system

Abstract

Provide a system. The system includes: means for receiving and processing a query containing specific information from a user; a means for retrieving relevant documents based on a query; A method for generating new manuals using generative AI based on the searched documents, and means for transmitting the generated manual to a user terminal; A system including:

Description

[Technical Field]

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

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

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

[0004] In modern companies, a large amount of documents are scattered both inside and outside the company, making it difficult to quickly and accurately obtain the necessary information. This problem is particularly pronounced with important documents such as business manuals and construction procedures, and users often waste time searching for information and understanding the content. As a result, work efficiency declines and employee productivity is affected. The present invention aims to solve this problem by providing a system that allows users to quickly and accurately access the necessary information. [Means for solving the problem]

[0005] The present invention solves the problems by the following means. It includes a means for receiving and processing a query containing specific information from a user. It then includes a means for searching for related documents based on the query. It then provides a means for generating a new manual using generative AI based on the searched documents. Finally, it includes a means for sending the generated manual to a user terminal, allowing the user to quickly access the information they need. This system can significantly reduce the time previously spent searching for documents and understanding their contents, improving work efficiency.

[0006] A "user" is a person or entity that utilizes the system to search for and obtain specific information.

[0007] A "query" is specific information or keywords that a user enters into the system to perform a search.

[0008] The "application server" is a core computing system that receives and analyzes queries, makes search requests to the database server, and also interacts with generative AI.

[0009] A "database server" is a computer system that stores and manages various documents and provides results in response to search requests for that data.

[0010] "Documents" are electronic documents containing information such as business manuals, construction procedures, and past materials.

[0011] "Generative AI" is an artificial intelligence system that generates new manuals or guides based on specified documents and query information.

[0012] A "manual" is a guide or instruction manual that describes a specific task or operation method.

[0013] A "user terminal" is a computing device that allows a user to use the system to enter queries and view results, and includes, for example, a computer or a smartphone.

[0014] The "query analysis module" is the part of the system that analyzes received queries, extracts meaning, and generates search terms. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0023] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] The present invention provides a system for quickly and accurately retrieving necessary information from a large number of scattered documents. This system has the function of allowing users to search for specific information and generate an optimal manual based on that information. Specific aspects of this system are described below.

[0037] System Structure

[0038] The system consists of a user terminal, an application server, and a database server. Furthermore, a generative AI is integrated into the application server.

[0039] 1. User Device

[0040] The user terminal provides an interface for the user to search for specific information, such as a web browser or a dedicated application for entering a query into a search bar.

[0041] 2. Application Server

[0042] The application server is responsible for receiving queries from users, analyzing the queries, and generating search requests to the database server based on the results of the analysis.

[0043] The application server also has the function of requesting the generative AI to generate an optimal manual based on the search results obtained from the database server. This generated manual is ultimately sent to the user's device.

[0044] 3. Database Server

[0045] The database server stores a large number of documents and provides relevant documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[0046] 4. Generative AI

[0047] The generative AI generates new manuals based on the documents and query information sent from the application server, generating specific and easy-to-understand content, including related progress management methods and best practices.

[0048] System Operation

[0049] User query input

[0050] The user enters a query such as "how to manage project progress" into the search bar of the device.

[0051] Query Analysis and Database Search

[0052] The application server analyzes the query received from the user, and extracts keywords such as "project management" and "progress management" from the analysis.

[0053] The application server sends a search request to the database server based on the analysis result.

[0054] The database server searches for relevant documents and returns the results to the application server.

[0055] Manual generation using generative AI

[0056] If the application server determines that the search results do not meet the user's needs, it requests the generative AI to generate a new manual.

[0057] The generative AI generates a new manual based on the provided document and query information, including, for example, detailed instructions on specific progress management methods and tools to be used.

[0058] Providing results

[0059] The application server sends the new manual returned by the generative AI to the user's terminal.

