system

Abstract

Provide a system. A means for acquiring internal regulation data; A means for learning and analyzing the internal regulation data using a generating AI; A means for updating the analyzed internal regulation data to the latest state; means for accepting questions from users; means for generating a customized answer based on the question using the generation AI; means for providing the customized answer to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] In a company's business environment, internal regulations are frequently revised due to changes in relevant laws and policies of higher authorities. This makes it difficult to keep up to date with the latest regulations and respond appropriately. There is also a high risk of errors occurring due to overlooking changes in regulations or proceeding with work based on incorrect information. There is a need to mitigate these risks, efficiently manage internal regulations, and provide appropriate information. With conventional methods, managing regulations and providing the latest information is often done manually, which requires a lot of effort and time, posing major challenges. [Means for solving the problem]

[0005] The present invention provides a system including a means for acquiring internal regulation data, a means for learning and analyzing the internal regulation data using a generation AI, a means for updating the analyzed internal regulation data to the latest state, a means for accepting questions from users, a means for generating customized answers based on the questions using the generation AI, and a means for providing the customized answers to users. This enables efficient management of internal company regulations and quick provision of customized answers based on the latest information. Furthermore, by further including a means for managing the change history of the internal regulation data and recording differences from past versions, and a means for converting answers provided to users into a user-friendly format, the system can also improve the user experience.

[0006] "Internal regulations data" refers to information such as regulations, procedures, policies, and guidelines used within a company.

[0007] "Generative AI" is a type of artificial intelligence, specifically a system that has the ability to analyze and learn from large amounts of text data using natural language processing.

[0008] "User" refers to a person who uses the system or a person in charge within a company.

[0009] "Database" refers to an electronic storage system for organizing, storing, and managing various data, including internal regulation data.

[0010] "Means for accepting questions" refers to the interface or function for receiving and processing inquiries or questions from users.

[0011] "Means of analysis" refers to the processes and functions used to analyze internal regulatory data using generative AI and extract important information and changes.

[0012] "Means for updating to the latest state" refers to the processes and functions for keeping the content of internal regulation data up to date when it is changed.

[0013] "Customized answers" refers to the results of a generative AI creating answers to user questions that are appropriate for individual situations based on internally defined data.

[0014] "Means for providing" refers to an interface or function for notifying and presenting the generated answer to the user.

[0015] "Means for managing change history" refers to the processes and functions for keeping records of past and current versions of internal regulation data and managing the differences between them.

[0016] "User-friendly format" refers to a format or presentation that presents information or answers in a way that is easy for users to understand and use. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system for improving the efficiency of internal company regulation management and information provision, and aims to acquire, learn, and update internal regulation data and provide appropriate answers to user questions. A specific embodiment of this system is described below.

[0039] System configuration

[0040] 1. Means of obtaining internal regulation data:

[0041] The terminal has the function of uploading new regulation documents. The user sends modified or new internal regulation documents from the terminal to the server.

[0042] Example: A user drags and drops the document for a new contract onto the device to upload it.

[0043] 2. How generative AI can learn and analyze data:

[0044] The server receives the uploaded specification document and provides it to the generation AI.

[0045] Generative AI tokenizes prescribed documents and extracts important keywords and phrases.

[0046] Example: The server identifies specific compliance regulation changes for new regulatory documents and updates the model.

[0047] 3. How to update:

[0048] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[0049] Manage change history and save differences from previous versions.

[0050] Example: The server updates its internal regulations database to register amendments based on new legislation.

[0051] 4. How to receive user questions:

[0052] The terminal has an interface that accepts questions input from the user.

[0053] Example: A user uses a terminal interface and types the question, "How do I keep my receipts up to date?"

[0054] 5. A way to generate customized answers based on questions:

[0055] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[0056] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[0057] Example: The generation AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[0058] 6. Means of providing users with customized answers:

[0059] The server sends the generated response to the terminal.

[0060] The terminal displays the answer to the user.

[0061] Example: The answer created by the generation AI will be displayed on the user's device, allowing the user to check it.

[0062] Specific examples

[0063] Scenario: How to submit a contract for approval based on the new regulations

[0064] 1. A user uploads a new policy document as a document file created by the department manager.

[0065] 2. The terminal sends this new specification document to the server and registers the data.

[0066] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[0067] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[0068] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[0069] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[0070] 7. The device sends this question to the server, completing the question acceptance.

[0071] 8. The server receives the question and asks the generation AI to process it.

[0072] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[0073] 10. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[0074] 11. The device displays to the user, "To apply for approval under the new regulations, please follow these steps: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department."

[0075] As described above, the present invention is a system that improves the efficiency of in-company regulation management and information provision, and always provides information based on the latest regulations, thereby reducing operational errors within a company and supporting smooth business operations.

[0076] The processing flow will be explained below.

[0077] Step 1:

[0078] A user creates new internal regulations documents, including changes to legislation and revisions to company policies.

[0079] Step 2:

[0080] The terminal receives the new prescribed document file from the user and uploads it to the server. Through the terminal interface, the user selects and sends the changed prescribed document.

[0081] Step 3:

[0082] The server receives the uploaded document, saves it in the appropriate folder, and waits for the database to be updated.

[0083] Step 4:

[0084] The server provides the uploaded document to the generation AI, which then analyzes the document and begins tokenization.

[0085] Step 5:

[0086] Generative AI tokenizes internal regulatory documents and extracts key keywords and phrases, which involves semantic analysis of the content using natural language processing techniques.

[0087] Step 6:

[0088] The generative AI uses the extracted data to learn about changes in internal regulations and update the model, thereby building an updated model that reflects the new regulations.

[0089] Step 7:

[0090] The server updates the internal database with new analysis results, ensuring that the database is always up to date. It also manages the history of changes to the regulation data and records the differences between the latest and previous versions.

[0091] Step 8:

[0092] The user types a policy question into the terminal, for example, "How do I keep my receipts up to date?"

[0093] Step 9:

[0094] The device sends the user's question to the server, which includes properly formatting the question and forwarding it to the server.

[0095] Step 10:

[0096] The server provides the user's question to the AI ​​generator and requests it to generate an answer based on that question. The server analyzes the question and provides the AI ​​with the necessary data.

[0097] Step 11:

[0098] The generation AI refers to an internal database of regulations and generates the optimal answer. The generation AI creates an accurate answer to the question based on the latest regulation data.

[0099] Step 12:

[0100] The AI ​​generates a response and returns it to the server, which processes it and converts it into a format that is easy for the user to understand.

[0101] Step 13:

[0102] The server sends the generated answer to the terminal and notifies the user, thereby allowing the user to obtain the latest provision information.

[0103] Step 14:

[0104] The terminal displays the response received from the server to the user, who can then confirm that "the latest receipt storage method is to store receipts as electronic data for five years."

[0105] The above are the specific processing steps of this system.

[0106] Example 1

[0107] 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."

[0108] Internal company regulation management and information provision require the management and updating of huge amounts of document data, tracking of change history, and prompt and appropriate responses to user questions. This process is often done manually, prone to errors, and takes time and effort. Another issue is the difficulty of providing the latest information in a timely manner. The goal is to provide a system that solves these issues and enables efficient and accurate regulation management and information provision.

[0109] 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.

[0110] In this invention, the server includes a means for acquiring internal regulation data, a means for learning and analyzing the internal regulation data using a generative AI model, and a means for updating the analyzed internal regulation data to the latest state, thereby improving the efficiency of internal regulation management and information provision within the company and enabling the prompt provision of the latest information.

[0111] "Internal regulation data" is document information such as rules and guidelines used within a company.

[0112] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze and learn from text data.

[0113] "Learning" is the process by which a generative AI model analyzes internally defined data and extracts important keywords and phrases.

[0114] "Analysis" refers to the process in which the generative AI model performs a detailed analysis of the content of the internally defined data and extracts semantic information and patterns.

[0115] "Updating" refers to the act of reflecting new analysis results in the internal regulations database and keeping the data up to date.

[0116] A "question" is text that a user enters into the system requesting information about internal regulations.

[0117] A "customized answer" is an answer that a generative AI model creates based on a user's question and is tailored to specific situations and conditions.

[0118] A "user-friendly format" is a presentation of information that is formatted to be easy for users to understand and use.

[0119] The present invention is a system for improving the efficiency of internal company regulation management and information provision. The system aims to provide the latest internal regulation information by acquiring internal regulation data and using a generative AI model to learn and analyze it. Specific embodiments for implementing the present invention are described below.

[0120] The main components of this system are a server, a terminal, and a generative AI model.

[0121] 1. Acquisition of internal regulation data:

[0122] The user uploads a new contract document using the device, for example, by dragging and dropping the PDF file of the new contract into a designated folder on the device.

[0123] The terminal sends the new specified document to the server. The terminal detects the addition of the document and automatically sends an HTTP request to the server to transfer the file.

[0124] 2. Data training and analysis:

[0125] The server receives the uploaded specification document and provides it to the generative AI model. The server saves the received file in a specific directory, and after saving, sends the file path to the generative AI model endpoint using socket communication.

[0126] The generative AI model tokenizes the specified document and extracts important keywords and phrases. Specifically, the generative AI model tokenizes the document (divides it into words and phrases) and extracts important keywords using techniques such as TF-IDF (Term Frequency-Inverse Document Frequency).

[0127] 3. Update the internal regulations database:

[0128] The server reflects the analysis results in an internal regulation database, always maintaining the latest regulation information. The server saves the keyword groups received from the generative AI model in the database and records differences from previous versions in a log.

[0129] The server manages the change history and saves the differences from previous versions. A trigger is set in the database, and when new data is added, the differences are automatically recorded in the change history table.

[0130] 4. Accepting user questions:

[0131] The user uses the terminal interface to enter a question. The user uses the web application interface to enter "How do I store my latest receipt?" in the text box and clicks the submit button.

[0132] The device sends this question to the server and completes the question reception. The device serializes the entered question into JSON format and sends an HTTP POST request to the API endpoint.

[0133] 5. Generate customized answers:

[0134] The server provides the user's question to the generative AI model and requests it to generate an appropriate answer. The server preprocesses the received question with an NLU (Natural Language Understanding) module and sends the query to the generative AI model.

[0135] The generative AI model refers to an internal database of regulations and creates answers based on the latest information. The generative AI model analyzes the intent of the question and searches for relevant information from the internal database to generate an answer.

[0136] 6. Providing answers to users:

[0137] The server sends the generated answer to the device. The server formats the answer received from the generative AI model into a user-friendly format and sends it to the device in JSON format.

[0138] The terminal displays the answer to the user. The terminal analyzes the received answer and displays on the web interface, "The latest method for storing receipts is to store them as electronic data for five years."

[0139] Examples:

[0140] As a concrete example, the following shows a scene where a user checks how to submit a contract for approval based on the new regulations.

[0141] 1. A user uploads a new policy document as a document file created by the department manager.

[0142] 2. The terminal sends this new specification document to the server and registers the data.

[0143] 3. The server saves the file and immediately submits it to the analysis process of the generative AI model.

[0144] 4. A generative AI model reads the file contents and extracts keywords and important changes through natural language processing.

[0145] 5. Once the generative AI model has completed the learning process, the server updates its internal regulatory database with this new information.

[0146] 6. The user enters a question on the terminal asking "How to apply for approval of a contract based on the new regulations" and submits it.

[0147] 7. The device sends this question to the server, completing the question acceptance.

[0148] 8. The server receives the question and requests the generative AI model to process it.

[0149] 9. The generative AI model references the latest prescribed data and generates an appropriate answer.

[0150] 10. The server receives the answer from the generative AI model and sends it to the device in a user-friendly format.

[0151] 11. The device displays to the user, "To apply for approval under the new regulations, please follow these steps: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department."

[0152] This specific example of the system will improve the efficiency of internal company regulation management and information provision, making it possible to always carry out business operations based on the latest information.

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1:

[0155] The user uploads a new regulation document as a document file created by the department manager.

[0156] Input: New regulation document (e.g. PDF file)

[0157] Output: Uploaded regulatory document

[0158] Specific operation: The user drags and drops a new rule document into the designated folder on the device, which activates the device's upload function and sends the file to the server.

[0159] Step 2:

[0160] The terminal sends this new provision document to the server.

[0161] Input: User uploaded regulatory document

[0162] Output: The document sent to the server

[0163] Specific operation: The device detects that a document has been added to the specified folder and automatically generates an HTTP request to transfer the file to the server.

[0164] Step 3:

[0165] The server receives the uploaded specification document and provides it to the generative AI model.

[0166] Input: Regulation document sent from the terminal

[0167] Output: Document data provided to the generative AI model

[0168] Specific operation: The server saves the received file in a specific directory, and after confirming that the file has been saved, it notifies the endpoint of the generative AI model of the file path using socket communication.

[0169] Step 4:

[0170] A generative AI model tokenizes the prescribed document and extracts key keywords and phrases.

[0171] Input: A prescribed document provided to a generative AI model

[0172] Output: Extracted keywords and phrases

[0173] How it works: The generative AI model tokenizes (divides) the specified document into words and phrases, and then analyzes and extracts important keywords and phrases using techniques such as TF-IDF.

[0174] Step 5:

[0175] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information.

[0176] Input: Analysis results from a generative AI model

[0177] Output: Updated internal regulations database

[0178] Specific operation: The server stores the keywords received from the generative AI model in a database and records the differences with previous versions in a log. A trigger is set in the database, and when new data is added, the differences are automatically recorded in a change history table.

[0179] Step 6:

[0180] The user uses the terminal interface to input a question.

[0181] Input: User question (e.g., "How do I keep my most recent receipts?")

[0182] Output: Question data sent from the terminal to the server

[0183] Specific operation: A user enters a question into the text box in the web application interface and clicks the submit button. The device serializes the entered question into JSON format and sends it as an HTTP POST request to the API endpoint.

[0184] Step 7:

[0185] The terminal sends this question to the server, completing the question reception.

[0186] Input: Send request from user

[0187] Output: Query data arriving at the server

[0188] Specific operation: The device receives the user's question and sends the serialized data to the server, which receives the request and passes the question to a process for analysis.

[0189] Step 8:

[0190] The server provides the user's question to the generative AI model, requesting it to generate an appropriate answer.

[0191] Input: The user's question that arrives at the server

[0192] Output: Question data sent to the generative AI model

[0193] Specific operation: The server preprocesses the received question using the NLU module and sends a query to the generative AI model endpoint.

[0194] Step 9:

[0195] The generative AI model references an internal regulatory database to create answers based on the most up-to-date information.

[0196] Input: Question data sent from the server

[0197] Output: Generated response data

[0198] How it works: The generative AI model analyzes the intent of the question, searches for relevant information from an internal database, and generates an answer.

[0199] Step 10:

[0200] The server receives the answer from the generative AI model, formats it in a user-friendly format, and sends it to the device.

[0201] Input: Answer data generated by the generative AI model

[0202] Output: Formatted response data sent to the device

[0203] Specific operation: The server formats the answer received from the generative AI model and sends it to the terminal as JSON data in a user-friendly format.

[0204] Step 11:

[0205] The terminal displays the answer to the user.

[0206] Input: Response data sent from the server

[0207] Output: The answer that is displayed to the user

[0208] Specific operation: The device analyzes the received response and displays it on the web interface, allowing the user to confirm and obtain the required information.

[0209] (Application example 1)

[0210] 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."

[0211] By streamlining internal regulation management and information provision and introducing a system that always provides information based on the latest regulations, it is necessary to reduce operational errors that may occur within the company and support smooth business operations.In addition, it is also necessary to quickly communicate the latest regulations and changes in safety standards to robots operated in factories and apply and implement actions based on them.

[0212] 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.

[0213] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from users, means for generating customized answers based on the questions using a generation AI, means for providing the customized answers to the users, means for applying the contents of the uploaded regulation document to the robot, and means for adapting the behavior of the robot to the latest internal regulations. This not only enables efficient management of regulations and information provision within the company, but also makes it possible to adapt the behavior of robots in the factory based on the latest regulations.

[0214] "Internal regulations data" refers to data containing information about rules and regulations that must be observed within a company.

[0215] "Generative AI" is a system that uses artificial intelligence technology to analyze and learn from data and generate appropriate answers and information.

[0216] "Analyzing" refers to the process of examining the contents of data in detail, understanding its structure, and extracting its meaning and importance.

[0217] "Updating" means updating the information in a database or system to conform to current regulations and standards, and keeping the information up to date at all times.

[0218] The "means for accepting questions" refers to an interface that receives questions from users in an input format and obtains the content of the questions in a form that can be processed by the system.

[0219] "Generating customized answers" means using generative AI to create appropriate answers to user questions based on specific conditions and context.

[0220] "Applying the contents of the uploaded regulation document to the robot" means that the new regulation document uploaded by the administrator is reflected in the robot's behavior.

[0221] "Adapting the robot's behavior to the latest internal regulations" means that the robot understands the latest regulations and safety standards and performs actions based on them.

[0222] The present invention provides a system that improves the efficiency of internal company regulation management and information provision, and also quickly reflects the latest regulations and safety standards in robots operated in factories. Specific embodiments are described below.

[0223] System configuration

[0224] This system consists of the following components:

[0225] 1. Upload regulatory documents

[0226] The user (factory manager) uploads a new regulation document by dragging and dropping it onto the terminal, which then sends the document to the server.

[0227] Example: An administrator drags and drops a new safety regulations document onto a terminal and sends it to the server.

[0228] 2. Analysis by generative AI

[0229] The server provides the received specified document to the generation AI, which then analyzes its contents. The generation AI uses, for example, GPT-3 (registered trademark) to tokenize the document and extract important keywords and phrases.

[0230] Example: The server receives a regulation document and passes it to a generator AI, which identifies changes to specific safety regulations.

[0231] 3. Updating the internal regulations database

[0232] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[0233] Example: The server updates the database with the new policy and records the differences between the old version.

[0234] 4. Robot Applications

[0235] The server applies the latest regulatory document to the robots in the factory, and uses the robot control library to adapt the robot's behavior to the latest regulations.

[0236] Example: The server updates the robot's operating instructions based on new rules, and the robot acts according to the new rules.

[0237] 5. Question Response System

[0238] The user's question is sent from the device to the server, which then asks the AI ​​to process the question. The AI ​​then refers to an internal database of rules and generates an appropriate answer.

[0239] Example: An administrator types the question "What are the latest safety regulations?" into a terminal, and the generating AI responds, "The latest safety regulations require the wearing of protective equipment."

[0240] Hardware and software used

[0241] Hardware: Terminals (computers used by administrators), servers, factory robots

[0242] Software: Python scripts, generative AI models (e.g., GPT-3), databases (e.g., SQLite), robot control libraries

[0243] Specific examples

[0244] Example prompt sentence:

[0245] "We've uploaded a new safety document. Please update your robot's behavior accordingly."

[0246] "What are the latest safety regulations?"

[0247] This system will improve the efficiency of internal company regulation management and information provision, and will also enable factory robots to adapt their behavior to the latest internal regulations. In addition, users can input questions, and the generative AI will provide appropriate answers based on the latest regulations.

[0248] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0249] Step 1:

[0250] The user uploads a new rule document by dragging and dropping it onto the device, which then sends the document to the server.

[0251] Specific operation: Using the terminal's file upload function, select a document from a directory specified by the user and transfer it to the server.

[0252] Input: Specified document file

[0253] Output: Specified document data transferred to the server

[0254] Step 2:

[0255] The server provides the received specification document to the generation AI, which then analyzes its contents.

[0256] Specific operation: After the server receives the file, it inputs the document data into a generative AI model (e.g., GPT-3) and performs natural language processing such as tokenization and key keyword extraction.

[0257] Input: Regulatory document data

[0258] Output: Parsed keywords and changes

[0259] Step 3:

[0260] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[0261] Specific operation: The server connects to a database (e.g., SQLite), adds the parsed content of the regulations as a new record, and simultaneously saves the difference information between the old regulations and the changes.

