means for searching for the most suitable expert based on the user's desired conditions and reserving a schedule for the provided service; a means for notifying the expert of the reservation information and sending a reminder. This allows users to easily match with a wider variety of professional service providers and efficiently receive services that meet their specific conditions. Furthermore, the reminder function improves the reliability and certainty of service provision.

[1552] A "user" is an individual or organization that wishes to use the system and enters their requirements.

[1553] An "expert" is an individual or organization that has a particular qualification or skill and is registered.

[1554] A "qualification" is a formal certification that attests to a particular skill or knowledge.

[1555] "Conditions" refers to information such as specific requirements regarding the service or qualifications desired by the user, and the desired date and time.

[1556] A "means" is a method or device for achieving a specific function or purpose.

[1557] A "database" is an information management system that stores and searches registered information in an organized manner.

[1558] "Search" is the act of retrieving information that matches a condition from a database.

[1559] A "schedule" is a timetable showing the schedules of service providers and users.

[1560] "Reminders" is a feature that notifies you of specific actions or events.

[1561] "Remuneration" means the consideration received by a professional for services rendered.

[1562] A "log" is data that records the progress and history of a service.

[1563] "Notification" is the act of informing a user or expert of specific information.

[1564] "Reservation" means the act of setting and reserving a date and time for a service or lesson.

[1565] A "service" is a specific task or support provided by an expert to a user.

[1566] This invention is a system that efficiently matches users with experts and provides a variety of professional services. This system is composed of three main elements: a server, a terminal, and a user, and includes the following means:

[1567] 1. User and Professional Registration

[1568] First, the user and expert register their respective information using a device. User information includes name, email address, password, desired qualifications, and service conditions, while expert information includes name, email address, password, qualifications and skills held, years of experience, and pricing. The server receives this information, verifies it, and then stores it in a database. The device can be a standard smartphone or tablet.

[1569] 2. Matching Process

[1570] The user inputs the desired qualifications and service requirements from the terminal. The server uses this information to search for the most suitable specialist from its database and provides the search results to the user as a list. The user selects the desired specialist from the displayed list, and the server checks the specialist's schedule and reserves the service for the desired date and time.

[1571] 3. Lesson or service provision and progress management

[1572] As the reserved service date and time approaches, the server sends reminder notifications to the user and the expert. On the day of the service, the user and the expert perform the service using online platforms such as video calls or chat tools. The server records the progress in real time and saves the execution log in a database. Notifications are ensured by using SMS notification services such as the Twilio API.

[1573] 4. Reward Calculation and Payment

[1574] Once the service is completed, the server calculates the fee to the expert based on the saved execution log. The calculated fee is provided to the expert via the terminal, and once the expert confirms and approves it, the payment is processed. Payment is processed using electronic payment services such as Stripe and PayPal.

[1575] Specific examples

[1576] For example, if a user requests a chef who can provide healthy Japanese meals twice a week, the server searches the database for chefs who meet the criteria and provides the results to the user. The user then selects the most suitable chef and reserves delivery for meals every Monday and Thursday at 7 p.m. The system notifies the chef of the reservation information and also sends reminders, ensuring that the service is provided reliably. After the service is completed, the remuneration is automatically calculated and paid to the chef. This allows users to easily find the right expert and receive services efficiently.

[1577] Prompt Sentence Examples

[1578] "I would like to have healthy Japanese meals served twice a week. Please help me build a platform that can match chefs with food requests. Please provide an overview of the process for supply and demand analysis, ingredient sourcing, lesson implementation, and compensation calculation."

[1579] This system allows users to efficiently use various professional services, and experts can efficiently provide services and receive compensation. Specific infrastructure includes the use of the Django framework, notifications via the Twilio API, and electronic payments via Stripe and PayPal.

[1580] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1581] Step 1:

[1582] User and Expert Registration

[1583] Users and experts use terminals to enter information such as name, email address, password, desired services and qualifications, etc. The server receives this information, verifies its accuracy and completeness, and then stores it in a database.

[1584] Input: Registration information (username, email address, password, credentials and service details)

[1585] Data processing: Verify that the format and content of the entered data are correct

[1586] Output: Save to database (user information and expert information)

[1587] Specifically, we will use the Django framework to create API endpoints that provide user and expert registration forms and save them to a database.

