System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving a registration condition requiring different conditions for each enterprise, a generation means for generating required registration information based on the condition, a means for storing the generated registration information and transmitting it to a user, a means for receiving the registration information inputted by the user, a verification means for confirming whether the inputted registration information matches the condition, and a means for transmitting the verification result 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] Corporate mobile registration, which requires different conditions for each company, is time-consuming and laborious, and there are frequent errors and inconsistencies based on the conditions. Traditional manual methods are prone to human error when generating registration data and checking input information, which not only reduces operational efficiency but also poses the risk of disrupting service provision to customers. For this reason, there is a need for a system that can generate and check registration information efficiently and accurately, responding to each company's conditions. [Means for solving the problem]

[0005] The present invention provides a system that receives registration conditions, which vary from company to company, and automatically generates the required registration information based on these conditions. Specifically, the system includes: (1) a means for receiving company configuration information; (2) a generating means for generating the required registration information based on the received conditions; (3) a means for saving the generated registration information and sending it to the user; (4) a means for receiving registration information entered by the user; (5) a verifying means for verifying whether the entered registration information matches the received conditions; and (6) a means for sending the verification results to the user. This allows the registration information generation and verification processes to be carried out efficiently and accurately, responding to the requirements of each company. In particular, using artificial intelligence in the verifying means reduces human error and increases the accuracy of the registration information.

[0006] An "enterprise" is a legal entity or association organized to carry on a particular trade or business.

[0007] "Conditions" refer to the criteria or constraints that must be met to achieve a particular result or action.

[0008] "Registration Information" refers to any data or information entered or stored in the System.

[0009] "Generation means" refers to the function of a device or software that automatically creates the necessary information or data based on specified conditions.

[0010] "Verification means" refers to the function of a device or software that verifies whether input information or data matches specified conditions.

[0011] "User" means any person or legal entity that uses the System to enter or confirm registration information.

[0012] "Artificial intelligence" refers to computer systems that mimic intelligent human behavior through large-scale data analysis and automated learning.

[0013] "Data Field" refers to a specific data item or attribute in the registration information.

[0014] "Feedback" refers to the information, error messages, and other reactions that a system provides to a user. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0036] The present invention is a system that generates registration information that requires different conditions for each company and checks whether the information is appropriate. The system of the present invention will be described from the perspective of including a server, a terminal, and a generation AI.

[0037] Natural language explanations

[0038] Automatic generation of registration information

[0039] Receiving configuration information

[0040] The server receives the company's conditions. For example, if a company requires a "customer code" and a "department ID" as registration conditions, the server receives those conditions.

[0041] Communication of Terms

[0042] The server sends the received setting information to the generation AI, instructing it to generate a "customer code" and a "department ID."

[0043] Information generation

[0044] The AI ​​generates the necessary registration information based on the conditions received from the server. For example, the generated registration information might be "Customer code: 12345" and "Department ID: 67890."

[0045] Storage and transmission of information

[0046] The server stores the registration information received from the generation AI in a database and sends it to the user, who can then refer to it when entering the registration information.

[0047] Confirmation of registration details

[0048] Entering information

[0049] The user enters registration information into the system through a terminal. For example, the user enters "Customer code: 12345" and "Department ID: 67890."

[0050] Sending information

[0051] The device sends the entered registration information to the server, which then passes the received information to the generation AI and requests confirmation.

[0052] Verifying Information

[0053] The generation AI checks whether the input information matches the conditions. For example, it checks whether the input "customer code" matches the generated "12345."

[0054] Receiving Feedback

[0055] The server receives the verification result from the generation AI, which is sent as a "match" or "no match."

[0056] Send Feedback

[0057] The server sends the verification result to the terminal and notifies the user, so that the user can check whether the information is correct. If it does not match, the user will receive an error message and be prompted to enter the correct information again.

[0058] Specific examples

[0059] Example of generating registration information

[0060] 1. The server receives the registration requirements for a specific company, "Company X." For example, "Company X" requires a "Customer Code" and a "Department ID."

[0061] 2. The server communicates these conditions to the generation AI and asks it to generate registration information.

[0062] 3. The generation AI generates "Customer code: 67890" and "Department ID: 12345".

[0063] 4. The server stores the generated information and sends it to employees of "Company X" for reference.

[0064] Specific examples of confirmation of registered information

[0065] 1. A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345."

[0066] 2. The terminal sends the entered information to the server.

[0067] 3. The server sends the received information to the generation AI and asks it to verify whether it matches the conditions.

[0068] 4. The generation AI checks whether the input information is correct and returns the result to the server. For example, it determines that the customer code is "67890" and matches.

[0069] 5. The server sends the results from the generated AI to the user's device, where the user verifies the accuracy of the information. If there are any errors, the user is prompted to re-enter the information.

[0070] In this way, the system of the present invention can efficiently and accurately generate registration information based on conditions that vary from company to company and verify the accuracy of that information, thereby improving business efficiency and ensuring the accuracy of registered data.

[0071] The processing flow will be explained below.

[0072] Automatic generation of registration information

[0073] Step 1:

[0074] The server receives the company's registration conditions.

[0075] The server receives the configuration information for a particular company and checks the specific conditions (e.g., customer code and department ID) required by that company for registration.

[0076] Step 2:

[0077] The server sends the received conditions to the generation AI.

[0078] The server passes the received conditions to the generation AI and instructs it to generate registration information.

[0079] Step 3:

[0080] The generation AI generates registration information based on the conditions.

[0081] The generation AI automatically generates customer codes and department IDs based on the company's conditions.

[0082] Step 4:

[0083] The server stores the generated registration information and sends it to the user.

[0084] The server stores the registration information received from the generation AI in a database and sends the information to the user.

[0085] Specific working example:

[0086] The server receives information that the conditions for "Company X" require a "Customer Code" and a "Department ID."

[0087] The server instructs the generation AI to generate "Customer code: 12345" and "Department ID: 67890".

[0088] The generation AI generates this and returns it to the server.

[0089] The server stores this in a database where it can be accessed by employees of "Company X."

[0090] Confirmation of registration details

[0091] Step 1:

[0092] The user enters registration information through the terminal.

[0093] The user enters the fields based on the registration information provided. For example, enter "Customer Code: 12345" and "Department ID: 67890."

[0094] Step 2:

[0095] The terminal transmits the entered registration information to the server.

[0096] Once the user has completed the input, the terminal sends this information to the server.

[0097] Step 3:

[0098] The server transmits the received information to the generation AI and requests confirmation.

[0099] The server sends the registration information received from the device to the generation AI and asks it to check whether the entered information matches the conditions.

[0100] Step 4:

[0101] The generating AI verifies the consistency of the information.

[0102] The generation AI checks whether the entered "Customer Code" and "Department ID" match the generated information.

[0103] Step 5:

[0104] The server receives the verification result and sends it to the user.

[0105] The server receives the verification results from the generation AI and sends them to the user's device.

[0106] Step 6:

[0107] The user reviews the feedback and makes corrections as necessary.

[0108] The user checks the feedback from the server, corrects any errors, and re-enters the data.

[0109] Specific working example:

[0110] The user enters "Customer code: 12345" and "Department ID: 67890" into the terminal.

[0111] The terminal sends this information to the server.

[0112] The server sends the information to the generating AI and requests verification.

[0113] The generation AI checks against a series of conditions, confirms that the "customer code" and "department ID" match, and returns the results to the server.

[0114] The server sends the results back to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[0115] Example 1

[0116] 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."

[0117] There is a need for a system that can efficiently and accurately generate registration information that meets the different conditions for each company and quickly verify the accuracy of the information entered by users. However, current systems require manual data entry and complex condition setting, which can lead to problems such as reduced work efficiency and the occurrence of errors. Therefore, there is a need to automate the process of generating information based on the registration conditions that vary from company to company and verifying its accuracy.

[0118] 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.

[0119] In this invention, the server includes a means for receiving registration conditions that differ from company to company, an artificial intelligence means for generating the necessary registration information based on the conditions, and a means for saving the generated registration information and transmitting it to the user, thereby enabling automatic generation of registration information under conditions that differ from company to company and confirmation of its accuracy.

[0120] The "means for receiving registration conditions that differ for each company" is a function that allows the server to receive information for each company to register its specific conditions.

[0121] The "artificial intelligence means for generating the necessary registration information based on the conditions" is a function that uses artificial intelligence to generate the necessary registration information based on the company-specific conditions received by the server.

[0122] "Means for saving the generated registration information and sending it to the user" refers to a function for saving the registration information generated by artificial intelligence in a database and providing that information to the user.

[0123] The "means for receiving registration information entered by a user" is a function that allows the server to receive registration information entered by a user through a terminal.

[0124] "Means for querying the artificial intelligence generated to confirm whether the entered registration information matches the conditions" is a function that queries the artificial intelligence to confirm whether the information entered by the user matches the pre-set conditions.

[0125] "Means for transmitting the verification results to the user" refers to a function by which the server transmits the results verified by artificial intelligence to the terminal in order to inform the user.

[0126] The present invention is a system that generates registration information that requires different conditions for each company and checks whether the information is appropriate. The system of the present invention will be described from the perspective of including a server, a terminal, and a generation AI model.

[0127] Hardware and software used

[0128] Server: Receives requirements from the enterprise, sends prompts to the generative AI model, and stores and transmits the generated information.

[0129] Terminal: The user enters registration information and sends it to the server

[0130] Generative AI model: Generates registration information based on company-specific conditions and validates the entered information

[0131] Automatic generation of registration information

[0132] Receiving configuration information

[0133] The server receives registration conditions from the company. These conditions include specific items required by the company (e.g., customer code, department ID, etc.). For example, the server receives a condition requiring a "customer code" and a "department ID."

[0134] Communication of Terms

[0135] The server sends the received conditions to the generation AI model. The specific prompt might be, "Generate registration information based on the following conditions. 'Customer code' and 'Department ID' are required."

[0136] Information generation

[0137] The generative AI model generates the necessary registration information based on prompts from the server. For example, the generated registration information is "Customer code: 12345" and "Department ID: 67890."

[0138] Storage and transmission of information

[0139] The server stores the registration information received from the generative AI model in a database and sends the information to the user's device, allowing the user to refer to the necessary registration information.

[0140] Confirmation of registration details

[0141] Entering information

[0142] The user accesses the system using a terminal and enters the specified registration information. For example, the user enters "Customer Code: 12345" and "Department ID: 67890."

[0143] Sending information

[0144] The terminal sends the entered registration information to the server, which receives it and proceeds to the next step.

[0145] Verifying Information

[0146] The server transmits the received registration information to the generative AI model and sends a prompt to confirm whether it matches the conditions. The specific prompt text might be, "Please confirm whether the following information matches the pre-set conditions: 'Customer code: 12345', 'Department ID: 67890'."

[0147] Receiving Feedback

[0148] The generative AI model checks whether the input information matches the conditions and sends the verification results to the server as either "match" or "mismatch."

[0149] Send Feedback

[0150] The server sends the verification results from the generative AI model to the user's device. The user can check the verification results through the device. If the information matches, the user proceeds to the next step. If it does not match, an error message is displayed and the user is prompted to re-enter the information.

[0151] Example prompt sentences

[0152] Below is an example of a prompt sentence.

[0153] Prompt text for generating registration information

[0154] Please generate registration information based on the following conditions. "Customer Code" and "Department ID" are required.

[0155] Information confirmation prompt

[0156] Please check whether the following information matches the pre-defined conditions: "Customer Code: 12345" "Department ID: 67890"

[0157] This allows the system of the present invention to efficiently and accurately generate and verify registration information based on conditions that vary from company to company, thereby improving business efficiency and ensuring the accuracy of registered data.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1: Receiving configuration information

[0160] The server receives registration conditions from the company. The input is the specific conditions required by the company (e.g., "Customer Code" and "Department ID"). The server stores the received conditions in its internal memory and prepares them for the next processing step. The output is the registration condition data.

[0161] Step 2: Communicate conditions

[0162] The server sends the received registration conditions to the generative AI model. Here, it generates a prompt to send to the generative AI model. It creates a prompt saying, "Please generate registration information based on the following conditions. 'Customer code' and 'Department ID' are required," and sends it to the generative AI model. The input is the registration conditions received earlier, and the output is the prompt to be sent to the generative AI model.

[0163] Step 3: Generate information

[0164] The generative AI model generates the necessary registration information based on prompts from the server. For example, it generates data such as "Customer code: 12345" and "Department ID: 67890." The input is the prompt, and the output is the generated registration information.

[0165] Step 4: Saving and sending information

[0166] The server stores the registration information received from the generative AI model in a database. At the same time, it sends this information to the user's device. The input is the generated registration information, and the output is updating the database and sending it to the user's device.

[0167] Step 5: Enter your information

[0168] A user uses a terminal to access the system and enters the specified registration information. For example, "Customer code: 12345" and "Department ID: 67890." The input is the user's registration information, and the output is the data to be sent to the server.

[0169] Step 6: Submit your information

[0170] The terminal sends the entered registration information to the server. The input is the registration information entered by the user, and the output is the data sent to the server.

[0171] Step 7: Verify the information

[0172] The server transmits the received registration information to the generative AI model and sends a prompt to confirm whether it matches the conditions. It generates a prompt such as, "Please check whether the following information matches the pre-set conditions: 'Customer code: 12345', 'Department ID: 67890'." The input is the user's registration information, and the output is the prompt sent to the generative AI model.

[0173] Step 8: Receiving feedback

[0174] The generative AI model checks whether the input information matches the conditions and sends the verification result to the server. The input is a verification prompt, and the output is a verification result such as "match" or "no match."

[0175] Step 9: Submit your feedback

[0176] The server sends the verification results from the generative AI model to the user's device. The user checks the verification results through the device. The input is the verification result, and the output is a notification to the user's device. If there is a mismatch, an error message is displayed prompting the user to re-enter the information.

[0177] (Application example 1)

[0178] 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."

[0179] Electronic payment services require a system that generates registration information based on conditions that vary from company to company and quickly and efficiently verifies its accuracy. Conventional methods involve manual data entry and verification, which is prone to errors and reduces operational efficiency. This poses a challenge, particularly for companies that handle a large volume of transactions, as generating and verifying registration information requires a great deal of time and effort.

[0180] 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.

[0181] In this invention, the server includes: means for receiving different conditions for a company; generation means for providing an artificial intelligence model that automatically generates registration information based on the conditions; means for saving the generated registration information in a database and transmitting it to the user; means for receiving registration information entered by the user; verification means using an artificial intelligence model to confirm whether the entered registration information matches the conditions; and means for transmitting the verification results to the user and providing feedback. This makes it possible to quickly and efficiently generate registration information based on conditions that vary from company to company in electronic payment services and verify its accuracy, thereby improving business efficiency and reducing errors.

[0182] "Company-specific conditions" refer to the individual requirements and standards that each company sets for its particular registration information and transactions.

[0183] "Registration conditions" are requirements and standards required when generating registration information.

[0184] "Registered information" is information related to a specific business partner or department, and is data generated based on conditions that differ for each company.

[0185] "Generation means" refers to a technique or device for automatically creating the necessary registration information based on set conditions.

[0186] "Verification means" refers to a technique or device for verifying whether the generated registration information matches the information entered by the user.

[0187] An "artificial intelligence model" is an algorithm or system that learns from large amounts of data and generates new information or analyzes and judges existing information.

[0188] A "database" is a collection of data constructed to efficiently store, search, update, and delete information.

[0189] "Feedback" refers to the provision of information or notification of results to system users, and is a response to a specific operation or input.

[0190] A "prompt sentence" is a sentence that is input as an instruction or question required for an artificial intelligence model to operate.

[0191] The system for implementing this invention consists of the following major hardware and software components: a server, a user's smartphone, a generative AI model, and a database.

[0192] Hardware & Software Details

[0193] 1. Server

[0194] Function: Receives registration conditions that vary from company to company, works with the generative AI model to generate registration information, saves the registration information, and notifies users.

[0195] Specific name: Cloud server (e.g., AWS (registered trademark), Google (registered trademark) Cloud Platform)

[0196] 2. Smartphone

[0197] Function: Provides an interface for users to enter registration information and submit that information to a server.

[0198] Specific name: iOS or ANDROID (registered trademark) smartphone

[0199] 3. Generative AI Models

[0200] Function: Generates registration information based on set criteria and verifies that the entered information matches the criteria.

[0201] Specific names: GPT-3 (registered trademark) (OpenAI (registered trademark)), BERT (Google)

[0202] 4. Database

[0203] Function: Stores generated registration information for quick access when needed.

[0204] Specific name: SQL database (e.g., MySQL (registered trademark), PostgreSQL)

[0205] System processing overview

[0206] 1. Receiving setting information

[0207] The server receives the registration conditions (for example, "customer code" and "department ID") provided by the company.

[0208] 2. Information Generation

[0209] The server sends the received conditions to the generation AI model and generates registration information using prompts. The example prompts used in this case are as follows:

[0210] Generate a customer code and department ID based on your company's requirements. The customer code and department ID should each be a five-digit number. Example: "Customer Code: 24680" "Department ID: 13579"

[0211] 3. Storage and transmission of information

[0212] The registration information generated by the generative AI model (for example, "Customer code: 24680" and "Department ID: 13579") is returned to the server and stored in a database. It is then sent to the user's smartphone, where the user can refer to it to enter their information.

[0213] 4. Entering and submitting information

[0214] The user uses a smartphone to enter the specified registration information and transmits it from the device to the server.

[0215] 5. Verification of Information

[0216] The server sends the input information back to the generative AI model and sets up prompts to confirm that the input information matches the conditions.

[0217] 6. Receiving Feedback

[0218] The generative AI model verifies the input information and sends the results to the server, which then sends the results to the smartphone and notifies the user.

[0219] If the verification is successful, the user can continue.

[0220] If validation fails, an error message will be displayed and you will be prompted to try again.

[0221] Adding specific examples

[0222] For company "Z", the "Customer Code" and "Department ID" are required. The server receives the conditions for "Z" and sends the following prompt to the generative AI model:

[0223] Generate a customer code and department ID based on your company's requirements. The customer code and department ID should each be a five-digit number. Example: "Customer Code: 98765" "Department ID: 43210"

[0224] The generative AI model generates "Customer code: 98765" and "Department ID: 43210," which the server stores in a database and sends to the user's smartphone. The user enters this information on their smartphone and sends it to the server. The server again uses the generative AI model to check whether the input information is correct and notifies the user of the results.

[0225] This system allows companies to efficiently generate registration information based on different conditions and verify its accuracy.

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

[0227] Step 1:

[0228] The server receives registration conditions from the company. For example, it receives conditions such as "client code" and "department ID." This input is text data, and the server prepares to send the received conditions to the generative AI model.

[0229] Step 2:

[0230] The server sends the received registration conditions to the generative AI model. It generates a prompt message, such as the following: "Please generate a customer code and department ID based on the company's conditions. The customer code and department ID will each be expressed as five-digit numbers. For example: 'Customer code: 98765', 'Department ID: 43210'." The generative AI model then generates the registration information based on this.

[0231] Step 3:

[0232] The generative AI model generates the necessary registration information (e.g., "Customer code: 98765" and "Department ID: 43210") based on the prompts from the server. This data is returned to the server as structured output.

[0233] Step 4:

[0234] The server stores the generated registration information in a database and sends it to the user's smartphone, creating a database entry and formatting the stored items as notification data.

[0235] Step 5:

[0236] Users access the system using their smartphones and enter their registration information (e.g., "Customer code: 98765" and "Department ID: 43210"). This input is done through text boxes and forms, and users are guided to enter the information accurately.

[0237] Step 6:

[0238] The device sends the entered registration information to the server. It receives the user's input and generates a request to forward it to the server, sometimes checking the format of the input data.

[0239] Step 7:

[0240] The server then sends the received registration information back to the AI ​​model, requesting confirmation that the input information matches the conditions. This process generates a prompt for verification, such as the following: "Please confirm that the customer code and department ID entered by the user are correct. Input: Customer code 98765, Department ID 43210."

[0241] Step 8:

[0242] The generative AI model verifies the input information based on the conditions and returns a result to the server indicating whether or not it matches. This data processing is performed by comparing the input information with reference information.

[0243] Step 9:

[0244] The server receives the verification results from the generative AI model and sends them to the user's smartphone. If the results match, it generates feedback data containing a success message, and if they do not match, it generates an error message.

[0245] Step 10:

[0246] The user checks the verification result on their smartphone and re-enters the information if necessary. For example, if an error message is displayed, the user re-enters the correct registration information.

[0247] Through this series of processing steps, registration information based on conditions that differ for each company and the accuracy of the information are efficiently generated and verified.

[0248] 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.

[0249] This invention combines a system that generates registration information that requires different conditions for each company and checks whether that information is appropriate, with an emotion engine that recognizes the user's emotions. The system of this invention will be explained from the perspective of including a server, terminal, generation AI, and emotion engine.

