System

Abstract

A system is provided.SOLUTION: The system includes means for receiving basic information of a user as an input form and transmitting the information to a server, means for storing the received user information in a database in the server, means for referring to a qualification information database and suggesting appropriate qualifications based on the information of the user, means for providing the suggested qualifications to the user, means for generating a study method and a curriculum plan based on the qualifications selected by the user, means for providing the generated study method and curriculum plan to the user, and means for periodically receiving a progress status of the user and additional information, generating latest advice based on the information, and providing the latest advice to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] For many people aiming to obtain qualifications, it is extremely difficult to select the right qualification for them from the vast amount of information available and to find an effective study method. Furthermore, there is a lack of appropriate qualification suggestions based on individual interests and living environments, and a lack of effective study plans. For this reason, there is a need for a system that can provide efficient and effective support during the qualification acquisition process. [Means for solving the problem]

[0005] The present invention provides consistent support for the entire process of obtaining a qualification through a system including means for receiving a user's basic information as an input form and sending that information to a server, means for storing the received user information in a database in the server, means for consulting the qualification information database and proposing appropriate qualifications based on the user's information, means for providing the user with the proposed qualifications, means for generating a study method and curriculum plan based on the qualification selected by the user, means for providing the generated study method and curriculum plan to the user, and means for periodically receiving the user's progress and additional information, and generating and providing the user with the latest advice based on that information. This makes it possible to suggest the most suitable qualification for each individual, provide an efficient study plan, and provide continuous support.

[0006] "User" refers to an individual who uses this system to receive support in obtaining qualifications.

[0007] A "terminal" is a device or equipment used by a user, and is an interface that transmits and receives information between the user and the server.

[0008] "Server" refers to the central computer system that stores and manages user information and credentials, and generates various advice and suggestions using generative AI models.

[0009] A "generative AI model" is an artificial intelligence model that automatically generates qualification suggestions, study methods, and curriculum plans based on user information.

[0010] The "qualification information database" is a database for storing and managing detailed information on various qualifications.

[0011] "Study method" refers to an effective way of studying to obtain a specific qualification.

[0012] A "curriculum plan" refers to the specific study schedule and content required to obtain a qualification.

[0013] "Suggestion" refers to presenting appropriate qualifications and learning methods based on the user's information.

[0014] "Progress" refers to the current achievement level and progress of the user in progressing with their studies toward obtaining a qualification.

[0015] "Advice" refers to specific guidance and advice generated according to the user's learning progress and situation. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to an AI chatbot system that supports qualification acquisition. This system acquires basic information about the user, suggests appropriate qualification information, and provides efficient study methods and curriculum plans. The specific operation of this system is explained below.

[0038] 1. First Interaction and User Registration

[0039] User Registration

[0040] The user accesses the qualification acquisition support AI chatbot from their device.

[0041] When the device is accessed for the first time, it displays a welcome message along with a form for entering basic information (such as name, areas of interest, current job, and qualification goals).

[0042] The user enters the required information into the input form and presses the submit button.

[0043] The terminal transmits the information input by the user to the server.

[0044] The server stores the received user information in a database.

[0045] 2. Qualification proposal

[0046] Qualification Suggestion

[0047] The server queries the qualification database and selects appropriate qualifications based on the user's information (areas of interest, current occupation, goals, etc.).

[0048] The server uses a generative AI model to create a list of selected qualifications.

[0049] The server sends the generated credential proposal list to the terminal.

[0050] The terminal displays the qualification proposal list to the user.

[0051] 3. Support after qualification selection

[0052] Study methods and curriculum

[0053] The user selects the desired qualification from a list of suggested qualifications.

[0054] The device sends the selected credentials to the server.

[0055] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[0056] For example, if you select "IT Passport," the generative AI model will generate a detailed plan such as "study for three hours a week" and "use official textbooks and past exam questions."

[0057] The server sends the generated study method and curriculum plan to the device.

[0058] The device displays the study plan to the user.

[0059] 4. Ongoing support

[0060] Progress check and advice

[0061] Users periodically enter their learning progress and additional questions.

[0062] The device sends progress information and questions to the server.

[0063] The server uses a generative AI model to generate up-to-date advice based on the progress information and questions entered.

[0064] For example, if learning progress is lagging behind, the generative AI model will generate specific advice such as "areas that should be prioritized for relearning" and "effective review methods."

[0065] The server transmits the generated advice to the terminal.

[0066] The terminal displays the latest advice to the user.

[0067] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. It also allows users to progress through their studies effectively with ongoing support, making it possible to provide consistent support throughout the entire qualification acquisition process.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] The user accesses the qualification acquisition support AI chatbot from their device.

[0071] Step 2:

[0072] When the device is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (such as name, areas of interest, current job, and qualification goals).

[0073] Step 3:

[0074] The user enters the required information into the input form and presses the submit button.

[0075] Step 4:

[0076] The terminal transmits the information provided by the user to the server.

[0077] Step 5:

[0078] The server stores the received user information in a database.

[0079] Step 6:

[0080] The server queries the credentials database to find appropriate credentials based on the user's information (interests, current occupation, goals, etc.).

[0081] Step 7:

[0082] The server uses a generative AI model to create a list of entitlements based on the user's information.

[0083] Step 8:

[0084] The server sends the generated credential proposal list to the terminal.

[0085] Step 9:

[0086] The terminal displays the qualification proposal list to the user.

[0087] Step 10:

[0088] The user selects the desired qualification from a list of suggested qualifications.

[0089] Step 11:

[0090] The device sends the selected credentials to the server.

[0091] Step 12:

[0092] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[0093] (For example, if you select "IT Passport," a plan will be generated that involves studying for 3 hours per week and using official textbooks and past exam questions.)

[0094] Step 13:

[0095] The server sends the generated study method and curriculum plan to the device.

[0096] Step 14:

[0097] The device displays the study plan to the user.

[0098] Step 15:

[0099] Users periodically enter their learning progress and additional questions.

[0100] Step 16:

[0101] The device sends progress information and questions to the server.

[0102] Step 17:

[0103] The server uses a generative AI model to generate up-to-date advice based on the progress information and questions entered.

[0104] (For example, if your learning progress is behind, it will generate specific advice such as what areas you should focus on relearning and effective review methods.)

[0105] Step 18:

[0106] The server transmits the generated advice to the terminal.

[0107] Step 19:

[0108] The terminal displays the latest advice to the user.

[0109] Example 1

[0110] 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."

[0111] Conventional qualification support systems provide users with uniform study methods and curriculum plans, making it difficult to provide support tailored to each user's individual needs. They also lack a mechanism for providing appropriate advice in real time according to the user's learning progress. This makes the process of users obtaining qualifications inefficient and results in insufficient support for achieving their goals.

[0112] 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.

[0113] In this invention, the server includes means for receiving a user's basic information as an input form and sending the information to the server, means for storing the received user information in a database in the server, means for querying a qualification information database and proposing appropriate qualifications based on the user's information, means for providing the proposed qualifications to the user, means for generating a study method and curriculum plan based on a qualification selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for generating customized qualification information, study methods, and curriculum plans using a generative AI model, and means for providing input to the generative AI model using prompt sentences. This makes it possible to consistently provide efficient and personalized support for the user to obtain qualifications.

[0114] A "user" is a person who uses the qualification acquisition support system.

[0115] "Basic information" refers to information such as name, areas of interest, current job, and qualification goals that a user provides through an input form.

[0116] "Input form" refers to an interface for users to enter basic information.

[0117] "Server" means a central processing unit that processes information received from users, stores it in a database, and selects and proposes appropriate credentials.

[0118] A "database" is a digital storage system for storing and managing data such as user information and qualification information.

[0119] The "Qualification Information Database" is a database for storing and querying detailed information on various qualifications.

[0120] "Suggestion" is the act of querying a qualification information database, selecting appropriate qualifications based on the user's information, and presenting them to the user.

[0121] "Study method" refers to the learning method or approach that is considered optimal for obtaining a particular qualification.

[0122] A "curriculum plan" is a study schedule created to help users efficiently obtain qualifications.

[0123] "Progress" is information indicating how much progress the user has made in their studies according to the set curriculum plan.

[0124] "Advice" is advice on how to improve learning or what the next step should be based on the user's progress and additional information.

[0125] A "generative AI model" is an artificial intelligence program that automatically generates credentials, study methods, curriculum plans, and advice based on prompt input.

[0126] A "prompt" is an instruction entered into a generative AI model, and is specific text that guides the content to be generated.

[0127] The present invention relates to an AI chatbot system for assisting users in obtaining qualifications. This system acquires basic information about the user, suggests appropriate qualifications, and provides efficient study methods and curriculum plans. Specific embodiments of the system are described below.

[0128] User Registration

[0129] The user accesses the qualification acquisition support AI chatbot using a browser or a dedicated app. Once access is confirmed, the device displays a welcome message and a form for entering basic information. This form includes fields for entering information such as name, areas of interest, current job, and qualification acquisition goals. When the user enters and submits this information, the device sends the input information to the server. The server stores the received information in a database.

[0130] Qualification proposal

[0131] The server then queries the credential database based on the stored user information to select appropriate credentials. During this process, it uses a generative AI model (e.g., GPT-4) to create a list of credentials that best fit the user's profile. The server then sends this list of proposed credentials to the device, which then displays it to the user.

[0132] An example of a prompt sentence is, "The qualification selected by the user is the IT Passport. Please create an efficient study method and curriculum plan for this user. Please provide specific suggestions for the amount of time each week to study, the study materials to use, and important review points."

[0133] Support after qualification selection

[0134] When the user selects the desired qualification from a list of suggested qualifications, the device sends the selected qualification information to the server. The server again uses the generative AI model to create a detailed study method and curriculum plan based on the selected qualification. This information is again sent from the server to the device, which displays it to the user. As a specific example, a curriculum plan may be generated that reads, "We recommend studying for three hours a week. Use the official textbook and past exam papers, and spend one hour reviewing every Saturday."

[0135] Ongoing support

[0136] Users can periodically input their learning progress and additional questions. The input information is sent from the device to the server, and the server uses the generative AI model to generate the latest advice. For example, if learning progress is lagging behind, the generative AI model will suggest areas that should be re-studyed before moving on to the next chapter and effective review methods. This advice is sent from the server to the device, which then displays it to the user.

[0137] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. Users can also receive ongoing support and progress in their studies effectively, providing consistent support throughout the entire qualification acquisition process.

[0138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0139] Step 1: User access and display of input form

[0140] Users access the qualification acquisition support AI chatbot using a browser or a dedicated app.

[0141] Input: The user's access request.

[0142] The device will confirm access and display a welcome message and a form to enter basic information.

[0143] Output: Basic information input form (name, area of ​​interest, current job, qualification goals).

[0144] Step 2: Enter and submit user information

[0145] The user enters basic information into the input form and clicks the submit button.

[0146] Input: Basic information entered by the user (e.g., Tanaka Taro, IT, systems engineer, hoping to obtain an IT passport).

[0147] The terminal sends the input information to the server.

[0148] Output: Basic information sent by the user.

[0149] Step 3: Save user information

[0150] The server stores the received basic information in a database.

[0151] Input: Basic information sent from the device.

[0152] Data processing: User information is processed into an appropriate format and stored in a database.

[0153] Output: User information stored in the database.

[0154] Step 4: Query and select credentials

[0155] The server queries the credentials database based on the stored user information and selects the appropriate credentials.

[0156] Input: User information stored in the database.

[0157] Data Computing: Using a generative AI model (e.g., GPT-4), we generate a list of entitlements that best fit a user's profile.

[0158] An example prompt for a generative AI model: "The qualification selected by the user is the IT Passport. Please create an efficient study method and curriculum plan for this user. Please provide specific suggestions for the amount of time each week to study, the study materials to use, and important points to review."

[0159] Output: A list of qualification suggestions generated by the generative AI model (e.g., IT Passport, Fundamental Information Technology Engineer, Applied Information Technology Engineer).

[0160] Step 5: Submit and view the qualification proposal list

[0161] The server sends the generated credential proposal list to the terminal.

[0162] Input: The generated qualification proposal list.

[0163] The terminal displays the qualification proposal list to the user.

[0164] Output: The qualification proposal list displayed to the user.

[0165] Step 6: Select and submit qualifications

[0166] The user selects the desired qualification from a list of suggested qualifications.

[0167] Input: The qualification selected by the user.

[0168] The device sends the selected credentials to the server.

[0169] Output: The selected credentials sent to the server.

[0170] Step 7: Create a study method and curriculum plan

[0171] Based on the selected qualifications, the server uses a generative AI model to generate a detailed study method and curriculum plan.

[0172] Enter: Selected credentials.

[0173] Data calculation: A generative AI model provides specific recommendations for weekly study time, study materials to use, and key review points.

[0174] Output: Generated study methods and curriculum plans.

[0175] Example: "We recommend three hours of study per week, using the official textbook and past exam papers, with one hour of review every Saturday."

[0176] Step 8: Submit and view your study plan

[0177] The server sends the generated study method and curriculum plan to the device.

[0178] Input: Generated study methods and curriculum plans.

[0179] The device displays the study plan to the user.

[0180] Output: The study plan displayed to the user.

[0181] Step 9: Enter and submit progress information

[0182] Users periodically enter their learning progress and follow-up questions.

[0183] Input: User progress information and questions.

[0184] The device sends progress information and questions to the server.

[0185] Output: Progress information and questions sent to the server.

[0186] Step 10: Generate and display updated advice

[0187] The server uses a generative AI model to generate up-to-date advice based on progress information and questions.

[0188] Input: Progress information and questions.

[0189] Data computation: Generate new advice using generative AI models.

[0190] Specific examples: Suggest "areas that should be re-studyed before moving on to the next chapter" and "effective review methods that also serve as a refresher."

[0191] Output: The latest advice generated.

[0192] The server transmits the generated advice to the terminal.

[0193] The terminal displays the latest advice to the user.

[0194] Output: The most recent advice shown to the user.

[0195] (Application example 1)

[0196] 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."

[0197] While existing certification support systems can provide users with appropriate certification suggestions and study plans, they lack interactive support for workers to study efficiently on the factory floor. There is also a need for customized learning support tailored to the specific needs of factory workers.

[0198] 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.

[0199] In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying a qualification information database and suggesting appropriate qualifications based on the user information, means for providing the suggested qualifications to the user, means for generating a study method and curriculum plan based on the qualification selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for interactively providing appropriate qualification information and learning content to factory workers, means for displaying the progress of the study plan and curriculum on a robot display, means for generating a study plan and curriculum using an artificial intelligence model, and means for creating a detailed study plan based on prompts using the generative AI model when generating the study plan. This enables factory workers to efficiently study for qualifications.

[0200] "Basic user information" refers to information about the user, such as the user's name, areas of interest, current occupation, and goals for obtaining qualifications.

[0201] An "input form" is a means of providing a screen or fields for a user to enter basic information about themselves.

[0202] "Server" means the central processing unit that stores and processes information obtained from Users and generates and provides credentials and study plans.

[0203] A "database" is a system that systematically stores and manages user information and qualification information stored on a server.

[0204] A "Credentials Database" is a dedicated database in which information about various credentials is stored.

[0205] A "generative AI model" is a model that uses artificial intelligence to generate customized study methods and curricula based on user information.

[0206] A "prompt sentence" is an input sentence that is fed into a generative AI model and instructs it to produce a specific output.

[0207] "Providing interactively" means providing information and services to users in a two-way manner.

[0208] A "robot display" is a display device mounted on a robot, and is a means for displaying notifications and information to the user.

[0209] "User progress" is information that indicates the progress and degree of progress of learning toward obtaining a qualification.

[0210] "Additional information" refers to information added later other than the basic information provided by the user at the time of initial registration.

[0211] "Latest advice" refers to the most appropriate learning guidelines and advice at the current time based on the user's progress and additional information.

[0212] This invention is an AI chatbot system for supporting qualification acquisition, particularly for factory workers to efficiently advance their studies for qualification acquisition. Specific embodiments of this system are described below.

[0213] First, a user accesses the qualification acquisition support system through a robot in the factory. When the user uses the terminal for the first time, the terminal displays a welcome message and a basic information input form. The user enters basic information such as name, field of interest, current occupation, and qualification acquisition goal into this input form and submits it. This information is sent from the terminal to the server.

[0214] The server receives the user information and stores it in a database. The server then queries the credentials database and suggests appropriate credentials based on the user's areas of interest, current occupation, and goals for obtaining credentials. The list of suggested credentials is created using a generative AI model and sent to the device. The device then displays this list of suggested credentials to the user.

[0215] When a user selects the desired qualification from a list of suggested qualifications, the device sends the selected qualification information to the server. Based on the received qualification information, the server uses a generative AI model to create a detailed study method and curriculum plan. For example, if "electrician" is selected, the generative AI model generates a specific plan based on the prompt, such as "study for three hours a week" and "use official textbooks and past exam questions." To create such plans, generative AI models such as the OpenAI API are used.

[0216] The generated study plan and curriculum are sent to the terminal and provided to the user, who can then check the study plan displayed on the robot display and proceed with their studies.

[0217] Furthermore, users can periodically enter their learning progress and additional questions. The device sends the progress information and questions to the server. Based on this information, the server uses the generative AI model to generate the latest advice and provides it to the user. For example, if learning progress is lagging behind, the generative AI model will generate specific advice such as "areas that should be prioritized for relearning" and "effective review methods."

[0218] Hardware and software used

[0219] This system uses the following hardware and software:

[0220] Server: Stores and processes user information and runs the generative AI model.

[0221] Terminal: An interface through which a user enters and views information.

[0222] Robot Display: Displays generated credentials and study plans to the user.

[0223] Generative AI model: Uses OpenAI API to generate detailed study plans and advice.

[0224] Example prompt: "Please tell me an effective study plan to pass the electrician's exam."

[0225] This system allows factory workers to efficiently study for qualifications.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] The user accesses the AI ​​chatbot to assist with qualification acquisition from a device. The device displays a welcome message and an input form for basic information (name, area of ​​interest, current occupation, goal of qualification acquisition, etc.). When the user enters information into the input form and presses the send button, the entered information is sent from the device to the server. The input data is sent in JSON format or similar.

[0229] Step 2:

[0230] The server receives the user information sent from the terminal and stores it in a database. In this process, the user information is inserted into a database table. For example, the name, areas of interest, etc. are saved.

[0231] Step 3:

[0232] The server queries the qualification database and selects appropriate qualifications based on the user's areas of interest, current occupation, and qualification goals. Based on the query results, a generative AI model generates a list of qualification suggestions. The qualification data is filtered to select the most suitable qualifications for the user.

[0233] Step 4:

[0234] The server sends the qualification proposal list generated by the generative AI model to the terminal. The terminal receives the qualification proposal list and displays it to the user. The qualification names and brief descriptions are displayed in list format.

[0235] Step 5:

[0236] The user selects the desired credential from the credential proposal list. The selected credential information is sent from the terminal to the server. This data includes the selected credential name and ID.

[0237] Step 6:

[0238] Based on the selected qualifications, the server uses a generative AI model to generate a detailed study method and curriculum plan. For example, if "electrician" is selected, the generative AI model generates a plan based on the prompt, such as "study three hours a week" and "use official textbooks and past exam questions." The prompt is "Please tell me an efficient study plan to pass the electrician's exam," and the generated plan is output.

[0239] Step 7:

[0240] The generated study plan and curriculum are sent from the server to the terminal, which then provides it to the user. The study plan is then displayed in real time on the robot display.

[0241] Step 8:

[0242] The user periodically inputs their learning progress and additional questions into the device, which then sends this input data to the server. Specifically, the progress information includes completed tasks and study time.

[0243] Step 9:

[0244] The server uses a generative AI model to generate up-to-date advice based on the input progress information and questions. For example, if a student is behind in their studies, it generates specific advice such as "areas that should be prioritized for relearning" and "effective review methods." The generated advice is then output.

[0245] Step 10:

[0246] The server sends the latest generated advice to the terminal, which displays it to the user, who can check the new advice through the robot display.

[0247] 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.

[0248] This invention relates to an AI chatbot system that supports qualification acquisition and combines it with an emotion engine that recognizes the user's emotions. This system acquires the user's basic information to suggest appropriate qualifications, provides efficient study methods and curriculum plans, and provides advice and support based on the user's emotional state. The specific operation of this system is explained below.

[0249] 1. First Interaction and User Registration

[0250] User Registration

[0251] The user accesses the qualification acquisition support AI chatbot from their device.

[0252] When the device is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (such as name, areas of interest, current job, and qualification goals).

[0253] The user enters the required information into the input form and presses the submit button.

[0254] The terminal transmits the information provided by the user to the server.

[0255] The server stores the received user information in a database.

[0256] 2. Qualification proposal

[0257] Qualification Suggestion

[0258] The server queries the credentials database to find appropriate credentials based on the user's information (interests, current occupation, goals, etc.).

[0259] The server uses a generative AI model to create a list of entitlements based on the user's information.