[1588] Step 2:

[1589] User condition input

[1590] The user inputs the desired qualifications and conditions for the service from the terminal. For example, they input specific requirements such as "I would like to have healthy Japanese meals twice a week." The server receives the input conditions and stores them in a database.

[1591] Input: Desired conditions (specific service and qualification requirements)

[1592] Data processing: Verification of the form and content of the conditions

[1593] Output: Save to database (desired conditions)

[1594] Specifically, it creates an input form, sends data asynchronously using an AJAX request, and saves it in a database on the server side.

[1595] Step 3:

[1596] Expert Search

[1597] The server searches the database for the most suitable expert based on the criteria entered by the user. For example, it searches for "experts who are qualified in Japanese cuisine and can provide health-conscious dishes." The search results are provided to the user in a list format.

[1598] Input: Desired conditions (match with conditions in the database)

[1599] Data operations: Applying criteria-based filtering and search algorithms

[1600] Output: Search result list (list of relevant experts)

[1601] Specifically, it uses Django ORM to execute a database query to find experts that meet the criteria.

[1602] Step 4:

[1603] Check schedule and make reservations

[1604] The user selects the desired expert from the displayed search result list, checks the expert's schedule, and makes a reservation. The server saves the reservation information in a database and updates the expert's schedule.

[1605] Input: ID of selected expert and desired date and time

[1606] Data processing: Check and update schedule availability

[1607] Output: Save reservation information (update database)

[1608] Specifically, it uses a Python script to update the schedule information in the database and notify the user that the reservation has been confirmed.

[1609] Step 5:

[1610] Reservation notifications and reminders

[1611] The server notifies the expert and user of the appointment information and also sends reminder notifications when the appointment time approaches, using the Twilio API to send SMS.

[1612] Input: Reservation information

[1613] Data processing: Converting to SMS format using Twilio API

[1614] Output: Send notification message (SMS)

[1615] Specifically, it uses the Twilio library and uses API keys to send appointment information and reminders to experts and users via SMS.

[1616] Step 6:

[1617] Service progress management and logging

[1618] On the day of the service delivery, the user and the expert communicate via video call or chat. The server manages the progress of the service in real time and records it as a log.

[1619] Input: Service progress information (start time, end time, other meter data)

[1620] Data calculation: Real-time update and recording of progress information

[1621] Output: Save progress log (database)

[1622] Specifically, we will build a dashboard that displays progress information along with real-time database updates.

[1623] Step 7:

[1624] Reward calculation and payment

[1625] Once the service is completed, the server calculates the fee to the expert based on the recorded log. After the calculation, a fee statement is sent to the expert and payment is made. Electronic payments are made using Stripe or PayPal.

[1626] Input: Service log (time, service content, etc.)

[1627] Data calculation: Applying reward calculation algorithms

[1628] Output: Compensation statement generation and payment execution

[1629] Specifically, it uses the Stripe API or PayPal API to automate fee calculation and payment to experts.

[1630] Prompt Sentence Examples

[1631] "I would like to have healthy Japanese meals served twice a week. Please help me build a platform that can match chefs with food requests. Please provide an overview of the process for supply and demand analysis, ingredient sourcing, lesson implementation, and compensation calculation."

[1632] 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.

[1633] This invention is a system that effectively matches users seeking qualifications with qualified experts, and provides optimal lessons by recognizing and considering the user's emotions. This system consists of three main elements: a server, a terminal, and a user, and an emotion engine is built into each processing step.

[1634] User and Expert Registration

[1635] First, users and experts who wish to use the system must register their information. Using a terminal, users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in the database.

[1636] Matching Process

[1637] The user inputs the qualifications they desire and sends them from their terminal. The emotion engine recognizes the user's emotions during this input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed with reserving the date and time of the lesson.

[1638] Conducting and logging lessons

[1639] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[1640] Adjusting lesson content

[1641] If the emotion engine detects stress or confusion in the user during a lesson, it will notify the expert and prompt them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[1642] Reward calculation and payment

[1643] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[1644] Specific examples

[1645] For example, if a user inputs a desire to "obtain an English proficiency test" and the emotion engine recognizes the user's lack of confidence, the server will search for and recommend experts with a particularly gentle teaching style. As a result, an expert named Mr. Tanaka is found and suggested to the user. The user selects Mr. Tanaka and books a lesson at 7 p.m. next Monday.