[0262] Input: Parsed keywords and changes

[0263] Output: Updated internal regulations database

[0264] Step 4:

[0265] The server applies the latest regulatory document to the robots in the factory, and uses the robot control library to adapt the robot's behavior to the latest regulations.

[0266] Specific operation: The server sends operation instructions based on the new regulations to the factory robot via the robot control library (e.g., robot control API).

[0267] Input: Updated internal regulations database contents

[0268] Output: Robot operation instructions based on the latest regulations

[0269] Step 5:

[0270] A question from the user is sent from the terminal to the server, and the server asks the generating AI to process the question.

[0271] How it works: The user enters a question into the device interface and sends it to the server, which passes it to the generation AI and receives an appropriate answer.

[0272] Input: User question (e.g., "What are the latest safety regulations?")

[0273] Output: A suitable answer from the generative AI

[0274] Step 6:

[0275] The generation AI refers to an internal database of regulations and generates appropriate answers.

[0276] How it works: The generative AI queries an internal rules database to retrieve the latest rules, then generates a textual answer appropriate to the question.

[0277] Input: User questions, internal regulations database

[0278] Output: Correct answer text

[0279] Step 7:

[0280] The server provides the user with the answer from the generated AI.

[0281] Specific operation: The server sends the answer received from the generation AI to the device, which then displays it to the user.

[0282] Input: Answer text from the generation AI

[0283] Output: The answer displayed on the user's terminal

[0284] 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.

[0285] The present invention is a system for streamlining internal company policy management and information provision, and also combines it with an emotion engine that recognizes user emotions. This system acquires, learns, and updates internal policy data, and not only provides appropriate answers to user questions but also customizes answers according to the user's emotions. A specific embodiment of this system is described below.

[0286] System configuration

[0287] 1. Means of obtaining internal regulation data:

[0288] The terminal has the function of uploading new regulation documents. The user sends modified or new internal regulation documents from the terminal to the server.

[0289] Example: A user drags and drops the document for a new contract onto the device to upload it.

[0290] 2. How generative AI can learn and analyze data:

[0291] The server receives the uploaded specification document and provides it to the generation AI.

[0292] Generative AI tokenizes prescribed documents and extracts important keywords and phrases.

[0293] Example: The server identifies specific compliance regulation changes for new regulatory documents and updates the model.

[0294] 3. How to update:

[0295] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[0296] Manage change history and save differences from previous versions.

[0297] Example: The server updates its internal regulations database to register amendments based on new legislation.

[0298] 4. How to receive user questions:

[0299] The terminal has an interface that accepts questions input from the user.

[0300] Example: A user uses a terminal interface and types the question, "How do I keep my receipts up to date?"

[0301] 5. A way to generate customized answers based on questions:

[0302] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[0303] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[0304] Example: The generation AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[0305] 6. Means of providing users with customized answers:

[0306] The server sends the generated response to the terminal.

[0307] The terminal displays the answer to the user.

[0308] Example: The answer created by the generation AI will be displayed on the user's device, allowing the user to check it.

[0309] 7. Emotion engine recognizes user emotions:

[0310] The terminal receives the user's text or voice input and provides it to the emotion engine.

[0311] An emotion engine analyzes the user's input and recognizes their emotional state.

[0312] Example: If a user types a question while in an anxious state, the emotion engine will detect the "anxious" state.

[0313] 8. Tailoring customized responses based on emotional state:

[0314] The server adjusts the tone and content of the answers created by the generative AI based on the emotional data obtained from the emotion engine.

[0315] For example, if the emotion engine detects a user's anxiety, the generative AI will generate a response that includes clear explanations and encouraging words.

[0316] Specific examples

[0317] Scenario: Confirming how to submit a contract for approval based on new regulations and recognizing user sentiment

[0318] 1. A user uploads a new policy document as a document file created by the department manager.

[0319] 2. The terminal sends this new specification document to the server and registers the data.

[0320] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[0321] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[0322] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[0323] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[0324] 7. The device sends this question to the server, completing the question acceptance.

[0325] 8. The server receives the question and asks the generation AI to process it.

[0326] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[0327] 10. The emotion engine analyzes the user's input and recognizes their emotional state.

[0328] 11. The server adjusts the generative AI's responses based on information from the emotion engine.

[0329] 12. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[0330] 13. The terminal displays the following message: "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. If you have any questions, please feel free to ask."

[0331] The emotion engine recognizes the user's emotions and provides customized responses based on those emotions, allowing the user to receive a more friendly response.In this way, the present invention is a system that streamlines internal company policy management and information provision, improving the user experience.

[0332] The processing flow will be explained below.

[0333] Step 1:

[0334] A user creates new internal regulations documents, including changes to legislation and revisions to company policies.

[0335] Step 2:

[0336] The terminal receives the new document file from the user and uploads it to the server. The user selects the document and sends it through the terminal interface.

[0337] Step 3:

[0338] The server receives the uploaded prescribed document and stores the document file in a database.

[0339] Step 4:

[0340] The server provides the stored prescribed data to the generation AI and instructs it to begin the analysis process.

[0341] Step 5:

[0342] Generative AI tokenizes documents and extracts important keywords and phrases, then uses natural language processing techniques to analyze the content.

[0343] Step 6:

[0344] The generative AI updates the internal regulation model based on the extracted data to reflect the new regulations.

[0345] Step 7:

[0346] The server updates the internal regulations database with the latest analysis results, keeping it up to date. It also manages the change history and saves the differences between previous versions.

[0347] Step 8:

[0348] The user inputs a question from the terminal. For example, "Please tell me how to submit a request for approval for a new contract."

[0349] Step 9:

[0350] The device sends the user's question to the server, which then formats the question appropriately and sends it.

[0351] Step 10:

[0352] The server receives the question, provides the question to the generation AI, and requests it to generate an answer.

[0353] Step 11:

[0354] The generative AI refers to an internal database of regulations and generates the best answer to the question.

[0355] Step 12:

[0356] The terminal provides the user's input data to the emotion engine, which passes the user's text input or voice input to the emotion engine.

[0357] Step 13:

[0358] The emotion engine analyzes user input data to recognize emotional states, including text and speech analysis.

[0359] Step 14:

[0360] The server adjusts the tone and content of the AI's responses based on the emotional data obtained from the emotion engine. For example, if the server determines that the user is nervous, it will add more friendly expressions.

[0361] Step 15:

[0362] The server then sends the final adjusted response to the terminal.

[0363] Step 16:

[0364] The device will then display the adjusted response to the user, for example, "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. Also, please feel free to ask us if you have any questions."

[0365] The above is a specific processing flow of the system of the present invention, which improves the efficiency of internal company policy management and provides customized responses according to the user's emotional state.

[0366] Example 2

[0367] 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."

[0368] In modern companies, managing internal regulations and providing information is important, but doing so efficiently is difficult. In particular, managing the history of changes to regulations and providing quick and accurate answers to user questions are required. Furthermore, there are very few systems that can respond with consideration for user feelings. The purpose of this invention is to address these challenges and provide a system that streamlines internal regulation management and information provision within a company and improves the user experience.

[0369] 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.

[0370] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from a user, means for generating a customized answer based on the question using the generation AI, means for providing the customized answer to the user, means including an emotion engine for analyzing the emotional state of the user, and means for adjusting the answer generated by the generation AI based on emotion data obtained by the emotion engine. This not only enables the user to efficiently acquire the latest internal regulation information, but also enables customized answers that take emotions into consideration.

[0371] "Internal regulation data" refers to documents and information such as rules, regulations, and procedures established within a company.

[0372] "Generative AI" refers to artificial intelligence technology that uses large amounts of data to perform natural language processing and generate and analyze text.

[0373] An "emotion engine" refers to software or a system that analyzes the emotional state of a user's input data and classifies it into emotional categories such as positive, negative, or neutral.

[0374] "Server" refers to a computer system that processes, stores, distributes, etc. data.

[0375] "Terminal" refers to a device that allows a user to input or output information, such as a computer, smartphone, or tablet.

[0376] "User" refers to a person or position who uses this system to enter questions and check answers.

[0377] "Database" refers to a system for efficiently storing, searching, retrieving, and managing data.

[0378] "Tokenization" refers to the process of dividing sentences or text into small units (tokens) for analysis.

[0379] The present invention is a system for streamlining internal company policy management and information provision, and also combines it with an emotion engine that recognizes user emotions. This system acquires, learns, and updates internal policy data, and not only provides appropriate answers to user questions but also customizes answers according to the user's emotions. A specific embodiment of this system is described below.

[0380] System configuration

[0381] 1. Means of obtaining internal regulation data:

[0382] The user uploads a new regulation document from the terminal to the server as a document file created by the department manager.

[0383] The terminal has the function of receiving new regulation documents uploaded and sending them to the server.

[0384] Examples of software used: Web browser, file upload tool.

[0385] Example: A user drags and drops the document for a new contract from their device to upload it to the server.

[0386] 2. Data learning and analysis methods:

[0387] The server receives the uploaded internal regulation data and provides it to the generative AI model.

[0388] The generative AI tokenizes the prescribed document and processes it to extract important keywords and phrases.

[0389] Example software used: Generative AI models (e.g., OpenAI® GPT-4®).

[0390] Example: The server identifies and models specific compliance regulation changes for new regulatory documents.

[0391] 3. How to update data:

[0392] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[0393] Examples of software used: Database management system (e.g., MySQL (registered trademark)).

[0394] Example: A server updates an internal regulations database to register amendments based on new legislation.

[0395] 4. How to receive user questions:

[0396] An interface is used for users to input and submit questions at their terminals.

[0397] The terminal receives the query and sends it to the server.

[0398] Examples of software used: web forms, chatbot interfaces.

[0399] Example: A user uses a terminal interface to input the question, "How do I keep my most recent receipts?"

[0400] 5. How to generate customized answers based on questions:

[0401] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[0402] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[0403] Examples of software used: Generative AI models (e.g., OpenAI GPT-4).

[0404] Example: The generating AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[0405] 6. Means of providing responses:

[0406] The server sends the generated response to the terminal.

[0407] The terminal displays the answer to the user.

[0408] Examples of software used: web browsers, notification systems.

[0409] Example: The answer created by the generation AI is displayed on the user's device, allowing the user to check it.

[0410] 7. User Emotion Recognition Method:

[0411] The terminal receives the user's text or voice input and provides it to the emotion engine.

[0412] An emotion engine analyzes the user's input and recognizes their emotional state.

[0413] Examples of software used: sentiment analysis engine (e.g., IBM Watson® Tone Analyzer).

[0414] Example: If a user types a question while in an anxious state, the emotion engine will detect the "anxious" state.

[0415] 8. Tailoring your customized answers:

[0416] The server adjusts the tone and content of the answers created by the generative AI based on the emotional data obtained from the emotion engine.

[0417] Generative AI generates answers that are easier for users to understand and provide a sense of security.

[0418] Examples of software used: generative AI models, emotion engines.

[0419] Example: If the emotion engine detects a user's anxiety, the generative AI will generate a response that "includes clear explanations and encouraging words."

[0420] Specific scenarios and prompt examples

[0421] scenario

[0422] Confirmation of how to submit contracts for approval based on the new regulations and recognition of user sentiment.

[0423] 1. The user uploads a new regulation document from the terminal to the server as a document file created by the department manager.

[0424] 2. The terminal sends this new specification document to the server and registers the data.

[0425] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[0426] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[0427] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[0428] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[0429] 7. The device sends this question to the server, completing the question acceptance.

[0430] 8. The server receives the question and asks the generation AI to process it.

[0431] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[0432] 10. The emotion engine analyzes the user's input and recognizes their emotional state.

[0433] 11. The server adjusts the generative AI's responses based on information from the emotion engine.

[0434] 12. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[0435] 13. The terminal displays the following message: "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. If you have any questions, please feel free to ask."

[0436] Prompt Sentence Examples

[0437] "What are the steps to uploading a new contract document?"

[0438] "What's the latest way to store receipts?"

[0439] "Please tell me how to submit a contract for approval based on the new regulations."

[0440] In this way, this system allows users to acquire information more efficiently and deepen their understanding. In addition, the emotion engine enables communication that takes into consideration the user's emotions.

[0441] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0442] Step 1:

[0443] The user uploads a new internal regulations document from the terminal to the server.

[0444] Input: The default document file selected by the user on the terminal

[0445] Specific operation: The user uses the upload function of the web browser to drag and drop a new regulation document and presses the upload button.

[0446] Output: The prescribed document is sent to the server and stored.

[0447] Step 2:

[0448] The terminal sends a new provision document to the server, and the server receives it.

[0449] Input: Specified document file sent from the terminal

[0450] Specific operation: The file is sent to the server while displaying a notification to the user that the upload is complete.

[0451] Output: The server receives and stores the document.

[0452] Step 3:

[0453] The server provides the uploaded prescribed document to the generative AI model and begins analysis.

[0454] Input: Standard document file stored on the server

[0455] How it works: The server passes the prescribed document to the Generator AI, which starts the analysis process. The Generator AI tokenizes the prescribed document and extracts important keywords and phrases.

[0456] Output: Key keywords and phrases as analysis results.

[0457] Step 4:

[0458] The generating AI reflects the analysis results in the internal regulations database.

[0459] Input: Keywords and phrases extracted by the generation AI

[0460] Specific operation: The generating AI adds keywords and phrases to the database, and the server updates the database.

[0461] Output: Updated internal regulations database.

[0462] Step 5:

[0463] The user types a question into the terminal, which then sends it to the server.

[0464] Input: The question typed by the user into the device interface

[0465] Specific operation: The user enters a question into the terminal interface and presses the send button.

[0466] Output: The terminal sends a query to the server, which receives it.

[0467] Step 6:

[0468] The server provides the received question to the generation AI, which generates an appropriate answer.

[0469] Input: User's question received by the server

[0470] Specific operation: The server sends the question to the generation AI, which then refers to an internal database of specifications and generates the optimal answer.

[0471] Output: The answer provided by the generation AI.

[0472] Step 7:

[0473] The server provides the response text from the generation AI to the emotion engine to recognize the user's emotional state.

[0474] Input: Answers provided by the generation AI, user input

[0475] Specific operation: The server sends the user's input to the emotion engine, which analyzes and recognizes the emotional state.

[0476] Output: User's emotional state data (e.g., "anxious").

[0477] Step 8:

[0478] The server adjusts the generative AI's responses based on the emotional data.

[0479] Input: User's emotional state data

[0480] How it works: The server provides emotional data to the generation AI, which then adjusts the tone and content of the response.

[0481] Output: The adjusted answer.

[0482] Step 9:

[0483] The server sends the adjusted response text to the terminal, which displays it to the user.

[0484] Input: Adjusted answer

[0485] Specific operation: The server sends the answer to the terminal, which displays it on the user's screen.

[0486] Output: The answer displayed on the user's device screen.

[0487] (Application example 2)

[0488] 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."

[0489] In modern corporate operations, managing internal regulations and providing information to employees are extremely important issues. However, not only is updating regulations and responding to questions labor-intensive, but many systems also lack the ability to respond to employees' emotional states. This can lead to anxiety and confusion among employees, affecting their productivity and satisfaction. Therefore, the objective of the present invention is to provide a system that improves the efficiency of managing internal regulations and providing information in corporate operations, and provides appropriate responses in response to employees' emotions.

[0490] 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.

[0491] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from users, means for generating customized answers based on the questions using the generation AI, means for recognizing the emotional state of the user using an emotion engine that recognizes the user's emotions, means for adjusting the customized answers based on the emotion engine, and means for providing the customized answers to users. This makes it possible to streamline regulation management within a company, provide appropriate answers to employee questions immediately, and respond in consideration of the user's emotions.

[0492] "Internal regulation data" refers to data such as rules, procedures, policies, and guidelines established within a company or organization.

[0493] "Generative AI" is a system that uses artificial intelligence techniques to learn and analyze data, using methods such as natural language processing.

[0494] A "server" is a computer system that stores, manages, and processes data, and processes requests from clients over a network.

[0495] The "question receiving means" is an interface that receives questions from users, and is input by the users using the terminal.

[0496] A "customized answer" is a specific answer that the AI ​​generates in response to a user's question based on internally specified data, and is tailored to the user's needs.

[0497] An "emotion engine" is a system that analyzes a user's text and voice input and recognizes their emotional state.

[0498] An "emotional state recognizer" is a process that uses an emotion engine to recognize a user's emotional state.

[0499] The "answer adjustment means" is a process of adjusting the answers created by the generation AI based on the emotion engine to suit the user's emotional state.

[0500] A "user-friendly format" is a format that is designed to be easy for users to understand and use.

[0501] The present invention is a system for improving the efficiency of corporate policy management and information provision, and is combined with an emotion engine that recognizes user emotions. Hereinafter, embodiments of the present invention will be described in detail.

[0502] System Configuration

[0503] The system of the present invention consists of the following major components:

[0504] 1. Server:

[0505] Internal regulation data acquisition means: has the function of receiving new regulation documents from the terminal. The user can send modified or new internal regulation documents from the terminal to the server.

[0506] Learning and analysis method using generative AI: The server provides the received prescribed document to the generative AI model, which tokenizes the prescribed document and extracts important keywords and phrases.

[0507] How to update to the latest version: The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[0508] Question receiving means: A question input from a user is received. For example, the user uses the terminal interface to input a question such as "How do I store the latest receipts?"

[0509] Customized answer generation: The generative AI model references an internal database of predefined answers to generate appropriate answers based on the question.

[0510] Emotion recognition means: An emotion engine is used to analyze the user's input and recognize their emotional state. For example, if a user enters a question while in an anxious state, the emotion engine will detect the "anxious" state.

[0511] Response adjustment measures: Adjust the tone and content of responses created by the generative AI model based on the emotion engine.

[0512] User provision means: The final generated answer is sent to the terminal and displayed to the user.

[0513] Specific examples

[0514] The following scenario is a specific example of server processing:

[0515] Scenario: Confirming how to submit a contract for approval based on new regulations and recognizing user sentiment

[0516] 1. The user drags and drops the new regulation document created by the department manager onto the terminal to upload it. The terminal then sends the document to the server.

[0517] 2. The server saves the file and immediately sends it to the generative AI analysis process, where the generative AI model uses natural language processing to extract keywords and important changes.

[0518] 3. The server updates the internal regulations database with new information, maintaining the latest regulations at all times.

[0519] 4. The user inputs a question from the terminal asking, "How do I apply for approval for a contract based on the new regulations?" The terminal then sends this question to the server.

[0520] 5. The server receives the question and requests the generative AI model to process it. The generative AI model references the latest prescribed data and generates an appropriate answer.

[0521] 6. The emotion engine analyzes the user's input and recognizes their emotional state. For example, if a user types, "This new rule is very complicated and I'm worried," the state of anxiety will be detected.

[0522] 7. The server adjusts the generative AI's response based on the information from the emotion engine, for example adding reassuring words such as "There's no need to worry. All the information has already been verified."

[0523] 8. The server sends the final answer to the terminal and displays it to the user.

[0524] Prompt Sentence Examples

[0525] Below are some example prompts to input to the generative AI model:

[0526] Please explain the new safety guidelines in simple terms.

[0527] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0528] Step 1:

[0529] The user drags and drops a new regulation document created by the department manager onto the terminal to upload it. The terminal then sends the document to the server, along with the document metadata (author, date, version, etc.).

[0530] Step 2:

[0531] The server receives the document and stores it in the file system. The server then prepares the document for injecting into the generative AI model. This preparation includes tokenizing the document and pre-processing it (e.g., removing unnecessary formatting).

[0532] Step 3:

[0533] The server uses generative AI to tokenize documents and extract important keywords and phrases. The extracted data is reflected in an internal rules database and added as new rules. For example, the generative AI model might pick out important keywords like "hard hat" and "safety glasses."

[0534] Step 4:

[0535] The server updates the internal regulations database, maintains a change history, and records the differences between old and new versions of documents, making it easy to compare them with previous versions.

[0536] Step 5:

[0537] The user enters a question into the interface from their device, asking "How do I apply for approval for a contract based on the new regulations?" The device then sends this question to the server. Along with the question, the user's metadata (user ID, question date and time, etc.) is also sent.