[0060] The user terminal displays the generated manual to the user, and quickly provides the information the user needs.

[0061] Specific examples

[0062] For example, if a user searches for information on "testing methods," the following scenario may occur:

[0063] 1. User: Enter "test method" and press the search button.

[0064] 2. Application server: Analyzes the query and extracts the keywords "test" and "method".

[0065] 3. Database Server: Searches for relevant documents and returns the results.

[0066] 4. Generative AI: Generates manuals for new "test methods" as needed.

[0067] 5. User terminal: Displays the generated manual and makes it viewable by the user.

[0068] This invention speeds up access to necessary information, significantly improving work efficiency, and also contributes to improving productivity by reducing the time users spend searching for information.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The user enters "project management progress management method" into the search interface on the terminal and presses the search button.

[0072] Step 2:

[0073] The terminal transmits the input query "Project management progress management method" to the application server.

[0074] Step 3:

[0075] The application server receives the query and analyzes its meaning using the query analysis module. For example, it extracts keywords such as "project management" and "progress management."

[0076] Step 4:

[0077] The application server sends a search request to the database server based on the extracted keywords.

[0078] Step 5:

[0079] The database server searches for relevant documents and sends the results to the application server.

[0080] Step 6:

[0081] The application server reviews the search results received from the database server and determines whether they meet the user's needs.

[0082] Step 7:

[0083] If the application server determines that the search results are insufficient, it sends the query information and the retrieved documents to the generative AI to request that the generative AI generate a new manual.

[0084] Step 8:

[0085] Based on the provided information, the generative AI generates a new manual for "Project Management Progress Management Methods," including detailed content on progress management procedures, tools to be used, and best practices.

[0086] Step 9:

[0087] The generative AI sends the generated manual to the application server.

[0088] Step 10:

[0089] The application server sends the new manual received from the generative AI to the user's terminal.

[0090] Step 11:

[0091] The terminal displays the generated manual to the user, allowing the user to quickly view the required information.

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] It is difficult to quickly and accurately obtain the necessary information from a large number of scattered documents and provide users with the most appropriate instruction manual. Furthermore, when existing documents are outdated or unclear, there is a lack of a way to determine this and generate appropriate new documents. This makes it difficult for users to quickly access the information they need, resulting in a decline in work efficiency.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for analyzing the query and extracting keywords, means for searching for related documents based on the query, means for determining the relevance of the documents in the search results, means for generating a new tutorial manual using generative artificial intelligence based on the searched documents, and means for transmitting the generated tutorial manual to a user terminal. This allows the user to quickly and accurately obtain the information they need and always have the latest and most appropriate tutorial manual.

[0097] A "query" is a search term or phrase that a user enters to search for specific information.

[0098] A "user terminal" is a device that a user operates to input queries and receive information, and includes a personal computer, a smartphone, etc.

[0099] The "application server" is a server whose role is to analyze queries received from user terminals, send search requests to the database server, and then request the generative artificial intelligence to generate new instruction manuals.

[0100] A "database server" is a server that stores a large number of documents, searches for related documents based on a query, and returns the search results to the application server.

[0101] "Generative AI" is an AI technology that generates new instruction manuals based on documents and query information sent from an application server.

[0102] "Keywords" are important words or phrases in a query that are used to find relevant documents.

[0103] "Relevance" is a rating that indicates how well a document in the search results meets the requirements of the user's query.

[0104] A "tutorial manual" is a detailed guide or manual that uses generative artificial intelligence to create new project management and progress management methods.

[0105] The present invention provides a system that allows users to search for specific information and generates optimal instruction manuals based on that information. This system consists of a user terminal, an application server, and a database server. Furthermore, generative artificial intelligence is integrated into the application server.

[0106] System Configuration

[0107] User terminal

[0108] A user device provides an interface for users to search for specific information. Specifically, this corresponds to a PC or smartphone, and provides the user interface via a web browser or dedicated application. For example, a user enters a query such as "project management progress management method" into the search bar.

