system

Abstract

We provide the system. [Solution] In an interactive knowledge base system that allows employees to search for company information using generating AI, A means of receiving questions entered by employees, A means of sending a query to a generating AI based on an input question and generating an appropriate response, A method for cleaning up the responses obtained from the generated AI and sending them back to the employees, Methods for importing and using internal company policies, procedures, service information, and other data into the system, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In companies that provide a wide variety of products and services, it is difficult for employees to grasp all the information, and there is a problem that it takes time to search for and obtain information. In addition, since the internal information is frequently updated, there is also a problem that it is difficult to always access the latest information. There is a demand for a system that solves such problems and enables employees to obtain necessary information quickly and accurately.

Means for Solving the Problems

[0005] This invention provides an interactive knowledge base system that allows employees to search for internal company information using a generative AI. Specifically, the system includes means for receiving questions entered by employees, means for sending queries to a generative AI based on the entered questions and generating appropriate responses, means for cleaning up the responses obtained from the generative AI and returning them to the employees, and means for importing and using data such as internal policies, procedures, and service information into the system. It also includes means for continuously updating the internal information database so that the latest information is always available, and means for providing a function that allows employees to search for information through an interactive interface. In this way, employees can quickly and accurately obtain the information they need, thereby improving the efficiency of their work.

[0006] "Generative AI" is a type of artificial intelligence technology that can learn from large amounts of data and automatically generate new information and responses.

[0007] An "interactive knowledge base system" is an information management system that allows users to input questions in natural language and generate and provide appropriate answers.

[0008] An "employee" is a person who belongs to a company or organization and needs information in order to perform their duties.

[0009] A "query" is a term that refers to a question or request entered by a user for a system to process.

[0010] "Means" refers to various machine or program components provided to achieve the objectives of the present invention.

[0011] "Internal company policy" refers to a document that sets out the norms and rules that employees must follow within a company or organization.

[0012] A "procedure manual" is a document that details the specific steps and methods for performing a particular task or operation.

[0013] "Service information" refers to information that includes detailed descriptions and instructions on how to use various services provided by a company or organization.

[0014] "Import" refers to the operation of taking in data from an external source or another system and converting it into a format that can be used within one's own system.

[0015] A "database" is a system or collection of data for efficiently storing, managing, and retrieving structured information.

[0016] "Continuous updating" refers to the process of regularly supplementing and correcting data and information in order to keep it up-to-date.

[0017] An "interactive interface" refers to a screen display or means of operation that allows a user to directly interact with the system, inputting information or asking questions.

[0018] "Cleanup" refers to the process of removing unnecessary parts from generated responses and data, and organizing them into a format that is easy for users to understand.

[0019] An "appropriate response" refers to a response that provides accurate and useful information in response to a user's questions or requests. [Brief explanation of the drawing]

[0020] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0022] First, the language used in the following description will be explained.

[0023] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] As shown in Figure 1, the 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.

[0031] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0033] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0034] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0040] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0041] This invention relates to an interactive knowledge base system that allows employees to search for company information using generating AI. Embodiments of the present invention are described in detail below.

[0042] System Configuration

[0043] This system consists of three main components: servers, terminals, and users. Each component functions as follows:

[0044] server

[0045] The server has the functionality to set and initialize API keys for integration with the generation AI. It also imports and manages internal data such as company policies, procedures, and service information as a database. When it receives a question from an employee, it sends a query to the generation AI to generate an appropriate response. It is also responsible for cleaning up the response obtained from the generation AI and returning it to the employee.

[0046] terminal

[0047] Users access the system through a terminal and ask questions about specific information. The terminal communicates with the server and provides an interface for sending questions and receiving responses.

[0048] User

[0049] Users, acting as employees, input questions from their terminals and obtain the necessary information. This system enables users to efficiently search for information and perform their tasks quickly.

[0050] Program Processing Description

[0051] Initial setup

[0052] The server first configures the API key and establishes integration with the generating AI. It also imports internal data such as company policies, procedures, and service information, making them available within the system.

[0053] Received a question

[0054] When a user enters a specific question through their device, the device sends that question to the server.

[0055] Query generation

[0056] The server sends the received question as a query to the generating AI. The generating AI then generates an appropriate response based on internal company data.

[0057] Response generation and cleanup

[0058] The responses generated by the AI ​​are cleaned up by the server. Specifically, unnecessary information and noise are removed, and the responses are formatted into a user-friendly format.

[0059] Sending a response

[0060] The server returns the cleaned-up response to the terminal and provides it to the user. The user receives the response through the terminal and refers to the necessary information.

[0061] Specific example

[0062] For example, consider a scenario where a user asks a question about a new service proposal. When the user enters the question, "What new service should we offer?", from their device, the question is sent to the server. The server sends the query to the AI ​​generator, which then generates an appropriate response. For example, a response such as, "According to market research, the new service customers are looking for is a data analysis tool," is generated, cleaned up, and then sent back to the user. The user can then use this response to propose a new service.

[0063] This system allows employees to quickly obtain necessary information, thereby improving work efficiency. Furthermore, by using AI-generated responses, it is possible to flexibly respond to changing market and internal information, as responses are always based on the latest information.

[0064] The following describes the processing flow.

[0065] Step 1:

[0066] The server configures the API key to enable connection to the Generative AI service. To this end, it holds the Generative AI's API key and prepares for authentication. This establishes the foundation for the server to communicate with the Generative AI.

[0067] Step 2:

[0068] The server imports data such as company policies, procedures, and service information into the system. This data is stored in a structured format, such as a dictionary, and used in subsequent query processing. This import process allows the server to retrieve and manage the necessary information.

[0069] Step 3:

[0070] The user enters a specific question through the terminal. For example, they might enter a question like, "What new services should we offer?" The terminal then sends this question to the server in the appropriate format.

[0071] Step 4:

[0072] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand and combines them into a single query.

[0073] Step 5:

[0074] The server sends a query to the generative AI. The generative AI receives this query and generates an appropriate response based on the underlying knowledge database and trained models. This response also takes into account the internal data imported by the server.

[0075] Step 6:

[0076] Once the AI ​​generates a response, the server cleans it up. Specifically, it removes unnecessary information and noise and formats it in a way that is easy for the user to understand. This process refines the response.

[0077] Step 7:

[0078] The server sends a cleaned-up response back to the terminal. The response is provided in a format easily understandable to the user. The terminal displays this response and provides it to the user.

[0079] Step 8:

[0080] The user receives the response sent back from the server via their terminal and verifies the information. For example, they might confirm something like, "According to market research, the new service customers are looking for is a data analysis tool," and then use that information in their work.

[0081] In this way, a system is realized in which servers, terminals, and users work together to efficiently process information and quickly provide the necessary information.

[0082] (Example 1)

[0083] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] In modern businesses, there is a growing need for systems that allow employees to quickly and accurately find the information they need. However, traditional search systems struggle to efficiently extract necessary information from vast amounts of internal data. Furthermore, even with methods utilizing generative AI, insufficient initial setup and cleanup functions prevent users from obtaining information properly. This leads to decreased work efficiency and longer work processes.

[0085] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0086] In this invention, the server includes means for receiving questions entered by employees, means for analyzing the entered questions and sending queries to a generating AI, means for cleaning up the responses generated in response to the queries sent to the generating AI and returning them to the employees, means for importing data such as internal policies, procedures, and service information into the system and managing it as a database, and means for setting an API key and establishing cooperation with the generating AI. This enables employees to quickly and accurately search for internal information and improve the efficiency of their work.

[0087] "Generative AI" refers to algorithms that use artificial intelligence technology to automatically generate text and responses.

[0088] An "interactive knowledge base system" is an information management system that allows users to input questions and provides responses in real time.

[0089] An "employee" is a person who works within a company or organization and performs their duties.

[0090] "Means for receiving questions" refers to interfaces or systems for users to input questions or queries.

[0091] A "query" refers to an inquiry made to a database or information system, and is used to retrieve specific information.

[0092] "Cleanup" refers to the process of formatting the responses obtained from the generated AI into an appropriate format and removing unnecessary information.

[0093] A "database" is a system that stores a structured collection of data and allows for efficient management and retrieval.

[0094] A "policy" is a document that outlines the guidelines and rules for conduct within an organization.

[0095] A "procedure manual" is a document that specifically describes the steps involved in performing a task or operation.

[0096] "Service information" refers to various types of information about the services provided by companies and organizations.

[0097] "Import" refers to the process of taking in data from an external source and making it available for use within a system.

[0098] An "API key" is a set of authentication credentials required to access the interface of an application or service.

[0099] This invention relates to an interactive knowledge base system that allows employees to search for company information using generating AI. Embodiments of the present invention are described in detail below.

[0100] This system primarily consists of three elements: servers, terminals, and users. Specific data processing and calculations are performed using the following hardware and software.

[0101] Server configuration and functionality

[0102] The server sets up and initializes API keys to establish integration with the generated AI model. It also imports and manages internal data such as company policies, procedures, and service information as a database. The server functions as follows:

[0103] 1. API Key Setup and Integration with Generating AI: The server sets an API key (for example, an OpenAI® API key) to use the generating AI and initializes itself to enable communication with the generating AI.

[0104] 2. Data Import and Management: The server imports important company documents into the database using SQL queries and other methods, and manages them.

[0105] 3. Receiving and analyzing questions: The server receives questions from users as HTTP requests, analyzes those questions, and generates queries to send to the AI.

[0106] 4. Query submission and response cleanup: Receive responses from the generating AI, clean them up (remove unnecessary information and correct ambiguous expressions), and send them back to the user.

[0107] Device configuration and functions

[0108] The terminal provides an interface for the user to access the system and enter questions. The terminal functions as follows:

[0109] 1. Question Input and Submission: Provides a user interface for users to input questions about specific information. Once a question is entered, the question data is sent to the server.

[0110] 2. Receiving and displaying responses: Receive responses sent back from the server and display them in the user interface.

[0111] User roles

[0112] Users, as employees, will utilize this system to quickly and accurately search for necessary information and efficiently perform their duties. The following steps are possible for the user's specific operational procedures:

[0113] 1. Entering the question: The user enters a question via their terminal, such as "Please tell me about the new employee benefits."

[0114] 2. Confirm the response: Confirm the response from the server and determine the next business action based on that information.

[0115] Specific examples and prompt statements

[0116] For example, when asking a question about a proposed new service, the user would type "What new service should we offer?" on their device. This question is sent from the device to the server, where a generating AI produces a response such as "According to market research, the new service customers are looking for is a data analysis tool." After a cleanup process, the response is finally displayed on the user's device.

[0117] Example of a prompt

[0118] "Please provide suggestions for new services that we should offer."

[0119] "What is the latest information regarding new employee benefits?"

[0120] As described above, close collaboration between servers, terminals, and users enables efficient internal information retrieval using generated AI models. This system allows employees to quickly and accurately obtain necessary information and carry out their work smoothly.

[0121] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0122] Step 1: System Initial Setup

[0123] After startup, the server configures the API key and establishes communication with the generating AI. Specifically, it reads the API key and applies it to the connection settings of the generating AI. The server also imports internal data such as company policies, procedures, and service information and manages it as a database. For example, it executes SQL queries to store data in database tables, making internal information searchable.

[0124] Input: API key, internal document data

[0125] Data processing: API key setup, data import

[0126] Output: Establishment of integration with the generation AI, initialization of the database.

[0127] Step 2: Entering and receiving questions

[0128] The user enters a question through the terminal's interface. For example, they might enter the question, "Tell me about the new project management tool." Once a question is entered, the terminal sends this question data to the server as an HTTP request in packet format. The server receives the request and parses the question data.

[0129] Input: User question (e.g., Tell me about the new project management tool)

[0130] Data processing: Packeting and transmission of question data.

[0131] Output: Transmission of query data to the server

[0132] Step 3: Question analysis and query generation

[0133] The server analyzes the received question and generates a query in a format suitable for the generating AI. Specifically, it normalizes the question content and extracts important keywords. For example, parts like "new project management tool" and "please tell me" are tagged. Based on this, it creates a query to send to the generating AI.

[0134] Input: Question data (Please tell me about the new project management tool)

[0135] Data processing: Questionnaire analysis, keyword extraction, query generation

[0136] Output: Query to send to the generating AI

[0137] Step 4: Submitting the query and generating the response

[0138] The server sends a query to the generating AI. Based on this query, the generating AI generates an appropriate response from internal data. For example, it might generate a response such as, "Tool X is now recommended as a new project management tool." The generated response is then returned to the server.

[0139] Input: Query

[0140] Data processing: Response generation by generative AI

[0141] Output: Generated response (e.g., Tool X is recommended)

[0142] Step 5: Clean up the response

[0143] The server cleans up the responses obtained from the generated AI. Specifically, it removes redundant information and noise and formats them in a user-friendly format. For example, it might revise them to a short and clear format such as "Tool X is recommended."

[0144] Input: Response obtained from the generating AI

[0145] Data processing: Information cleanup, removal of redundant information, formatting.

[0146] Output: Cleaned-up response (e.g., Tool X is recommended)

[0147] Step 6: Sending and displaying the response

[0148] The server sends the cleaned-up response back to the terminal. The terminal receives the response data and displays it in the user interface. The user can then review this response and decide on their next action.

[0149] Input: Cleaned-up response

[0150] Data processing: Packeting and transmission of response data

[0151] Output: Response displayed in the user interface (e.g., Tool X is recommended)

[0152] In this way, each step works in conjunction with the others, allowing users to quickly and accurately obtain the information they need.

[0153] (Application Example 1)

[0154] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0155] There is a need to provide internal security personnel with a means to quickly and accurately search for specific internal protocols and security information. Especially at night or during emergencies, delays or omissions in information can cause serious problems, making the establishment of a proper knowledge base system an urgent necessity.

[0156] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0157] In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generating AI based on the entered questions and generating appropriate responses, means for cleaning up the responses obtained from the generating AI and returning them to the employees, means for importing and using data such as internal policies, procedures, and service information into the system, and means equipped with a function that allows security personnel to quickly search for specific internal protocols. This enables security personnel to quickly and accurately obtain necessary information even in emergencies or during nighttime patrols, allowing for problem solving and appropriate responses.

[0158] "Generative AI" is a type of artificial intelligence that has the function of generating new information and content based on large amounts of data.

[0159] A "query" refers to a question or request made to a database or generative AI, and is a means of obtaining information as a result.

[0160] "Cleanup" is the process of removing unnecessary information and noise from the responses obtained from the generating AI and organizing them into a format that is easy for the user to understand.

[0161] The term "employee" refers to individuals who belong to a company or organization and perform their duties.

[0162] A "policy" refers to a document that sets out guidelines and rules for conduct within a company or organization.

[0163] A "procedure manual" is a document that describes the methods and procedures for performing a specific task or operation.

[0164] "Service information" refers to detailed information and descriptions about the services provided by a company or organization.

[0165] An "interactive knowledge base system" is an interactive information retrieval system in which the system provides appropriate information when the user inputs questions in natural language.

[0166] "Security personnel" refers to people whose job it is to be responsible for the security of a company or organization.

[0167] "Internal protocol" refers to guidelines or rules that describe specific procedures and response methods established within a company or organization.

[0168] Modes for carrying out the invention

[0169] This invention relates to an interactive knowledge base system that uses generative AI to enable security personnel to quickly search for internal protocols and security information. The embodiments for carrying out this invention are described in detail below.

[0170] System Configuration

[0171] This system consists of three main components: servers, terminals, and users (security personnel). Each component functions as follows:

[0172] server

[0173] The server has the functionality to set and initialize API keys for integration with the Generative AI. It also imports and manages data such as internal policies, procedures, service information, and security protocols as a database. When it receives a question from security personnel, it sends a query to the Generative AI and generates an appropriate response. It is also responsible for cleaning up the response obtained from the Generative AI and returning it to the security personnel.

[0174] terminal

[0175] The terminal provides an interface for security personnel to access the system and ask questions about specific information. The terminal communicates with the server to send questions and receive responses.

[0176] User (security personnel)

[0177] Users input questions into the system via their terminals and quickly obtain the necessary information. This allows security personnel to perform their duties quickly and accurately.

[0178] Program Processing Description

[0179] The server first sets up an API key and establishes communication with the generating AI. It also imports data such as internal policies, procedures, service information, and security protocols, making them available within the system. When a user enters a question from a terminal, the terminal sends the question to the server. The server sends the received question as a query to the generating AI, which generates an appropriate response. This response is cleaned up by the server and sent back to the user.

[0180] Recommended hardware includes smartphones and cloud-based servers. Software used includes generative AI (e.g., OpenAI's GPT-4® API), a database (e.g., MySQL®), and a backend framework (e.g., Django or Flask). The generative AI generates information based on the received query and returns the results to the server.

[0181] Specific example

[0182] For example, suppose a security personnel member needs to quickly obtain information about the company's security protocols while on nighttime patrol. They type "What are the nighttime emergency response protocols?" into a smartphone app. This question is immediately sent to the server. The AI ​​generator produces an appropriate response, returning the answer, "The nighttime emergency response protocol is to first report to emergency contacts and then secure the site." The security personnel member can then respond quickly according to this.

[0183] Example of a prompt

[0184] When security personnel request a specific internal protocol, generate an appropriate response based on the following information:

[0185] User input: "What is the emergency response protocol for nighttime?"

[0186] Example output: "The nighttime emergency response protocol is to first report to emergency contacts, and then secure the scene."

[0187] This system allows security personnel to quickly and accurately obtain necessary information, improving operational efficiency and enabling rapid responses in emergencies.

[0188] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0189] Step 1:

[0190] The user enters a question into the device. For example, a security personnel member might use a smartphone app to enter "What is the nighttime emergency response protocol?" This is the input for Step 1.

[0191] Step 2:

[0192] The terminal sends the user's question to the server. The entered question data is sent from the terminal to the server. Specifically, the text data entered in the question form is sent to the server as an HTTP request.

[0193] Step 3:

[0194] The server receives the question and sends the query to the generating AI. The server receives the HTTP request and formats the question text as a query for the generating AI. For example, the question "What is the nighttime emergency response protocol?" is converted into an API request to the generating AI. This is the input for step 3.