[0538] Step 6:

[0539] The server receives a question from the user and provides it to the generative AI model. The generative AI model then refers to the internal regulations database and generates the most appropriate answer to the question. For example, it might generate an answer like, "To apply for approval based on the new regulations, follow the steps below: 1. Draft a contract, 2. Have the draft reviewed by the department manager, 3. Submit to the corporate planning department."

[0540] Step 7:

[0541] The device provides the user's question input data to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input text and detects the emotional state (e.g., "anxiety" or "anger").

[0542] Step 8:

[0543] The server adjusts the answers created by the generative AI based on the output data of the emotion engine. For example, if the emotion engine detects a state of "anxiety," it adds reassuring words such as "There's no need to worry. All the information has already been confirmed" to the generative AI model's answer.

[0544] Step 9:

[0545] The server sends the final adjusted answer to the terminal, and the terminal displays the answer to the user, who can check the final adjusted answer through the interface.

[0546] 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.

[0547] 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.

[0548] 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.

[0549] [Second embodiment]

[0550] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0551] 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.

[0552] 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).

[0553] 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.

[0554] 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.

[0555] 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).

[0556] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 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."

[0562] The present invention is a system for improving the efficiency of internal company regulation management and information provision, and aims to acquire, learn, and update internal regulation data and provide appropriate answers to user questions. A specific embodiment of this system is described below.

[0563] System configuration

[0564] 1. Means of obtaining internal regulation data:

[0565] The terminal has the function of uploading new regulation documents. The user sends modified or new internal regulation documents from the terminal to the server.

[0566] Example: A user drags and drops the document for a new contract onto the device to upload it.

[0567] 2. How generative AI can learn and analyze data:

[0568] The server receives the uploaded specification document and provides it to the generation AI.

[0569] Generative AI tokenizes prescribed documents and extracts important keywords and phrases.

[0570] Example: The server identifies specific compliance regulation changes for new regulatory documents and updates the model.

[0571] 3. How to update:

[0572] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[0573] Manage change history and save differences from previous versions.

[0574] Example: The server updates its internal regulations database to register amendments based on new legislation.

[0575] 4. How to receive user questions:

[0576] The terminal has an interface that accepts questions input from the user.

[0577] Example: A user uses a terminal interface and types the question, "How do I keep my receipts up to date?"

[0578] 5. A way to generate customized answers based on questions:

[0579] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[0580] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[0581] Example: The generation AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[0582] 6. Means of providing users with customized answers:

[0583] The server sends the generated response to the terminal.

[0584] The terminal displays the answer to the user.

[0585] Example: The answer created by the generation AI will be displayed on the user's device, allowing the user to check it.

[0586] Specific examples

[0587] Scenario: How to submit a contract for approval based on the new regulations

[0588] 1. A user uploads a new policy document as a document file created by the department manager.

[0589] 2. The terminal sends this new specification document to the server and registers the data.

[0590] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[0591] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[0592] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[0593] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[0594] 7. The device sends this question to the server, completing the question acceptance.

[0595] 8. The server receives the question and asks the generation AI to process it.

[0596] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[0597] 10. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[0598] 11. The device displays to the user, "To apply for approval under the new regulations, please follow these steps: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department."

[0599] As described above, the present invention is a system that improves the efficiency of in-company regulation management and information provision, and always provides information based on the latest regulations, thereby reducing operational errors within a company and supporting smooth business operations.

[0600] The processing flow will be explained below.

[0601] Step 1:

[0602] A user creates new internal regulations documents, including changes to legislation and revisions to company policies.

[0603] Step 2:

[0604] The terminal receives the new prescribed document file from the user and uploads it to the server. Through the terminal interface, the user selects and sends the changed prescribed document.

[0605] Step 3:

[0606] The server receives the uploaded document, saves it in the appropriate folder, and waits for the database to be updated.

[0607] Step 4:

[0608] The server provides the uploaded document to the generation AI, which then analyzes the document and begins tokenization.

[0609] Step 5:

[0610] Generative AI tokenizes internal regulatory documents and extracts key keywords and phrases, which involves semantic analysis of the content using natural language processing techniques.

[0611] Step 6:

[0612] The generative AI uses the extracted data to learn about changes in internal regulations and update the model, thereby building an updated model that reflects the new regulations.

[0613] Step 7:

[0614] The server updates the internal database with new analysis results, ensuring that the database is always up to date. It also manages the history of changes to the regulation data and records the differences between the latest and previous versions.

[0615] Step 8:

[0616] The user types a policy question into the terminal, for example, "How do I keep my receipts up to date?"

[0617] Step 9:

[0618] The device sends the user's question to the server, which includes properly formatting the question and forwarding it to the server.

[0619] Step 10:

[0620] The server provides the user's question to the AI ​​generator and requests it to generate an answer based on that question. The server analyzes the question and provides the AI ​​with the necessary data.

[0621] Step 11:

[0622] The generation AI refers to an internal database of regulations and generates the optimal answer. The generation AI creates an accurate answer to the question based on the latest regulation data.

[0623] Step 12:

[0624] The AI ​​generates a response and returns it to the server, which processes it and converts it into a format that is easy for the user to understand.

[0625] Step 13:

[0626] The server sends the generated answer to the terminal and notifies the user, thereby allowing the user to obtain the latest provision information.

[0627] Step 14:

[0628] The terminal displays the response received from the server to the user, who can then confirm that "the latest receipt storage method is to store receipts as electronic data for five years."

[0629] The above are the specific processing steps of this system.

[0630] Example 1

[0631] 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."

[0632] Internal company regulation management and information provision require the management and updating of huge amounts of document data, tracking of change history, and prompt and appropriate responses to user questions. This process is often done manually, prone to errors, and takes time and effort. Another issue is the difficulty of providing the latest information in a timely manner. The goal is to provide a system that solves these issues and enables efficient and accurate regulation management and information provision.

[0633] 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.

[0634] In this invention, the server includes a means for acquiring internal regulation data, a means for learning and analyzing the internal regulation data using a generative AI model, and a means for updating the analyzed internal regulation data to the latest state, thereby improving the efficiency of internal regulation management and information provision within the company and enabling the prompt provision of the latest information.

[0635] "Internal regulation data" is document information such as rules and guidelines used within a company.

[0636] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze and learn from text data.

[0637] "Learning" is the process by which a generative AI model analyzes internally defined data and extracts important keywords and phrases.

[0638] "Analysis" refers to the process in which the generative AI model performs a detailed analysis of the content of the internally defined data and extracts semantic information and patterns.

[0639] "Updating" refers to the act of reflecting new analysis results in the internal regulations database and keeping the data up to date.

[0640] A "question" is text that a user enters into the system requesting information about internal regulations.

[0641] A "customized answer" is an answer that a generative AI model creates based on a user's question and is tailored to specific situations and conditions.

[0642] A "user-friendly format" is a presentation of information that is formatted to be easy for users to understand and use.

[0643] The present invention is a system for improving the efficiency of internal company regulation management and information provision. The system aims to provide the latest internal regulation information by acquiring internal regulation data and using a generative AI model to learn and analyze it. Specific embodiments for implementing the present invention are described below.

[0644] The main components of this system are a server, a terminal, and a generative AI model.

[0645] 1. Acquisition of internal regulation data:

[0646] The user uploads a new contract document using the device, for example, by dragging and dropping the PDF file of the new contract into a designated folder on the device.

[0647] The terminal sends the new specified document to the server. The terminal detects the addition of the document and automatically sends an HTTP request to the server to transfer the file.

[0648] 2. Data training and analysis:

[0649] The server receives the uploaded specification document and provides it to the generative AI model. The server saves the received file in a specific directory, and after saving, sends the file path to the generative AI model endpoint using socket communication.

[0650] The generative AI model tokenizes the specified document and extracts important keywords and phrases. Specifically, the generative AI model tokenizes the document (divides it into words and phrases) and extracts important keywords using techniques such as TF-IDF (Term Frequency-Inverse Document Frequency).

[0651] 3. Update the internal regulations database:

[0652] The server reflects the analysis results in an internal regulation database, always maintaining the latest regulation information. The server saves the keyword groups received from the generative AI model in the database and records differences from previous versions in a log.

[0653] The server manages the change history and saves the differences from previous versions. A trigger is set in the database, and when new data is added, the differences are automatically recorded in the change history table.

[0654] 4. Accepting user questions:

[0655] The user uses the terminal interface to enter a question. The user uses the web application interface to enter "How do I store my latest receipt?" in the text box and clicks the submit button.

[0656] The device sends this question to the server and completes the question reception. The device serializes the entered question into JSON format and sends an HTTP POST request to the API endpoint.

[0657] 5. Generate customized answers:

[0658] The server provides the user's question to the generative AI model and requests it to generate an appropriate answer. The server preprocesses the received question with an NLU (Natural Language Understanding) module and sends the query to the generative AI model.

[0659] The generative AI model refers to an internal database of regulations and creates answers based on the latest information. The generative AI model analyzes the intent of the question and searches for relevant information from the internal database to generate an answer.

[0660] 6. Providing answers to users:

[0661] The server sends the generated answer to the device. The server formats the answer received from the generative AI model into a user-friendly format and sends it to the device in JSON format.

[0662] The terminal displays the answer to the user. The terminal analyzes the received answer and displays on the web interface, "The latest method for storing receipts is to store them as electronic data for five years."

[0663] Examples:

[0664] As a concrete example, the following shows a scene where a user checks how to submit a contract for approval based on the new regulations.

[0665] 1. A user uploads a new policy document as a document file created by the department manager.

[0666] 2. The terminal sends this new specification document to the server and registers the data.

[0667] 3. The server saves the file and immediately submits it to the analysis process of the generative AI model.

[0668] 4. A generative AI model reads the file contents and extracts keywords and important changes through natural language processing.

[0669] 5. Once the generative AI model has completed the learning process, the server updates its internal regulatory database with this new information.

[0670] 6. The user enters a question on the terminal asking "How to apply for approval of a contract based on the new regulations" and submits it.

[0671] 7. The device sends this question to the server, completing the question acceptance.

[0672] 8. The server receives the question and requests the generative AI model to process it.

[0673] 9. The generative AI model references the latest prescribed data and generates an appropriate answer.

[0674] 10. The server receives the answer from the generative AI model and sends it to the device in a user-friendly format.

[0675] 11. The device displays to the user, "To apply for approval under the new regulations, please follow these steps: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department."

[0676] This specific example of the system will improve the efficiency of internal company regulation management and information provision, making it possible to always carry out business operations based on the latest information.

[0677] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0678] Step 1:

[0679] The user uploads a new regulation document as a document file created by the department manager.

[0680] Input: New regulation document (e.g. PDF file)

[0681] Output: Uploaded regulatory document

[0682] Specific operation: The user drags and drops a new rule document into the designated folder on the device, which activates the device's upload function and sends the file to the server.

[0683] Step 2:

[0684] The terminal sends this new provision document to the server.

[0685] Input: User uploaded regulatory document

[0686] Output: The document sent to the server

[0687] Specific operation: The device detects that a document has been added to the specified folder and automatically generates an HTTP request to transfer the file to the server.

[0688] Step 3:

[0689] The server receives the uploaded specification document and provides it to the generative AI model.

[0690] Input: Regulation document sent from the terminal

[0691] Output: Document data provided to the generative AI model

[0692] Specific operation: The server saves the received file in a specific directory, and after confirming that the file has been saved, it notifies the endpoint of the generative AI model of the file path using socket communication.

[0693] Step 4:

[0694] A generative AI model tokenizes the prescribed document and extracts key keywords and phrases.

[0695] Input: A prescribed document provided to a generative AI model

[0696] Output: Extracted keywords and phrases

[0697] How it works: The generative AI model tokenizes (divides) the specified document into words and phrases, and then analyzes and extracts important keywords and phrases using techniques such as TF-IDF.

[0698] Step 5:

[0699] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information.

[0700] Input: Analysis results from a generative AI model

[0701] Output: Updated internal regulations database

[0702] Specific operation: The server stores the keywords received from the generative AI model in a database and records the differences with previous versions in a log. A trigger is set in the database, and when new data is added, the differences are automatically recorded in a change history table.

[0703] Step 6:

[0704] The user uses the terminal interface to input a question.

[0705] Input: User question (e.g., "How do I keep my most recent receipts?")

[0706] Output: Question data sent from the terminal to the server

[0707] Specific operation: A user enters a question into the text box in the web application interface and clicks the submit button. The device serializes the entered question into JSON format and sends it as an HTTP POST request to the API endpoint.

[0708] Step 7:

[0709] The terminal sends this question to the server, completing the question reception.

[0710] Input: Send request from user

[0711] Output: Query data arriving at the server

[0712] Specific operation: The device receives the user's question and sends the serialized data to the server, which receives the request and passes the question to a process for analysis.

[0713] Step 8:

[0714] The server provides the user's question to the generative AI model, requesting it to generate an appropriate answer.

[0715] Input: The user's question that arrives at the server

[0716] Output: Question data sent to the generative AI model

[0717] Specific operation: The server preprocesses the received question using the NLU module and sends a query to the generative AI model endpoint.

[0718] Step 9:

[0719] The generative AI model references an internal regulatory database to create answers based on the most up-to-date information.

[0720] Input: Question data sent from the server

[0721] Output: Generated response data

[0722] How it works: The generative AI model analyzes the intent of the question, searches for relevant information from an internal database, and generates an answer.

[0723] Step 10:

[0724] The server receives the answer from the generative AI model, formats it in a user-friendly format, and sends it to the device.

[0725] Input: Answer data generated by the generative AI model

[0726] Output: Formatted response data sent to the device

[0727] Specific operation: The server formats the answer received from the generative AI model and sends it to the terminal as JSON data in a user-friendly format.

[0728] Step 11:

[0729] The terminal displays the answer to the user.

[0730] Input: Response data sent from the server

[0731] Output: The answer that is displayed to the user

[0732] Specific operation: The device analyzes the received response and displays it on the web interface, allowing the user to confirm and obtain the required information.

[0733] (Application example 1)

[0734] 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."

[0735] By streamlining internal regulation management and information provision and introducing a system that always provides information based on the latest regulations, it is necessary to reduce operational errors that may occur within the company and support smooth business operations.In addition, it is also necessary to quickly communicate the latest regulations and changes in safety standards to robots operated in factories and apply and implement actions based on them.

[0736] 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.

[0737] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from users, means for generating customized answers based on the questions using a generation AI, means for providing the customized answers to the users, means for applying the contents of the uploaded regulation document to the robot, and means for adapting the behavior of the robot to the latest internal regulations. This not only enables efficient management of regulations and information provision within the company, but also makes it possible to adapt the behavior of robots in the factory based on the latest regulations.

[0738] "Internal regulations data" refers to data containing information about rules and regulations that must be observed within a company.

[0739] "Generative AI" is a system that uses artificial intelligence technology to analyze and learn from data and generate appropriate answers and information.

[0740] "Analyzing" refers to the process of examining the contents of data in detail, understanding its structure, and extracting its meaning and importance.

[0741] "Updating" means updating the information in a database or system to conform to current regulations and standards, and keeping the information up to date at all times.

[0742] The "means for accepting questions" refers to an interface that receives questions from users in an input format and obtains the content of the questions in a form that can be processed by the system.

[0743] "Generating customized answers" means using generative AI to create appropriate answers to user questions based on specific conditions and context.

[0744] "Applying the contents of the uploaded regulation document to the robot" means that the new regulation document uploaded by the administrator is reflected in the robot's behavior.

[0745] "Adapting the robot's behavior to the latest internal regulations" means that the robot understands the latest regulations and safety standards and performs actions based on them.

[0746] The present invention provides a system that improves the efficiency of internal company regulation management and information provision, and also quickly reflects the latest regulations and safety standards in robots operated in factories. Specific embodiments are described below.

[0747] System configuration

[0748] This system consists of the following components:

[0749] 1. Upload regulatory documents

[0750] The user (factory manager) uploads a new regulation document by dragging and dropping it onto the terminal, which then sends the document to the server.

[0751] Example: An administrator drags and drops a new safety regulations document onto a terminal and sends it to the server.

[0752] 2. Analysis by generative AI

[0753] The server provides the received prescribed document to the generation AI, which then analyzes its contents. The generation AI then tokenizes the document using, for example, GPT-3 and extracts important keywords and phrases.

[0754] Example: The server receives a regulation document and passes it to a generator AI, which identifies changes to specific safety regulations.

[0755] 3. Updating the internal regulations database

[0756] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[0757] Example: The server updates the database with the new policy and records the differences between the old version.

[0758] 4. Robot Applications

[0759] The server applies the latest regulatory document to the robots in the factory, and uses the robot control library to adapt the robot's behavior to the latest regulations.

[0760] Example: The server updates the robot's operating instructions based on new rules, and the robot acts according to the new rules.

[0761] 5. Question Response System

[0762] The user's question is sent from the device to the server, which then asks the AI ​​to process the question. The AI ​​then refers to an internal database of rules and generates an appropriate answer.

[0763] Example: An administrator types the question "What are the latest safety regulations?" into a terminal, and the generating AI responds, "The latest safety regulations require the wearing of protective equipment."

[0764] Hardware and software used

[0765] Hardware: Terminals (computers used by administrators), servers, factory robots

[0766] Software: Python scripts, generative AI models (e.g., GPT-3), databases (e.g., SQLite), robot control libraries

[0767] Specific examples

[0768] Example prompt sentence:

[0769] "We've uploaded a new safety document. Please update your robot's behavior accordingly."

[0770] "What are the latest safety regulations?"

[0771] This system will improve the efficiency of internal company regulation management and information provision, and will also enable factory robots to adapt their behavior to the latest internal regulations. In addition, users can input questions, and the generative AI will provide appropriate answers based on the latest regulations.

[0772] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0773] Step 1:

[0774] The user uploads a new rule document by dragging and dropping it onto the device, which then sends the document to the server.

[0775] Specific operation: Using the terminal's file upload function, select a document from a directory specified by the user and transfer it to the server.

[0776] Input: Specified document file

[0777] Output: Specified document data transferred to the server

[0778] Step 2:

[0779] The server provides the received specification document to the generation AI, which then analyzes its contents.

[0780] Specific operation: After the server receives the file, it inputs the document data into a generative AI model (e.g., GPT-3) and performs natural language processing such as tokenization and key keyword extraction.

[0781] Input: Regulatory document data

[0782] Output: Parsed keywords and changes

[0783] Step 3:

[0784] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[0785] Specific operation: The server connects to a database (e.g., SQLite), adds the parsed content of the regulations as a new record, and simultaneously saves the difference information between the old regulations and the changes.

[0786] Input: Parsed keywords and changes

[0787] Output: Updated internal regulations database

[0788] Step 4:

[0789] The server applies the latest regulatory document to the robots in the factory, and uses the robot control library to adapt the robot's behavior to the latest regulations.

[0790] Specific operation: The server sends operation instructions based on the new regulations to the factory robot via the robot control library (e.g., robot control API).

[0791] Input: Updated internal regulations database contents

[0792] Output: Robot operation instructions based on the latest regulations

[0793] Step 5:

[0794] A question from the user is sent from the terminal to the server, and the server asks the generating AI to process the question.

[0795] How it works: The user enters a question into the device interface and sends it to the server, which passes it to the generation AI and receives an appropriate answer.

[0796] Input: User question (e.g., "What are the latest safety regulations?")

[0797] Output: A suitable answer from the generative AI

[0798] Step 6:

[0799] The generation AI refers to an internal database of regulations and generates appropriate answers.

[0800] How it works: The generative AI queries an internal rules database to retrieve the latest rules, then generates a textual answer appropriate to the question.

[0801] Input: User questions, internal regulations database

[0802] Output: Correct answer text

[0803] Step 7:

[0804] The server provides the user with the answer from the generated AI.

[0805] Specific operation: The server sends the answer received from the generation AI to the device, which then displays it to the user.

[0806] Input: Answer text from the generation AI

[0807] Output: The answer displayed on the user's terminal

[0808] 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.