[0109] Application Server

[0110] The application server is responsible for analyzing queries received from user devices and sending search requests to the database server. Query analysis uses NLP (natural language processing) technology, using libraries such as Python's NLTK and spaCy. It also has the function of requesting a generative AI to generate new tutorials based on search results obtained from the database server. Generative AI uses generative models such as OpenAI's GPT-4.

[0111] As a specific example of query analysis, if a user enters "project management progress management methods," the application server extracts keywords such as "project management" and "progress management." Based on the analysis results, the application server sends a search request to the database server.

[0112] Database Server

[0113] The database server stores a large number of documents and searches for relevant documents based on queries sent from the application server. MySQL (registered trademark) or PostgreSQL is used as the database management system (DBMS). For example, it searches past literature and business manuals related to "project management" and returns the results to the application server.

[0114] Generative Artificial Intelligence

[0115] The generative AI generates new tutorials based on the document and query information sent from the application server. It uses OpenAI's GPT-4 and other technologies. The generative AI generates tutorials using the following prompt as input:

[0116] "Generate detailed instruction on how to track project management progress."

[0117] Specific examples

[0118] For example, if a user searches for information on "testing methods," the flow is as follows:

[0119] 1. The user enters "test method" in the device's search bar and presses the search button.

[0120] 2. The application server analyzes the query and extracts the keywords "test" and "method."

[0121] 3. The application server sends a search request to the database server based on the analysis results.

[0122] 4. The database server searches for relevant documents and returns the results to the application server.

[0123] 5. If the application server determines that the search results do not meet the user's needs, it will ask the generative AI to generate a new instruction manual for the "test method," using the prompt "Please generate detailed instructions for the test method."

[0124] 6. The generative AI generates a new tutorial and sends it back to the application server. The generated tutorial includes specific test methods and procedures.

[0125] 7. The application server sends the generated instruction manual to the user terminal.

[0126] 8. The user terminal displays the instruction manual and makes it available for the user to view. For example, the user can view a new instruction manual that describes detailed test methods and use it in their work.

[0127] In this way, this system provides quick access to necessary information, significantly improving work efficiency, and reducing the time spent searching for information, contributing to increased productivity.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1: User Query Input

[0130] A user uses a device (PC or smartphone) to input specific information into a search bar. For example, they might input "progress management methods for project management" and press the search button. This query is sent to the application server. The input is "progress management methods for project management," and the output is the query data sent to the application server.

[0131] Step 2: Parsing the query

[0132] The application server analyzes the received query, in particular using NLP techniques to extract important keywords within the query. Libraries used include Python's NLTK and spaCy. For example, the query "How to track progress in project management" is analyzed to extract the keywords "project management" and "progress management." The input is the user's query data, and the output is the extracted keywords.

[0133] Step 3: Database search

[0134] The application server sends a search request to the database server based on the extracted keywords. The database server searches for related documents and returns the results to the application server. MySQL or PostgreSQL is used as the DBMS. For example, searching for past literature or business manuals related to "project management." The input is the extracted keywords, and the output is a list of related documents.

[0135] Step 4: Determine the relevance of search results

[0136] The application server analyzes the returned search results and determines the relevance of the document. This determination is based on criteria such as the document's age, the vagueness of its content, and its specificity to the user's query. The input is the document data of the search results, and the output is the relevance determination result.

[0137] Step 5: Manual generation instructions

[0138] If the degree of compatibility is determined to be low, the application server requests the generative AI to generate a new instruction manual. The specific prompt used is "Please generate a detailed instruction manual on how to manage project progress." The input is the prompt text and related document data, and the output is a generation request.

[0139] Step 6: Receive the generated manual

[0140] The generative AI generates a new tutorial based on the provided document and query information and returns it to the application server. OpenAI's GPT-4 and other technologies are used for generation, and the generated tutorial includes specific progress management methods and explanations of the tools used. The input is the generation request and related data, and the output is the generated tutorial.