[0195] Step 4:

[0196] The generating AI generates a response based on the query. The generating AI analyzes the input query and generates an appropriate response based on the database. For example, it might generate the response, "The nighttime emergency response protocol is to first report to emergency contacts and then secure the scene." This is the output of step 4.

[0197] Step 5:

[0198] The server cleans up the response received from the generating AI. The server receives the response from the generating AI, removes unnecessary information and noise, and formats it in a user-friendly format. Specifically, it adjusts line breaks and punctuation to make the response text easier to read. This is the output of step 5.

[0199] Step 6:

[0200] The server sends a cleaned-up response back to the terminal. The formatted response text is sent from the server to the terminal. Specifically, text data is returned as an HTTP response.

[0201] Step 7:

[0202] The terminal receives a response from the server and displays it to the user. The terminal displays the received response text in the user interface. For example, the smartphone app screen might display the text: "Nighttime emergency response protocol is to first report to emergency contacts and then secure the scene."

[0203] In this way, security personnel can quickly and accurately obtain the necessary information, enabling more efficient operations and faster response times.

[0204] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0205] This invention relates to an interactive knowledge base system and its configuration method that enables employees to effectively search for company information using a generative AI and an emotion engine. Embodiments of the present invention are described in detail below.

[0206] System Configuration

[0207] This system consists of four main components: the server, the terminal, the emotion engine, and the user. The function of each component is as follows:

[0208] server

[0209] The server establishes cooperation with the generative AI and emotion engine to manage internal company information. Specifically, it imports data such as internal policies, procedures, and service information, and stores and manages them in a database. It also receives questions, sends queries to the generative AI to generate responses, and has the functionality to recognize the user's emotions with the emotion engine and adjust the response accordingly.

[0210] terminal

[0211] The terminal provides an interface for the user to access the system. Here, the user enters questions and receives responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[0212] Emotional Engine

[0213] The emotion engine recognizes emotions from the user's input questions and conversation content. The recognized emotion data is sent to the server and reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[0214] User

[0215] Users access the system through their terminals to search for company information. Questions entered by users are processed through the server and a sentiment engine, providing appropriate responses.

[0216] Program Processing Description

[0217] Initial setup

[0218] The server configures API keys to connect to the AI ​​generation and emotion engine services. It also imports internal data such as company policies, procedures, and service information, and stores and manages them as a database.

[0219] Received a question

[0220] The user enters a specific question through the terminal. For example, they might enter a question like, "What new service should we offer?" The terminal then sends this question to the server.

[0221] Query generation

[0222] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand.

[0223] Recognition of emotions

[0224] Simultaneously, the terminal sends the user's input to the emotion engine, which then recognizes the emotion. The emotion engine analyzes the user's emotion and sends the results back to the server.

[0225] Response generation and adjustment

[0226] The server sends queries to the generative AI and generates appropriate responses. The responses obtained from the generative AI are then cleaned up by the server. Furthermore, the tone and content of the responses are adjusted based on the sentiment data returned from the sentiment engine.

[0227] Sending a response

[0228] The server sends a cleaned and refined response back to the terminal. The terminal displays this response to the user, enabling the user to efficiently utilize the information.

[0229] Specific example

[0230] For example, consider a scenario where an employee enters a question via a terminal, such as, "What new service should we offer?" The question is sent from the terminal to the server. The server sends the query to the generation AI, which then generates an appropriate response. The emotion engine also recognizes emotions from the user's input, detecting feelings such as "anxiety" or "excitement." Based on these findings, the server adjusts the content and tone of the response. For example, it might generate a response like, "According to market research, the new service customers are looking for is a data analysis tool," which is then cleaned up and emotionally adjusted before being provided to the user. The user then uses this response to propose new services and incorporates them into their work.

[0231] In this way, a system is realized in which the server, terminal, emotion engine, and user work together to efficiently process information and quickly provide the necessary information. The introduction of the emotion engine enables a more personalized experience by providing responses based on the user's emotions.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The server configures API keys to enable connection to the Generative AI and Emotion Engine services. To this end, it holds the API keys for the Generative AI and Emotion Engine and prepares for authentication. This establishes the foundation for the server to communicate with the Generative AI and Emotion Engine.

[0235] Step 2:

[0236] The server imports data such as company policies, procedures, and service information into the system. This data is stored in a structured format, such as a dictionary, and used in subsequent query processing. This import process allows the server to retrieve and manage the necessary information.

[0237] Step 3:

[0238] The user enters a specific question through the terminal. For example, they might enter a question like, "What new services should we offer?" The terminal then sends this question to the server in the appropriate format.

[0239] Step 4:

[0240] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand and combines them into a single query.

[0241] Step 5:

[0242] The device sends user input to an emotion engine to recognize the user's emotions. The emotion engine analyzes the input and identifies the user's emotions (e.g., anxiety, excitement, calmness). This emotion data is then sent from the device to the server.

[0243] Step 6:

[0244] The server sends a query to the generative AI. The generative AI receives this query and generates an appropriate response based on the underlying knowledge database and trained models. This response also takes into account the internal data imported by the server.

[0245] Step 7:

[0246] Once the AI ​​generates a response, the server cleans it up. Specifically, it removes unnecessary information and noise and formats it in a way that is easy for the user to understand. For example, it changes redundant parts and technical jargon into more concise expressions.

[0247] Step 8:

[0248] The server adjusts the content and tone of the generated response based on the emotional data sent from the emotion engine. For example, if the user is expressing anxiety, the response will have an encouraging tone added.

[0249] Step 9:

[0250] The server sends a cleaned-up, sentiment-based response back to the terminal. The terminal displays this response to the user, enabling them to efficiently utilize the information.

[0251] Step 10:

[0252] The user receives the response sent back from the server via their terminal and verifies the information. For example, they might see a response such as, "According to market research, the new service customers are looking for is a data analysis tool," and then use that information in their work.

[0253] In this way, a system is realized in which the server, terminal, emotion engine, and user work together to efficiently process information and quickly provide the necessary information. The introduction of the emotion engine enables a more personalized experience by providing responses based on the user's emotions.

[0254] (Example 2)

[0255] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0256] Traditional internal information retrieval systems have a problem in that they struggle to provide optimal responses to employee questions that take context and emotions into account. This makes it difficult for employees to effectively retrieve information, potentially leading to decreased work efficiency. Furthermore, if the responses generated by the AI ​​are inappropriate, it could undermine employee satisfaction and the reliability of the system.

[0257] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generation AI based on the entered questions and generating appropriate responses, means for cleaning up the responses obtained from the generation AI and returning them to the employees, means for importing and using data such as company policies, procedures, and service information into the system, means for recognizing the employee's emotions from the input content using an emotion engine, and means for adjusting the tone and content of the response based on the recognized emotion data. As a result, employees can obtain optimal responses that take context and emotions into consideration, improving the efficiency of information acquisition and enabling smoother business operations.

[0258] "Employee" refers to an employee who belongs to a company or organization.

[0259] "Means for receiving questions" refers to the interface or mechanism for receiving questions entered by employees into the system.

[0260] "Generative AI" refers to an artificial intelligence system that generates relevant responses to questions in natural language.

[0261] "Means of sending queries" refers to a mechanism for converting employee questions into a format that the generation AI can understand and then sending it to the generation AI.

[0262] "Means for cleaning up responses" refers to a mechanism for checking responses sent back by the generating AI, removing unnecessary information, and providing it to employees.

[0263] "Means of importing and using data into a system" refers to a mechanism for incorporating internal company policies, procedures, service information, and other data into a system, and accessing and using it as needed.

[0264] An "emotion engine" refers to a system or software that recognizes emotions from employee input and generates corresponding data.

[0265] "Means of recognizing emotions" refers to a mechanism that uses an emotion engine to analyze emotions from employees' questions and inputs, and generate emotion data.

[0266] "Means for adjusting the tone and content of responses" refers to a mechanism for appropriately adjusting the tone and content of generated responses based on recognized emotional data.

[0267] This invention relates to an interactive knowledge base system that uses generative AI and an emotion engine to enable employees to effectively search for company information. Embodiments of the present invention are described in detail below.

[0268] System Configuration

[0269] This system consists of four main components: the server, the terminal, the emotion engine, and the user. The specific functions of each component are described in detail below.

[0270] server

[0271] The server establishes connections with generative AI and emotion engines and manages internal company information. Specifically, the server sets API keys to connect to the generative AI and emotion engine services (e.g., OpenAI API). The server imports data such as internal policies, procedures, and service information into the system and stores and manages it in a NoSQL database (e.g., MongoDB). Furthermore, it has the functionality to analyze user questions, send queries to the generative AI to generate responses, and use the emotion engine to recognize the user's emotions and adjust the response accordingly.

[0272] terminal

[0273] The terminal provides an interface for the user to access the system. The terminal allows the user to input questions and receive responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[0274] Emotional Engine

[0275] The emotion engine recognizes emotions from the questions and conversations entered by the user. Using natural language processing technology, the emotion engine analyzes the user's emotions and sends the results to the server. The recognized emotion data is reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[0276] User

[0277] Users access the system through their terminals and search for company information. When a user enters a question, an appropriate response is provided via the server and a sentiment engine.

[0278] Specific example

[0279] For example, consider a scenario where an employee enters a question via a terminal, such as, "What new service should we offer?" The question is sent from the terminal to the server. The server sends a query to the generation AI, which generates a response such as, "According to market research, the new service customers are looking for is a data analysis tool." Simultaneously, the emotion engine recognizes emotions such as "anxiety" or "excitement" from the user's input. Based on this, the server appropriately adjusts the tone of the response and sends the adjusted response back to the terminal. The user then uses this information to propose new services and apply it to their work.

[0280] Example of a prompt

[0281] 1. "Please tell us what opinions your employees have regarding the new project."

[0282] 2. "Are there any items that should be on the agenda for the next meeting?"

[0283] 3. "Please tell us about any problems with the current progress of the project."

[0284] The flow of the specific process in Example 2 will be described using FIG. 13.

[0285] Step 1: The user inputs a specific question through the terminal. For example, the user inputs a question such as "What is the service to be newly provided?" The terminal sends this question to the server as an HTTP request. The input is the question from the user, and the output is the request to the server.

[0286] Step 2: The server receives the question sent from the user. Specifically, the server analyzes the HTTP request and extracts the question part. The input is the request from the terminal, and the output is the analyzed question.

[0287] Step 3: The server converts the analyzed question into a format that the generative AI can understand. For example, the natural language processing module (e.g., spaCy) is used to tokenize the question and convert it into the prompt format of the generative AI. The input is the analyzed question, and the output is the prompt sentence for the generative AI.

[0288] Step 4: The server sends the prompt sentence to the generative AI. Specifically, the API of the generative AI (e.g., OpenAI API) is used to send the prompt sentence as a query. The input is the prompt sentence for the generative AI, and the output is the response sentence from the generative AI.

[0289] Step 5: The server sends the input content of the user from the terminal to the sentiment engine. Specifically, the API of the sentiment engine (e.g., sentiment analysis API) is used to analyze the input content. The input is the question content of the user, and the output is the recognized sentiment data.

[0290] Step 6: The sentiment engine recognizes the sentiment from the input content of the user and sends the result to the server. Specifically, the natural language processing algorithm is used to analyze the sentiment, and the result is returned to the server as structured data. The input is the question content of the user, and the output is the sentiment analysis result.

[0291] Step 7: The server cleans up the response sentence obtained from the generating AI. Specifically, it filters out inappropriate phrases and redundant information, extracting only the necessary information. The input is the response sentence from the generating AI, and the output is the cleaned-up response sentence.

[0292] Step 8: The server adjusts the tone and content of the response based on the emotion data returned from the emotion engine. For example, if anxiety is detected, the response is changed to a calmer tone. The input is the cleaned-up response and emotion data, and the output is the adjusted response.

[0293] Step 9: The server sends the prepared response back to the terminal. Specifically, it sends it back to the terminal as an HTTP response. The input is the prepared response, and the output is the response to the terminal.

[0294] Step 10: The terminal receives the prepared response from the server and displays it to the user. Specifically, the response is displayed on the screen through the terminal's user interface. The input is the response from the server, and the output is the information displayed to the user.

[0295] (Application Example 2)

[0296] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0297] Traditional interactive knowledge base systems have a drawback: they fail to provide responses that take user emotions into account, resulting in a poor user experience. Furthermore, providing appropriate support to factory workers, especially during emergencies or when they are confused, is difficult. This can lead to a decrease in work efficiency. Additionally, responses that do not incorporate emotion analysis provide users with insufficient assistance, making it difficult to achieve overall improvements in work efficiency.

[0298] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0299] In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generating AI based on the entered questions to generate appropriate responses, means for importing and utilizing data such as internal policies, procedures, and service information into the system, means for analyzing employees' emotions using an emotion engine and adjusting the tone and content of responses based on the analysis results, and means for enabling employees to efficiently search for information through an interactive interface. This provides appropriate and personalized responses that take into account the user's emotions, enabling quick and accurate support, especially for factory workers, even in emergencies or when they are confused.

[0300] "Generative AI" refers to artificial intelligence models that perform natural language generation, and is a technology used to generate appropriate responses to specific queries.

[0301] An "interactive knowledge base system" is a system that automatically generates and responds with appropriate information and answers when a user inputs a question in natural language.

[0302] An "emotion engine" is a technology that analyzes user emotions from their input and conversation content, and reflects the results in the response.

[0303] A "query" is the information used when a user asks a question or makes a request to a specific system or database.

[0304] A "server" is a computer system that establishes cooperation with generative AI and emotion engines, and manages data and generates and adjusts responses.

[0305] A "terminal" is a device that provides an interface for users to access a system, input questions and instructions, and receive responses.

[0306] "Import" refers to the process of taking external data into the system and making it available for use.

[0307] "Cleanup" refers to the process of removing unnecessary information and redundant parts from the generated response and organizing it to make it easier for the user to understand.

[0308] "Sentiment analysis" refers to the technology of automatically recognizing and classifying the sentiment from the user's input content.

[0309] "Response tone" refers to the element that adjusts the atmosphere and nuance of the generated response and is appropriately changed according to the user's sentiment.

[0310] This invention relates to an interactive knowledge-based system and its configuration method that enable employees to effectively search for in-company information using generative AI and an emotion engine. The main components are a server, a terminal, an emotion engine, and a user.

[0311] System Configuration

[0312] Server

[0313] The server plays a central role in this system. The server establishes cooperation between the generative AI and the emotion engine and manages in-company information. Specifically, it imports data such as in-company policies, procedure manuals, service information, etc., and stores and manages them in a database. Also, the server receives the user's questions, sends queries to the generative AI to generate responses, and has the function of recognizing the user's sentiment using the emotion engine and adjusting the tone and content of the response based on the result.

[0314] Terminal

[0315] [[ID=3

[0316] Emotional Engine

[0317] The emotion engine recognizes emotions from the user's input questions and conversation content. The recognized emotion data is sent to the server and reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[0318] User

[0319] Users access the system through their terminals to search for company information. The questions entered by users are processed through the server and sentiment engine, and appropriate responses are provided.

[0320] Hardware and software to be used

[0321] Server: Python-based web framework (e.g., Flask, Django)

[0322] Generative AI: Natural language generation models (e.g., OpenAI's GPT model)

[0323] Emotion engine: Emotion analysis API (e.g., Microsoft® Azure® Cognitive Services Emotion API)

[0324] Device: User interface device (e.g., PC, smartphone, tablet)

[0325] Software interface: Operating system software (e.g., ROS)

[0326] Program processing

[0327] The server connects to the generative AI and sentiment engine services using API keys. It also imports and manages internal company data (policies, procedures, service information, etc.) as a database. When a user enters a question through a terminal, the server receives it and generates a query for the generative AI. Simultaneously, the terminal sends the entered question to the sentiment engine for sentiment analysis. Using the response obtained from the generative AI and the sentiment analysis results, the server adjusts the tone and content of the response. This ensures that the user receives the most appropriate response.

[0328] Specific example

[0329] For example, consider a scenario where a factory worker types "Please tell me how to repair this machine" into a terminal. The question is sent to the server, which then sends a query to the generating AI. Simultaneously, the emotion engine detects emotions such as "anxiety" from the input. Based on this, the server adjusts the tone and content of the response, providing a response like, "Don't worry, first turn off the machine. Next, open the panel and remove the filter." In this way, the introduction of an emotion engine enables more nuanced support based on the user's emotions.

[0330] Example of a prompt

[0331] Operator asks: "Could you please explain the repair procedure for this machine?"

[0332] Query to the generating AI: "Please tell me the repair procedure for factory machinery."

[0333] Emotional analysis result from the emotional engine: "Anxiety"

[0334] Generated response: "Don't worry, first turn off the machine, then open the panel and remove the filter."

[0335] In this way, interactive knowledge base systems enable employees to efficiently search for internal company information and provide emotion-based, customized responses. Therefore, they can not only improve operational efficiency but also enhance the user experience.

[0336] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0337] Step 1:

[0338] The user enters a question through the terminal. For example, they might enter, "Please tell me how to repair this machine." The terminal accepts the user's question and sends it to the server. The data entered is in text format, and the user's question is sent to the server exactly as it is.

[0339] Step 2:

[0340] The server analyzes the question received from the terminal and converts it into a format for sending queries to the generative AI. Specifically, it uses natural language processing (NLP) algorithms to extract the intent of the question and convert it into an appropriate query. This query is then sent to the generative AI. The input data is the user's question, and the output data is the query sent to the generative AI.

[0341] Step 3:

[0342] Simultaneously, the terminal sends the user's input question to the emotion engine for emotional analysis. The emotion engine identifies emotions from the input text and generates emotion tags such as "anxiety" or "excitement." The input data is the user's question, and the output data is the analyzed emotion tags. Specifically, the emotion analysis API is called and the results are received.

[0343] Step 4:

[0344] The server receives the response from the generating AI. The generating AI generates an appropriate response based on the sent query. This response is in text format and is sent back to the server. The input data is the query sent to the generating AI, and the output data is the generated response. Specifically, this involves reading the response to an API call.