[0809] The present invention is a system for streamlining internal company policy management and information provision, and also combines it with an emotion engine that recognizes user emotions. This system acquires, learns, and updates internal policy data, and not only provides appropriate answers to user questions but also customizes answers according to the user's emotions. A specific embodiment of this system is described below.

[0810] System configuration

[0811] 1. Means of obtaining internal regulation data:

[0812] The terminal has the function of uploading new regulation documents. The user sends modified or new internal regulation documents from the terminal to the server.

[0813] Example: A user drags and drops the document for a new contract onto the device to upload it.

[0814] 2. How generative AI can learn and analyze data:

[0815] The server receives the uploaded specification document and provides it to the generation AI.

[0816] Generative AI tokenizes prescribed documents and extracts important keywords and phrases.

[0817] Example: The server identifies specific compliance regulation changes for new regulatory documents and updates the model.

[0818] 3. How to update:

[0819] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[0820] Manage change history and save differences from previous versions.

[0821] Example: The server updates its internal regulations database to register amendments based on new legislation.

[0822] 4. How to receive user questions:

[0823] The terminal has an interface that accepts questions input from the user.

[0824] Example: A user uses a terminal interface and types the question, "How do I keep my receipts up to date?"

[0825] 5. A way to generate customized answers based on questions:

[0826] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[0827] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[0828] Example: The generation AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[0829] 6. Means of providing users with customized answers:

[0830] The server sends the generated response to the terminal.

[0831] The terminal displays the answer to the user.

[0832] Example: The answer created by the generation AI will be displayed on the user's device, allowing the user to check it.

[0833] 7. Emotion engine recognizes user emotions:

[0834] The terminal receives the user's text or voice input and provides it to the emotion engine.

[0835] An emotion engine analyzes the user's input and recognizes their emotional state.

[0836] Example: If a user types a question while in an anxious state, the emotion engine will detect the "anxious" state.

[0837] 8. Tailoring customized responses based on emotional state:

[0838] The server adjusts the tone and content of the answers created by the generative AI based on the emotional data obtained from the emotion engine.

[0839] For example, if the emotion engine detects a user's anxiety, the generative AI will generate a response that includes clear explanations and encouraging words.

[0840] Specific examples

[0841] Scenario: Confirming how to submit a contract for approval based on new regulations and recognizing user sentiment

[0842] 1. A user uploads a new policy document as a document file created by the department manager.

[0843] 2. The terminal sends this new specification document to the server and registers the data.

[0844] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[0845] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[0846] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[0847] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[0848] 7. The device sends this question to the server, completing the question acceptance.

[0849] 8. The server receives the question and asks the generation AI to process it.

[0850] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[0851] 10. The emotion engine analyzes the user's input and recognizes their emotional state.

[0852] 11. The server adjusts the generative AI's responses based on information from the emotion engine.

[0853] 12. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[0854] 13. The terminal displays the following message: "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. If you have any questions, please feel free to ask."

[0855] The emotion engine recognizes the user's emotions and provides customized responses based on those emotions, allowing the user to receive a more friendly response.In this way, the present invention is a system that streamlines internal company policy management and information provision, improving the user experience.

[0856] The processing flow will be explained below.

[0857] Step 1:

[0858] A user creates new internal regulations documents, including changes to legislation and revisions to company policies.

[0859] Step 2:

[0860] The terminal receives the new document file from the user and uploads it to the server. The user selects the document and sends it through the terminal interface.

[0861] Step 3:

[0862] The server receives the uploaded prescribed document and stores the document file in a database.

[0863] Step 4:

[0864] The server provides the stored prescribed data to the generation AI and instructs it to begin the analysis process.

[0865] Step 5:

[0866] Generative AI tokenizes documents and extracts important keywords and phrases, then uses natural language processing techniques to analyze the content.

[0867] Step 6:

[0868] The generative AI updates the internal regulation model based on the extracted data to reflect the new regulations.

[0869] Step 7:

[0870] The server updates the internal regulations database with the latest analysis results, keeping it up to date. It also manages the change history and saves the differences between previous versions.

[0871] Step 8:

[0872] The user inputs a question from the terminal. For example, "Please tell me how to submit a request for approval for a new contract."

[0873] Step 9:

[0874] The device sends the user's question to the server, which then formats the question appropriately and sends it.

[0875] Step 10:

[0876] The server receives the question, provides the question to the generation AI, and requests it to generate an answer.

[0877] Step 11:

[0878] The generative AI refers to an internal database of regulations and generates the best answer to the question.

[0879] Step 12:

[0880] The terminal provides the user's input data to the emotion engine, which passes the user's text input or voice input to the emotion engine.

[0881] Step 13:

[0882] The emotion engine analyzes user input data to recognize emotional states, including text and speech analysis.

[0883] Step 14:

[0884] The server adjusts the tone and content of the AI's responses based on the emotional data obtained from the emotion engine. For example, if the server determines that the user is nervous, it will add more friendly expressions.

[0885] Step 15:

[0886] The server then sends the final adjusted response to the terminal.

[0887] Step 16:

[0888] The device will then display the adjusted response to the user, for example, "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. Also, please feel free to ask us if you have any questions."

[0889] The above is a specific processing flow of the system of the present invention, which improves the efficiency of internal company policy management and provides customized responses according to the user's emotional state.

[0890] Example 2

[0891] 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."

[0892] In modern companies, managing internal regulations and providing information is important, but doing so efficiently is difficult. In particular, managing the history of changes to regulations and providing quick and accurate answers to user questions are required. Furthermore, there are very few systems that can respond with consideration for user feelings. The purpose of this invention is to address these challenges and provide a system that streamlines internal regulation management and information provision within a company and improves the user experience.

[0893] 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.

[0894] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from a user, means for generating a customized answer based on the question using the generation AI, means for providing the customized answer to the user, means including an emotion engine for analyzing the emotional state of the user, and means for adjusting the answer generated by the generation AI based on emotion data obtained by the emotion engine. This not only enables the user to efficiently acquire the latest internal regulation information, but also enables customized answers that take emotions into consideration.

[0895] "Internal regulation data" refers to documents and information such as rules, regulations, and procedures established within a company.

[0896] "Generative AI" refers to artificial intelligence technology that uses large amounts of data to perform natural language processing and generate and analyze text.

[0897] An "emotion engine" refers to software or a system that analyzes the emotional state of a user's input data and classifies it into emotional categories such as positive, negative, or neutral.

[0898] "Server" refers to a computer system that processes, stores, distributes, etc. data.

[0899] "Terminal" refers to a device that allows a user to input or output information, such as a computer, smartphone, or tablet.

[0900] "User" refers to a person or position who uses this system to enter questions and check answers.

[0901] "Database" refers to a system for efficiently storing, searching, retrieving, and managing data.

[0902] "Tokenization" refers to the process of dividing sentences or text into small units (tokens) for analysis.

[0903] The present invention is a system for streamlining internal company policy management and information provision, and also combines it with an emotion engine that recognizes user emotions. This system acquires, learns, and updates internal policy data, and not only provides appropriate answers to user questions but also customizes answers according to the user's emotions. A specific embodiment of this system is described below.

[0904] System configuration

[0905] 1. Means of obtaining internal regulation data:

[0906] The user uploads a new regulation document from the terminal to the server as a document file created by the department manager.

[0907] The terminal has the function of receiving new regulation documents uploaded and sending them to the server.

[0908] Examples of software used: Web browser, file upload tool.

[0909] Example: A user drags and drops the document for a new contract from their device to upload it to the server.

[0910] 2. Data learning and analysis methods:

[0911] The server receives the uploaded internal regulation data and provides it to the generative AI model.

[0912] The generative AI tokenizes the prescribed document and processes it to extract important keywords and phrases.

[0913] Examples of software used: Generative AI models (e.g., OpenAI GPT-4).

[0914] Example: The server identifies and models specific compliance regulation changes for new regulatory documents.

[0915] 3. How to update data:

[0916] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[0917] Examples of software used: Database management systems (e.g., MySQL).

[0918] Example: A server updates an internal regulations database to register amendments based on new legislation.

[0919] 4. How to receive user questions:

[0920] An interface is used for users to input and submit questions at their terminals.

[0921] The terminal receives the query and sends it to the server.

[0922] Examples of software used: web forms, chatbot interfaces.

[0923] Example: A user uses a terminal interface to input the question, "How do I keep my most recent receipts?"

[0924] 5. How to generate customized answers based on questions:

[0925] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[0926] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[0927] Examples of software used: Generative AI models (e.g., OpenAI GPT-4).

[0928] Example: The generating AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[0929] 6. Means of providing responses:

[0930] The server sends the generated response to the terminal.

[0931] The terminal displays the answer to the user.

[0932] Examples of software used: web browsers, notification systems.

[0933] Example: The answer created by the generation AI is displayed on the user's device, allowing the user to check it.

[0934] 7. User Emotion Recognition Method:

[0935] The terminal receives the user's text or voice input and provides it to the emotion engine.

[0936] An emotion engine analyzes the user's input and recognizes their emotional state.

[0937] Examples of software used: Sentiment analysis engines (e.g., IBM Watson Tone Analyzer).

[0938] Example: If a user types a question while in an anxious state, the emotion engine will detect the "anxious" state.

[0939] 8. Tailoring your customized answers:

[0940] The server adjusts the tone and content of the answers created by the generative AI based on the emotional data obtained from the emotion engine.

[0941] Generative AI generates answers that are easier for users to understand and provide a sense of security.

[0942] Examples of software used: generative AI models, emotion engines.

[0943] Example: If the emotion engine detects a user's anxiety, the generative AI will generate a response that "includes clear explanations and encouraging words."

[0944] Specific scenarios and prompt examples

[0945] scenario

[0946] Confirmation of how to submit contracts for approval based on the new regulations and recognition of user sentiment.

[0947] 1. The user uploads a new regulation document from the terminal to the server as a document file created by the department manager.

[0948] 2. The terminal sends this new specification document to the server and registers the data.

[0949] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[0950] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[0951] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[0952] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[0953] 7. The device sends this question to the server, completing the question acceptance.

[0954] 8. The server receives the question and asks the generation AI to process it.

[0955] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[0956] 10. The emotion engine analyzes the user's input and recognizes their emotional state.

[0957] 11. The server adjusts the generative AI's responses based on information from the emotion engine.

[0958] 12. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[0959] 13. The terminal displays the following message: "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. If you have any questions, please feel free to ask."

[0960] Prompt Sentence Examples

[0961] "What are the steps to uploading a new contract document?"

[0962] "What's the latest way to store receipts?"

[0963] "Please tell me how to submit a contract for approval based on the new regulations."

[0964] In this way, this system allows users to acquire information more efficiently and deepen their understanding. In addition, the emotion engine enables communication that takes into consideration the user's emotions.

[0965] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0966] Step 1:

[0967] The user uploads a new internal regulations document from the terminal to the server.

[0968] Input: The default document file selected by the user on the terminal

[0969] Specific operation: The user uses the upload function of the web browser to drag and drop a new regulation document and presses the upload button.

[0970] Output: The prescribed document is sent to the server and stored.

[0971] Step 2:

[0972] The terminal sends a new provision document to the server, and the server receives it.

[0973] Input: Specified document file sent from the terminal

[0974] Specific operation: The file is sent to the server while displaying a notification to the user that the upload is complete.

[0975] Output: The server receives and stores the document.

[0976] Step 3:

[0977] The server provides the uploaded prescribed document to the generative AI model and begins analysis.

[0978] Input: Standard document file stored on the server

[0979] How it works: The server passes the prescribed document to the Generator AI, which starts the analysis process. The Generator AI tokenizes the prescribed document and extracts important keywords and phrases.

[0980] Output: Key keywords and phrases as analysis results.

[0981] Step 4:

[0982] The generating AI reflects the analysis results in the internal regulations database.

[0983] Input: Keywords and phrases extracted by the generation AI

[0984] Specific operation: The generating AI adds keywords and phrases to the database, and the server updates the database.

[0985] Output: Updated internal regulations database.

[0986] Step 5:

[0987] The user types a question into the terminal, which then sends it to the server.

[0988] Input: The question typed by the user into the device interface

[0989] Specific operation: The user enters a question into the terminal interface and presses the send button.

[0990] Output: The terminal sends a query to the server, which receives it.

[0991] Step 6:

[0992] The server provides the received question to the generation AI, which generates an appropriate answer.

[0993] Input: User's question received by the server

[0994] Specific operation: The server sends the question to the generation AI, which then refers to an internal database of specifications and generates the optimal answer.

[0995] Output: The answer provided by the generation AI.

[0996] Step 7:

[0997] The server provides the response text from the generation AI to the emotion engine to recognize the user's emotional state.

[0998] Input: Answers provided by the generation AI, user input

[0999] Specific operation: The server sends the user's input to the emotion engine, which analyzes and recognizes the emotional state.

[1000] Output: User's emotional state data (e.g., "anxious").

[1001] Step 8:

[1002] The server adjusts the generative AI's responses based on the emotional data.

[1003] Input: User's emotional state data

[1004] How it works: The server provides emotional data to the generation AI, which then adjusts the tone and content of the response.

[1005] Output: The adjusted answer.

[1006] Step 9:

[1007] The server sends the adjusted response text to the terminal, which displays it to the user.

[1008] Input: Adjusted answer

[1009] Specific operation: The server sends the answer to the terminal, which displays it on the user's screen.

[1010] Output: The answer displayed on the user's device screen.

[1011] (Application example 2)

[1012] 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."

[1013] In modern corporate operations, managing internal regulations and providing information to employees are extremely important issues. However, not only is updating regulations and responding to questions labor-intensive, but many systems also lack the ability to respond to employees' emotional states. This can lead to anxiety and confusion among employees, affecting their productivity and satisfaction. Therefore, the objective of the present invention is to provide a system that improves the efficiency of managing internal regulations and providing information in corporate operations, and provides appropriate responses in response to employees' emotions.

[1014] 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.

[1015] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from users, means for generating customized answers based on the questions using the generation AI, means for recognizing the emotional state of the user using an emotion engine that recognizes the user's emotions, means for adjusting the customized answers based on the emotion engine, and means for providing the customized answers to users. This makes it possible to streamline regulation management within a company, provide appropriate answers to employee questions immediately, and respond in consideration of the user's emotions.

[1016] "Internal regulation data" refers to data such as rules, procedures, policies, and guidelines established within a company or organization.

[1017] "Generative AI" is a system that uses artificial intelligence techniques to learn and analyze data, using methods such as natural language processing.

[1018] A "server" is a computer system that stores, manages, and processes data, and processes requests from clients over a network.

[1019] The "question receiving means" is an interface that receives questions from users, and is input by the users using the terminal.

[1020] A "customized answer" is a specific answer that the AI ​​generates in response to a user's question based on internally specified data, and is tailored to the user's needs.

[1021] An "emotion engine" is a system that analyzes a user's text and voice input and recognizes their emotional state.

[1022] An "emotional state recognizer" is a process that uses an emotion engine to recognize a user's emotional state.

[1023] The "answer adjustment means" is a process of adjusting the answers created by the generation AI based on the emotion engine to suit the user's emotional state.

[1024] A "user-friendly format" is a format that is designed to be easy for users to understand and use.

[1025] The present invention is a system for improving the efficiency of corporate policy management and information provision, and is combined with an emotion engine that recognizes user emotions. Hereinafter, embodiments of the present invention will be described in detail.

[1026] System Configuration

[1027] The system of the present invention consists of the following major components:

[1028] 1. Server:

[1029] Internal regulation data acquisition means: has the function of receiving new regulation documents from the terminal. The user can send modified or new internal regulation documents from the terminal to the server.

[1030] Learning and analysis method using generative AI: The server provides the received prescribed document to the generative AI model, which tokenizes the prescribed document and extracts important keywords and phrases.

[1031] How to update to the latest version: The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[1032] Question receiving means: A question input from a user is received. For example, the user uses the terminal interface to input a question such as "How do I store the latest receipts?"

[1033] Customized answer generation: The generative AI model references an internal database of predefined answers to generate appropriate answers based on the question.

[1034] Emotion recognition means: An emotion engine is used to analyze the user's input and recognize their emotional state. For example, if a user enters a question while in an anxious state, the emotion engine will detect the "anxious" state.

[1035] Response adjustment measures: Adjust the tone and content of responses created by the generative AI model based on the emotion engine.

[1036] User provision means: The final generated answer is sent to the terminal and displayed to the user.

[1037] Specific examples

[1038] The following scenario is a specific example of server processing:

[1039] Scenario: Confirming how to submit a contract for approval based on new regulations and recognizing user sentiment

[1040] 1. The user drags and drops the new regulation document created by the department manager onto the terminal to upload it. The terminal then sends the document to the server.

[1041] 2. The server saves the file and immediately sends it to the generative AI analysis process, where the generative AI model uses natural language processing to extract keywords and important changes.

[1042] 3. The server updates the internal regulations database with new information, maintaining the latest regulations at all times.

[1043] 4. The user inputs a question from the terminal asking, "How do I apply for approval for a contract based on the new regulations?" The terminal then sends this question to the server.

[1044] 5. The server receives the question and requests the generative AI model to process it. The generative AI model references the latest prescribed data and generates an appropriate answer.

[1045] 6. The emotion engine analyzes the user's input and recognizes their emotional state. For example, if a user types, "This new rule is very complicated and I'm worried," the state of anxiety will be detected.

[1046] 7. The server adjusts the generative AI's response based on the information from the emotion engine, for example adding reassuring words such as "There's no need to worry. All the information has already been verified."

[1047] 8. The server sends the final answer to the terminal and displays it to the user.

[1048] Prompt Sentence Examples

[1049] Below are some example prompts to input to the generative AI model:

[1050] Please explain the new safety guidelines in simple terms.

[1051] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1052] Step 1:

[1053] The user drags and drops a new regulation document created by the department manager onto the terminal to upload it. The terminal then sends the document to the server, along with the document metadata (author, date, version, etc.).

[1054] Step 2:

[1055] The server receives the document and stores it in the file system. The server then prepares the document for injecting into the generative AI model. This preparation includes tokenizing the document and pre-processing it (e.g., removing unnecessary formatting).

[1056] Step 3:

[1057] The server uses generative AI to tokenize documents and extract important keywords and phrases. The extracted data is reflected in an internal rules database and added as new rules. For example, the generative AI model might pick out important keywords like "hard hat" and "safety glasses."

[1058] Step 4:

[1059] The server updates the internal regulations database, maintains a change history, and records the differences between old and new versions of documents, making it easy to compare them with previous versions.

[1060] Step 5:

[1061] The user enters a question into the interface from their device, asking "How do I apply for approval for a contract based on the new regulations?" The device then sends this question to the server. Along with the question, the user's metadata (user ID, question date and time, etc.) is also sent.

[1062] Step 6:

[1063] The server receives a question from the user and provides it to the generative AI model. The generative AI model then refers to the internal regulations database and generates the most appropriate answer to the question. For example, it might generate an answer like, "To apply for approval based on the new regulations, follow the steps below: 1. Draft a contract, 2. Have the draft reviewed by the department manager, 3. Submit to the corporate planning department."

[1064] Step 7:

[1065] The device provides the user's question input data to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input text and detects the emotional state (e.g., "anxiety" or "anger").

[1066] Step 8:

[1067] The server adjusts the answers created by the generative AI based on the output data of the emotion engine. For example, if the emotion engine detects a state of "anxiety," it adds reassuring words such as "There's no need to worry. All the information has already been confirmed" to the generative AI model's answer.

[1068] Step 9:

[1069] The server sends the final adjusted answer to the terminal, and the terminal displays the answer to the user, who can check the final adjusted answer through the interface.

[1070] 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.

[1071] 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.

[1072] 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.

[1073] [Third embodiment]

[1074] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1075] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1076] 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).

[1077] 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.

[1078] 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.

[1079] 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).

[1080] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1081] 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.

[1082] 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.

[1083] 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.

[1084] 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.

[1085] 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."