[0250] Natural language explanations

[0251] Automatic generation of registration information

[0252] Receiving configuration information

[0253] The server receives the company's registration conditions. For example, if a company requires a "client code" and a "department ID" as registration conditions, the server receives those conditions.

[0254] Communication of Terms

[0255] The server sends the received setting information to the generation AI, instructing it to generate a "customer code" and a "department ID."

[0256] Information generation

[0257] The AI ​​generates the necessary registration information based on the conditions received from the server. For example, the generated registration information might be "Customer code: 12345" and "Department ID: 67890."

[0258] Storage and transmission of information

[0259] The server stores the registration information received from the generation AI in a database and sends it to the user, who can then refer to it when entering the registration information.

[0260] Confirmation of registration details

[0261] Entering information

[0262] The user enters registration information into the system through a terminal. For example, the user enters "Customer code: 12345" and "Department ID: 67890."

[0263] Sending information

[0264] The device sends the entered registration information to the server, which then passes the received information to the generation AI and requests confirmation.

[0265] Verifying Information

[0266] The generation AI checks whether the input information matches the conditions. For example, it checks whether the input "customer code" matches the generated "12345."

[0267] Receiving Feedback

[0268] The server receives the verification result from the generation AI, which is sent as a "match" or "no match."

[0269] Send Feedback

[0270] The server sends the verification result to the terminal and notifies the user, so that the user can check whether the information is correct. If it does not match, the user will receive an error message and be prompted to enter the correct information again.

[0271] Incorporating an emotion engine

[0272] Emotion Engine Operation

[0273] Emotion recognition

[0274] The device sends emotional information such as the user's facial expressions and voice to the emotion engine, which then analyzes the user's emotions during input or when error feedback is provided.

[0275] Emotion-based assistance

[0276] The emotion engine assists with the next action based on the user's emotional state. For example, if the user is confused or frustrated, the emotion engine notifies the server that support is needed.

[0277] Feedback adjustment

[0278] The emotion engine adjusts the feedback content depending on the user's emotional state. For example, if the user is feeling stressed, the emotion engine will display helpful guidance or comforting messages.

[0279] Specific examples

[0280] Example of generating registration information

[0281] 1. The server receives the registration conditions for a specific company, "Company X." For example, "Company X" requires a "Customer Code" and a "Department ID."

[0282] 2. The server communicates these conditions to the generation AI and asks it to generate registration information.

[0283] 3. The generation AI generates "Customer code: 67890" and "Department ID: 12345".

[0284] 4. The server stores the generated information and sends it to employees of "Company X" for reference.

[0285] Specific examples of confirmation of registered information

[0286] 1. A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345."

[0287] 2. The terminal sends the entered information to the server.

[0288] 3. The server sends the information to the generation AI and asks it to verify whether it matches the conditions.

[0289] 4. The generation AI checks against a set of conditions, confirms that the "Customer Code" and "Department ID" match, and returns the results to the server.

[0290] 5. The server sends the results to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[0291] Examples of emotion engines

[0292] 1. When the user enters the "customer code," the device sends the user's facial expressions and voice to the emotion engine.

[0293] 2. The emotion engine analyzes the user's emotions and determines that the user is confused.

[0294] 3. The emotion engine notifies the server and suggests providing additional support.

[0295] 4. The server sends messages containing helpful instructions and warnings to the user, helping the user to enter information without stress.

[0296] In this way, the system of the present invention can efficiently and accurately generate registration information based on the conditions that vary from company to company and verify the accuracy of that information. Furthermore, by incorporating an emotion engine, it is possible to provide feedback and support according to the user's emotional state, improving the system's user experience.

[0297] The processing flow will be explained below.

[0298] Automatic generation of registration information

[0299] Step 1:

[0300] The server receives the company's registration conditions.

[0301] The server receives the configuration information for a particular company and checks the specific conditions (e.g., customer code and department ID) required by that company for registration.

[0302] Step 2:

[0303] The server sends the received conditions to the generation AI.

[0304] The server passes the received conditions to the generation AI and instructs it to generate registration information.

[0305] Step 3:

[0306] The generation AI generates registration information based on the conditions.

[0307] The generation AI automatically generates, for example, customer codes and department IDs based on the company's conditions.

[0308] Step 4:

[0309] The server stores the generated registration information and sends it to the user.

[0310] The server stores the registration information received from the generation AI in a database and sends the information to the user.

[0311] Specific working example:

[0312] The server receives information that the conditions for "Company X" require a "Customer Code" and a "Department ID."

[0313] The server instructs the generation AI to generate "Customer code: 12345" and "Department ID: 67890".

[0314] The generation AI generates this and returns it to the server.

[0315] The server stores this in a database where it can be accessed by employees of "Company X."

[0316] Confirmation of registration details

[0317] Step 1:

[0318] The user enters registration information through the terminal.

[0319] The user enters the fields based on the registration information provided. For example, enter "Customer Code: 12345" and "Department ID: 67890."

[0320] Step 2:

[0321] The terminal transmits the entered registration information to the server.

[0322] Once the user has completed the input, the terminal sends this information to the server.

[0323] Step 3:

[0324] The server transmits the received information to the generation AI and requests confirmation.

[0325] The server sends the registration information received from the device to the generation AI and asks it to check whether the entered information matches the conditions.

[0326] Step 4:

[0327] The generating AI verifies the consistency of the information.

[0328] The generation AI checks whether the entered "Customer Code" and "Department ID" match the generated information.

[0329] Step 5:

[0330] The server receives the verification result and sends it to the user.

[0331] The server receives the verification results from the generation AI and sends them to the user's device.

[0332] Step 6:

[0333] The user reviews the feedback and makes corrections as necessary.

[0334] The user checks the feedback from the server, corrects any errors, and re-enters the data.

[0335] Specific working example:

[0336] The user enters "Customer code: 12345" and "Department ID: 67890" into the terminal.

[0337] The terminal sends this information to the server.

[0338] The server sends the information to the generating AI and requests verification.

[0339] The generation AI checks against a series of conditions, confirms that the "customer code" and "department ID" match, and returns the results to the server.

[0340] The server sends the results back to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[0341] Incorporating an emotion engine

[0342] Step 1:

[0343] The terminal transmits emotional information such as the user's facial expressions and voice to the emotion engine.

[0344] When the terminal is in the middle of user input or when error feedback is received, it sends data to the emotion engine to analyze the user's emotions.

[0345] Step 2:

[0346] The emotion engine recognizes and analyzes the user's emotions.

[0347] The emotion engine analyzes the user's emotional state (happiness, sadness, irritation, etc.) based on the data received.

[0348] Step 3:

[0349] The emotion engine determines the next action based on the emotional state.

[0350] For example, if the user is frustrated, the emotion engine will notify the server that support is needed.

[0351] Step 4:

[0352] The server generates feedback according to the user's emotional state.

[0353] The server generates a message including friendly guidance and warnings for the user based on the notification from the emotion engine.

[0354] Step 5:

[0355] The user receives feedback according to their emotional state.

[0356] To allow users to receive feedback from the server and continue working without stress or confusion.

[0357] Specific working example:

[0358] When the user enters the "customer code," the terminal transmits the user's facial expressions and voice to the emotion engine.

[0359] The emotion engine analyzes the user's emotions and determines that the user is confused.

[0360] The emotion engine notifies the server of this and suggests providing additional support.

[0361] The server sends messages containing helpful guidance and cautions to the user, thereby helping the user to input information without feeling stressed.

[0362] Example 2

[0363] 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."

[0364] Conventional systems require a complex process for generating and verifying registration information based on company-specific requirements, and provide limited feedback on user input. Furthermore, they do not provide support tailored to the user's emotional state, resulting in a lack of user experience. Therefore, there is a need for a system that efficiently generates and verifies registration information, provides feedback tailored to the user's emotional state, and improves the user experience.

[0365] 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.

[0366] In this invention, the server includes means for receiving registration conditions, which may differ from company to company, means for generating the required registration information based on the conditions, means for saving the generated registration information and transmitting it to the user, means for receiving registration information entered by the user, means for verifying whether the entered registration information matches the conditions, means for transmitting the verification results to the user, emotion recognition means for recognizing the user's emotion information, and means for adjusting feedback to the user based on the emotion information. This makes it possible to generate and verify registration information more efficiently and accurately, and to provide appropriate feedback according to the user's emotional state.

[0367] "Registration conditions" refer to conditions that differ for each company and are elements required to register specific information.

[0368] "Generation means" refers to a device or software having the function of generating the necessary registration information based on the received registration conditions.

[0369] "Storage means" refers to a device or software that has the function of storing the generated registration information in a database or storage.

[0370] "Receiving means" refers to a device or software that has the function of receiving registration information entered by a user.

[0371] "Verification means" refers to a device or software that has the function of verifying whether the entered registration information matches pre-set conditions or generated information.

[0372] "Emotion recognition means" refers to a device or software that has the function of recognizing and analyzing emotional information such as a user's facial expression and voice.

[0373] The "feedback adjustment means" refers to a device or software that has the function of adjusting the content of feedback to the user based on the emotional information recognized by the emotional recognition means.

[0374] The present invention combines a system that generates registration information that requires different conditions for each company and checks whether that information is appropriate, with an emotion engine that recognizes user emotions. The system of the present invention will be described from the perspective of including a server, a terminal, a generation AI, and an emotion engine. Specific embodiments will be described below.

[0375] Automatic generation of registration information

[0376] Receiving configuration information

[0377] The server receives the company's registration conditions. For example, if a company requires a "client code" and a "department ID" as registration conditions, the server receives those conditions. This information is received via API and temporarily stored in an internal database.

[0378] Communication of Terms

[0379] The server sends the received configuration information to the generation AI. It instructs the generation AI to generate a "customer code" and a "department ID." This communication uses HTTP requests and WebSockets.

[0380] Information generation

[0381] The generative AI generates the necessary registration information based on the conditions received from the server. The generative AI model (e.g., GPT-3) generates text data such as "Customer code: 12345" and "Department ID: 67890" based on the prompt sentence.

[0382] Storage and transmission of information

[0383] The server stores the registration information received from the generation AI in a database and sends it to the user so that the user can refer to it when entering registration information.

[0384] Confirmation of registration details

[0385] Entering information

[0386] The user enters registration information into the system via a terminal. For example, the user uses a web browser or a dedicated application to enter registration information such as "Customer code: 12345" and "Department ID: 67890" into a form.

[0387] Sending information

[0388] The terminal sends the entered registration information to the server. When the user enters information and presses the send button, the terminal sends the information to the server as a POST request.

[0389] Verifying Information

[0390] The server transmits the received information to the generation AI and requests confirmation. The generation AI compares the received information with the pre-set conditions and the generated information to determine whether they match.

[0391] Receiving and sending feedback

[0392] The server receives the verification results from the generation AI and sends them to the terminal as an API response. The terminal displays the results so that the user can confirm them. For example, it checks whether the entered "customer code" matches the generated "12345" and notifies the user of the result.

[0393] Incorporating an emotion engine

[0394] Emotion recognition

[0395] The device sends emotional information such as the user's facial expressions and voice to the emotion engine. The device uses a camera and microphone to detect the user's facial expressions and voice, and sends the data to the emotion engine in real time.

[0396] Emotion-based assistance

[0397] The emotion engine assists with next actions based on the user's emotional state. For example, if it determines that the user is confused, the emotion engine notifies the server and suggests that additional support is needed.

[0398] Feedback adjustment

[0399] An emotion engine tailors the feedback depending on the user's emotional state: for example, if the user is feeling stressed, the server will send helpful guidance or comforting messages.

[0400] Specific examples

[0401] Example of generating registration information

[0402] The server receives the registration conditions for a specific company. For example, "Company X" requires a "Customer Code" and a "Department ID." The server communicates these conditions to the generation AI and asks it to generate the registration information. The generation AI generates "Customer Code: 67890" and "Department ID: 12345." The server then stores the generated information and sends it to employees of "Company X" for reference.

[0403] Specific examples of confirmation of registered information

[0404] A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345." The terminal sends the entered information to the server, which then sends the information to the generation AI, requesting it to verify whether it matches the conditions. The generation AI checks the information against a set of conditions, confirms that the "Customer Code" and "Department ID" match, and returns the results to the server. The server then sends the results to the user, who confirms that the entered information is accurate.

[0405] Examples of emotion engines

[0406] When the user enters the "customer code," the terminal sends the user's facial expression and voice to the emotion engine. The emotion engine analyzes the user's emotions and determines that the user is confused. The emotion engine notifies the server and suggests providing additional support. The server then sends the user a message containing helpful instructions and warnings, helping the user enter information without stress.

[0407] In this way, the system of the present invention can efficiently and accurately generate registration information based on the conditions that vary from company to company and verify the accuracy of that information. Furthermore, by incorporating an emotion engine, it is possible to provide feedback and support according to the user's emotional state, improving the system's user experience.

[0408] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0409] Program processing flow

[0410] Automatic generation of registration information

[0411] Step 1:

[0412] The server receives the registration conditions.

[0413] Input: Registration conditions sent by the company (e.g. "Customer Code" and "Department ID")

[0414] What happens: The server receives the API request and temporarily stores these registration conditions in an internal database.

[0415] Step 2:

[0416] The server sends the registration conditions to the generated AI.

[0417] Input: Registration conditions saved on the server

[0418] Output: Data in prompt format

[0419] Specific operation: The server sends the registration conditions as a prompt to the generation AI (e.g., "Generate a customer code and department ID") via an HTTP request.

[0420] Step 3:

[0421] Generative AI generates registration information

[0422] Input: prompt statement

[0423] Output: Registration information (e.g., "Customer code: 12345" and "Department ID: 67890")

[0424] Specific operation: The generative AI model (e.g., GPT-3) generates registration information based on the prompt sentence and returns it as text data.

[0425] Step 4:

[0426] The server stores the generated registration information and sends it to the user.

[0427] Input: Registration information returned by the generation AI

[0428] Output: Registration information stored in a database and sent to the user

[0429] Specific operation: The server stores the acquired registration information in an internal database and then sends it to the company's system administrator via email or API response.

[0430] Confirmation of registration details

[0431] Step 5:

[0432] The user enters registration information through the terminal.

[0433] Input: Registration information provided by the company (e.g., "Customer code: 12345" and "Department ID: 67890")

[0434] What happens: The user uses a web browser or a dedicated application to enter registration information into the appropriate form.

[0435] Step 6:

[0436] The device sends the entered registration information to the server.

[0437] Input: Information entered by the user

[0438] Output: POST request to the server

[0439] Specific operation: When the user presses the send button, the terminal sends the entered information to the server as a POST request.

[0440] Step 7:

[0441] The server sends the received information to the generation AI and checks whether it matches the conditions.

[0442] Input: Your registered information

[0443] Output: Confirmation request to the generating AI

[0444] Specific operation: The server sends the received registration information as a verification request to the generation AI to determine whether it matches.

[0445] Step 8:

[0446] Generative AI checks for information consistency

[0447] Input: Registration information sent from the server

[0448] Output: Match / No-Match results

[0449] Specific operation: The generation AI compares the registration information received from the server with the information generated in advance and returns the match / mismatch result to the server.

[0450] Step 9:

[0451] The server sends the verification result to the device.

[0452] Input: Match / No-Match results from the generation AI

[0453] Output: Notification of validation results to the user

[0454] Specific operation: The server returns the received verification result to the device as an API response so that the user can check it.

[0455] Incorporating an emotion engine

[0456] Step 10:

[0457] The device sends the user's emotional information to the emotion engine.

[0458] Input: User facial and voice data

[0459] Output: Emotional information data

[0460] How it works: The device uses a camera and microphone to detect the user's facial expressions and voice, and sends that data to the emotion engine in real time.

[0461] Step 11:

[0462] Emotion engine helps guide next actions based on emotions

[0463] Input: Emotional information data

[0464] Output: Instructions for auxiliary actions

[0465] Specific behavior: If the emotion engine performs analysis and determines that the user is confused or frustrated, it notifies the server and suggests the need for additional support.

[0466] Step 12:

[0467] The server adjusts the feedback and notifies the user.

[0468] Input: Notification from the emotion engine

[0469] Output: Adjusted feedback message

[0470] Specific operation: Based on the notification from the emotion engine, the server sends messages containing helpful guidance and warnings to the user, helping the user to enter information without stress.

[0471] (Application example 2)

[0472] 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."

[0473] Conventional systems for generating and verifying registration information have the problem that the information entered by users is complex, making it prone to input errors and mismatches with requirements. Furthermore, users often experience difficulty or stress when entering information, which can lead to a decline in service quality. The present invention aims to solve these issues and improve the user experience. Furthermore, the present invention provides a system that recognizes the user's emotions and provides appropriate support, allowing the input process to proceed smoothly.

[0474] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0475] In this invention, the server includes: means for receiving registration conditions, which may vary from company to company; means for generating the required registration information based on the conditions; means for saving the generated registration information and transmitting it to the user; means for receiving the registration information entered by the user; means for verifying whether the entered registration information matches the conditions; means for transmitting the verification result to the user; emotion recognition means for analyzing the user's emotions and providing support according to the emotional state; and means for providing additional support if the emotion recognition means determines that the user's emotional state is unstable. This not only enables efficient and accurate generation and verification of registration information, but also makes it possible to reduce stress and improve the user experience by providing appropriate support according to the user's emotional state.

[0476] "Conditions that vary from company to company" refers to the specific requirements and conditions required for registration information that each company sets on its own.

[0477] "Registration information" refers to a series of data and information generated based on the conditions required by a company.

[0478] "Generation means" refers to a mechanism or method for automatically generating registration information based on conditions that vary from company to company.

[0479] "Storage means" refers to a function or system for storing the generated registration information in a database or the like.

[0480] "Receiving means" refers to a function for receiving information sent from a user or a server.

[0481] "Verification measures" refer to mechanisms or methods for verifying whether the registration information entered by the user matches the conditions set by the company.

[0482] "Transmission means" refers to a function for transmitting the generated registration information and verification results to the user or other systems.

[0483] "Emotion recognition means" refers to a mechanism or method for analyzing emotions from the user's facial expressions, voice, etc., and determining their current emotional state.

[0484] "Means for providing additional support" refers to functions or methods for providing appropriate assistance or guidance when the emotion recognition means determines that the user is experiencing difficulty or stress.

[0485] The system for implementing this invention includes the following main components: a server, a terminal, a generation AI, and an emotion recognition means. Below, we will explain how each component works together and processes data.

[0486] First, the server receives the registration conditions, which vary from company to company. These conditions are specific data fields, such as a customer code or department ID. The server then sends the received conditions to the generation AI.

[0487] The generation AI generates the necessary registration information based on the prompt sent from the server. The generated registration information is saved in a database and later provided to the user. An example of a prompt statement for the generation AI is, "The client code and department ID are required as registration conditions for Company X. Please generate the client code and department ID."

[0488] The terminal is a device for transmitting the registration information entered by the user to the server. This terminal may be a smartphone, tablet, or PC, and can transmit the information entered by the user to the server in real time. Furthermore, the terminal can transmit the user's facial expression and voice data to the emotion recognition means.

[0489] The verification method checks whether the entered registration information matches the company's set conditions. This includes a matching process using the generation AI. For example, if a user enters "Customer Code 12345" and "Department ID 67890," it verifies that this matches the generated information.

[0490] The emotion recognition means analyzes the user's facial expressions and voice data to determine their emotional state. If the user is confused or stressed, this information is sent to the server, and additional support is provided. For example, if the user is determined to be confused, a helpful guidance message may be displayed.

[0491] This not only enables efficient and accurate generation and confirmation of registration information, but also reduces stress and improves the user experience by providing appropriate support according to the user's emotional state.

[0492] Specific examples

[0493] For example, when a new employee of a certain company, "Company X," attempts to register in the system, the server requests the generation AI to provide the "customer code" and "department ID" as the registration conditions for Company X. The "customer code 12345" and "department ID 67890" generated by the generation AI are stored in the database. The user enters this information using a terminal, and the system verifies it. After confirming that the input matches, the user's facial expressions and voice are analyzed using emotion recognition means, and additional support is provided if necessary.

[0494] Example prompt sentence:

[0495] "Company X requires a customer code and department ID to register. Please generate a customer code and department ID."

[0496] In this way, the system of the invention can efficiently generate and verify registration information, and provide support according to the user's emotional state.

[0497] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0498] Step 1:

[0499] The server receives registration conditions, which vary from company to company. As input, the condition data provided by the company is required. This includes specific data fields such as customer code and department ID. The server receives this information and temporarily stores it.

[0500] Step 2:

[0501] The server sends the received conditions to the generation AI, which generates a prompt to generate the required registration information. The company's condition data is used as input. The generation AI generates registration information based on the provided conditions and returns specific registration information as output (e.g., "Customer code 12345" and "Department ID 67890").