[0345] Step 5:

[0346] The server receives emotion tags from the emotion engine and adjusts the tone and content of the response generated by the AI ​​based on the received emotion tags. For example, if the emotion tag "anxiety" is detected, the server changes the response to a more reassuring tone. The input data is the generated response and emotion tags, and the output data is the adjusted response. In terms of specific operations, string manipulation and template application are performed.

[0347] Step 6:

[0348] The server sends a pre-formatted response back to the terminal. The terminal displays this response to the user or outputs it as audio. The input data is the pre-formatted response, and the output data is the response in a format that is easy for the user to understand. Specifically, this can involve text display or speech synthesis.

[0349] In this way, users can efficiently search for internal company information and receive personalized responses based on their emotions. As a result, appropriate support becomes possible, especially for users in emergencies or when they are confused, improving work efficiency and user experience.

[0350] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0351] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0352] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0353] [Second Embodiment]

[0354] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0355] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0356] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0357] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0358] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0359] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0360] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0361] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0362] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0363] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0364] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0365] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0366] This invention relates to an interactive knowledge base system that allows employees to search for company information using generating AI. Embodiments of the present invention are described in detail below.

[0367] System Configuration

[0368] This system consists of three main components: servers, terminals, and users. Each component functions as follows:

[0369] server

[0370] The server has the functionality to set and initialize API keys for integration with the generation AI. It also imports and manages internal data such as company policies, procedures, and service information as a database. When it receives a question from an employee, it sends a query to the generation AI to generate an appropriate response. It is also responsible for cleaning up the response obtained from the generation AI and returning it to the employee.

[0371] terminal

[0372] Users access the system through a terminal and ask questions about specific information. The terminal communicates with the server and provides an interface for sending questions and receiving responses.

[0373] User

[0374] Users, acting as employees, input questions from their terminals and obtain the necessary information. This system enables users to efficiently search for information and perform their tasks quickly.

[0375] Program Processing Description

[0376] Initial setup

[0377] The server first configures the API key and establishes integration with the generating AI. It also imports internal data such as company policies, procedures, and service information, making them available within the system.

[0378] Received a question

[0379] When a user enters a specific question through their device, the device sends that question to the server.

[0380] Query generation

[0381] The server sends the received question as a query to the generating AI. The generating AI then generates an appropriate response based on internal company data.

[0382] Response generation and cleanup

[0383] The responses generated by the AI ​​are cleaned up by the server. Specifically, unnecessary information and noise are removed, and the responses are formatted into a user-friendly format.

[0384] Sending a response

[0385] The server returns the cleaned-up response to the terminal and provides it to the user. The user receives the response through the terminal and refers to the necessary information.

[0386] Specific example

[0387] For example, consider a scenario where a user asks a question about a new service proposal. When the user enters the question, "What new service should we offer?", from their device, the question is sent to the server. The server sends the query to the AI ​​generator, which then generates an appropriate response. For example, a response such as, "According to market research, the new service customers are looking for is a data analysis tool," is generated, cleaned up, and then sent back to the user. The user can then use this response to propose a new service.

[0388] This system allows employees to quickly obtain necessary information, thereby improving work efficiency. Furthermore, by using AI-generated responses, it is possible to flexibly respond to changing market and internal information, as responses are always based on the latest information.

[0389] The following describes the processing flow.

[0390] Step 1:

[0391] The server configures the API key to enable connection to the Generative AI service. To this end, it holds the Generative AI's API key and prepares for authentication. This establishes the foundation for the server to communicate with the Generative AI.

[0392] Step 2:

[0393] The server imports data such as company policies, procedures, and service information into the system. This data is stored in a structured format, such as a dictionary, and used in subsequent query processing. This import process allows the server to retrieve and manage the necessary information.

[0394] Step 3:

[0395] The user enters a specific question through the terminal. For example, they might enter a question like, "What new services should we offer?" The terminal then sends this question to the server in the appropriate format.

[0396] Step 4:

[0397] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand and combines them into a single query.

[0398] Step 5:

[0399] The server sends a query to the generative AI. The generative AI receives this query and generates an appropriate response based on the underlying knowledge database and trained models. This response also takes into account the internal data imported by the server.

[0400] Step 6:

[0401] Once the AI ​​generates a response, the server cleans it up. Specifically, it removes unnecessary information and noise and formats it in a way that is easy for the user to understand. This process refines the response.

[0402] Step 7:

[0403] The server sends a cleaned-up response back to the terminal. The response is provided in a format easily understandable to the user. The terminal displays this response and provides it to the user.

[0404] Step 8:

[0405] The user receives the response sent back from the server via their terminal and verifies the information. For example, they might confirm something like, "According to market research, the new service customers are looking for is a data analysis tool," and then use that information in their work.

[0406] In this way, a system is realized in which servers, terminals, and users work together to efficiently process information and quickly provide the necessary information.

[0407] (Example 1)

[0408] Next, we will describe Example 1. 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."

[0409] In modern businesses, there is a growing need for systems that allow employees to quickly and accurately find the information they need. However, traditional search systems struggle to efficiently extract necessary information from vast amounts of internal data. Furthermore, even with methods utilizing generative AI, insufficient initial setup and cleanup functions prevent users from obtaining information properly. This leads to decreased work efficiency and longer work processes.

[0410] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0411] In this invention, the server includes means for receiving questions entered by employees, means for analyzing the entered questions and sending queries to a generating AI, means for cleaning up the responses generated in response to the queries sent to the generating AI and returning them to the employees, means for importing data such as internal policies, procedures, and service information into the system and managing it as a database, and means for setting an API key and establishing cooperation with the generating AI. This enables employees to quickly and accurately search for internal information and improve the efficiency of their work.

[0412] "Generative AI" refers to algorithms that use artificial intelligence technology to automatically generate text and responses.

[0413] An "interactive knowledge base system" is an information management system that allows users to input questions and provides responses in real time.

[0414] An "employee" is a person who works within a company or organization and performs their duties.

[0415] "Means for receiving questions" refers to interfaces or systems for users to input questions or queries.

[0416] A "query" refers to an inquiry made to a database or information system, and is used to retrieve specific information.

[0417] "Cleanup" refers to the process of formatting the responses obtained from the generated AI into an appropriate format and removing unnecessary information.

[0418] A "database" is a system that stores a structured collection of data and allows for efficient management and retrieval.

[0419] A "policy" is a document that outlines the guidelines and rules for conduct within an organization.

[0420] A "procedure manual" is a document that specifically describes the steps involved in performing a task or operation.

[0421] "Service information" refers to various types of information about the services provided by companies and organizations.

[0422] "Import" refers to the process of taking in data from an external source and making it available for use within a system.

[0423] An "API key" is a set of authentication credentials required to access the interface of an application or service.

[0424] This invention relates to an interactive knowledge base system that allows employees to search for company information using generating AI. Embodiments of the present invention are described in detail below.

[0425] This system primarily consists of three elements: servers, terminals, and users. Specific data processing and calculations are performed using the following hardware and software.

[0426] Server configuration and functionality

[0427] The server sets up and initializes API keys to establish integration with the generated AI model. It also imports and manages internal data such as company policies, procedures, and service information as a database. The server functions as follows:

[0428] 1. API key setup and integration with the generation AI: The server sets an API key (for example, an OpenAI API key) to use the generation AI and initializes itself to enable communication with the generation AI.

[0429] 2. Data Import and Management: The server imports important company documents into the database using SQL queries and other methods, and manages them.

[0430] 3. Receiving and analyzing questions: The server receives questions from users as HTTP requests, analyzes those questions, and generates queries to send to the AI.

[0431] 4. Query submission and response cleanup: Receive responses from the generating AI, clean them up (remove unnecessary information and correct ambiguous expressions), and send them back to the user.

[0432] Device configuration and functions

[0433] The terminal provides an interface for the user to access the system and enter questions. The terminal functions as follows:

[0434] 1. Question Input and Submission: Provides a user interface for users to input questions about specific information. Once a question is entered, the question data is sent to the server.

[0435] 2. Receiving and displaying responses: Receive responses sent back from the server and display them in the user interface.

[0436] User roles

[0437] Users, as employees, will utilize this system to quickly and accurately search for necessary information and efficiently perform their duties. The following steps are possible for the user's specific operational procedures:

[0438] 1. Entering the question: The user enters a question via their terminal, such as "Please tell me about the new employee benefits."

[0439] 2. Confirm the response: Confirm the response from the server and determine the next business action based on that information.

[0440] Specific examples and prompt statements

[0441] For example, when asking a question about a proposed new service, the user would type "What new service should we offer?" on their device. This question is sent from the device to the server, where a generating AI produces a response such as "According to market research, the new service customers are looking for is a data analysis tool." After a cleanup process, the response is finally displayed on the user's device.

[0442] Example of a prompt

[0443] "Please provide suggestions for new services that we should offer."

[0444] "What is the latest information regarding new employee benefits?"

[0445] As described above, close collaboration between servers, terminals, and users enables efficient internal information retrieval using generated AI models. This system allows employees to quickly and accurately obtain necessary information and carry out their work smoothly.

[0446] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0447] Step 1: System Initial Setup

[0448] After startup, the server configures the API key and establishes communication with the generating AI. Specifically, it reads the API key and applies it to the connection settings of the generating AI. The server also imports internal data such as company policies, procedures, and service information and manages it as a database. For example, it executes SQL queries to store data in database tables, making internal information searchable.

[0449] Input: API key, internal document data

[0450] Data processing: API key setup, data import

[0451] Output: Establishment of integration with the generation AI, initialization of the database.

[0452] Step 2: Entering and receiving questions

[0453] The user enters a question through the terminal's interface. For example, they might enter the question, "Tell me about the new project management tool." Once a question is entered, the terminal sends this question data to the server as an HTTP request in packet format. The server receives the request and parses the question data.

[0454] Input: User question (e.g., Tell me about the new project management tool)

[0455] Data processing: Packeting and transmission of question data.

[0456] Output: Transmission of query data to the server

[0457] Step 3: Question analysis and query generation

[0458] The server analyzes the received question and generates a query in a format suitable for the generating AI. Specifically, it normalizes the question content and extracts important keywords. For example, parts like "new project management tool" and "please tell me" are tagged. Based on this, it creates a query to send to the generating AI.

[0459] Input: Question data (Please tell me about the new project management tool)

[0460] Data processing: Questionnaire analysis, keyword extraction, query generation

[0461] Output: Query to send to the generating AI

[0462] Step 4: Submitting the query and generating the response

[0463] The server sends a query to the generating AI. Based on this query, the generating AI generates an appropriate response from internal data. For example, it might generate a response such as, "Tool X is now recommended as a new project management tool." The generated response is then returned to the server.

[0464] Input: Query

[0465] Data processing: Response generation by generative AI

[0466] Output: Generated response (e.g., Tool X is recommended)

[0467] Step 5: Clean up the response

[0468] The server cleans up the responses obtained from the generated AI. Specifically, it removes redundant information and noise and formats them in a user-friendly format. For example, it might revise them to a short and clear format such as "Tool X is recommended."

[0469] Input: Response obtained from the generating AI

[0470] Data processing: Information cleanup, removal of redundant information, formatting.

[0471] Output: Cleaned-up response (e.g., Tool X is recommended)

[0472] Step 6: Sending and displaying the response

[0473] The server sends the cleaned-up response back to the terminal. The terminal receives the response data and displays it in the user interface. The user can then review this response and decide on their next action.

[0474] Input: Cleaned-up response

[0475] Data processing: Packeting and transmission of response data

[0476] Output: Response displayed in the user interface (e.g., Tool X is recommended)

[0477] In this way, each step works in conjunction with the others, allowing users to quickly and accurately obtain the information they need.

[0478] (Application Example 1)

[0479] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0480] There is a need to provide internal security personnel with a means to quickly and accurately search for specific internal protocols and security information. Especially at night or during emergencies, delays or omissions in information can cause serious problems, making the establishment of a proper knowledge base system an urgent necessity.

[0481] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0482] In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generating AI based on the entered questions and generating appropriate responses, means for cleaning up the responses obtained from the generating AI and returning them to the employees, means for importing and using data such as internal policies, procedures, and service information into the system, and means equipped with a function that allows security personnel to quickly search for specific internal protocols. This enables security personnel to quickly and accurately obtain necessary information even in emergencies or during nighttime patrols, allowing for problem solving and appropriate responses.

[0483] "Generative AI" is a type of artificial intelligence that has the function of generating new information and content based on large amounts of data.

[0484] A "query" refers to a question or request made to a database or generative AI, and is a means of obtaining information as a result.

[0485] "Cleanup" is the process of removing unnecessary information and noise from the responses obtained from the generating AI and organizing them into a format that is easy for the user to understand.

[0486] The term "employee" refers to individuals who belong to a company or organization and perform their duties.

[0487] A "policy" refers to a document that sets out guidelines and rules for conduct within a company or organization.

[0488] A "procedure manual" is a document that describes the methods and procedures for performing a specific task or operation.

[0489] "Service information" refers to detailed information and descriptions about the services provided by a company or organization.

[0490] An "interactive knowledge base system" is an interactive information retrieval system in which the system provides appropriate information when the user inputs questions in natural language.

[0491] "Security personnel" refers to people whose job it is to be responsible for the security of a company or organization.

[0492] "Internal protocol" refers to guidelines or rules that describe specific procedures and response methods established within a company or organization.

[0493] Modes for carrying out the invention

[0494] This invention relates to an interactive knowledge base system that uses generative AI to enable security personnel to quickly search for internal protocols and security information. The embodiments for carrying out this invention are described in detail below.

[0495] System Configuration

[0496] This system consists of three main components: servers, terminals, and users (security personnel). Each component functions as follows:

[0497] server

[0498] The server has the functionality to set and initialize API keys for integration with the Generative AI. It also imports and manages data such as internal policies, procedures, service information, and security protocols as a database. When it receives a question from security personnel, it sends a query to the Generative AI and generates an appropriate response. It is also responsible for cleaning up the response obtained from the Generative AI and returning it to the security personnel.

[0499] terminal

[0500] The terminal provides an interface for security personnel to access the system and ask questions about specific information. The terminal communicates with the server to send questions and receive responses.

[0501] User (security personnel)

[0502] Users input questions into the system via their terminals and quickly obtain the necessary information. This allows security personnel to perform their duties quickly and accurately.

[0503] Program Processing Description

[0504] The server first sets up an API key and establishes communication with the generating AI. It also imports data such as internal policies, procedures, service information, and security protocols, making them available within the system. When a user enters a question from a terminal, the terminal sends the question to the server. The server sends the received question as a query to the generating AI, which generates an appropriate response. This response is cleaned up by the server and sent back to the user.

[0505] Recommended hardware includes smartphones and cloud-based servers. Software used includes generative AI (e.g., OpenAI's GPT-4 API), a database (e.g., MySQL), and a backend framework (e.g., Django or Flask). The generative AI generates information based on the received query and returns the results to the server.

[0506] Specific example

[0507] For example, suppose a security personnel member needs to quickly obtain information about the company's security protocols while on nighttime patrol. They type "What are the nighttime emergency response protocols?" into a smartphone app. This question is immediately sent to the server. The AI ​​generator produces an appropriate response, returning the answer, "The nighttime emergency response protocol is to first report to emergency contacts and then secure the site." The security personnel member can then respond quickly according to this.

[0508] Example of a prompt

[0509] When security personnel request a specific internal protocol, generate an appropriate response based on the following information:

[0510] User input: "What is the emergency response protocol for nighttime?"

[0511] Example output: "The nighttime emergency response protocol is to first report to emergency contacts, and then secure the scene."

[0512] This system allows security personnel to quickly and accurately obtain necessary information, improving operational efficiency and enabling rapid responses in emergencies.

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The user enters a question into the device. For example, a security personnel member might use a smartphone app to enter "What is the nighttime emergency response protocol?" This is the input for Step 1.

[0516] Step 2:

[0517] The terminal sends the user's question to the server. The entered question data is sent from the terminal to the server. Specifically, the text data entered in the question form is sent to the server as an HTTP request.

[0518] Step 3:

[0519] The server receives the question and sends the query to the generating AI. The server receives the HTTP request and formats the question text as a query for the generating AI. For example, the question "What is the nighttime emergency response protocol?" is converted into an API request to the generating AI. This is the input for step 3.

[0520] Step 4:

[0521] The generating AI generates a response based on the query. The generating AI analyzes the input query and generates an appropriate response based on the database. For example, it might generate the response, "The nighttime emergency response protocol is to first report to emergency contacts and then secure the scene." This is the output of step 4.

[0522] Step 5:

[0523] The server cleans up the response received from the generating AI. The server receives the response from the generating AI, removes unnecessary information and noise, and formats it in a user-friendly format. Specifically, it adjusts line breaks and punctuation to make the response text easier to read. This is the output of step 5.

[0524] Step 6:

[0525] The server sends a cleaned-up response back to the terminal. The formatted response text is sent from the server to the terminal. Specifically, text data is returned as an HTTP response.

[0526] Step 7:

[0527] The terminal receives a response from the server and displays it to the user. The terminal displays the received response text in the user interface. For example, the smartphone app screen might display the text: "Nighttime emergency response protocol is to first report to emergency contacts and then secure the scene."

[0528] In this way, security personnel can quickly and accurately obtain the necessary information, enabling more efficient operations and faster response times.

[0529] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0530] This invention relates to an interactive knowledge base system and its configuration method that enables employees to effectively search for company information using a generative AI and an emotion engine. Embodiments of the present invention are described in detail below.

[0531] System Configuration

[0532] This system consists of four main components: the server, the terminal, the emotion engine, and the user. The function of each component is as follows:

[0533] server

[0534] The server establishes cooperation with the generative AI and emotion engine to manage internal company information. Specifically, it imports data such as internal policies, procedures, and service information, and stores and manages them in a database. It also receives questions, sends queries to the generative AI to generate responses, and has the functionality to recognize the user's emotions with the emotion engine and adjust the response accordingly.

[0535] terminal

[0536] The terminal provides an interface for the user to access the system. Here, the user enters questions and receives responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[0537] Emotional Engine

[0538] The emotion engine recognizes emotions from the user's input questions and conversation content. The recognized emotion data is sent to the server and reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[0539] User

[0540] Users access the system through their terminals to search for company information. Questions entered by users are processed through the server and a sentiment engine, providing appropriate responses.