[1086] The present invention is a system for improving the efficiency of internal company regulation management and information provision, and aims to acquire, learn, and update internal regulation data and provide appropriate answers to user questions. A specific embodiment of this system is described below.

[1087] System configuration

[1088] 1. Means of obtaining internal regulation data:

[1089] The terminal has the function of uploading new regulation documents. The user sends modified or new internal regulation documents from the terminal to the server.

[1090] Example: A user drags and drops the document for a new contract onto the device to upload it.

[1091] 2. How generative AI can learn and analyze data:

[1092] The server receives the uploaded specification document and provides it to the generation AI.

[1093] Generative AI tokenizes prescribed documents and extracts important keywords and phrases.

[1094] Example: The server identifies specific compliance regulation changes for new regulatory documents and updates the model.

[1095] 3. How to update:

[1096] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[1097] Manage change history and save differences from previous versions.

[1098] Example: The server updates its internal regulations database to register amendments based on new legislation.

[1099] 4. How to receive user questions:

[1100] The terminal has an interface that accepts questions input from the user.

[1101] Example: A user uses a terminal interface and types the question, "How do I keep my receipts up to date?"

[1102] 5. A way to generate customized answers based on questions:

[1103] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[1104] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[1105] Example: The generation AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[1106] 6. Means of providing users with customized answers:

[1107] The server sends the generated response to the terminal.

[1108] The terminal displays the answer to the user.

[1109] Example: The answer created by the generation AI will be displayed on the user's device, allowing the user to check it.

[1110] Specific examples

[1111] Scenario: How to submit a contract for approval based on the new regulations

[1112] 1. A user uploads a new policy document as a document file created by the department manager.

[1113] 2. The terminal sends this new specification document to the server and registers the data.

[1114] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[1115] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[1116] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[1117] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[1118] 7. The device sends this question to the server, completing the question acceptance.

[1119] 8. The server receives the question and asks the generation AI to process it.

[1120] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[1121] 10. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[1122] 11. The device displays to the user, "To apply for approval under the new regulations, please follow these steps: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department."

[1123] As described above, the present invention is a system that improves the efficiency of in-company regulation management and information provision, and always provides information based on the latest regulations, thereby reducing operational errors within a company and supporting smooth business operations.

[1124] The processing flow will be explained below.

[1125] Step 1:

[1126] A user creates new internal regulations documents, including changes to legislation and revisions to company policies.

[1127] Step 2:

[1128] The terminal receives the new prescribed document file from the user and uploads it to the server. Through the terminal interface, the user selects and sends the changed prescribed document.

[1129] Step 3:

[1130] The server receives the uploaded document, saves it in the appropriate folder, and waits for the database to be updated.

[1131] Step 4:

[1132] The server provides the uploaded document to the generation AI, which then analyzes the document and begins tokenization.

[1133] Step 5:

[1134] Generative AI tokenizes internal regulatory documents and extracts key keywords and phrases, which involves semantic analysis of the content using natural language processing techniques.

[1135] Step 6:

[1136] The generative AI uses the extracted data to learn about changes in internal regulations and update the model, thereby building an updated model that reflects the new regulations.

[1137] Step 7:

[1138] The server updates the internal database with new analysis results, ensuring that the database is always up to date. It also manages the history of changes to the regulation data and records the differences between the latest and previous versions.

[1139] Step 8:

[1140] The user types a policy question into the terminal, for example, "How do I keep my receipts up to date?"

[1141] Step 9:

[1142] The device sends the user's question to the server, which includes properly formatting the question and forwarding it to the server.

[1143] Step 10:

[1144] The server provides the user's question to the AI ​​generator and requests it to generate an answer based on that question. The server analyzes the question and provides the AI ​​with the necessary data.

[1145] Step 11:

[1146] The generation AI refers to an internal database of regulations and generates the optimal answer. The generation AI creates an accurate answer to the question based on the latest regulation data.

[1147] Step 12:

[1148] The AI ​​generates a response and returns it to the server, which processes it and converts it into a format that is easy for the user to understand.

[1149] Step 13:

[1150] The server sends the generated answer to the terminal and notifies the user, thereby allowing the user to obtain the latest provision information.

[1151] Step 14:

[1152] The terminal displays the response received from the server to the user, who can then confirm that "the latest receipt storage method is to store receipts as electronic data for five years."

[1153] The above are the specific processing steps of this system.

[1154] Example 1

[1155] 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."

[1156] Internal company regulation management and information provision require the management and updating of huge amounts of document data, tracking of change history, and prompt and appropriate responses to user questions. This process is often done manually, prone to errors, and takes time and effort. Another issue is the difficulty of providing the latest information in a timely manner. The goal is to provide a system that solves these issues and enables efficient and accurate regulation management and information provision.

[1157] 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.

[1158] In this invention, the server includes a means for acquiring internal regulation data, a means for learning and analyzing the internal regulation data using a generative AI model, and a means for updating the analyzed internal regulation data to the latest state, thereby improving the efficiency of internal regulation management and information provision within the company and enabling the prompt provision of the latest information.

[1159] "Internal regulation data" is document information such as rules and guidelines used within a company.

[1160] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze and learn from text data.

[1161] "Learning" is the process by which a generative AI model analyzes internally defined data and extracts important keywords and phrases.

[1162] "Analysis" refers to the process in which the generative AI model performs a detailed analysis of the content of the internally defined data and extracts semantic information and patterns.

[1163] "Updating" refers to the act of reflecting new analysis results in the internal regulations database and keeping the data up to date.

[1164] A "question" is text that a user enters into the system requesting information about internal regulations.

[1165] A "customized answer" is an answer that a generative AI model creates based on a user's question and is tailored to specific situations and conditions.

[1166] A "user-friendly format" is a presentation of information that is formatted to be easy for users to understand and use.

[1167] The present invention is a system for improving the efficiency of internal company regulation management and information provision. The system aims to provide the latest internal regulation information by acquiring internal regulation data and using a generative AI model to learn and analyze it. Specific embodiments for implementing the present invention are described below.

[1168] The main components of this system are a server, a terminal, and a generative AI model.

[1169] 1. Acquisition of internal regulation data:

[1170] The user uploads a new contract document using the device, for example, by dragging and dropping the PDF file of the new contract into a designated folder on the device.

[1171] The terminal sends the new specified document to the server. The terminal detects the addition of the document and automatically sends an HTTP request to the server to transfer the file.

[1172] 2. Data training and analysis:

[1173] The server receives the uploaded specification document and provides it to the generative AI model. The server saves the received file in a specific directory, and after saving, sends the file path to the generative AI model endpoint using socket communication.

[1174] The generative AI model tokenizes the specified document and extracts important keywords and phrases. Specifically, the generative AI model tokenizes the document (divides it into words and phrases) and extracts important keywords using techniques such as TF-IDF (Term Frequency-Inverse Document Frequency).

[1175] 3. Update the internal regulations database:

[1176] The server reflects the analysis results in an internal regulation database, always maintaining the latest regulation information. The server saves the keyword groups received from the generative AI model in the database and records differences from previous versions in a log.

[1177] The server manages the change history and saves the differences from previous versions. A trigger is set in the database, and when new data is added, the differences are automatically recorded in the change history table.

[1178] 4. Accepting user questions:

[1179] The user uses the terminal interface to enter a question. The user uses the web application interface to enter "How do I store my latest receipt?" in the text box and clicks the submit button.

[1180] The device sends this question to the server and completes the question reception. The device serializes the entered question into JSON format and sends an HTTP POST request to the API endpoint.

[1181] 5. Generate customized answers:

[1182] The server provides the user's question to the generative AI model and requests it to generate an appropriate answer. The server preprocesses the received question with an NLU (Natural Language Understanding) module and sends the query to the generative AI model.

[1183] The generative AI model refers to an internal database of regulations and creates answers based on the latest information. The generative AI model analyzes the intent of the question and searches for relevant information from the internal database to generate an answer.

[1184] 6. Providing answers to users:

[1185] The server sends the generated answer to the device. The server formats the answer received from the generative AI model into a user-friendly format and sends it to the device in JSON format.

[1186] The terminal displays the answer to the user. The terminal analyzes the received answer and displays on the web interface, "The latest method for storing receipts is to store them as electronic data for five years."

[1187] Examples:

[1188] As a concrete example, the following shows a scene where a user checks how to submit a contract for approval based on the new regulations.

[1189] 1. A user uploads a new policy document as a document file created by the department manager.

[1190] 2. The terminal sends this new specification document to the server and registers the data.

[1191] 3. The server saves the file and immediately submits it to the analysis process of the generative AI model.

[1192] 4. A generative AI model reads the file contents and extracts keywords and important changes through natural language processing.

[1193] 5. Once the generative AI model has completed the learning process, the server updates its internal regulatory database with this new information.

[1194] 6. The user enters a question on the terminal asking "How to apply for approval of a contract based on the new regulations" and submits it.

[1195] 7. The device sends this question to the server, completing the question acceptance.

[1196] 8. The server receives the question and requests the generative AI model to process it.

[1197] 9. The generative AI model references the latest prescribed data and generates an appropriate answer.

[1198] 10. The server receives the answer from the generative AI model and sends it to the device in a user-friendly format.

[1199] 11. The device displays to the user, "To apply for approval under the new regulations, please follow these steps: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department."

[1200] This specific example of the system will improve the efficiency of internal company regulation management and information provision, making it possible to always carry out business operations based on the latest information.

[1201] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1202] Step 1:

[1203] The user uploads a new regulation document as a document file created by the department manager.

[1204] Input: New regulation document (e.g. PDF file)

[1205] Output: Uploaded regulatory document

[1206] Specific operation: The user drags and drops a new rule document into the designated folder on the device, which activates the device's upload function and sends the file to the server.

[1207] Step 2:

[1208] The terminal sends this new provision document to the server.

[1209] Input: User uploaded regulatory document

[1210] Output: The document sent to the server

[1211] Specific operation: The device detects that a document has been added to the specified folder and automatically generates an HTTP request to transfer the file to the server.

[1212] Step 3:

[1213] The server receives the uploaded specification document and provides it to the generative AI model.

[1214] Input: Regulation document sent from the terminal

[1215] Output: Document data provided to the generative AI model

[1216] Specific operation: The server saves the received file in a specific directory, and after confirming that the file has been saved, it notifies the endpoint of the generative AI model of the file path using socket communication.

[1217] Step 4:

[1218] A generative AI model tokenizes the prescribed document and extracts key keywords and phrases.

[1219] Input: A prescribed document provided to a generative AI model

[1220] Output: Extracted keywords and phrases

[1221] How it works: The generative AI model tokenizes (divides) the specified document into words and phrases, and then analyzes and extracts important keywords and phrases using techniques such as TF-IDF.

[1222] Step 5:

[1223] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information.

[1224] Input: Analysis results from a generative AI model

[1225] Output: Updated internal regulations database

[1226] Specific operation: The server stores the keywords received from the generative AI model in a database and records the differences with previous versions in a log. A trigger is set in the database, and when new data is added, the differences are automatically recorded in a change history table.

[1227] Step 6:

[1228] The user uses the terminal interface to input a question.

[1229] Input: User question (e.g., "How do I keep my most recent receipts?")

[1230] Output: Question data sent from the terminal to the server

[1231] Specific operation: A user enters a question into the text box in the web application interface and clicks the submit button. The device serializes the entered question into JSON format and sends it as an HTTP POST request to the API endpoint.

[1232] Step 7:

[1233] The terminal sends this question to the server, completing the question reception.

[1234] Input: Send request from user

[1235] Output: Query data arriving at the server

[1236] Specific operation: The device receives the user's question and sends the serialized data to the server, which receives the request and passes the question to a process for analysis.

[1237] Step 8:

[1238] The server provides the user's question to the generative AI model, requesting it to generate an appropriate answer.

[1239] Input: The user's question that arrives at the server

[1240] Output: Question data sent to the generative AI model

[1241] Specific operation: The server preprocesses the received question using the NLU module and sends a query to the generative AI model endpoint.

[1242] Step 9:

[1243] The generative AI model references an internal regulatory database to create answers based on the most up-to-date information.

[1244] Input: Question data sent from the server

[1245] Output: Generated response data

[1246] How it works: The generative AI model analyzes the intent of the question, searches for relevant information from an internal database, and generates an answer.

[1247] Step 10:

[1248] The server receives the answer from the generative AI model, formats it in a user-friendly format, and sends it to the device.

[1249] Input: Answer data generated by the generative AI model

[1250] Output: Formatted response data sent to the device

[1251] Specific operation: The server formats the answer received from the generative AI model and sends it to the terminal as JSON data in a user-friendly format.

[1252] Step 11:

[1253] The terminal displays the answer to the user.

[1254] Input: Response data sent from the server

[1255] Output: The answer that is displayed to the user

[1256] Specific operation: The device analyzes the received response and displays it on the web interface, allowing the user to confirm and obtain the required information.

[1257] (Application example 1)

[1258] 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."

[1259] By streamlining internal regulation management and information provision and introducing a system that always provides information based on the latest regulations, it is necessary to reduce operational errors that may occur within the company and support smooth business operations.In addition, it is also necessary to quickly communicate the latest regulations and changes in safety standards to robots operated in factories and apply and implement actions based on them.

[1260] 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.

[1261] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from users, means for generating customized answers based on the questions using a generation AI, means for providing the customized answers to the users, means for applying the contents of the uploaded regulation document to the robot, and means for adapting the behavior of the robot to the latest internal regulations. This not only enables efficient management of regulations and information provision within the company, but also makes it possible to adapt the behavior of robots in the factory based on the latest regulations.

[1262] "Internal regulations data" refers to data containing information about rules and regulations that must be observed within a company.

[1263] "Generative AI" is a system that uses artificial intelligence technology to analyze and learn from data and generate appropriate answers and information.

[1264] "Analyzing" refers to the process of examining the contents of data in detail, understanding its structure, and extracting its meaning and importance.

[1265] "Updating" means updating the information in a database or system to conform to current regulations and standards, and keeping the information up to date at all times.

[1266] The "means for accepting questions" refers to an interface that receives questions from users in an input format and obtains the content of the questions in a form that can be processed by the system.

[1267] "Generating customized answers" means using generative AI to create appropriate answers to user questions based on specific conditions and context.

[1268] "Applying the contents of the uploaded regulation document to the robot" means that the new regulation document uploaded by the administrator is reflected in the robot's behavior.

[1269] "Adapting the robot's behavior to the latest internal regulations" means that the robot understands the latest regulations and safety standards and performs actions based on them.

[1270] The present invention provides a system that improves the efficiency of internal company regulation management and information provision, and also quickly reflects the latest regulations and safety standards in robots operated in factories. Specific embodiments are described below.

[1271] System configuration

[1272] This system consists of the following components:

[1273] 1. Upload regulatory documents

[1274] The user (factory manager) uploads a new regulation document by dragging and dropping it onto the terminal, which then sends the document to the server.

[1275] Example: An administrator drags and drops a new safety regulations document onto a terminal and sends it to the server.

[1276] 2. Analysis by generative AI

[1277] The server provides the received prescribed document to the generation AI, which then analyzes its contents. The generation AI then tokenizes the document using, for example, GPT-3 and extracts important keywords and phrases.

[1278] Example: The server receives a regulation document and passes it to a generator AI, which identifies changes to specific safety regulations.

[1279] 3. Updating the internal regulations database

[1280] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[1281] Example: The server updates the database with the new policy and records the differences between the old version.

[1282] 4. Robot Applications

[1283] The server applies the latest regulatory document to the robots in the factory, and uses the robot control library to adapt the robot's behavior to the latest regulations.

[1284] Example: The server updates the robot's operating instructions based on new rules, and the robot acts according to the new rules.

[1285] 5. Question Response System

[1286] The user's question is sent from the device to the server, which then asks the AI ​​to process the question. The AI ​​then refers to an internal database of rules and generates an appropriate answer.

[1287] Example: An administrator types the question "What are the latest safety regulations?" into a terminal, and the generating AI responds, "The latest safety regulations require the wearing of protective equipment."

[1288] Hardware and software used

[1289] Hardware: Terminals (computers used by administrators), servers, factory robots

[1290] Software: Python scripts, generative AI models (e.g., GPT-3), databases (e.g., SQLite), robot control libraries

[1291] Specific examples

[1292] Example prompt sentence:

[1293] "We've uploaded a new safety document. Please update your robot's behavior accordingly."

[1294] "What are the latest safety regulations?"

[1295] This system will improve the efficiency of internal company regulation management and information provision, and will also enable factory robots to adapt their behavior to the latest internal regulations. In addition, users can input questions, and the generative AI will provide appropriate answers based on the latest regulations.

[1296] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1297] Step 1:

[1298] The user uploads a new rule document by dragging and dropping it onto the device, which then sends the document to the server.

[1299] Specific operation: Using the terminal's file upload function, select a document from a directory specified by the user and transfer it to the server.

[1300] Input: Specified document file

[1301] Output: Specified document data transferred to the server

[1302] Step 2:

[1303] The server provides the received specification document to the generation AI, which then analyzes its contents.

[1304] Specific operation: After the server receives the file, it inputs the document data into a generative AI model (e.g., GPT-3) and performs natural language processing such as tokenization and key keyword extraction.

[1305] Input: Regulatory document data

[1306] Output: Parsed keywords and changes

[1307] Step 3:

[1308] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[1309] Specific operation: The server connects to a database (e.g., SQLite), adds the parsed content of the regulations as a new record, and simultaneously saves the difference information between the old regulations and the changes.

[1310] Input: Parsed keywords and changes

[1311] Output: Updated internal regulations database

[1312] Step 4:

[1313] The server applies the latest regulatory document to the robots in the factory, and uses the robot control library to adapt the robot's behavior to the latest regulations.

[1314] Specific operation: The server sends operation instructions based on the new regulations to the factory robot via the robot control library (e.g., robot control API).

[1315] Input: Updated internal regulations database contents

[1316] Output: Robot operation instructions based on the latest regulations

[1317] Step 5:

[1318] A question from the user is sent from the terminal to the server, and the server asks the generating AI to process the question.

[1319] How it works: The user enters a question into the device interface and sends it to the server, which passes it to the generation AI and receives an appropriate answer.

[1320] Input: User question (e.g., "What are the latest safety regulations?")

[1321] Output: A suitable answer from the generative AI

[1322] Step 6:

[1323] The generation AI refers to an internal database of regulations and generates appropriate answers.

[1324] How it works: The generative AI queries an internal rules database to retrieve the latest rules, then generates a textual answer appropriate to the question.

[1325] Input: User questions, internal regulations database

[1326] Output: Correct answer text

[1327] Step 7:

[1328] The server provides the user with the answer from the generated AI.

[1329] Specific operation: The server sends the answer received from the generation AI to the device, which then displays it to the user.

[1330] Input: Answer text from the generation AI

[1331] Output: The answer displayed on the user's terminal

[1332] 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.

[1333] The present invention is a system for streamlining internal company policy management and information provision, and also combines it with an emotion engine that recognizes user emotions. This system acquires, learns, and updates internal policy data, and not only provides appropriate answers to user questions but also customizes answers according to the user's emotions. A specific embodiment of this system is described below.

[1334] System configuration

[1335] 1. Means of obtaining internal regulation data:

[1336] The terminal has the function of uploading new regulation documents. The user sends modified or new internal regulation documents from the terminal to the server.

[1337] Example: A user drags and drops the document for a new contract onto the device to upload it.

[1338] 2. How generative AI can learn and analyze data:

[1339] The server receives the uploaded specification document and provides it to the generation AI.

[1340] Generative AI tokenizes prescribed documents and extracts important keywords and phrases.

[1341] Example: The server identifies specific compliance regulation changes for new regulatory documents and updates the model.

[1342] 3. How to update:

[1343] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[1344] Manage change history and save differences from previous versions.

[1345] Example: The server updates its internal regulations database to register amendments based on new legislation.

[1346] 4. How to receive user questions:

[1347] The terminal has an interface that accepts questions input from the user.

[1348] Example: A user uses a terminal interface and types the question, "How do I keep my receipts up to date?"