[0502] Step 3:

[0503] The server saves the generated registration information in a database. The input is the registration information received from the generation AI. The output is that the registration information is saved accurately in the database.

[0504] Step 4:

[0505] A user accesses the system using a terminal and enters their registration information. As input, the user enters the registration information (e.g., "Customer code 12345" and "Department ID 67890") into the terminal. The terminal then sends this information to the server.

[0506] Step 5:

[0507] The server receives the user's input information and sends it to the generation AI for confirmation. The information entered by the user is used as input. The generation AI compares the received information with existing registration information and returns a result of whether or not it matches.

[0508] Step 6:

[0509] The server receives the verification result and notifies the user. As input, the verification result from the generation AI is used. As output, the user is provided with feedback of "match" or "mismatch."

[0510] Step 7:

[0511] The emotion recognition means analyzes the user's facial and voice data to determine their emotional state. The facial and voice data collected by the device is used as input. The analyzed user's emotional state (e.g., confusion, stress, etc.) is output.

[0512] Step 8:

[0513] If the emotion recognition means determines that the user's emotional state is unstable, the server provides additional support. The judgment data from the emotion recognition means is used as input. The server provides the user with friendly guidance messages and additional support, and as output, creates an environment where the user can input information with confidence.

[0514] 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.

[0515] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0516] 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.

[0517] [Second embodiment]

[0518] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0519] 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.

[0520] 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).

[0521] 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.

[0522] 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.

[0523] 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).

[0524] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0525] 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.

[0526] 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.

[0527] 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.

[0528] 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.

[0529] 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."

[0530] The present invention is a system that generates registration information that requires different conditions for each company and checks whether the information is appropriate. The system of the present invention will be described from the perspective of including a server, a terminal, and a generation AI.

[0531] Natural language explanations

[0532] Automatic generation of registration information

[0533] Receiving configuration information

[0534] The server receives the company's conditions. For example, if a company requires a "customer code" and a "department ID" as registration conditions, the server receives those conditions.

[0535] Communication of Terms

[0536] The server sends the received setting information to the generation AI, instructing it to generate a "customer code" and a "department ID."

[0537] Information generation

[0538] The AI ​​generates the necessary registration information based on the conditions received from the server. For example, the generated registration information might be "Customer code: 12345" and "Department ID: 67890."

[0539] Storage and transmission of information

[0540] The server stores the registration information received from the generation AI in a database and sends it to the user, who can then refer to it when entering the registration information.

[0541] Confirmation of registration details

[0542] Entering information

[0543] The user enters registration information into the system through a terminal. For example, the user enters "Customer code: 12345" and "Department ID: 67890."

[0544] Sending information

[0545] The device sends the entered registration information to the server, which then passes the received information to the generation AI and requests confirmation.

[0546] Verifying Information

[0547] The generation AI checks whether the input information matches the conditions. For example, it checks whether the input "customer code" matches the generated "12345."

[0548] Receiving Feedback

[0549] The server receives the verification result from the generation AI, which is sent as a "match" or "no match."

[0550] Send Feedback

[0551] The server sends the verification result to the terminal and notifies the user, so that the user can check whether the information is correct. If it does not match, the user will receive an error message and be prompted to enter the correct information again.

[0552] Specific examples

[0553] Example of generating registration information

[0554] 1. The server receives the registration requirements for a specific company, "Company X." For example, "Company X" requires a "Customer Code" and a "Department ID."

[0555] 2. The server communicates these conditions to the generation AI and asks it to generate registration information.

[0556] 3. The generation AI generates "Customer code: 67890" and "Department ID: 12345".

[0557] 4. The server stores the generated information and sends it to employees of "Company X" for reference.

[0558] Specific examples of confirmation of registered information

[0559] 1. A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345."

[0560] 2. The terminal sends the entered information to the server.

[0561] 3. The server sends the received information to the generation AI and asks it to verify whether it matches the conditions.

[0562] 4. The generation AI checks whether the input information is correct and returns the result to the server. For example, it determines that the customer code is "67890" and matches.

[0563] 5. The server sends the results from the generated AI to the user's device, where the user verifies the accuracy of the information. If there are any errors, the user is prompted to re-enter the information.

[0564] In this way, the system of the present invention can efficiently and accurately generate registration information based on conditions that vary from company to company and verify the accuracy of that information, thereby improving business efficiency and ensuring the accuracy of registered data.

[0565] The processing flow will be explained below.

[0566] Automatic generation of registration information

[0567] Step 1:

[0568] The server receives the company's registration conditions.

[0569] The server receives the configuration information for a particular company and checks the specific conditions (e.g., customer code and department ID) required by that company for registration.

[0570] Step 2:

[0571] The server sends the received conditions to the generation AI.

[0572] The server passes the received conditions to the generation AI and instructs it to generate registration information.

[0573] Step 3:

[0574] The generation AI generates registration information based on the conditions.

[0575] The generation AI automatically generates customer codes and department IDs based on the company's conditions.

[0576] Step 4:

[0577] The server stores the generated registration information and sends it to the user.

[0578] The server stores the registration information received from the generation AI in a database and sends the information to the user.

[0579] Specific working example:

[0580] The server receives information that the conditions for "Company X" require a "Customer Code" and a "Department ID."

[0581] The server instructs the generation AI to generate "Customer code: 12345" and "Department ID: 67890".

[0582] The generation AI generates this and returns it to the server.

[0583] The server stores this in a database where it can be accessed by employees of "Company X."

[0584] Confirmation of registration details

[0585] Step 1:

[0586] The user enters registration information through the terminal.

[0587] The user enters the fields based on the registration information provided. For example, enter "Customer Code: 12345" and "Department ID: 67890."

[0588] Step 2:

[0589] The terminal transmits the entered registration information to the server.

[0590] Once the user has completed the input, the terminal sends this information to the server.

[0591] Step 3:

[0592] The server transmits the received information to the generation AI and requests confirmation.

[0593] The server sends the registration information received from the device to the generation AI and asks it to check whether the entered information matches the conditions.

[0594] Step 4:

[0595] The generating AI verifies the consistency of the information.

[0596] The generation AI checks whether the entered "Customer Code" and "Department ID" match the generated information.

[0597] Step 5:

[0598] The server receives the verification result and sends it to the user.

[0599] The server receives the verification results from the generation AI and sends them to the user's device.

[0600] Step 6:

[0601] The user reviews the feedback and makes corrections as necessary.

[0602] The user checks the feedback from the server, corrects any errors, and re-enters the data.

[0603] Specific working example:

[0604] The user enters "Customer code: 12345" and "Department ID: 67890" into the terminal.

[0605] The terminal sends this information to the server.

[0606] The server sends the information to the generating AI and requests verification.

[0607] The generation AI checks against a series of conditions, confirms that the "customer code" and "department ID" match, and returns the results to the server.

[0608] The server sends the results back to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[0609] Example 1

[0610] 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."

[0611] There is a need for a system that can efficiently and accurately generate registration information that meets the different conditions for each company and quickly verify the accuracy of the information entered by users. However, current systems require manual data entry and complex condition setting, which can lead to problems such as reduced work efficiency and the occurrence of errors. Therefore, there is a need to automate the process of generating information based on the registration conditions that vary from company to company and verifying its accuracy.

[0612] 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.

[0613] In this invention, the server includes a means for receiving registration conditions that differ from company to company, an artificial intelligence means for generating the necessary registration information based on the conditions, and a means for saving the generated registration information and transmitting it to the user, thereby enabling automatic generation of registration information under conditions that differ from company to company and confirmation of its accuracy.

[0614] The "means for receiving registration conditions that differ for each company" is a function that allows the server to receive information for each company to register its specific conditions.

[0615] The "artificial intelligence means for generating the necessary registration information based on the conditions" is a function that uses artificial intelligence to generate the necessary registration information based on the company-specific conditions received by the server.

[0616] "Means for saving the generated registration information and sending it to the user" refers to a function for saving the registration information generated by artificial intelligence in a database and providing that information to the user.

[0617] The "means for receiving registration information entered by a user" is a function that allows the server to receive registration information entered by a user through a terminal.

[0618] "Means for querying the artificial intelligence generated to confirm whether the entered registration information matches the conditions" is a function that queries the artificial intelligence to confirm whether the information entered by the user matches the pre-set conditions.

[0619] "Means for transmitting the verification results to the user" refers to a function by which the server transmits the results verified by artificial intelligence to the terminal in order to inform the user.

[0620] The present invention is a system that generates registration information that requires different conditions for each company and checks whether the information is appropriate. The system of the present invention will be described from the perspective of including a server, a terminal, and a generation AI model.

[0621] Hardware and software used

[0622] Server: Receives requirements from the enterprise, sends prompts to the generative AI model, and stores and transmits the generated information.

[0623] Terminal: The user enters registration information and sends it to the server

[0624] Generative AI model: Generates registration information based on company-specific conditions and validates the entered information

[0625] Automatic generation of registration information

[0626] Receiving configuration information

[0627] The server receives registration conditions from the company. These conditions include specific items required by the company (e.g., customer code, department ID, etc.). For example, the server receives a condition requiring a "customer code" and a "department ID."

[0628] Communication of Terms

[0629] The server sends the received conditions to the generation AI model. The specific prompt might be, "Generate registration information based on the following conditions. 'Customer code' and 'Department ID' are required."

[0630] Information generation

[0631] The generative AI model generates the necessary registration information based on prompts from the server. For example, the generated registration information is "Customer code: 12345" and "Department ID: 67890."

[0632] Storage and transmission of information

[0633] The server stores the registration information received from the generative AI model in a database and sends the information to the user's device, allowing the user to refer to the necessary registration information.

[0634] Confirmation of registration details

[0635] Entering information

[0636] The user accesses the system using a terminal and enters the specified registration information. For example, the user enters "Customer Code: 12345" and "Department ID: 67890."

[0637] Sending information

[0638] The terminal sends the entered registration information to the server, which receives it and proceeds to the next step.

[0639] Verifying Information

[0640] The server transmits the received registration information to the generative AI model and sends a prompt to confirm whether it matches the conditions. The specific prompt text might be, "Please confirm whether the following information matches the pre-set conditions: 'Customer code: 12345', 'Department ID: 67890'."

[0641] Receiving Feedback

[0642] The generative AI model checks whether the input information matches the conditions and sends the verification results to the server as either "match" or "mismatch."

[0643] Send Feedback

[0644] The server sends the verification results from the generative AI model to the user's device. The user can check the verification results through the device. If the information matches, the user proceeds to the next step. If it does not match, an error message is displayed and the user is prompted to re-enter the information.

[0645] Example prompt sentences

[0646] Below is an example of a prompt sentence.

[0647] Prompt text for generating registration information

[0648] Please generate registration information based on the following conditions. "Customer Code" and "Department ID" are required.

[0649] Information confirmation prompt

[0650] Please check whether the following information matches the pre-defined conditions: "Customer Code: 12345" "Department ID: 67890"

[0651] This allows the system of the present invention to efficiently and accurately generate and verify registration information based on conditions that vary from company to company, thereby improving business efficiency and ensuring the accuracy of registered data.

[0652] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0653] Step 1: Receiving configuration information

[0654] The server receives registration conditions from the company. The input is the specific conditions required by the company (e.g., "Customer Code" and "Department ID"). The server stores the received conditions in its internal memory and prepares them for the next processing step. The output is the registration condition data.

[0655] Step 2: Communicate conditions

[0656] The server sends the received registration conditions to the generative AI model. Here, it generates a prompt to send to the generative AI model. It creates a prompt saying, "Please generate registration information based on the following conditions. 'Customer code' and 'Department ID' are required," and sends it to the generative AI model. The input is the registration conditions received earlier, and the output is the prompt to be sent to the generative AI model.

[0657] Step 3: Generate information

[0658] The generative AI model generates the necessary registration information based on prompts from the server. For example, it generates data such as "Customer code: 12345" and "Department ID: 67890." The input is the prompt, and the output is the generated registration information.

[0659] Step 4: Saving and sending information

[0660] The server stores the registration information received from the generative AI model in a database. At the same time, it sends this information to the user's device. The input is the generated registration information, and the output is updating the database and sending it to the user's device.

[0661] Step 5: Enter your information

[0662] A user uses a terminal to access the system and enters the specified registration information. For example, "Customer code: 12345" and "Department ID: 67890." The input is the user's registration information, and the output is the data to be sent to the server.

[0663] Step 6: Submit your information

[0664] The terminal sends the entered registration information to the server. The input is the registration information entered by the user, and the output is the data sent to the server.

[0665] Step 7: Verify the information

[0666] The server transmits the received registration information to the generative AI model and sends a prompt to confirm whether it matches the conditions. It generates a prompt such as, "Please check whether the following information matches the pre-set conditions: 'Customer code: 12345', 'Department ID: 67890'." The input is the user's registration information, and the output is the prompt sent to the generative AI model.

[0667] Step 8: Receiving feedback

[0668] The generative AI model checks whether the input information matches the conditions and sends the verification result to the server. The input is a verification prompt, and the output is a verification result such as "match" or "no match."

[0669] Step 9: Submit your feedback

[0670] The server sends the verification results from the generative AI model to the user's device. The user checks the verification results through the device. The input is the verification result, and the output is a notification to the user's device. If there is a mismatch, an error message is displayed prompting the user to re-enter the information.

[0671] (Application example 1)

[0672] 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."

[0673] Electronic payment services require a system that generates registration information based on conditions that vary from company to company and quickly and efficiently verifies its accuracy. Conventional methods involve manual data entry and verification, which is prone to errors and reduces operational efficiency. This poses a challenge, particularly for companies that handle a large volume of transactions, as generating and verifying registration information requires a great deal of time and effort.

[0674] 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.

[0675] In this invention, the server includes: means for receiving different conditions for a company; generation means for providing an artificial intelligence model that automatically generates registration information based on the conditions; means for saving the generated registration information in a database and transmitting it to the user; means for receiving registration information entered by the user; verification means using an artificial intelligence model to confirm whether the entered registration information matches the conditions; and means for transmitting the verification results to the user and providing feedback. This makes it possible to quickly and efficiently generate registration information based on conditions that vary from company to company in electronic payment services and verify its accuracy, thereby improving business efficiency and reducing errors.

[0676] "Company-specific conditions" refer to the individual requirements and standards that each company sets for its particular registration information and transactions.

[0677] "Registration conditions" are requirements and standards required when generating registration information.

[0678] "Registered information" is information related to a specific business partner or department, and is data generated based on conditions that differ for each company.

[0679] "Generation means" refers to a technique or device for automatically creating the necessary registration information based on set conditions.

[0680] "Verification means" refers to a technique or device for verifying whether the generated registration information matches the information entered by the user.

[0681] An "artificial intelligence model" is an algorithm or system that learns from large amounts of data and generates new information or analyzes and judges existing information.

[0682] A "database" is a collection of data constructed to efficiently store, search, update, and delete information.

[0683] "Feedback" refers to the provision of information or notification of results to system users, and is a response to a specific operation or input.

[0684] A "prompt sentence" is a sentence that is input as an instruction or question required for an artificial intelligence model to operate.

[0685] The system for implementing this invention consists of the following major hardware and software components: a server, a user's smartphone, a generative AI model, and a database.

[0686] Hardware & Software Details

[0687] 1. Server

[0688] Function: Receives registration conditions that vary from company to company, works with the generative AI model to generate registration information, saves the registration information, and notifies users.

[0689] Specific name: Cloud server (e.g. AWS, Google Cloud Platform)

[0690] 2. Smartphone

[0691] Function: Provides an interface for users to enter registration information and submit that information to a server.

[0692] Specific name: iOS or Android smartphone

[0693] 3. Generative AI Models

[0694] Function: Generates registration information based on set criteria and verifies that the entered information matches the criteria.

[0695] Specific names: GPT-3 (OpenAI), BERT (Google)

[0696] 4. Database

[0697] Function: Stores generated registration information for quick access when needed.

[0698] Specific name: SQL database (e.g., MySQL, PostgreSQL)

[0699] System processing overview

[0700] 1. Receiving setting information

[0701] The server receives the registration conditions (for example, "customer code" and "department ID") provided by the company.

[0702] 2. Information Generation

[0703] The server sends the received conditions to the generation AI model and generates registration information using prompts. The example prompts used in this case are as follows:

[0704] Generate a customer code and department ID based on your company's requirements. The customer code and department ID should each be a five-digit number. Example: "Customer Code: 24680" "Department ID: 13579"

[0705] 3. Storage and transmission of information

[0706] The registration information generated by the generative AI model (for example, "Customer code: 24680" and "Department ID: 13579") is returned to the server and stored in a database. It is then sent to the user's smartphone, where the user can refer to it to enter their information.

[0707] 4. Entering and submitting information

[0708] The user uses a smartphone to enter the specified registration information and transmits it from the device to the server.

[0709] 5. Verification of Information

[0710] The server sends the input information back to the generative AI model and sets up prompts to confirm that the input information matches the conditions.

[0711] 6. Receiving Feedback

[0712] The generative AI model verifies the input information and sends the results to the server, which then sends the results to the smartphone and notifies the user.

[0713] If the verification is successful, the user can continue.

[0714] If validation fails, an error message will be displayed and you will be prompted to try again.

[0715] Adding specific examples

[0716] For company "Z", the "Customer Code" and "Department ID" are required. The server receives the conditions for "Z" and sends the following prompt to the generative AI model:

[0717] Generate a customer code and department ID based on your company's requirements. The customer code and department ID should each be a five-digit number. Example: "Customer Code: 98765" "Department ID: 43210"

[0718] The generative AI model generates "Customer code: 98765" and "Department ID: 43210," which the server stores in a database and sends to the user's smartphone. The user enters this information on their smartphone and sends it to the server. The server again uses the generative AI model to check whether the input information is correct and notifies the user of the results.

[0719] This system allows companies to efficiently generate registration information based on different conditions and verify its accuracy.

[0720] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0721] Step 1:

[0722] The server receives registration conditions from the company. For example, it receives conditions such as "client code" and "department ID." This input is text data, and the server prepares to send the received conditions to the generative AI model.

[0723] Step 2:

[0724] The server sends the received registration conditions to the generative AI model. It generates a prompt message, such as the following: "Please generate a customer code and department ID based on the company's conditions. The customer code and department ID will each be expressed as five-digit numbers. For example: 'Customer code: 98765', 'Department ID: 43210'." The generative AI model then generates the registration information based on this.

[0725] Step 3:

[0726] The generative AI model generates the necessary registration information (e.g., "Customer code: 98765" and "Department ID: 43210") based on the prompts from the server. This data is returned to the server as structured output.

[0727] Step 4:

[0728] The server stores the generated registration information in a database and sends it to the user's smartphone, creating a database entry and formatting the stored items as notification data.

[0729] Step 5:

[0730] Users access the system using their smartphones and enter their registration information (e.g., "Customer code: 98765" and "Department ID: 43210"). This input is done through text boxes and forms, and users are guided to enter the information accurately.

[0731] Step 6:

[0732] The device sends the entered registration information to the server. It receives the user's input and generates a request to forward it to the server, sometimes checking the format of the input data.

[0733] Step 7:

[0734] The server then sends the received registration information back to the AI ​​model, requesting confirmation that the input information matches the conditions. This process generates a prompt for verification, such as the following: "Please confirm that the customer code and department ID entered by the user are correct. Input: Customer code 98765, Department ID 43210."

[0735] Step 8:

[0736] The generative AI model verifies the input information based on the conditions and returns a result to the server indicating whether or not it matches. This data processing is performed by comparing the input information with reference information.

[0737] Step 9:

[0738] The server receives the verification results from the generative AI model and sends them to the user's smartphone. If the results match, it generates feedback data containing a success message, and if they do not match, it generates an error message.

[0739] Step 10:

[0740] The user checks the verification result on their smartphone and re-enters the information if necessary. For example, if an error message is displayed, the user re-enters the correct registration information.

[0741] Through this series of processing steps, registration information based on conditions that differ for each company and the accuracy of the information are efficiently generated and verified.

[0742] 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.

[0743] This invention combines a system that generates registration information that requires different conditions for each company and checks whether that information is appropriate, with an emotion engine that recognizes the user's emotions. The system of this invention will be explained from the perspective of including a server, terminal, generation AI, and emotion engine.

[0744] Natural language explanations

[0745] Automatic generation of registration information

[0746] Receiving configuration information

[0747] The server receives the company's registration conditions. For example, if a company requires a "client code" and a "department ID" as registration conditions, the server receives those conditions.

[0748] Communication of Terms

[0749] The server sends the received setting information to the generation AI, instructing it to generate a "customer code" and a "department ID."

[0750] Information generation

[0751] The AI ​​generates the necessary registration information based on the conditions received from the server. For example, the generated registration information might be "Customer code: 12345" and "Department ID: 67890."

[0752] Storage and transmission of information

[0753] The server stores the registration information received from the generation AI in a database and sends it to the user, who can then refer to it when entering the registration information.