[0541] Program Processing Description

[0542] Initial setup

[0543] The server configures API keys to connect to the AI ​​generation and emotion engine services. It also imports internal data such as company policies, procedures, and service information, and stores and manages them as a database.

[0544] Received a question

[0545] The user enters a specific question through the terminal. For example, they might enter a question like, "What new service should we offer?" The terminal then sends this question to the server.

[0546] Query generation

[0547] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand.

[0548] Recognition of emotions

[0549] Simultaneously, the terminal sends the user's input to the emotion engine, which then recognizes the emotion. The emotion engine analyzes the user's emotion and sends the results back to the server.

[0550] Response generation and adjustment

[0551] The server sends queries to the generative AI and generates appropriate responses. The responses obtained from the generative AI are then cleaned up by the server. Furthermore, the tone and content of the responses are adjusted based on the sentiment data returned from the sentiment engine.

[0552] Sending a response

[0553] The server sends a cleaned and refined response back to the terminal. The terminal displays this response to the user, enabling the user to efficiently utilize the information.

[0554] Specific example

[0555] For example, consider a scenario where an employee enters a question via a terminal, such as, "What new service should we offer?" The question is sent from the terminal to the server. The server sends the query to the generation AI, which then generates an appropriate response. The emotion engine also recognizes emotions from the user's input, detecting feelings such as "anxiety" or "excitement." Based on these findings, the server adjusts the content and tone of the response. For example, it might generate a response like, "According to market research, the new service customers are looking for is a data analysis tool," which is then cleaned up and emotionally adjusted before being provided to the user. The user then uses this response to propose new services and incorporates them into their work.

[0556] In this way, a system is realized in which the server, terminal, emotion engine, and user work together to efficiently process information and quickly provide the necessary information. The introduction of the emotion engine enables a more personalized experience by providing responses based on the user's emotions.

[0557] The following describes the processing flow.

[0558] Step 1:

[0559] The server configures API keys to enable connection to the Generative AI and Emotion Engine services. To this end, it holds the API keys for the Generative AI and Emotion Engine and prepares for authentication. This establishes the foundation for the server to communicate with the Generative AI and Emotion Engine.

[0560] Step 2:

[0561] The server imports data such as company policies, procedures, and service information into the system. This data is stored in a structured format, such as a dictionary, and used in subsequent query processing. This import process allows the server to retrieve and manage the necessary information.

[0562] Step 3:

[0563] The user enters a specific question through the terminal. For example, they might enter a question like, "What new services should we offer?" The terminal then sends this question to the server in the appropriate format.

[0564] Step 4:

[0565] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand and combines them into a single query.

[0566] Step 5:

[0567] The device sends user input to an emotion engine to recognize the user's emotions. The emotion engine analyzes the input and identifies the user's emotions (e.g., anxiety, excitement, calmness). This emotion data is then sent from the device to the server.

[0568] Step 6:

[0569] The server sends a query to the generative AI. The generative AI receives this query and generates an appropriate response based on the underlying knowledge database and trained models. This response also takes into account the internal data imported by the server.

[0570] Step 7:

[0571] Once the AI ​​generates a response, the server cleans it up. Specifically, it removes unnecessary information and noise and formats it in a way that is easy for the user to understand. For example, it changes redundant parts and technical jargon into more concise expressions.

[0572] Step 8:

[0573] The server adjusts the content and tone of the generated response based on the emotional data sent from the emotion engine. For example, if the user is expressing anxiety, the response will have an encouraging tone added.

[0574] Step 9:

[0575] The server sends a cleaned-up, sentiment-based response back to the terminal. The terminal displays this response to the user, enabling them to efficiently utilize the information.

[0576] Step 10:

[0577] The user receives the response sent back from the server via their terminal and verifies the information. For example, they might see a response such as, "According to market research, the new service customers are looking for is a data analysis tool," and then use that information in their work.

[0578] In this way, a system is realized in which the server, terminal, emotion engine, and user work together to efficiently process information and quickly provide the necessary information. The introduction of the emotion engine enables a more personalized experience by providing responses based on the user's emotions.

[0579] (Example 2)

[0580] Next, we will describe Example 2. 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".

[0581] Traditional internal information retrieval systems have a problem in that they struggle to provide optimal responses to employee questions that take context and emotions into account. This makes it difficult for employees to effectively retrieve information, potentially leading to decreased work efficiency. Furthermore, if the responses generated by the AI ​​are inappropriate, it could undermine employee satisfaction and the reliability of the system.

[0582] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generation AI based on the entered questions and generating appropriate responses, means for cleaning up the responses obtained from the generation AI and returning them to the employees, means for importing and using data such as company policies, procedures, and service information into the system, means for recognizing the employee's emotions from the input content using an emotion engine, and means for adjusting the tone and content of the response based on the recognized emotion data. As a result, employees can obtain optimal responses that take context and emotions into consideration, improving the efficiency of information acquisition and enabling smoother business operations.

[0583] "Employee" refers to an employee who belongs to a company or organization.

[0584] "Means for receiving questions" refers to the interface or mechanism for receiving questions entered by employees into the system.

[0585] "Generative AI" refers to an artificial intelligence system that generates relevant responses to questions in natural language.

[0586] "Means of sending queries" refers to a mechanism for converting employee questions into a format that the generation AI can understand and then sending it to the generation AI.

[0587] "Means for cleaning up responses" refers to a mechanism for checking responses sent back by the generating AI, removing unnecessary information, and providing it to employees.

[0588] "Means of importing and using data into a system" refers to a mechanism for incorporating internal company policies, procedures, service information, and other data into a system, and accessing and using it as needed.

[0589] An "emotion engine" refers to a system or software that recognizes emotions from employee input and generates corresponding data.

[0590] "Means of recognizing emotions" refers to a mechanism that uses an emotion engine to analyze emotions from employees' questions and inputs, and generate emotion data.

[0591] "Means for adjusting the tone and content of responses" refers to a mechanism for appropriately adjusting the tone and content of generated responses based on recognized emotional data.

[0592] This invention relates to an interactive knowledge base system that uses generative AI and an emotion engine to enable employees to effectively search for company information. Embodiments of the present invention are described in detail below.

[0593] System Configuration

[0594] This system consists of four main components: the server, the terminal, the emotion engine, and the user. The specific functions of each component are described in detail below.

[0595] server

[0596] The server establishes connections with generative AI and emotion engines and manages internal company information. Specifically, the server sets API keys to connect to the generative AI and emotion engine services (e.g., OpenAI API). The server imports data such as internal policies, procedures, and service information into the system and stores and manages it in a NoSQL database (e.g., MongoDB). Furthermore, it has the functionality to analyze user questions, send queries to the generative AI to generate responses, and use the emotion engine to recognize the user's emotions and adjust the response accordingly.

[0597] terminal

[0598] The terminal provides an interface for the user to access the system. The terminal allows the user to input questions and receive responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[0599] Emotional Engine

[0600] The emotion engine recognizes emotions from the questions and conversations entered by the user. Using natural language processing technology, the emotion engine analyzes the user's emotions and sends the results to the server. The recognized emotion data is reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[0601] User

[0602] Users access the system through their terminals and search for company information. When a user enters a question, an appropriate response is provided via the server and a sentiment engine.

[0603] Specific example

[0604] For example, consider a scenario where an employee enters a question via a terminal, such as, "What new service should we offer?" The question is sent from the terminal to the server. The server sends a query to the generation AI, which generates a response such as, "According to market research, the new service customers are looking for is a data analysis tool." Simultaneously, the emotion engine recognizes emotions such as "anxiety" or "excitement" from the user's input. Based on this, the server appropriately adjusts the tone of the response and sends the adjusted response back to the terminal. The user then uses this information to propose new services and apply it to their work.

[0605] Example of a prompt

[0606] 1. "Please tell us what opinions your employees have regarding the new project."

[0607] 2. "Are there any items that should be on the agenda for the next meeting?"

[0608] 3. "Please tell us about any problems with the current progress of the project."

[0609] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0610] Step 1: The user enters a specific question through the terminal. For example, they might enter a question like, "What new service should we offer?" The terminal sends this question to the server as an HTTP request. The input is the question from the user, and the output is the request to the server.

[0611] Step 2: The server receives the question sent by the user. Specifically, the server parses the HTTP request and extracts the question portion. The input is the request from the terminal, and the output is the parsed question.

[0612] Step 3: The server converts the parsed question into a format that the generating AI can understand. For example, it uses a natural language processing module (e.g., spaCy) to tokenize the question and convert it into a prompt format for the generating AI. The input is the parsed question, and the output is a prompt for the generating AI.

[0613] Step 4: The server sends a prompt to the generating AI. Specifically, it sends the prompt as a query using the generating AI's API (e.g., OpenAI API). The input is the prompt for the generating AI, and the output is the response from the generating AI.

[0614] Step 5: The server sends the user's input from the terminal to the emotion engine. Specifically, it analyzes the input using the emotion engine's API (e.g., emotion analysis API). The input is the user's question, and the output is the recognized emotion data.

[0615] Step 6: The emotion engine recognizes emotions from the user's input and sends the results to the server. Specifically, it uses a natural language processing algorithm to analyze emotions and returns the results to the server as structured data. The input is the user's question, and the output is the emotion analysis result.

[0616] Step 7: The server cleans up the response sentence obtained from the generating AI. Specifically, it filters out inappropriate phrases and redundant information, extracting only the necessary information. The input is the response sentence from the generating AI, and the output is the cleaned-up response sentence.

[0617] Step 8: The server adjusts the tone and content of the response based on the emotion data returned from the emotion engine. For example, if anxiety is detected, the response is changed to a calmer tone. The input is the cleaned-up response and emotion data, and the output is the adjusted response.

[0618] Step 9: The server sends the prepared response back to the terminal. Specifically, it sends it back to the terminal as an HTTP response. The input is the prepared response, and the output is the response to the terminal.

[0619] Step 10: The terminal receives the prepared response from the server and displays it to the user. Specifically, the response is displayed on the screen through the terminal's user interface. The input is the response from the server, and the output is the information displayed to the user.

[0620] (Application Example 2)

[0621] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0622] Traditional interactive knowledge base systems have a drawback: they fail to provide responses that take user emotions into account, resulting in a poor user experience. Furthermore, providing appropriate support to factory workers, especially during emergencies or when they are confused, is difficult. This can lead to a decrease in work efficiency. Additionally, responses that do not incorporate emotion analysis provide users with insufficient assistance, making it difficult to achieve overall improvements in work efficiency.

[0623] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0624] In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generating AI based on the entered questions to generate appropriate responses, means for importing and utilizing data such as internal policies, procedures, and service information into the system, means for analyzing employees' emotions using an emotion engine and adjusting the tone and content of responses based on the analysis results, and means for enabling employees to efficiently search for information through an interactive interface. This provides appropriate and personalized responses that take into account the user's emotions, enabling quick and accurate support, especially for factory workers, even in emergencies or when they are confused.

[0625] "Generative AI" refers to artificial intelligence models that perform natural language generation, and is a technology used to generate appropriate responses to specific queries.

[0626] An "interactive knowledge base system" is a system that automatically generates and responds with appropriate information and answers when a user inputs a question in natural language.

[0627] An "emotion engine" is a technology that analyzes user emotions from their input and conversation content, and reflects the results in the response.

[0628] A "query" is the information used when a user asks a question or makes a request to a specific system or database.

[0629] A "server" is a computer system that establishes cooperation with generative AI and emotion engines, and manages data and generates and adjusts responses.

[0630] A "terminal" is a device that provides an interface for users to access a system, input questions and instructions, and receive responses.

[0631] "Import" is the process of bringing external data into a system and making it available for use.

[0632] "Cleanup" is the process of removing unnecessary information and redundant parts from a generated response and organizing it to make it easier for the user to understand.

[0633] "Emotion analysis" is a technology that automatically recognizes and classifies emotions based on user input.

[0634] "Response tone" refers to elements that adjust the atmosphere and nuances of the generated response, and are appropriately changed according to the user's emotions.

[0635] This invention relates to an interactive knowledge base system and its configuration method that allows employees to effectively search for company information using generative AI and an emotion engine. The main components are a server, a terminal, an emotion engine, and a user.

[0636] System Configuration

[0637] server

[0638] The server plays a central role in this system. The server establishes coordination between the generative AI and the emotion engine and manages internal company information. Specifically, it imports data such as internal policies, procedures, and service information, and stores and manages it in a database. The server also receives user questions, sends queries to the generative AI to generate responses, and uses the emotion engine to recognize the user's emotions, adjusting the tone and content of the response based on the results.

[0639] terminal

[0640] The terminal provides an interface for the user to access the system. The user inputs questions through the terminal and receives responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[0641] Emotional Engine

[0642] The emotion engine recognizes emotions from the user's input questions and conversation content. The recognized emotion data is sent to the server and reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[0643] User

[0644] Users access the system through their terminals to search for company information. The questions entered by users are processed through the server and sentiment engine, and appropriate responses are provided.

[0645] Hardware and software to be used

[0646] Server: Python-based web framework (e.g., Flask, Django)

[0647] Generative AI: Natural language generation models (e.g., OpenAI's GPT model)

[0648] Emotion engine: Emotion analysis API (e.g., Microsoft Azure Cognitive Services' Emotion API)

[0649] Device: User interface device (e.g., PC, smartphone, tablet)

[0650] Software interface: Operating system software (e.g., ROS)

[0651] Program processing

[0652] The server connects to the generative AI and sentiment engine services using API keys. It also imports and manages internal company data (policies, procedures, service information, etc.) as a database. When a user enters a question through a terminal, the server receives it and generates a query for the generative AI. Simultaneously, the terminal sends the entered question to the sentiment engine for sentiment analysis. Using the response obtained from the generative AI and the sentiment analysis results, the server adjusts the tone and content of the response. This ensures that the user receives the most appropriate response.

[0653] Specific example

[0654] For example, consider a scenario where a factory worker types "Please tell me how to repair this machine" into a terminal. The question is sent to the server, which then sends a query to the generating AI. Simultaneously, the emotion engine detects emotions such as "anxiety" from the input. Based on this, the server adjusts the tone and content of the response, providing a response like, "Don't worry, first turn off the machine. Next, open the panel and remove the filter." In this way, the introduction of an emotion engine enables more nuanced support based on the user's emotions.

[0655] Example of a prompt

[0656] Operator asks: "Could you please explain the repair procedure for this machine?"

[0657] Query to the generating AI: "Please tell me the repair procedure for factory machinery."

[0658] Emotional analysis result from the emotional engine: "Anxiety"

[0659] Generated response: "Don't worry, first turn off the machine, then open the panel and remove the filter."

[0660] In this way, interactive knowledge base systems enable employees to efficiently search for internal company information and provide emotion-based, customized responses. Therefore, they can not only improve operational efficiency but also enhance the user experience.

[0661] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0662] Step 1:

[0663] The user enters a question through the terminal. For example, they might enter, "Please tell me how to repair this machine." The terminal accepts the user's question and sends it to the server. The data entered is in text format, and the user's question is sent to the server exactly as it is.

[0664] Step 2:

[0665] The server analyzes the question received from the terminal and converts it into a format for sending queries to the generative AI. Specifically, it uses natural language processing (NLP) algorithms to extract the intent of the question and convert it into an appropriate query. This query is then sent to the generative AI. The input data is the user's question, and the output data is the query sent to the generative AI.

[0666] Step 3:

[0667] Simultaneously, the terminal sends the user's input question to the emotion engine for emotional analysis. The emotion engine identifies emotions from the input text and generates emotion tags such as "anxiety" or "excitement." The input data is the user's question, and the output data is the analyzed emotion tags. Specifically, the emotion analysis API is called and the results are received.

[0668] Step 4:

[0669] The server receives the response from the generating AI. The generating AI generates an appropriate response based on the sent query. This response is in text format and is sent back to the server. The input data is the query sent to the generating AI, and the output data is the generated response. Specifically, this involves reading the response to an API call.

[0670] Step 5:

[0671] The server receives emotion tags from the emotion engine and adjusts the tone and content of the response generated by the AI ​​based on the received emotion tags. For example, if the emotion tag "anxiety" is detected, the server changes the response to a more reassuring tone. The input data is the generated response and emotion tags, and the output data is the adjusted response. In terms of specific operations, string manipulation and template application are performed.

[0672] Step 6:

[0673] The server sends a pre-formatted response back to the terminal. The terminal displays this response to the user or outputs it as audio. The input data is the pre-formatted response, and the output data is the response in a format that is easy for the user to understand. Specifically, this can involve text display or speech synthesis.

[0674] In this way, users can efficiently search for internal company information and receive personalized responses based on their emotions. As a result, appropriate support becomes possible, especially for users in emergencies or when they are confused, improving work efficiency and user experience.

[0675] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0676] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0677] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0678] [Third Embodiment]

[0679] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0680] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0681] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0682] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0683] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0684] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0685] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0686] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0687] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0688] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0689] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0690] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0691] This invention relates to an interactive knowledge base system that allows employees to search for company information using generating AI. Embodiments of the present invention are described in detail below.

[0692] System Configuration

[0693] This system consists of three main components: servers, terminals, and users. Each component functions as follows:

[0694] server

[0695] The server has the functionality to set and initialize API keys for integration with the generation AI. It also imports and manages internal data such as company policies, procedures, and service information as a database. When it receives a question from an employee, it sends a query to the generation AI to generate an appropriate response. It is also responsible for cleaning up the response obtained from the generation AI and returning it to the employee.

[0696] terminal

[0697] Users access the system through a terminal and ask questions about specific information. The terminal communicates with the server and provides an interface for sending questions and receiving responses.

[0698] User

[0699] Users, acting as employees, input questions from their terminals and obtain the necessary information. This system enables users to efficiently search for information and perform their tasks quickly.

[0700] Program Processing Description

[0701] Initial setup

[0702] The server first configures the API key and establishes integration with the generating AI. It also imports internal data such as company policies, procedures, and service information, making them available within the system.

[0703] Received a question

[0704] When a user enters a specific question through their device, the device sends that question to the server.