[0141] Step 7: Send and view the manual

[0142] The application server sends the generated instruction manual to the user's terminal. The user's terminal displays the received instruction manual to the user, providing the necessary information quickly and accurately. For example, the user can view the new instruction manual that contains detailed progress management methods and use it in their work. The input is the generated instruction manual, and the output is the instruction manual displayed on the user's terminal.

[0143] Through these detailed processing steps, the system enables users to quickly and accurately access the information they need, thereby improving work efficiency.

[0144] (Application example 1)

[0145] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0146] To improve work efficiency at logistics centers, a system that allows quick and accurate access to work procedures and troubleshooting information on-site is required. However, with the current system, it takes time to search for information and obtain manuals, which can lead to work delays and errors. This results in a problem of reduced efficiency of the entire business.

[0147] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0148] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related data based on the query, means for generating a new manual using generative AI based on the searched data, means for transmitting the generated manual to a user terminal, means for receiving specific information by voice input or text input, and means for using a smartphone or head-mounted display as the user terminal. This allows workers at a logistics center to efficiently access work procedures and troubleshooting information, improving the efficiency of overall operations.

[0149] A "user" is a person or robot that uses the system to search for specific information and obtain a manual.

[0150] A "query" is a search request representing specific information entered by a user.

[0151] A "smartphone" is a portable computing device capable of communicating, processing information, and running applications.

[0152] A "head-mounted display" is a device worn on the head that displays visual information.

[0153] A "server" is a central computer that receives and processes user requests.

[0154] An "application server" is a server that is responsible for analyzing queries, searching data, and sending generated manuals.

[0155] A "database server" is a server that stores large amounts of data and provides the data in response to search requests.

[0156] "Generative AI" is artificial intelligence that generates new manuals based on given data and queries.

[0157] A "Manual" is a document that contains specific procedure and troubleshooting information.

[0158] An "interface" is an operation screen or input means for exchanging information between the system and the user.

[0159] This invention is a system for dramatically improving work efficiency in logistics centers. The system is designed to enable workers to quickly and appropriately access work procedures and troubleshooting information using smartphones and head-mounted displays (HMDs).

[0160] System Configuration

[0161] The system is broadly composed of a user terminal, an application server, and a database server. Generative AI is also integrated into the application server.

[0162] User terminal

[0163] User devices provide an interface for workers to search for specific information. User devices include smartphones and head-mounted displays, allowing workers to enter queries via voice or text input.

[0164] Application Server

[0165] The application server receives queries from users and analyzes them. Based on the analysis results, it generates a search request to the database server. The application server also has the function of requesting the generative AI to generate an optimal manual based on the search results obtained from the database server. This generated manual is ultimately sent to the user's device.

[0166] Database Server

[0167] The database server stores large amounts of data and provides relevant data in response to queries from the application server, such as product sorting procedures, troubleshooting methods, and safety measures.

[0168] Generative AI

[0169] The generative AI generates new manuals based on the data and query information sent from the application server, generating specific and easy-to-understand content, such as work procedures and troubleshooting methods.

[0170] System operation explanation

[0171] 1. User query input

[0172] The worker (user) sends queries such as "product sorting procedures" and "how to handle malfunctions" to the system by voice or text input through the interface of their smartphone or HMD.

[0173] 2. Query Analysis and Database Search

[0174] The application server receives the query and analyzes it. From this analysis, it extracts keywords such as "product sorting" and "fault handling" and sends a search request to the database server. The database server searches for related data and returns the results.

[0175] 3. Manual generation using generative AI

[0176] The application server analyzes the search results and sends data and query information to the generative AI as needed. The generative AI generates a new manual based on this data. For example, the following prompt sentence could be sent to the generative AI:

[0177] Example prompt sentence:

[0178] "Generate the most appropriate manual based on the following information: product sorting procedures, safety measures, and troubleshooting methods."