[0260] The server sends the generated credential proposal list to the terminal.

[0261] The terminal displays the qualification proposal list to the user.

[0262] 3. Support after qualification selection

[0263] Utilizing the Emotion Engine

[0264] The device uses an emotion engine to recognize emotions from the user's facial expressions and text input.

[0265] The device transmits the recognized emotion information to the server.

[0266] Study methods and curriculum

[0267] The user selects the desired qualification from a list of suggested qualifications.

[0268] The device sends the selected credentials to the server.

[0269] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[0270] For example, if you select "IT Passport," the generative AI model will generate a detailed plan such as "study for three hours a week" and "use official textbooks and past exam questions."

[0271] The server sends the generated study method and curriculum plan to the device.

[0272] The device displays the study plan to the user.

[0273] 4. Ongoing support

[0274] Progress check and advice

[0275] Users periodically enter their progress and follow-up questions, and changes in their emotions are recognized along the way.

[0276] The device sends progress information, emotional data, and questions to the server.

[0277] The server uses a generative AI model to generate the latest advice based on the input progress information, emotional data, and questions.

[0278] For example, if a user's learning progress is slow and the emotion engine recognizes that they are feeling "impatient" or "anxious," the generative AI model will generate specific advice that takes emotions into consideration, such as "stay calm" and "get as much rest as possible."

[0279] The server transmits the generated advice to the terminal.

[0280] The terminal displays the latest advice to the user.

[0281] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. Furthermore, by receiving ongoing support and assistance that takes into account their emotional state, they can progress through their studies effectively. This makes it possible to provide consistent support throughout the entire qualification acquisition process.

[0282] The processing flow will be explained below.

[0283] Step 1:

[0284] The user accesses the qualification acquisition support AI chatbot from their device.

[0285] Step 2:

[0286] When the device is accessed for the first time, it displays a welcome message and provides a form for entering basic information (name, areas of interest, current job, and qualification goals).

[0287] Step 3:

[0288] The user enters the required information into the input form and presses the submit button.

[0289] Step 4:

[0290] The terminal transmits the user's input information to the server.

[0291] Step 5:

[0292] The server stores the received user information in a database.

[0293] Step 6:

[0294] The server queries the credential database to find the appropriate credentials based on the user's information.

[0295] Step 7:

[0296] The server uses the generative AI model to create a list of qualification suggestions based on the user information.

[0297] Step 8:

[0298] The server sends the generated credential proposal list to the terminal.

[0299] Step 9:

[0300] The terminal displays the qualification proposal list to the user.

[0301] Step 10:

[0302] The user selects the desired qualification from a list of suggested qualifications.

[0303] Step 11:

[0304] The device sends the selected credentials to the server.

[0305] Step 12:

[0306] The device uses an emotion engine to analyze the user's facial expressions and text input and sends the results to the server.

[0307] Step 13:

[0308] The server holds the emotion information received from the emotion engine.

[0309] Step 14:

[0310] The server uses a generative AI model for the selected qualification to create a detailed study method and curriculum plan.

[0311] (For example, if you select "IT Passport," a plan will be generated that involves studying for 3 hours per week and using official textbooks and past exam questions.)

[0312] Step 15:

[0313] The server sends the generated study method and curriculum plan to the device.

[0314] Step 16:

[0315] The device displays the study plan to the user.

[0316] Step 17:

[0317] Users periodically enter their learning progress and additional questions.

[0318] Step 18:

[0319] The device sends learning progress information, additional questions, and the user's current emotional data to the server.

[0320] Step 19:

[0321] The server analyzes the user's emotions using an emotion engine and feeds that information back to the generative AI model.

[0322] Step 20:

[0323] The server uses a generative AI model to generate the latest advice based on the user's progress and emotional information.

[0324] (For example, if the system recognizes that the user is feeling anxious because their learning progress is slow, it generates advice on how to stay calm and how to take effective breaks.)

[0325] Step 21:

[0326] The server transmits the generated advice to the terminal.

[0327] Step 22:

[0328] The terminal displays the latest advice to the user.

[0329] Example 2

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

[0331] While conventional qualification acquisition support systems have the ability to suggest qualifications based on the user's basic information and provide study methods and curriculum plans, they lack support that takes into account the user's emotional state. As a result, users often study while feeling mentally burdened, which can lead to a decline in motivation and stress, resulting in a decrease in learning effectiveness. Furthermore, there is also the issue of it being difficult to fully meet the user's needs, as qualification list generation and curriculum customization are not adequately performed.

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

[0333] In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying the qualification information database and proposing appropriate qualifications based on the user information, means for providing the proposed qualifications to the user, means for generating a study method and curriculum plan based on the qualifications selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for recognizing emotions from the user's facial expressions and text input, means for providing emotion-sensitive advice to the user based on the recognized emotional information, means for using a generative AI model to create a list of qualifications based on the user information, and means for providing the generated qualification list to the user. This enables support based on the user's emotional state and consistent support from qualification selection to providing study methods and curricula.

[0334] "Basic user information" refers to information such as name, areas of interest, current job, and qualification goals that users provide when using the system.

[0335] An "input form" refers to a screen or interface that a user uses to input information into a system.

[0336] A "server" is a computer system that receives, stores, and processes information submitted by users.

[0337] A "database" is a data management system for systematically storing received user information, qualification information, and the like.

[0338] "Credentials information database" means a database that stores information about the credentials managed by the system.

[0339] "Qualification proposal" is the process of selecting and presenting appropriate qualifications based on the user's basic information.

[0340] "Study method" refers to an efficient learning method for obtaining the qualification selected by the user.

[0341] A "curriculum plan" is a plan that defines a specific study schedule and how to use study materials to obtain a qualification.

[0342] "Progress" refers to the state that indicates how far the user has progressed in their studies.

[0343] "Advice" is specific advice or guidance provided to the user, taking into account their progress and emotional state.

[0344] "Emotion recognition" is a technology that identifies the user's current emotions from their facial expressions and text input.

[0345] "Generative AI model" refers to an artificial intelligence model used to generate qualification lists, study methods, and curriculum plans based on user information.

[0346] This invention relates to an AI chatbot system for assisting users in obtaining qualifications. This system acquires basic information about the user, suggests appropriate qualifications, provides efficient study methods and curriculum plans, and provides advice and support based on the user's emotional state. Specific embodiments for implementing this invention are described below.

[0347] Hardware and software used

[0348] Device: A device through which a user enters information (e.g., smartphone, tablet, computer, etc.).

[0349] Server: A computer system that receives, stores, and processes user information.

[0350] Database: A data management system that systematically stores user information and credentials.

[0351] Generative AI model: An artificial intelligence model that generates qualification lists, study methods, and curriculum plans based on user information.

[0352] Emotion recognition engine: Technology that recognizes emotions from a user's facial expressions and text input.

[0353] First Interaction and User Registration

[0354] When a user accesses the qualification acquisition support AI chatbot from their device, the device displays a welcome message along with a form for entering the user's basic information (such as name, areas of interest, current job, and qualification acquisition goals). When the user enters the required information in the form and submits it, the device sends the information to the server, which then stores the received information in a database.

[0355] Qualification proposal

[0356] The server queries the credentials database to find appropriate qualifications based on the user's information. The server then uses a generative AI model to generate a prompt, such as "Please list IT qualifications suitable for a systems engineer." The generative AI model then creates a list of qualifications based on the prompt. The resulting list of proposed qualifications is then sent to the terminal and displayed to the user.

[0357] Support after qualification selection

[0358] When a user selects a desired qualification from a list of suggested qualifications, the device sends the selected information to the server. The server uses a generative AI model to create a detailed study method and curriculum plan for the selected qualification. For example, if the user selects "IT Passport," the server inputs the prompt "Please generate a study plan for the IT Passport," and the generative AI model creates a detailed plan including "study three hours per week" and "use official textbooks and past exam papers." The generated study method and curriculum plan are sent to the device and displayed to the user.

[0359] Ongoing support

[0360] The user periodically inputs their learning progress and any follow-up questions into the device. The device then sends the progress information, questions, and emotional data recognized by the emotion recognition engine to the server. The server uses the generative AI model based on the input information to generate the latest advice. For example, if the server recognizes that learning progress is lagging behind or that the user is feeling "impatient" or "anxious," it will use the generative AI model to provide specific advice that takes into account the user's emotions, such as "stay calm" and "take as much rest as possible." This advice is displayed to the user via the device.

[0361] In this way, users can aim to obtain qualifications while receiving consistent support, and comprehensive assistance can be provided, from selecting appropriate qualifications to efficient learning and emotionally sensitive advice.

[0362] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0363] Processing Steps

[0364] First Interaction and User Registration

[0365] Step 1:

[0366] The user accesses the qualification acquisition support AI chatbot from their device.

[0367] Input: Actions to access the device (launching an app, accessing a website, etc.)

[0368] Output: A screen showing the chatbot's welcome message and input form

[0369] Specific operation: The user launches the dedicated app, and the device displays a welcome message along with a form for entering "name," "areas of interest," "current job," and "qualification goals."

[0370] Step 2:

[0371] The user enters the required information into the input form and presses the submit button.

[0372] Input: User inputs information (name, interests, current job, qualification goals)

[0373] Output: The entered information is sent to the server

[0374] Specific operation: The user enters information into each field and presses the send button. The device displays "Sending..." and sends the data to the server.

[0375] Step 3:

[0376] The server stores the received user information in a database.

[0377] Input: User information (name, area of ​​interest, current job, qualification goal)

[0378] Output: User information stored in the database

[0379] Specific operation: The server analyzes the user information and stores it in the database in the appropriate format. The server records in the log "New user information has been saved in the database."

[0380] Qualification proposal

[0381] Step 4:

[0382] The server queries the credential database to find the appropriate credentials based on the user's information.

[0383] Input: User information stored in the database

[0384] Output: A list of entitlements based on the user information

[0385] Specific operation: The server searches the database for qualification data related to "System Engineer" and "IT".

[0386] Step 5:

[0387] The server uses a generative AI model to create a list of entitlements based on the user's information.

[0388] Input: Certification data retrieved from the credentials database, prompt "Please list IT certifications suitable for systems engineers."

[0389] Output: A list of entitlements created by the generative AI model

[0390] How it works: The server inputs the prompt into the generative AI model, which returns a list of appropriate qualifications, including the IT Passport and the Fundamental Information Technology Engineer Examination.

[0391] Step 6:

[0392] The server sends the generated credential proposal list to the terminal.

[0393] Input: A list of entitlements created by a generative AI model

[0394] Output: List of qualification proposals sent to the terminal

[0395] Specific operation: The server sends the credential list to the terminal, notifying it that the credential list is ready.

[0396] Step 7:

[0397] The terminal displays the qualification proposal list to the user.

[0398] Input: Qualification proposal list sent to the terminal

[0399] Output: A list of qualification suggestions displayed on the screen

[0400] Specific operation: The terminal displays a list of suggested qualifications to the user, suggesting qualifications such as "IT Passport" and "Fundamental Information Technology Engineer Examination."

[0401] Support after qualification selection

[0402] Step 8:

[0403] The user selects the desired qualification from a list of suggested qualifications.

[0404] Input: Proposed Eligibility List

[0405] Output: Selected credentials

[0406] Specific operation: The user selects "IT Passport" and the terminal sends the selected information to the server.

[0407] Step 9:

[0408] The device sends the selected credentials to the server.

[0409] Enter: Selected Credentials

[0410] Output: Credential selection information sent to the server

[0411] Specific operation: The terminal notifies the server that "User 1 has selected IT Passport."

[0412] Step 10:

[0413] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[0414] Input: Selected credentials, prompt "Generate a study plan for the IT Passport"

[0415] Output: Detailed study methods and curriculum plans created by the generative AI model

[0416] Specific operation: The server inputs prompt statements into the generative AI model, and the returned plan includes things like "study three hours a week" and "use official textbooks and past exam questions."

[0417] Step 11:

[0418] The server sends the generated study method and curriculum plan to the device.

[0419] Input: Study methods and curriculum plans created by a generative AI model

[0420] Output: Study methods and curriculum plans sent to the device

[0421] Specific operation: The server notifies the device that "the study plan has been sent" and sends a detailed plan.

[0422] Step 12:

[0423] The device displays the study plan to the user.

[0424] Input: Study methods and curriculum plans sent to your device

[0425] Output: Study plan displayed on screen

[0426] Specific operation: The device displays a plan to the user, such as "study 3 hours a week" and "use official textbooks and past exam questions."

[0427] Ongoing support

[0428] Step 13:

[0429] The user periodically inputs information about their learning progress and any follow-up questions into the device, and any changes in their emotions along the way are also recognized.

[0430] Input: User progress, follow-up questions, emotional information

[0431] Output: Input progress information and questions, recognized emotion data

[0432] Specific operation: The user types "I'm on track this week" or "What's next?", and the device analyzes the user's emotions using an emotion recognition engine.

[0433] Step 14:

[0434] The device sends progress information, emotional data, and questions to the server.

[0435] Input: Progress information, emotion data, questions

[0436] Output: Progress information, emotion data, and questions sent to the server

[0437] Specific operation: The device displays "Sending progress information and questions..." and sends it to the server.

[0438] Step 15:

[0439] The server uses a generative AI model to generate the latest advice based on input progress information, emotional data, and questions.

[0440] Input: Progress information, emotion data, question, prompt "Advice if learning progress is slow"

[0441] Output: Updated advice produced by the generative AI model

[0442] Specific operation: The server inputs the necessary prompt sentences into the generated AI model, and generates advice such as "calm down" and "get as much rest as possible."

[0443] Step 16:

[0444] The server transmits the generated advice to the terminal.

[0445] Input: The latest advice generated by the generative AI model

[0446] Output: Advice sent to terminal

[0447] Specific operation: The server sends the advice to the device, notifying it that "the advice is ready."

[0448] Step 17:

[0449] The terminal displays the latest advice to the user.

[0450] Input: Latest advice sent to the terminal

[0451] Output: The most recent advice displayed on the screen.

[0452] What happens: Your device will display advice such as "Slow down a bit and take a break."

[0453] This series of processes will enable users to effectively study towards obtaining qualifications.

[0454] (Application example 2)

[0455] 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."

[0456] Conventional food delivery systems lack personalized menu suggestions based on the user's basic information and preferences, making it difficult for users to make satisfying choices. Furthermore, they are unable to provide advice based on the user's emotions and state, making it difficult to improve the user experience.

[0457] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying a food information database and proposing an appropriate menu based on the user information, means for providing the proposed menu to the user, means for generating discount information based on a menu selected by the user, means for providing the generated discount information to the user, and means for periodically receiving the user's progress status and emotional data, and generating and providing the user with the latest suggestions and advice based on that information. This makes it possible to propose personalized menus based on the user's basic information and preferences, and to provide advice and discount information that takes emotions into consideration.

[0458] "Basic user information" is personal data such as the user's name, favorite dishes, allergy information, budget, etc.

[0459] The "means for sending to the server" is a communication means for sending the user's basic information from the input form to the server.

[0460] The "means for storing in a database" refers to a means for storing the received information in a database and making it possible to query the information as needed.

[0461] "Querying a food information database" refers to referencing the database to search for an appropriate menu based on the user's information.

[0462] The "means for suggesting a menu" is a means for generating an appropriate menu based on the user's basic information and preferences and presenting it to the user.

[0463] The "means for generating discount information" is a means for generating applicable discount information based on the menu selected by the user.

[0464] "Periodic receipt of progress status and emotional data" means periodically obtaining feedback and emotional state from the user.

[0465] "Generating up-to-date suggestions and advice" means using collected progress and sentiment data to generate appropriate menus and advice for the next step.

[0466] This invention relates to an AI chatbot system that combines a food delivery order support system with an emotion engine that recognizes user emotions. This system acquires basic information about the user, suggests appropriate menu items, and provides discount information and advice. The specific operation is explained below.

[0467] First Interaction and User Registration

[0468] User Registration

[0469] A user accesses a food delivery app on their smartphone.

[0470] When the device is accessed for the first time, it displays a welcome message along with a form for entering the user's basic information (name, favorite dishes, allergy information, budget, etc.).

[0471] The user enters the required information into the input form and presses the submit button.

[0472] The terminal transmits the information provided by the user to the server.

[0473] The server stores the received user information in a database using Firebase.

[0474] Menu suggestions

[0475] Menu suggestions

[0476] The server queries the food information database and searches for an appropriate menu based on the user's information (preferred dishes, allergy information, budget, etc.).

[0477] The server uses a generative AI model (GPT-4) to create a list of menus based on user information.

[0478] The server generates a menu suggestion list and sends it to the terminal.

[0479] The terminal displays a menu suggestion list to the user.

[0480] Utilizing the Emotion Engine

[0481] Analysis by emotion engine

[0482] The device recognizes emotions using an emotion engine (Emotion API) from the user's facial expressions and text input.

[0483] The device transmits the recognized emotion information to the server.

[0484] Providing discount information

[0485] Generate and provide discount information

[0486] The user selects the desired menu from the suggested menu list.

[0487] The terminal transmits the selected menu information to the server.

[0488] The server uses a generative AI model to create discount information for the selected menu.

[0489] The server transmits the generated discount information to the terminal.

[0490] The terminal displays the discount information to the user.

[0491] Ongoing support

[0492] Progress check and advice

[0493] Users periodically enter feedback and sentiment about their orders.

[0494] The device sends feedback information and emotion data to the server.

[0495] The server uses a generative AI model to generate the latest suggestions and advice based on the input feedback information and emotional data.

[0496] The server sends the generated suggestions and advice to the device.

[0497] The device displays the latest suggestions and advice to the user.

[0498] This allows users to select the menu that best suits them and receive emotionally sensitive advice and discount information, providing a highly satisfying food delivery experience.

[0499] Prompt Sentence Examples

[0500] markdown

[0501] System prompt:

[0502] User attribute information: {Name: 'Yamada Taro', Favorite food: 'Japanese food', Allergy: 'Peanuts', Budget: 'Under 2000 yen'}

[0503] Past orders: 'Sushi, Ramen, Udon'

[0504] User sentiment: 'Frustrated'

[0505] Generate menu suggestions and discount coupons.

[0506] Example output to the user:

[0507] Hello Taro Yamada! How are you today?

[0508] We've created menu recommendations for you based on your recent orders.

[0509] Tempura set meal

[0510] Boiled fish

[0511] Plus, we offer special coupons for new customers!

[0512] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0513] Step 1:

[0514] A user accesses a food delivery app from their smartphone. When the app is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (name, favorite dishes, allergy information, budget, etc.). The user enters the necessary information into the input form and presses the submit button.

[0515] Input: User basic information

[0516] Output: Basic information filled in and ready to send

[0517] Step 2:

[0518] The device sends the information provided by the user to the server, which then stores the received user information in a database (Firebase).

[0519] Input: User's basic information input data

[0520] Output: User information is saved in the database

[0521] Step 3:

[0522] The server queries a food information database and searches for appropriate menu items based on the user's information (preferred dishes, allergies, budget, etc.). The server uses a generative AI model (GPT-4) to create a list of menu items based on the user's information.

[0523] Input: User basic information

[0524] Data processing / calculation: Generate menu suggestions using AI models

[0525] Output: Menu suggestion list

[0526] Step 4:

[0527] The server sends the generated menu suggestion list to the terminal, which displays the menu suggestion list to the user.

[0528] Input: Menu suggestion list

[0529] Output: A list of menu suggestions is displayed on the terminal.

[0530] Step 5:

[0531] The device recognizes emotions from the user's facial expressions and text input using the emotion engine (Emotion API). The recognized emotion information is sent to the server.

[0532] Input: facial expressions and text as the user types

[0533] Data processing / calculation: Analysis using emotion recognition engine

[0534] Output: Emotional information

[0535] Step 6:

[0536] The user selects the desired menu from the proposed menu list, and the terminal transmits the selected menu information to the server.

[0537] Input: User menu selection

[0538] Output: Selected menu information is sent to the server

[0539] Step 7:

[0540] The server uses a generative AI model to create discount information for the selected menu item. The discount information is then sent from the server to the device, which then displays the discount information to the user.

[0541] Input: Selected menu information

[0542] Data processing / calculation: Generate discount information using AI models

[0543] Output: Discount information displayed on terminal

[0544] Step 8:

[0545] The user periodically inputs feedback about orders and changes in emotions, and the terminal transmits the feedback information and emotion data to the server.

[0546] Input: User feedback and sentiment data

[0547] Output: Feedback information and emotion data are sent to the server.

[0548] Step 9:

[0549] The server uses a generative AI model to generate the latest suggestions and advice based on the input feedback information and emotion data. The server then sends the generated suggestions and advice to the device, which then displays the latest suggestions and advice to the user.

[0550] Input: Feedback information and emotion data

[0551] Data processing / calculation: Generate suggestions and advice using AI models

[0552] Output: The latest suggestions and advice will be displayed on your terminal.

[0553] 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.

[0554] 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.

[0555] 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.