[0754] Confirmation of registration details

[0755] Entering information

[0756] The user enters registration information into the system through a terminal. For example, the user enters "Customer code: 12345" and "Department ID: 67890."

[0757] Sending information

[0758] The device sends the entered registration information to the server, which then passes the received information to the generation AI and requests confirmation.

[0759] Verifying Information

[0760] The generation AI checks whether the input information matches the conditions. For example, it checks whether the input "customer code" matches the generated "12345."

[0761] Receiving Feedback

[0762] The server receives the verification result from the generation AI, which is sent as a "match" or "no match."

[0763] Send Feedback

[0764] The server sends the verification result to the terminal and notifies the user, so that the user can check whether the information is correct. If it does not match, the user will receive an error message and be prompted to enter the correct information again.

[0765] Incorporating an emotion engine

[0766] Emotion Engine Operation

[0767] Emotion recognition

[0768] The device sends emotional information such as the user's facial expressions and voice to the emotion engine, which then analyzes the user's emotions during input or when error feedback is provided.

[0769] Emotion-based assistance

[0770] The emotion engine assists with the next action based on the user's emotional state. For example, if the user is confused or frustrated, the emotion engine notifies the server that support is needed.

[0771] Feedback adjustment

[0772] The emotion engine adjusts the feedback content depending on the user's emotional state. For example, if the user is feeling stressed, the emotion engine will display helpful guidance or comforting messages.

[0773] Specific examples

[0774] Example of generating registration information

[0775] 1. The server receives the registration conditions for a specific company, "Company X." For example, "Company X" requires a "Customer Code" and a "Department ID."

[0776] 2. The server communicates these conditions to the generation AI and asks it to generate registration information.

[0777] 3. The generation AI generates "Customer code: 67890" and "Department ID: 12345".

[0778] 4. The server stores the generated information and sends it to employees of "Company X" for reference.

[0779] Specific examples of confirmation of registered information

[0780] 1. A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345."

[0781] 2. The terminal sends the entered information to the server.

[0782] 3. The server sends the information to the generation AI and asks it to verify whether it matches the conditions.

[0783] 4. The generation AI checks against a set of conditions, confirms that the "Customer Code" and "Department ID" match, and returns the results to the server.

[0784] 5. The server sends the results to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[0785] Examples of emotion engines

[0786] 1. When the user enters the "customer code," the device sends the user's facial expressions and voice to the emotion engine.

[0787] 2. The emotion engine analyzes the user's emotions and determines that the user is confused.

[0788] 3. The emotion engine notifies the server and suggests providing additional support.

[0789] 4. The server sends messages containing helpful instructions and warnings to the user, helping the user to enter information without stress.

[0790] In this way, the system of the present invention can efficiently and accurately generate registration information based on the conditions that vary from company to company and verify the accuracy of that information. Furthermore, by incorporating an emotion engine, it is possible to provide feedback and support according to the user's emotional state, improving the system's user experience.

[0791] The processing flow will be explained below.

[0792] Automatic generation of registration information

[0793] Step 1:

[0794] The server receives the company's registration conditions.

[0795] The server receives the configuration information for a particular company and checks the specific conditions (e.g., customer code and department ID) required by that company for registration.

[0796] Step 2:

[0797] The server sends the received conditions to the generation AI.

[0798] The server passes the received conditions to the generation AI and instructs it to generate registration information.

[0799] Step 3:

[0800] The generation AI generates registration information based on the conditions.

[0801] The generation AI automatically generates, for example, customer codes and department IDs based on the company's conditions.

[0802] Step 4:

[0803] The server stores the generated registration information and sends it to the user.

[0804] The server stores the registration information received from the generation AI in a database and sends the information to the user.

[0805] Specific working example:

[0806] The server receives information that the conditions for "Company X" require a "Customer Code" and a "Department ID."

[0807] The server instructs the generation AI to generate "Customer code: 12345" and "Department ID: 67890".

[0808] The generation AI generates this and returns it to the server.

[0809] The server stores this in a database where it can be accessed by employees of "Company X."

[0810] Confirmation of registration details

[0811] Step 1:

[0812] The user enters registration information through the terminal.

[0813] The user enters the fields based on the registration information provided. For example, enter "Customer Code: 12345" and "Department ID: 67890."

[0814] Step 2:

[0815] The terminal transmits the entered registration information to the server.

[0816] Once the user has completed the input, the terminal sends this information to the server.

[0817] Step 3:

[0818] The server transmits the received information to the generation AI and requests confirmation.

[0819] The server sends the registration information received from the device to the generation AI and asks it to check whether the entered information matches the conditions.

[0820] Step 4:

[0821] The generating AI verifies the consistency of the information.

[0822] The generation AI checks whether the entered "Customer Code" and "Department ID" match the generated information.

[0823] Step 5:

[0824] The server receives the verification result and sends it to the user.

[0825] The server receives the verification results from the generation AI and sends them to the user's device.

[0826] Step 6:

[0827] The user reviews the feedback and makes corrections as necessary.

[0828] The user checks the feedback from the server, corrects any errors, and re-enters the data.

[0829] Specific working example:

[0830] The user enters "Customer code: 12345" and "Department ID: 67890" into the terminal.

[0831] The terminal sends this information to the server.

[0832] The server sends the information to the generating AI and requests verification.

[0833] The generation AI checks against a series of conditions, confirms that the "customer code" and "department ID" match, and returns the results to the server.

[0834] The server sends the results back to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[0835] Incorporating an emotion engine

[0836] Step 1:

[0837] The terminal transmits emotional information such as the user's facial expressions and voice to the emotion engine.

[0838] When the terminal is in the middle of user input or when error feedback is received, it sends data to the emotion engine to analyze the user's emotions.

[0839] Step 2:

[0840] The emotion engine recognizes and analyzes the user's emotions.

[0841] The emotion engine analyzes the user's emotional state (happiness, sadness, irritation, etc.) based on the data received.

[0842] Step 3:

[0843] The emotion engine determines the next action based on the emotional state.

[0844] For example, if the user is frustrated, the emotion engine will notify the server that support is needed.

[0845] Step 4:

[0846] The server generates feedback according to the user's emotional state.

[0847] The server generates a message including friendly guidance and warnings for the user based on the notification from the emotion engine.

[0848] Step 5:

[0849] The user receives feedback according to their emotional state.

[0850] To allow users to receive feedback from the server and continue working without stress or confusion.

[0851] Specific working example:

[0852] When the user enters the "customer code," the terminal transmits the user's facial expressions and voice to the emotion engine.

[0853] The emotion engine analyzes the user's emotions and determines that the user is confused.

[0854] The emotion engine notifies the server of this and suggests providing additional support.

[0855] The server sends messages containing helpful guidance and cautions to the user, thereby helping the user to input information without feeling stressed.

[0856] Example 2

[0857] 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."

[0858] Conventional systems require a complex process for generating and verifying registration information based on company-specific requirements, and provide limited feedback on user input. Furthermore, they do not provide support tailored to the user's emotional state, resulting in a lack of user experience. Therefore, there is a need for a system that efficiently generates and verifies registration information, provides feedback tailored to the user's emotional state, and improves the user experience.

[0859] 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.

[0860] In this invention, the server includes means for receiving registration conditions, which may differ from company to company, means for generating the required registration information based on the conditions, means for saving the generated registration information and transmitting it to the user, means for receiving registration information entered by the user, means for verifying whether the entered registration information matches the conditions, means for transmitting the verification results to the user, emotion recognition means for recognizing the user's emotion information, and means for adjusting feedback to the user based on the emotion information. This makes it possible to generate and verify registration information more efficiently and accurately, and to provide appropriate feedback according to the user's emotional state.

[0861] "Registration conditions" refer to conditions that differ for each company and are elements required to register specific information.

[0862] "Generation means" refers to a device or software having the function of generating the necessary registration information based on the received registration conditions.

[0863] "Storage means" refers to a device or software that has the function of storing the generated registration information in a database or storage.

[0864] "Receiving means" refers to a device or software that has the function of receiving registration information entered by a user.

[0865] "Verification means" refers to a device or software that has the function of verifying whether the entered registration information matches pre-set conditions or generated information.

[0866] "Emotion recognition means" refers to a device or software that has the function of recognizing and analyzing emotional information such as a user's facial expression and voice.

[0867] The "feedback adjustment means" refers to a device or software that has the function of adjusting the content of feedback to the user based on the emotional information recognized by the emotional recognition means.

[0868] The present invention combines a system that generates registration information that requires different conditions for each company and checks whether that information is appropriate, with an emotion engine that recognizes user emotions. The system of the present invention will be described from the perspective of including a server, a terminal, a generation AI, and an emotion engine. Specific embodiments will be described below.

[0869] Automatic generation of registration information

[0870] Receiving configuration information

[0871] The server receives the company's registration conditions. For example, if a company requires a "client code" and a "department ID" as registration conditions, the server receives those conditions. This information is received via API and temporarily stored in an internal database.

[0872] Communication of Terms

[0873] The server sends the received configuration information to the generation AI. It instructs the generation AI to generate a "customer code" and a "department ID." This communication uses HTTP requests and WebSockets.

[0874] Information generation

[0875] The generative AI generates the necessary registration information based on the conditions received from the server. The generative AI model (e.g., GPT-3) generates text data such as "Customer code: 12345" and "Department ID: 67890" based on the prompt sentence.

[0876] Storage and transmission of information

[0877] The server stores the registration information received from the generation AI in a database and sends it to the user so that the user can refer to it when entering registration information.

[0878] Confirmation of registration details

[0879] Entering information

[0880] The user enters registration information into the system via a terminal. For example, the user uses a web browser or a dedicated application to enter registration information such as "Customer code: 12345" and "Department ID: 67890" into a form.

[0881] Sending information

[0882] The terminal sends the entered registration information to the server. When the user enters information and presses the send button, the terminal sends the information to the server as a POST request.

[0883] Verifying Information

[0884] The server transmits the received information to the generation AI and requests confirmation. The generation AI compares the received information with the pre-set conditions and the generated information to determine whether they match.

[0885] Receiving and sending feedback

[0886] The server receives the verification results from the generation AI and sends them to the terminal as an API response. The terminal displays the results so that the user can confirm them. For example, it checks whether the entered "customer code" matches the generated "12345" and notifies the user of the result.

[0887] Incorporating an emotion engine

[0888] Emotion recognition

[0889] The device sends emotional information such as the user's facial expressions and voice to the emotion engine. The device uses a camera and microphone to detect the user's facial expressions and voice, and sends the data to the emotion engine in real time.

[0890] Emotion-based assistance

[0891] The emotion engine assists with next actions based on the user's emotional state. For example, if it determines that the user is confused, the emotion engine notifies the server and suggests that additional support is needed.

[0892] Feedback adjustment

[0893] An emotion engine tailors the feedback depending on the user's emotional state: for example, if the user is feeling stressed, the server will send helpful guidance or comforting messages.

[0894] Specific examples

[0895] Example of generating registration information

[0896] The server receives the registration conditions for a specific company. For example, "Company X" requires a "Customer Code" and a "Department ID." The server communicates these conditions to the generation AI and asks it to generate the registration information. The generation AI generates "Customer Code: 67890" and "Department ID: 12345." The server then stores the generated information and sends it to employees of "Company X" for reference.

[0897] Specific examples of confirmation of registered information

[0898] A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345." The terminal sends the entered information to the server, which then sends the information to the generation AI, requesting it to verify whether it matches the conditions. The generation AI checks the information against a set of conditions, confirms that the "Customer Code" and "Department ID" match, and returns the results to the server. The server then sends the results to the user, who confirms that the entered information is accurate.

[0899] Examples of emotion engines

[0900] When the user enters the "customer code," the terminal sends the user's facial expression and voice to the emotion engine. The emotion engine analyzes the user's emotions and determines that the user is confused. The emotion engine notifies the server and suggests providing additional support. The server then sends the user a message containing helpful instructions and warnings, helping the user enter information without stress.

[0901] In this way, the system of the present invention can efficiently and accurately generate registration information based on the conditions that vary from company to company and verify the accuracy of that information. Furthermore, by incorporating an emotion engine, it is possible to provide feedback and support according to the user's emotional state, improving the system's user experience.

[0902] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0903] Program processing flow

[0904] Automatic generation of registration information

[0905] Step 1:

[0906] The server receives the registration conditions.

[0907] Input: Registration conditions sent by the company (e.g. "Customer Code" and "Department ID")

[0908] What happens: The server receives the API request and temporarily stores these registration conditions in an internal database.

[0909] Step 2:

[0910] The server sends the registration conditions to the generated AI.

[0911] Input: Registration conditions saved on the server

[0912] Output: Data in prompt format

[0913] Specific operation: The server sends the registration conditions as a prompt to the generation AI (e.g., "Generate a customer code and department ID") via an HTTP request.

[0914] Step 3:

[0915] Generative AI generates registration information

[0916] Input: prompt statement

[0917] Output: Registration information (e.g., "Customer code: 12345" and "Department ID: 67890")

[0918] Specific operation: The generative AI model (e.g., GPT-3) generates registration information based on the prompt sentence and returns it as text data.

[0919] Step 4:

[0920] The server stores the generated registration information and sends it to the user.

[0921] Input: Registration information returned by the generation AI

[0922] Output: Registration information stored in a database and sent to the user

[0923] Specific operation: The server stores the acquired registration information in an internal database and then sends it to the company's system administrator via email or API response.

[0924] Confirmation of registration details

[0925] Step 5:

[0926] The user enters registration information through the terminal.

[0927] Input: Registration information provided by the company (e.g., "Customer code: 12345" and "Department ID: 67890")

[0928] What happens: The user uses a web browser or a dedicated application to enter registration information into the appropriate form.

[0929] Step 6:

[0930] The device sends the entered registration information to the server.

[0931] Input: Information entered by the user

[0932] Output: POST request to the server

[0933] Specific operation: When the user presses the send button, the terminal sends the entered information to the server as a POST request.

[0934] Step 7:

[0935] The server sends the received information to the generation AI and checks whether it matches the conditions.

[0936] Input: Your registered information

[0937] Output: Confirmation request to the generating AI

[0938] Specific operation: The server sends the received registration information as a verification request to the generation AI to determine whether it matches.

[0939] Step 8:

[0940] Generative AI checks for information consistency

[0941] Input: Registration information sent from the server

[0942] Output: Match / No-Match results

[0943] Specific operation: The generation AI compares the registration information received from the server with the information generated in advance and returns the match / mismatch result to the server.

[0944] Step 9:

[0945] The server sends the verification result to the device.

[0946] Input: Match / No-Match results from the generation AI

[0947] Output: Notification of validation results to the user

[0948] Specific operation: The server returns the received verification result to the device as an API response so that the user can check it.

[0949] Incorporating an emotion engine

[0950] Step 10:

[0951] The device sends the user's emotional information to the emotion engine.

[0952] Input: User facial and voice data

[0953] Output: Emotional information data

[0954] How it works: The device uses a camera and microphone to detect the user's facial expressions and voice, and sends that data to the emotion engine in real time.

[0955] Step 11:

[0956] Emotion engine helps guide next actions based on emotions

[0957] Input: Emotional information data

[0958] Output: Instructions for auxiliary actions

[0959] Specific behavior: If the emotion engine performs analysis and determines that the user is confused or frustrated, it notifies the server and suggests the need for additional support.

[0960] Step 12:

[0961] The server adjusts the feedback and notifies the user.

[0962] Input: Notification from the emotion engine

[0963] Output: Adjusted feedback message

[0964] Specific operation: Based on the notification from the emotion engine, the server sends messages containing helpful guidance and warnings to the user, helping the user to enter information without stress.

[0965] (Application example 2)

[0966] 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."

[0967] Conventional systems for generating and verifying registration information have the problem that the information entered by users is complex, making it prone to input errors and mismatches with requirements. Furthermore, users often experience difficulty or stress when entering information, which can lead to a decline in service quality. The present invention aims to solve these issues and improve the user experience. Furthermore, the present invention provides a system that recognizes the user's emotions and provides appropriate support, allowing the input process to proceed smoothly.

[0968] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0969] In this invention, the server includes: means for receiving registration conditions, which may vary from company to company; means for generating the required registration information based on the conditions; means for saving the generated registration information and transmitting it to the user; means for receiving the registration information entered by the user; means for verifying whether the entered registration information matches the conditions; means for transmitting the verification result to the user; emotion recognition means for analyzing the user's emotions and providing support according to the emotional state; and means for providing additional support if the emotion recognition means determines that the user's emotional state is unstable. This not only enables efficient and accurate generation and verification of registration information, but also makes it possible to reduce stress and improve the user experience by providing appropriate support according to the user's emotional state.

[0970] "Conditions that vary from company to company" refers to the specific requirements and conditions required for registration information that each company sets on its own.

[0971] "Registration information" refers to a series of data and information generated based on the conditions required by a company.

[0972] "Generation means" refers to a mechanism or method for automatically generating registration information based on conditions that vary from company to company.

[0973] "Storage means" refers to a function or system for storing the generated registration information in a database or the like.

[0974] "Receiving means" refers to a function for receiving information sent from a user or a server.

[0975] "Verification measures" refer to mechanisms or methods for verifying whether the registration information entered by the user matches the conditions set by the company.

[0976] "Transmission means" refers to a function for transmitting the generated registration information and verification results to the user or other systems.

[0977] "Emotion recognition means" refers to a mechanism or method for analyzing emotions from the user's facial expressions, voice, etc., and determining their current emotional state.

[0978] "Means for providing additional support" refers to functions or methods for providing appropriate assistance or guidance when the emotion recognition means determines that the user is experiencing difficulty or stress.

[0979] The system for implementing this invention includes the following main components: a server, a terminal, a generation AI, and an emotion recognition means. Below, we will explain how each component works together and processes data.

[0980] First, the server receives the registration conditions, which vary from company to company. These conditions are specific data fields, such as a customer code or department ID. The server then sends the received conditions to the generation AI.

[0981] The generation AI generates the necessary registration information based on the prompt sent from the server. The generated registration information is saved in a database and later provided to the user. An example of a prompt statement for the generation AI is, "The client code and department ID are required as registration conditions for Company X. Please generate the client code and department ID."

[0982] The terminal is a device for transmitting the registration information entered by the user to the server. This terminal may be a smartphone, tablet, or PC, and can transmit the information entered by the user to the server in real time. Furthermore, the terminal can transmit the user's facial expression and voice data to the emotion recognition means.

[0983] The verification method checks whether the entered registration information matches the company's set conditions. This includes a matching process using the generation AI. For example, if a user enters "Customer Code 12345" and "Department ID 67890," it verifies that this matches the generated information.

[0984] The emotion recognition means analyzes the user's facial expressions and voice data to determine their emotional state. If the user is confused or stressed, this information is sent to the server, and additional support is provided. For example, if the user is determined to be confused, a helpful guidance message may be displayed.

[0985] This not only enables efficient and accurate generation and confirmation of registration information, but also reduces stress and improves the user experience by providing appropriate support according to the user's emotional state.

[0986] Specific examples

[0987] For example, when a new employee of a certain company, "Company X," attempts to register in the system, the server requests the generation AI to provide the "customer code" and "department ID" as the registration conditions for Company X. The "customer code 12345" and "department ID 67890" generated by the generation AI are stored in the database. The user enters this information using a terminal, and the system verifies it. After confirming that the input matches, the user's facial expressions and voice are analyzed using emotion recognition means, and additional support is provided if necessary.

[0988] Example prompt sentence:

[0989] "Company X requires a customer code and department ID to register. Please generate a customer code and department ID."

[0990] In this way, the system of the invention can efficiently generate and verify registration information, and provide support according to the user's emotional state.

[0991] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0992] Step 1:

[0993] The server receives registration conditions, which vary from company to company. As input, the condition data provided by the company is required. This includes specific data fields such as customer code and department ID. The server receives this information and temporarily stores it.

[0994] Step 2:

[0995] The server sends the received conditions to the generation AI, which generates a prompt to generate the required registration information. The company's condition data is used as input. The generation AI generates registration information based on the provided conditions and returns specific registration information as output (e.g., "Customer code 12345" and "Department ID 67890").

[0996] Step 3:

[0997] The server saves the generated registration information in a database. The input is the registration information received from the generation AI. The output is that the registration information is saved accurately in the database.

[0998] Step 4:

[0999] A user accesses the system using a terminal and enters their registration information. As input, the user enters the registration information (e.g., "Customer code 12345" and "Department ID 67890") into the terminal. The terminal then sends this information to the server.

[1000] Step 5:

[1001] The server receives the user's input information and sends it to the generation AI for confirmation. The information entered by the user is used as input. The generation AI compares the received information with existing registration information and returns a result of whether or not it matches.

[1002] Step 6:

[1003] The server receives the verification result and notifies the user. As input, the verification result from the generation AI is used. As output, the user is provided with feedback of "match" or "mismatch."