[0179] 4. Providing results

[0180] The application server receives the manual sent back from the generative AI and displays it on the interface of a smartphone or HMD, allowing workers to access the appropriate information in real time.

[0181] Specific examples

[0182] For example, when a worker searches for information on "how to handle a malfunction," the following operations are performed.

[0183] 1. Worker: Send a query to the system by voice or text input, saying "How to handle the malfunction."

[0184] 2. Application server: Analyzes the query and extracts the keyword "failure response."

[0185] 3. Database Server: Searches for relevant data and returns the results to the application server.

[0186] 4. Generative AI: Generates new manuals on how to respond to failures as needed.

[0187] 5. User terminal: Displays the generated manual and makes it available for workers to view.

[0188] This invention will significantly improve operational efficiency at logistics centers, allowing workers to work quickly and accurately.

[0189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0190] Step 1:

[0191] Entering a query

[0192] A user inputs a query for specific information via voice or text input through the interface of a smartphone or head-mounted display. The input query (e.g., "product sorting procedure") is received by the user device.

[0193] Step 2:

[0194] Submitting a query

[0195] The user terminal sends the input query to the application server, at which point the input data is treated as a query and sent to the application server.

[0196] Step 3:

[0197] Parsing a query

[0198] The application server analyzes the received query using natural language processing (e.g., Spacy) to extract important keywords from the query (e.g., "product sorting"). These keywords form the basis for subsequent data retrieval.

[0199] Step 4:

[0200] Sending a search request to the database

[0201] The application server sends a search request to the database server based on the extracted keywords, which includes the keywords and searches for related data.

[0202] Step 5:

[0203] Database search

[0204] The database server searches for relevant data in the database based on the received search request. As a search result, relevant data (e.g., "documents regarding product sorting procedures") is generated and sent to the application server.

[0205] Step 6:

[0206] Receiving and analyzing search results

[0207] The application server receives the search results sent from the database server and checks their contents. Here, it determines whether the search result data is old or unclear (e.g., checks the update date and time of the data and whether it is unclear).

[0208] Step 7:

[0209] Sending to generative AI

[0210] The application server sends search results and queries to the generative AI as needed. The generative AI receives prompts to generate new manuals based on the given data and query information (e.g., "Generate the optimal manual based on the following information: product sorting procedures, safety measures, and troubleshooting methods").

[0211] Step 8:

[0212] Generate manual

[0213] The generative AI analyzes the prompt and generates an appropriate manual, which includes new information and concrete examples and is designed to meet the user's needs (e.g., "Detailed instructions for product sorting procedures").

[0214] Step 9:

[0215] Sending the generated results

[0216] The generative AI sends the generated manual to the application server, which receives it and prepares it for delivery to the user.

[0217] Step 10:

[0218] Providing manuals

[0219] The application server sends the generated manual to the user terminal, which displays the manual and makes it available for workers to view, allowing workers to access the information they need in real time.

[0220] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0221] The present invention provides a system for quickly and accurately retrieving necessary information from a large number of scattered documents. This system has the functions of allowing users to search for specific information and generating an optimal manual based on that information, as well as the functions of recognizing the user's emotions and providing a response that corresponds to the emotion. Specific embodiments of this system are described below.

[0222] System Structure

[0223] The system consists of a user terminal, an application server, a database server, and an emotion engine. Generative AI is also integrated into the application server.

[0224] 1. User Device

[0225] The user terminal provides an interface for the user to search for specific information, such as a web browser or a dedicated application for entering a query into a search bar.

[0226] It also has sensors and input devices, such as a camera and microphone, to recognize the user's emotions.

[0227] 2. Application Server

[0228] The application server is responsible for receiving queries from users, analyzing the queries, and generating search requests to the database server based on the results of the analysis.

[0229] It also receives user emotional data from the emotion engine and has the ability to request manual adjustments from the generative AI based on this data.