[0705] Query generation

[0706] The server sends the received question as a query to the generating AI. The generating AI then generates an appropriate response based on internal company data.

[0707] Response generation and cleanup

[0708] The responses generated by the AI ​​are cleaned up by the server. Specifically, unnecessary information and noise are removed, and the responses are formatted into a user-friendly format.

[0709] Sending a response

[0710] The server returns the cleaned-up response to the terminal and provides it to the user. The user receives the response through the terminal and refers to the necessary information.

[0711] Specific example

[0712] For example, consider a scenario where a user asks a question about a new service proposal. When the user enters the question, "What new service should we offer?", from their device, the question is sent to the server. The server sends the query to the AI ​​generator, which then generates an appropriate response. For example, a response such as, "According to market research, the new service customers are looking for is a data analysis tool," is generated, cleaned up, and then sent back to the user. The user can then use this response to propose a new service.

[0713] This system allows employees to quickly obtain necessary information, thereby improving work efficiency. Furthermore, by using AI-generated responses, it is possible to flexibly respond to changing market and internal information, as responses are always based on the latest information.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The server configures the API key to enable connection to the Generative AI service. To this end, it holds the Generative AI's API key and prepares for authentication. This establishes the foundation for the server to communicate with the Generative AI.

[0717] Step 2:

[0718] The server imports data such as company policies, procedures, and service information into the system. This data is stored in a structured format, such as a dictionary, and used in subsequent query processing. This import process allows the server to retrieve and manage the necessary information.

[0719] Step 3:

[0720] The user enters a specific question through the terminal. For example, they might enter a question like, "What new services should we offer?" The terminal then sends this question to the server in the appropriate format.

[0721] Step 4:

[0722] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand and combines them into a single query.

[0723] Step 5:

[0724] The server sends a query to the generative AI. The generative AI receives this query and generates an appropriate response based on the underlying knowledge database and trained models. This response also takes into account the internal data imported by the server.

[0725] Step 6:

[0726] Once the AI ​​generates a response, the server cleans it up. Specifically, it removes unnecessary information and noise and formats it in a way that is easy for the user to understand. This process refines the response.

[0727] Step 7:

[0728] The server sends a cleaned-up response back to the terminal. The response is provided in a format easily understandable to the user. The terminal displays this response and provides it to the user.

[0729] Step 8:

[0730] The user receives the response sent back from the server via their terminal and verifies the information. For example, they might confirm something like, "According to market research, the new service customers are looking for is a data analysis tool," and then use that information in their work.

[0731] In this way, a system is realized in which servers, terminals, and users work together to efficiently process information and quickly provide the necessary information.

[0732] (Example 1)

[0733] Next, we will describe Example 1. 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."

[0734] In modern businesses, there is a growing need for systems that allow employees to quickly and accurately find the information they need. However, traditional search systems struggle to efficiently extract necessary information from vast amounts of internal data. Furthermore, even with methods utilizing generative AI, insufficient initial setup and cleanup functions prevent users from obtaining information properly. This leads to decreased work efficiency and longer work processes.

[0735] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0736] In this invention, the server includes means for receiving questions entered by employees, means for analyzing the entered questions and sending queries to a generating AI, means for cleaning up the responses generated in response to the queries sent to the generating AI and returning them to the employees, means for importing data such as internal policies, procedures, and service information into the system and managing it as a database, and means for setting an API key and establishing cooperation with the generating AI. This enables employees to quickly and accurately search for internal information and improve the efficiency of their work.

[0737] "Generative AI" refers to algorithms that use artificial intelligence technology to automatically generate text and responses.

[0738] An "interactive knowledge base system" is an information management system that allows users to input questions and provides responses in real time.

[0739] An "employee" is a person who works within a company or organization and performs their duties.

[0740] "Means for receiving questions" refers to interfaces or systems for users to input questions or queries.

[0741] A "query" refers to an inquiry made to a database or information system, and is used to retrieve specific information.

[0742] "Cleanup" refers to the process of formatting the responses obtained from the generated AI into an appropriate format and removing unnecessary information.

[0743] A "database" is a system that stores a structured collection of data and allows for efficient management and retrieval.

[0744] A "policy" is a document that outlines the guidelines and rules for conduct within an organization.

[0745] A "procedure manual" is a document that specifically describes the steps involved in performing a task or operation.

[0746] "Service information" refers to various types of information about the services provided by companies and organizations.

[0747] "Import" refers to the process of taking in data from an external source and making it available for use within a system.

[0748] An "API key" is a set of authentication credentials required to access the interface of an application or service.

[0749] This invention relates to an interactive knowledge base system that allows employees to search for company information using generating AI. Embodiments of the present invention are described in detail below.

[0750] This system primarily consists of three elements: servers, terminals, and users. Specific data processing and calculations are performed using the following hardware and software.

[0751] Server configuration and functionality

[0752] The server sets up and initializes API keys to establish integration with the generated AI model. It also imports and manages internal data such as company policies, procedures, and service information as a database. The server functions as follows:

[0753] 1. API key setup and integration with the generation AI: The server sets an API key (for example, an OpenAI API key) to use the generation AI and initializes itself to enable communication with the generation AI.

[0754] 2. Data Import and Management: The server imports important company documents into the database using SQL queries and other methods, and manages them.

[0755] 3. Receiving and analyzing questions: The server receives questions from users as HTTP requests, analyzes those questions, and generates queries to send to the AI.

[0756] 4. Query submission and response cleanup: Receive responses from the generating AI, clean them up (remove unnecessary information and correct ambiguous expressions), and send them back to the user.

[0757] Device configuration and functions

[0758] The terminal provides an interface for the user to access the system and enter questions. The terminal functions as follows:

[0759] 1. Question Input and Submission: Provides a user interface for users to input questions about specific information. Once a question is entered, the question data is sent to the server.

[0760] 2. Receiving and displaying responses: Receive responses sent back from the server and display them in the user interface.

[0761] User roles

[0762] Users, as employees, will utilize this system to quickly and accurately search for necessary information and efficiently perform their duties. The following steps are possible for the user's specific operational procedures:

[0763] 1. Entering the question: The user enters a question via their terminal, such as "Please tell me about the new employee benefits."

[0764] 2. Confirm the response: Confirm the response from the server and determine the next business action based on that information.

[0765] Specific examples and prompt statements

[0766] For example, when asking a question about a proposed new service, the user would type "What new service should we offer?" on their device. This question is sent from the device to the server, where a generating AI produces a response such as "According to market research, the new service customers are looking for is a data analysis tool." After a cleanup process, the response is finally displayed on the user's device.

[0767] Example of a prompt

[0768] "Please provide suggestions for new services that we should offer."

[0769] "What is the latest information regarding new employee benefits?"

[0770] As described above, close collaboration between servers, terminals, and users enables efficient internal information retrieval using generated AI models. This system allows employees to quickly and accurately obtain necessary information and carry out their work smoothly.

[0771] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0772] Step 1: System Initial Setup

[0773] After startup, the server configures the API key and establishes communication with the generating AI. Specifically, it reads the API key and applies it to the connection settings of the generating AI. The server also imports internal data such as company policies, procedures, and service information and manages it as a database. For example, it executes SQL queries to store data in database tables, making internal information searchable.

[0774] Input: API key, internal document data

[0775] Data processing: API key setup, data import

[0776] Output: Establishment of integration with the generation AI, initialization of the database.

[0777] Step 2: Entering and receiving questions

[0778] The user enters a question through the terminal's interface. For example, they might enter the question, "Tell me about the new project management tool." Once a question is entered, the terminal sends this question data to the server as an HTTP request in packet format. The server receives the request and parses the question data.

[0779] Input: User question (e.g., Tell me about the new project management tool)

[0780] Data processing: Packeting and transmission of question data.

[0781] Output: Transmission of query data to the server

[0782] Step 3: Question analysis and query generation

[0783] The server analyzes the received question and generates a query in a format suitable for the generating AI. Specifically, it normalizes the question content and extracts important keywords. For example, parts like "new project management tool" and "please tell me" are tagged. Based on this, it creates a query to send to the generating AI.

[0784] Input: Question data (Please tell me about the new project management tool)

[0785] Data processing: Questionnaire analysis, keyword extraction, query generation

[0786] Output: Query to send to the generating AI

[0787] Step 4: Submitting the query and generating the response

[0788] The server sends a query to the generating AI. Based on this query, the generating AI generates an appropriate response from internal data. For example, it might generate a response such as, "Tool X is now recommended as a new project management tool." The generated response is then returned to the server.

[0789] Input: Query

[0790] Data processing: Response generation by generative AI

[0791] Output: Generated response (e.g., Tool X is recommended)

[0792] Step 5: Clean up the response

[0793] The server cleans up the responses obtained from the generated AI. Specifically, it removes redundant information and noise and formats them in a user-friendly format. For example, it might revise them to a short and clear format such as "Tool X is recommended."

[0794] Input: Response obtained from the generating AI

[0795] Data processing: Information cleanup, removal of redundant information, formatting.

[0796] Output: Cleaned-up response (e.g., Tool X is recommended)

[0797] Step 6: Sending and displaying the response

[0798] The server sends the cleaned-up response back to the terminal. The terminal receives the response data and displays it in the user interface. The user can then review this response and decide on their next action.

[0799] Input: Cleaned-up response

[0800] Data processing: Packeting and transmission of response data

[0801] Output: Response displayed in the user interface (e.g., Tool X is recommended)

[0802] In this way, each step works in conjunction with the others, allowing users to quickly and accurately obtain the information they need.

[0803] (Application Example 1)

[0804] Next, we will explain Application Example 1. In the following explanation, 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."

[0805] There is a need to provide internal security personnel with a means to quickly and accurately search for specific internal protocols and security information. Especially at night or during emergencies, delays or omissions in information can cause serious problems, making the establishment of a proper knowledge base system an urgent necessity.

[0806] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0807] In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generating AI based on the entered questions and generating appropriate responses, means for cleaning up the responses obtained from the generating AI and returning them to the employees, means for importing and using data such as internal policies, procedures, and service information into the system, and means equipped with a function that allows security personnel to quickly search for specific internal protocols. This enables security personnel to quickly and accurately obtain necessary information even in emergencies or during nighttime patrols, allowing for problem solving and appropriate responses.

[0808] "Generative AI" is a type of artificial intelligence that has the function of generating new information and content based on large amounts of data.

[0809] A "query" refers to a question or request made to a database or generative AI, and is a means of obtaining information as a result.

[0810] "Cleanup" is the process of removing unnecessary information and noise from the responses obtained from the generating AI and organizing them into a format that is easy for the user to understand.

[0811] The term "employee" refers to individuals who belong to a company or organization and perform their duties.

[0812] A "policy" refers to a document that sets out guidelines and rules for conduct within a company or organization.

[0813] A "procedure manual" is a document that describes the methods and procedures for performing a specific task or operation.

[0814] "Service information" refers to detailed information and descriptions about the services provided by a company or organization.

[0815] An "interactive knowledge base system" is an interactive information retrieval system in which the system provides appropriate information when the user inputs questions in natural language.

[0816] "Security personnel" refers to people whose job it is to be responsible for the security of a company or organization.

[0817] "Internal protocol" refers to guidelines or rules that describe specific procedures and response methods established within a company or organization.

[0818] Modes for carrying out the invention

[0819] This invention relates to an interactive knowledge base system that uses generative AI to enable security personnel to quickly search for internal protocols and security information. The embodiments for carrying out this invention are described in detail below.

[0820] System Configuration

[0821] This system consists of three main components: servers, terminals, and users (security personnel). Each component functions as follows:

[0822] server

[0823] The server has the functionality to set and initialize API keys for integration with the Generative AI. It also imports and manages data such as internal policies, procedures, service information, and security protocols as a database. When it receives a question from security personnel, it sends a query to the Generative AI and generates an appropriate response. It is also responsible for cleaning up the response obtained from the Generative AI and returning it to the security personnel.

[0824] terminal

[0825] The terminal provides an interface for security personnel to access the system and ask questions about specific information. The terminal communicates with the server to send questions and receive responses.

[0826] User (security personnel)

[0827] Users input questions into the system via their terminals and quickly obtain the necessary information. This allows security personnel to perform their duties quickly and accurately.

[0828] Program Processing Description

[0829] The server first sets up an API key and establishes communication with the generating AI. It also imports data such as internal policies, procedures, service information, and security protocols, making them available within the system. When a user enters a question from a terminal, the terminal sends the question to the server. The server sends the received question as a query to the generating AI, which generates an appropriate response. This response is cleaned up by the server and sent back to the user.

[0830] Recommended hardware includes smartphones and cloud-based servers. Software used includes generative AI (e.g., OpenAI's GPT-4 API), a database (e.g., MySQL), and a backend framework (e.g., Django or Flask). The generative AI generates information based on the received query and returns the results to the server.

[0831] Specific example

[0832] For example, suppose a security personnel member needs to quickly obtain information about the company's security protocols while on nighttime patrol. They type "What are the nighttime emergency response protocols?" into a smartphone app. This question is immediately sent to the server. The AI ​​generator produces an appropriate response, returning the answer, "The nighttime emergency response protocol is to first report to emergency contacts and then secure the site." The security personnel member can then respond quickly according to this.

[0833] Example of a prompt

[0834] When security personnel request a specific internal protocol, generate an appropriate response based on the following information:

[0835] User input: "What is the emergency response protocol for nighttime?"

[0836] Example output: "The nighttime emergency response protocol is to first report to emergency contacts, and then secure the scene."

[0837] This system allows security personnel to quickly and accurately obtain necessary information, improving operational efficiency and enabling rapid responses in emergencies.

[0838] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0839] Step 1:

[0840] The user enters a question into the device. For example, a security personnel member might use a smartphone app to enter "What is the nighttime emergency response protocol?" This is the input for Step 1.

[0841] Step 2:

[0842] The terminal sends the user's question to the server. The entered question data is sent from the terminal to the server. Specifically, the text data entered in the question form is sent to the server as an HTTP request.

[0843] Step 3:

[0844] The server receives the question and sends the query to the generating AI. The server receives the HTTP request and formats the question text as a query for the generating AI. For example, the question "What is the nighttime emergency response protocol?" is converted into an API request to the generating AI. This is the input for step 3.

[0845] Step 4:

[0846] The generating AI generates a response based on the query. The generating AI analyzes the input query and generates an appropriate response based on the database. For example, it might generate the response, "The nighttime emergency response protocol is to first report to emergency contacts and then secure the scene." This is the output of step 4.

[0847] Step 5:

[0848] The server cleans up the response received from the generating AI. The server receives the response from the generating AI, removes unnecessary information and noise, and formats it in a user-friendly format. Specifically, it adjusts line breaks and punctuation to make the response text easier to read. This is the output of step 5.

[0849] Step 6:

[0850] The server sends a cleaned-up response back to the terminal. The formatted response text is sent from the server to the terminal. Specifically, text data is returned as an HTTP response.

[0851] Step 7:

[0852] The terminal receives a response from the server and displays it to the user. The terminal displays the received response text in the user interface. For example, the smartphone app screen might display the text: "Nighttime emergency response protocol is to first report to emergency contacts and then secure the scene."

[0853] In this way, security personnel can quickly and accurately obtain the necessary information, enabling more efficient operations and faster response times.

[0854] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0855] This invention relates to an interactive knowledge base system and its configuration method that enables employees to effectively search for company information using a generative AI and an emotion engine. Embodiments of the present invention are described in detail below.

[0856] System Configuration

[0857] This system consists of four main components: the server, the terminal, the emotion engine, and the user. The function of each component is as follows:

[0858] server

[0859] The server establishes cooperation with the generative AI and emotion engine to manage internal company information. Specifically, it imports data such as internal policies, procedures, and service information, and stores and manages them in a database. It also receives questions, sends queries to the generative AI to generate responses, and has the functionality to recognize the user's emotions with the emotion engine and adjust the response accordingly.

[0860] terminal

[0861] The terminal provides an interface for the user to access the system. Here, the user enters questions and receives responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[0862] Emotional Engine

[0863] The emotion engine recognizes emotions from the user's input questions and conversation content. The recognized emotion data is sent to the server and reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[0864] User

[0865] Users access the system through their terminals to search for company information. Questions entered by users are processed through the server and a sentiment engine, providing appropriate responses.

[0866] Program Processing Description

[0867] Initial setup

[0868] The server configures API keys to connect to the AI ​​generation and emotion engine services. It also imports internal data such as company policies, procedures, and service information, and stores and manages them as a database.

[0869] Received a question

[0870] The user enters a specific question through the terminal. For example, they might enter a question like, "What new service should we offer?" The terminal then sends this question to the server.

[0871] Query generation

[0872] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand.

[0873] Recognition of emotions

[0874] Simultaneously, the terminal sends the user's input to the emotion engine, which then recognizes the emotion. The emotion engine analyzes the user's emotion and sends the results back to the server.

[0875] Response generation and adjustment

[0876] The server sends queries to the generative AI and generates appropriate responses. The responses obtained from the generative AI are then cleaned up by the server. Furthermore, the tone and content of the responses are adjusted based on the sentiment data returned from the sentiment engine.

[0877] Sending a response

[0878] The server sends a cleaned and refined response back to the terminal. The terminal displays this response to the user, enabling the user to efficiently utilize the information.

[0879] Specific example

[0880] For example, consider a scenario where an employee enters a question via a terminal, such as, "What new service should we offer?" The question is sent from the terminal to the server. The server sends the query to the generation AI, which then generates an appropriate response. The emotion engine also recognizes emotions from the user's input, detecting feelings such as "anxiety" or "excitement." Based on these findings, the server adjusts the content and tone of the response. For example, it might generate a response like, "According to market research, the new service customers are looking for is a data analysis tool," which is then cleaned up and emotionally adjusted before being provided to the user. The user then uses this response to propose new services and incorporates them into their work.

[0881] In this way, a system is realized in which the server, terminal, emotion engine, and user work together to efficiently process information and quickly provide the necessary information. The introduction of the emotion engine enables a more personalized experience by providing responses based on the user's emotions.

[0882] The following describes the processing flow.