[1349] 5. A way to generate customized answers based on questions:

[1350] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[1351] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[1352] Example: The generation AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[1353] 6. Means of providing users with customized answers:

[1354] The server sends the generated response to the terminal.

[1355] The terminal displays the answer to the user.

[1356] Example: The answer created by the generation AI will be displayed on the user's device, allowing the user to check it.

[1357] 7. Emotion engine recognizes user emotions:

[1358] The terminal receives the user's text or voice input and provides it to the emotion engine.

[1359] An emotion engine analyzes the user's input and recognizes their emotional state.

[1360] Example: If a user types a question while in an anxious state, the emotion engine will detect the "anxious" state.

[1361] 8. Tailoring customized responses based on emotional state:

[1362] The server adjusts the tone and content of the answers created by the generative AI based on the emotional data obtained from the emotion engine.

[1363] For example, if the emotion engine detects a user's anxiety, the generative AI will generate a response that includes clear explanations and encouraging words.

[1364] Specific examples

[1365] Scenario: Confirming how to submit a contract for approval based on new regulations and recognizing user sentiment

[1366] 1. A user uploads a new policy document as a document file created by the department manager.

[1367] 2. The terminal sends this new specification document to the server and registers the data.

[1368] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[1369] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[1370] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[1371] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[1372] 7. The device sends this question to the server, completing the question acceptance.

[1373] 8. The server receives the question and asks the generation AI to process it.

[1374] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[1375] 10. The emotion engine analyzes the user's input and recognizes their emotional state.

[1376] 11. The server adjusts the generative AI's responses based on information from the emotion engine.

[1377] 12. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[1378] 13. The terminal displays the following message: "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. If you have any questions, please feel free to ask."

[1379] The emotion engine recognizes the user's emotions and provides customized responses based on those emotions, allowing the user to receive a more friendly response.In this way, the present invention is a system that streamlines internal company policy management and information provision, improving the user experience.

[1380] The processing flow will be explained below.

[1381] Step 1:

[1382] A user creates new internal regulations documents, including changes to legislation and revisions to company policies.

[1383] Step 2:

[1384] The terminal receives the new document file from the user and uploads it to the server. The user selects the document and sends it through the terminal interface.

[1385] Step 3:

[1386] The server receives the uploaded prescribed document and stores the document file in a database.

[1387] Step 4:

[1388] The server provides the stored prescribed data to the generation AI and instructs it to begin the analysis process.

[1389] Step 5:

[1390] Generative AI tokenizes documents and extracts important keywords and phrases, then uses natural language processing techniques to analyze the content.

[1391] Step 6:

[1392] The generative AI updates the internal regulation model based on the extracted data to reflect the new regulations.

[1393] Step 7:

[1394] The server updates the internal regulations database with the latest analysis results, keeping it up to date. It also manages the change history and saves the differences between previous versions.

[1395] Step 8:

[1396] The user inputs a question from the terminal. For example, "Please tell me how to submit a request for approval for a new contract."

[1397] Step 9:

[1398] The device sends the user's question to the server, which then formats the question appropriately and sends it.

[1399] Step 10:

[1400] The server receives the question, provides the question to the generation AI, and requests it to generate an answer.

[1401] Step 11:

[1402] The generative AI refers to an internal database of regulations and generates the best answer to the question.

[1403] Step 12:

[1404] The terminal provides the user's input data to the emotion engine, which passes the user's text input or voice input to the emotion engine.

[1405] Step 13:

[1406] The emotion engine analyzes user input data to recognize emotional states, including text and speech analysis.

[1407] Step 14:

[1408] The server adjusts the tone and content of the AI's responses based on the emotional data obtained from the emotion engine. For example, if the server determines that the user is nervous, it will add more friendly expressions.

[1409] Step 15:

[1410] The server then sends the final adjusted response to the terminal.

[1411] Step 16:

[1412] The device will then display the adjusted response to the user, for example, "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. Also, please feel free to ask us if you have any questions."

[1413] The above is a specific processing flow of the system of the present invention, which improves the efficiency of internal company policy management and provides customized responses according to the user's emotional state.

[1414] Example 2

[1415] 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."

[1416] In modern companies, managing internal regulations and providing information is important, but doing so efficiently is difficult. In particular, managing the history of changes to regulations and providing quick and accurate answers to user questions are required. Furthermore, there are very few systems that can respond with consideration for user feelings. The purpose of this invention is to address these challenges and provide a system that streamlines internal regulation management and information provision within a company and improves the user experience.

[1417] 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.

[1418] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from a user, means for generating a customized answer based on the question using the generation AI, means for providing the customized answer to the user, means including an emotion engine for analyzing the emotional state of the user, and means for adjusting the answer generated by the generation AI based on emotion data obtained by the emotion engine. This not only enables the user to efficiently acquire the latest internal regulation information, but also enables customized answers that take emotions into consideration.

[1419] "Internal regulation data" refers to documents and information such as rules, regulations, and procedures established within a company.

[1420] "Generative AI" refers to artificial intelligence technology that uses large amounts of data to perform natural language processing and generate and analyze text.

[1421] An "emotion engine" refers to software or a system that analyzes the emotional state of a user's input data and classifies it into emotional categories such as positive, negative, or neutral.

[1422] "Server" refers to a computer system that processes, stores, distributes, etc. data.

[1423] "Terminal" refers to a device that allows a user to input or output information, such as a computer, smartphone, or tablet.

[1424] "User" refers to a person or position who uses this system to enter questions and check answers.

[1425] "Database" refers to a system for efficiently storing, searching, retrieving, and managing data.

[1426] "Tokenization" refers to the process of dividing sentences or text into small units (tokens) for analysis.

[1427] The present invention is a system for streamlining internal company policy management and information provision, and also combines it with an emotion engine that recognizes user emotions. This system acquires, learns, and updates internal policy data, and not only provides appropriate answers to user questions but also customizes answers according to the user's emotions. A specific embodiment of this system is described below.

[1428] System configuration

[1429] 1. Means of obtaining internal regulation data:

[1430] The user uploads a new regulation document from the terminal to the server as a document file created by the department manager.

[1431] The terminal has the function of receiving new regulation documents uploaded and sending them to the server.

[1432] Examples of software used: Web browser, file upload tool.

[1433] Example: A user drags and drops the document for a new contract from their device to upload it to the server.

[1434] 2. Data learning and analysis methods:

[1435] The server receives the uploaded internal regulation data and provides it to the generative AI model.

[1436] The generative AI tokenizes the prescribed document and processes it to extract important keywords and phrases.

[1437] Examples of software used: Generative AI models (e.g., OpenAI GPT-4).

[1438] Example: The server identifies and models specific compliance regulation changes for new regulatory documents.

[1439] 3. How to update data:

[1440] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[1441] Examples of software used: Database management systems (e.g., MySQL).

[1442] Example: A server updates an internal regulations database to register amendments based on new legislation.

[1443] 4. How to receive user questions:

[1444] An interface is used for users to input and submit questions at their terminals.

[1445] The terminal receives the query and sends it to the server.

[1446] Examples of software used: web forms, chatbot interfaces.

[1447] Example: A user uses a terminal interface to input the question, "How do I keep my most recent receipts?"

[1448] 5. How to generate customized answers based on questions:

[1449] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[1450] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[1451] Examples of software used: Generative AI models (e.g., OpenAI GPT-4).

[1452] Example: The generating AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[1453] 6. Means of providing responses:

[1454] The server sends the generated response to the terminal.

[1455] The terminal displays the answer to the user.

[1456] Examples of software used: web browsers, notification systems.

[1457] Example: The answer created by the generation AI is displayed on the user's device, allowing the user to check it.

[1458] 7. User Emotion Recognition Method:

[1459] The terminal receives the user's text or voice input and provides it to the emotion engine.

[1460] An emotion engine analyzes the user's input and recognizes their emotional state.

[1461] Examples of software used: Sentiment analysis engines (e.g., IBM Watson Tone Analyzer).

[1462] Example: If a user types a question while in an anxious state, the emotion engine will detect the "anxious" state.

[1463] 8. Tailoring your customized answers:

[1464] The server adjusts the tone and content of the answers created by the generative AI based on the emotional data obtained from the emotion engine.

[1465] Generative AI generates answers that are easier for users to understand and provide a sense of security.

[1466] Examples of software used: generative AI models, emotion engines.

[1467] Example: If the emotion engine detects a user's anxiety, the generative AI will generate a response that "includes clear explanations and encouraging words."

[1468] Specific scenarios and prompt examples

[1469] scenario

[1470] Confirmation of how to submit contracts for approval based on the new regulations and recognition of user sentiment.

[1471] 1. The user uploads a new regulation document from the terminal to the server as a document file created by the department manager.

[1472] 2. The terminal sends this new specification document to the server and registers the data.

[1473] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[1474] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[1475] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[1476] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[1477] 7. The device sends this question to the server, completing the question acceptance.

[1478] 8. The server receives the question and asks the generation AI to process it.

[1479] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[1480] 10. The emotion engine analyzes the user's input and recognizes their emotional state.

[1481] 11. The server adjusts the generative AI's responses based on information from the emotion engine.

[1482] 12. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[1483] 13. The terminal displays the following message: "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. If you have any questions, please feel free to ask."

[1484] Prompt Sentence Examples

[1485] "What are the steps to uploading a new contract document?"

[1486] "What's the latest way to store receipts?"

[1487] "Please tell me how to submit a contract for approval based on the new regulations."

[1488] In this way, this system allows users to acquire information more efficiently and deepen their understanding. In addition, the emotion engine enables communication that takes into consideration the user's emotions.

[1489] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1490] Step 1:

[1491] The user uploads a new internal regulations document from the terminal to the server.

[1492] Input: The default document file selected by the user on the terminal

[1493] Specific operation: The user uses the upload function of the web browser to drag and drop a new regulation document and presses the upload button.

[1494] Output: The prescribed document is sent to the server and stored.

[1495] Step 2:

[1496] The terminal sends a new provision document to the server, and the server receives it.

[1497] Input: Specified document file sent from the terminal

[1498] Specific operation: The file is sent to the server while displaying a notification to the user that the upload is complete.

[1499] Output: The server receives and stores the document.

[1500] Step 3:

[1501] The server provides the uploaded prescribed document to the generative AI model and begins analysis.

[1502] Input: Standard document file stored on the server

[1503] How it works: The server passes the prescribed document to the Generator AI, which starts the analysis process. The Generator AI tokenizes the prescribed document and extracts important keywords and phrases.

[1504] Output: Key keywords and phrases as analysis results.

[1505] Step 4:

[1506] The generating AI reflects the analysis results in the internal regulations database.

[1507] Input: Keywords and phrases extracted by the generation AI

[1508] Specific operation: The generating AI adds keywords and phrases to the database, and the server updates the database.

[1509] Output: Updated internal regulations database.

[1510] Step 5:

[1511] The user types a question into the terminal, which then sends it to the server.

[1512] Input: The question typed by the user into the device interface

[1513] Specific operation: The user enters a question into the terminal interface and presses the send button.

[1514] Output: The terminal sends a query to the server, which receives it.

[1515] Step 6:

[1516] The server provides the received question to the generation AI, which generates an appropriate answer.

[1517] Input: User's question received by the server

[1518] Specific operation: The server sends the question to the generation AI, which then refers to an internal database of specifications and generates the optimal answer.

[1519] Output: The answer provided by the generation AI.

[1520] Step 7:

[1521] The server provides the response text from the generation AI to the emotion engine to recognize the user's emotional state.

[1522] Input: Answers provided by the generation AI, user input

[1523] Specific operation: The server sends the user's input to the emotion engine, which analyzes and recognizes the emotional state.

[1524] Output: User's emotional state data (e.g., "anxious").

[1525] Step 8:

[1526] The server adjusts the generative AI's responses based on the emotional data.

[1527] Input: User's emotional state data

[1528] How it works: The server provides emotional data to the generation AI, which then adjusts the tone and content of the response.

[1529] Output: The adjusted answer.

[1530] Step 9:

[1531] The server sends the adjusted response text to the terminal, which displays it to the user.

[1532] Input: Adjusted answer

[1533] Specific operation: The server sends the answer to the terminal, which displays it on the user's screen.

[1534] Output: The answer displayed on the user's device screen.

[1535] (Application example 2)

[1536] 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."

[1537] In modern corporate operations, managing internal regulations and providing information to employees are extremely important issues. However, not only is updating regulations and responding to questions labor-intensive, but many systems also lack the ability to respond to employees' emotional states. This can lead to anxiety and confusion among employees, affecting their productivity and satisfaction. Therefore, the objective of the present invention is to provide a system that improves the efficiency of managing internal regulations and providing information in corporate operations, and provides appropriate responses in response to employees' emotions.

[1538] 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.

[1539] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from users, means for generating customized answers based on the questions using the generation AI, means for recognizing the emotional state of the user using an emotion engine that recognizes the user's emotions, means for adjusting the customized answers based on the emotion engine, and means for providing the customized answers to users. This makes it possible to streamline regulation management within a company, provide appropriate answers to employee questions immediately, and respond in consideration of the user's emotions.

[1540] "Internal regulation data" refers to data such as rules, procedures, policies, and guidelines established within a company or organization.

[1541] "Generative AI" is a system that uses artificial intelligence techniques to learn and analyze data, using methods such as natural language processing.

[1542] A "server" is a computer system that stores, manages, and processes data, and processes requests from clients over a network.

[1543] The "question receiving means" is an interface that receives questions from users, and is input by the users using the terminal.

[1544] A "customized answer" is a specific answer that the AI ​​generates in response to a user's question based on internally specified data, and is tailored to the user's needs.

[1545] An "emotion engine" is a system that analyzes a user's text and voice input and recognizes their emotional state.

[1546] An "emotional state recognizer" is a process that uses an emotion engine to recognize a user's emotional state.

[1547] The "answer adjustment means" is a process of adjusting the answers created by the generation AI based on the emotion engine to suit the user's emotional state.

[1548] A "user-friendly format" is a format that is designed to be easy for users to understand and use.

[1549] The present invention is a system for improving the efficiency of corporate policy management and information provision, and is combined with an emotion engine that recognizes user emotions. Hereinafter, embodiments of the present invention will be described in detail.

[1550] System Configuration

[1551] The system of the present invention consists of the following major components:

[1552] 1. Server:

[1553] Internal regulation data acquisition means: has the function of receiving new regulation documents from the terminal. The user can send modified or new internal regulation documents from the terminal to the server.

[1554] Learning and analysis method using generative AI: The server provides the received prescribed document to the generative AI model, which tokenizes the prescribed document and extracts important keywords and phrases.

[1555] How to update to the latest version: The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[1556] Question receiving means: A question input from a user is received. For example, the user uses the terminal interface to input a question such as "How do I store the latest receipts?"

[1557] Customized answer generation: The generative AI model references an internal database of predefined answers to generate appropriate answers based on the question.

[1558] Emotion recognition means: An emotion engine is used to analyze the user's input and recognize their emotional state. For example, if a user enters a question while in an anxious state, the emotion engine will detect the "anxious" state.

[1559] Response adjustment measures: Adjust the tone and content of responses created by the generative AI model based on the emotion engine.

[1560] User provision means: The final generated answer is sent to the terminal and displayed to the user.

[1561] Specific examples

[1562] The following scenario is a specific example of server processing:

[1563] Scenario: Confirming how to submit a contract for approval based on new regulations and recognizing user sentiment

[1564] 1. The user drags and drops the new regulation document created by the department manager onto the terminal to upload it. The terminal then sends the document to the server.

[1565] 2. The server saves the file and immediately sends it to the generative AI analysis process, where the generative AI model uses natural language processing to extract keywords and important changes.

[1566] 3. The server updates the internal regulations database with new information, maintaining the latest regulations at all times.

[1567] 4. The user inputs a question from the terminal asking, "How do I apply for approval for a contract based on the new regulations?" The terminal then sends this question to the server.

[1568] 5. The server receives the question and requests the generative AI model to process it. The generative AI model references the latest prescribed data and generates an appropriate answer.

[1569] 6. The emotion engine analyzes the user's input and recognizes their emotional state. For example, if a user types, "This new rule is very complicated and I'm worried," the state of anxiety will be detected.

[1570] 7. The server adjusts the generative AI's response based on the information from the emotion engine, for example adding reassuring words such as "There's no need to worry. All the information has already been verified."

[1571] 8. The server sends the final answer to the terminal and displays it to the user.

[1572] Prompt Sentence Examples

[1573] Below are some example prompts to input to the generative AI model:

[1574] Please explain the new safety guidelines in simple terms.

[1575] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1576] Step 1:

[1577] The user drags and drops a new regulation document created by the department manager onto the terminal to upload it. The terminal then sends the document to the server, along with the document metadata (author, date, version, etc.).

[1578] Step 2:

[1579] The server receives the document and stores it in the file system. The server then prepares the document for injecting into the generative AI model. This preparation includes tokenizing the document and pre-processing it (e.g., removing unnecessary formatting).

[1580] Step 3:

[1581] The server uses generative AI to tokenize documents and extract important keywords and phrases. The extracted data is reflected in an internal rules database and added as new rules. For example, the generative AI model might pick out important keywords like "hard hat" and "safety glasses."

[1582] Step 4:

[1583] The server updates the internal regulations database, maintains a change history, and records the differences between old and new versions of documents, making it easy to compare them with previous versions.

[1584] Step 5:

[1585] The user enters a question into the interface from their device, asking "How do I apply for approval for a contract based on the new regulations?" The device then sends this question to the server. Along with the question, the user's metadata (user ID, question date and time, etc.) is also sent.

[1586] Step 6:

[1587] The server receives a question from the user and provides it to the generative AI model. The generative AI model then refers to the internal regulations database and generates the most appropriate answer to the question. For example, it might generate an answer like, "To apply for approval based on the new regulations, follow the steps below: 1. Draft a contract, 2. Have the draft reviewed by the department manager, 3. Submit to the corporate planning department."

[1588] Step 7:

[1589] The device provides the user's question input data to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input text and detects the emotional state (e.g., "anxiety" or "anger").

[1590] Step 8:

[1591] The server adjusts the answers created by the generative AI based on the output data of the emotion engine. For example, if the emotion engine detects a state of "anxiety," it adds reassuring words such as "There's no need to worry. All the information has already been confirmed" to the generative AI model's answer.

[1592] Step 9:

[1593] The server sends the final adjusted answer to the terminal, and the terminal displays the answer to the user, who can check the final adjusted answer through the interface.

[1594] 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.

[1595] 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.

[1596] 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.

[1597] [Fourth embodiment]

[1598] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1599] 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.

[1600] 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).

[1601] 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.

[1602] 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.

[1603] 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).

[1604] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1605] 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.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] 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."

[1611] The present invention is a system for improving the efficiency of internal company regulation management and information provision, and aims to acquire, learn, and update internal regulation data and provide appropriate answers to user questions. A specific embodiment of this system is described below.

[1612] System configuration

[1613] 1. Means of obtaining internal regulation data:

[1614] The terminal has the function of uploading new regulation documents. The user sends modified or new internal regulation documents from the terminal to the server.

[1615] Example: A user drags and drops the document for a new contract onto the device to upload it.

[1616] 2. How generative AI can learn and analyze data:

[1617] The server receives the uploaded specification document and provides it to the generation AI.

[1618] Generative AI tokenizes prescribed documents and extracts important keywords and phrases.

[1619] Example: The server identifies specific compliance regulation changes for new regulatory documents and updates the model.

[1620] 3. How to update:

[1621] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[1622] Manage change history and save differences from previous versions.

[1623] Example: The server updates its internal regulations database to register amendments based on new legislation.

[1624] 4. How to receive user questions:

[1625] The terminal has an interface that accepts questions input from the user.

[1626] Example: A user uses a terminal interface and types the question, "How do I keep my receipts up to date?"

[1627] 5. A way to generate customized answers based on questions:

[1628] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[1629] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[1630] Example: The generation AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[1631] 6. Means of providing users with customized answers:

[1632] The server sends the generated response to the terminal.

[1633] The terminal displays the answer to the user.