[0556] [Second embodiment]

[0557] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0558] 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.

[0559] 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).

[0560] 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.

[0561] 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.

[0562] 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).

[0563] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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."

[0569] This invention relates to an AI chatbot system that supports qualification acquisition. This system acquires basic information about the user, suggests appropriate qualification information, and provides efficient study methods and curriculum plans. The specific operation of this system is explained below.

[0570] 1. First Interaction and User Registration

[0571] User Registration

[0572] The user accesses the qualification acquisition support AI chatbot from their device.

[0573] When the device is accessed for the first time, it displays a welcome message along with a form for entering basic information (such as name, areas of interest, current job, and qualification goals).

[0574] The user enters the required information into the input form and presses the submit button.

[0575] The terminal transmits the information input by the user to the server.

[0576] The server stores the received user information in a database.

[0577] 2. Qualification proposal

[0578] Qualification Suggestion

[0579] The server queries the qualification database and selects appropriate qualifications based on the user's information (areas of interest, current occupation, goals, etc.).

[0580] The server uses a generative AI model to create a list of selected qualifications.

[0581] The server sends the generated credential proposal list to the terminal.

[0582] The terminal displays the qualification proposal list to the user.

[0583] 3. Support after qualification selection

[0584] Study methods and curriculum

[0585] The user selects the desired qualification from a list of suggested qualifications.

[0586] The device sends the selected credentials to the server.

[0587] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[0588] For example, if you select "IT Passport," the generative AI model will generate a detailed plan such as "study for three hours a week" and "use official textbooks and past exam questions."

[0589] The server sends the generated study method and curriculum plan to the device.

[0590] The device displays the study plan to the user.

[0591] 4. Ongoing support

[0592] Progress check and advice

[0593] Users periodically enter their learning progress and additional questions.

[0594] The device sends progress information and questions to the server.

[0595] The server uses a generative AI model to generate up-to-date advice based on the progress information and questions entered.

[0596] For example, if learning progress is lagging behind, the generative AI model will generate specific advice such as "areas that should be prioritized for relearning" and "effective review methods."

[0597] The server transmits the generated advice to the terminal.

[0598] The terminal displays the latest advice to the user.

[0599] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. It also allows users to progress through their studies effectively with ongoing support, making it possible to provide consistent support throughout the entire qualification acquisition process.

[0600] The processing flow will be explained below.

[0601] Step 1:

[0602] The user accesses the qualification acquisition support AI chatbot from their device.

[0603] Step 2:

[0604] When the device is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (such as name, areas of interest, current job, and qualification goals).

[0605] Step 3:

[0606] The user enters the required information into the input form and presses the submit button.

[0607] Step 4:

[0608] The terminal transmits the information provided by the user to the server.

[0609] Step 5:

[0610] The server stores the received user information in a database.

[0611] Step 6:

[0612] The server queries the credentials database to find appropriate credentials based on the user's information (interests, current occupation, goals, etc.).

[0613] Step 7:

[0614] The server uses a generative AI model to create a list of entitlements based on the user's information.

[0615] Step 8:

[0616] The server sends the generated credential proposal list to the terminal.

[0617] Step 9:

[0618] The terminal displays the qualification proposal list to the user.

[0619] Step 10:

[0620] The user selects the desired qualification from a list of suggested qualifications.

[0621] Step 11:

[0622] The device sends the selected credentials to the server.

[0623] Step 12:

[0624] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[0625] (For example, if you select "IT Passport," a plan will be generated that involves studying for 3 hours per week and using official textbooks and past exam questions.)

[0626] Step 13:

[0627] The server sends the generated study method and curriculum plan to the device.

[0628] Step 14:

[0629] The device displays the study plan to the user.

[0630] Step 15:

[0631] Users periodically enter their learning progress and additional questions.

[0632] Step 16:

[0633] The device sends progress information and questions to the server.

[0634] Step 17:

[0635] The server uses a generative AI model to generate up-to-date advice based on the progress information and questions entered.

[0636] (For example, if your learning progress is behind, it will generate specific advice such as what areas you should focus on relearning and effective review methods.)

[0637] Step 18:

[0638] The server transmits the generated advice to the terminal.

[0639] Step 19:

[0640] The terminal displays the latest advice to the user.

[0641] Example 1

[0642] 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."

[0643] Conventional qualification support systems provide users with uniform study methods and curriculum plans, making it difficult to provide support tailored to each user's individual needs. They also lack a mechanism for providing appropriate advice in real time according to the user's learning progress. This makes the process of users obtaining qualifications inefficient and results in insufficient support for achieving their goals.

[0644] 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.

[0645] In this invention, the server includes means for receiving a user's basic information as an input form and sending the information to the server, means for storing the received user information in a database in the server, means for querying a qualification information database and proposing appropriate qualifications based on the user's information, means for providing the proposed qualifications to the user, means for generating a study method and curriculum plan based on a qualification selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for generating customized qualification information, study methods, and curriculum plans using a generative AI model, and means for providing input to the generative AI model using prompt sentences. This makes it possible to consistently provide efficient and personalized support for the user to obtain qualifications.

[0646] A "user" is a person who uses the qualification acquisition support system.

[0647] "Basic information" refers to information such as name, areas of interest, current job, and qualification goals that a user provides through an input form.

[0648] "Input form" refers to an interface for users to enter basic information.

[0649] "Server" means a central processing unit that processes information received from users, stores it in a database, and selects and proposes appropriate credentials.

[0650] A "database" is a digital storage system for storing and managing data such as user information and qualification information.

[0651] The "Qualification Information Database" is a database for storing and querying detailed information on various qualifications.

[0652] "Suggestion" is the act of querying a qualification information database, selecting appropriate qualifications based on the user's information, and presenting them to the user.

[0653] "Study method" refers to the learning method or approach that is considered optimal for obtaining a particular qualification.

[0654] A "curriculum plan" is a study schedule created to help users efficiently obtain qualifications.

[0655] "Progress" is information indicating how much progress the user has made in their studies according to the set curriculum plan.

[0656] "Advice" is advice on how to improve learning or what the next step should be based on the user's progress and additional information.

[0657] A "generative AI model" is an artificial intelligence program that automatically generates credentials, study methods, curriculum plans, and advice based on prompt input.

[0658] A "prompt" is an instruction entered into a generative AI model, and is specific text that guides the content to be generated.

[0659] The present invention relates to an AI chatbot system for assisting users in obtaining qualifications. This system acquires basic information about the user, suggests appropriate qualifications, and provides efficient study methods and curriculum plans. Specific embodiments of the system are described below.

[0660] User Registration

[0661] The user accesses the qualification acquisition support AI chatbot using a browser or a dedicated app. Once access is confirmed, the device displays a welcome message and a form for entering basic information. This form includes fields for entering information such as name, areas of interest, current job, and qualification acquisition goals. When the user enters and submits this information, the device sends the input information to the server. The server stores the received information in a database.

[0662] Qualification proposal

[0663] The server then queries the credential database based on the stored user information to select appropriate credentials. During this process, it uses a generative AI model (e.g., GPT-4) to create a list of credentials that best fit the user's profile. The server then sends this list of proposed credentials to the device, which then displays it to the user.

[0664] An example of a prompt sentence is, "The qualification selected by the user is the IT Passport. Please create an efficient study method and curriculum plan for this user. Please provide specific suggestions for the amount of time each week to study, the study materials to use, and important review points."

[0665] Support after qualification selection

[0666] When the user selects the desired qualification from a list of suggested qualifications, the device sends the selected qualification information to the server. The server again uses the generative AI model to create a detailed study method and curriculum plan based on the selected qualification. This information is again sent from the server to the device, which displays it to the user. As a specific example, a curriculum plan may be generated that reads, "We recommend studying for three hours a week. Use the official textbook and past exam papers, and spend one hour reviewing every Saturday."

[0667] Ongoing support

[0668] Users can periodically input their learning progress and additional questions. The input information is sent from the device to the server, and the server uses the generative AI model to generate the latest advice. For example, if learning progress is lagging behind, the generative AI model will suggest areas that should be re-studyed before moving on to the next chapter and effective review methods. This advice is sent from the server to the device, which then displays it to the user.

[0669] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. Users can also receive ongoing support and progress in their studies effectively, providing consistent support throughout the entire qualification acquisition process.

[0670] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0671] Step 1: User access and display of input form

[0672] Users access the qualification acquisition support AI chatbot using a browser or a dedicated app.

[0673] Input: The user's access request.

[0674] The device will confirm access and display a welcome message and a form to enter basic information.

[0675] Output: Basic information input form (name, area of ​​interest, current job, qualification goals).

[0676] Step 2: Enter and submit user information

[0677] The user enters basic information into the input form and clicks the submit button.

[0678] Input: Basic information entered by the user (e.g., Tanaka Taro, IT, systems engineer, hoping to obtain an IT passport).

[0679] The terminal sends the input information to the server.

[0680] Output: Basic information sent by the user.

[0681] Step 3: Save user information

[0682] The server stores the received basic information in a database.

[0683] Input: Basic information sent from the device.

[0684] Data processing: User information is processed into an appropriate format and stored in a database.

[0685] Output: User information stored in the database.

[0686] Step 4: Query and select credentials

[0687] The server queries the credentials database based on the stored user information and selects the appropriate credentials.

[0688] Input: User information stored in the database.

[0689] Data Computing: Using a generative AI model (e.g., GPT-4), we generate a list of entitlements that best fit a user's profile.

[0690] An example prompt for a generative AI model: "The qualification selected by the user is the IT Passport. Please create an efficient study method and curriculum plan for this user. Please provide specific suggestions for the amount of time each week to study, the study materials to use, and important points to review."

[0691] Output: A list of qualification suggestions generated by the generative AI model (e.g., IT Passport, Fundamental Information Technology Engineer, Applied Information Technology Engineer).

[0692] Step 5: Submit and view the qualification proposal list

[0693] The server sends the generated credential proposal list to the terminal.

[0694] Input: The generated qualification proposal list.

[0695] The terminal displays the qualification proposal list to the user.

[0696] Output: The qualification proposal list displayed to the user.

[0697] Step 6: Select and submit qualifications

[0698] The user selects the desired qualification from a list of suggested qualifications.

[0699] Input: The qualification selected by the user.

[0700] The device sends the selected credentials to the server.

[0701] Output: The selected credentials sent to the server.

[0702] Step 7: Create a study method and curriculum plan

[0703] Based on the selected qualifications, the server uses a generative AI model to generate a detailed study method and curriculum plan.

[0704] Enter: Selected credentials.

[0705] Data calculation: A generative AI model provides specific recommendations for weekly study time, study materials to use, and key review points.

[0706] Output: Generated study methods and curriculum plans.

[0707] Example: "We recommend three hours of study per week, using the official textbook and past exam papers, with one hour of review every Saturday."

[0708] Step 8: Submit and view your study plan

[0709] The server sends the generated study method and curriculum plan to the device.

[0710] Input: Generated study methods and curriculum plans.

[0711] The device displays the study plan to the user.

[0712] Output: The study plan displayed to the user.

[0713] Step 9: Enter and submit progress information

[0714] Users periodically enter their learning progress and follow-up questions.

[0715] Input: User progress information and questions.

[0716] The device sends progress information and questions to the server.

[0717] Output: Progress information and questions sent to the server.

[0718] Step 10: Generate and display updated advice

[0719] The server uses a generative AI model to generate up-to-date advice based on progress information and questions.

[0720] Input: Progress information and questions.

[0721] Data computation: Generate new advice using generative AI models.

[0722] Specific examples: Suggest "areas that should be re-studyed before moving on to the next chapter" and "effective review methods that also serve as a refresher."

[0723] Output: The latest advice generated.

[0724] The server transmits the generated advice to the terminal.

[0725] The terminal displays the latest advice to the user.

[0726] Output: The most recent advice shown to the user.

[0727] (Application example 1)

[0728] 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."

[0729] While existing certification support systems can provide users with appropriate certification suggestions and study plans, they lack interactive support for workers to study efficiently on the factory floor. There is also a need for customized learning support tailored to the specific needs of factory workers.

[0730] 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.

[0731] In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying a qualification information database and suggesting appropriate qualifications based on the user information, means for providing the suggested qualifications to the user, means for generating a study method and curriculum plan based on the qualification selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for interactively providing appropriate qualification information and learning content to factory workers, means for displaying the progress of the study plan and curriculum on a robot display, means for generating a study plan and curriculum using an artificial intelligence model, and means for creating a detailed study plan based on prompts using the generative AI model when generating the study plan. This enables factory workers to efficiently study for qualifications.

[0732] "Basic user information" refers to information about the user, such as the user's name, areas of interest, current occupation, and goals for obtaining qualifications.

[0733] An "input form" is a means of providing a screen or fields for a user to enter basic information about themselves.

[0734] "Server" means the central processing unit that stores and processes information obtained from Users and generates and provides credentials and study plans.

[0735] A "database" is a system that systematically stores and manages user information and qualification information stored on a server.

[0736] A "Credentials Database" is a dedicated database in which information about various credentials is stored.

[0737] A "generative AI model" is a model that uses artificial intelligence to generate customized study methods and curricula based on user information.

[0738] A "prompt sentence" is an input sentence that is fed into a generative AI model and instructs it to produce a specific output.

[0739] "Providing interactively" means providing information and services to users in a two-way manner.

[0740] A "robot display" is a display device mounted on a robot, and is a means for displaying notifications and information to the user.

[0741] "User progress" is information that indicates the progress and degree of progress of learning toward obtaining a qualification.

[0742] "Additional information" refers to information added later other than the basic information provided by the user at the time of initial registration.

[0743] "Latest advice" refers to the most appropriate learning guidelines and advice at the current time based on the user's progress and additional information.

[0744] This invention is an AI chatbot system for supporting qualification acquisition, particularly for factory workers to efficiently advance their studies for qualification acquisition. Specific embodiments of this system are described below.

[0745] First, a user accesses the qualification acquisition support system through a robot in the factory. When the user uses the terminal for the first time, the terminal displays a welcome message and a basic information input form. The user enters basic information such as name, field of interest, current occupation, and qualification acquisition goal into this input form and submits it. This information is sent from the terminal to the server.

[0746] The server receives the user information and stores it in a database. The server then queries the credentials database and suggests appropriate credentials based on the user's areas of interest, current occupation, and goals for obtaining credentials. The list of suggested credentials is created using a generative AI model and sent to the device. The device then displays this list of suggested credentials to the user.

[0747] When a user selects the desired qualification from a list of suggested qualifications, the device sends the selected qualification information to the server. Based on the received qualification information, the server uses a generative AI model to create a detailed study method and curriculum plan. For example, if "electrician" is selected, the generative AI model generates a specific plan based on the prompt, such as "study for three hours a week" and "use official textbooks and past exam questions." To create such plans, generative AI models such as the OpenAI API are used.

[0748] The generated study plan and curriculum are sent to the terminal and provided to the user, who can then check the study plan displayed on the robot display and proceed with their studies.

[0749] Furthermore, users can periodically enter their learning progress and additional questions. The device sends the progress information and questions to the server. Based on this information, the server uses the generative AI model to generate the latest advice and provides it to the user. For example, if learning progress is lagging behind, the generative AI model will generate specific advice such as "areas that should be prioritized for relearning" and "effective review methods."

[0750] Hardware and software used

[0751] This system uses the following hardware and software:

[0752] Server: Stores and processes user information and runs the generative AI model.

[0753] Terminal: An interface through which a user enters and views information.

[0754] Robot Display: Displays generated credentials and study plans to the user.

[0755] Generative AI model: Uses OpenAI API to generate detailed study plans and advice.

[0756] Example prompt: "Please tell me an effective study plan to pass the electrician's exam."

[0757] This system allows factory workers to efficiently study for qualifications.

[0758] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0759] Step 1:

[0760] The user accesses the AI ​​chatbot to assist with qualification acquisition from a device. The device displays a welcome message and an input form for basic information (name, area of ​​interest, current occupation, goal of qualification acquisition, etc.). When the user enters information into the input form and presses the send button, the entered information is sent from the device to the server. The input data is sent in JSON format or similar.

[0761] Step 2:

[0762] The server receives the user information sent from the terminal and stores it in a database. In this process, the user information is inserted into a database table. For example, the name, areas of interest, etc. are saved.

[0763] Step 3:

[0764] The server queries the qualification database and selects appropriate qualifications based on the user's areas of interest, current occupation, and qualification goals. Based on the query results, a generative AI model generates a list of qualification suggestions. The qualification data is filtered to select the most suitable qualifications for the user.

[0765] Step 4:

[0766] The server sends the qualification proposal list generated by the generative AI model to the terminal. The terminal receives the qualification proposal list and displays it to the user. The qualification names and brief descriptions are displayed in list format.

[0767] Step 5:

[0768] The user selects the desired credential from the credential proposal list. The selected credential information is sent from the terminal to the server. This data includes the selected credential name and ID.

[0769] Step 6:

[0770] Based on the selected qualifications, the server uses a generative AI model to generate a detailed study method and curriculum plan. For example, if "electrician" is selected, the generative AI model generates a plan based on the prompt, such as "study three hours a week" and "use official textbooks and past exam questions." The prompt is "Please tell me an efficient study plan to pass the electrician's exam," and the generated plan is output.

[0771] Step 7:

[0772] The generated study plan and curriculum are sent from the server to the terminal, which then provides it to the user. The study plan is then displayed in real time on the robot display.

[0773] Step 8:

[0774] The user periodically inputs their learning progress and additional questions into the device, which then sends this input data to the server. Specifically, the progress information includes completed tasks and study time.

[0775] Step 9:

[0776] The server uses a generative AI model to generate up-to-date advice based on the input progress information and questions. For example, if a student is behind in their studies, it generates specific advice such as "areas that should be prioritized for relearning" and "effective review methods." The generated advice is then output.

[0777] Step 10:

[0778] The server sends the latest generated advice to the terminal, which displays it to the user, who can check the new advice through the robot display.

[0779] 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.

[0780] This invention relates to an AI chatbot system that supports qualification acquisition and combines it with an emotion engine that recognizes the user's emotions. This system acquires the user's basic information to suggest appropriate qualifications, provides efficient study methods and curriculum plans, and provides advice and support based on the user's emotional state. The specific operation of this system is explained below.

[0781] 1. First Interaction and User Registration

[0782] User Registration

[0783] The user accesses the qualification acquisition support AI chatbot from their device.

[0784] When the device is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (such as name, areas of interest, current job, and qualification goals).

[0785] The user enters the required information into the input form and presses the submit button.

[0786] The terminal transmits the information provided by the user to the server.

[0787] The server stores the received user information in a database.

[0788] 2. Qualification proposal

[0789] Qualification Suggestion

[0790] The server queries the credentials database to find appropriate credentials based on the user's information (interests, current occupation, goals, etc.).

[0791] The server uses a generative AI model to create a list of entitlements based on the user's information.

[0792] The server sends the generated credential proposal list to the terminal.

[0793] The terminal displays the qualification proposal list to the user.

[0794] 3. Support after qualification selection

[0795] Utilizing the Emotion Engine

[0796] The device uses an emotion engine to recognize emotions from the user's facial expressions and text input.

[0797] The device transmits the recognized emotion information to the server.

[0798] Study methods and curriculum

[0799] The user selects the desired qualification from a list of suggested qualifications.

[0800] The device sends the selected credentials to the server.

[0801] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[0802] For example, if you select "IT Passport," the generative AI model will generate a detailed plan such as "study for three hours a week" and "use official textbooks and past exam questions."

[0803] The server sends the generated study method and curriculum plan to the device.

[0804] The device displays the study plan to the user.

[0805] 4. Ongoing support

[0806] Progress check and advice

[0807] Users periodically enter their progress and follow-up questions, and changes in their emotions are recognized along the way.

[0808] The device sends progress information, emotional data, and questions to the server.

[0809] The server uses a generative AI model to generate the latest advice based on the input progress information, emotional data, and questions.

[0810] For example, if a user's learning progress is slow and the emotion engine recognizes that they are feeling "impatient" or "anxious," the generative AI model will generate specific advice that takes emotions into consideration, such as "stay calm" and "get as much rest as possible."

[0811] The server transmits the generated advice to the terminal.

[0812] The terminal displays the latest advice to the user.

[0813] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. Furthermore, by receiving ongoing support and assistance that takes into account their emotional state, they can progress through their studies effectively. This makes it possible to provide consistent support throughout the entire qualification acquisition process.

[0814] The processing flow will be explained below.

[0815] Step 1:

[0816] The user accesses the qualification acquisition support AI chatbot from their device.

[0817] Step 2:

[0818] When the device is accessed for the first time, it displays a welcome message and provides a form for entering basic information (name, areas of interest, current job, and qualification goals).

[0819] Step 3:

[0820] The user enters the required information into the input form and presses the submit button.

[0821] Step 4:

[0822] The terminal transmits the user's input information to the server.

[0823] Step 5:

[0824] The server stores the received user information in a database.