[0883] Step 1:

[0884] The server configures API keys to enable connection to the Generative AI and Emotion Engine services. To this end, it holds the API keys for the Generative AI and Emotion Engine and prepares for authentication. This establishes the foundation for the server to communicate with the Generative AI and Emotion Engine.

[0885] Step 2:

[0886] The server imports data such as company policies, procedures, and service information into the system. This data is stored in a structured format, such as a dictionary, and used in subsequent query processing. This import process allows the server to retrieve and manage the necessary information.

[0887] Step 3:

[0888] The user enters a specific question through the terminal. For example, they might enter a question like, "What new services should we offer?" The terminal then sends this question to the server in the appropriate format.

[0889] Step 4:

[0890] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand and combines them into a single query.

[0891] Step 5:

[0892] The device sends user input to an emotion engine to recognize the user's emotions. The emotion engine analyzes the input and identifies the user's emotions (e.g., anxiety, excitement, calmness). This emotion data is then sent from the device to the server.

[0893] Step 6:

[0894] The server sends a query to the generative AI. The generative AI receives this query and generates an appropriate response based on the underlying knowledge database and trained models. This response also takes into account the internal data imported by the server.

[0895] Step 7:

[0896] Once the AI ​​generates a response, the server cleans it up. Specifically, it removes unnecessary information and noise and formats it in a way that is easy for the user to understand. For example, it changes redundant parts and technical jargon into more concise expressions.

[0897] Step 8:

[0898] The server adjusts the content and tone of the generated response based on the emotional data sent from the emotion engine. For example, if the user is expressing anxiety, the response will have an encouraging tone added.

[0899] Step 9:

[0900] The server sends a cleaned-up, sentiment-based response back to the terminal. The terminal displays this response to the user, enabling them to efficiently utilize the information.

[0901] Step 10:

[0902] The user receives the response sent back from the server via their terminal and verifies the information. For example, they might see a response such as, "According to market research, the new service customers are looking for is a data analysis tool," and then use that information in their work.

[0903] In this way, a system is realized in which the server, terminal, emotion engine, and user work together to efficiently process information and quickly provide the necessary information. The introduction of the emotion engine enables a more personalized experience by providing responses based on the user's emotions.

[0904] (Example 2)

[0905] Next, we will describe Example 2. 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."

[0906] Traditional internal information retrieval systems have a problem in that they struggle to provide optimal responses to employee questions that take context and emotions into account. This makes it difficult for employees to effectively retrieve information, potentially leading to decreased work efficiency. Furthermore, if the responses generated by the AI ​​are inappropriate, it could undermine employee satisfaction and the reliability of the system.

[0907] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generation AI based on the entered questions and generating appropriate responses, means for cleaning up the responses obtained from the generation AI and returning them to the employees, means for importing and using data such as company policies, procedures, and service information into the system, means for recognizing the employee's emotions from the input content using an emotion engine, and means for adjusting the tone and content of the response based on the recognized emotion data. As a result, employees can obtain optimal responses that take context and emotions into consideration, improving the efficiency of information acquisition and enabling smoother business operations.

[0908] "Employee" refers to an employee who belongs to a company or organization.

[0909] "Means for receiving questions" refers to the interface or mechanism for receiving questions entered by employees into the system.

[0910] "Generative AI" refers to an artificial intelligence system that generates relevant responses to questions in natural language.

[0911] "Means of sending queries" refers to a mechanism for converting employee questions into a format that the generation AI can understand and then sending it to the generation AI.

[0912] "Means for cleaning up responses" refers to a mechanism for checking responses sent back by the generating AI, removing unnecessary information, and providing it to employees.

[0913] "Means of importing and using data into a system" refers to a mechanism for incorporating internal company policies, procedures, service information, and other data into a system, and accessing and using it as needed.

[0914] An "emotion engine" refers to a system or software that recognizes emotions from employee input and generates corresponding data.

[0915] "Means of recognizing emotions" refers to a mechanism that uses an emotion engine to analyze emotions from employees' questions and inputs, and generate emotion data.

[0916] "Means for adjusting the tone and content of responses" refers to a mechanism for appropriately adjusting the tone and content of generated responses based on recognized emotional data.

[0917] This invention relates to an interactive knowledge base system that uses generative AI and an emotion engine to enable employees to effectively search for company information. Embodiments of the present invention are described in detail below.

[0918] System Configuration

[0919] This system consists of four main components: the server, the terminal, the emotion engine, and the user. The specific functions of each component are described in detail below.

[0920] server

[0921] The server establishes connections with generative AI and emotion engines and manages internal company information. Specifically, the server sets API keys to connect to the generative AI and emotion engine services (e.g., OpenAI API). The server imports data such as internal policies, procedures, and service information into the system and stores and manages it in a NoSQL database (e.g., MongoDB). Furthermore, it has the functionality to analyze user questions, send queries to the generative AI to generate responses, and use the emotion engine to recognize the user's emotions and adjust the response accordingly.

[0922] terminal

[0923] The terminal provides an interface for the user to access the system. The terminal allows the user to input questions and receive responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[0924] Emotional Engine

[0925] The emotion engine recognizes emotions from the questions and conversations entered by the user. Using natural language processing technology, the emotion engine analyzes the user's emotions and sends the results to the server. The recognized emotion data is reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[0926] User

[0927] Users access the system through their terminals and search for company information. When a user enters a question, an appropriate response is provided via the server and a sentiment engine.

[0928] Specific example

[0929] For example, consider a scenario where an employee enters a question via a terminal, such as, "What new service should we offer?" The question is sent from the terminal to the server. The server sends a query to the generation AI, which generates a response such as, "According to market research, the new service customers are looking for is a data analysis tool." Simultaneously, the emotion engine recognizes emotions such as "anxiety" or "excitement" from the user's input. Based on this, the server appropriately adjusts the tone of the response and sends the adjusted response back to the terminal. The user then uses this information to propose new services and apply it to their work.

[0930] Example of a prompt

[0931] 1. "Please tell us what opinions your employees have regarding the new project."

[0932] 2. "Are there any items that should be on the agenda for the next meeting?"

[0933] 3. "Please tell us about any problems with the current progress of the project."

[0934] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0935] Step 1: The user enters a specific question through the terminal. For example, they might enter a question like, "What new service should we offer?" The terminal sends this question to the server as an HTTP request. The input is the question from the user, and the output is the request to the server.

[0936] Step 2: The server receives the question sent by the user. Specifically, the server parses the HTTP request and extracts the question portion. The input is the request from the terminal, and the output is the parsed question.

[0937] Step 3: The server converts the parsed question into a format that the generating AI can understand. For example, it uses a natural language processing module (e.g., spaCy) to tokenize the question and convert it into a prompt format for the generating AI. The input is the parsed question, and the output is a prompt for the generating AI.

[0938] Step 4: The server sends a prompt to the generating AI. Specifically, it sends the prompt as a query using the generating AI's API (e.g., OpenAI API). The input is the prompt for the generating AI, and the output is the response from the generating AI.

[0939] Step 5: The server sends the user's input from the terminal to the emotion engine. Specifically, it analyzes the input using the emotion engine's API (e.g., emotion analysis API). The input is the user's question, and the output is the recognized emotion data.

[0940] Step 6: The emotion engine recognizes emotions from the user's input and sends the results to the server. Specifically, it uses a natural language processing algorithm to analyze emotions and returns the results to the server as structured data. The input is the user's question, and the output is the emotion analysis result.

[0941] Step 7: The server cleans up the response sentence obtained from the generating AI. Specifically, it filters out inappropriate phrases and redundant information, extracting only the necessary information. The input is the response sentence from the generating AI, and the output is the cleaned-up response sentence.

[0942] Step 8: The server adjusts the tone and content of the response based on the emotion data returned from the emotion engine. For example, if anxiety is detected, the response is changed to a calmer tone. The input is the cleaned-up response and emotion data, and the output is the adjusted response.

[0943] Step 9: The server sends the prepared response back to the terminal. Specifically, it sends it back to the terminal as an HTTP response. The input is the prepared response, and the output is the response to the terminal.

[0944] Step 10: The terminal receives the prepared response from the server and displays it to the user. Specifically, the response is displayed on the screen through the terminal's user interface. The input is the response from the server, and the output is the information displayed to the user.

[0945] (Application Example 2)

[0946] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0947] Traditional interactive knowledge base systems have a drawback: they fail to provide responses that take user emotions into account, resulting in a poor user experience. Furthermore, providing appropriate support to factory workers, especially during emergencies or when they are confused, is difficult. This can lead to a decrease in work efficiency. Additionally, responses that do not incorporate emotion analysis provide users with insufficient assistance, making it difficult to achieve overall improvements in work efficiency.

[0948] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0949] In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generating AI based on the entered questions to generate appropriate responses, means for importing and utilizing data such as internal policies, procedures, and service information into the system, means for analyzing employees' emotions using an emotion engine and adjusting the tone and content of responses based on the analysis results, and means for enabling employees to efficiently search for information through an interactive interface. This provides appropriate and personalized responses that take into account the user's emotions, enabling quick and accurate support, especially for factory workers, even in emergencies or when they are confused.

[0950] "Generative AI" refers to artificial intelligence models that perform natural language generation, and is a technology used to generate appropriate responses to specific queries.

[0951] An "interactive knowledge base system" is a system that automatically generates and responds with appropriate information and answers when a user inputs a question in natural language.

[0952] An "emotion engine" is a technology that analyzes user emotions from their input and conversation content, and reflects the results in the response.

[0953] A "query" is the information used when a user asks a question or makes a request to a specific system or database.

[0954] A "server" is a computer system that establishes cooperation with generative AI and emotion engines, and manages data and generates and adjusts responses.

[0955] A "terminal" is a device that provides an interface for users to access a system, input questions and instructions, and receive responses.

[0956] "Import" is the process of bringing external data into a system and making it available for use.

[0957] "Cleanup" is the process of removing unnecessary information and redundant parts from a generated response and organizing it to make it easier for the user to understand.

[0958] "Emotion analysis" is a technology that automatically recognizes and classifies emotions based on user input.

[0959] "Response tone" refers to elements that adjust the atmosphere and nuances of the generated response, and are appropriately changed according to the user's emotions.

[0960] This invention relates to an interactive knowledge base system and its configuration method that allows employees to effectively search for company information using generative AI and an emotion engine. The main components are a server, a terminal, an emotion engine, and a user.

[0961] System Configuration

[0962] server

[0963] The server plays a central role in this system. The server establishes coordination between the generative AI and the emotion engine and manages internal company information. Specifically, it imports data such as internal policies, procedures, and service information, and stores and manages it in a database. The server also receives user questions, sends queries to the generative AI to generate responses, and uses the emotion engine to recognize the user's emotions, adjusting the tone and content of the response based on the results.

[0964] terminal

[0965] The terminal provides an interface for the user to access the system. The user inputs questions through the terminal and receives responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[0966] Emotional Engine

[0967] The emotion engine recognizes emotions from the user's input questions and conversation content. The recognized emotion data is sent to the server and reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[0968] User

[0969] Users access the system through their terminals to search for company information. The questions entered by users are processed through the server and sentiment engine, and appropriate responses are provided.

[0970] Hardware and software to be used

[0971] Server: Python-based web framework (e.g., Flask, Django)

[0972] Generative AI: Natural language generation models (e.g., OpenAI's GPT model)

[0973] Emotion engine: Emotion analysis API (e.g., Microsoft Azure Cognitive Services' Emotion API)

[0974] Device: User interface device (e.g., PC, smartphone, tablet)

[0975] Software interface: Operating system software (e.g., ROS)

[0976] Program processing

[0977] The server connects to the generative AI and sentiment engine services using API keys. It also imports and manages internal company data (policies, procedures, service information, etc.) as a database. When a user enters a question through a terminal, the server receives it and generates a query for the generative AI. Simultaneously, the terminal sends the entered question to the sentiment engine for sentiment analysis. Using the response obtained from the generative AI and the sentiment analysis results, the server adjusts the tone and content of the response. This ensures that the user receives the most appropriate response.

[0978] Specific example

[0979] For example, consider a scenario where a factory worker types "Please tell me how to repair this machine" into a terminal. The question is sent to the server, which then sends a query to the generating AI. Simultaneously, the emotion engine detects emotions such as "anxiety" from the input. Based on this, the server adjusts the tone and content of the response, providing a response like, "Don't worry, first turn off the machine. Next, open the panel and remove the filter." In this way, the introduction of an emotion engine enables more nuanced support based on the user's emotions.

[0980] Example of a prompt

[0981] Operator asks: "Could you please explain the repair procedure for this machine?"

[0982] Query to the generating AI: "Please tell me the repair procedure for factory machinery."

[0983] Emotional analysis result from the emotional engine: "Anxiety"

[0984] Generated response: "Don't worry, first turn off the machine, then open the panel and remove the filter."

[0985] In this way, interactive knowledge base systems enable employees to efficiently search for internal company information and provide emotion-based, customized responses. Therefore, they can not only improve operational efficiency but also enhance the user experience.

[0986] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0987] Step 1:

[0988] The user enters a question through the terminal. For example, they might enter, "Please tell me how to repair this machine." The terminal accepts the user's question and sends it to the server. The data entered is in text format, and the user's question is sent to the server exactly as it is.

[0989] Step 2:

[0990] The server analyzes the question received from the terminal and converts it into a format for sending queries to the generative AI. Specifically, it uses natural language processing (NLP) algorithms to extract the intent of the question and convert it into an appropriate query. This query is then sent to the generative AI. The input data is the user's question, and the output data is the query sent to the generative AI.

[0991] Step 3:

[0992] Simultaneously, the terminal sends the user's input question to the emotion engine for emotional analysis. The emotion engine identifies emotions from the input text and generates emotion tags such as "anxiety" or "excitement." The input data is the user's question, and the output data is the analyzed emotion tags. Specifically, the emotion analysis API is called and the results are received.

[0993] Step 4:

[0994] The server receives the response from the generating AI. The generating AI generates an appropriate response based on the sent query. This response is in text format and is sent back to the server. The input data is the query sent to the generating AI, and the output data is the generated response. Specifically, this involves reading the response to an API call.

[0995] Step 5:

[0996] The server receives emotion tags from the emotion engine and adjusts the tone and content of the response generated by the AI ​​based on the received emotion tags. For example, if the emotion tag "anxiety" is detected, the server changes the response to a more reassuring tone. The input data is the generated response and emotion tags, and the output data is the adjusted response. In terms of specific operations, string manipulation and template application are performed.

[0997] Step 6:

[0998] The server sends a pre-formatted response back to the terminal. The terminal displays this response to the user or outputs it as audio. The input data is the pre-formatted response, and the output data is the response in a format that is easy for the user to understand. Specifically, this can involve text display or speech synthesis.

[0999] In this way, users can efficiently search for internal company information and receive personalized responses based on their emotions. As a result, appropriate support becomes possible, especially for users in emergencies or when they are confused, improving work efficiency and user experience.

[1000] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1001] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1002] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1003] [Fourth Embodiment]

[1004] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1005] As shown in Figure 7, the 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.

[1006] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1007] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1008] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1009] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1010] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1011] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1012] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1013] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1014] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1015] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1016] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1017] This invention relates to an interactive knowledge base system that allows employees to search for company information using generating AI. Embodiments of the present invention are described in detail below.

[1018] System Configuration

[1019] This system consists of three main components: servers, terminals, and users. Each component functions as follows:

[1020] server

[1021] The server has the functionality to set and initialize API keys for integration with the generation AI. It also imports and manages internal data such as company policies, procedures, and service information as a database. When it receives a question from an employee, it sends a query to the generation AI to generate an appropriate response. It is also responsible for cleaning up the response obtained from the generation AI and returning it to the employee.

[1022] terminal

[1023] Users access the system through a terminal and ask questions about specific information. The terminal communicates with the server and provides an interface for sending questions and receiving responses.

[1024] User

[1025] Users, acting as employees, input questions from their terminals and obtain the necessary information. This system enables users to efficiently search for information and perform their tasks quickly.

[1026] Program Processing Description

[1027] Initial setup

[1028] The server first configures the API key and establishes integration with the generating AI. It also imports internal data such as company policies, procedures, and service information, making them available within the system.

[1029] Received a question

[1030] When a user enters a specific question through their device, the device sends that question to the server.

[1031] Query generation

[1032] The server sends the received question as a query to the generating AI. The generating AI then generates an appropriate response based on internal company data.

[1033] Response generation and cleanup

[1034] The responses generated by the AI ​​are cleaned up by the server. Specifically, unnecessary information and noise are removed, and the responses are formatted into a user-friendly format.

[1035] Sending a response

[1036] The server returns the cleaned-up response to the terminal and provides it to the user. The user receives the response through the terminal and refers to the necessary information.

[1037] Specific example

[1038] For example, consider a scenario where a user asks a question about a new service proposal. When the user enters the question, "What new service should we offer?", from their device, the question is sent to the server. The server sends the query to the AI ​​generator, which then generates an appropriate response. For example, a response such as, "According to market research, the new service customers are looking for is a data analysis tool," is generated, cleaned up, and then sent back to the user. The user can then use this response to propose a new service.

[1039] This system allows employees to quickly obtain necessary information, thereby improving work efficiency. Furthermore, by using AI-generated responses, it is possible to flexibly respond to changing market and internal information, as responses are always based on the latest information.

[1040] The following describes the processing flow.

[1041] Step 1:

[1042] The server configures the API key to enable connection to the Generative AI service. To this end, it holds the Generative AI's API key and prepares for authentication. This establishes the foundation for the server to communicate with the Generative AI.

[1043] Step 2:

[1044] The server imports data such as company policies, procedures, and service information into the system. This data is stored in a structured format, such as a dictionary, and used in subsequent query processing. This import process allows the server to retrieve and manage the necessary information.

[1045] Step 3:

[1046] The user enters a specific question through the terminal. For example, they might enter a question like, "What new services should we offer?" The terminal then sends this question to the server in the appropriate format.

[1047] Step 4:

[1048] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand and combines them into a single query.

[1049] Step 5:

[1050] The server sends a query to the generative AI. The generative AI receives this query and generates an appropriate response based on the underlying knowledge database and trained models. This response also takes into account the internal data imported by the server.