[1634] Example: The answer created by the generation AI will be displayed on the user's device, allowing the user to check it.

[1635] Specific examples

[1636] Scenario: How to submit a contract for approval based on the new regulations

[1637] 1. A user uploads a new policy document as a document file created by the department manager.

[1638] 2. The terminal sends this new specification document to the server and registers the data.

[1639] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[1640] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[1641] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[1642] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[1643] 7. The device sends this question to the server, completing the question acceptance.

[1644] 8. The server receives the question and asks the generation AI to process it.

[1645] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[1646] 10. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[1647] 11. The device displays to the user, "To apply for approval under the new regulations, please follow these steps: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department."

[1648] As described above, the present invention is a system that improves the efficiency of in-company regulation management and information provision, and always provides information based on the latest regulations, thereby reducing operational errors within a company and supporting smooth business operations.

[1649] The processing flow will be explained below.

[1650] Step 1:

[1651] A user creates new internal regulations documents, including changes to legislation and revisions to company policies.

[1652] Step 2:

[1653] The terminal receives the new prescribed document file from the user and uploads it to the server. Through the terminal interface, the user selects and sends the changed prescribed document.

[1654] Step 3:

[1655] The server receives the uploaded document, saves it in the appropriate folder, and waits for the database to be updated.

[1656] Step 4:

[1657] The server provides the uploaded document to the generation AI, which then analyzes the document and begins tokenization.

[1658] Step 5:

[1659] Generative AI tokenizes internal regulatory documents and extracts key keywords and phrases, which involves semantic analysis of the content using natural language processing techniques.

[1660] Step 6:

[1661] The generative AI uses the extracted data to learn about changes in internal regulations and update the model, thereby building an updated model that reflects the new regulations.

[1662] Step 7:

[1663] The server updates the internal database with new analysis results, ensuring that the database is always up to date. It also manages the history of changes to the regulation data and records the differences between the latest and previous versions.

[1664] Step 8:

[1665] The user types a policy question into the terminal, for example, "How do I keep my receipts up to date?"

[1666] Step 9:

[1667] The device sends the user's question to the server, which includes properly formatting the question and forwarding it to the server.

[1668] Step 10:

[1669] The server provides the user's question to the AI ​​generator and requests it to generate an answer based on that question. The server analyzes the question and provides the AI ​​with the necessary data.

[1670] Step 11:

[1671] The generation AI refers to an internal database of regulations and generates the optimal answer. The generation AI creates an accurate answer to the question based on the latest regulation data.

[1672] Step 12:

[1673] The AI ​​generates a response and returns it to the server, which processes it and converts it into a format that is easy for the user to understand.

[1674] Step 13:

[1675] The server sends the generated answer to the terminal and notifies the user, thereby allowing the user to obtain the latest provision information.

[1676] Step 14:

[1677] The terminal displays the response received from the server to the user, who can then confirm that "the latest receipt storage method is to store receipts as electronic data for five years."

[1678] The above are the specific processing steps of this system.

[1679] Example 1

[1680] 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."

[1681] Internal company regulation management and information provision require the management and updating of huge amounts of document data, tracking of change history, and prompt and appropriate responses to user questions. This process is often done manually, prone to errors, and takes time and effort. Another issue is the difficulty of providing the latest information in a timely manner. The goal is to provide a system that solves these issues and enables efficient and accurate regulation management and information provision.

[1682] 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.

[1683] In this invention, the server includes a means for acquiring internal regulation data, a means for learning and analyzing the internal regulation data using a generative AI model, and a means for updating the analyzed internal regulation data to the latest state, thereby improving the efficiency of internal regulation management and information provision within the company and enabling the prompt provision of the latest information.

[1684] "Internal regulation data" is document information such as rules and guidelines used within a company.

[1685] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze and learn from text data.

[1686] "Learning" is the process by which a generative AI model analyzes internally defined data and extracts important keywords and phrases.

[1687] "Analysis" refers to the process in which the generative AI model performs a detailed analysis of the content of the internally defined data and extracts semantic information and patterns.

[1688] "Updating" refers to the act of reflecting new analysis results in the internal regulations database and keeping the data up to date.

[1689] A "question" is text that a user enters into the system requesting information about internal regulations.

[1690] A "customized answer" is an answer that a generative AI model creates based on a user's question and is tailored to specific situations and conditions.

[1691] A "user-friendly format" is a presentation of information that is formatted to be easy for users to understand and use.

[1692] The present invention is a system for improving the efficiency of internal company regulation management and information provision. The system aims to provide the latest internal regulation information by acquiring internal regulation data and using a generative AI model to learn and analyze it. Specific embodiments for implementing the present invention are described below.

[1693] The main components of this system are a server, a terminal, and a generative AI model.

[1694] 1. Acquisition of internal regulation data:

[1695] The user uploads a new contract document using the device, for example, by dragging and dropping the PDF file of the new contract into a designated folder on the device.

[1696] The terminal sends the new specified document to the server. The terminal detects the addition of the document and automatically sends an HTTP request to the server to transfer the file.

[1697] 2. Data training and analysis:

[1698] The server receives the uploaded specification document and provides it to the generative AI model. The server saves the received file in a specific directory, and after saving, sends the file path to the generative AI model endpoint using socket communication.

[1699] The generative AI model tokenizes the specified document and extracts important keywords and phrases. Specifically, the generative AI model tokenizes the document (divides it into words and phrases) and extracts important keywords using techniques such as TF-IDF (Term Frequency-Inverse Document Frequency).

[1700] 3. Update the internal regulations database:

[1701] The server reflects the analysis results in an internal regulation database, always maintaining the latest regulation information. The server saves the keyword groups received from the generative AI model in the database and records differences from previous versions in a log.

[1702] The server manages the change history and saves the differences from previous versions. A trigger is set in the database, and when new data is added, the differences are automatically recorded in the change history table.

[1703] 4. Accepting user questions:

[1704] The user uses the terminal interface to enter a question. The user uses the web application interface to enter "How do I store my latest receipt?" in the text box and clicks the submit button.

[1705] The device sends this question to the server and completes the question reception. The device serializes the entered question into JSON format and sends an HTTP POST request to the API endpoint.

[1706] 5. Generate customized answers:

[1707] The server provides the user's question to the generative AI model and requests it to generate an appropriate answer. The server preprocesses the received question with an NLU (Natural Language Understanding) module and sends the query to the generative AI model.

[1708] The generative AI model refers to an internal database of regulations and creates answers based on the latest information. The generative AI model analyzes the intent of the question and searches for relevant information from the internal database to generate an answer.

[1709] 6. Providing answers to users:

[1710] The server sends the generated answer to the device. The server formats the answer received from the generative AI model into a user-friendly format and sends it to the device in JSON format.

[1711] The terminal displays the answer to the user. The terminal analyzes the received answer and displays on the web interface, "The latest method for storing receipts is to store them as electronic data for five years."

[1712] Examples:

[1713] As a concrete example, the following shows a scene where a user checks how to submit a contract for approval based on the new regulations.

[1714] 1. A user uploads a new policy document as a document file created by the department manager.

[1715] 2. The terminal sends this new specification document to the server and registers the data.

[1716] 3. The server saves the file and immediately submits it to the analysis process of the generative AI model.

[1717] 4. A generative AI model reads the file contents and extracts keywords and important changes through natural language processing.

[1718] 5. Once the generative AI model has completed the learning process, the server updates its internal regulatory database with this new information.

[1719] 6. The user enters a question on the terminal asking "How to apply for approval of a contract based on the new regulations" and submits it.

[1720] 7. The device sends this question to the server, completing the question acceptance.

[1721] 8. The server receives the question and requests the generative AI model to process it.

[1722] 9. The generative AI model references the latest prescribed data and generates an appropriate answer.

[1723] 10. The server receives the answer from the generative AI model and sends it to the device in a user-friendly format.

[1724] 11. The device displays to the user, "To apply for approval under the new regulations, please follow these steps: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department."

[1725] This specific example of the system will improve the efficiency of internal company regulation management and information provision, making it possible to always carry out business operations based on the latest information.

[1726] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1727] Step 1:

[1728] The user uploads a new regulation document as a document file created by the department manager.

[1729] Input: New regulation document (e.g. PDF file)

[1730] Output: Uploaded regulatory document

[1731] Specific operation: The user drags and drops a new rule document into the designated folder on the device, which activates the device's upload function and sends the file to the server.

[1732] Step 2:

[1733] The terminal sends this new provision document to the server.

[1734] Input: User uploaded regulatory document

[1735] Output: The document sent to the server

[1736] Specific operation: The device detects that a document has been added to the specified folder and automatically generates an HTTP request to transfer the file to the server.

[1737] Step 3:

[1738] The server receives the uploaded specification document and provides it to the generative AI model.

[1739] Input: Regulation document sent from the terminal

[1740] Output: Document data provided to the generative AI model

[1741] Specific operation: The server saves the received file in a specific directory, and after confirming that the file has been saved, it notifies the endpoint of the generative AI model of the file path using socket communication.

[1742] Step 4:

[1743] A generative AI model tokenizes the prescribed document and extracts key keywords and phrases.

[1744] Input: A prescribed document provided to a generative AI model

[1745] Output: Extracted keywords and phrases

[1746] How it works: The generative AI model tokenizes (divides) the specified document into words and phrases, and then analyzes and extracts important keywords and phrases using techniques such as TF-IDF.

[1747] Step 5:

[1748] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information.

[1749] Input: Analysis results from a generative AI model

[1750] Output: Updated internal regulations database

[1751] Specific operation: The server stores the keywords received from the generative AI model in a database and records the differences with previous versions in a log. A trigger is set in the database, and when new data is added, the differences are automatically recorded in a change history table.

[1752] Step 6:

[1753] The user uses the terminal interface to input a question.

[1754] Input: User question (e.g., "How do I keep my most recent receipts?")

[1755] Output: Question data sent from the terminal to the server

[1756] Specific operation: A user enters a question into the text box in the web application interface and clicks the submit button. The device serializes the entered question into JSON format and sends it as an HTTP POST request to the API endpoint.

[1757] Step 7:

[1758] The terminal sends this question to the server, completing the question reception.

[1759] Input: Send request from user

[1760] Output: Query data arriving at the server

[1761] Specific operation: The device receives the user's question and sends the serialized data to the server, which receives the request and passes the question to a process for analysis.

[1762] Step 8:

[1763] The server provides the user's question to the generative AI model, requesting it to generate an appropriate answer.

[1764] Input: The user's question that arrives at the server

[1765] Output: Question data sent to the generative AI model

[1766] Specific operation: The server preprocesses the received question using the NLU module and sends a query to the generative AI model endpoint.

[1767] Step 9:

[1768] The generative AI model references an internal regulatory database to create answers based on the most up-to-date information.

[1769] Input: Question data sent from the server

[1770] Output: Generated response data

[1771] How it works: The generative AI model analyzes the intent of the question, searches for relevant information from an internal database, and generates an answer.

[1772] Step 10:

[1773] The server receives the answer from the generative AI model, formats it in a user-friendly format, and sends it to the device.

[1774] Input: Answer data generated by the generative AI model

[1775] Output: Formatted response data sent to the device

[1776] Specific operation: The server formats the answer received from the generative AI model and sends it to the terminal as JSON data in a user-friendly format.

[1777] Step 11:

[1778] The terminal displays the answer to the user.

[1779] Input: Response data sent from the server

[1780] Output: The answer that is displayed to the user

[1781] Specific operation: The device analyzes the received response and displays it on the web interface, allowing the user to confirm and obtain the required information.

[1782] (Application example 1)

[1783] 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."

[1784] By streamlining internal regulation management and information provision and introducing a system that always provides information based on the latest regulations, it is necessary to reduce operational errors that may occur within the company and support smooth business operations.In addition, it is also necessary to quickly communicate the latest regulations and changes in safety standards to robots operated in factories and apply and implement actions based on them.

[1785] 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.

[1786] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from users, means for generating customized answers based on the questions using a generation AI, means for providing the customized answers to the users, means for applying the contents of the uploaded regulation document to the robot, and means for adapting the behavior of the robot to the latest internal regulations. This not only enables efficient management of regulations and information provision within the company, but also makes it possible to adapt the behavior of robots in the factory based on the latest regulations.

[1787] "Internal regulations data" refers to data containing information about rules and regulations that must be observed within a company.

[1788] "Generative AI" is a system that uses artificial intelligence technology to analyze and learn from data and generate appropriate answers and information.

[1789] "Analyzing" refers to the process of examining the contents of data in detail, understanding its structure, and extracting its meaning and importance.

[1790] "Updating" means updating the information in a database or system to conform to current regulations and standards, and keeping the information up to date at all times.

[1791] The "means for accepting questions" refers to an interface that receives questions from users in an input format and obtains the content of the questions in a form that can be processed by the system.

[1792] "Generating customized answers" means using generative AI to create appropriate answers to user questions based on specific conditions and context.

[1793] "Applying the contents of the uploaded regulation document to the robot" means that the new regulation document uploaded by the administrator is reflected in the robot's behavior.

[1794] "Adapting the robot's behavior to the latest internal regulations" means that the robot understands the latest regulations and safety standards and performs actions based on them.

[1795] The present invention provides a system that improves the efficiency of internal company regulation management and information provision, and also quickly reflects the latest regulations and safety standards in robots operated in factories. Specific embodiments are described below.

[1796] System configuration

[1797] This system consists of the following components:

[1798] 1. Upload regulatory documents

[1799] The user (factory manager) uploads a new regulation document by dragging and dropping it onto the terminal, which then sends the document to the server.

[1800] Example: An administrator drags and drops a new safety regulations document onto a terminal and sends it to the server.

[1801] 2. Analysis by generative AI

[1802] The server provides the received prescribed document to the generation AI, which then analyzes its contents. The generation AI then tokenizes the document using, for example, GPT-3 and extracts important keywords and phrases.

[1803] Example: The server receives a regulation document and passes it to a generator AI, which identifies changes to specific safety regulations.

[1804] 3. Updating the internal regulations database

[1805] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[1806] Example: The server updates the database with the new policy and records the differences between the old version.

[1807] 4. Robot Applications

[1808] The server applies the latest regulatory document to the robots in the factory, and uses the robot control library to adapt the robot's behavior to the latest regulations.

[1809] Example: The server updates the robot's operating instructions based on new rules, and the robot acts according to the new rules.

[1810] 5. Question Response System

[1811] The user's question is sent from the device to the server, which then asks the AI ​​to process the question. The AI ​​then refers to an internal database of rules and generates an appropriate answer.

[1812] Example: An administrator types the question "What are the latest safety regulations?" into a terminal, and the generating AI responds, "The latest safety regulations require the wearing of protective equipment."

[1813] Hardware and software used

[1814] Hardware: Terminals (computers used by administrators), servers, factory robots

[1815] Software: Python scripts, generative AI models (e.g., GPT-3), databases (e.g., SQLite), robot control libraries

[1816] Specific examples

[1817] Example prompt sentence:

[1818] "We've uploaded a new safety document. Please update your robot's behavior accordingly."

[1819] "What are the latest safety regulations?"

[1820] This system will improve the efficiency of internal company regulation management and information provision, and will also enable factory robots to adapt their behavior to the latest internal regulations. In addition, users can input questions, and the generative AI will provide appropriate answers based on the latest regulations.

[1821] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1822] Step 1:

[1823] The user uploads a new rule document by dragging and dropping it onto the device, which then sends the document to the server.

[1824] Specific operation: Using the terminal's file upload function, select a document from a directory specified by the user and transfer it to the server.

[1825] Input: Specified document file

[1826] Output: Specified document data transferred to the server

[1827] Step 2:

[1828] The server provides the received specification document to the generation AI, which then analyzes its contents.

[1829] Specific operation: After the server receives the file, it inputs the document data into a generative AI model (e.g., GPT-3) and performs natural language processing such as tokenization and key keyword extraction.

[1830] Input: Regulatory document data

[1831] Output: Parsed keywords and changes

[1832] Step 3:

[1833] The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[1834] Specific operation: The server connects to a database (e.g., SQLite), adds the parsed content of the regulations as a new record, and simultaneously saves the difference information between the old regulations and the changes.

[1835] Input: Parsed keywords and changes

[1836] Output: Updated internal regulations database

[1837] Step 4:

[1838] The server applies the latest regulatory document to the robots in the factory, and uses the robot control library to adapt the robot's behavior to the latest regulations.

[1839] Specific operation: The server sends operation instructions based on the new regulations to the factory robot via the robot control library (e.g., robot control API).

[1840] Input: Updated internal regulations database contents

[1841] Output: Robot operation instructions based on the latest regulations

[1842] Step 5:

[1843] A question from the user is sent from the terminal to the server, and the server asks the generating AI to process the question.

[1844] How it works: The user enters a question into the device interface and sends it to the server, which passes it to the generation AI and receives an appropriate answer.

[1845] Input: User question (e.g., "What are the latest safety regulations?")

[1846] Output: A suitable answer from the generative AI

[1847] Step 6:

[1848] The generation AI refers to an internal database of regulations and generates appropriate answers.

[1849] How it works: The generative AI queries an internal rules database to retrieve the latest rules, then generates a textual answer appropriate to the question.

[1850] Input: User questions, internal regulations database

[1851] Output: Correct answer text

[1852] Step 7:

[1853] The server provides the user with the answer from the generated AI.

[1854] Specific operation: The server sends the answer received from the generation AI to the device, which then displays it to the user.

[1855] Input: Answer text from the generation AI

[1856] Output: The answer displayed on the user's terminal

[1857] 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.

[1858] The present invention is a system for streamlining internal company policy management and information provision, and also combines it with an emotion engine that recognizes user emotions. This system acquires, learns, and updates internal policy data, and not only provides appropriate answers to user questions but also customizes answers according to the user's emotions. A specific embodiment of this system is described below.

[1859] System configuration

[1860] 1. Means of obtaining internal regulation data:

[1861] The terminal has the function of uploading new regulation documents. The user sends modified or new internal regulation documents from the terminal to the server.

[1862] Example: A user drags and drops the document for a new contract onto the device to upload it.

[1863] 2. How generative AI can learn and analyze data:

[1864] The server receives the uploaded specification document and provides it to the generation AI.

[1865] Generative AI tokenizes prescribed documents and extracts important keywords and phrases.

[1866] Example: The server identifies specific compliance regulation changes for new regulatory documents and updates the model.

[1867] 3. How to update:

[1868] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[1869] Manage change history and save differences from previous versions.

[1870] Example: The server updates its internal regulations database to register amendments based on new legislation.

[1871] 4. How to receive user questions:

[1872] The terminal has an interface that accepts questions input from the user.

[1873] Example: A user uses a terminal interface and types the question, "How do I keep my receipts up to date?"

[1874] 5. A way to generate customized answers based on questions:

[1875] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[1876] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[1877] Example: The generation AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[1878] 6. Means of providing users with customized answers:

[1879] The server sends the generated response to the terminal.

[1880] The terminal displays the answer to the user.

[1881] Example: The answer created by the generation AI will be displayed on the user's device, allowing the user to check it.

[1882] 7. Emotion engine recognizes user emotions:

[1883] The terminal receives the user's text or voice input and provides it to the emotion engine.

[1884] An emotion engine analyzes the user's input and recognizes their emotional state.

[1885] Example: If a user types a question while in an anxious state, the emotion engine will detect the "anxious" state.

[1886] 8. Tailoring customized responses based on emotional state:

[1887] The server adjusts the tone and content of the answers created by the generative AI based on the emotional data obtained from the emotion engine.

[1888] For example, if the emotion engine detects a user's anxiety, the generative AI will generate a response that includes clear explanations and encouraging words.

[1889] Specific examples

[1890] Scenario: Confirming how to submit a contract for approval based on new regulations and recognizing user sentiment

[1891] 1. A user uploads a new policy document as a document file created by the department manager.

[1892] 2. The terminal sends this new specification document to the server and registers the data.

[1893] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[1894] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[1895] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[1896] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[1897] 7. The device sends this question to the server, completing the question acceptance.