[1004] Step 7:

[1005] The emotion recognition means analyzes the user's facial and voice data to determine their emotional state. The facial and voice data collected by the device is used as input. The analyzed user's emotional state (e.g., confusion, stress, etc.) is output.

[1006] Step 8:

[1007] If the emotion recognition means determines that the user's emotional state is unstable, the server provides additional support. The judgment data from the emotion recognition means is used as input. The server provides the user with friendly guidance messages and additional support, and as output, creates an environment where the user can input information with confidence.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] [Third embodiment]

[1012] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1013] 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.

[1014] 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).

[1015] 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.

[1016] 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.

[1017] 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).

[1018] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1019] 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.

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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."

[1024] The present invention is a system that generates registration information that requires different conditions for each company and checks whether the information is appropriate. The system of the present invention will be described from the perspective of including a server, a terminal, and a generation AI.

[1025] Natural language explanations

[1026] Automatic generation of registration information

[1027] Receiving configuration information

[1028] The server receives the company's conditions. For example, if a company requires a "customer code" and a "department ID" as registration conditions, the server receives those conditions.

[1029] Communication of Terms

[1030] The server sends the received setting information to the generation AI, instructing it to generate a "customer code" and a "department ID."

[1031] Information generation

[1032] The AI ​​generates the necessary registration information based on the conditions received from the server. For example, the generated registration information might be "Customer code: 12345" and "Department ID: 67890."

[1033] Storage and transmission of information

[1034] The server stores the registration information received from the generation AI in a database and sends it to the user, who can then refer to it when entering the registration information.

[1035] Confirmation of registration details

[1036] Entering information

[1037] The user enters registration information into the system through a terminal. For example, the user enters "Customer code: 12345" and "Department ID: 67890."

[1038] Sending information

[1039] The device sends the entered registration information to the server, which then passes the received information to the generation AI and requests confirmation.

[1040] Verifying Information

[1041] The generation AI checks whether the input information matches the conditions. For example, it checks whether the input "customer code" matches the generated "12345."

[1042] Receiving Feedback

[1043] The server receives the verification result from the generation AI, which is sent as a "match" or "no match."

[1044] Send Feedback

[1045] The server sends the verification result to the terminal and notifies the user, so that the user can check whether the information is correct. If it does not match, the user will receive an error message and be prompted to enter the correct information again.

[1046] Specific examples

[1047] Example of generating registration information

[1048] 1. The server receives the registration requirements for a specific company, "Company X." For example, "Company X" requires a "Customer Code" and a "Department ID."

[1049] 2. The server communicates these conditions to the generation AI and asks it to generate registration information.

[1050] 3. The generation AI generates "Customer code: 67890" and "Department ID: 12345".

[1051] 4. The server stores the generated information and sends it to employees of "Company X" for reference.

[1052] Specific examples of confirmation of registered information

[1053] 1. A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345."

[1054] 2. The terminal sends the entered information to the server.

[1055] 3. The server sends the received information to the generation AI and asks it to verify whether it matches the conditions.

[1056] 4. The generation AI checks whether the input information is correct and returns the result to the server. For example, it determines that the customer code is "67890" and matches.

[1057] 5. The server sends the results from the generated AI to the user's device, where the user verifies the accuracy of the information. If there are any errors, the user is prompted to re-enter the information.

[1058] In this way, the system of the present invention can efficiently and accurately generate registration information based on conditions that vary from company to company and verify the accuracy of that information, thereby improving business efficiency and ensuring the accuracy of registered data.

[1059] The processing flow will be explained below.

[1060] Automatic generation of registration information

[1061] Step 1:

[1062] The server receives the company's registration conditions.

[1063] The server receives the configuration information for a particular company and checks the specific conditions (e.g., customer code and department ID) required by that company for registration.

[1064] Step 2:

[1065] The server sends the received conditions to the generation AI.

[1066] The server passes the received conditions to the generation AI and instructs it to generate registration information.

[1067] Step 3:

[1068] The generation AI generates registration information based on the conditions.

[1069] The generation AI automatically generates customer codes and department IDs based on the company's conditions.

[1070] Step 4:

[1071] The server stores the generated registration information and sends it to the user.

[1072] The server stores the registration information received from the generation AI in a database and sends the information to the user.

[1073] Specific working example:

[1074] The server receives information that the conditions for "Company X" require a "Customer Code" and a "Department ID."

[1075] The server instructs the generation AI to generate "Customer code: 12345" and "Department ID: 67890".

[1076] The generation AI generates this and returns it to the server.

[1077] The server stores this in a database where it can be accessed by employees of "Company X."

[1078] Confirmation of registration details

[1079] Step 1:

[1080] The user enters registration information through the terminal.

[1081] The user enters the fields based on the registration information provided. For example, enter "Customer Code: 12345" and "Department ID: 67890."

[1082] Step 2:

[1083] The terminal transmits the entered registration information to the server.

[1084] Once the user has completed the input, the terminal sends this information to the server.

[1085] Step 3:

[1086] The server transmits the received information to the generation AI and requests confirmation.

[1087] The server sends the registration information received from the device to the generation AI and asks it to check whether the entered information matches the conditions.

[1088] Step 4:

[1089] The generating AI verifies the consistency of the information.

[1090] The generation AI checks whether the entered "Customer Code" and "Department ID" match the generated information.

[1091] Step 5:

[1092] The server receives the verification result and sends it to the user.

[1093] The server receives the verification results from the generation AI and sends them to the user's device.

[1094] Step 6:

[1095] The user reviews the feedback and makes corrections as necessary.

[1096] The user checks the feedback from the server, corrects any errors, and re-enters the data.

[1097] Specific working example:

[1098] The user enters "Customer code: 12345" and "Department ID: 67890" into the terminal.

[1099] The terminal sends this information to the server.

[1100] The server sends the information to the generating AI and requests verification.

[1101] The generation AI checks against a series of conditions, confirms that the "customer code" and "department ID" match, and returns the results to the server.

[1102] The server sends the results back to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[1103] Example 1

[1104] 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."

[1105] There is a need for a system that can efficiently and accurately generate registration information that meets the different conditions for each company and quickly verify the accuracy of the information entered by users. However, current systems require manual data entry and complex condition setting, which can lead to problems such as reduced work efficiency and the occurrence of errors. Therefore, there is a need to automate the process of generating information based on the registration conditions that vary from company to company and verifying its accuracy.

[1106] 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.

[1107] In this invention, the server includes a means for receiving registration conditions that differ from company to company, an artificial intelligence means for generating the necessary registration information based on the conditions, and a means for saving the generated registration information and transmitting it to the user, thereby enabling automatic generation of registration information under conditions that differ from company to company and confirmation of its accuracy.

[1108] The "means for receiving registration conditions that differ for each company" is a function that allows the server to receive information for each company to register its specific conditions.

[1109] The "artificial intelligence means for generating the necessary registration information based on the conditions" is a function that uses artificial intelligence to generate the necessary registration information based on the company-specific conditions received by the server.

[1110] "Means for saving the generated registration information and sending it to the user" refers to a function for saving the registration information generated by artificial intelligence in a database and providing that information to the user.

[1111] The "means for receiving registration information entered by a user" is a function that allows the server to receive registration information entered by a user through a terminal.

[1112] "Means for querying the artificial intelligence generated to confirm whether the entered registration information matches the conditions" is a function that queries the artificial intelligence to confirm whether the information entered by the user matches the pre-set conditions.

[1113] "Means for transmitting the verification results to the user" refers to a function by which the server transmits the results verified by artificial intelligence to the terminal in order to inform the user.

[1114] The present invention is a system that generates registration information that requires different conditions for each company and checks whether the information is appropriate. The system of the present invention will be described from the perspective of including a server, a terminal, and a generation AI model.

[1115] Hardware and software used

[1116] Server: Receives requirements from the enterprise, sends prompts to the generative AI model, and stores and transmits the generated information.

[1117] Terminal: The user enters registration information and sends it to the server

[1118] Generative AI model: Generates registration information based on company-specific conditions and validates the entered information

[1119] Automatic generation of registration information

[1120] Receiving configuration information

[1121] The server receives registration conditions from the company. These conditions include specific items required by the company (e.g., customer code, department ID, etc.). For example, the server receives a condition requiring a "customer code" and a "department ID."

[1122] Communication of Terms

[1123] The server sends the received conditions to the generation AI model. The specific prompt might be, "Generate registration information based on the following conditions. 'Customer code' and 'Department ID' are required."

[1124] Information generation

[1125] The generative AI model generates the necessary registration information based on prompts from the server. For example, the generated registration information is "Customer code: 12345" and "Department ID: 67890."

[1126] Storage and transmission of information

[1127] The server stores the registration information received from the generative AI model in a database and sends the information to the user's device, allowing the user to refer to the necessary registration information.

[1128] Confirmation of registration details

[1129] Entering information

[1130] The user accesses the system using a terminal and enters the specified registration information. For example, the user enters "Customer Code: 12345" and "Department ID: 67890."

[1131] Sending information

[1132] The terminal sends the entered registration information to the server, which receives it and proceeds to the next step.

[1133] Verifying Information

[1134] The server transmits the received registration information to the generative AI model and sends a prompt to confirm whether it matches the conditions. The specific prompt text might be, "Please confirm whether the following information matches the pre-set conditions: 'Customer code: 12345', 'Department ID: 67890'."

[1135] Receiving Feedback

[1136] The generative AI model checks whether the input information matches the conditions and sends the verification results to the server as either "match" or "mismatch."

[1137] Send Feedback

[1138] The server sends the verification results from the generative AI model to the user's device. The user can check the verification results through the device. If the information matches, the user proceeds to the next step. If it does not match, an error message is displayed and the user is prompted to re-enter the information.

[1139] Example prompt sentences

[1140] Below is an example of a prompt sentence.

[1141] Prompt text for generating registration information

[1142] Please generate registration information based on the following conditions. "Customer Code" and "Department ID" are required.

[1143] Information confirmation prompt

[1144] Please check whether the following information matches the pre-defined conditions: "Customer Code: 12345" "Department ID: 67890"

[1145] This allows the system of the present invention to efficiently and accurately generate and verify registration information based on conditions that vary from company to company, thereby improving business efficiency and ensuring the accuracy of registered data.

[1146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1147] Step 1: Receiving configuration information

[1148] The server receives registration conditions from the company. The input is the specific conditions required by the company (e.g., "Customer Code" and "Department ID"). The server stores the received conditions in its internal memory and prepares them for the next processing step. The output is the registration condition data.

[1149] Step 2: Communicate conditions

[1150] The server sends the received registration conditions to the generative AI model. Here, it generates a prompt to send to the generative AI model. It creates a prompt saying, "Please generate registration information based on the following conditions. 'Customer code' and 'Department ID' are required," and sends it to the generative AI model. The input is the registration conditions received earlier, and the output is the prompt to be sent to the generative AI model.

[1151] Step 3: Generate information

[1152] The generative AI model generates the necessary registration information based on prompts from the server. For example, it generates data such as "Customer code: 12345" and "Department ID: 67890." The input is the prompt, and the output is the generated registration information.

[1153] Step 4: Saving and sending information

[1154] The server stores the registration information received from the generative AI model in a database. At the same time, it sends this information to the user's device. The input is the generated registration information, and the output is updating the database and sending it to the user's device.

[1155] Step 5: Enter your information

[1156] A user uses a terminal to access the system and enters the specified registration information. For example, "Customer code: 12345" and "Department ID: 67890." The input is the user's registration information, and the output is the data to be sent to the server.

[1157] Step 6: Submit your information

[1158] The terminal sends the entered registration information to the server. The input is the registration information entered by the user, and the output is the data sent to the server.

[1159] Step 7: Verify the information

[1160] The server transmits the received registration information to the generative AI model and sends a prompt to confirm whether it matches the conditions. It generates a prompt such as, "Please check whether the following information matches the pre-set conditions: 'Customer code: 12345', 'Department ID: 67890'." The input is the user's registration information, and the output is the prompt sent to the generative AI model.

[1161] Step 8: Receiving feedback

[1162] The generative AI model checks whether the input information matches the conditions and sends the verification result to the server. The input is a verification prompt, and the output is a verification result such as "match" or "no match."

[1163] Step 9: Submit your feedback

[1164] The server sends the verification results from the generative AI model to the user's device. The user checks the verification results through the device. The input is the verification result, and the output is a notification to the user's device. If there is a mismatch, an error message is displayed prompting the user to re-enter the information.

[1165] (Application example 1)

[1166] 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."

[1167] Electronic payment services require a system that generates registration information based on conditions that vary from company to company and quickly and efficiently verifies its accuracy. Conventional methods involve manual data entry and verification, which is prone to errors and reduces operational efficiency. This poses a challenge, particularly for companies that handle a large volume of transactions, as generating and verifying registration information requires a great deal of time and effort.

[1168] 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.

[1169] In this invention, the server includes: means for receiving different conditions for a company; generation means for providing an artificial intelligence model that automatically generates registration information based on the conditions; means for saving the generated registration information in a database and transmitting it to the user; means for receiving registration information entered by the user; verification means using an artificial intelligence model to confirm whether the entered registration information matches the conditions; and means for transmitting the verification results to the user and providing feedback. This makes it possible to quickly and efficiently generate registration information based on conditions that vary from company to company in electronic payment services and verify its accuracy, thereby improving business efficiency and reducing errors.

[1170] "Company-specific conditions" refer to the individual requirements and standards that each company sets for its particular registration information and transactions.

[1171] "Registration conditions" are requirements and standards required when generating registration information.

[1172] "Registered information" is information related to a specific business partner or department, and is data generated based on conditions that differ for each company.

[1173] "Generation means" refers to a technique or device for automatically creating the necessary registration information based on set conditions.

[1174] "Verification means" refers to a technique or device for verifying whether the generated registration information matches the information entered by the user.

[1175] An "artificial intelligence model" is an algorithm or system that learns from large amounts of data and generates new information or analyzes and judges existing information.

[1176] A "database" is a collection of data constructed to efficiently store, search, update, and delete information.

[1177] "Feedback" refers to the provision of information or notification of results to system users, and is a response to a specific operation or input.

[1178] A "prompt sentence" is a sentence that is input as an instruction or question required for an artificial intelligence model to operate.

[1179] The system for implementing this invention consists of the following major hardware and software components: a server, a user's smartphone, a generative AI model, and a database.

[1180] Hardware & Software Details

[1181] 1. Server

[1182] Function: Receives registration conditions that vary from company to company, works with the generative AI model to generate registration information, saves the registration information, and notifies users.

[1183] Specific name: Cloud server (e.g. AWS, Google Cloud Platform)

[1184] 2. Smartphone

[1185] Function: Provides an interface for users to enter registration information and submit that information to a server.

[1186] Specific name: iOS or Android smartphone

[1187] 3. Generative AI Models

[1188] Function: Generates registration information based on set criteria and verifies that the entered information matches the criteria.

[1189] Specific names: GPT-3 (OpenAI), BERT (Google)

[1190] 4. Database

[1191] Function: Stores generated registration information for quick access when needed.

[1192] Specific name: SQL database (e.g., MySQL, PostgreSQL)

[1193] System processing overview

[1194] 1. Receiving setting information

[1195] The server receives the registration conditions (for example, "customer code" and "department ID") provided by the company.

[1196] 2. Information Generation

[1197] The server sends the received conditions to the generation AI model and generates registration information using prompts. The example prompts used in this case are as follows:

[1198] Generate a customer code and department ID based on your company's requirements. The customer code and department ID should each be a five-digit number. Example: "Customer Code: 24680" "Department ID: 13579"

[1199] 3. Storage and transmission of information

[1200] The registration information generated by the generative AI model (for example, "Customer code: 24680" and "Department ID: 13579") is returned to the server and stored in a database. It is then sent to the user's smartphone, where the user can refer to it to enter their information.

[1201] 4. Entering and submitting information

[1202] The user uses a smartphone to enter the specified registration information and transmits it from the device to the server.

[1203] 5. Verification of Information

[1204] The server sends the input information back to the generative AI model and sets up prompts to confirm that the input information matches the conditions.

[1205] 6. Receiving Feedback

[1206] The generative AI model verifies the input information and sends the results to the server, which then sends the results to the smartphone and notifies the user.

[1207] If the verification is successful, the user can continue.

[1208] If validation fails, an error message will be displayed and you will be prompted to try again.

[1209] Adding specific examples

[1210] For company "Z", the "Customer Code" and "Department ID" are required. The server receives the conditions for "Z" and sends the following prompt to the generative AI model:

[1211] Generate a customer code and department ID based on your company's requirements. The customer code and department ID should each be a five-digit number. Example: "Customer Code: 98765" "Department ID: 43210"

[1212] The generative AI model generates "Customer code: 98765" and "Department ID: 43210," which the server stores in a database and sends to the user's smartphone. The user enters this information on their smartphone and sends it to the server. The server again uses the generative AI model to check whether the input information is correct and notifies the user of the results.

[1213] This system allows companies to efficiently generate registration information based on different conditions and verify its accuracy.

[1214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1215] Step 1:

[1216] The server receives registration conditions from the company. For example, it receives conditions such as "client code" and "department ID." This input is text data, and the server prepares to send the received conditions to the generative AI model.

[1217] Step 2:

[1218] The server sends the received registration conditions to the generative AI model. It generates a prompt message, such as the following: "Please generate a customer code and department ID based on the company's conditions. The customer code and department ID will each be expressed as five-digit numbers. For example: 'Customer code: 98765', 'Department ID: 43210'." The generative AI model then generates the registration information based on this.

[1219] Step 3:

[1220] The generative AI model generates the necessary registration information (e.g., "Customer code: 98765" and "Department ID: 43210") based on the prompts from the server. This data is returned to the server as structured output.

[1221] Step 4:

[1222] The server stores the generated registration information in a database and sends it to the user's smartphone, creating a database entry and formatting the stored items as notification data.

[1223] Step 5:

[1224] Users access the system using their smartphones and enter their registration information (e.g., "Customer code: 98765" and "Department ID: 43210"). This input is done through text boxes and forms, and users are guided to enter the information accurately.

[1225] Step 6:

[1226] The device sends the entered registration information to the server. It receives the user's input and generates a request to forward it to the server, sometimes checking the format of the input data.

[1227] Step 7:

[1228] The server then sends the received registration information back to the AI ​​model, requesting confirmation that the input information matches the conditions. This process generates a prompt for verification, such as the following: "Please confirm that the customer code and department ID entered by the user are correct. Input: Customer code 98765, Department ID 43210."

[1229] Step 8:

[1230] The generative AI model verifies the input information based on the conditions and returns a result to the server indicating whether or not it matches. This data processing is performed by comparing the input information with reference information.

[1231] Step 9:

[1232] The server receives the verification results from the generative AI model and sends them to the user's smartphone. If the results match, it generates feedback data containing a success message, and if they do not match, it generates an error message.

[1233] Step 10:

[1234] The user checks the verification result on their smartphone and re-enters the information if necessary. For example, if an error message is displayed, the user re-enters the correct registration information.

[1235] Through this series of processing steps, registration information based on conditions that differ for each company and the accuracy of the information are efficiently generated and verified.

[1236] 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.

[1237] This invention combines a system that generates registration information that requires different conditions for each company and checks whether that information is appropriate, with an emotion engine that recognizes the user's emotions. The system of this invention will be explained from the perspective of including a server, terminal, generation AI, and emotion engine.

[1238] Natural language explanations

[1239] Automatic generation of registration information

[1240] Receiving configuration information

[1241] The server receives the company's registration conditions. For example, if a company requires a "client code" and a "department ID" as registration conditions, the server receives those conditions.

[1242] Communication of Terms

[1243] The server sends the received setting information to the generation AI, instructing it to generate a "customer code" and a "department ID."

[1244] Information generation

[1245] The AI ​​generates the necessary registration information based on the conditions received from the server. For example, the generated registration information might be "Customer code: 12345" and "Department ID: 67890."

[1246] Storage and transmission of information

[1247] The server stores the registration information received from the generation AI in a database and sends it to the user, who can then refer to it when entering the registration information.

[1248] Confirmation of registration details

[1249] Entering information

[1250] The user enters registration information into the system through a terminal. For example, the user enters "Customer code: 12345" and "Department ID: 67890."

[1251] Sending information

[1252] The device sends the entered registration information to the server, which then passes the received information to the generation AI and requests confirmation.

[1253] Verifying Information

[1254] The generation AI checks whether the input information matches the conditions. For example, it checks whether the input "customer code" matches the generated "12345."

[1255] Receiving Feedback

[1256] The server receives the verification result from the generation AI, which is sent as a "match" or "no match."

[1257] Send Feedback

[1258] The server sends the verification result to the terminal and notifies the user, so that the user can check whether the information is correct. If it does not match, the user will receive an error message and be prompted to enter the correct information again.