[1646] On the day of the lesson, the user and Professor Tanaka will start a video call via the selected online platform. The emotion engine will monitor the user's reactions during the lesson. For example, if the user shows signs of confusion, the server will relay this information to Professor Tanaka. Professor Tanaka will then adjust his / her teaching method, providing easier-to-understand explanations and adjusting the pace.

[1647] This system allows users to learn efficiently from experts who are best suited to their emotions and situations, and experts can receive appropriate compensation for the lessons they provide, making the qualification process more effective and comfortable.

[1648] The processing flow will be explained below.

[1649] Step 1: Enter your user information

[1650] Terminal: Presents a form where the user enters their name, email address, password, and desired credentials.

[1651] User: Enter the required information and click the submit button.

[1652] Terminal: Sends input information to the emotion engine and recognizes the user's emotions.

[1653] Step 2: Recognizing and transmitting emotions

[1654] Emotion engine: Analyzes user input and behavior to recognize emotions.

[1655] Device: Sends the recognized emotion information to the server.

[1656] Server: Receives input information and emotion information and performs verification.

[1657] Step 3: Verify and save the information

[1658] Server: Validate the received user information and sentiment information, check that all required fields are filled in, and that the email address format is correct.

[1659] Server: Save the verified information in the database.

[1660] Step 4: Enter expert information

[1661] Device: Displays a form where the expert can enter their name, email address, password, certifications, years of experience, and pricing information.

[1662] Expert: Enter the required information and click the submit button.

[1663] Terminal: Sends the entered information to the server.

[1664] Step 5: Verify and save expert information

[1665] Server: Validates received expert information and checks for accuracy.

[1666] Server: Save the verified information in the database.

[1667] Step 6: Enter criteria and search

[1668] Terminal: Displays a form for the user to enter the lesson conditions they desire (e.g. time slot, number of times per week, fee, etc.).

[1669] User: Enter the required information and click the submit button.

[1670] Terminal: Sends the input conditions and the results of the emotion engine to the server.

[1671] Server: Searches the database for the most suitable experts based on the input conditions and emotional information, and generates a list of search results.

[1672] Step 7: View and select search results

[1673] Server: Sends the search result list to the device.

[1674] Terminal: Display search results to the user in a list format.

[1675] User: Select the desired expert from the list of suggested experts.

[1676] Step 8: Book a lesson

[1677] Terminal: View the selected expert's schedule.

[1678] User: Select the desired lesson date and time and click the submit button.

[1679] Device: Send the selected date and time to the server.

[1680] Server: Stores lesson reservation information in a database and sends reservation notifications to experts.

[1681] Step 9: Reminders

[1682] Server: Send reminder notifications to users and experts before the lesson starts.

[1683] Step 10: Lesson implementation

[1684] User: Follow the reminder notification and launch the video calling platform to connect with the expert.

[1685] Experts: Launch a video chat platform during lesson time to connect with users.

[1686] Server: Monitors the progress of lessons and records progress and emotion logs.

[1687] Step 11: Adjust the lesson content

[1688] Emotion Engine: Analyzes user emotions in real time during lessons.

[1689] Server: Sends the user's emotional information to the expert, prompting the expert to adjust the content and pace of the lesson according to the user's emotions.

[1690] Step 12: Complete the lesson and calculate your rewards

[1691] Server: After the lesson is completed, calculate the reward for the expert based on the recorded log and emotion log.

[1692] Server: Generates reward statements and sends them to experts.

[1693] Terminal: Shows the expert a fee statement.

[1694] Step 13: Payment

[1695] Expert: Review and approve the fee statement.

[1696] Server: After receiving the expert's approval, the server executes the payment process.

[1697] Server: Notifies the expert that payment has been made.

[1698] Terminal: Shows the expert a notification that payment has been completed.

[1699] Example 2

[1700] 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."

[1701] Conventional qualification support systems provide lessons without considering the user's emotional state, which can lead to stress and reduced learning effectiveness. Furthermore, the calculation of compensation for experts is based solely on lesson logs, which means that the experts' efforts and the quality of the lessons are not properly evaluated.