[1051] Step 6:

[1052] Once the AI ​​generates a response, the server cleans it up. Specifically, it removes unnecessary information and noise and formats it in a way that is easy for the user to understand. This process refines the response.

[1053] Step 7:

[1054] The server sends a cleaned-up response back to the terminal. The response is provided in a format easily understandable to the user. The terminal displays this response and provides it to the user.

[1055] Step 8:

[1056] The user receives the response sent back from the server via their terminal and verifies the information. For example, they might confirm something like, "According to market research, the new service customers are looking for is a data analysis tool," and then use that information in their work.

[1057] In this way, a system is realized in which servers, terminals, and users work together to efficiently process information and quickly provide the necessary information.

[1058] (Example 1)

[1059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1060] In modern businesses, there is a growing need for systems that allow employees to quickly and accurately find the information they need. However, traditional search systems struggle to efficiently extract necessary information from vast amounts of internal data. Furthermore, even with methods utilizing generative AI, insufficient initial setup and cleanup functions prevent users from obtaining information properly. This leads to decreased work efficiency and longer work processes.

[1061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1062] In this invention, the server includes means for receiving questions entered by employees, means for analyzing the entered questions and sending queries to a generating AI, means for cleaning up the responses generated in response to the queries sent to the generating AI and returning them to the employees, means for importing data such as internal policies, procedures, and service information into the system and managing it as a database, and means for setting an API key and establishing cooperation with the generating AI. This enables employees to quickly and accurately search for internal information and improve the efficiency of their work.

[1063] "Generative AI" refers to algorithms that use artificial intelligence technology to automatically generate text and responses.

[1064] An "interactive knowledge base system" is an information management system that allows users to input questions and provides responses in real time.

[1065] An "employee" is a person who works within a company or organization and performs their duties.

[1066] "Means for receiving questions" refers to interfaces or systems for users to input questions or queries.

[1067] A "query" refers to an inquiry made to a database or information system, and is used to retrieve specific information.

[1068] "Cleanup" refers to the process of formatting the responses obtained from the generated AI into an appropriate format and removing unnecessary information.

[1069] A "database" is a system that stores a structured collection of data and allows for efficient management and retrieval.

[1070] A "policy" is a document that outlines the guidelines and rules for conduct within an organization.

[1071] A "procedure manual" is a document that specifically describes the steps involved in performing a task or operation.

[1072] "Service information" refers to various types of information about the services provided by companies and organizations.

[1073] "Import" refers to the process of taking in data from an external source and making it available for use within a system.

[1074] An "API key" is a set of authentication credentials required to access the interface of an application or service.

[1075] This invention relates to an interactive knowledge base system that allows employees to search for company information using generating AI. Embodiments of the present invention are described in detail below.

[1076] This system primarily consists of three elements: servers, terminals, and users. Specific data processing and calculations are performed using the following hardware and software.

[1077] Server configuration and functionality

[1078] The server sets up and initializes API keys to establish integration with the generated AI model. It also imports and manages internal data such as company policies, procedures, and service information as a database. The server functions as follows:

[1079] 1. API key setup and integration with the generation AI: The server sets an API key (for example, an OpenAI API key) to use the generation AI and initializes itself to enable communication with the generation AI.

[1080] 2. Data Import and Management: The server imports important company documents into the database using SQL queries and other methods, and manages them.

[1081] 3. Receiving and analyzing questions: The server receives questions from users as HTTP requests, analyzes those questions, and generates queries to send to the AI.

[1082] 4. Query submission and response cleanup: Receive responses from the generating AI, clean them up (remove unnecessary information and correct ambiguous expressions), and send them back to the user.

[1083] Device configuration and functions

[1084] The terminal provides an interface for the user to access the system and enter questions. The terminal functions as follows:

[1085] 1. Question Input and Submission: Provides a user interface for users to input questions about specific information. Once a question is entered, the question data is sent to the server.

[1086] 2. Receiving and displaying responses: Receive responses sent back from the server and display them in the user interface.

[1087] User roles

[1088] Users, as employees, will utilize this system to quickly and accurately search for necessary information and efficiently perform their duties. The following steps are possible for the user's specific operational procedures:

[1089] 1. Entering the question: The user enters a question via their terminal, such as "Please tell me about the new employee benefits."

[1090] 2. Confirm the response: Confirm the response from the server and determine the next business action based on that information.

[1091] Specific examples and prompt statements

[1092] For example, when asking a question about a proposed new service, the user would type "What new service should we offer?" on their device. This question is sent from the device to the server, where a generating AI produces a response such as "According to market research, the new service customers are looking for is a data analysis tool." After a cleanup process, the response is finally displayed on the user's device.

[1093] Example of a prompt

[1094] "Please provide suggestions for new services that we should offer."

[1095] "What is the latest information regarding new employee benefits?"

[1096] As described above, close collaboration between servers, terminals, and users enables efficient internal information retrieval using generated AI models. This system allows employees to quickly and accurately obtain necessary information and carry out their work smoothly.

[1097] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1098] Step 1: System Initial Setup

[1099] After startup, the server configures the API key and establishes communication with the generating AI. Specifically, it reads the API key and applies it to the connection settings of the generating AI. The server also imports internal data such as company policies, procedures, and service information and manages it as a database. For example, it executes SQL queries to store data in database tables, making internal information searchable.

[1100] Input: API key, internal document data

[1101] Data processing: API key setup, data import

[1102] Output: Establishment of integration with the generation AI, initialization of the database.

[1103] Step 2: Entering and receiving questions

[1104] The user enters a question through the terminal's interface. For example, they might enter the question, "Tell me about the new project management tool." Once a question is entered, the terminal sends this question data to the server as an HTTP request in packet format. The server receives the request and parses the question data.

[1105] Input: User question (e.g., Tell me about the new project management tool)

[1106] Data processing: Packeting and transmission of question data.

[1107] Output: Transmission of query data to the server

[1108] Step 3: Question analysis and query generation

[1109] The server analyzes the received question and generates a query in a format suitable for the generating AI. Specifically, it normalizes the question content and extracts important keywords. For example, parts like "new project management tool" and "please tell me" are tagged. Based on this, it creates a query to send to the generating AI.

[1110] Input: Question data (Please tell me about the new project management tool)

[1111] Data processing: Questionnaire analysis, keyword extraction, query generation

[1112] Output: Query to send to the generating AI

[1113] Step 4: Submitting the query and generating the response

[1114] The server sends a query to the generating AI. Based on this query, the generating AI generates an appropriate response from internal data. For example, it might generate a response such as, "Tool X is now recommended as a new project management tool." The generated response is then returned to the server.

[1115] Input: Query

[1116] Data processing: Response generation by generative AI

[1117] Output: Generated response (e.g., Tool X is recommended)

[1118] Step 5: Clean up the response

[1119] The server cleans up the responses obtained from the generated AI. Specifically, it removes redundant information and noise and formats them in a user-friendly format. For example, it might revise them to a short and clear format such as "Tool X is recommended."

[1120] Input: Response obtained from the generating AI

[1121] Data processing: Information cleanup, removal of redundant information, formatting.

[1122] Output: Cleaned-up response (e.g., Tool X is recommended)

[1123] Step 6: Sending and displaying the response

[1124] The server sends the cleaned-up response back to the terminal. The terminal receives the response data and displays it in the user interface. The user can then review this response and decide on their next action.

[1125] Input: Cleaned-up response

[1126] Data processing: Packeting and transmission of response data

[1127] Output: Response displayed in the user interface (e.g., Tool X is recommended)

[1128] In this way, each step works in conjunction with the others, allowing users to quickly and accurately obtain the information they need.

[1129] (Application Example 1)

[1130] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1131] There is a need to provide internal security personnel with a means to quickly and accurately search for specific internal protocols and security information. Especially at night or during emergencies, delays or omissions in information can cause serious problems, making the establishment of a proper knowledge base system an urgent necessity.

[1132] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1133] In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generating AI based on the entered questions and generating appropriate responses, means for cleaning up the responses obtained from the generating AI and returning them to the employees, means for importing and using data such as internal policies, procedures, and service information into the system, and means equipped with a function that allows security personnel to quickly search for specific internal protocols. This enables security personnel to quickly and accurately obtain necessary information even in emergencies or during nighttime patrols, allowing for problem solving and appropriate responses.

[1134] "Generative AI" is a type of artificial intelligence that has the function of generating new information and content based on large amounts of data.

[1135] A "query" refers to a question or request made to a database or generative AI, and is a means of obtaining information as a result.

[1136] "Cleanup" is the process of removing unnecessary information and noise from the responses obtained from the generating AI and organizing them into a format that is easy for the user to understand.

[1137] The term "employee" refers to individuals who belong to a company or organization and perform their duties.

[1138] A "policy" refers to a document that sets out guidelines and rules for conduct within a company or organization.

[1139] A "procedure manual" is a document that describes the methods and procedures for performing a specific task or operation.

[1140] "Service information" refers to detailed information and descriptions about the services provided by a company or organization.

[1141] An "interactive knowledge base system" is an interactive information retrieval system in which the system provides appropriate information when the user inputs questions in natural language.

[1142] "Security personnel" refers to people whose job it is to be responsible for the security of a company or organization.

[1143] "Internal protocol" refers to guidelines or rules that describe specific procedures and response methods established within a company or organization.

[1144] Modes for carrying out the invention

[1145] This invention relates to an interactive knowledge base system that uses generative AI to enable security personnel to quickly search for internal protocols and security information. The embodiments for carrying out this invention are described in detail below.

[1146] System Configuration

[1147] This system consists of three main components: servers, terminals, and users (security personnel). Each component functions as follows:

[1148] server

[1149] The server has the functionality to set and initialize API keys for integration with the Generative AI. It also imports and manages data such as internal policies, procedures, service information, and security protocols as a database. When it receives a question from security personnel, it sends a query to the Generative AI and generates an appropriate response. It is also responsible for cleaning up the response obtained from the Generative AI and returning it to the security personnel.

[1150] terminal

[1151] The terminal provides an interface for security personnel to access the system and ask questions about specific information. The terminal communicates with the server to send questions and receive responses.

[1152] User (security personnel)

[1153] Users input questions into the system via their terminals and quickly obtain the necessary information. This allows security personnel to perform their duties quickly and accurately.

[1154] Program Processing Description

[1155] The server first sets up an API key and establishes communication with the generating AI. It also imports data such as internal policies, procedures, service information, and security protocols, making them available within the system. When a user enters a question from a terminal, the terminal sends the question to the server. The server sends the received question as a query to the generating AI, which generates an appropriate response. This response is cleaned up by the server and sent back to the user.

[1156] Recommended hardware includes smartphones and cloud-based servers. Software used includes generative AI (e.g., OpenAI's GPT-4 API), a database (e.g., MySQL), and a backend framework (e.g., Django or Flask). The generative AI generates information based on the received query and returns the results to the server.

[1157] Specific example

[1158] For example, suppose a security personnel member needs to quickly obtain information about the company's security protocols while on nighttime patrol. They type "What are the nighttime emergency response protocols?" into a smartphone app. This question is immediately sent to the server. The AI ​​generator produces an appropriate response, returning the answer, "The nighttime emergency response protocol is to first report to emergency contacts and then secure the site." The security personnel member can then respond quickly according to this.

[1159] Example of a prompt

[1160] When security personnel request a specific internal protocol, generate an appropriate response based on the following information:

[1161] User input: "What is the emergency response protocol for nighttime?"

[1162] Example output: "The nighttime emergency response protocol is to first report to emergency contacts, and then secure the scene."

[1163] This system allows security personnel to quickly and accurately obtain necessary information, improving operational efficiency and enabling rapid responses in emergencies.

[1164] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1165] Step 1:

[1166] The user enters a question into the device. For example, a security personnel member might use a smartphone app to enter "What is the nighttime emergency response protocol?" This is the input for Step 1.

[1167] Step 2:

[1168] The terminal sends the user's question to the server. The entered question data is sent from the terminal to the server. Specifically, the text data entered in the question form is sent to the server as an HTTP request.

[1169] Step 3:

[1170] The server receives the question and sends the query to the generating AI. The server receives the HTTP request and formats the question text as a query for the generating AI. For example, the question "What is the nighttime emergency response protocol?" is converted into an API request to the generating AI. This is the input for step 3.

[1171] Step 4:

[1172] The generating AI generates a response based on the query. The generating AI analyzes the input query and generates an appropriate response based on the database. For example, it might generate the response, "The nighttime emergency response protocol is to first report to emergency contacts and then secure the scene." This is the output of step 4.

[1173] Step 5:

[1174] The server cleans up the response received from the generating AI. The server receives the response from the generating AI, removes unnecessary information and noise, and formats it in a user-friendly format. Specifically, it adjusts line breaks and punctuation to make the response text easier to read. This is the output of step 5.

[1175] Step 6:

[1176] The server sends a cleaned-up response back to the terminal. The formatted response text is sent from the server to the terminal. Specifically, text data is returned as an HTTP response.

[1177] Step 7:

[1178] The terminal receives a response from the server and displays it to the user. The terminal displays the received response text in the user interface. For example, the smartphone app screen might display the text: "Nighttime emergency response protocol is to first report to emergency contacts and then secure the scene."

[1179] In this way, security personnel can quickly and accurately obtain the necessary information, enabling more efficient operations and faster response times.

[1180] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1181] This invention relates to an interactive knowledge base system and its configuration method that enables employees to effectively search for company information using a generative AI and an emotion engine. Embodiments of the present invention are described in detail below.

[1182] System Configuration

[1183] This system consists of four main components: the server, the terminal, the emotion engine, and the user. The function of each component is as follows:

[1184] server

[1185] The server establishes cooperation with the generative AI and emotion engine to manage internal company information. Specifically, it imports data such as internal policies, procedures, and service information, and stores and manages them in a database. It also receives questions, sends queries to the generative AI to generate responses, and has the functionality to recognize the user's emotions with the emotion engine and adjust the response accordingly.

[1186] terminal

[1187] The terminal provides an interface for the user to access the system. Here, the user enters questions and receives responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[1188] Emotional Engine

[1189] The emotion engine recognizes emotions from the user's input questions and conversation content. The recognized emotion data is sent to the server and reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[1190] User

[1191] Users access the system through their terminals to search for company information. Questions entered by users are processed through the server and a sentiment engine, providing appropriate responses.

[1192] Program Processing Description

[1193] Initial setup

[1194] The server configures API keys to connect to the AI ​​generation and emotion engine services. It also imports internal data such as company policies, procedures, and service information, and stores and manages them as a database.

[1195] Received a question

[1196] The user enters a specific question through the terminal. For example, they might enter a question like, "What new service should we offer?" The terminal then sends this question to the server.

[1197] Query generation

[1198] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand.

[1199] Recognition of emotions

[1200] Simultaneously, the terminal sends the user's input to the emotion engine, which then recognizes the emotion. The emotion engine analyzes the user's emotion and sends the results back to the server.

[1201] Response generation and adjustment

[1202] The server sends queries to the generative AI and generates appropriate responses. The responses obtained from the generative AI are then cleaned up by the server. Furthermore, the tone and content of the responses are adjusted based on the sentiment data returned from the sentiment engine.

[1203] Sending a response

[1204] The server sends a cleaned and refined response back to the terminal. The terminal displays this response to the user, enabling the user to efficiently utilize the information.

[1205] Specific example

[1206] For example, consider a scenario where an employee enters a question via a terminal, such as, "What new service should we offer?" The question is sent from the terminal to the server. The server sends the query to the generation AI, which then generates an appropriate response. The emotion engine also recognizes emotions from the user's input, detecting feelings such as "anxiety" or "excitement." Based on these findings, the server adjusts the content and tone of the response. For example, it might generate a response like, "According to market research, the new service customers are looking for is a data analysis tool," which is then cleaned up and emotionally adjusted before being provided to the user. The user then uses this response to propose new services and incorporates them into their work.

[1207] In this way, a system is realized in which the server, terminal, emotion engine, and user work together to efficiently process information and quickly provide the necessary information. The introduction of the emotion engine enables a more personalized experience by providing responses based on the user's emotions.

[1208] The following describes the processing flow.

[1209] Step 1:

[1210] The server configures API keys to enable connection to the Generative AI and Emotion Engine services. To this end, it holds the API keys for the Generative AI and Emotion Engine and prepares for authentication. This establishes the foundation for the server to communicate with the Generative AI and Emotion Engine.

[1211] Step 2:

[1212] The server imports data such as company policies, procedures, and service information into the system. This data is stored in a structured format, such as a dictionary, and used in subsequent query processing. This import process allows the server to retrieve and manage the necessary information.

[1213] Step 3:

[1214] The user enters a specific question through the terminal. For example, they might enter a question like, "What new services should we offer?" The terminal then sends this question to the server in the appropriate format.

[1215] Step 4:

[1216] The server analyzes the questions received from the user and prepares them to be sent to the generating AI. Specifically, it converts the questions into a format that the generating AI can understand and combines them into a single query.

[1217] Step 5:

[1218] The device sends user input to an emotion engine to recognize the user's emotions. The emotion engine analyzes the input and identifies the user's emotions (e.g., anxiety, excitement, calmness). This emotion data is then sent from the device to the server.

[1219] Step 6:

[1220] The server sends a query to the generative AI. The generative AI receives this query and generates an appropriate response based on the underlying knowledge database and trained models. This response also takes into account the internal data imported by the server.

[1221] Step 7:

[1222] Once the AI ​​generates a response, the server cleans it up. Specifically, it removes unnecessary information and noise and formats it in a way that is easy for the user to understand. For example, it changes redundant parts and technical jargon into more concise expressions.

[1223] Step 8:

[1224] The server adjusts the content and tone of the generated response based on the emotional data sent from the emotion engine. For example, if the user is expressing anxiety, the response will have an encouraging tone added.

[1225] Step 9:

[1226] The server sends a cleaned-up, sentiment-based response back to the terminal. The terminal displays this response to the user, enabling them to efficiently utilize the information.

[1227] Step 10:

[1228] The user receives the response sent back from the server via their terminal and verifies the information. For example, they might see a response such as, "According to market research, the new service customers are looking for is a data analysis tool," and then use that information in their work.