[0825] Step 6:

[0826] The server queries the credential database to find the appropriate credentials based on the user's information.

[0827] Step 7:

[0828] The server uses the generative AI model to create a list of qualification suggestions based on the user information.

[0829] Step 8:

[0830] The server sends the generated credential proposal list to the terminal.

[0831] Step 9:

[0832] The terminal displays the qualification proposal list to the user.

[0833] Step 10:

[0834] The user selects the desired qualification from a list of suggested qualifications.

[0835] Step 11:

[0836] The device sends the selected credentials to the server.

[0837] Step 12:

[0838] The device uses an emotion engine to analyze the user's facial expressions and text input and sends the results to the server.

[0839] Step 13:

[0840] The server holds the emotion information received from the emotion engine.

[0841] Step 14:

[0842] The server uses a generative AI model for the selected qualification to create a detailed study method and curriculum plan.

[0843] (For example, if you select "IT Passport," a plan will be generated that involves studying for 3 hours per week and using official textbooks and past exam questions.)

[0844] Step 15:

[0845] The server sends the generated study method and curriculum plan to the device.

[0846] Step 16:

[0847] The device displays the study plan to the user.

[0848] Step 17:

[0849] Users periodically enter their learning progress and additional questions.

[0850] Step 18:

[0851] The device sends learning progress information, additional questions, and the user's current emotional data to the server.

[0852] Step 19:

[0853] The server analyzes the user's emotions using an emotion engine and feeds that information back to the generative AI model.

[0854] Step 20:

[0855] The server uses a generative AI model to generate the latest advice based on the user's progress and emotional information.

[0856] (For example, if the system recognizes that the user is feeling anxious because their learning progress is slow, it generates advice on how to stay calm and how to take effective breaks.)

[0857] Step 21:

[0858] The server transmits the generated advice to the terminal.

[0859] Step 22:

[0860] The terminal displays the latest advice to the user.

[0861] Example 2

[0862] 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."

[0863] While conventional qualification acquisition support systems have the ability to suggest qualifications based on the user's basic information and provide study methods and curriculum plans, they lack support that takes into account the user's emotional state. As a result, users often study while feeling mentally burdened, which can lead to a decline in motivation and stress, resulting in a decrease in learning effectiveness. Furthermore, there is also the issue of it being difficult to fully meet the user's needs, as qualification list generation and curriculum customization are not adequately performed.

[0864] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0865] In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying the qualification information database and proposing appropriate qualifications based on the user information, means for providing the proposed qualifications to the user, means for generating a study method and curriculum plan based on the qualifications selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for recognizing emotions from the user's facial expressions and text input, means for providing emotion-sensitive advice to the user based on the recognized emotional information, means for using a generative AI model to create a list of qualifications based on the user information, and means for providing the generated qualification list to the user. This enables support based on the user's emotional state and consistent support from qualification selection to providing study methods and curricula.

[0866] "Basic user information" refers to information such as name, areas of interest, current job, and qualification goals that users provide when using the system.

[0867] An "input form" refers to a screen or interface that a user uses to input information into a system.

[0868] A "server" is a computer system that receives, stores, and processes information submitted by users.

[0869] A "database" is a data management system for systematically storing received user information, qualification information, and the like.

[0870] "Credentials information database" means a database that stores information about the credentials managed by the system.

[0871] "Qualification proposal" is the process of selecting and presenting appropriate qualifications based on the user's basic information.

[0872] "Study method" refers to an efficient learning method for obtaining the qualification selected by the user.

[0873] A "curriculum plan" is a plan that defines a specific study schedule and how to use study materials to obtain a qualification.

[0874] "Progress" refers to the state that indicates how far the user has progressed in their studies.

[0875] "Advice" is specific advice or guidance provided to the user, taking into account their progress and emotional state.

[0876] "Emotion recognition" is a technology that identifies the user's current emotions from their facial expressions and text input.

[0877] "Generative AI model" refers to an artificial intelligence model used to generate qualification lists, study methods, and curriculum plans based on user information.

[0878] This invention relates to an AI chatbot system for assisting users in obtaining qualifications. This system acquires basic information about the user, suggests appropriate qualifications, provides efficient study methods and curriculum plans, and provides advice and support based on the user's emotional state. Specific embodiments for implementing this invention are described below.

[0879] Hardware and software used

[0880] Device: A device through which a user enters information (e.g., smartphone, tablet, computer, etc.).

[0881] Server: A computer system that receives, stores, and processes user information.

[0882] Database: A data management system that systematically stores user information and credentials.

[0883] Generative AI model: An artificial intelligence model that generates qualification lists, study methods, and curriculum plans based on user information.

[0884] Emotion recognition engine: Technology that recognizes emotions from a user's facial expressions and text input.

[0885] First Interaction and User Registration

[0886] When a user accesses the qualification acquisition support AI chatbot from their device, the device displays a welcome message along with a form for entering the user's basic information (such as name, areas of interest, current job, and qualification acquisition goals). When the user enters the required information in the form and submits it, the device sends the information to the server, which then stores the received information in a database.

[0887] Qualification proposal

[0888] The server queries the credentials database to find appropriate qualifications based on the user's information. The server then uses a generative AI model to generate a prompt, such as "Please list IT qualifications suitable for a systems engineer." The generative AI model then creates a list of qualifications based on the prompt. The resulting list of proposed qualifications is then sent to the terminal and displayed to the user.

[0889] Support after qualification selection

[0890] When a user selects a desired qualification from a list of suggested qualifications, the device sends the selected information to the server. The server uses a generative AI model to create a detailed study method and curriculum plan for the selected qualification. For example, if the user selects "IT Passport," the server inputs the prompt "Please generate a study plan for the IT Passport," and the generative AI model creates a detailed plan including "study three hours per week" and "use official textbooks and past exam papers." The generated study method and curriculum plan are sent to the device and displayed to the user.

[0891] Ongoing support

[0892] The user periodically inputs their learning progress and any follow-up questions into the device. The device then sends the progress information, questions, and emotional data recognized by the emotion recognition engine to the server. The server uses the generative AI model based on the input information to generate the latest advice. For example, if the server recognizes that learning progress is lagging behind or that the user is feeling "impatient" or "anxious," it will use the generative AI model to provide specific advice that takes into account the user's emotions, such as "stay calm" and "take as much rest as possible." This advice is displayed to the user via the device.

[0893] In this way, users can aim to obtain qualifications while receiving consistent support, and comprehensive assistance can be provided, from selecting appropriate qualifications to efficient learning and emotionally sensitive advice.

[0894] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0895] Processing Steps

[0896] First Interaction and User Registration

[0897] Step 1:

[0898] The user accesses the qualification acquisition support AI chatbot from their device.

[0899] Input: Actions to access the device (launching an app, accessing a website, etc.)

[0900] Output: A screen showing the chatbot's welcome message and input form

[0901] Specific operation: The user launches the dedicated app, and the device displays a welcome message along with a form for entering "name," "areas of interest," "current job," and "qualification goals."

[0902] Step 2:

[0903] The user enters the required information into the input form and presses the submit button.

[0904] Input: User inputs information (name, interests, current job, qualification goals)

[0905] Output: The entered information is sent to the server

[0906] Specific operation: The user enters information into each field and presses the send button. The device displays "Sending..." and sends the data to the server.

[0907] Step 3:

[0908] The server stores the received user information in a database.

[0909] Input: User information (name, area of ​​interest, current job, qualification goal)

[0910] Output: User information stored in the database

[0911] Specific operation: The server analyzes the user information and stores it in the database in the appropriate format. The server records in the log "New user information has been saved in the database."

[0912] Qualification proposal

[0913] Step 4:

[0914] The server queries the credential database to find the appropriate credentials based on the user's information.

[0915] Input: User information stored in the database

[0916] Output: A list of entitlements based on the user information

[0917] Specific operation: The server searches the database for qualification data related to "System Engineer" and "IT".

[0918] Step 5:

[0919] The server uses a generative AI model to create a list of entitlements based on the user's information.

[0920] Input: Certification data retrieved from the credentials database, prompt "Please list IT certifications suitable for systems engineers."

[0921] Output: A list of entitlements created by the generative AI model

[0922] How it works: The server inputs the prompt into the generative AI model, which returns a list of appropriate qualifications, including the IT Passport and the Fundamental Information Technology Engineer Examination.

[0923] Step 6:

[0924] The server sends the generated credential proposal list to the terminal.

[0925] Input: A list of entitlements created by a generative AI model

[0926] Output: List of qualification proposals sent to the terminal

[0927] Specific operation: The server sends the credential list to the terminal, notifying it that the credential list is ready.

[0928] Step 7:

[0929] The terminal displays the qualification proposal list to the user.

[0930] Input: Qualification proposal list sent to the terminal

[0931] Output: A list of qualification suggestions displayed on the screen

[0932] Specific operation: The terminal displays a list of suggested qualifications to the user, suggesting qualifications such as "IT Passport" and "Fundamental Information Technology Engineer Examination."

[0933] Support after qualification selection

[0934] Step 8:

[0935] The user selects the desired qualification from a list of suggested qualifications.

[0936] Input: Proposed Eligibility List

[0937] Output: Selected credentials

[0938] Specific operation: The user selects "IT Passport" and the terminal sends the selected information to the server.

[0939] Step 9:

[0940] The device sends the selected credentials to the server.

[0941] Enter: Selected Credentials

[0942] Output: Credential selection information sent to the server

[0943] Specific operation: The terminal notifies the server that "User 1 has selected IT Passport."

[0944] Step 10:

[0945] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[0946] Input: Selected credentials, prompt "Generate a study plan for the IT Passport"

[0947] Output: Detailed study methods and curriculum plans created by the generative AI model

[0948] Specific operation: The server inputs prompt statements into the generative AI model, and the returned plan includes things like "study three hours a week" and "use official textbooks and past exam questions."

[0949] Step 11:

[0950] The server sends the generated study method and curriculum plan to the device.

[0951] Input: Study methods and curriculum plans created by a generative AI model

[0952] Output: Study methods and curriculum plans sent to the device

[0953] Specific operation: The server notifies the device that "the study plan has been sent" and sends a detailed plan.

[0954] Step 12:

[0955] The device displays the study plan to the user.

[0956] Input: Study methods and curriculum plans sent to your device

[0957] Output: Study plan displayed on screen

[0958] Specific operation: The device displays a plan to the user, such as "study 3 hours a week" and "use official textbooks and past exam questions."

[0959] Ongoing support

[0960] Step 13:

[0961] The user periodically inputs information about their learning progress and any follow-up questions into the device, and any changes in their emotions along the way are also recognized.

[0962] Input: User progress, follow-up questions, emotional information

[0963] Output: Input progress information and questions, recognized emotion data

[0964] Specific operation: The user types "I'm on track this week" or "What's next?", and the device analyzes the user's emotions using an emotion recognition engine.

[0965] Step 14:

[0966] The device sends progress information, emotional data, and questions to the server.

[0967] Input: Progress information, emotion data, questions

[0968] Output: Progress information, emotion data, and questions sent to the server

[0969] Specific operation: The device displays "Sending progress information and questions..." and sends it to the server.

[0970] Step 15:

[0971] The server uses a generative AI model to generate the latest advice based on input progress information, emotional data, and questions.

[0972] Input: Progress information, emotion data, question, prompt "Advice if learning progress is slow"

[0973] Output: Updated advice produced by the generative AI model

[0974] Specific operation: The server inputs the necessary prompt sentences into the generated AI model, and generates advice such as "calm down" and "get as much rest as possible."

[0975] Step 16:

[0976] The server transmits the generated advice to the terminal.

[0977] Input: The latest advice generated by the generative AI model

[0978] Output: Advice sent to terminal

[0979] Specific operation: The server sends the advice to the device, notifying it that "the advice is ready."

[0980] Step 17:

[0981] The terminal displays the latest advice to the user.

[0982] Input: Latest advice sent to the terminal

[0983] Output: The most recent advice displayed on the screen.

[0984] What happens: Your device will display advice such as "Slow down a bit and take a break."

[0985] This series of processes will enable users to effectively study towards obtaining qualifications.

[0986] (Application example 2)

[0987] 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."

[0988] Conventional food delivery systems lack personalized menu suggestions based on the user's basic information and preferences, making it difficult for users to make satisfying choices. Furthermore, they are unable to provide advice based on the user's emotions and state, making it difficult to improve the user experience.

[0989] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying a food information database and proposing an appropriate menu based on the user information, means for providing the proposed menu to the user, means for generating discount information based on a menu selected by the user, means for providing the generated discount information to the user, and means for periodically receiving the user's progress status and emotional data, and generating and providing the user with the latest suggestions and advice based on that information. This makes it possible to propose personalized menus based on the user's basic information and preferences, and to provide advice and discount information that takes emotions into consideration.

[0990] "Basic user information" is personal data such as the user's name, favorite dishes, allergy information, budget, etc.

[0991] The "means for sending to the server" is a communication means for sending the user's basic information from the input form to the server.

[0992] The "means for storing in a database" refers to a means for storing the received information in a database and making it possible to query the information as needed.

[0993] "Querying a food information database" refers to referencing the database to search for an appropriate menu based on the user's information.

[0994] The "means for suggesting a menu" is a means for generating an appropriate menu based on the user's basic information and preferences and presenting it to the user.

[0995] The "means for generating discount information" is a means for generating applicable discount information based on the menu selected by the user.

[0996] "Periodic receipt of progress status and emotional data" means periodically obtaining feedback and emotional state from the user.

[0997] "Generating up-to-date suggestions and advice" means using collected progress and sentiment data to generate appropriate menus and advice for the next step.

[0998] This invention relates to an AI chatbot system that combines a food delivery order support system with an emotion engine that recognizes user emotions. This system acquires basic information about the user, suggests appropriate menu items, and provides discount information and advice. The specific operation is explained below.

[0999] First Interaction and User Registration

[1000] User Registration

[1001] A user accesses a food delivery app on their smartphone.

[1002] When the device is accessed for the first time, it displays a welcome message along with a form for entering the user's basic information (name, favorite dishes, allergy information, budget, etc.).

[1003] The user enters the required information into the input form and presses the submit button.

[1004] The terminal transmits the information provided by the user to the server.

[1005] The server stores the received user information in a database using Firebase.

[1006] Menu suggestions

[1007] Menu suggestions

[1008] The server queries the food information database and searches for an appropriate menu based on the user's information (preferred dishes, allergy information, budget, etc.).

[1009] The server uses a generative AI model (GPT-4) to create a list of menus based on user information.

[1010] The server generates a menu suggestion list and sends it to the terminal.

[1011] The terminal displays a menu suggestion list to the user.

[1012] Utilizing the Emotion Engine

[1013] Analysis by emotion engine

[1014] The device recognizes emotions using an emotion engine (Emotion API) from the user's facial expressions and text input.

[1015] The device transmits the recognized emotion information to the server.

[1016] Providing discount information

[1017] Generate and provide discount information

[1018] The user selects the desired menu from the suggested menu list.

[1019] The terminal transmits the selected menu information to the server.

[1020] The server uses a generative AI model to create discount information for the selected menu.

[1021] The server transmits the generated discount information to the terminal.

[1022] The terminal displays the discount information to the user.

[1023] Ongoing support

[1024] Progress check and advice

[1025] Users periodically enter feedback and sentiment about their orders.

[1026] The device sends feedback information and emotion data to the server.

[1027] The server uses a generative AI model to generate the latest suggestions and advice based on the input feedback information and emotional data.

[1028] The server sends the generated suggestions and advice to the device.

[1029] The device displays the latest suggestions and advice to the user.

[1030] This allows users to select the menu that best suits them and receive emotionally sensitive advice and discount information, providing a highly satisfying food delivery experience.

[1031] Prompt Sentence Examples

[1032] markdown

[1033] System prompt:

[1034] User attribute information: {Name: 'Yamada Taro', Favorite food: 'Japanese food', Allergy: 'Peanuts', Budget: 'Under 2000 yen'}

[1035] Past orders: 'Sushi, Ramen, Udon'

[1036] User sentiment: 'Frustrated'

[1037] Generate menu suggestions and discount coupons.

[1038] Example output to the user:

[1039] Hello Taro Yamada! How are you today?

[1040] We've created menu recommendations for you based on your recent orders.

[1041] Tempura set meal

[1042] Boiled fish

[1043] Plus, we offer special coupons for new customers!

[1044] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1045] Step 1:

[1046] A user accesses a food delivery app from their smartphone. When the app is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (name, favorite dishes, allergy information, budget, etc.). The user enters the necessary information into the input form and presses the submit button.

[1047] Input: User basic information

[1048] Output: Basic information filled in and ready to send

[1049] Step 2:

[1050] The device sends the information provided by the user to the server, which then stores the received user information in a database (Firebase).

[1051] Input: User's basic information input data

[1052] Output: User information is saved in the database

[1053] Step 3:

[1054] The server queries a food information database and searches for appropriate menu items based on the user's information (preferred dishes, allergies, budget, etc.). The server uses a generative AI model (GPT-4) to create a list of menu items based on the user's information.

[1055] Input: User basic information

[1056] Data processing / calculation: Generate menu suggestions using AI models

[1057] Output: Menu suggestion list

[1058] Step 4:

[1059] The server sends the generated menu suggestion list to the terminal, which displays the menu suggestion list to the user.

[1060] Input: Menu suggestion list

[1061] Output: A list of menu suggestions is displayed on the terminal.

[1062] Step 5:

[1063] The device recognizes emotions from the user's facial expressions and text input using the emotion engine (Emotion API). The recognized emotion information is sent to the server.

[1064] Input: facial expressions and text as the user types

[1065] Data processing / calculation: Analysis using emotion recognition engine

[1066] Output: Emotional information

[1067] Step 6:

[1068] The user selects the desired menu from the proposed menu list, and the terminal transmits the selected menu information to the server.

[1069] Input: User menu selection

[1070] Output: Selected menu information is sent to the server

[1071] Step 7:

[1072] The server uses a generative AI model to create discount information for the selected menu item. The discount information is then sent from the server to the device, which then displays the discount information to the user.

[1073] Input: Selected menu information

[1074] Data processing / calculation: Generate discount information using AI models

[1075] Output: Discount information displayed on terminal

[1076] Step 8:

[1077] The user periodically inputs feedback about orders and changes in emotions, and the terminal transmits the feedback information and emotion data to the server.

[1078] Input: User feedback and sentiment data

[1079] Output: Feedback information and emotion data are sent to the server.

[1080] Step 9:

[1081] The server uses a generative AI model to generate the latest suggestions and advice based on the input feedback information and emotion data. The server then sends the generated suggestions and advice to the device, which then displays the latest suggestions and advice to the user.

[1082] Input: Feedback information and emotion data

[1083] Data processing / calculation: Generate suggestions and advice using AI models

[1084] Output: The latest suggestions and advice will be displayed on your terminal.

[1085] 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.

[1086] 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.

[1087] 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.

[1088] [Third embodiment]

[1089] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1090] 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.

[1091] 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).

[1092] 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.

[1093] 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.

[1094] 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).

[1095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1096] 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.

[1097] 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.

[1098] 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.

[1099] 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.

[1100] 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."

[1101] This invention relates to an AI chatbot system that supports qualification acquisition. This system acquires basic information about the user, suggests appropriate qualification information, and provides efficient study methods and curriculum plans. The specific operation of this system is explained below.

[1102] 1. First Interaction and User Registration

[1103] User Registration

[1104] The user accesses the qualification acquisition support AI chatbot from their device.

[1105] When the device is accessed for the first time, it displays a welcome message along with a form for entering basic information (such as name, areas of interest, current job, and qualification goals).

[1106] The user enters the required information into the input form and presses the submit button.

[1107] The terminal transmits the information input by the user to the server.

[1108] The server stores the received user information in a database.

[1109] 2. Qualification proposal

[1110] Qualification Suggestion

[1111] The server queries the qualification database and selects appropriate qualifications based on the user's information (areas of interest, current occupation, goals, etc.).

[1112] The server uses a generative AI model to create a list of selected qualifications.

[1113] The server sends the generated credential proposal list to the terminal.

[1114] The terminal displays the qualification proposal list to the user.

[1115] 3. Support after qualification selection

[1116] Study methods and curriculum

[1117] The user selects the desired qualification from a list of suggested qualifications.

[1118] The device sends the selected credentials to the server.

[1119] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[1120] For example, if you select "IT Passport," the generative AI model will generate a detailed plan such as "study for three hours a week" and "use official textbooks and past exam questions."

[1121] The server sends the generated study method and curriculum plan to the device.

[1122] The device displays the study plan to the user.

[1123] 4. Ongoing support

[1124] Progress check and advice

[1125] Users periodically enter their learning progress and additional questions.

[1126] The device sends progress information and questions to the server.

[1127] The server uses a generative AI model to generate up-to-date advice based on the progress information and questions entered.

[1128] For example, if learning progress is lagging behind, the generative AI model will generate specific advice such as "areas that should be prioritized for relearning" and "effective review methods."

[1129] The server transmits the generated advice to the terminal.