[1702] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: a means for inputting conditions related to the qualifications desired by the user; a means for registering information on qualified experts; a means for saving information on the registered experts in a database; a means for searching for the most suitable expert based on the conditions input by the user; a means for providing the search results to the user; a means for checking the schedule of the expert selected by the user and reserving a lesson; a means for notifying the expert of the lesson reservation information; a means for recognizing the user's emotions in real time and recording the results in a database; a means for managing the progress of the lesson and recording a performance log and an emotion log; a means for calculating the fee to the expert based on the recorded lesson log and emotion log; and a means for paying the fee to the expert. This enables the provision of an optimal lesson that takes the user's emotional state into consideration, and also enables the expert's efforts and the quality of the lesson to be appropriately evaluated.

[1703] "User" means an individual or organization that uses this system to obtain qualifications.

[1704] An "expert" is a person who holds a specific qualification and is able to provide guidance and advice based on that qualification.

[1705] An "emotion engine" is a software or hardware component that analyzes and recognizes a user's emotional state in real time based on their facial expressions, voice, etc.

[1706] The "database" is a system for centrally storing and managing data such as user and expert information, lesson logs, and emotion logs.

[1707] "Input conditions" refers to the conditions and requirements desired by the user regarding qualification acquisition.

[1708] A "lesson" is a teaching and educational session conducted by an expert for a user.

[1709] "Lesson Log" refers to a record of the content, progress and various interactions of a lesson.

[1710] An "emotion log" refers to a record of a user's emotional state.

[1711] "Remuneration Statement" means a document or data summarizing the details of the compensation paid to the Expert after the Lesson is completed.

[1712] "Reminder notification" refers to alerts and notifications that notify users and experts in advance of lesson dates and times, etc.

[1713] "Schedule" refers to a list of an Expert's available dates, times, and booked slots.

[1714] MODE FOR CARRYING OUT THE INVENTION

[1715] This invention is a system that effectively matches users seeking qualifications with qualified experts and provides optimal lessons taking into account the user's emotions. This system is composed of three main elements: a server, a terminal, and a user, and an emotion engine is built into each processing step.

[1716] User and Expert Registration

[1717] First, users and experts who wish to use the system must register their information using a terminal. Users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications held, years of experience, and pricing. The server receives this input information, verifies it, and then stores it in a database.

[1718] Matching Process

[1719] The user inputs the qualifications they desire and sends them from their terminal. At this time, the emotion engine recognizes the user's emotions during the input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the user with a list of search results. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and provides a procedure that allows the user to reserve a date and time for a lesson.

[1720] Conducting and logging lessons

[1721] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[1722] Adjusting lesson content

[1723] If the emotion engine detects stress or confusion in the user during a lesson, the server notifies the expert and prompts them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[1724] Reward calculation and payment

[1725] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[1726] Specific examples

[1727] For example, if a user inputs their desire to "obtain an English proficiency test" and the emotion engine recognizes the user's lack of confidence, the server will search for and recommend experts with a particularly gentle teaching style. As a result, a specific expert is found and suggested to the user. The user selects that expert and books a lesson for the following Monday at 7 p.m. On the day of the lesson, the user and the expert will initiate a video call on the specified online platform. The emotion engine will monitor the user's reactions during the lesson, and if the user shows signs of confusion, for example, the server will relay that information to the expert. The expert will then adjust their teaching method, providing easier-to-understand explanations and a more pacing-oriented lesson.

[1728] Prompt Sentence Examples

[1729] An example of a prompt that allows the user to enter their wishes into the system is:

[1730] If a user enters their desire to "obtain an English proficiency test" but feels unsure, please recommend the most suitable expert.

[1731] This system allows users to learn efficiently from experts who are best suited to their emotions and situations, and experts can receive appropriate compensation for the lessons they provide, making the qualification process more effective and comfortable.

[1732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1733] Step 1:

[1734] User and Expert Registration

[1735] The user uses the terminal to enter information such as their name, email address, password, and desired qualification. Specifically, they enter their name "Yamada Taro," their email address "taro@example.com," their password "password123," and their desired qualification "TOEIC750."

[1736] The terminal transmits these input data to the server.

[1737] The server validates the data it receives, checking, for example, the format of the email address and the strength of the password, and if the data is correct, stores it in the database.

[1738] Similarly, experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. Specifically, they enter their name "Sato Hanako," email address "hanako@example.com," password "securePass456," qualifications "TOEIC 950," years of experience "5 years," and pricing "5,000 yen per hour."