[1898] 8. The server receives the question and asks the generation AI to process it.

[1899] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[1900] 10. The emotion engine analyzes the user's input and recognizes their emotional state.

[1901] 11. The server adjusts the generative AI's responses based on information from the emotion engine.

[1902] 12. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[1903] 13. The terminal displays the following message: "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. If you have any questions, please feel free to ask."

[1904] The emotion engine recognizes the user's emotions and provides customized responses based on those emotions, allowing the user to receive a more friendly response.In this way, the present invention is a system that streamlines internal company policy management and information provision, improving the user experience.

[1905] The processing flow will be explained below.

[1906] Step 1:

[1907] A user creates new internal regulations documents, including changes to legislation and revisions to company policies.

[1908] Step 2:

[1909] The terminal receives the new document file from the user and uploads it to the server. The user selects the document and sends it through the terminal interface.

[1910] Step 3:

[1911] The server receives the uploaded prescribed document and stores the document file in a database.

[1912] Step 4:

[1913] The server provides the stored prescribed data to the generation AI and instructs it to begin the analysis process.

[1914] Step 5:

[1915] Generative AI tokenizes documents and extracts important keywords and phrases, then uses natural language processing techniques to analyze the content.

[1916] Step 6:

[1917] The generative AI updates the internal regulation model based on the extracted data to reflect the new regulations.

[1918] Step 7:

[1919] The server updates the internal regulations database with the latest analysis results, keeping it up to date. It also manages the change history and saves the differences between previous versions.

[1920] Step 8:

[1921] The user inputs a question from the terminal. For example, "Please tell me how to submit a request for approval for a new contract."

[1922] Step 9:

[1923] The device sends the user's question to the server, which then formats the question appropriately and sends it.

[1924] Step 10:

[1925] The server receives the question, provides the question to the generation AI, and requests it to generate an answer.

[1926] Step 11:

[1927] The generative AI refers to an internal database of regulations and generates the best answer to the question.

[1928] Step 12:

[1929] The terminal provides the user's input data to the emotion engine, which passes the user's text input or voice input to the emotion engine.

[1930] Step 13:

[1931] The emotion engine analyzes user input data to recognize emotional states, including text and speech analysis.

[1932] Step 14:

[1933] The server adjusts the tone and content of the AI's responses based on the emotional data obtained from the emotion engine. For example, if the server determines that the user is nervous, it will add more friendly expressions.

[1934] Step 15:

[1935] The server then sends the final adjusted response to the terminal.

[1936] Step 16:

[1937] The device will then display the adjusted response to the user, for example, "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. Also, please feel free to ask us if you have any questions."

[1938] The above is a specific processing flow of the system of the present invention, which improves the efficiency of internal company policy management and provides customized responses according to the user's emotional state.

[1939] Example 2

[1940] 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."

[1941] In modern companies, managing internal regulations and providing information is important, but doing so efficiently is difficult. In particular, managing the history of changes to regulations and providing quick and accurate answers to user questions are required. Furthermore, there are very few systems that can respond with consideration for user feelings. The purpose of this invention is to address these challenges and provide a system that streamlines internal regulation management and information provision within a company and improves the user experience.

[1942] 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.

[1943] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from a user, means for generating a customized answer based on the question using the generation AI, means for providing the customized answer to the user, means including an emotion engine for analyzing the emotional state of the user, and means for adjusting the answer generated by the generation AI based on emotion data obtained by the emotion engine. This not only enables the user to efficiently acquire the latest internal regulation information, but also enables customized answers that take emotions into consideration.

[1944] "Internal regulation data" refers to documents and information such as rules, regulations, and procedures established within a company.

[1945] "Generative AI" refers to artificial intelligence technology that uses large amounts of data to perform natural language processing and generate and analyze text.

[1946] An "emotion engine" refers to software or a system that analyzes the emotional state of a user's input data and classifies it into emotional categories such as positive, negative, or neutral.

[1947] "Server" refers to a computer system that processes, stores, distributes, etc. data.

[1948] "Terminal" refers to a device that allows a user to input or output information, such as a computer, smartphone, or tablet.

[1949] "User" refers to a person or position who uses this system to enter questions and check answers.

[1950] "Database" refers to a system for efficiently storing, searching, retrieving, and managing data.

[1951] "Tokenization" refers to the process of dividing sentences or text into small units (tokens) for analysis.

[1952] The present invention is a system for streamlining internal company policy management and information provision, and also combines it with an emotion engine that recognizes user emotions. This system acquires, learns, and updates internal policy data, and not only provides appropriate answers to user questions but also customizes answers according to the user's emotions. A specific embodiment of this system is described below.

[1953] System configuration

[1954] 1. Means of obtaining internal regulation data:

[1955] The user uploads a new regulation document from the terminal to the server as a document file created by the department manager.

[1956] The terminal has the function of receiving new regulation documents uploaded and sending them to the server.

[1957] Examples of software used: Web browser, file upload tool.

[1958] Example: A user drags and drops the document for a new contract from their device to upload it to the server.

[1959] 2. Data learning and analysis methods:

[1960] The server receives the uploaded internal regulation data and provides it to the generative AI model.

[1961] The generative AI tokenizes the prescribed document and processes it to extract important keywords and phrases.

[1962] Examples of software used: Generative AI models (e.g., OpenAI GPT-4).

[1963] Example: The server identifies and models specific compliance regulation changes for new regulatory documents.

[1964] 3. How to update data:

[1965] The server updates the internal regulation database with new analysis results, always maintaining the latest regulation information.

[1966] Examples of software used: Database management systems (e.g., MySQL).

[1967] Example: A server updates an internal regulations database to register amendments based on new legislation.

[1968] 4. How to receive user questions:

[1969] An interface is used for users to input and submit questions at their terminals.

[1970] The terminal receives the query and sends it to the server.

[1971] Examples of software used: web forms, chatbot interfaces.

[1972] Example: A user uses a terminal interface to input the question, "How do I keep my most recent receipts?"

[1973] 5. How to generate customized answers based on questions:

[1974] The server provides the user's question to the generation AI and requests it to generate an appropriate answer.

[1975] The generating AI refers to an internal regulatory database and creates answers based on the latest information.

[1976] Examples of software used: Generative AI models (e.g., OpenAI GPT-4).

[1977] Example: The generating AI generates the answer, "The latest method for storing receipts is to store them as electronic data for five years."

[1978] 6. Means of providing responses:

[1979] The server sends the generated response to the terminal.

[1980] The terminal displays the answer to the user.

[1981] Examples of software used: web browsers, notification systems.

[1982] Example: The answer created by the generation AI is displayed on the user's device, allowing the user to check it.

[1983] 7. User Emotion Recognition Method:

[1984] The terminal receives the user's text or voice input and provides it to the emotion engine.

[1985] An emotion engine analyzes the user's input and recognizes their emotional state.

[1986] Examples of software used: Sentiment analysis engines (e.g., IBM Watson Tone Analyzer).

[1987] Example: If a user types a question while in an anxious state, the emotion engine will detect the "anxious" state.

[1988] 8. Tailoring your customized answers:

[1989] The server adjusts the tone and content of the answers created by the generative AI based on the emotional data obtained from the emotion engine.

[1990] Generative AI generates answers that are easier for users to understand and provide a sense of security.

[1991] Examples of software used: generative AI models, emotion engines.

[1992] Example: If the emotion engine detects a user's anxiety, the generative AI will generate a response that "includes clear explanations and encouraging words."

[1993] Specific scenarios and prompt examples

[1994] scenario

[1995] Confirmation of how to submit contracts for approval based on the new regulations and recognition of user sentiment.

[1996] 1. The user uploads a new regulation document from the terminal to the server as a document file created by the department manager.

[1997] 2. The terminal sends this new specification document to the server and registers the data.

[1998] 3. The server saves the file and immediately submits it to the generative AI's analysis process.

[1999] 4. Generative AI reads the file contents and extracts keywords and important changes through natural language processing.

[2000] 5. Once the generative AI has completed the learning process, the server updates its internal regulatory database with this new information.

[2001] 6. The user enters a question on the terminal asking, "How do I apply for approval for a contract based on the new regulations?"

[2002] 7. The device sends this question to the server, completing the question acceptance.

[2003] 8. The server receives the question and asks the generation AI to process it.

[2004] 9. The generative AI refers to the latest standard data and generates an appropriate answer.

[2005] 10. The emotion engine analyzes the user's input and recognizes their emotional state.

[2006] 11. The server adjusts the generative AI's responses based on information from the emotion engine.

[2007] 12. The server receives the answer from the generating AI and sends it to the device in a user-friendly format.

[2008] 13. The terminal displays the following message: "To apply for approval under the new regulations, please follow the steps below: 1. Draft the contract, 2. Have the draft reviewed by the department manager, 3. Submit to the Corporate Planning Department. If you have any questions, please feel free to ask."

[2009] Prompt Sentence Examples

[2010] "What are the steps to uploading a new contract document?"

[2011] "What's the latest way to store receipts?"

[2012] "Please tell me how to submit a contract for approval based on the new regulations."

[2013] In this way, this system allows users to acquire information more efficiently and deepen their understanding. In addition, the emotion engine enables communication that takes into consideration the user's emotions.

[2014] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2015] Step 1:

[2016] The user uploads a new internal regulations document from the terminal to the server.

[2017] Input: The default document file selected by the user on the terminal

[2018] Specific operation: The user uses the upload function of the web browser to drag and drop a new regulation document and presses the upload button.

[2019] Output: The prescribed document is sent to the server and stored.

[2020] Step 2:

[2021] The terminal sends a new provision document to the server, and the server receives it.

[2022] Input: Specified document file sent from the terminal

[2023] Specific operation: The file is sent to the server while displaying a notification to the user that the upload is complete.

[2024] Output: The server receives and stores the document.

[2025] Step 3:

[2026] The server provides the uploaded prescribed document to the generative AI model and begins analysis.

[2027] Input: Standard document file stored on the server

[2028] How it works: The server passes the prescribed document to the Generator AI, which starts the analysis process. The Generator AI tokenizes the prescribed document and extracts important keywords and phrases.

[2029] Output: Key keywords and phrases as analysis results.

[2030] Step 4:

[2031] The generating AI reflects the analysis results in the internal regulations database.

[2032] Input: Keywords and phrases extracted by the generation AI

[2033] Specific operation: The generating AI adds keywords and phrases to the database, and the server updates the database.

[2034] Output: Updated internal regulations database.

[2035] Step 5:

[2036] The user types a question into the terminal, which then sends it to the server.

[2037] Input: The question typed by the user into the device interface

[2038] Specific operation: The user enters a question into the terminal interface and presses the send button.

[2039] Output: The terminal sends a query to the server, which receives it.

[2040] Step 6:

[2041] The server provides the received question to the generation AI, which generates an appropriate answer.

[2042] Input: User's question received by the server

[2043] Specific operation: The server sends the question to the generation AI, which then refers to an internal database of specifications and generates the optimal answer.

[2044] Output: The answer provided by the generation AI.

[2045] Step 7:

[2046] The server provides the response text from the generation AI to the emotion engine to recognize the user's emotional state.

[2047] Input: Answers provided by the generation AI, user input

[2048] Specific operation: The server sends the user's input to the emotion engine, which analyzes and recognizes the emotional state.

[2049] Output: User's emotional state data (e.g., "anxious").

[2050] Step 8:

[2051] The server adjusts the generative AI's responses based on the emotional data.

[2052] Input: User's emotional state data

[2053] How it works: The server provides emotional data to the generation AI, which then adjusts the tone and content of the response.

[2054] Output: The adjusted answer.

[2055] Step 9:

[2056] The server sends the adjusted response text to the terminal, which displays it to the user.

[2057] Input: Adjusted answer

[2058] Specific operation: The server sends the answer to the terminal, which displays it on the user's screen.

[2059] Output: The answer displayed on the user's device screen.

[2060] (Application example 2)

[2061] 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."

[2062] In modern corporate operations, managing internal regulations and providing information to employees are extremely important issues. However, not only is updating regulations and responding to questions labor-intensive, but many systems also lack the ability to respond to employees' emotional states. This can lead to anxiety and confusion among employees, affecting their productivity and satisfaction. Therefore, the objective of the present invention is to provide a system that improves the efficiency of managing internal regulations and providing information in corporate operations, and provides appropriate responses in response to employees' emotions.

[2063] 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.

[2064] In this invention, the server includes means for acquiring internal regulation data, means for learning and analyzing the internal regulation data using a generation AI, means for updating the analyzed internal regulation data to the latest state, means for accepting questions from users, means for generating customized answers based on the questions using the generation AI, means for recognizing the emotional state of the user using an emotion engine that recognizes the user's emotions, means for adjusting the customized answers based on the emotion engine, and means for providing the customized answers to users. This makes it possible to streamline regulation management within a company, provide appropriate answers to employee questions immediately, and respond in consideration of the user's emotions.

[2065] "Internal regulation data" refers to data such as rules, procedures, policies, and guidelines established within a company or organization.

[2066] "Generative AI" is a system that uses artificial intelligence techniques to learn and analyze data, using methods such as natural language processing.

[2067] A "server" is a computer system that stores, manages, and processes data, and processes requests from clients over a network.

[2068] The "question receiving means" is an interface that receives questions from users, and is input by the users using the terminal.

[2069] A "customized answer" is a specific answer that the AI ​​generates in response to a user's question based on internally specified data, and is tailored to the user's needs.

[2070] An "emotion engine" is a system that analyzes a user's text and voice input and recognizes their emotional state.

[2071] An "emotional state recognizer" is a process that uses an emotion engine to recognize a user's emotional state.

[2072] The "answer adjustment means" is a process of adjusting the answers created by the generation AI based on the emotion engine to suit the user's emotional state.

[2073] A "user-friendly format" is a format that is designed to be easy for users to understand and use.

[2074] The present invention is a system for improving the efficiency of corporate policy management and information provision, and is combined with an emotion engine that recognizes user emotions. Hereinafter, embodiments of the present invention will be described in detail.

[2075] System Configuration

[2076] The system of the present invention consists of the following major components:

[2077] 1. Server:

[2078] Internal regulation data acquisition means: has the function of receiving new regulation documents from the terminal. The user can send modified or new internal regulation documents from the terminal to the server.

[2079] Learning and analysis method using generative AI: The server provides the received prescribed document to the generative AI model, which tokenizes the prescribed document and extracts important keywords and phrases.

[2080] How to update to the latest version: The server reflects the analysis results in the internal regulations database, always maintaining the latest regulations information. It also manages the change history and saves the differences from previous versions.

[2081] Question receiving means: A question input from a user is received. For example, the user uses the terminal interface to input a question such as "How do I store the latest receipts?"

[2082] Customized answer generation: The generative AI model references an internal database of predefined answers to generate appropriate answers based on the question.

[2083] Emotion recognition means: An emotion engine is used to analyze the user's input and recognize their emotional state. For example, if a user enters a question while in an anxious state, the emotion engine will detect the "anxious" state.

[2084] Response adjustment measures: Adjust the tone and content of responses created by the generative AI model based on the emotion engine.

[2085] User provision means: The final generated answer is sent to the terminal and displayed to the user.

[2086] Specific examples

[2087] The following scenario is a specific example of server processing:

[2088] Scenario: Confirming how to submit a contract for approval based on new regulations and recognizing user sentiment

[2089] 1. The user drags and drops the new regulation document created by the department manager onto the terminal to upload it. The terminal then sends the document to the server.

[2090] 2. The server saves the file and immediately sends it to the generative AI analysis process, where the generative AI model uses natural language processing to extract keywords and important changes.

[2091] 3. The server updates the internal regulations database with new information, maintaining the latest regulations at all times.

[2092] 4. The user inputs a question from the terminal asking, "How do I apply for approval for a contract based on the new regulations?" The terminal then sends this question to the server.

[2093] 5. The server receives the question and requests the generative AI model to process it. The generative AI model references the latest prescribed data and generates an appropriate answer.

[2094] 6. The emotion engine analyzes the user's input and recognizes their emotional state. For example, if a user types, "This new rule is very complicated and I'm worried," the state of anxiety will be detected.

[2095] 7. The server adjusts the generative AI's response based on the information from the emotion engine, for example adding reassuring words such as "There's no need to worry. All the information has already been verified."

[2096] 8. The server sends the final answer to the terminal and displays it to the user.

[2097] Prompt Sentence Examples

[2098] Below are some example prompts to input to the generative AI model:

[2099] Please explain the new safety guidelines in simple terms.

[2100] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2101] Step 1:

[2102] The user drags and drops a new regulation document created by the department manager onto the terminal to upload it. The terminal then sends the document to the server, along with the document metadata (author, date, version, etc.).

[2103] Step 2:

[2104] The server receives the document and stores it in the file system. The server then prepares the document for injecting into the generative AI model. This preparation includes tokenizing the document and pre-processing it (e.g., removing unnecessary formatting).

[2105] Step 3:

[2106] The server uses generative AI to tokenize documents and extract important keywords and phrases. The extracted data is reflected in an internal rules database and added as new rules. For example, the generative AI model might pick out important keywords like "hard hat" and "safety glasses."

[2107] Step 4:

[2108] The server updates the internal regulations database, maintains a change history, and records the differences between old and new versions of documents, making it easy to compare them with previous versions.

[2109] Step 5:

[2110] The user enters a question into the interface from their device, asking "How do I apply for approval for a contract based on the new regulations?" The device then sends this question to the server. Along with the question, the user's metadata (user ID, question date and time, etc.) is also sent.

[2111] Step 6:

[2112] The server receives a question from the user and provides it to the generative AI model. The generative AI model then refers to the internal regulations database and generates the most appropriate answer to the question. For example, it might generate an answer like, "To apply for approval based on the new regulations, follow the steps below: 1. Draft a contract, 2. Have the draft reviewed by the department manager, 3. Submit to the corporate planning department."

[2113] Step 7:

[2114] The device provides the user's question input data to the emotion engine to recognize the user's emotional state. The emotion engine analyzes the user's input text and detects the emotional state (e.g., "anxiety" or "anger").

[2115] Step 8:

[2116] The server adjusts the answers created by the generative AI based on the output data of the emotion engine. For example, if the emotion engine detects a state of "anxiety," it adds reassuring words such as "There's no need to worry. All the information has already been confirmed" to the generative AI model's answer.

[2117] Step 9:

[2118] The server sends the final adjusted answer to the terminal, and the terminal displays the answer to the user, who can check the final adjusted answer through the interface.

[2119] 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.

[2120] 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.

[2121] 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.

[2122] 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.

[2123] 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.

[2124] 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.

[2125] 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).

[2126] 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.

[2127] 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."

[2128] 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.

[2129] 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).

[2130] 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.

[2131] 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.

[2132] 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.

[2133] 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.

[2134] 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.

[2135] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2136] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2138] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2139] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2140] The following is further disclosed regarding the above embodiment.

[2141] (Claim 1)

[2142] a means for obtaining internal regulatory data;

[2143] A means for learning and analyzing the internal regulation data using a generating AI;

[2144] A means for updating the analyzed internal regulation data to the latest state;

[2145] means for accepting questions from users;

[2146] means for generating a customized answer based on the question using the generation AI;

[2147] means for providing the customized answer to a user;

[2148] A system including:

[2149] (Claim 2)

[2150] 2. The system according to claim 1, further comprising means for managing a change history of said internal specification data and recording differences from past versions.

[2151] (Claim 3)

[2152] 10. The system of claim 1, further comprising: means for converting the answers provided to the user into a user-friendly format.

[2153] "Example 1"

[2154] (Claim 1)

[2155] a means for o...

Claims

1. a means for obtaining internal regulatory data; A means for learning and analyzing the internal regulation data using a generating AI; A means for updating the analyzed internal regulation data to the latest state; means for accepting questions from users; means for generating a customized answer based on the question using the generation AI; means for providing the customized answer to a user; A system including:

2. 2. The system according to claim 1, further comprising means for managing a history of changes to said internal specification data and recording differences from past versions.

3. The system of claim 1 further comprising: means for converting answers provided to the user into a user-friendly format.

Images