[1130] The terminal displays the latest advice to the user.

[1131] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. It also allows users to progress through their studies effectively with ongoing support, making it possible to provide consistent support throughout the entire qualification acquisition process.

[1132] The processing flow will be explained below.

[1133] Step 1:

[1134] The user accesses the qualification acquisition support AI chatbot from their device.

[1135] Step 2:

[1136] When the device is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (such as name, areas of interest, current job, and qualification goals).

[1137] Step 3:

[1138] The user enters the required information into the input form and presses the submit button.

[1139] Step 4:

[1140] The terminal transmits the information provided by the user to the server.

[1141] Step 5:

[1142] The server stores the received user information in a database.

[1143] Step 6:

[1144] The server queries the credentials database to find appropriate credentials based on the user's information (interests, current occupation, goals, etc.).

[1145] Step 7:

[1146] The server uses a generative AI model to create a list of entitlements based on the user's information.

[1147] Step 8:

[1148] The server sends the generated credential proposal list to the terminal.

[1149] Step 9:

[1150] The terminal displays the qualification proposal list to the user.

[1151] Step 10:

[1152] The user selects the desired qualification from a list of suggested qualifications.

[1153] Step 11:

[1154] The device sends the selected credentials to the server.

[1155] Step 12:

[1156] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[1157] (For example, if you select "IT Passport," a plan will be generated that involves studying for 3 hours per week and using official textbooks and past exam questions.)

[1158] Step 13:

[1159] The server sends the generated study method and curriculum plan to the device.

[1160] Step 14:

[1161] The device displays the study plan to the user.

[1162] Step 15:

[1163] Users periodically enter their learning progress and additional questions.

[1164] Step 16:

[1165] The device sends progress information and questions to the server.

[1166] Step 17:

[1167] The server uses a generative AI model to generate up-to-date advice based on the progress information and questions entered.

[1168] (For example, if your learning progress is behind, it will generate specific advice such as what areas you should focus on relearning and effective review methods.)

[1169] Step 18:

[1170] The server transmits the generated advice to the terminal.

[1171] Step 19:

[1172] The terminal displays the latest advice to the user.

[1173] Example 1

[1174] 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."

[1175] Conventional qualification support systems provide users with uniform study methods and curriculum plans, making it difficult to provide support tailored to each user's individual needs. They also lack a mechanism for providing appropriate advice in real time according to the user's learning progress. This makes the process of users obtaining qualifications inefficient and results in insufficient support for achieving their goals.

[1176] 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.

[1177] In this invention, the server includes means for receiving a user's basic information as an input form and sending the information to the server, means for storing the received user information in a database in the server, means for querying a qualification information database and proposing appropriate qualifications based on the user's information, means for providing the proposed qualifications to the user, means for generating a study method and curriculum plan based on a qualification selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for generating customized qualification information, study methods, and curriculum plans using a generative AI model, and means for providing input to the generative AI model using prompt sentences. This makes it possible to consistently provide efficient and personalized support for the user to obtain qualifications.

[1178] A "user" is a person who uses the qualification acquisition support system.

[1179] "Basic information" refers to information such as name, areas of interest, current job, and qualification goals that a user provides through an input form.

[1180] "Input form" refers to an interface for users to enter basic information.

[1181] "Server" means a central processing unit that processes information received from users, stores it in a database, and selects and proposes appropriate credentials.

[1182] A "database" is a digital storage system for storing and managing data such as user information and qualification information.

[1183] The "Qualification Information Database" is a database for storing and querying detailed information on various qualifications.

[1184] "Suggestion" is the act of querying a qualification information database, selecting appropriate qualifications based on the user's information, and presenting them to the user.

[1185] "Study method" refers to the learning method or approach that is considered optimal for obtaining a particular qualification.

[1186] A "curriculum plan" is a study schedule created to help users efficiently obtain qualifications.

[1187] "Progress" is information indicating how much progress the user has made in their studies according to the set curriculum plan.

[1188] "Advice" is advice on how to improve learning or what the next step should be based on the user's progress and additional information.

[1189] A "generative AI model" is an artificial intelligence program that automatically generates credentials, study methods, curriculum plans, and advice based on prompt input.

[1190] A "prompt" is an instruction entered into a generative AI model, and is specific text that guides the content to be generated.

[1191] The present invention relates to an AI chatbot system for assisting users in obtaining qualifications. This system acquires basic information about the user, suggests appropriate qualifications, and provides efficient study methods and curriculum plans. Specific embodiments of the system are described below.

[1192] User Registration

[1193] The user accesses the qualification acquisition support AI chatbot using a browser or a dedicated app. Once access is confirmed, the device displays a welcome message and a form for entering basic information. This form includes fields for entering information such as name, areas of interest, current job, and qualification acquisition goals. When the user enters and submits this information, the device sends the input information to the server. The server stores the received information in a database.

[1194] Qualification proposal

[1195] The server then queries the credential database based on the stored user information to select appropriate credentials. During this process, it uses a generative AI model (e.g., GPT-4) to create a list of credentials that best fit the user's profile. The server then sends this list of proposed credentials to the device, which then displays it to the user.

[1196] An example of a prompt sentence is, "The qualification selected by the user is the IT Passport. Please create an efficient study method and curriculum plan for this user. Please provide specific suggestions for the amount of time each week to study, the study materials to use, and important review points."

[1197] Support after qualification selection

[1198] When the user selects the desired qualification from a list of suggested qualifications, the device sends the selected qualification information to the server. The server again uses the generative AI model to create a detailed study method and curriculum plan based on the selected qualification. This information is again sent from the server to the device, which displays it to the user. As a specific example, a curriculum plan may be generated that reads, "We recommend studying for three hours a week. Use the official textbook and past exam papers, and spend one hour reviewing every Saturday."

[1199] Ongoing support

[1200] Users can periodically input their learning progress and additional questions. The input information is sent from the device to the server, and the server uses the generative AI model to generate the latest advice. For example, if learning progress is lagging behind, the generative AI model will suggest areas that should be re-studyed before moving on to the next chapter and effective review methods. This advice is sent from the server to the device, which then displays it to the user.

[1201] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. Users can also receive ongoing support and progress in their studies effectively, providing consistent support throughout the entire qualification acquisition process.

[1202] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1203] Step 1: User access and display of input form

[1204] Users access the qualification acquisition support AI chatbot using a browser or a dedicated app.

[1205] Input: The user's access request.

[1206] The device will confirm access and display a welcome message and a form to enter basic information.

[1207] Output: Basic information input form (name, area of ​​interest, current job, qualification goals).

[1208] Step 2: Enter and submit user information

[1209] The user enters basic information into the input form and clicks the submit button.

[1210] Input: Basic information entered by the user (e.g., Tanaka Taro, IT, systems engineer, hoping to obtain an IT passport).

[1211] The terminal sends the input information to the server.

[1212] Output: Basic information sent by the user.

[1213] Step 3: Save user information

[1214] The server stores the received basic information in a database.

[1215] Input: Basic information sent from the device.

[1216] Data processing: User information is processed into an appropriate format and stored in a database.

[1217] Output: User information stored in the database.

[1218] Step 4: Query and select credentials

[1219] The server queries the credentials database based on the stored user information and selects the appropriate credentials.

[1220] Input: User information stored in the database.

[1221] Data Computing: Using a generative AI model (e.g., GPT-4), we generate a list of entitlements that best fit a user's profile.

[1222] An example prompt for a generative AI model: "The qualification selected by the user is the IT Passport. Please create an efficient study method and curriculum plan for this user. Please provide specific suggestions for the amount of time each week to study, the study materials to use, and important points to review."

[1223] Output: A list of qualification suggestions generated by the generative AI model (e.g., IT Passport, Fundamental Information Technology Engineer, Applied Information Technology Engineer).

[1224] Step 5: Submit and view the qualification proposal list

[1225] The server sends the generated credential proposal list to the terminal.

[1226] Input: The generated qualification proposal list.

[1227] The terminal displays the qualification proposal list to the user.

[1228] Output: The qualification proposal list displayed to the user.

[1229] Step 6: Select and submit qualifications

[1230] The user selects the desired qualification from a list of suggested qualifications.

[1231] Input: The qualification selected by the user.

[1232] The device sends the selected credentials to the server.

[1233] Output: The selected credentials sent to the server.

[1234] Step 7: Create a study method and curriculum plan

[1235] Based on the selected qualifications, the server uses a generative AI model to generate a detailed study method and curriculum plan.

[1236] Enter: Selected credentials.

[1237] Data calculation: A generative AI model provides specific recommendations for weekly study time, study materials to use, and key review points.

[1238] Output: Generated study methods and curriculum plans.

[1239] Example: "We recommend three hours of study per week, using the official textbook and past exam papers, with one hour of review every Saturday."

[1240] Step 8: Submit and view your study plan

[1241] The server sends the generated study method and curriculum plan to the device.

[1242] Input: Generated study methods and curriculum plans.

[1243] The device displays the study plan to the user.

[1244] Output: The study plan displayed to the user.

[1245] Step 9: Enter and submit progress information

[1246] Users periodically enter their learning progress and follow-up questions.

[1247] Input: User progress information and questions.

[1248] The device sends progress information and questions to the server.

[1249] Output: Progress information and questions sent to the server.

[1250] Step 10: Generate and display updated advice

[1251] The server uses a generative AI model to generate up-to-date advice based on progress information and questions.

[1252] Input: Progress information and questions.

[1253] Data computation: Generate new advice using generative AI models.

[1254] Specific examples: Suggest "areas that should be re-studyed before moving on to the next chapter" and "effective review methods that also serve as a refresher."

[1255] Output: The latest advice generated.

[1256] The server transmits the generated advice to the terminal.

[1257] The terminal displays the latest advice to the user.

[1258] Output: The most recent advice shown to the user.

[1259] (Application example 1)

[1260] 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."

[1261] While existing certification support systems can provide users with appropriate certification suggestions and study plans, they lack interactive support for workers to study efficiently on the factory floor. There is also a need for customized learning support tailored to the specific needs of factory workers.

[1262] 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.

[1263] In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying a qualification information database and suggesting appropriate qualifications based on the user information, means for providing the suggested qualifications to the user, means for generating a study method and curriculum plan based on the qualification selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for interactively providing appropriate qualification information and learning content to factory workers, means for displaying the progress of the study plan and curriculum on a robot display, means for generating a study plan and curriculum using an artificial intelligence model, and means for creating a detailed study plan based on prompts using the generative AI model when generating the study plan. This enables factory workers to efficiently study for qualifications.

[1264] "Basic user information" refers to information about the user, such as the user's name, areas of interest, current occupation, and goals for obtaining qualifications.

[1265] An "input form" is a means of providing a screen or fields for a user to enter basic information about themselves.

[1266] "Server" means the central processing unit that stores and processes information obtained from Users and generates and provides credentials and study plans.

[1267] A "database" is a system that systematically stores and manages user information and qualification information stored on a server.

[1268] A "Credentials Database" is a dedicated database in which information about various credentials is stored.

[1269] A "generative AI model" is a model that uses artificial intelligence to generate customized study methods and curricula based on user information.

[1270] A "prompt sentence" is an input sentence that is fed into a generative AI model and instructs it to produce a specific output.

[1271] "Providing interactively" means providing information and services to users in a two-way manner.

[1272] A "robot display" is a display device mounted on a robot, and is a means for displaying notifications and information to the user.

[1273] "User progress" is information that indicates the progress and degree of progress of learning toward obtaining a qualification.

[1274] "Additional information" refers to information added later other than the basic information provided by the user at the time of initial registration.

[1275] "Latest advice" refers to the most appropriate learning guidelines and advice at the current time based on the user's progress and additional information.

[1276] This invention is an AI chatbot system for supporting qualification acquisition, particularly for factory workers to efficiently advance their studies for qualification acquisition. Specific embodiments of this system are described below.

[1277] First, a user accesses the qualification acquisition support system through a robot in the factory. When the user uses the terminal for the first time, the terminal displays a welcome message and a basic information input form. The user enters basic information such as name, field of interest, current occupation, and qualification acquisition goal into this input form and submits it. This information is sent from the terminal to the server.

[1278] The server receives the user information and stores it in a database. The server then queries the credentials database and suggests appropriate credentials based on the user's areas of interest, current occupation, and goals for obtaining credentials. The list of suggested credentials is created using a generative AI model and sent to the device. The device then displays this list of suggested credentials to the user.

[1279] When a user selects the desired qualification from a list of suggested qualifications, the device sends the selected qualification information to the server. Based on the received qualification information, the server uses a generative AI model to create a detailed study method and curriculum plan. For example, if "electrician" is selected, the generative AI model generates a specific plan based on the prompt, such as "study for three hours a week" and "use official textbooks and past exam questions." To create such plans, generative AI models such as the OpenAI API are used.

[1280] The generated study plan and curriculum are sent to the terminal and provided to the user, who can then check the study plan displayed on the robot display and proceed with their studies.

[1281] Furthermore, users can periodically enter their learning progress and additional questions. The device sends the progress information and questions to the server. Based on this information, the server uses the generative AI model to generate the latest advice and provides it to the user. For example, if learning progress is lagging behind, the generative AI model will generate specific advice such as "areas that should be prioritized for relearning" and "effective review methods."

[1282] Hardware and software used

[1283] This system uses the following hardware and software:

[1284] Server: Stores and processes user information and runs the generative AI model.

[1285] Terminal: An interface through which a user enters and views information.

[1286] Robot Display: Displays generated credentials and study plans to the user.

[1287] Generative AI model: Uses OpenAI API to generate detailed study plans and advice.

[1288] Example prompt: "Please tell me an effective study plan to pass the electrician's exam."

[1289] This system allows factory workers to efficiently study for qualifications.

[1290] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1291] Step 1:

[1292] The user accesses the AI ​​chatbot to assist with qualification acquisition from a device. The device displays a welcome message and an input form for basic information (name, area of ​​interest, current occupation, goal of qualification acquisition, etc.). When the user enters information into the input form and presses the send button, the entered information is sent from the device to the server. The input data is sent in JSON format or similar.

[1293] Step 2:

[1294] The server receives the user information sent from the terminal and stores it in a database. In this process, the user information is inserted into a database table. For example, the name, areas of interest, etc. are saved.

[1295] Step 3:

[1296] The server queries the qualification database and selects appropriate qualifications based on the user's areas of interest, current occupation, and qualification goals. Based on the query results, a generative AI model generates a list of qualification suggestions. The qualification data is filtered to select the most suitable qualifications for the user.

[1297] Step 4:

[1298] The server sends the qualification proposal list generated by the generative AI model to the terminal. The terminal receives the qualification proposal list and displays it to the user. The qualification names and brief descriptions are displayed in list format.

[1299] Step 5:

[1300] The user selects the desired credential from the credential proposal list. The selected credential information is sent from the terminal to the server. This data includes the selected credential name and ID.

[1301] Step 6:

[1302] Based on the selected qualifications, the server uses a generative AI model to generate a detailed study method and curriculum plan. For example, if "electrician" is selected, the generative AI model generates a plan based on the prompt, such as "study three hours a week" and "use official textbooks and past exam questions." The prompt is "Please tell me an efficient study plan to pass the electrician's exam," and the generated plan is output.

[1303] Step 7:

[1304] The generated study plan and curriculum are sent from the server to the terminal, which then provides it to the user. The study plan is then displayed in real time on the robot display.

[1305] Step 8:

[1306] The user periodically inputs their learning progress and additional questions into the device, which then sends this input data to the server. Specifically, the progress information includes completed tasks and study time.

[1307] Step 9:

[1308] The server uses a generative AI model to generate up-to-date advice based on the input progress information and questions. For example, if a student is behind in their studies, it generates specific advice such as "areas that should be prioritized for relearning" and "effective review methods." The generated advice is then output.

[1309] Step 10:

[1310] The server sends the latest generated advice to the terminal, which displays it to the user, who can check the new advice through the robot display.

[1311] 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.

[1312] This invention relates to an AI chatbot system that supports qualification acquisition and combines it with an emotion engine that recognizes the user's emotions. This system acquires the user's basic information to suggest appropriate qualifications, provides efficient study methods and curriculum plans, and provides advice and support based on the user's emotional state. The specific operation of this system is explained below.

[1313] 1. First Interaction and User Registration

[1314] User Registration

[1315] The user accesses the qualification acquisition support AI chatbot from their device.

[1316] When the device is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (such as name, areas of interest, current job, and qualification goals).

[1317] The user enters the required information into the input form and presses the submit button.

[1318] The terminal transmits the information provided by the user to the server.

[1319] The server stores the received user information in a database.

[1320] 2. Qualification proposal

[1321] Qualification Suggestion

[1322] The server queries the credentials database to find appropriate credentials based on the user's information (interests, current occupation, goals, etc.).

[1323] The server uses a generative AI model to create a list of entitlements based on the user's information.

[1324] The server sends the generated credential proposal list to the terminal.

[1325] The terminal displays the qualification proposal list to the user.

[1326] 3. Support after qualification selection

[1327] Utilizing the Emotion Engine

[1328] The device uses an emotion engine to recognize emotions from the user's facial expressions and text input.

[1329] The device transmits the recognized emotion information to the server.

[1330] Study methods and curriculum

[1331] The user selects the desired qualification from a list of suggested qualifications.

[1332] The device sends the selected credentials to the server.

[1333] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[1334] For example, if you select "IT Passport," the generative AI model will generate a detailed plan such as "study for three hours a week" and "use official textbooks and past exam questions."

[1335] The server sends the generated study method and curriculum plan to the device.

[1336] The device displays the study plan to the user.

[1337] 4. Ongoing support

[1338] Progress check and advice

[1339] Users periodically enter their progress and follow-up questions, and changes in their emotions are recognized along the way.

[1340] The device sends progress information, emotional data, and questions to the server.

[1341] The server uses a generative AI model to generate the latest advice based on the input progress information, emotional data, and questions.

[1342] For example, if a user's learning progress is slow and the emotion engine recognizes that they are feeling "impatient" or "anxious," the generative AI model will generate specific advice that takes emotions into consideration, such as "stay calm" and "get as much rest as possible."

[1343] The server transmits the generated advice to the terminal.

[1344] The terminal displays the latest advice to the user.

[1345] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. Furthermore, by receiving ongoing support and assistance that takes into account their emotional state, they can progress through their studies effectively. This makes it possible to provide consistent support throughout the entire qualification acquisition process.

[1346] The processing flow will be explained below.

[1347] Step 1:

[1348] The user accesses the qualification acquisition support AI chatbot from their device.

[1349] Step 2:

[1350] When the device is accessed for the first time, it displays a welcome message and provides a form for entering basic information (name, areas of interest, current job, and qualification goals).

[1351] Step 3:

[1352] The user enters the required information into the input form and presses the submit button.

[1353] Step 4:

[1354] The terminal transmits the user's input information to the server.

[1355] Step 5:

[1356] The server stores the received user information in a database.

[1357] Step 6:

[1358] The server queries the credential database to find the appropriate credentials based on the user's information.

[1359] Step 7:

[1360] The server uses the generative AI model to create a list of qualification suggestions based on the user information.

[1361] Step 8:

[1362] The server sends the generated credential proposal list to the terminal.

[1363] Step 9:

[1364] The terminal displays the qualification proposal list to the user.

[1365] Step 10:

[1366] The user selects the desired qualification from a list of suggested qualifications.

[1367] Step 11:

[1368] The device sends the selected credentials to the server.

[1369] Step 12:

[1370] The device uses an emotion engine to analyze the user's facial expressions and text input and sends the results to the server.

[1371] Step 13:

[1372] The server holds the emotion information received from the emotion engine.

[1373] Step 14:

[1374] The server uses a generative AI model for the selected qualification to create a detailed study method and curriculum plan.

[1375] (For example, if you select "IT Passport," a plan will be generated that involves studying for 3 hours per week and using official textbooks and past exam questions.)

[1376] Step 15:

[1377] The server sends the generated study method and curriculum plan to the device.

[1378] Step 16:

[1379] The device displays the study plan to the user.

[1380] Step 17:

[1381] Users periodically enter their learning progress and additional questions.

[1382] Step 18:

[1383] The device sends learning progress information, additional questions, and the user's current emotional data to the server.

[1384] Step 19:

[1385] The server analyzes the user's emotions using an emotion engine and feeds that information back to the generative AI model.

[1386] Step 20:

[1387] The server uses a generative AI model to generate the latest advice based on the user's progress and emotional information.

[1388] (For example, if the system recognizes that the user is feeling anxious because their learning progress is slow, it generates advice on how to stay calm and how to take effective breaks.)

[1389] Step 21:

[1390] The server transmits the generated advice to the terminal.

[1391] Step 22:

[1392] The terminal displays the latest advice to the user.

[1393] Example 2

[1394] 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."