[1739] The terminal sends the input data to the server, which verifies the received data and, if correct, stores it in a database.

[1740] Step 2:

[1741] User qualifications and emotion recognition

[1742] The user inputs the desired qualifications (e.g., "TOEIC 750") into the terminal.

[1743] The terminal transmits the input data to the server.

[1744] The emotion engine monitors the user's input process and analyzes facial expressions and tone of voice to recognize emotional states (e.g., "unconfident").

[1745] The server stores the user's conditions and the emotion engine results in a database.

[1746] Step 3:

[1747] Providing matching results and reservations

[1748] The server searches the database for the most suitable expert based on the user's input criteria and emotional state, using a search algorithm that takes into account the expert's teaching style and qualifications.

[1749] The server sends the search results (e.g., "experts with a gentle teaching style") in the form of a list to the user's terminal.

[1750] The user selects the desired expert from the list.

[1751] The server obtains the schedule information of the experts and presents the user with available reservation dates and times.

[1752] The user selects the desired date and time and confirms the reservation through the terminal.

[1753] The server stores the reservation data in a database and notifies the specialist.

[1754] Step 4:

[1755] Lesson implementation and logging

[1756] The server sends reminders to the user and the expert for the day of the lesson.

[1757] The user and the expert initiate a video call on a designated online platform (e.g., Zoom).

[1758] The emotion engine analyzes the user's facial expressions and voice during the lesson to recognize their emotional state in real time, for example, analyzing a "confused expression."

[1759] The server records the progress of the lesson, the dialogue content, and the emotion log in a database.

[1760] Step 5:

[1761] Adjusting lesson content

[1762] When the emotion engine recognizes a change in the user's emotion (e.g., "high stress"), it sends that information to the server.

[1763] The server notifies the expert of the information on the emotional changes.

[1764] The expert will use the information to adjust the content and pace of the lesson, for example by providing more detailed explanations or slowing down the pace.

[1765] Step 6:

[1766] Reward calculation and payment

[1767] After the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. Specifically, it calculates the lesson time and evaluation items based on a formula.

[1768] The server sends the calculated remuneration details to the expert's terminal.

[1769] The expert checks and approves the compensation details on the terminal.

[1770] The server processes the approved payment by transferring it to the expert's bank account.

[1771] These steps result in a system that takes into account the user's emotional state, provides optimal lessons, and appropriately rewards the experts.

[1772] (Application example 2)

[1773] 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."

[1774] Conventional qualification support systems have the problem that lessons proceed without taking into account the user's emotions or current skill state, which makes it difficult to alleviate the stress and confusion felt by users. Furthermore, when it comes to improving the skills of employees in a company's factory, there is a lack of a way to grasp the emotional state of each employee in real time and provide optimal guidance. This makes effective learning difficult.

[1775] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for a user to input conditions related to desired qualifications, means for registering information on qualified experts, means for saving information on registered experts in a database, means for searching for the most suitable expert based on the conditions input by the user, means for providing the search results to the user, means for checking the schedule of the expert selected by the user and reserving a lesson, means for notifying the expert of the lesson reservation information, means for managing the progress of the lesson and recording a lesson log, means for calculating the fee to the expert based on the recorded lesson log, means for paying the fee to the expert, and means for recognizing the user's emotions and adjusting the lesson content in real time. This makes it possible to provide effective lessons that take the user's emotional state into consideration.

[1776] The "means for users to input conditions related to the qualifications they desire" is a function that allows users who wish to obtain qualifications to input their desired qualifications and related conditions into the system.

[1777] The "means for registering information on qualified professionals" is a function that allows professionals who hold specific qualifications to register their information in the system.

[1778] The "means for storing information on registered experts in a database" is a function that uses a database to stably store and manage information on registered experts.

[1779] "Means for searching for the most suitable expert based on the conditions entered by the user" refers to a function that has algorithms and logic for searching for the most suitable expert from within the database based on the conditions entered by the user.

[1780] The "means for providing search results to the user" is a function for displaying or providing information on the searched experts to the user.

[1781] The "means for checking the schedule of the expert selected by the user and reserving a lesson" is a function for checking the schedule of the expert selected by the user and reserving a lesson based on that schedule.

[1782] The "means for notifying the expert of lesson reservation information" is a function for notifying the expert of details of the reserved lesson.