[0230] 3. Database Server

[0231] The database server stores a large number of documents and provides relevant documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[0232] 4. Generative AI

[0233] The generative AI generates a new manual based on the document and query information sent from the application server and the user's emotional data, including detailed content such as progress management procedures, tools to be used, and best practices.

[0234] 5. Emotion Engine

[0235] The emotion engine recognizes the user's emotions and sends the data to the application server, which makes it possible to generate an optimal manual based on the user's emotions.

[0236] System Operation

[0237] User query input

[0238] The user enters "project management progress management methods" into the search interface of the device and presses the search button. At this time, the emotion engine recognizes the user's facial expressions and tone of voice.

[0239] Query Analysis and Database Search

[0240] The application server analyzes the query received from the user, and extracts keywords such as "project management" and "progress management" from the analysis.

[0241] The application server sends a search request to the database server based on the analysis result.

[0242] The database server searches for relevant documents and returns the results to the application server.

[0243] Use of Emotional Data

[0244] The emotion engine analyzes the user's emotion data in real time and sends the results to the application server.

[0245] Based on this emotional data, the application server requests the generative AI to generate the optimal manual based on the query.

[0246] Manual generation using generative AI

[0247] Based on the provided information and the user's emotional data, the generative AI generates a manual on "Project Management Progress Management Methods" that is more user-friendly. Specifically, if the user is feeling stressed, the AI ​​adjusts the manual's writing style and content to be gentle and explanatory.

[0248] Providing results

[0249] The application server sends the new manual returned by the generative AI to the user's terminal.

[0250] The user terminal displays the generated manual to the user, allowing the user to quickly view the required information.

[0251] Specific examples

[0252] For example, when a user searches for information on "testing methods," the following scenario may occur:

[0253] 1. User: Enters "test method" and presses the search button. The emotion engine analyzes the user's facial expressions and voice at this time and detects that the user is in a hurry.

[0254] 2. Application server: Analyzes the query and extracts the keywords "test" and "method".

[0255] 3. Database Server: Searches for relevant documents and returns the results.

[0256] 4. Emotion engine: Sends the user's hurry emotion data to the application server.

[0257] 5. Generative AI: Based on emotional data, it generates a concise and easily understandable manual on the "test method."

[0258] 6. User terminal: Displays the generated manual and helps users quickly obtain information.

[0259] This invention not only speeds up access to necessary information and significantly improves business efficiency, but also provides a flexible system that responds to the user's emotions.

[0260] The processing flow will be explained below.

[0261] Step 1:

[0262] The user enters "project management progress management methods" into the search interface on their device and presses the search button. At this time, the emotion engine recognizes the user's facial expressions and tone of voice in real time.

[0263] Step 2:

[0264] The terminal sends the input query "Project management progress management method" to the application server. At the same time, the emotion engine sends the user's emotion data to the application server.

[0265] Step 3:

[0266] The application server receives the query and analyzes its meaning using the query analysis module. For example, it extracts keywords such as "project management" and "progress management."

[0267] Step 4:

[0268] The application server sends a search request to the database server based on the extracted keywords.

[0269] Step 5:

[0270] The database server searches for relevant documents and sends the results to the application server. For example, past progress management manuals related to "project management" are retrieved.

[0271] Step 6:

[0272] The application server reviews the search result documents received from the database server to determine whether the retrieved documents meet the user's needs.

[0273] Step 7:

[0274] If the application server determines that the documentation is insufficient, it sends the retrieved documentation and query information to the generative AI and requests it to generate a new manual. At this time, the user's emotional data sent from the emotion engine is also sent.

[0275] Step 8:

[0276] The generative AI generates a new manual for "Project Management Progress Management Methods" based on the provided document, query information, and emotion data. Specifically, if the user is feeling stressed, the AI ​​adjusts the manual's writing style to be gentler and provide more explanations.