[1259] Incorporating an emotion engine

[1260] Emotion Engine Operation

[1261] Emotion recognition

[1262] The device sends emotional information such as the user's facial expressions and voice to the emotion engine, which then analyzes the user's emotions during input or when error feedback is provided.

[1263] Emotion-based assistance

[1264] The emotion engine assists with the next action based on the user's emotional state. For example, if the user is confused or frustrated, the emotion engine notifies the server that support is needed.

[1265] Feedback adjustment

[1266] The emotion engine adjusts the feedback content depending on the user's emotional state. For example, if the user is feeling stressed, the emotion engine will display helpful guidance or comforting messages.

[1267] Specific examples

[1268] Example of generating registration information

[1269] 1. The server receives the registration conditions for a specific company, "Company X." For example, "Company X" requires a "Customer Code" and a "Department ID."

[1270] 2. The server communicates these conditions to the generation AI and asks it to generate registration information.

[1271] 3. The generation AI generates "Customer code: 67890" and "Department ID: 12345".

[1272] 4. The server stores the generated information and sends it to employees of "Company X" for reference.

[1273] Specific examples of confirmation of registered information

[1274] 1. A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345."

[1275] 2. The terminal sends the entered information to the server.

[1276] 3. The server sends the information to the generation AI and asks it to verify whether it matches the conditions.

[1277] 4. The generation AI checks against a set of conditions, confirms that the "Customer Code" and "Department ID" match, and returns the results to the server.

[1278] 5. The server sends the results to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[1279] Examples of emotion engines

[1280] 1. When the user enters the "customer code," the device sends the user's facial expressions and voice to the emotion engine.

[1281] 2. The emotion engine analyzes the user's emotions and determines that the user is confused.

[1282] 3. The emotion engine notifies the server and suggests providing additional support.

[1283] 4. The server sends messages containing helpful instructions and warnings to the user, helping the user to enter information without stress.

[1284] In this way, the system of the present invention can efficiently and accurately generate registration information based on the conditions that vary from company to company and verify the accuracy of that information. Furthermore, by incorporating an emotion engine, it is possible to provide feedback and support according to the user's emotional state, improving the system's user experience.

[1285] The processing flow will be explained below.

[1286] Automatic generation of registration information

[1287] Step 1:

[1288] The server receives the company's registration conditions.

[1289] The server receives the configuration information for a particular company and checks the specific conditions (e.g., customer code and department ID) required by that company for registration.

[1290] Step 2:

[1291] The server sends the received conditions to the generation AI.

[1292] The server passes the received conditions to the generation AI and instructs it to generate registration information.

[1293] Step 3:

[1294] The generation AI generates registration information based on the conditions.

[1295] The generation AI automatically generates, for example, customer codes and department IDs based on the company's conditions.

[1296] Step 4:

[1297] The server stores the generated registration information and sends it to the user.

[1298] The server stores the registration information received from the generation AI in a database and sends the information to the user.

[1299] Specific working example:

[1300] The server receives information that the conditions for "Company X" require a "Customer Code" and a "Department ID."

[1301] The server instructs the generation AI to generate "Customer code: 12345" and "Department ID: 67890".

[1302] The generation AI generates this and returns it to the server.

[1303] The server stores this in a database where it can be accessed by employees of "Company X."

[1304] Confirmation of registration details

[1305] Step 1:

[1306] The user enters registration information through the terminal.

[1307] The user enters the fields based on the registration information provided. For example, enter "Customer Code: 12345" and "Department ID: 67890."

[1308] Step 2:

[1309] The terminal transmits the entered registration information to the server.

[1310] Once the user has completed the input, the terminal sends this information to the server.

[1311] Step 3:

[1312] The server transmits the received information to the generation AI and requests confirmation.

[1313] The server sends the registration information received from the device to the generation AI and asks it to check whether the entered information matches the conditions.

[1314] Step 4:

[1315] The generating AI verifies the consistency of the information.

[1316] The generation AI checks whether the entered "Customer Code" and "Department ID" match the generated information.

[1317] Step 5:

[1318] The server receives the verification result and sends it to the user.

[1319] The server receives the verification results from the generation AI and sends them to the user's device.

[1320] Step 6:

[1321] The user reviews the feedback and makes corrections as necessary.

[1322] The user checks the feedback from the server, corrects any errors, and re-enters the data.

[1323] Specific working example:

[1324] The user enters "Customer code: 12345" and "Department ID: 67890" into the terminal.

[1325] The terminal sends this information to the server.

[1326] The server sends the information to the generating AI and requests verification.

[1327] The generation AI checks against a series of conditions, confirms that the "customer code" and "department ID" match, and returns the results to the server.

[1328] The server sends the results back to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[1329] Incorporating an emotion engine

[1330] Step 1:

[1331] The terminal transmits emotional information such as the user's facial expressions and voice to the emotion engine.

[1332] When the terminal is in the middle of user input or when error feedback is received, it sends data to the emotion engine to analyze the user's emotions.

[1333] Step 2:

[1334] The emotion engine recognizes and analyzes the user's emotions.

[1335] The emotion engine analyzes the user's emotional state (happiness, sadness, irritation, etc.) based on the data received.

[1336] Step 3:

[1337] The emotion engine determines the next action based on the emotional state.

[1338] For example, if the user is frustrated, the emotion engine will notify the server that support is needed.

[1339] Step 4:

[1340] The server generates feedback according to the user's emotional state.

[1341] The server generates a message including friendly guidance and warnings for the user based on the notification from the emotion engine.

[1342] Step 5:

[1343] The user receives feedback according to their emotional state.

[1344] To allow users to receive feedback from the server and continue working without stress or confusion.

[1345] Specific working example:

[1346] When the user enters the "customer code," the terminal transmits the user's facial expressions and voice to the emotion engine.

[1347] The emotion engine analyzes the user's emotions and determines that the user is confused.

[1348] The emotion engine notifies the server of this and suggests providing additional support.

[1349] The server sends messages containing helpful guidance and cautions to the user, thereby helping the user to input information without feeling stressed.

[1350] Example 2

[1351] 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."

[1352] Conventional systems require a complex process for generating and verifying registration information based on company-specific requirements, and provide limited feedback on user input. Furthermore, they do not provide support tailored to the user's emotional state, resulting in a lack of user experience. Therefore, there is a need for a system that efficiently generates and verifies registration information, provides feedback tailored to the user's emotional state, and improves the user experience.

[1353] 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.

[1354] In this invention, the server includes means for receiving registration conditions, which may differ from company to company, means for generating the required registration information based on the conditions, means for saving the generated registration information and transmitting it to the user, means for receiving registration information entered by the user, means for verifying whether the entered registration information matches the conditions, means for transmitting the verification results to the user, emotion recognition means for recognizing the user's emotion information, and means for adjusting feedback to the user based on the emotion information. This makes it possible to generate and verify registration information more efficiently and accurately, and to provide appropriate feedback according to the user's emotional state.

[1355] "Registration conditions" refer to conditions that differ for each company and are elements required to register specific information.

[1356] "Generation means" refers to a device or software having the function of generating the necessary registration information based on the received registration conditions.

[1357] "Storage means" refers to a device or software that has the function of storing the generated registration information in a database or storage.

[1358] "Receiving means" refers to a device or software that has the function of receiving registration information entered by a user.

[1359] "Verification means" refers to a device or software that has the function of verifying whether the entered registration information matches pre-set conditions or generated information.

[1360] "Emotion recognition means" refers to a device or software that has the function of recognizing and analyzing emotional information such as a user's facial expression and voice.

[1361] The "feedback adjustment means" refers to a device or software that has the function of adjusting the content of feedback to the user based on the emotional information recognized by the emotional recognition means.

[1362] The present invention combines a system that generates registration information that requires different conditions for each company and checks whether that information is appropriate, with an emotion engine that recognizes user emotions. The system of the present invention will be described from the perspective of including a server, a terminal, a generation AI, and an emotion engine. Specific embodiments will be described below.

[1363] Automatic generation of registration information

[1364] Receiving configuration information

[1365] The server receives the company's registration conditions. For example, if a company requires a "client code" and a "department ID" as registration conditions, the server receives those conditions. This information is received via API and temporarily stored in an internal database.

[1366] Communication of Terms

[1367] The server sends the received configuration information to the generation AI. It instructs the generation AI to generate a "customer code" and a "department ID." This communication uses HTTP requests and WebSockets.

[1368] Information generation

[1369] The generative AI generates the necessary registration information based on the conditions received from the server. The generative AI model (e.g., GPT-3) generates text data such as "Customer code: 12345" and "Department ID: 67890" based on the prompt sentence.

[1370] Storage and transmission of information

[1371] The server stores the registration information received from the generation AI in a database and sends it to the user so that the user can refer to it when entering registration information.

[1372] Confirmation of registration details

[1373] Entering information

[1374] The user enters registration information into the system via a terminal. For example, the user uses a web browser or a dedicated application to enter registration information such as "Customer code: 12345" and "Department ID: 67890" into a form.

[1375] Sending information

[1376] The terminal sends the entered registration information to the server. When the user enters information and presses the send button, the terminal sends the information to the server as a POST request.

[1377] Verifying Information

[1378] The server transmits the received information to the generation AI and requests confirmation. The generation AI compares the received information with the pre-set conditions and the generated information to determine whether they match.

[1379] Receiving and sending feedback

[1380] The server receives the verification results from the generation AI and sends them to the terminal as an API response. The terminal displays the results so that the user can confirm them. For example, it checks whether the entered "customer code" matches the generated "12345" and notifies the user of the result.

[1381] Incorporating an emotion engine

[1382] Emotion recognition

[1383] The device sends emotional information such as the user's facial expressions and voice to the emotion engine. The device uses a camera and microphone to detect the user's facial expressions and voice, and sends the data to the emotion engine in real time.

[1384] Emotion-based assistance

[1385] The emotion engine assists with next actions based on the user's emotional state. For example, if it determines that the user is confused, the emotion engine notifies the server and suggests that additional support is needed.

[1386] Feedback adjustment

[1387] An emotion engine tailors the feedback depending on the user's emotional state: for example, if the user is feeling stressed, the server will send helpful guidance or comforting messages.

[1388] Specific examples

[1389] Example of generating registration information

[1390] The server receives the registration conditions for a specific company. For example, "Company X" requires a "Customer Code" and a "Department ID." The server communicates these conditions to the generation AI and asks it to generate the registration information. The generation AI generates "Customer Code: 67890" and "Department ID: 12345." The server then stores the generated information and sends it to employees of "Company X" for reference.

[1391] Specific examples of confirmation of registered information

[1392] A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345." The terminal sends the entered information to the server, which then sends the information to the generation AI, requesting it to verify whether it matches the conditions. The generation AI checks the information against a set of conditions, confirms that the "Customer Code" and "Department ID" match, and returns the results to the server. The server then sends the results to the user, who confirms that the entered information is accurate.

[1393] Examples of emotion engines

[1394] When the user enters the "customer code," the terminal sends the user's facial expression and voice to the emotion engine. The emotion engine analyzes the user's emotions and determines that the user is confused. The emotion engine notifies the server and suggests providing additional support. The server then sends the user a message containing helpful instructions and warnings, helping the user enter information without stress.

[1395] In this way, the system of the present invention can efficiently and accurately generate registration information based on the conditions that vary from company to company and verify the accuracy of that information. Furthermore, by incorporating an emotion engine, it is possible to provide feedback and support according to the user's emotional state, improving the system's user experience.

[1396] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1397] Program processing flow

[1398] Automatic generation of registration information

[1399] Step 1:

[1400] The server receives the registration conditions.

[1401] Input: Registration conditions sent by the company (e.g. "Customer Code" and "Department ID")

[1402] What happens: The server receives the API request and temporarily stores these registration conditions in an internal database.

[1403] Step 2:

[1404] The server sends the registration conditions to the generated AI.

[1405] Input: Registration conditions saved on the server

[1406] Output: Data in prompt format

[1407] Specific operation: The server sends the registration conditions as a prompt to the generation AI (e.g., "Generate a customer code and department ID") via an HTTP request.

[1408] Step 3:

[1409] Generative AI generates registration information

[1410] Input: prompt statement

[1411] Output: Registration information (e.g., "Customer code: 12345" and "Department ID: 67890")

[1412] Specific operation: The generative AI model (e.g., GPT-3) generates registration information based on the prompt sentence and returns it as text data.

[1413] Step 4:

[1414] The server stores the generated registration information and sends it to the user.

[1415] Input: Registration information returned by the generation AI

[1416] Output: Registration information stored in a database and sent to the user

[1417] Specific operation: The server stores the acquired registration information in an internal database and then sends it to the company's system administrator via email or API response.

[1418] Confirmation of registration details

[1419] Step 5:

[1420] The user enters registration information through the terminal.

[1421] Input: Registration information provided by the company (e.g., "Customer code: 12345" and "Department ID: 67890")

[1422] What happens: The user uses a web browser or a dedicated application to enter registration information into the appropriate form.

[1423] Step 6:

[1424] The device sends the entered registration information to the server.

[1425] Input: Information entered by the user

[1426] Output: POST request to the server

[1427] Specific operation: When the user presses the send button, the terminal sends the entered information to the server as a POST request.

[1428] Step 7:

[1429] The server sends the received information to the generation AI and checks whether it matches the conditions.

[1430] Input: Your registered information

[1431] Output: Confirmation request to the generating AI

[1432] Specific operation: The server sends the received registration information as a verification request to the generation AI to determine whether it matches.

[1433] Step 8:

[1434] Generative AI checks for information consistency

[1435] Input: Registration information sent from the server

[1436] Output: Match / No-Match results

[1437] Specific operation: The generation AI compares the registration information received from the server with the information generated in advance and returns the match / mismatch result to the server.

[1438] Step 9:

[1439] The server sends the verification result to the device.

[1440] Input: Match / No-Match results from the generation AI

[1441] Output: Notification of validation results to the user

[1442] Specific operation: The server returns the received verification result to the device as an API response so that the user can check it.

[1443] Incorporating an emotion engine

[1444] Step 10:

[1445] The device sends the user's emotional information to the emotion engine.

[1446] Input: User facial and voice data

[1447] Output: Emotional information data

[1448] How it works: The device uses a camera and microphone to detect the user's facial expressions and voice, and sends that data to the emotion engine in real time.

[1449] Step 11:

[1450] Emotion engine helps guide next actions based on emotions

[1451] Input: Emotional information data

[1452] Output: Instructions for auxiliary actions

[1453] Specific behavior: If the emotion engine performs analysis and determines that the user is confused or frustrated, it notifies the server and suggests the need for additional support.

[1454] Step 12:

[1455] The server adjusts the feedback and notifies the user.

[1456] Input: Notification from the emotion engine

[1457] Output: Adjusted feedback message

[1458] Specific operation: Based on the notification from the emotion engine, the server sends messages containing helpful guidance and warnings to the user, helping the user to enter information without stress.

[1459] (Application example 2)

[1460] 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."

[1461] Conventional systems for generating and verifying registration information have the problem that the information entered by users is complex, making it prone to input errors and mismatches with requirements. Furthermore, users often experience difficulty or stress when entering information, which can lead to a decline in service quality. The present invention aims to solve these issues and improve the user experience. Furthermore, the present invention provides a system that recognizes the user's emotions and provides appropriate support, allowing the input process to proceed smoothly.

[1462] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1463] In this invention, the server includes: means for receiving registration conditions, which may vary from company to company; means for generating the required registration information based on the conditions; means for saving the generated registration information and transmitting it to the user; means for receiving the registration information entered by the user; means for verifying whether the entered registration information matches the conditions; means for transmitting the verification result to the user; emotion recognition means for analyzing the user's emotions and providing support according to the emotional state; and means for providing additional support if the emotion recognition means determines that the user's emotional state is unstable. This not only enables efficient and accurate generation and verification of registration information, but also makes it possible to reduce stress and improve the user experience by providing appropriate support according to the user's emotional state.

[1464] "Conditions that vary from company to company" refers to the specific requirements and conditions required for registration information that each company sets on its own.

[1465] "Registration information" refers to a series of data and information generated based on the conditions required by a company.

[1466] "Generation means" refers to a mechanism or method for automatically generating registration information based on conditions that vary from company to company.

[1467] "Storage means" refers to a function or system for storing the generated registration information in a database or the like.

[1468] "Receiving means" refers to a function for receiving information sent from a user or a server.

[1469] "Verification measures" refer to mechanisms or methods for verifying whether the registration information entered by the user matches the conditions set by the company.

[1470] "Transmission means" refers to a function for transmitting the generated registration information and verification results to the user or other systems.

[1471] "Emotion recognition means" refers to a mechanism or method for analyzing emotions from the user's facial expressions, voice, etc., and determining their current emotional state.

[1472] "Means for providing additional support" refers to functions or methods for providing appropriate assistance or guidance when the emotion recognition means determines that the user is experiencing difficulty or stress.

[1473] The system for implementing this invention includes the following main components: a server, a terminal, a generation AI, and an emotion recognition means. Below, we will explain how each component works together and processes data.

[1474] First, the server receives the registration conditions, which vary from company to company. These conditions are specific data fields, such as a customer code or department ID. The server then sends the received conditions to the generation AI.

[1475] The generation AI generates the necessary registration information based on the prompt sent from the server. The generated registration information is saved in a database and later provided to the user. An example of a prompt statement for the generation AI is, "The client code and department ID are required as registration conditions for Company X. Please generate the client code and department ID."

[1476] The terminal is a device for transmitting the registration information entered by the user to the server. This terminal may be a smartphone, tablet, or PC, and can transmit the information entered by the user to the server in real time. Furthermore, the terminal can transmit the user's facial expression and voice data to the emotion recognition means.

[1477] The verification method checks whether the entered registration information matches the company's set conditions. This includes a matching process using the generation AI. For example, if a user enters "Customer Code 12345" and "Department ID 67890," it verifies that this matches the generated information.

[1478] The emotion recognition means analyzes the user's facial expressions and voice data to determine their emotional state. If the user is confused or stressed, this information is sent to the server, and additional support is provided. For example, if the user is determined to be confused, a helpful guidance message may be displayed.

[1479] This not only enables efficient and accurate generation and confirmation of registration information, but also reduces stress and improves the user experience by providing appropriate support according to the user's emotional state.

[1480] Specific examples

[1481] For example, when a new employee of a certain company, "Company X," attempts to register in the system, the server requests the generation AI to provide the "customer code" and "department ID" as the registration conditions for Company X. The "customer code 12345" and "department ID 67890" generated by the generation AI are stored in the database. The user enters this information using a terminal, and the system verifies it. After confirming that the input matches, the user's facial expressions and voice are analyzed using emotion recognition means, and additional support is provided if necessary.

[1482] Example prompt sentence:

[1483] "Company X requires a customer code and department ID to register. Please generate a customer code and department ID."

[1484] In this way, the system of the invention can efficiently generate and verify registration information, and provide support according to the user's emotional state.

[1485] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1486] Step 1:

[1487] The server receives registration conditions, which vary from company to company. As input, the condition data provided by the company is required. This includes specific data fields such as customer code and department ID. The server receives this information and temporarily stores it.

[1488] Step 2:

[1489] The server sends the received conditions to the generation AI, which generates a prompt to generate the required registration information. The company's condition data is used as input. The generation AI generates registration information based on the provided conditions and returns specific registration information as output (e.g., "Customer code 12345" and "Department ID 67890").

[1490] Step 3:

[1491] The server saves the generated registration information in a database. The input is the registration information received from the generation AI. The output is that the registration information is saved accurately in the database.

[1492] Step 4:

[1493] A user accesses the system using a terminal and enters their registration information. As input, the user enters the registration information (e.g., "Customer code 12345" and "Department ID 67890") into the terminal. The terminal then sends this information to the server.

[1494] Step 5:

[1495] The server receives the user's input information and sends it to the generation AI for confirmation. The information entered by the user is used as input. The generation AI compares the received information with existing registration information and returns a result of whether or not it matches.

[1496] Step 6:

[1497] The server receives the verification result and notifies the user. As input, the verification result from the generation AI is used. As output, the user is provided with feedback of "match" or "mismatch."

[1498] Step 7:

[1499] The emotion recognition means analyzes the user's facial and voice data to determine their emotional state. The facial and voice data collected by the device is used as input. The analyzed user's emotional state (e.g., confusion, stress, etc.) is output.

[1500] Step 8:

[1501] If the emotion recognition means determines that the user's emotional state is unstable, the server provides additional support. The judgment data from the emotion recognition means is used as input. The server provides the user with friendly guidance messages and additional support, and as output, creates an environment where the user can input information with confidence.

[1502] 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.

[1503] 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.

[1504] 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.

[1505] [Fourth embodiment]

[1506] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1507] 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.

[1508] 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).

[1509] 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.

[1510] 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.

[1511] 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).

[1512] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1513] 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.

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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."