[1783] The "means for managing the progress of a lesson and recording an implementation log" is a function for managing the progress of a lesson in progress and recording the details as a log.

[1784] The "means for calculating the remuneration to the expert based on the recorded lesson log" is a function for calculating the remuneration to the expert based on the lesson implementation log.

[1785] The "means for paying the remuneration to the expert" is a function for actually paying the calculated remuneration to the expert.

[1786] "Means for recognizing the user's emotions and adjusting the lesson content in real time" is a function that uses emotion recognition technology to recognize the user's emotions and adjusts the optimal instruction in real time according to the user's situation during the lesson.

[1787] This invention is a system that effectively matches users seeking qualifications with qualified professionals, and provides optimal lessons by recognizing and considering the user's emotions. It can also be applied to improving the skills of employees in factories. This system consists of three main elements: a server, a terminal, and a user.

[1788] User and Expert Registration

[1789] First, users and experts who wish to use the system must register their information. Using a terminal, users enter their name, email address, password, and desired qualifications. Experts enter information such as their name, email address, password, qualifications, years of experience, and pricing. The server receives this information, verifies it, and then stores it in a database.

[1790] Matching Process

[1791] The user inputs the qualifications they desire and sends them from their terminal. The emotion engine recognizes the user's emotions during this input process and also sends this information to the server. The server searches the database for the most suitable experts based on the input information and the emotion engine's results, and provides the search results to the user as a list. The user then selects the desired expert from the displayed list. Based on this selection, the server checks the expert's schedule and the user can proceed to reserve a date and time for a lesson.

[1792] Conducting and logging lessons

[1793] When a lesson is booked, the server notifies the expert of the reservation information. On the day of the lesson, the user and the expert conduct the lesson via an online platform such as a video call. The emotion engine recognizes the user's emotions in real time during the lesson and transfers them to the server. The server records the emotion log along with the lesson progress.

[1794] Adjusting lesson content

[1795] If the emotion engine detects stress or confusion in the user during a lesson, it will notify the expert and prompt them to adjust the pace and content of the lesson, providing a more effective learning environment for the user.

[1796] Reward calculation and payment

[1797] When the lesson is completed, the server calculates the reward for the expert based on the lesson log and emotion log. The calculated reward details are provided to the expert via the terminal. Once the expert checks and approves the reward details, the server executes the reward payment process.

[1798] Factory Applications

[1799] A "skill improvement support robot" can be installed for factory workers. This robot will monitor each employee's work skills and emotional state in real time and provide appropriate training and guidance. It will capture facial images of employees via webcam and analyze their emotions using an emotion recognition model. Based on the employee's emotional data, the robot will display appropriate messages and adjust the work content and guidance methods.

[1800] For example, if employee A shows the emotion of "anger," the robot can display the message, "A, you seem to be feeling angry right now. Please stay calm and continue working." If employee A shows the emotion of "joy," the robot can display the message, "A, you're doing well. Keep going and do your best."

[1801] Specific prompt examples

[1802] "Build a response system that takes employees' facial and text data as input, recognizes their emotions, and suggests optimal coaching methods."

[1803] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1804] Step 1:

[1805] User and Expert Registration

[1806] Input: The user enters their name, email address, password, and desired qualification information into the terminal. Similarly, the expert enters their name, email address, password, qualifications, years of experience, pricing, and other information into the terminal.

[1807] Processing: The device sends this information to the server, which then verifies the received information, checking the authenticity of the credentials, the format of the email address, etc.

[1808] Output: After the verification is complete, the server saves the information in the database. The user and the expert receive a notification that their registration is complete.

[1809] Step 2:

[1810] User condition input and emotion recognition

[1811] Input: The user inputs the desired qualifications and conditions into the terminal. At this time, the user's facial expression is captured through the terminal's camera and emotion recognition is performed.

[1812] Processing: The device sends the user's input conditions and emotion recognition data to the server. Emotion recognition is performed using an emotion engine that analyzes the user's facial image.

[1813] Output: The server receives the user's input conditions and emotional data. Based on the emotional data, the server outputs the user's state (e.g., stress, confusion, etc.) as a numerical value or label.

[1814] Step 3:

[1815] Expert Matching

[1816] Input: The server searches the database based on the user's input conditions and emotion data.

[1817] Processing: Using a search algorithm, we list the experts best suited to the user's situation. The search takes into account not only the user's conditions but also their emotional state.