[1229] In this way, a system is realized in which the server, terminal, emotion engine, and user work together to efficiently process information and quickly provide the necessary information. The introduction of the emotion engine enables a more personalized experience by providing responses based on the user's emotions.

[1230] (Example 2)

[1231] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1232] Traditional internal information retrieval systems have a problem in that they struggle to provide optimal responses to employee questions that take context and emotions into account. This makes it difficult for employees to effectively retrieve information, potentially leading to decreased work efficiency. Furthermore, if the responses generated by the AI ​​are inappropriate, it could undermine employee satisfaction and the reliability of the system.

[1233] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generation AI based on the entered questions and generating appropriate responses, means for cleaning up the responses obtained from the generation AI and returning them to the employees, means for importing and using data such as company policies, procedures, and service information into the system, means for recognizing the employee's emotions from the input content using an emotion engine, and means for adjusting the tone and content of the response based on the recognized emotion data. As a result, employees can obtain optimal responses that take context and emotions into consideration, improving the efficiency of information acquisition and enabling smoother business operations.

[1234] "Employee" refers to an employee who belongs to a company or organization.

[1235] "Means for receiving questions" refers to the interface or mechanism for receiving questions entered by employees into the system.

[1236] "Generative AI" refers to an artificial intelligence system that generates relevant responses to questions in natural language.

[1237] "Means of sending queries" refers to a mechanism for converting employee questions into a format that the generation AI can understand and then sending it to the generation AI.

[1238] "Means for cleaning up responses" refers to a mechanism for checking responses sent back by the generating AI, removing unnecessary information, and providing it to employees.

[1239] "Means of importing and using data into a system" refers to a mechanism for incorporating internal company policies, procedures, service information, and other data into a system, and accessing and using it as needed.

[1240] An "emotion engine" refers to a system or software that recognizes emotions from employee input and generates corresponding data.

[1241] "Means of recognizing emotions" refers to a mechanism that uses an emotion engine to analyze emotions from employees' questions and inputs, and generate emotion data.

[1242] "Means for adjusting the tone and content of responses" refers to a mechanism for appropriately adjusting the tone and content of generated responses based on recognized emotional data.

[1243] This invention relates to an interactive knowledge base system that uses generative AI and an emotion engine to enable employees to effectively search for company information. Embodiments of the present invention are described in detail below.

[1244] System Configuration

[1245] This system consists of four main components: the server, the terminal, the emotion engine, and the user. The specific functions of each component are described in detail below.

[1246] server

[1247] The server establishes connections with generative AI and emotion engines and manages internal company information. Specifically, the server sets API keys to connect to the generative AI and emotion engine services (e.g., OpenAI API). The server imports data such as internal policies, procedures, and service information into the system and stores and manages it in a NoSQL database (e.g., MongoDB). Furthermore, it has the functionality to analyze user questions, send queries to the generative AI to generate responses, and use the emotion engine to recognize the user's emotions and adjust the response accordingly.

[1248] terminal

[1249] The terminal provides an interface for the user to access the system. The terminal allows the user to input questions and receive responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[1250] Emotional Engine

[1251] The emotion engine recognizes emotions from the questions and conversations entered by the user. Using natural language processing technology, the emotion engine analyzes the user's emotions and sends the results to the server. The recognized emotion data is reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[1252] User

[1253] Users access the system through their terminals and search for company information. When a user enters a question, an appropriate response is provided via the server and a sentiment engine.

[1254] Specific example

[1255] For example, consider a scenario where an employee enters a question via a terminal, such as, "What new service should we offer?" The question is sent from the terminal to the server. The server sends a query to the generation AI, which generates a response such as, "According to market research, the new service customers are looking for is a data analysis tool." Simultaneously, the emotion engine recognizes emotions such as "anxiety" or "excitement" from the user's input. Based on this, the server appropriately adjusts the tone of the response and sends the adjusted response back to the terminal. The user then uses this information to propose new services and apply it to their work.

[1256] Example of a prompt

[1257] 1. "Please tell us what opinions your employees have regarding the new project."

[1258] 2. "Are there any items that should be on the agenda for the next meeting?"

[1259] 3. "Please tell us about any problems with the current progress of the project."

[1260] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1261] Step 1: The user enters a specific question through the terminal. For example, they might enter a question like, "What new service should we offer?" The terminal sends this question to the server as an HTTP request. The input is the question from the user, and the output is the request to the server.

[1262] Step 2: The server receives the question sent by the user. Specifically, the server parses the HTTP request and extracts the question portion. The input is the request from the terminal, and the output is the parsed question.

[1263] Step 3: The server converts the parsed question into a format that the generating AI can understand. For example, it uses a natural language processing module (e.g., spaCy) to tokenize the question and convert it into a prompt format for the generating AI. The input is the parsed question, and the output is a prompt for the generating AI.

[1264] Step 4: The server sends a prompt to the generating AI. Specifically, it sends the prompt as a query using the generating AI's API (e.g., OpenAI API). The input is the prompt for the generating AI, and the output is the response from the generating AI.

[1265] Step 5: The server sends the user's input from the terminal to the emotion engine. Specifically, it analyzes the input using the emotion engine's API (e.g., emotion analysis API). The input is the user's question, and the output is the recognized emotion data.

[1266] Step 6: The emotion engine recognizes emotions from the user's input and sends the results to the server. Specifically, it uses a natural language processing algorithm to analyze emotions and returns the results to the server as structured data. The input is the user's question, and the output is the emotion analysis result.

[1267] Step 7: The server cleans up the response sentence obtained from the generating AI. Specifically, it filters out inappropriate phrases and redundant information, extracting only the necessary information. The input is the response sentence from the generating AI, and the output is the cleaned-up response sentence.

[1268] Step 8: The server adjusts the tone and content of the response based on the emotion data returned from the emotion engine. For example, if anxiety is detected, the response is changed to a calmer tone. The input is the cleaned-up response and emotion data, and the output is the adjusted response.

[1269] Step 9: The server sends the prepared response back to the terminal. Specifically, it sends it back to the terminal as an HTTP response. The input is the prepared response, and the output is the response to the terminal.

[1270] Step 10: The terminal receives the prepared response from the server and displays it to the user. Specifically, the response is displayed on the screen through the terminal's user interface. The input is the response from the server, and the output is the information displayed to the user.

[1271] (Application Example 2)

[1272] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1273] Traditional interactive knowledge base systems have a drawback: they fail to provide responses that take user emotions into account, resulting in a poor user experience. Furthermore, providing appropriate support to factory workers, especially during emergencies or when they are confused, is difficult. This can lead to a decrease in work efficiency. Additionally, responses that do not incorporate emotion analysis provide users with insufficient assistance, making it difficult to achieve overall improvements in work efficiency.

[1274] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1275] In this invention, the server includes means for receiving questions entered by employees, means for sending queries to a generating AI based on the entered questions to generate appropriate responses, means for importing and utilizing data such as internal policies, procedures, and service information into the system, means for analyzing employees' emotions using an emotion engine and adjusting the tone and content of responses based on the analysis results, and means for enabling employees to efficiently search for information through an interactive interface. This provides appropriate and personalized responses that take into account the user's emotions, enabling quick and accurate support, especially for factory workers, even in emergencies or when they are confused.

[1276] "Generative AI" refers to artificial intelligence models that perform natural language generation, and is a technology used to generate appropriate responses to specific queries.

[1277] An "interactive knowledge base system" is a system that automatically generates and responds with appropriate information and answers when a user inputs a question in natural language.

[1278] An "emotion engine" is a technology that analyzes user emotions from their input and conversation content, and reflects the results in the response.

[1279] A "query" is the information used when a user asks a question or makes a request to a specific system or database.

[1280] A "server" is a computer system that establishes cooperation with generative AI and emotion engines, and manages data and generates and adjusts responses.

[1281] A "terminal" is a device that provides an interface for users to access a system, input questions and instructions, and receive responses.

[1282] "Import" is the process of bringing external data into a system and making it available for use.

[1283] "Cleanup" is the process of removing unnecessary information and redundant parts from a generated response and organizing it to make it easier for the user to understand.

[1284] "Emotion analysis" is a technology that automatically recognizes and classifies emotions based on user input.

[1285] "Response tone" refers to elements that adjust the atmosphere and nuances of the generated response, and are appropriately changed according to the user's emotions.

[1286] This invention relates to an interactive knowledge base system and its configuration method that allows employees to effectively search for company information using generative AI and an emotion engine. The main components are a server, a terminal, an emotion engine, and a user.

[1287] System Configuration

[1288] server

[1289] The server plays a central role in this system. The server establishes coordination between the generative AI and the emotion engine and manages internal company information. Specifically, it imports data such as internal policies, procedures, and service information, and stores and manages it in a database. The server also receives user questions, sends queries to the generative AI to generate responses, and uses the emotion engine to recognize the user's emotions, adjusting the tone and content of the response based on the results.

[1290] terminal

[1291] The terminal provides an interface for the user to access the system. The user inputs questions through the terminal and receives responses from the server. The terminal also sends the user's input to the emotion engine for emotion recognition.

[1292] Emotional Engine

[1293] The emotion engine recognizes emotions from the user's input questions and conversation content. The recognized emotion data is sent to the server and reflected in the tone and content of the response. Furthermore, the emotion data is accumulated and used to improve the accuracy of future conversations.

[1294] User

[1295] Users access the system through their terminals to search for company information. The questions entered by users are processed through the server and sentiment engine, and appropriate responses are provided.

[1296] Hardware and software to be used

[1297] Server: Python-based web framework (e.g., Flask, Django)

[1298] Generative AI: Natural language generation models (e.g., OpenAI's GPT model)

[1299] Emotion engine: Emotion analysis API (e.g., Microsoft Azure Cognitive Services' Emotion API)

[1300] Device: User interface device (e.g., PC, smartphone, tablet)

[1301] Software interface: Operating system software (e.g., ROS)

[1302] Program processing

[1303] The server connects to the generative AI and sentiment engine services using API keys. It also imports and manages internal company data (policies, procedures, service information, etc.) as a database. When a user enters a question through a terminal, the server receives it and generates a query for the generative AI. Simultaneously, the terminal sends the entered question to the sentiment engine for sentiment analysis. Using the response obtained from the generative AI and the sentiment analysis results, the server adjusts the tone and content of the response. This ensures that the user receives the most appropriate response.

[1304] Specific example

[1305] For example, consider a scenario where a factory worker types "Please tell me how to repair this machine" into a terminal. The question is sent to the server, which then sends a query to the generating AI. Simultaneously, the emotion engine detects emotions such as "anxiety" from the input. Based on this, the server adjusts the tone and content of the response, providing a response like, "Don't worry, first turn off the machine. Next, open the panel and remove the filter." In this way, the introduction of an emotion engine enables more nuanced support based on the user's emotions.

[1306] Example of a prompt

[1307] Operator asks: "Could you please explain the repair procedure for this machine?"

[1308] Query to the generating AI: "Please tell me the repair procedure for factory machinery."

[1309] Emotional analysis result from the emotional engine: "Anxiety"

[1310] Generated response: "Don't worry, first turn off the machine, then open the panel and remove the filter."

[1311] In this way, interactive knowledge base systems enable employees to efficiently search for internal company information and provide emotion-based, customized responses. Therefore, they can not only improve operational efficiency but also enhance the user experience.

[1312] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1313] Step 1:

[1314] The user enters a question through the terminal. For example, they might enter, "Please tell me how to repair this machine." The terminal accepts the user's question and sends it to the server. The data entered is in text format, and the user's question is sent to the server exactly as it is.

[1315] Step 2:

[1316] The server analyzes the question received from the terminal and converts it into a format for sending queries to the generative AI. Specifically, it uses natural language processing (NLP) algorithms to extract the intent of the question and convert it into an appropriate query. This query is then sent to the generative AI. The input data is the user's question, and the output data is the query sent to the generative AI.

[1317] Step 3:

[1318] Simultaneously, the terminal sends the user's input question to the emotion engine for emotional analysis. The emotion engine identifies emotions from the input text and generates emotion tags such as "anxiety" or "excitement." The input data is the user's question, and the output data is the analyzed emotion tags. Specifically, the emotion analysis API is called and the results are received.

[1319] Step 4:

[1320] The server receives the response from the generating AI. The generating AI generates an appropriate response based on the sent query. This response is in text format and is sent back to the server. The input data is the query sent to the generating AI, and the output data is the generated response. Specifically, this involves reading the response to an API call.

[1321] Step 5:

[1322] The server receives emotion tags from the emotion engine and adjusts the tone and content of the response generated by the AI ​​based on the received emotion tags. For example, if the emotion tag "anxiety" is detected, the server changes the response to a more reassuring tone. The input data is the generated response and emotion tags, and the output data is the adjusted response. In terms of specific operations, string manipulation and template application are performed.

[1323] Step 6:

[1324] The server sends a pre-formatted response back to the terminal. The terminal displays this response to the user or outputs it as audio. The input data is the pre-formatted response, and the output data is the response in a format that is easy for the user to understand. Specifically, this can involve text display or speech synthesis.

[1325] In this way, users can efficiently search for internal company information and receive personalized responses based on their emotions. As a result, appropriate support becomes possible, especially for users in emergencies or when they are confused, improving work efficiency and user experience.

[1326] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1327] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1328] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1329] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1330] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1331] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1332] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1333] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1334] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1335] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1336] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1337] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1338] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1339] 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.

[1340] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1341] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1342] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1343] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1344] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1345] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1346] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1347] The following is further disclosed regarding the embodiments described above.

[1348] (Claim 1)

[1349] In an interactive knowledge base system that uses generation AI to allow employees to search for company information,

[1350] A means of receiving questions entered by employees,

[1351] A means of sending a query to a generating AI based on an input question and generating an appropriate response,

[1352] A method for cleaning up the responses obtained from the generated AI and sending them back to the employees,

[1353] Methods for importing and using internal company policies, procedures, service information, and other data into the system,

[1354] A system that includes this.

[1355] (Claim 2)

[1356] The system according to claim 1, further comprising means for continuously updating a database of internal company information so that the latest information is always available.

[1357] (Claim 3)

[1358] The system according to claim 1, comprising means for providing a function that allows employees to search for information through an interactive interface.

[1359] "Example 1"

[1360] (Claim 1)

[1361] In an interactive knowledge base system that uses generation AI to allow employees to search for company information,

[1362] A means of receiving questions entered by employees,

[1363] A means for analyzing the input question and sending the query to the generating AI,

[1364] A means of cleaning up the responses generated in response to queries sent to the generation AI and sending them back to the employee,

[1365] One method is to import internal company policies, procedures, service information, and other data into a system and manage them as a database.

[1366] A system that includes a means to set an API key and establish a connection with the generating AI.

[1367] (Claim 2)

[1368] The system according to claim 1, further comprising means for continuously updating a database of internal company information so that the latest information is always available.

[1369] (Claim 3)

[1370] The system according to claim 1, comprising means for providing a function that allows employees to search for information through an interactive interface.

[1371] "Application Example 1"

[1372] (Claim 1)

[1373] In an interactive knowledge base system that uses generation AI to allow employees to search for company information,

[1374] A means of receiving questions entered by employees,

[1375] A means of sending a query to a generating AI based on an input question and generating an appropriate response,

[1376] A method for cleaning up the responses obtained from the generated AI and sending them back to the employees,

[1377] Methods for importing and using internal company policies, procedures, service information, and other data into the system,

[1378] A means that allows security personnel to quickly search for specific internal protocols,

[1379] A system that includes this.

[1380] (Claim 2)

[1381] The system according to claim 1, further comprising means for continuously updating a database of internal company information so that the latest information is always available.

[1382] (Claim 3)

[1383] The system according to claim 1, comprising means for providing a function that allows employees to search for information through an interactive interface.

[1384] "Example 2 of combining an emotion engine"

[1385] (Claim 1)

[1386] A means of receiving questions entered by employees,

[1387] A means of sending a query to a generating AI based on an input question and generating an appropriate response,

[1388] A method for cleaning up the responses obtained from the generated AI and sending them back to the employees,

[1389] Methods for importing and using internal company policies, procedures, service information, and other data into the system,

[1390] A method for recognizing an employee's emotions from input content using an emotion engine,

[1391] A means of adjusting the tone and content of responses based on recognized emotional data,

[1392] A system that includes this.

[1393] (Claim 2)

[1394] The system according to claim 1, further comprising means for continuously updating a database of internal company information so that the latest information is always available.

[1395] (Claim 3)

[1396] The system according to claim 1, comprising means for providing a function that allows employees to search for information through an interactive interface, and providing emotion-based responses.

[1397] "Application example 2 of combining emotional engines"

[1398] (Claim 1)

[1399] In an interactive knowledge base system that uses generation AI to allow employees to search for company information,

[1400] A means of receiving questions entered by employees,

[1401] A means of sending a query to a generating AI based on an input question and generating an appropriate response,

[1402] A method for cleaning up the responses obtained from the generated AI and sending them back to the employees,

[1403] Methods for importing and using internal company policies, procedures, service information, and other data into the system,

[1404] A means of analyzing employees' emotions using an emotion engine and adjusting the tone and content of responses based on the analysis results,

[1405] Through an interactive interface, employees can efficiently search for information,

[1406] ...

[1407] A system that includes this.

[1408] (Claim 2)

[1409] The system according to claim 1, further comprising means for continuously updating a database of internal company information so that the latest information is always available.

[1410] (Claim 3)

[1411] The system according to claim 1, further comprising means for providing a function that recognizes the emotions of employees using an emotion engine and adjusts the tone and content of the dialogue based on those emotions. [Explanation of Symbols]

[1412] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. In an interactive knowledge base system that uses generation AI to allow employees to search for company information, A means of receiving questions entered by employees, A means of sending a query to a generating AI based on an input question and generating an appropriate response, A method for cleaning up the responses obtained from the generated AI and sending them back to the employees, Methods for importing and using internal company policies, procedures, service information, and other data into the system, A system that includes this.

2. The system according to claim 1, further comprising means for continuously updating the internal information database so that the latest information is always available.

3. The system according to claim 1, comprising means for providing a function that allows employees to search for information through an interactive interface.

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