[1519] The present invention is a system that generates registration information that requires different conditions for each company and checks whether the information is appropriate. The system of the present invention will be described from the perspective of including a server, a terminal, and a generation AI.

[1520] Natural language explanations

[1521] Automatic generation of registration information

[1522] Receiving configuration information

[1523] The server receives the company's conditions. For example, if a company requires a "customer code" and a "department ID" as registration conditions, the server receives those conditions.

[1524] Communication of Terms

[1525] The server sends the received setting information to the generation AI, instructing it to generate a "customer code" and a "department ID."

[1526] Information generation

[1527] The AI ​​generates the necessary registration information based on the conditions received from the server. For example, the generated registration information might be "Customer code: 12345" and "Department ID: 67890."

[1528] Storage and transmission of information

[1529] The server stores the registration information received from the generation AI in a database and sends it to the user, who can then refer to it when entering the registration information.

[1530] Confirmation of registration details

[1531] Entering information

[1532] The user enters registration information into the system through a terminal. For example, the user enters "Customer code: 12345" and "Department ID: 67890."

[1533] Sending information

[1534] The device sends the entered registration information to the server, which then passes the received information to the generation AI and requests confirmation.

[1535] Verifying Information

[1536] The generation AI checks whether the input information matches the conditions. For example, it checks whether the input "customer code" matches the generated "12345."

[1537] Receiving Feedback

[1538] The server receives the verification result from the generation AI, which is sent as a "match" or "no match."

[1539] Send Feedback

[1540] The server sends the verification result to the terminal and notifies the user, so that the user can check whether the information is correct. If it does not match, the user will receive an error message and be prompted to enter the correct information again.

[1541] Specific examples

[1542] Example of generating registration information

[1543] 1. The server receives the registration requirements for a specific company, "Company X." For example, "Company X" requires a "Customer Code" and a "Department ID."

[1544] 2. The server communicates these conditions to the generation AI and asks it to generate registration information.

[1545] 3. The generation AI generates "Customer code: 67890" and "Department ID: 12345".

[1546] 4. The server stores the generated information and sends it to employees of "Company X" for reference.

[1547] Specific examples of confirmation of registered information

[1548] 1. A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345."

[1549] 2. The terminal sends the entered information to the server.

[1550] 3. The server sends the received information to the generation AI and asks it to verify whether it matches the conditions.

[1551] 4. The generation AI checks whether the input information is correct and returns the result to the server. For example, it determines that the customer code is "67890" and matches.

[1552] 5. The server sends the results from the generated AI to the user's device, where the user verifies the accuracy of the information. If there are any errors, the user is prompted to re-enter the information.

[1553] In this way, the system of the present invention can efficiently and accurately generate registration information based on conditions that vary from company to company and verify the accuracy of that information, thereby improving business efficiency and ensuring the accuracy of registered data.

[1554] The processing flow will be explained below.

[1555] Automatic generation of registration information

[1556] Step 1:

[1557] The server receives the company's registration conditions.

[1558] The server receives the configuration information for a particular company and checks the specific conditions (e.g., customer code and department ID) required by that company for registration.

[1559] Step 2:

[1560] The server sends the received conditions to the generation AI.

[1561] The server passes the received conditions to the generation AI and instructs it to generate registration information.

[1562] Step 3:

[1563] The generation AI generates registration information based on the conditions.

[1564] The generation AI automatically generates customer codes and department IDs based on the company's conditions.

[1565] Step 4:

[1566] The server stores the generated registration information and sends it to the user.

[1567] The server stores the registration information received from the generation AI in a database and sends the information to the user.

[1568] Specific working example:

[1569] The server receives information that the conditions for "Company X" require a "Customer Code" and a "Department ID."

[1570] The server instructs the generation AI to generate "Customer code: 12345" and "Department ID: 67890".

[1571] The generation AI generates this and returns it to the server.

[1572] The server stores this in a database where it can be accessed by employees of "Company X."

[1573] Confirmation of registration details

[1574] Step 1:

[1575] The user enters registration information through the terminal.

[1576] The user enters the fields based on the registration information provided. For example, enter "Customer Code: 12345" and "Department ID: 67890."

[1577] Step 2:

[1578] The terminal transmits the entered registration information to the server.

[1579] Once the user has completed the input, the terminal sends this information to the server.

[1580] Step 3:

[1581] The server transmits the received information to the generation AI and requests confirmation.

[1582] The server sends the registration information received from the device to the generation AI and asks it to check whether the entered information matches the conditions.

[1583] Step 4:

[1584] The generating AI verifies the consistency of the information.

[1585] The generation AI checks whether the entered "Customer Code" and "Department ID" match the generated information.

[1586] Step 5:

[1587] The server receives the verification result and sends it to the user.

[1588] The server receives the verification results from the generation AI and sends them to the user's device.

[1589] Step 6:

[1590] The user reviews the feedback and makes corrections as necessary.

[1591] The user checks the feedback from the server, corrects any errors, and re-enters the data.

[1592] Specific working example:

[1593] The user enters "Customer code: 12345" and "Department ID: 67890" into the terminal.

[1594] The terminal sends this information to the server.

[1595] The server sends the information to the generating AI and requests verification.

[1596] The generation AI checks against a series of conditions, confirms that the "customer code" and "department ID" match, and returns the results to the server.

[1597] The server sends the results back to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[1598] Example 1

[1599] 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."

[1600] There is a need for a system that can efficiently and accurately generate registration information that meets the different conditions for each company and quickly verify the accuracy of the information entered by users. However, current systems require manual data entry and complex condition setting, which can lead to problems such as reduced work efficiency and the occurrence of errors. Therefore, there is a need to automate the process of generating information based on the registration conditions that vary from company to company and verifying its accuracy.

[1601] 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.

[1602] In this invention, the server includes a means for receiving registration conditions that differ from company to company, an artificial intelligence means for generating the necessary registration information based on the conditions, and a means for saving the generated registration information and transmitting it to the user, thereby enabling automatic generation of registration information under conditions that differ from company to company and confirmation of its accuracy.

[1603] The "means for receiving registration conditions that differ for each company" is a function that allows the server to receive information for each company to register its specific conditions.

[1604] The "artificial intelligence means for generating the necessary registration information based on the conditions" is a function that uses artificial intelligence to generate the necessary registration information based on the company-specific conditions received by the server.

[1605] "Means for saving the generated registration information and sending it to the user" refers to a function for saving the registration information generated by artificial intelligence in a database and providing that information to the user.

[1606] The "means for receiving registration information entered by a user" is a function that allows the server to receive registration information entered by a user through a terminal.

[1607] "Means for querying the artificial intelligence generated to confirm whether the entered registration information matches the conditions" is a function that queries the artificial intelligence to confirm whether the information entered by the user matches the pre-set conditions.

[1608] "Means for transmitting the verification results to the user" refers to a function by which the server transmits the results verified by artificial intelligence to the terminal in order to inform the user.

[1609] The present invention is a system that generates registration information that requires different conditions for each company and checks whether the information is appropriate. The system of the present invention will be described from the perspective of including a server, a terminal, and a generation AI model.

[1610] Hardware and software used

[1611] Server: Receives requirements from the enterprise, sends prompts to the generative AI model, and stores and transmits the generated information.

[1612] Terminal: The user enters registration information and sends it to the server

[1613] Generative AI model: Generates registration information based on company-specific conditions and validates the entered information

[1614] Automatic generation of registration information

[1615] Receiving configuration information

[1616] The server receives registration conditions from the company. These conditions include specific items required by the company (e.g., customer code, department ID, etc.). For example, the server receives a condition requiring a "customer code" and a "department ID."

[1617] Communication of Terms

[1618] The server sends the received conditions to the generation AI model. The specific prompt might be, "Generate registration information based on the following conditions. 'Customer code' and 'Department ID' are required."

[1619] Information generation

[1620] The generative AI model generates the necessary registration information based on prompts from the server. For example, the generated registration information is "Customer code: 12345" and "Department ID: 67890."

[1621] Storage and transmission of information

[1622] The server stores the registration information received from the generative AI model in a database and sends the information to the user's device, allowing the user to refer to the necessary registration information.

[1623] Confirmation of registration details

[1624] Entering information

[1625] The user accesses the system using a terminal and enters the specified registration information. For example, the user enters "Customer Code: 12345" and "Department ID: 67890."

[1626] Sending information

[1627] The terminal sends the entered registration information to the server, which receives it and proceeds to the next step.

[1628] Verifying Information

[1629] The server transmits the received registration information to the generative AI model and sends a prompt to confirm whether it matches the conditions. The specific prompt text might be, "Please confirm whether the following information matches the pre-set conditions: 'Customer code: 12345', 'Department ID: 67890'."

[1630] Receiving Feedback

[1631] The generative AI model checks whether the input information matches the conditions and sends the verification results to the server as either "match" or "mismatch."

[1632] Send Feedback

[1633] The server sends the verification results from the generative AI model to the user's device. The user can check the verification results through the device. If the information matches, the user proceeds to the next step. If it does not match, an error message is displayed and the user is prompted to re-enter the information.

[1634] Example prompt sentences

[1635] Below is an example of a prompt sentence.

[1636] Prompt text for generating registration information

[1637] Please generate registration information based on the following conditions. "Customer Code" and "Department ID" are required.

[1638] Information confirmation prompt

[1639] Please check whether the following information matches the pre-defined conditions: "Customer Code: 12345" "Department ID: 67890"

[1640] This allows the system of the present invention to efficiently and accurately generate and verify registration information based on conditions that vary from company to company, thereby improving business efficiency and ensuring the accuracy of registered data.

[1641] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1642] Step 1: Receiving configuration information

[1643] The server receives registration conditions from the company. The input is the specific conditions required by the company (e.g., "Customer Code" and "Department ID"). The server stores the received conditions in its internal memory and prepares them for the next processing step. The output is the registration condition data.

[1644] Step 2: Communicate conditions

[1645] The server sends the received registration conditions to the generative AI model. Here, it generates a prompt to send to the generative AI model. It creates a prompt saying, "Please generate registration information based on the following conditions. 'Customer code' and 'Department ID' are required," and sends it to the generative AI model. The input is the registration conditions received earlier, and the output is the prompt to be sent to the generative AI model.

[1646] Step 3: Generate information

[1647] The generative AI model generates the necessary registration information based on prompts from the server. For example, it generates data such as "Customer code: 12345" and "Department ID: 67890." The input is the prompt, and the output is the generated registration information.

[1648] Step 4: Saving and sending information

[1649] The server stores the registration information received from the generative AI model in a database. At the same time, it sends this information to the user's device. The input is the generated registration information, and the output is updating the database and sending it to the user's device.

[1650] Step 5: Enter your information

[1651] A user uses a terminal to access the system and enters the specified registration information. For example, "Customer code: 12345" and "Department ID: 67890." The input is the user's registration information, and the output is the data to be sent to the server.

[1652] Step 6: Submit your information

[1653] The terminal sends the entered registration information to the server. The input is the registration information entered by the user, and the output is the data sent to the server.

[1654] Step 7: Verify the information

[1655] The server transmits the received registration information to the generative AI model and sends a prompt to confirm whether it matches the conditions. It generates a prompt such as, "Please check whether the following information matches the pre-set conditions: 'Customer code: 12345', 'Department ID: 67890'." The input is the user's registration information, and the output is the prompt sent to the generative AI model.

[1656] Step 8: Receiving feedback

[1657] The generative AI model checks whether the input information matches the conditions and sends the verification result to the server. The input is a verification prompt, and the output is a verification result such as "match" or "no match."

[1658] Step 9: Submit your feedback

[1659] The server sends the verification results from the generative AI model to the user's device. The user checks the verification results through the device. The input is the verification result, and the output is a notification to the user's device. If there is a mismatch, an error message is displayed prompting the user to re-enter the information.

[1660] (Application example 1)

[1661] 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."

[1662] Electronic payment services require a system that generates registration information based on conditions that vary from company to company and quickly and efficiently verifies its accuracy. Conventional methods involve manual data entry and verification, which is prone to errors and reduces operational efficiency. This poses a challenge, particularly for companies that handle a large volume of transactions, as generating and verifying registration information requires a great deal of time and effort.

[1663] 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.

[1664] In this invention, the server includes: means for receiving different conditions for a company; generation means for providing an artificial intelligence model that automatically generates registration information based on the conditions; means for saving the generated registration information in a database and transmitting it to the user; means for receiving registration information entered by the user; verification means using an artificial intelligence model to confirm whether the entered registration information matches the conditions; and means for transmitting the verification results to the user and providing feedback. This makes it possible to quickly and efficiently generate registration information based on conditions that vary from company to company in electronic payment services and verify its accuracy, thereby improving business efficiency and reducing errors.

[1665] "Company-specific conditions" refer to the individual requirements and standards that each company sets for its particular registration information and transactions.

[1666] "Registration conditions" are requirements and standards required when generating registration information.

[1667] "Registered information" is information related to a specific business partner or department, and is data generated based on conditions that differ for each company.

[1668] "Generation means" refers to a technique or device for automatically creating the necessary registration information based on set conditions.

[1669] "Verification means" refers to a technique or device for verifying whether the generated registration information matches the information entered by the user.

[1670] An "artificial intelligence model" is an algorithm or system that learns from large amounts of data and generates new information or analyzes and judges existing information.

[1671] A "database" is a collection of data constructed to efficiently store, search, update, and delete information.

[1672] "Feedback" refers to the provision of information or notification of results to system users, and is a response to a specific operation or input.

[1673] A "prompt sentence" is a sentence that is input as an instruction or question required for an artificial intelligence model to operate.

[1674] The system for implementing this invention consists of the following major hardware and software components: a server, a user's smartphone, a generative AI model, and a database.

[1675] Hardware & Software Details

[1676] 1. Server

[1677] Function: Receives registration conditions that vary from company to company, works with the generative AI model to generate registration information, saves the registration information, and notifies users.

[1678] Specific name: Cloud server (e.g. AWS, Google Cloud Platform)

[1679] 2. Smartphone

[1680] Function: Provides an interface for users to enter registration information and submit that information to a server.

[1681] Specific name: iOS or Android smartphone

[1682] 3. Generative AI Models

[1683] Function: Generates registration information based on set criteria and verifies that the entered information matches the criteria.

[1684] Specific names: GPT-3 (OpenAI), BERT (Google)

[1685] 4. Database

[1686] Function: Stores generated registration information for quick access when needed.

[1687] Specific name: SQL database (e.g., MySQL, PostgreSQL)

[1688] System processing overview

[1689] 1. Receiving setting information

[1690] The server receives the registration conditions (for example, "customer code" and "department ID") provided by the company.

[1691] 2. Information Generation

[1692] The server sends the received conditions to the generation AI model and generates registration information using prompts. The example prompts used in this case are as follows:

[1693] Generate a customer code and department ID based on your company's requirements. The customer code and department ID should each be a five-digit number. Example: "Customer Code: 24680" "Department ID: 13579"

[1694] 3. Storage and transmission of information

[1695] The registration information generated by the generative AI model (for example, "Customer code: 24680" and "Department ID: 13579") is returned to the server and stored in a database. It is then sent to the user's smartphone, where the user can refer to it to enter their information.

[1696] 4. Entering and submitting information

[1697] The user uses a smartphone to enter the specified registration information and transmits it from the device to the server.

[1698] 5. Verification of Information

[1699] The server sends the input information back to the generative AI model and sets up prompts to confirm that the input information matches the conditions.

[1700] 6. Receiving Feedback

[1701] The generative AI model verifies the input information and sends the results to the server, which then sends the results to the smartphone and notifies the user.

[1702] If the verification is successful, the user can continue.

[1703] If validation fails, an error message will be displayed and you will be prompted to try again.

[1704] Adding specific examples

[1705] For company "Z", the "Customer Code" and "Department ID" are required. The server receives the conditions for "Z" and sends the following prompt to the generative AI model:

[1706] Generate a customer code and department ID based on your company's requirements. The customer code and department ID should each be a five-digit number. Example: "Customer Code: 98765" "Department ID: 43210"

[1707] The generative AI model generates "Customer code: 98765" and "Department ID: 43210," which the server stores in a database and sends to the user's smartphone. The user enters this information on their smartphone and sends it to the server. The server again uses the generative AI model to check whether the input information is correct and notifies the user of the results.

[1708] This system allows companies to efficiently generate registration information based on different conditions and verify its accuracy.

[1709] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1710] Step 1:

[1711] The server receives registration conditions from the company. For example, it receives conditions such as "client code" and "department ID." This input is text data, and the server prepares to send the received conditions to the generative AI model.

[1712] Step 2:

[1713] The server sends the received registration conditions to the generative AI model. It generates a prompt message, such as the following: "Please generate a customer code and department ID based on the company's conditions. The customer code and department ID will each be expressed as five-digit numbers. For example: 'Customer code: 98765', 'Department ID: 43210'." The generative AI model then generates the registration information based on this.

[1714] Step 3:

[1715] The generative AI model generates the necessary registration information (e.g., "Customer code: 98765" and "Department ID: 43210") based on the prompts from the server. This data is returned to the server as structured output.

[1716] Step 4:

[1717] The server stores the generated registration information in a database and sends it to the user's smartphone, creating a database entry and formatting the stored items as notification data.

[1718] Step 5:

[1719] Users access the system using their smartphones and enter their registration information (e.g., "Customer code: 98765" and "Department ID: 43210"). This input is done through text boxes and forms, and users are guided to enter the information accurately.

[1720] Step 6:

[1721] The device sends the entered registration information to the server. It receives the user's input and generates a request to forward it to the server, sometimes checking the format of the input data.

[1722] Step 7:

[1723] The server then sends the received registration information back to the AI ​​model, requesting confirmation that the input information matches the conditions. This process generates a prompt for verification, such as the following: "Please confirm that the customer code and department ID entered by the user are correct. Input: Customer code 98765, Department ID 43210."

[1724] Step 8:

[1725] The generative AI model verifies the input information based on the conditions and returns a result to the server indicating whether or not it matches. This data processing is performed by comparing the input information with reference information.

[1726] Step 9:

[1727] The server receives the verification results from the generative AI model and sends them to the user's smartphone. If the results match, it generates feedback data containing a success message, and if they do not match, it generates an error message.

[1728] Step 10:

[1729] The user checks the verification result on their smartphone and re-enters the information if necessary. For example, if an error message is displayed, the user re-enters the correct registration information.

[1730] Through this series of processing steps, registration information based on conditions that differ for each company and the accuracy of the information are efficiently generated and verified.

[1731] 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.

[1732] This invention combines a system that generates registration information that requires different conditions for each company and checks whether that information is appropriate, with an emotion engine that recognizes the user's emotions. The system of this invention will be explained from the perspective of including a server, terminal, generation AI, and emotion engine.

[1733] Natural language explanations

[1734] Automatic generation of registration information

[1735] Receiving configuration information

[1736] The server receives the company's registration conditions. For example, if a company requires a "client code" and a "department ID" as registration conditions, the server receives those conditions.

[1737] Communication of Terms

[1738] The server sends the received setting information to the generation AI, instructing it to generate a "customer code" and a "department ID."

[1739] Information generation

[1740] The AI ​​generates the necessary registration information based on the conditions received from the server. For example, the generated registration information might be "Customer code: 12345" and "Department ID: 67890."

[1741] Storage and transmission of information

[1742] The server stores the registration information received from the generation AI in a database and sends it to the user, who can then refer to it when entering the registration information.

[1743] Confirmation of registration details

[1744] Entering information

[1745] The user enters registration information into the system through a terminal. For example, the user enters "Customer code: 12345" and "Department ID: 67890."

[1746] Sending information

[1747] The device sends the entered registration information to the server, which then passes the received information to the generation AI and requests confirmation.

[1748] Verifying Information

[1749] The generation AI checks whether the input information matches the conditions. For example, it checks whether the input "customer code" matches the generated "12345."

[1750] Receiving Feedback

[1751] The server receives the verification result from the generation AI, which is sent as a "match" or "no match."

[1752] Send Feedback

[1753] The server sends the verification result to the terminal and notifies the user, so that the user can check whether the information is correct. If it does not match, the user will receive an error message and be prompted to enter the correct information again.

[1754] Incorporating an emotion engine

[1755] Emotion Engine Operation

[1756] Emotion recognition

[1757] The device sends emotional information such as the user's facial expressions and voice to the emotion engine, which then analyzes the user's emotions during input or when error feedback is provided.

[1758] Emotion-based assistance

[1759] The emotion engine assists with the next action based on the user's emotional state. For example, if the user is confused or frustrated, the emotion engine notifies the server that support is needed.

[1760] Feedback adjustment

[1761] The emotion engine adjusts the feedback content depending on the user's emotional state. For example, if the user is feeling stressed, the emotion engine will display helpful guidance or comforting messages.

[1762] Specific examples

[1763] Example of generating registration information

[1764] 1. The server receives the registration conditions for a specific company, "Company X." For example, "Company X" requires a "Customer Code" and a "Department ID."