[1818] Output: The list of experts obtained as a search result is provided to the user. The search results are displayed on the user's terminal.

[1819] Step 4:

[1820] Book a lesson

[1821] Input: The user selects the desired expert from the list of experts presented and specifies the date and time of the lesson on the terminal.

[1822] Processing: The terminal sends the reservation information to the server, which checks the expert's schedule and confirms the reservation.

[1823] Output: The reservation confirmation information is notified to the expert and the user. The expert's schedule is updated.

[1824] Step 5:

[1825] Conducting and recording lessons

[1826] Input: On the day of the lesson, the user and the expert initiate a video call on the designated online platform.

[1827] Processing: The emotion engine monitors the user's emotions during the lesson in real time and sends the data to the server.

[1828] Output: The server records the lesson progress and execution log and saves it together with the emotion log. The real-time emotion data is notified to the expert.

[1829] Step 6:

[1830] Adjusting lesson content

[1831] Input: If the emotion engine detects that the user is stressed or confused, it sends that information to the server.

[1832] Processing: The server notifies the expert of the user's emotional state and prompts them to adjust their teaching methods.

[1833] Output: The expert adjusts the teaching method and content appropriately based on the user's emotional state.

[1834] Step 7:

[1835] Reward calculation and payment

[1836] Input: After the lesson is completed, the server calculates the reward for the expert based on the recorded lesson log and emotion log.

[1837] Processing: A calculation algorithm is used to calculate the required fee. The expert is provided with a fee breakdown.

[1838] Output: The expert confirms and approves the reward amount. The server executes the reward payment process and the reward is paid to the expert.

[1839] In this way, a system that takes into account the user's emotions is realized through specific processing steps to provide an effective lesson environment.

[1840] 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.

[1841] 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.

[1842] 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.

[1843] 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.

[1844] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1845] 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.

[1846] 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).

[1847] 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.

[1848] 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."

[1849] 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.

[1850] 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).

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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.

[1861] The following is further disclosed regarding the above embodiment.

[1862] (Claim 1)

[1863] a means for the user to input desired qualification criteria;

[1864] a means for registering information about qualified professionals;

[1865] a means for storing information of registered experts in a database;

[1866] A means for searching for the most suitable expert based on the user's input criteria;

[1867] a means for providing search results to a user;

[1868] A means for the user to view the schedule of the selected expert and book a lesson;

[1869] A means of notifying the specialist of lesson reservation information;

[1870] A means to manage the progress of lessons and record implementation logs;

[1871] A means for calculating the remuneration of the expert based on the recorded lesson log;

[1872] a means of paying the professional; and

[1873] A system including:

[1874] (Claim 2)

[1875] means for verifying the desired conditions entered by the user;

[1876] 10. The system of claim 1, further comprising means for verifying the registration information of the expert.

[1877] (Claim 3)

[1878] 10. The system of claim 1, further comprising means for sending a reminder notice before the start of a lesson.

[1879] "Example 1"

[1880] (Claim 1)

[1881] a means for the user to input desired qualification criteria;

[1882] a means for registering information about qualified professionals;

[1883] a means for storing information of registered experts in a database;

[1884] A means for searching for the most suitable expert based on the user's input criteria;

[1885] a means for providing search results to a user;

[1886] A means for the user to view the schedule of the selected expert and book a lesson;

[1887] A means of notifying the specialist of lesson reservation information;

[1888] A means for the user and the expert to conduct the lesson on the designated online platform on the day of the lesson reservation;

[1889] A means to manage the progress of lessons and record implementation logs;

[1890] A means for calculating the remuneration of the expert based on the rec...

Claims

1. a means for the user to input desired qualification criteria; a means for registering information about qualified professionals; a means for storing information of registered experts in a database; A means for searching for the most suitable expert based on the user's input criteria; a means for providing search results to a user; A means for the user to view the schedule of the selected expert and book a lesson; A means of notifying the specialist of lesson reservation information; A means to manage the progress of lessons and record implementation logs; A means for calculating the remuneration of the expert based on the recorded lesson log; a means of paying the professional; and A system including:

2. means for verifying the desired conditions entered by the user; 10. The system of claim 1, further comprising means for verifying the registration information of the professional.

3. 10. The system of claim 1, further comprising means for sending a reminder notice before the start of a lesson.

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