[1395] While conventional qualification acquisition support systems have the ability to suggest qualifications based on the user's basic information and provide study methods and curriculum plans, they lack support that takes into account the user's emotional state. As a result, users often study while feeling mentally burdened, which can lead to a decline in motivation and stress, resulting in a decrease in learning effectiveness. Furthermore, there is also the issue of it being difficult to fully meet the user's needs, as qualification list generation and curriculum customization are not adequately performed.

[1396] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1397] In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying the qualification information database and proposing appropriate qualifications based on the user information, means for providing the proposed qualifications to the user, means for generating a study method and curriculum plan based on the qualifications selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for recognizing emotions from the user's facial expressions and text input, means for providing emotion-sensitive advice to the user based on the recognized emotional information, means for using a generative AI model to create a list of qualifications based on the user information, and means for providing the generated qualification list to the user. This enables support based on the user's emotional state and consistent support from qualification selection to providing study methods and curricula.

[1398] "Basic user information" refers to information such as name, areas of interest, current job, and qualification goals that users provide when using the system.

[1399] An "input form" refers to a screen or interface that a user uses to input information into a system.

[1400] A "server" is a computer system that receives, stores, and processes information submitted by users.

[1401] A "database" is a data management system for systematically storing received user information, qualification information, and the like.

[1402] "Credentials information database" means a database that stores information about the credentials managed by the system.

[1403] "Qualification proposal" is the process of selecting and presenting appropriate qualifications based on the user's basic information.

[1404] "Study method" refers to an efficient learning method for obtaining the qualification selected by the user.

[1405] A "curriculum plan" is a plan that defines a specific study schedule and how to use study materials to obtain a qualification.

[1406] "Progress" refers to the state that indicates how far the user has progressed in their studies.

[1407] "Advice" is specific advice or guidance provided to the user, taking into account their progress and emotional state.

[1408] "Emotion recognition" is a technology that identifies the user's current emotions from their facial expressions and text input.

[1409] "Generative AI model" refers to an artificial intelligence model used to generate qualification lists, study methods, and curriculum plans based on user information.

[1410] This invention relates to an AI chatbot system for assisting users in obtaining qualifications. This system acquires basic information about the user, suggests appropriate qualifications, provides efficient study methods and curriculum plans, and provides advice and support based on the user's emotional state. Specific embodiments for implementing this invention are described below.

[1411] Hardware and software used

[1412] Device: A device through which a user enters information (e.g., smartphone, tablet, computer, etc.).

[1413] Server: A computer system that receives, stores, and processes user information.

[1414] Database: A data management system that systematically stores user information and credentials.

[1415] Generative AI model: An artificial intelligence model that generates qualification lists, study methods, and curriculum plans based on user information.

[1416] Emotion recognition engine: Technology that recognizes emotions from a user's facial expressions and text input.

[1417] First Interaction and User Registration

[1418] When a user accesses the qualification acquisition support AI chatbot from their device, the device displays a welcome message along with a form for entering the user's basic information (such as name, areas of interest, current job, and qualification acquisition goals). When the user enters the required information in the form and submits it, the device sends the information to the server, which then stores the received information in a database.

[1419] Qualification proposal

[1420] The server queries the credentials database to find appropriate qualifications based on the user's information. The server then uses a generative AI model to generate a prompt, such as "Please list IT qualifications suitable for a systems engineer." The generative AI model then creates a list of qualifications based on the prompt. The resulting list of proposed qualifications is then sent to the terminal and displayed to the user.

[1421] Support after qualification selection

[1422] When a user selects a desired qualification from a list of suggested qualifications, the device sends the selected information to the server. The server uses a generative AI model to create a detailed study method and curriculum plan for the selected qualification. For example, if the user selects "IT Passport," the server inputs the prompt "Please generate a study plan for the IT Passport," and the generative AI model creates a detailed plan including "study three hours per week" and "use official textbooks and past exam papers." The generated study method and curriculum plan are sent to the device and displayed to the user.

[1423] Ongoing support

[1424] The user periodically inputs their learning progress and any follow-up questions into the device. The device then sends the progress information, questions, and emotional data recognized by the emotion recognition engine to the server. The server uses the generative AI model based on the input information to generate the latest advice. For example, if the server recognizes that learning progress is lagging behind or that the user is feeling "impatient" or "anxious," it will use the generative AI model to provide specific advice that takes into account the user's emotions, such as "stay calm" and "take as much rest as possible." This advice is displayed to the user via the device.

[1425] In this way, users can aim to obtain qualifications while receiving consistent support, and comprehensive assistance can be provided, from selecting appropriate qualifications to efficient learning and emotionally sensitive advice.

[1426] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1427] Processing Steps

[1428] First Interaction and User Registration

[1429] Step 1:

[1430] The user accesses the qualification acquisition support AI chatbot from their device.

[1431] Input: Actions to access the device (launching an app, accessing a website, etc.)

[1432] Output: A screen showing the chatbot's welcome message and input form

[1433] Specific operation: The user launches the dedicated app, and the device displays a welcome message along with a form for entering "name," "areas of interest," "current job," and "qualification goals."

[1434] Step 2:

[1435] The user enters the required information into the input form and presses the submit button.

[1436] Input: User inputs information (name, interests, current job, qualification goals)

[1437] Output: The entered information is sent to the server

[1438] Specific operation: The user enters information into each field and presses the send button. The device displays "Sending..." and sends the data to the server.

[1439] Step 3:

[1440] The server stores the received user information in a database.

[1441] Input: User information (name, area of ​​interest, current job, qualification goal)

[1442] Output: User information stored in the database

[1443] Specific operation: The server analyzes the user information and stores it in the database in the appropriate format. The server records in the log "New user information has been saved in the database."

[1444] Qualification proposal

[1445] Step 4:

[1446] The server queries the credential database to find the appropriate credentials based on the user's information.

[1447] Input: User information stored in the database

[1448] Output: A list of entitlements based on the user information

[1449] Specific operation: The server searches the database for qualification data related to "System Engineer" and "IT".

[1450] Step 5:

[1451] The server uses a generative AI model to create a list of entitlements based on the user's information.

[1452] Input: Certification data retrieved from the credentials database, prompt "Please list IT certifications suitable for systems engineers."

[1453] Output: A list of entitlements created by the generative AI model

[1454] How it works: The server inputs the prompt into the generative AI model, which returns a list of appropriate qualifications, including the IT Passport and the Fundamental Information Technology Engineer Examination.

[1455] Step 6:

[1456] The server sends the generated credential proposal list to the terminal.

[1457] Input: A list of entitlements created by a generative AI model

[1458] Output: List of qualification proposals sent to the terminal

[1459] Specific operation: The server sends the credential list to the terminal, notifying it that the credential list is ready.

[1460] Step 7:

[1461] The terminal displays the qualification proposal list to the user.

[1462] Input: Qualification proposal list sent to the terminal

[1463] Output: A list of qualification suggestions displayed on the screen

[1464] Specific operation: The terminal displays a list of suggested qualifications to the user, suggesting qualifications such as "IT Passport" and "Fundamental Information Technology Engineer Examination."

[1465] Support after qualification selection

[1466] Step 8:

[1467] The user selects the desired qualification from a list of suggested qualifications.

[1468] Input: Proposed Eligibility List

[1469] Output: Selected credentials

[1470] Specific operation: The user selects "IT Passport" and the terminal sends the selected information to the server.

[1471] Step 9:

[1472] The device sends the selected credentials to the server.

[1473] Enter: Selected Credentials

[1474] Output: Credential selection information sent to the server

[1475] Specific operation: The terminal notifies the server that "User 1 has selected IT Passport."

[1476] Step 10:

[1477] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[1478] Input: Selected credentials, prompt "Generate a study plan for the IT Passport"

[1479] Output: Detailed study methods and curriculum plans created by the generative AI model

[1480] Specific operation: The server inputs prompt statements into the generative AI model, and the returned plan includes things like "study three hours a week" and "use official textbooks and past exam questions."

[1481] Step 11:

[1482] The server sends the generated study method and curriculum plan to the device.

[1483] Input: Study methods and curriculum plans created by a generative AI model

[1484] Output: Study methods and curriculum plans sent to the device

[1485] Specific operation: The server notifies the device that "the study plan has been sent" and sends a detailed plan.

[1486] Step 12:

[1487] The device displays the study plan to the user.

[1488] Input: Study methods and curriculum plans sent to your device

[1489] Output: Study plan displayed on screen

[1490] Specific operation: The device displays a plan to the user, such as "study 3 hours a week" and "use official textbooks and past exam questions."

[1491] Ongoing support

[1492] Step 13:

[1493] The user periodically inputs information about their learning progress and any follow-up questions into the device, and any changes in their emotions along the way are also recognized.

[1494] Input: User progress, follow-up questions, emotional information

[1495] Output: Input progress information and questions, recognized emotion data

[1496] Specific operation: The user types "I'm on track this week" or "What's next?", and the device analyzes the user's emotions using an emotion recognition engine.

[1497] Step 14:

[1498] The device sends progress information, emotional data, and questions to the server.

[1499] Input: Progress information, emotion data, questions

[1500] Output: Progress information, emotion data, and questions sent to the server

[1501] Specific operation: The device displays "Sending progress information and questions..." and sends it to the server.

[1502] Step 15:

[1503] The server uses a generative AI model to generate the latest advice based on input progress information, emotional data, and questions.

[1504] Input: Progress information, emotion data, question, prompt "Advice if learning progress is slow"

[1505] Output: Updated advice produced by the generative AI model

[1506] Specific operation: The server inputs the necessary prompt sentences into the generated AI model, and generates advice such as "calm down" and "get as much rest as possible."

[1507] Step 16:

[1508] The server transmits the generated advice to the terminal.

[1509] Input: The latest advice generated by the generative AI model

[1510] Output: Advice sent to terminal

[1511] Specific operation: The server sends the advice to the device, notifying it that "the advice is ready."

[1512] Step 17:

[1513] The terminal displays the latest advice to the user.

[1514] Input: Latest advice sent to the terminal

[1515] Output: The most recent advice displayed on the screen.

[1516] What happens: Your device will display advice such as "Slow down a bit and take a break."

[1517] This series of processes will enable users to effectively study towards obtaining qualifications.

[1518] (Application example 2)

[1519] 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."

[1520] Conventional food delivery systems lack personalized menu suggestions based on the user's basic information and preferences, making it difficult for users to make satisfying choices. Furthermore, they are unable to provide advice based on the user's emotions and state, making it difficult to improve the user experience.

[1521] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying a food information database and proposing an appropriate menu based on the user information, means for providing the proposed menu to the user, means for generating discount information based on a menu selected by the user, means for providing the generated discount information to the user, and means for periodically receiving the user's progress status and emotional data, and generating and providing the user with the latest suggestions and advice based on that information. This makes it possible to propose personalized menus based on the user's basic information and preferences, and to provide advice and discount information that takes emotions into consideration.

[1522] "Basic user information" is personal data such as the user's name, favorite dishes, allergy information, budget, etc.

[1523] The "means for sending to the server" is a communication means for sending the user's basic information from the input form to the server.

[1524] The "means for storing in a database" refers to a means for storing the received information in a database and making it possible to query the information as needed.

[1525] "Querying a food information database" refers to referencing the database to search for an appropriate menu based on the user's information.

[1526] The "means for suggesting a menu" is a means for generating an appropriate menu based on the user's basic information and preferences and presenting it to the user.

[1527] The "means for generating discount information" is a means for generating applicable discount information based on the menu selected by the user.

[1528] "Periodic receipt of progress status and emotional data" means periodically obtaining feedback and emotional state from the user.

[1529] "Generating up-to-date suggestions and advice" means using collected progress and sentiment data to generate appropriate menus and advice for the next step.

[1530] This invention relates to an AI chatbot system that combines a food delivery order support system with an emotion engine that recognizes user emotions. This system acquires basic information about the user, suggests appropriate menu items, and provides discount information and advice. The specific operation is explained below.

[1531] First Interaction and User Registration

[1532] User Registration

[1533] A user accesses a food delivery app on their smartphone.

[1534] When the device is accessed for the first time, it displays a welcome message along with a form for entering the user's basic information (name, favorite dishes, allergy information, budget, etc.).

[1535] The user enters the required information into the input form and presses the submit button.

[1536] The terminal transmits the information provided by the user to the server.

[1537] The server stores the received user information in a database using Firebase.

[1538] Menu suggestions

[1539] Menu suggestions

[1540] The server queries the food information database and searches for an appropriate menu based on the user's information (preferred dishes, allergy information, budget, etc.).

[1541] The server uses a generative AI model (GPT-4) to create a list of menus based on user information.

[1542] The server generates a menu suggestion list and sends it to the terminal.

[1543] The terminal displays a menu suggestion list to the user.

[1544] Utilizing the Emotion Engine

[1545] Analysis by emotion engine

[1546] The device recognizes emotions using an emotion engine (Emotion API) from the user's facial expressions and text input.

[1547] The device transmits the recognized emotion information to the server.

[1548] Providing discount information

[1549] Generate and provide discount information

[1550] The user selects the desired menu from the suggested menu list.

[1551] The terminal transmits the selected menu information to the server.

[1552] The server uses a generative AI model to create discount information for the selected menu.

[1553] The server transmits the generated discount information to the terminal.

[1554] The terminal displays the discount information to the user.

[1555] Ongoing support

[1556] Progress check and advice

[1557] Users periodically enter feedback and sentiment about their orders.

[1558] The device sends feedback information and emotion data to the server.

[1559] The server uses a generative AI model to generate the latest suggestions and advice based on the input feedback information and emotional data.

[1560] The server sends the generated suggestions and advice to the device.

[1561] The device displays the latest suggestions and advice to the user.

[1562] This allows users to select the menu that best suits them and receive emotionally sensitive advice and discount information, providing a highly satisfying food delivery experience.

[1563] Prompt Sentence Examples

[1564] markdown

[1565] System prompt:

[1566] User attribute information: {Name: 'Yamada Taro', Favorite food: 'Japanese food', Allergy: 'Peanuts', Budget: 'Under 2000 yen'}

[1567] Past orders: 'Sushi, Ramen, Udon'

[1568] User sentiment: 'Frustrated'

[1569] Generate menu suggestions and discount coupons.

[1570] Example output to the user:

[1571] Hello Taro Yamada! How are you today?

[1572] We've created menu recommendations for you based on your recent orders.

[1573] Tempura set meal

[1574] Boiled fish

[1575] Plus, we offer special coupons for new customers!

[1576] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1577] Step 1:

[1578] A user accesses a food delivery app from their smartphone. When the app is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (name, favorite dishes, allergy information, budget, etc.). The user enters the necessary information into the input form and presses the submit button.

[1579] Input: User basic information

[1580] Output: Basic information filled in and ready to send

[1581] Step 2:

[1582] The device sends the information provided by the user to the server, which then stores the received user information in a database (Firebase).

[1583] Input: User's basic information input data

[1584] Output: User information is saved in the database

[1585] Step 3:

[1586] The server queries a food information database and searches for appropriate menu items based on the user's information (preferred dishes, allergies, budget, etc.). The server uses a generative AI model (GPT-4) to create a list of menu items based on the user's information.

[1587] Input: User basic information

[1588] Data processing / calculation: Generate menu suggestions using AI models

[1589] Output: Menu suggestion list

[1590] Step 4:

[1591] The server sends the generated menu suggestion list to the terminal, which displays the menu suggestion list to the user.

[1592] Input: Menu suggestion list

[1593] Output: A list of menu suggestions is displayed on the terminal.

[1594] Step 5:

[1595] The device recognizes emotions from the user's facial expressions and text input using the emotion engine (Emotion API). The recognized emotion information is sent to the server.

[1596] Input: facial expressions and text as the user types

[1597] Data processing / calculation: Analysis using emotion recognition engine

[1598] Output: Emotional information

[1599] Step 6:

[1600] The user selects the desired menu from the proposed menu list, and the terminal transmits the selected menu information to the server.

[1601] Input: User menu selection

[1602] Output: Selected menu information is sent to the server

[1603] Step 7:

[1604] The server uses a generative AI model to create discount information for the selected menu item. The discount information is then sent from the server to the device, which then displays the discount information to the user.

[1605] Input: Selected menu information

[1606] Data processing / calculation: Generate discount information using AI models

[1607] Output: Discount information displayed on terminal

[1608] Step 8:

[1609] The user periodically inputs feedback about orders and changes in emotions, and the terminal transmits the feedback information and emotion data to the server.

[1610] Input: User feedback and sentiment data

[1611] Output: Feedback information and emotion data are sent to the server.

[1612] Step 9:

[1613] The server uses a generative AI model to generate the latest suggestions and advice based on the input feedback information and emotion data. The server then sends the generated suggestions and advice to the device, which then displays the latest suggestions and advice to the user.

[1614] Input: Feedback information and emotion data

[1615] Data processing / calculation: Generate suggestions and advice using AI models

[1616] Output: The latest suggestions and advice will be displayed on your terminal.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] [Fourth embodiment]

[1621] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1622] 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.

[1623] 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).

[1624] 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.

[1625] 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.

[1626] 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).

[1627] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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."

[1634] This invention relates to an AI chatbot system that supports qualification acquisition. This system acquires basic information about the user, suggests appropriate qualification information, and provides efficient study methods and curriculum plans. The specific operation of this system is explained below.

[1635] 1. First Interaction and User Registration

[1636] User Registration

[1637] The user accesses the qualification acquisition support AI chatbot from their device.

[1638] When the device is accessed for the first time, it displays a welcome message along with a form for entering basic information (such as name, areas of interest, current job, and qualification goals).

[1639] The user enters the required information into the input form and presses the submit button.

[1640] The terminal transmits the information input by the user to the server.

[1641] The server stores the received user information in a database.

[1642] 2. Qualification proposal

[1643] Qualification Suggestion

[1644] The server queries the qualification database and selects appropriate qualifications based on the user's information (areas of interest, current occupation, goals, etc.).

[1645] The server uses a generative AI model to create a list of selected qualifications.

[1646] The server sends the generated credential proposal list to the terminal.

[1647] The terminal displays the qualification proposal list to the user.

[1648] 3. Support after qualification selection

[1649] Study methods and curriculum

[1650] The user selects the desired qualification from a list of suggested qualifications.

[1651] The device sends the selected credentials to the server.

[1652] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[1653] For example, if you select "IT Passport," the generative AI model will generate a detailed plan such as "study for three hours a week" and "use official textbooks and past exam questions."

[1654] The server sends the generated study method and curriculum plan to the device.

[1655] The device displays the study plan to the user.

[1656] 4. Ongoing support

[1657] Progress check and advice

[1658] Users periodically enter their learning progress and additional questions.

[1659] The device sends progress information and questions to the server.

[1660] The server uses a generative AI model to generate up-to-date advice based on the progress information and questions entered.

[1661] For example, if learning progress is lagging behind, the generative AI model will generate specific advice such as "areas that should be prioritized for relearning" and "effective review methods."

[1662] The server transmits the generated advice to the terminal.

[1663] The terminal displays the latest advice to the user.

[1664] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. It also allows users to progress through their studies effectively with ongoing support, making it possible to provide consistent support throughout the entire qualification acquisition process.

[1665] The processing flow will be explained below.

[1666] Step 1:

[1667] The user accesses the qualification acquisition support AI chatbot from their device.

[1668] Step 2:

[1669] When the device is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (such as name, areas of interest, current job, and qualification goals).

[1670] Step 3:

[1671] The user enters the required information into the input form and presses the submit button.

[1672] Step 4:

[1673] The terminal transmits the information provided by the user to the server.

[1674] Step 5:

[1675] The server stores the received user information in a database.

[1676] Step 6:

[1677] The server queries the credentials database to find appropriate credentials based on the user's information (interests, current occupation, goals, etc.).

[1678] Step 7:

[1679] The server uses a generative AI model to create a list of entitlements based on the user's information.

[1680] Step 8:

[1681] The server sends the generated credential proposal list to the terminal.

[1682] Step 9:

[1683] The terminal displays the qualification proposal list to the user.

[1684] Step 10:

[1685] The user selects the desired qualification from a list of suggested qualifications.

[1686] Step 11:

[1687] The device sends the selected credentials to the server.

[1688] Step 12:

[1689] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[1690] (For example, if you select "IT Passport," a plan will be generated that involves studying for 3 hours per week and using official textbooks and past exam questions.)

[1691] Step 13:

[1692] The server sends the generated study method and curriculum plan to the device.

[1693] Step 14:

[1694] The device displays the study plan to the user.

[1695] Step 15:

[1696] Users periodically enter their learning progress and additional questions.

[1697] Step 16:

[1698] The device sends progress information and questions to the server.

[1699] Step 17:

[1700] The server uses a generative AI model to generate up-to-date advice based on the progress information and questions entered.

[1701] (For example, if your learning progress is behind, it will generate specific advice such as what areas you should focus on relearning and effective review methods.)

[1702] Step 18:

[1703] The server transmits the generated advice to the terminal.

[1704] Step 19:

[1705] The terminal displays the latest advice to the user.

[1706] Example 1

[1707] 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."