[1765] 2. The server communicates these conditions to the generation AI and asks it to generate registration information.

[1766] 3. The generation AI generates "Customer code: 67890" and "Department ID: 12345".

[1767] 4. The server stores the generated information and sends it to employees of "Company X" for reference.

[1768] Specific examples of confirmation of registered information

[1769] 1. A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345."

[1770] 2. The terminal sends the entered information to the server.

[1771] 3. The server sends the information to the generation AI and asks it to verify whether it matches the conditions.

[1772] 4. The generation AI checks against a set of conditions, confirms that the "Customer Code" and "Department ID" match, and returns the results to the server.

[1773] 5. The server sends the results to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[1774] Examples of emotion engines

[1775] 1. When the user enters the "customer code," the device sends the user's facial expressions and voice to the emotion engine.

[1776] 2. The emotion engine analyzes the user's emotions and determines that the user is confused.

[1777] 3. The emotion engine notifies the server and suggests providing additional support.

[1778] 4. The server sends messages containing helpful instructions and warnings to the user, helping the user to enter information without stress.

[1779] In this way, the system of the present invention can efficiently and accurately generate registration information based on the conditions that vary from company to company and verify the accuracy of that information. Furthermore, by incorporating an emotion engine, it is possible to provide feedback and support according to the user's emotional state, improving the system's user experience.

[1780] The processing flow will be explained below.

[1781] Automatic generation of registration information

[1782] Step 1:

[1783] The server receives the company's registration conditions.

[1784] The server receives the configuration information for a particular company and checks the specific conditions (e.g., customer code and department ID) required by that company for registration.

[1785] Step 2:

[1786] The server sends the received conditions to the generation AI.

[1787] The server passes the received conditions to the generation AI and instructs it to generate registration information.

[1788] Step 3:

[1789] The generation AI generates registration information based on the conditions.

[1790] The generation AI automatically generates, for example, customer codes and department IDs based on the company's conditions.

[1791] Step 4:

[1792] The server stores the generated registration information and sends it to the user.

[1793] The server stores the registration information received from the generation AI in a database and sends the information to the user.

[1794] Specific working example:

[1795] The server receives information that the conditions for "Company X" require a "Customer Code" and a "Department ID."

[1796] The server instructs the generation AI to generate "Customer code: 12345" and "Department ID: 67890".

[1797] The generation AI generates this and returns it to the server.

[1798] The server stores this in a database where it can be accessed by employees of "Company X."

[1799] Confirmation of registration details

[1800] Step 1:

[1801] The user enters registration information through the terminal.

[1802] The user enters the fields based on the registration information provided. For example, enter "Customer Code: 12345" and "Department ID: 67890."

[1803] Step 2:

[1804] The terminal transmits the entered registration information to the server.

[1805] Once the user has completed the input, the terminal sends this information to the server.

[1806] Step 3:

[1807] The server transmits the received information to the generation AI and requests confirmation.

[1808] The server sends the registration information received from the device to the generation AI and asks it to check whether the entered information matches the conditions.

[1809] Step 4:

[1810] The generating AI verifies the consistency of the information.

[1811] The generation AI checks whether the entered "Customer Code" and "Department ID" match the generated information.

[1812] Step 5:

[1813] The server receives the verification result and sends it to the user.

[1814] The server receives the verification results from the generation AI and sends them to the user's device.

[1815] Step 6:

[1816] The user reviews the feedback and makes corrections as necessary.

[1817] The user checks the feedback from the server, corrects any errors, and re-enters the data.

[1818] Specific working example:

[1819] The user enters "Customer code: 12345" and "Department ID: 67890" into the terminal.

[1820] The terminal sends this information to the server.

[1821] The server sends the information to the generating AI and requests verification.

[1822] The generation AI checks against a series of conditions, confirms that the "customer code" and "department ID" match, and returns the results to the server.

[1823] The server sends the results back to the user, who verifies the accuracy of the information entered and, if there are any errors, corrects and re-enters the information.

[1824] Incorporating an emotion engine

[1825] Step 1:

[1826] The terminal transmits emotional information such as the user's facial expressions and voice to the emotion engine.

[1827] When the terminal is in the middle of user input or when error feedback is received, it sends data to the emotion engine to analyze the user's emotions.

[1828] Step 2:

[1829] The emotion engine recognizes and analyzes the user's emotions.

[1830] The emotion engine analyzes the user's emotional state (happiness, sadness, irritation, etc.) based on the data received.

[1831] Step 3:

[1832] The emotion engine determines the next action based on the emotional state.

[1833] For example, if the user is frustrated, the emotion engine will notify the server that support is needed.

[1834] Step 4:

[1835] The server generates feedback according to the user's emotional state.

[1836] The server generates a message including friendly guidance and warnings for the user based on the notification from the emotion engine.

[1837] Step 5:

[1838] The user receives feedback according to their emotional state.

[1839] To allow users to receive feedback from the server and continue working without stress or confusion.

[1840] Specific working example:

[1841] When the user enters the "customer code," the terminal transmits the user's facial expressions and voice to the emotion engine.

[1842] The emotion engine analyzes the user's emotions and determines that the user is confused.

[1843] The emotion engine notifies the server of this and suggests providing additional support.

[1844] The server sends messages containing helpful guidance and cautions to the user, thereby helping the user to input information without feeling stressed.

[1845] Example 2

[1846] 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."

[1847] Conventional systems require a complex process for generating and verifying registration information based on company-specific requirements, and provide limited feedback on user input. Furthermore, they do not provide support tailored to the user's emotional state, resulting in a lack of user experience. Therefore, there is a need for a system that efficiently generates and verifies registration information, provides feedback tailored to the user's emotional state, and improves the user experience.

[1848] 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.

[1849] In this invention, the server includes means for receiving registration conditions, which may differ from company to company, means for generating the required registration information based on the conditions, means for saving the generated registration information and transmitting it to the user, means for receiving registration information entered by the user, means for verifying whether the entered registration information matches the conditions, means for transmitting the verification results to the user, emotion recognition means for recognizing the user's emotion information, and means for adjusting feedback to the user based on the emotion information. This makes it possible to generate and verify registration information more efficiently and accurately, and to provide appropriate feedback according to the user's emotional state.

[1850] "Registration conditions" refer to conditions that differ for each company and are elements required to register specific information.

[1851] "Generation means" refers to a device or software having the function of generating the necessary registration information based on the received registration conditions.

[1852] "Storage means" refers to a device or software that has the function of storing the generated registration information in a database or storage.

[1853] "Receiving means" refers to a device or software that has the function of receiving registration information entered by a user.

[1854] "Verification means" refers to a device or software that has the function of verifying whether the entered registration information matches pre-set conditions or generated information.

[1855] "Emotion recognition means" refers to a device or software that has the function of recognizing and analyzing emotional information such as a user's facial expression and voice.

[1856] The "feedback adjustment means" refers to a device or software that has the function of adjusting the content of feedback to the user based on the emotional information recognized by the emotional recognition means.

[1857] The present invention combines a system that generates registration information that requires different conditions for each company and checks whether that information is appropriate, with an emotion engine that recognizes user emotions. The system of the present invention will be described from the perspective of including a server, a terminal, a generation AI, and an emotion engine. Specific embodiments will be described below.

[1858] Automatic generation of registration information

[1859] Receiving configuration information

[1860] The server receives the company's registration conditions. For example, if a company requires a "client code" and a "department ID" as registration conditions, the server receives those conditions. This information is received via API and temporarily stored in an internal database.

[1861] Communication of Terms

[1862] The server sends the received configuration information to the generation AI. It instructs the generation AI to generate a "customer code" and a "department ID." This communication uses HTTP requests and WebSockets.

[1863] Information generation

[1864] The generative AI generates the necessary registration information based on the conditions received from the server. The generative AI model (e.g., GPT-3) generates text data such as "Customer code: 12345" and "Department ID: 67890" based on the prompt sentence.

[1865] Storage and transmission of information

[1866] The server stores the registration information received from the generation AI in a database and sends it to the user so that the user can refer to it when entering registration information.

[1867] Confirmation of registration details

[1868] Entering information

[1869] The user enters registration information into the system via a terminal. For example, the user uses a web browser or a dedicated application to enter registration information such as "Customer code: 12345" and "Department ID: 67890" into a form.

[1870] Sending information

[1871] The terminal sends the entered registration information to the server. When the user enters information and presses the send button, the terminal sends the information to the server as a POST request.

[1872] Verifying Information

[1873] The server transmits the received information to the generation AI and requests confirmation. The generation AI compares the received information with the pre-set conditions and the generated information to determine whether they match.

[1874] Receiving and sending feedback

[1875] The server receives the verification results from the generation AI and sends them to the terminal as an API response. The terminal displays the results so that the user can confirm them. For example, it checks whether the entered "customer code" matches the generated "12345" and notifies the user of the result.

[1876] Incorporating an emotion engine

[1877] Emotion recognition

[1878] The device sends emotional information such as the user's facial expressions and voice to the emotion engine. The device uses a camera and microphone to detect the user's facial expressions and voice, and sends the data to the emotion engine in real time.

[1879] Emotion-based assistance

[1880] The emotion engine assists with next actions based on the user's emotional state. For example, if it determines that the user is confused, the emotion engine notifies the server and suggests that additional support is needed.

[1881] Feedback adjustment

[1882] An emotion engine tailors the feedback depending on the user's emotional state: for example, if the user is feeling stressed, the server will send helpful guidance or comforting messages.

[1883] Specific examples

[1884] Example of generating registration information

[1885] The server receives the registration conditions for a specific company. For example, "Company X" requires a "Customer Code" and a "Department ID." The server communicates these conditions to the generation AI and asks it to generate the registration information. The generation AI generates "Customer Code: 67890" and "Department ID: 12345." The server then stores the generated information and sends it to employees of "Company X" for reference.

[1886] Specific examples of confirmation of registered information

[1887] A user accesses the system and enters the specified registration information. For example, they enter "Customer Code: 67890" and "Department ID: 12345." The terminal sends the entered information to the server, which then sends the information to the generation AI, requesting it to verify whether it matches the conditions. The generation AI checks the information against a set of conditions, confirms that the "Customer Code" and "Department ID" match, and returns the results to the server. The server then sends the results to the user, who confirms that the entered information is accurate.

[1888] Examples of emotion engines

[1889] When the user enters the "customer code," the terminal sends the user's facial expression and voice to the emotion engine. The emotion engine analyzes the user's emotions and determines that the user is confused. The emotion engine notifies the server and suggests providing additional support. The server then sends the user a message containing helpful instructions and warnings, helping the user enter information without stress.

[1890] In this way, the system of the present invention can efficiently and accurately generate registration information based on the conditions that vary from company to company and verify the accuracy of that information. Furthermore, by incorporating an emotion engine, it is possible to provide feedback and support according to the user's emotional state, improving the system's user experience.

[1891] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1892] Program processing flow

[1893] Automatic generation of registration information

[1894] Step 1:

[1895] The server receives the registration conditions.

[1896] Input: Registration conditions sent by the company (e.g. "Customer Code" and "Department ID")

[1897] What happens: The server receives the API request and temporarily stores these registration conditions in an internal database.

[1898] Step 2:

[1899] The server sends the registration conditions to the generated AI.

[1900] Input: Registration conditions saved on the server

[1901] Output: Data in prompt format

[1902] Specific operation: The server sends the registration conditions as a prompt to the generation AI (e.g., "Generate a customer code and department ID") via an HTTP request.

[1903] Step 3:

[1904] Generative AI generates registration information

[1905] Input: prompt statement

[1906] Output: Registration information (e.g., "Customer code: 12345" and "Department ID: 67890")

[1907] Specific operation: The generative AI model (e.g., GPT-3) generates registration information based on the prompt sentence and returns it as text data.

[1908] Step 4:

[1909] The server stores the generated registration information and sends it to the user.

[1910] Input: Registration information returned by the generation AI

[1911] Output: Registration information stored in a database and sent to the user

[1912] Specific operation: The server stores the acquired registration information in an internal database and then sends it to the company's system administrator via email or API response.

[1913] Confirmation of registration details

[1914] Step 5:

[1915] The user enters registration information through the terminal.

[1916] Input: Registration information provided by the company (e.g., "Customer code: 12345" and "Department ID: 67890")

[1917] What happens: The user uses a web browser or a dedicated application to enter registration information into the appropriate form.

[1918] Step 6:

[1919] The device sends the entered registration information to the server.

[1920] Input: Information entered by the user

[1921] Output: POST request to the server

[1922] Specific operation: When the user presses the send button, the terminal sends the entered information to the server as a POST request.

[1923] Step 7:

[1924] The server sends the received information to the generation AI and checks whether it matches the conditions.

[1925] Input: Your registered information

[1926] Output: Confirmation request to the generating AI

[1927] Specific operation: The server sends the received registration information as a verification request to the generation AI to determine whether it matches.

[1928] Step 8:

[1929] Generative AI checks for information consistency

[1930] Input: Registration information sent from the server

[1931] Output: Match / No-Match results

[1932] Specific operation: The generation AI compares the registration information received from the server with the information generated in advance and returns the match / mismatch result to the server.

[1933] Step 9:

[1934] The server sends the verification result to the device.

[1935] Input: Match / No-Match results from the generation AI

[1936] Output: Notification of validation results to the user

[1937] Specific operation: The server returns the received verification result to the device as an API response so that the user can check it.

[1938] Incorporating an emotion engine

[1939] Step 10:

[1940] The device sends the user's emotional information to the emotion engine.

[1941] Input: User facial and voice data

[1942] Output: Emotional information data

[1943] How it works: The device uses a camera and microphone to detect the user's facial expressions and voice, and sends that data to the emotion engine in real time.

[1944] Step 11:

[1945] Emotion engine helps guide next actions based on emotions

[1946] Input: Emotional information data

[1947] Output: Instructions for auxiliary actions

[1948] Specific behavior: If the emotion engine performs analysis and determines that the user is confused or frustrated, it notifies the server and suggests the need for additional support.

[1949] Step 12:

[1950] The server adjusts the feedback and notifies the user.

[1951] Input: Notification from the emotion engine

[1952] Output: Adjusted feedback message

[1953] Specific operation: Based on the notification from the emotion engine, the server sends messages containing helpful guidance and warnings to the user, helping the user to enter information without stress.

[1954] (Application example 2)

[1955] 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."

[1956] Conventional systems for generating and verifying registration information have the problem that the information entered by users is complex, making it prone to input errors and mismatches with requirements. Furthermore, users often experience difficulty or stress when entering information, which can lead to a decline in service quality. The present invention aims to solve these issues and improve the user experience. Furthermore, the present invention provides a system that recognizes the user's emotions and provides appropriate support, allowing the input process to proceed smoothly.

[1957] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1958] In this invention, the server includes: means for receiving registration conditions, which may vary from company to company; means for generating the required registration information based on the conditions; means for saving the generated registration information and transmitting it to the user; means for receiving the registration information entered by the user; means for verifying whether the entered registration information matches the conditions; means for transmitting the verification result to the user; emotion recognition means for analyzing the user's emotions and providing support according to the emotional state; and means for providing additional support if the emotion recognition means determines that the user's emotional state is unstable. This not only enables efficient and accurate generation and verification of registration information, but also makes it possible to reduce stress and improve the user experience by providing appropriate support according to the user's emotional state.

[1959] "Conditions that vary from company to company" refers to the specific requirements and conditions required for registration information that each company sets on its own.

[1960] "Registration information" refers to a series of data and information generated based on the conditions required by a company.

[1961] "Generation means" refers to a mechanism or method for automatically generating registration information based on conditions that vary from company to company.

[1962] "Storage means" refers to a function or system for storing the generated registration information in a database or the like.

[1963] "Receiving means" refers to a function for receiving information sent from a user or a server.

[1964] "Verification measures" refer to mechanisms or methods for verifying whether the registration information entered by the user matches the conditions set by the company.

[1965] "Transmission means" refers to a function for transmitting the generated registration information and verification results to the user or other systems.

[1966] "Emotion recognition means" refers to a mechanism or method for analyzing emotions from the user's facial expressions, voice, etc., and determining their current emotional state.

[1967] "Means for providing additional support" refers to functions or methods for providing appropriate assistance or guidance when the emotion recognition means determines that the user is experiencing difficulty or stress.

[1968] The system for implementing this invention includes the following main components: a server, a terminal, a generation AI, and an emotion recognition means. Below, we will explain how each component works together and processes data.

[1969] First, the server receives the registration conditions, which vary from company to company. These conditions are specific data fields, such as a customer code or department ID. The server then sends the received conditions to the generation AI.

[1970] The generation AI generates the necessary registration information based on the prompt sent from the server. The generated registration information is saved in a database and later provided to the user. An example of a prompt statement for the generation AI is, "The client code and department ID are required as registration conditions for Company X. Please generate the client code and department ID."

[1971] The terminal is a device for transmitting the registration information entered by the user to the server. This terminal may be a smartphone, tablet, or PC, and can transmit the information entered by the user to the server in real time. Furthermore, the terminal can transmit the user's facial expression and voice data to the emotion recognition means.

[1972] The verification method checks whether the entered registration information matches the company's set conditions. This includes a matching process using the generation AI. For example, if a user enters "Customer Code 12345" and "Department ID 67890," it verifies that this matches the generated information.

[1973] The emotion recognition means analyzes the user's facial expressions and voice data to determine their emotional state. If the user is confused or stressed, this information is sent to the server, and additional support is provided. For example, if the user is determined to be confused, a helpful guidance message may be displayed.

[1974] This not only enables efficient and accurate generation and confirmation of registration information, but also reduces stress and improves the user experience by providing appropriate support according to the user's emotional state.

[1975] Specific examples

[1976] For example, when a new employee of a certain company, "Company X," attempts to register in the system, the server requests the generation AI to provide the "customer code" and "department ID" as the registration conditions for Company X. The "customer code 12345" and "department ID 67890" generated by the generation AI are stored in the database. The user enters this information using a terminal, and the system verifies it. After confirming that the input matches, the user's facial expressions and voice are analyzed using emotion recognition means, and additional support is provided if necessary.

[1977] Example prompt sentence:

[1978] "Company X requires a customer code and department ID to register. Please generate a customer code and department ID."

[1979] In this way, the system of the invention can efficiently generate and verify registration information, and provide support according to the user's emotional state.

[1980] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1981] Step 1:

[1982] The server receives registration conditions, which vary from company to company. As input, the condition data provided by the company is required. This includes specific data fields such as customer code and department ID. The server receives this information and temporarily stores it.

[1983] Step 2:

[1984] The server sends the received conditions to the generation AI, which generates a prompt to generate the required registration information. The company's condition data is used as input. The generation AI generates registration information based on the provided conditions and returns specific registration information as output (e.g., "Customer code 12345" and "Department ID 67890").

[1985] Step 3:

[1986] The server saves the generated registration information in a database. The input is the registration information received from the generation AI. The output is that the registration information is saved accurately in the database.

[1987] Step 4:

[1988] A user accesses the system using a terminal and enters their registration information. As input, the user enters the registration information (e.g., "Customer code 12345" and "Department ID 67890") into the terminal. The terminal then sends this information to the server.

[1989] Step 5:

[1990] The server receives the user's input information and sends it to the generation AI for confirmation. The information entered by the user is used as input. The generation AI compares the received information with existing registration information and returns a result of whether or not it matches.

[1991] Step 6:

[1992] The server receives the verification result and notifies the user. As input, the verification result from the generation AI is used. As output, the user is provided with feedback of "match" or "mismatch."

[1993] Step 7:

[1994] The emotion recognition means analyzes the user's facial and voice data to determine their emotional state. The facial and voice data collected by the device is used as input. The analyzed user's emotional state (e.g., confusion, stress, etc.) is output.

[1995] Step 8:

[1996] If the emotion recognition means determines that the user's emotional state is unstable, the server provides additional support. The judgment data from the emotion recognition means is used as input. The server provides the user with friendly guidance messages and additional support, and as output, creates an environment where the user can input information with confidence.

[1997] 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.

[1998] 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.

[1999] 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.

[2000] 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.

[2001] 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.

[2002] 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.

[2003] 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).

[2004] 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.

[2005] 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."

[2006] 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.

[2007] 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).

[2008] 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.

[2009] 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.

[2010] 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.

[2011] 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.

[2012] 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.

[2013] 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 ty...

Claims

1. a means for receiving registration conditions, which may vary from company to company; a generating means for generating necessary registration information based on the conditions; means for storing and transmitting the generated registration information to the user; means for receiving registration information entered by a user; a verification means for verifying whether the input registration information matches the conditions; means for transmitting the verification result to a user; A system including:

2. 10. The system of claim 1, further comprising means for matching a plurality of data fields contained in the entered registration information with predefined conditions.

3. 2. The system of claim 1, wherein the verification means uses artificial intelligence to determine whether the entered registration information matches the generated registration information.

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