[1708] Conventional qualification support systems provide users with uniform study methods and curriculum plans, making it difficult to provide support tailored to each user's individual needs. They also lack a mechanism for providing appropriate advice in real time according to the user's learning progress. This makes the process of users obtaining qualifications inefficient and results in insufficient support for achieving their goals.

[1709] 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.

[1710] In this invention, the server includes means for receiving a user's basic information as an input form and sending the information to the server, means for storing the received user information in a database in the server, means for querying a qualification information database and proposing appropriate qualifications based on the user's information, means for providing the proposed qualifications to the user, means for generating a study method and curriculum plan based on a qualification selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for generating customized qualification information, study methods, and curriculum plans using a generative AI model, and means for providing input to the generative AI model using prompt sentences. This makes it possible to consistently provide efficient and personalized support for the user to obtain qualifications.

[1711] A "user" is a person who uses the qualification acquisition support system.

[1712] "Basic information" refers to information such as name, areas of interest, current job, and qualification goals that a user provides through an input form.

[1713] "Input form" refers to an interface for users to enter basic information.

[1714] "Server" means a central processing unit that processes information received from users, stores it in a database, and selects and proposes appropriate credentials.

[1715] A "database" is a digital storage system for storing and managing data such as user information and qualification information.

[1716] The "Qualification Information Database" is a database for storing and querying detailed information on various qualifications.

[1717] "Suggestion" is the act of querying a qualification information database, selecting appropriate qualifications based on the user's information, and presenting them to the user.

[1718] "Study method" refers to the learning method or approach that is considered optimal for obtaining a particular qualification.

[1719] A "curriculum plan" is a study schedule created to help users efficiently obtain qualifications.

[1720] "Progress" is information indicating how much progress the user has made in their studies according to the set curriculum plan.

[1721] "Advice" is advice on how to improve learning or what the next step should be based on the user's progress and additional information.

[1722] A "generative AI model" is an artificial intelligence program that automatically generates credentials, study methods, curriculum plans, and advice based on prompt input.

[1723] A "prompt" is an instruction entered into a generative AI model, and is specific text that guides the content to be generated.

[1724] The present invention relates to an AI chatbot system for assisting users in obtaining qualifications. This system acquires basic information about the user, suggests appropriate qualifications, and provides efficient study methods and curriculum plans. Specific embodiments of the system are described below.

[1725] User Registration

[1726] The user accesses the qualification acquisition support AI chatbot using a browser or a dedicated app. Once access is confirmed, the device displays a welcome message and a form for entering basic information. This form includes fields for entering information such as name, areas of interest, current job, and qualification acquisition goals. When the user enters and submits this information, the device sends the input information to the server. The server stores the received information in a database.

[1727] Qualification proposal

[1728] The server then queries the credential database based on the stored user information to select appropriate credentials. During this process, it uses a generative AI model (e.g., GPT-4) to create a list of credentials that best fit the user's profile. The server then sends this list of proposed credentials to the device, which then displays it to the user.

[1729] An example of a prompt sentence is, "The qualification selected by the user is the IT Passport. Please create an efficient study method and curriculum plan for this user. Please provide specific suggestions for the amount of time each week to study, the study materials to use, and important review points."

[1730] Support after qualification selection

[1731] When the user selects the desired qualification from a list of suggested qualifications, the device sends the selected qualification information to the server. The server again uses the generative AI model to create a detailed study method and curriculum plan based on the selected qualification. This information is again sent from the server to the device, which displays it to the user. As a specific example, a curriculum plan may be generated that reads, "We recommend studying for three hours a week. Use the official textbook and past exam papers, and spend one hour reviewing every Saturday."

[1732] Ongoing support

[1733] Users can periodically input their learning progress and additional questions. The input information is sent from the device to the server, and the server uses the generative AI model to generate the latest advice. For example, if learning progress is lagging behind, the generative AI model will suggest areas that should be re-studyed before moving on to the next chapter and effective review methods. This advice is sent from the server to the device, which then displays it to the user.

[1734] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. Users can also receive ongoing support and progress in their studies effectively, providing consistent support throughout the entire qualification acquisition process.

[1735] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1736] Step 1: User access and display of input form

[1737] Users access the qualification acquisition support AI chatbot using a browser or a dedicated app.

[1738] Input: The user's access request.

[1739] The device will confirm access and display a welcome message and a form to enter basic information.

[1740] Output: Basic information input form (name, area of ​​interest, current job, qualification goals).

[1741] Step 2: Enter and submit user information

[1742] The user enters basic information into the input form and clicks the submit button.

[1743] Input: Basic information entered by the user (e.g., Tanaka Taro, IT, systems engineer, hoping to obtain an IT passport).

[1744] The terminal sends the input information to the server.

[1745] Output: Basic information sent by the user.

[1746] Step 3: Save user information

[1747] The server stores the received basic information in a database.

[1748] Input: Basic information sent from the device.

[1749] Data processing: User information is processed into an appropriate format and stored in a database.

[1750] Output: User information stored in the database.

[1751] Step 4: Query and select credentials

[1752] The server queries the credentials database based on the stored user information and selects the appropriate credentials.

[1753] Input: User information stored in the database.

[1754] Data Computing: Using a generative AI model (e.g., GPT-4), we generate a list of entitlements that best fit a user's profile.

[1755] An example prompt for a generative AI model: "The qualification selected by the user is the IT Passport. Please create an efficient study method and curriculum plan for this user. Please provide specific suggestions for the amount of time each week to study, the study materials to use, and important points to review."

[1756] Output: A list of qualification suggestions generated by the generative AI model (e.g., IT Passport, Fundamental Information Technology Engineer, Applied Information Technology Engineer).

[1757] Step 5: Submit and view the qualification proposal list

[1758] The server sends the generated credential proposal list to the terminal.

[1759] Input: The generated qualification proposal list.

[1760] The terminal displays the qualification proposal list to the user.

[1761] Output: The qualification proposal list displayed to the user.

[1762] Step 6: Select and submit qualifications

[1763] The user selects the desired qualification from a list of suggested qualifications.

[1764] Input: The qualification selected by the user.

[1765] The device sends the selected credentials to the server.

[1766] Output: The selected credentials sent to the server.

[1767] Step 7: Create a study method and curriculum plan

[1768] Based on the selected qualifications, the server uses a generative AI model to generate a detailed study method and curriculum plan.

[1769] Enter: Selected credentials.

[1770] Data calculation: A generative AI model provides specific recommendations for weekly study time, study materials to use, and key review points.

[1771] Output: Generated study methods and curriculum plans.

[1772] Example: "We recommend three hours of study per week, using the official textbook and past exam papers, with one hour of review every Saturday."

[1773] Step 8: Submit and view your study plan

[1774] The server sends the generated study method and curriculum plan to the device.

[1775] Input: Generated study methods and curriculum plans.

[1776] The device displays the study plan to the user.

[1777] Output: The study plan displayed to the user.

[1778] Step 9: Enter and submit progress information

[1779] Users periodically enter their learning progress and follow-up questions.

[1780] Input: User progress information and questions.

[1781] The device sends progress information and questions to the server.

[1782] Output: Progress information and questions sent to the server.

[1783] Step 10: Generate and display updated advice

[1784] The server uses a generative AI model to generate up-to-date advice based on progress information and questions.

[1785] Input: Progress information and questions.

[1786] Data computation: Generate new advice using generative AI models.

[1787] Specific examples: Suggest "areas that should be re-studyed before moving on to the next chapter" and "effective review methods that also serve as a refresher."

[1788] Output: The latest advice generated.

[1789] The server transmits the generated advice to the terminal.

[1790] The terminal displays the latest advice to the user.

[1791] Output: The most recent advice shown to the user.

[1792] (Application example 1)

[1793] 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."

[1794] While existing certification support systems can provide users with appropriate certification suggestions and study plans, they lack interactive support for workers to study efficiently on the factory floor. There is also a need for customized learning support tailored to the specific needs of factory workers.

[1795] 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.

[1796] In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying a qualification information database and suggesting appropriate qualifications based on the user information, means for providing the suggested qualifications to the user, means for generating a study method and curriculum plan based on the qualification selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for interactively providing appropriate qualification information and learning content to factory workers, means for displaying the progress of the study plan and curriculum on a robot display, means for generating a study plan and curriculum using an artificial intelligence model, and means for creating a detailed study plan based on prompts using the generative AI model when generating the study plan. This enables factory workers to efficiently study for qualifications.

[1797] "Basic user information" refers to information about the user, such as the user's name, areas of interest, current occupation, and goals for obtaining qualifications.

[1798] An "input form" is a means of providing a screen or fields for a user to enter basic information about themselves.

[1799] "Server" means the central processing unit that stores and processes information obtained from Users and generates and provides credentials and study plans.

[1800] A "database" is a system that systematically stores and manages user information and qualification information stored on a server.

[1801] A "Credentials Database" is a dedicated database in which information about various credentials is stored.

[1802] A "generative AI model" is a model that uses artificial intelligence to generate customized study methods and curricula based on user information.

[1803] A "prompt sentence" is an input sentence that is fed into a generative AI model and instructs it to produce a specific output.

[1804] "Providing interactively" means providing information and services to users in a two-way manner.

[1805] A "robot display" is a display device mounted on a robot, and is a means for displaying notifications and information to the user.

[1806] "User progress" is information that indicates the progress and degree of progress of learning toward obtaining a qualification.

[1807] "Additional information" refers to information added later other than the basic information provided by the user at the time of initial registration.

[1808] "Latest advice" refers to the most appropriate learning guidelines and advice at the current time based on the user's progress and additional information.

[1809] This invention is an AI chatbot system for supporting qualification acquisition, particularly for factory workers to efficiently advance their studies for qualification acquisition. Specific embodiments of this system are described below.

[1810] First, a user accesses the qualification acquisition support system through a robot in the factory. When the user uses the terminal for the first time, the terminal displays a welcome message and a basic information input form. The user enters basic information such as name, field of interest, current occupation, and qualification acquisition goal into this input form and submits it. This information is sent from the terminal to the server.

[1811] The server receives the user information and stores it in a database. The server then queries the credentials database and suggests appropriate credentials based on the user's areas of interest, current occupation, and goals for obtaining credentials. The list of suggested credentials is created using a generative AI model and sent to the device. The device then displays this list of suggested credentials to the user.

[1812] When a user selects the desired qualification from a list of suggested qualifications, the device sends the selected qualification information to the server. Based on the received qualification information, the server uses a generative AI model to create a detailed study method and curriculum plan. For example, if "electrician" is selected, the generative AI model generates a specific plan based on the prompt, such as "study for three hours a week" and "use official textbooks and past exam questions." To create such plans, generative AI models such as the OpenAI API are used.

[1813] The generated study plan and curriculum are sent to the terminal and provided to the user, who can then check the study plan displayed on the robot display and proceed with their studies.

[1814] Furthermore, users can periodically enter their learning progress and additional questions. The device sends the progress information and questions to the server. Based on this information, the server uses the generative AI model to generate the latest advice and provides it to the user. For example, if learning progress is lagging behind, the generative AI model will generate specific advice such as "areas that should be prioritized for relearning" and "effective review methods."

[1815] Hardware and software used

[1816] This system uses the following hardware and software:

[1817] Server: Stores and processes user information and runs the generative AI model.

[1818] Terminal: An interface through which a user enters and views information.

[1819] Robot Display: Displays generated credentials and study plans to the user.

[1820] Generative AI model: Uses OpenAI API to generate detailed study plans and advice.

[1821] Example prompt: "Please tell me an effective study plan to pass the electrician's exam."

[1822] This system allows factory workers to efficiently study for qualifications.

[1823] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1824] Step 1:

[1825] The user accesses the AI ​​chatbot to assist with qualification acquisition from a device. The device displays a welcome message and an input form for basic information (name, area of ​​interest, current occupation, goal of qualification acquisition, etc.). When the user enters information into the input form and presses the send button, the entered information is sent from the device to the server. The input data is sent in JSON format or similar.

[1826] Step 2:

[1827] The server receives the user information sent from the terminal and stores it in a database. In this process, the user information is inserted into a database table. For example, the name, areas of interest, etc. are saved.

[1828] Step 3:

[1829] The server queries the qualification database and selects appropriate qualifications based on the user's areas of interest, current occupation, and qualification goals. Based on the query results, a generative AI model generates a list of qualification suggestions. The qualification data is filtered to select the most suitable qualifications for the user.

[1830] Step 4:

[1831] The server sends the qualification proposal list generated by the generative AI model to the terminal. The terminal receives the qualification proposal list and displays it to the user. The qualification names and brief descriptions are displayed in list format.

[1832] Step 5:

[1833] The user selects the desired credential from the credential proposal list. The selected credential information is sent from the terminal to the server. This data includes the selected credential name and ID.

[1834] Step 6:

[1835] Based on the selected qualifications, the server uses a generative AI model to generate a detailed study method and curriculum plan. For example, if "electrician" is selected, the generative AI model generates a plan based on the prompt, such as "study three hours a week" and "use official textbooks and past exam questions." The prompt is "Please tell me an efficient study plan to pass the electrician's exam," and the generated plan is output.

[1836] Step 7:

[1837] The generated study plan and curriculum are sent from the server to the terminal, which then provides it to the user. The study plan is then displayed in real time on the robot display.

[1838] Step 8:

[1839] The user periodically inputs their learning progress and additional questions into the device, which then sends this input data to the server. Specifically, the progress information includes completed tasks and study time.

[1840] Step 9:

[1841] The server uses a generative AI model to generate up-to-date advice based on the input progress information and questions. For example, if a student is behind in their studies, it generates specific advice such as "areas that should be prioritized for relearning" and "effective review methods." The generated advice is then output.

[1842] Step 10:

[1843] The server sends the latest generated advice to the terminal, which displays it to the user, who can check the new advice through the robot display.

[1844] 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.

[1845] This invention relates to an AI chatbot system that supports qualification acquisition and combines it with an emotion engine that recognizes the user's emotions. This system acquires the user's basic information to suggest appropriate qualifications, provides efficient study methods and curriculum plans, and provides advice and support based on the user's emotional state. The specific operation of this system is explained below.

[1846] 1. First Interaction and User Registration

[1847] User Registration

[1848] The user accesses the qualification acquisition support AI chatbot from their device.

[1849] When the device is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (such as name, areas of interest, current job, and qualification goals).

[1850] The user enters the required information into the input form and presses the submit button.

[1851] The terminal transmits the information provided by the user to the server.

[1852] The server stores the received user information in a database.

[1853] 2. Qualification proposal

[1854] Qualification Suggestion

[1855] The server queries the credentials database to find appropriate credentials based on the user's information (interests, current occupation, goals, etc.).

[1856] The server uses a generative AI model to create a list of entitlements based on the user's information.

[1857] The server sends the generated credential proposal list to the terminal.

[1858] The terminal displays the qualification proposal list to the user.

[1859] 3. Support after qualification selection

[1860] Utilizing the Emotion Engine

[1861] The device uses an emotion engine to recognize emotions from the user's facial expressions and text input.

[1862] The device transmits the recognized emotion information to the server.

[1863] Study methods and curriculum

[1864] The user selects the desired qualification from a list of suggested qualifications.

[1865] The device sends the selected credentials to the server.

[1866] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[1867] For example, if you select "IT Passport," the generative AI model will generate a detailed plan such as "study for three hours a week" and "use official textbooks and past exam questions."

[1868] The server sends the generated study method and curriculum plan to the device.

[1869] The device displays the study plan to the user.

[1870] 4. Ongoing support

[1871] Progress check and advice

[1872] Users periodically enter their progress and follow-up questions, and changes in their emotions are recognized along the way.

[1873] The device sends progress information, emotional data, and questions to the server.

[1874] The server uses a generative AI model to generate the latest advice based on the input progress information, emotional data, and questions.

[1875] For example, if a user's learning progress is slow and the emotion engine recognizes that they are feeling "impatient" or "anxious," the generative AI model will generate specific advice that takes emotions into consideration, such as "stay calm" and "get as much rest as possible."

[1876] The server transmits the generated advice to the terminal.

[1877] The terminal displays the latest advice to the user.

[1878] This system allows users to select the qualification that best suits them and aim to obtain it through efficient study methods and curriculum plans. Furthermore, by receiving ongoing support and assistance that takes into account their emotional state, they can progress through their studies effectively. This makes it possible to provide consistent support throughout the entire qualification acquisition process.

[1879] The processing flow will be explained below.

[1880] Step 1:

[1881] The user accesses the qualification acquisition support AI chatbot from their device.

[1882] Step 2:

[1883] When the device is accessed for the first time, it displays a welcome message and provides a form for entering basic information (name, areas of interest, current job, and qualification goals).

[1884] Step 3:

[1885] The user enters the required information into the input form and presses the submit button.

[1886] Step 4:

[1887] The terminal transmits the user's input information to the server.

[1888] Step 5:

[1889] The server stores the received user information in a database.

[1890] Step 6:

[1891] The server queries the credential database to find the appropriate credentials based on the user's information.

[1892] Step 7:

[1893] The server uses the generative AI model to create a list of qualification suggestions based on the user information.

[1894] Step 8:

[1895] The server sends the generated credential proposal list to the terminal.

[1896] Step 9:

[1897] The terminal displays the qualification proposal list to the user.

[1898] Step 10:

[1899] The user selects the desired qualification from a list of suggested qualifications.

[1900] Step 11:

[1901] The device sends the selected credentials to the server.

[1902] Step 12:

[1903] The device uses an emotion engine to analyze the user's facial expressions and text input and sends the results to the server.

[1904] Step 13:

[1905] The server holds the emotion information received from the emotion engine.

[1906] Step 14:

[1907] The server uses a generative AI model for the selected qualification to create a detailed study method and curriculum plan.

[1908] (For example, if you select "IT Passport," a plan will be generated that involves studying for 3 hours per week and using official textbooks and past exam questions.)

[1909] Step 15:

[1910] The server sends the generated study method and curriculum plan to the device.

[1911] Step 16:

[1912] The device displays the study plan to the user.

[1913] Step 17:

[1914] Users periodically enter their learning progress and additional questions.

[1915] Step 18:

[1916] The device sends learning progress information, additional questions, and the user's current emotional data to the server.

[1917] Step 19:

[1918] The server analyzes the user's emotions using an emotion engine and feeds that information back to the generative AI model.

[1919] Step 20:

[1920] The server uses a generative AI model to generate the latest advice based on the user's progress and emotional information.

[1921] (For example, if the system recognizes that the user is feeling anxious because their learning progress is slow, it generates advice on how to stay calm and how to take effective breaks.)

[1922] Step 21:

[1923] The server transmits the generated advice to the terminal.

[1924] Step 22:

[1925] The terminal displays the latest advice to the user.

[1926] Example 2

[1927] 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."

[1928] While conventional qualification acquisition support systems have the ability to suggest qualifications based on the user's basic information and provide study methods and curriculum plans, they lack support that takes into account the user's emotional state. As a result, users often study while feeling mentally burdened, which can lead to a decline in motivation and stress, resulting in a decrease in learning effectiveness. Furthermore, there is also the issue of it being difficult to fully meet the user's needs, as qualification list generation and curriculum customization are not adequately performed.

[1929] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1930] In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying the qualification information database and proposing appropriate qualifications based on the user information, means for providing the proposed qualifications to the user, means for generating a study method and curriculum plan based on the qualifications selected by the user, means for providing the generated study method and curriculum plan to the user, means for periodically receiving the user's progress and additional information and generating and providing updated advice to the user based on that information, means for recognizing emotions from the user's facial expressions and text input, means for providing emotion-sensitive advice to the user based on the recognized emotional information, means for using a generative AI model to create a list of qualifications based on the user information, and means for providing the generated qualification list to the user. This enables support based on the user's emotional state and consistent support from qualification selection to providing study methods and curricula.

[1931] "Basic user information" refers to information such as name, areas of interest, current job, and qualification goals that users provide when using the system.

[1932] An "input form" refers to a screen or interface that a user uses to input information into a system.

[1933] A "server" is a computer system that receives, stores, and processes information submitted by users.

[1934] A "database" is a data management system for systematically storing received user information, qualification information, and the like.

[1935] "Credentials information database" means a database that stores information about the credentials managed by the system.

[1936] "Qualification proposal" is the process of selecting and presenting appropriate qualifications based on the user's basic information.

[1937] "Study method" refers to an efficient learning method for obtaining the qualification selected by the user.

[1938] A "curriculum plan" is a plan that defines a specific study schedule and how to use study materials to obtain a qualification.

[1939] "Progress" refers to the state that indicates how far the user has progressed in their studies.

[1940] "Advice" is specific advice or guidance provided to the user, taking into account their progress and emotional state.

[1941] "Emotion recognition" is a technology that identifies the user's current emotions from their facial expressions and text input.

[1942] "Generative AI model" refers to an artificial intelligence model used to generate qualification lists, study methods, and curriculum plans based on user information.

[1943] This invention relates to an AI chatbot system for assisting users in obtaining qualifications. This system acquires basic information about the user, suggests appropriate qualifications, provides efficient study methods and curriculum plans, and provides advice and support based on the user's emotional state. Specific embodiments for implementing this invention are described below.

[1944] Hardware and software used

[1945] Device: A device through which a user enters information (e.g., smartphone, tablet, computer, etc.).

[1946] Server: A computer system that receives, stores, and processes user information.

[1947] Database: A data management system that systematically stores user information and credentials.

[1948] Generative AI model: An artificial intelligence model that generates qualification lists, study methods, and curriculum plans based on user information.

[1949] Emotion recognition engine: Technology that recognizes emotions from a user's facial expressions and text input.

[1950] First Interaction and User Registration

[1951] When a user accesses the qualification acquisition support AI chatbot from their device, the device displays a welcome message along with a form for entering the user's basic information (such as name, areas of interest, current job, and qualification acquisition goals). When the user enters the required information in the form and submits it, the device sends the information to the server, which then stores the received information in a database.

[1952] Qualification proposal

[1953] The server queries the credentials database to find appropriate qualifications based on the user's information. The server then uses a generative AI model to generate a prompt, such as "Please list IT qualifications suitable for a systems engineer." The generative AI model then creates a list of qualifications based on the prompt. The resulting list of proposed qualifications is then sent to the terminal and displayed to the user.

[1954] Support after qualification selection

[1955] When a user selects a desired qualification from a list of suggested qualifications, the device sends the selected information to the server. The server uses a generative AI model to create a detailed study method and curriculum plan for the selected qualification. For example, if the user selects "IT Passport," the server inputs the prompt "Please generate a study plan for the IT Passport," and the generative AI model creates a detailed plan including "study three hours per week" and "use official textbooks and past exam papers." The generated study method and curriculum plan are sent to the device and displayed to the user.

[1956] Ongoing support

[1957] The user periodically inputs their learning progress and any follow-up questions into the device. The device then sends the progress information, questions, and emotional data recognized by the emotion recognition engine to the server. The server uses the generative AI model based on the input information to generate the latest advice. For example, if the server recognizes that learning progress is lagging behind or that the user is feeling "impatient" or "anxious," it will use the generative AI model to provide specific advice that takes into account the user's emotions, such as "stay calm" and "take as much rest as possible." This advice is displayed to the user via the device.

[1958] In this way, users can aim to obtain qualifications while receiving consistent support, and comprehensive assistance can be provided, from selecting appropriate qualifications to efficient learning and emotionally sensitive advice.

[1959] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1960] Processing Steps

[1961] First Interaction and User Registration

[1962] Step 1:

[1963] The user accesses the qualification acquisition support AI chatbot from their device.

[1964] Input: Actions to access the device (launching an app, accessing a website, etc.)

[1965] Output: A screen showing the chatbot's welcome message and input form

[1966] Specific operation: The user launches the dedicated app, and the device displays a welcome message along with a form for entering "name," "areas of interest," "current job," and "qualification goals."

[1967] Step 2:

[1968] The user enters the required information into the input form and presses the submit button.

[1969] Input: User inputs information (name, interests, current job, qualification goals)

[1970] Output: The entered information is sent to the server

[1971] Specific operation: The user enters information into each field and presses the send button. The device displays "Sending..." and sends the data to the server.

[1972] Step 3:

[1973] The server stores the received user information in a database.

[1974] Input: User information (name, area of ​​interest, current job, qualification goal)

[1975] Output: User information stored in the database

[1976] Specific operation: The server analyzes the user information and stores it in the database in the appropriate format. The server records in the log "New user information has been saved in the database."

[1977] Qualification proposal

[1978] Step 4:

[1979] The server queries the credential database to find the appropriate credentials based on the user's information.

[1980] Input: User information stored in the database

[1981] Output: A list of entitlements based on the user information

[1982] Specific operation: The server searches the database for qualification data related to "System Engineer" and "IT".

[1983] Step 5:

[1984] The server uses a generative AI model to create a list of entitlements based on the user's information.

[1985] Input: Certification data retrieved from the credentials database, prompt "Please list IT certifications suitable for systems engineers."

[1986] Output: A list of entitlements created by the generative AI model

[1987] How it works: The server inputs the prompt into the generative AI model, which returns a list of appropriate qualifications, including the IT Passport and the Fundamental Information Technology Engineer Examination.

[1988] Step 6:

[1989] The server sends the generated credential proposal list to the terminal.

[1990] Input: A list of entitlements created by a generative AI model

[1991] Output: List of qualification proposals sent to the terminal

[1992] Specific operation: The server sends the credential list to the terminal, notifying it that the credential list is ready.

[1993] Step 7:

[1994] The terminal displays the qualification proposal list to the user.

[1995] Input: Qualification proposal list sent to the terminal

[1996] Output: A list of qualification suggestions displayed on the screen

[1997] Specific operation: The terminal displays a list of suggested qualifications to the user, suggesting qualifications such as "IT Passport" and "Fundamental Information Technology Engineer Examination."

[1998] Support after qualification selection

[1999] Step 8:

[2000] The user selects the desired qualification from a list of suggested qualifications.

[2001] Input: Proposed Eligibility List

[2002] Output: Selected credentials

[2003] Specific operation: The user selects "IT Passport" and the terminal sends the selected information to the server.

[2004] Step 9:

[2005] The device sends the selected credentials to the server.

[2006] Enter: Selected Credentials

[2007] Output: Credential selection information sent to the server

[2008] Specific operation: The terminal notifies the server that "User 1 has selected IT Passport."

[2009] Step 10:

[2010] For selected qualifications, the server uses a generative AI model to create detailed study methods and curriculum plans.

[2011] Input: Selected credentials, prompt "Generate a study plan for the IT Passport"

[2012] Output: Detailed study methods and curriculum plans created by the generative AI model

[2013] Specific operation: The server inputs prompt statements into the generative AI model, and the returned plan includes things like "study three hours a week" and "use official textbooks and past exam questions."

[2014] Step 11:

[2015] The server sends the generated study method and curriculum plan to the device.

[2016] Input: Study methods and curriculum plans created by a generative AI model

[2017] Output: Study methods and curriculum plans sent to the device

[2018] Specific operation: The server notifies the device that "the study plan has been sent" and sends a detailed plan.

[2019] Step 12:

[2020] The device displays the study plan to the user.

[2021] Input: Study methods and curriculum plans sent to your device

[2022] Output: Study plan displayed on screen

[2023] Specific operation: The device displays a plan to the user, such as "study 3 hours a week" and "use official textbooks and past exam questions."

[2024] Ongoing support

[2025] Step 13:

[2026] The user periodically inputs information about their learning progress and any follow-up questions into the device, and any changes in their emotions along the way are also recognized.

[2027] Input: User progress, follow-up questions, emotional information

[2028] Output: Input progress information and questions, recognized emotion data

[2029] Specific operation: The user types "I'm on track this week" or "What's next?", and the device analyzes the user's emotions using an emotion recognition engine.

[2030] Step 14:

[2031] The device sends progress information, emotional data, and questions to the server.

[2032] Input: Progress information, emotion data, questions

[2033] Output: Progress information, emotion data, and questions sent to the server

[2034] Specific operation: The device displays "Sending progress information and questions..." and sends it to the server.

[2035] Step 15:

[2036] The server uses a generative AI model to generate the latest advice based on input progress information, emotional data, and questions.

[2037] Input: Progress information, emotion data, question, prompt "Advice if learning progress is slow"

[2038] Output: Updated advice produced by the generative AI model

[2039] Specific operation: The server inputs the necessary prompt sentences into the generated AI model, and generates advice such as "calm down" and "get as much rest as possible."

[2040] Step 16:

[2041] The server transmits the generated advice to the terminal.

[2042] Input: The latest advice generated by the generative AI model

[2043] Output: Advice sent to terminal

[2044] Specific operation: The server sends the advice to the device, notifying it that "the advice is ready."

[2045] Step 17:

[2046] The terminal displays the latest advice to the user.

[2047] Input: Latest advice sent to the terminal

[2048] Output: The most recent advice displayed on the screen.

[2049] What happens: Your device will display advice such as "Slow down a bit and take a break."

[2050] This series of processes will enable users to effectively study towards obtaining qualifications.

[2051] (Application example 2)

[2052] 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."

[2053] Conventional food delivery systems lack personalized menu suggestions based on the user's basic information and preferences, making it difficult for users to make satisfying choices. Furthermore, they are unable to provide advice based on the user's emotions and state, making it difficult to improve the user experience.

[2054] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user's basic information as an input form and transmitting the information to the server, means for storing the received user information in a database in the server, means for querying a food information database and proposing an appropriate menu based on the user information, means for providing the proposed menu to the user, means for generating discount information based on a menu selected by the user, means for providing the generated discount information to the user, and means for periodically receiving the user's progress status and emotional data, and generating and providing the user with the latest suggestions and advice based on that information. This makes it possible to propose personalized menus based on the user's basic information and preferences, and to provide advice and discount information that takes emotions into consideration.

[2055] "Basic user information" is personal data such as the user's name, favorite dishes, allergy information, budget, etc.

[2056] The "means for sending to the server" is a communication means for sending the user's basic information from the input form to the server.

[2057] The "means for storing in a database" refers to a means for storing the received information in a database and making it possible to query the information as needed.

[2058] "Querying a food information database" refers to referencing the database to search for an appropriate menu based on the user's information.

[2059] The "means for suggesting a menu" is a means for generating an appropriate menu based on the user's basic information and preferences and presenting it to the user.

[2060] The "means for generating discount information" is a means for generating applicable discount information based on the menu selected by the user.

[2061] "Periodic receipt of progress status and emotional data" means periodically obtaining feedback and emotional state from the user.

[2062] "Generating up-to-date suggestions and advice" means using collected progress and sentiment data to generate appropriate menus and advice for the next step.

[2063] This invention relates to an AI chatbot system that combines a food delivery order support system with an emotion engine that recognizes user emotions. This system acquires basic information about the user, suggests appropriate menu items, and provides discount information and advice. The specific operation is explained below.

[2064] First Interaction and User Registration

[2065] User Registration

[2066] A user accesses a food delivery app on their smartphone.

[2067] When the device is accessed for the first time, it displays a welcome message along with a form for entering the user's basic information (name, favorite dishes, allergy information, budget, etc.).

[2068] The user enters the required information into the input form and presses the submit button.

[2069] The terminal transmits the information provided by the user to the server.

[2070] The server stores the received user information in a database using Firebase.

[2071] Menu suggestions

[2072] Menu suggestions

[2073] The server queries the food information database and searches for an appropriate menu based on the user's information (preferred dishes, allergy information, budget, etc.).

[2074] The server uses a generative AI model (GPT-4) to create a list of menus based on user information.

[2075] The server generates a menu suggestion list and sends it to the terminal.

[2076] The terminal displays a menu suggestion list to the user.

[2077] Utilizing the Emotion Engine

[2078] Analysis by emotion engine

[2079] The device recognizes emotions using an emotion engine (Emotion API) from the user's facial expressions and text input.

[2080] The device transmits the recognized emotion information to the server.

[2081] Providing discount information

[2082] Generate and provide discount information

[2083] The user selects the desired menu from the suggested menu list.

[2084] The terminal transmits the selected menu information to the server.

[2085] The server uses a generative AI model to create discount information for the selected menu.

[2086] The server transmits the generated discount information to the terminal.

[2087] The terminal displays the discount information to the user.

[2088] Ongoing support

[2089] Progress check and advice

[2090] Users periodically enter feedback and sentiment about their orders.

[2091] The device sends feedback information and emotion data to the server.

[2092] The server uses a generative AI model to generate the latest suggestions and advice based on the input feedback information and emotional data.

[2093] The server sends the generated suggestions and advice to the device.

[2094] The device displays the latest suggestions and advice to the user.

[2095] This allows users to select the menu that best suits them and receive emotionally sensitive advice and discount information, providing a highly satisfying food delivery experience.

[2096] Prompt Sentence Examples

[2097] markdown

[2098] System prompt:

[2099] User attribute information: {Name: 'Yamada Taro', Favorite food: 'Japanese food', Allergy: 'Peanuts', Budget: 'Under 2000 yen'}

[2100] Past orders: 'Sushi, Ramen, Udon'

[2101] User sentiment: 'Frustrated'

[2102] Generate menu suggestions and discount coupons.

[2103] Example output to the user:

[2104] Hello Taro Yamada! How are you today?

[2105] We've created menu recommendations for you based on your recent orders.

[2106] Tempura set meal

[2107] Boiled fish

[2108] Plus, we offer special coupons for new customers!

[2109] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2110] Step 1:

[2111] A user accesses a food delivery app from their smartphone. When the app is accessed for the first time, it displays a welcome message along with a form for inputting the user's basic information (name, favorite dishes, allergy information, budget, etc.). The user enters the necessary information into the input form and presses the submit button.

[2112] Input: User basic information

[2113] Output: Basic information filled in and ready to send

[2114] Step 2:

[2115] The device sends the information provided by the user to the server, which then stores the received user information in a database (Firebase).

[2116] Input: User's basic information input data

[2117] Output: User information is saved in the database

[2118] Step 3:

[2119] The server queries a food information database and searches for appropriate menu items based on the user's information (preferred dishes, allergies, budget, etc.). The server uses a generative AI model (GPT-4) to create a list of menu items based on the user's information.

[2120] Input: User basic information

[2121] Data processing / calculation: Generate menu suggestions using AI models

[2122] Output: Menu suggestion list

[2123] Step 4:

[2124] The server sends the generated menu suggestion list to the terminal, which displays the menu suggestion list to the user.

[2125] Input: Menu suggestion list

[2126] Output: A list of menu suggestions is displayed on the terminal.

[2127] Step 5:

[2128] The device recognizes emotions from the user's facial expressions and text input using the emotion engine (Emotion API). The recognized emotion information is sent to the server.

[2129] Input: facial expressions and text as the user types

[2130] Data processing / calculation: Analysis using emotion recognition engine

[2131] Output: Emotional information

[2132] Step 6:

[2133] The user selects the desired menu from the proposed menu list, and the terminal transmits the selected menu information to the server.

[2134] Input: User menu selection

[2135] Output: Selected menu information is sent to the server

[2136] Step 7:

[2137] The server uses a generative AI model to create discount information for the selected menu item. The discount information is then sent from the server to the device, which then displays the discount information to the user.

[2138] Input: Selected menu information

[2139] Data processing / calculation: Generate discount information using AI models

[2140] Output: Discount information displayed on terminal

[2141] Step 8:

[2142] The user periodically inputs feedback about orders and changes in emotions, and the terminal transmits the feedback information and emotion data to the server.

[2143] Input: User feedback and sentiment data

[2144] Output: Feedback information and emotion data are sent to the server.

[2145] Step 9:

[2146] The server uses a generative AI model to generate the latest suggestions and advice based on the input feedback information and emotion data. The server then sends the generated suggestions and advice to the device, which then displays the latest suggestions and advice to the user.

[2147] Input: Feedback information and emotion data

[2148] Data processing / calculation: Generate suggestions and advice using AI models

[2149] Output: The latest suggestions and advice will be displayed on your terminal.

[2150] 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.

[2151] 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.

[2152] 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.

[2153] 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.

[2154] 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.

[2155] 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.

[2156] 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).

[2157] 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.

[2158] 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."

[2159] 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.

[2160] 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).

[2161] 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.

[2162] 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.

[2163] 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.

[2164] 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.

[2165] 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.

[2166] 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.

[2167] 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.

[2168] 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.

[2169] 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.

[2170] 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.

[2171] The following is further disclosed regarding the above embodiment.

[2172] (Claim 1)

[2173] a means for receiving basic information of a user as an input form and transmitting the information to a server;

[2174] A means for storing the received user information in a database in the server;

[2175] means for querying a credentials database and suggesting appropriate credentials based on the user's information;

[2176] means for providing suggested entitlements to a user;

[2177] means for generating study methods and curriculum plans based on the user's selected qualifications;

[2178] a means for providing the generated study methods and curriculum plans to a user;

[2179] A system that includes a means for periodically receiving the user's progress and additional information, and generating and providing updated advice to the user based on that information.

[2180] (Claim 2)

[2181] 2. The system of claim 1, further comprising means for transmitting the generated qualification proposal list to a terminal and displaying it to a user.

[2182] (Claim 3)

[2183] 10. The system of claim 1, further comprising means for generating customized credentials and study methods according to a user's profile using a generative AI model.

[2184] "Example 1"

[2185] (Claim 1)

[2186] a means for receiving basic information of a user as an input form and transmitting the information to a server;

[2187] A means for storing the received user information in a database in the server;

[2188] means for querying a credentials database and suggesting appropriate credentials based on the user's information;

[2189] means for providing suggested entitlements to a user;

[2190] means for generating study methods and curriculum plans based on the user's selected qualifications;

[2191] a means for providing the generated study methods and curriculum plans to a user;

[2192] a means for periodically receiving the user's progress and additional information, and generating and providing updated advice to the user based on that information;

[2193] a means for generating customized credentials, study methods, and curriculum plans using generative AI models;

[2194] a means for providing input to the generative AI model using prompt sentences;

[2195] A system including:

[2196] (Claim 2)

[2197] 2. The system of claim 1, further comprising means for transmitting the generated qualification proposal list to a terminal and displaying it to a user.

[2198] (Claim 3)

[2199] 10. The system of claim 1, further comprising means for generating customized credentials and study methods according to a user profile.

[2200] "Application Example 1"

[2201] (Claim 1)

[2202] a means for receiving basic information of a user as an input form and transmitting the information to a server;

[2203] A means for storing the received user information in a database in the server;

[2204] means for querying a credentials database and suggesting appropriate credentials based on the user's information;

[2205] means for providing suggested entitlements to a user;

[2206] means for generating study methods and curriculum plans based on the user's selected qualifications;

[2207] a means for providing the generated study methods and curriculum plans to a user;

[2208] a means for periodically receiving the user's progress and additional information, and generating and providing updated advice to the user based on that information;

[2209] An interactive way to provide appropriate credentials and learning content to factory workers;

[2210] A means to display study plans and curriculum progress on the robot display, and

[2211] A means for generating study plans and curricula using artificial intelligence models;

[2212] The system includes a means for generating a detailed study plan based on a prompt sentence using a generative AI model when generating the study plan.

[2213] (Claim 2)

[2214] 2. The system of claim 1, further comprising means for transmitting the generated qualification proposal list to a terminal and displaying it to a user.

[2215] (Claim 3)

[2216] 10. The system of claim 1, further comprising means for generating customized credentials and study methods according to a user's profile using a generative AI model.

[2217] "Example 2: Combining Emotion Engines"

[2218] (Claim 1)

[2219] a means for receiving basic information of a user as an input form and transmitting the information to a server;

[2220] A means for storing the received user information in a database in the server;

[2221] means for querying a credentials database and suggesting appropriate credentials based on the user's information;

[2222] means for providing suggested entitlements to a user;

[2223] means for generating study methods and curriculum plans based on the user's selected qualifications;

[2224] a means for providing the generated study methods and curriculum plans to a user;

[2225] a means for periodically receiving the user's progress and additional information, and generating and providing updated advice to the user based on that information;

[2226] A means for recognizing emotions from a user's facial expressions and text input;

[2227] a means for providing emotion-sensitive advice to a user based on the recognized emotion information;

[2228] a means for generating a list of qualifications based on user information using a generative AI model;

[2229] The system includes a means for providing the generated entitlement list to a user.

[2230] (Claim 2)

[2231] 2. The system of claim 1, further comprising means for transmitting the generated qualification proposal list to a terminal and displaying it to a user.

[2232] (Claim 3)

[2233] 10. The system of claim 1, further comprising means for generating customized credentials and study methods according to a user's profile using a generative AI model.

[2234] "Application example 2 when combining emotion engines"

[2235] (Claim 1)

[2236] a means for receiving basic information of a user as an input form and transmitting the information to a server;

[2237] A means for storing the received user information in a database in the server;

[2238] means for querying a food information database and suggesting appropriate menu items based on user information;

[2239] means for providing the suggested menu to the user;

[2240] means for generating discount information based on a menu selected by a user;

[2241] means for providing the generated discount information to a user;

[2242] A system that includes a means for periodically receiving a user's progress and emotional data, and generating and providing the user with updated suggestions and advice bas...

Claims

1. a means for receiving basic information of a user as an input form and transmitting the information to a server; A means for storing the received user information in a database in the server; means for querying a credentials database and suggesting appropriate credentials based on the user's information; means for providing suggested entitlements to a user; means for generating study methods and curriculum plans based on the user's selected qualifications; a means for providing the generated study methods and curriculum plans to a user; A system that includes a means for periodically receiving the user's progress and additional information, and generating and providing updated advice to the user based on that information.

2. 2. The system of claim 1, further comprising means for transmitting the generated qualification proposal list to a terminal and displaying it to a user.

3. 10. The system of claim 1, further comprising means for generating customized credentials and study methods according to a user's profile using a generative AI model.

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