[0277] Step 9:

[0278] The generative AI sends the generated manual to the application server.

[0279] Step 10:

[0280] The application server sends the new manual received from the generative AI to the user's terminal.

[0281] Step 11:

[0282] The terminal displays the generated manual to the user, allowing the user to quickly view the information they need. In addition, appropriate information is provided according to the user's emotions, providing a more satisfying experience for the user.

[0283] Example 2

[0284] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0285] In today's business environment, it is extremely important to quickly and accurately retrieve necessary information from a large amount of scattered documents and generate optimal manuals based on that information. However, conventional systems lack the flexibility to respond to user emotions, making it difficult to provide appropriate information, especially when the user is stressed or in a hurry.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0287] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related data based on the query, means for generating new information using generative artificial intelligence based on the searched data, means for recognizing and analyzing user emotion data, and means for transmitting the generated information to a user terminal. This makes it possible to generate and provide a flexible and optimal manual that responds to the user's emotions.

[0288] "User" refers to an individual who operates a computer system or electronic device.

[0289] "Query" refers to a question or request that a user enters into a system to retrieve information.

[0290] "Data" refers to a collection of records or facts that comprise information, including digital documents and information stored in databases.

[0291] "Generative AI" refers to a type of AI that has the ability to generate new texts, manuals, etc. based on input information.

[0292] "Emotional Data" refers to data collected and analyzed to identify a user's emotions, including, specifically, a user's facial expressions and tone of voice.

[0293] "User terminal" refers to a hardware device that allows a user to access and operate the system, and includes a computer, smartphone, etc.

[0294] An "application server" is a server that receives and processes user requests and provides information in cooperation with a database.

[0295] A "database server" refers to a server that stores and manages large amounts of data and provides the data as needed and in response to requests.

[0296] "Search Request" means an instruction sent to a system to retrieve specific information.

[0297] "New information" refers to search results and new manuals and explanations generated by generative artificial intelligence.

[0298] This invention is a system that quickly and accurately acquires necessary information from a large amount of scattered data and generates an optimal manual based on the user's emotional data. This system is composed of a user terminal, an application server, a database server, an emotion engine, and generative artificial intelligence. The specific form of this system is described below.

[0299] 1. User Device

[0300] The user device provides an interface for searching for information. This can be achieved through a web browser or a dedicated application. It also has sensors such as a camera and microphone to recognize the user's emotions. The user requests information by entering a specific query into the search bar.

[0301] 2. Application Server

[0302] The application server is responsible for receiving and analyzing queries sent by users. This server breaks down queries into tokens and extracts important keywords. It also receives emotion data from the emotion engine and provides the analysis results to the generative AI.

[0303] 3. Database Server

[0304] The database server searches large amounts of data based on search requests sent from the application server and returns relevant results. The database stores business manuals, technical documents, past cases, etc.

[0305] 4. Generative Artificial Intelligence

[0306] Generative AI generates new manuals based on data and emotional data passed from the application server. This AI has the ability to customize content according to specific contexts and emotions, providing information that is relevant to the user's needs.

[0307] 5. Emotion Engine

[0308] The emotion engine analyzes the user's facial expressions and tone of voice in real time to generate emotion data, which is then sent to the application server to indicate whether the user is feeling stressed or in a hurry.

[0309] Specific examples

[0310] An example will be described in which a user searches for "project management progress management method."

[0311] User: Enters "Project management progress management methods" into the device's search bar and presses the search button. The emotion engine analyzes the user's facial expressions and tone of voice at this time and detects that the user is feeling impatient.

[0312] Application server: Breaks down the query into tokens and extracts keywords such as "project management" and "progress management." It then sends a search request to the database server based on the keywords.

[0313] Database Server: Searches for relevant data and returns the results to the application server.

[0314] Emotion engine: Sends user emotion data to the application server in real time.

[0315] Generative AI: Generates a new "progress management method" manual based on data and emotion data received from the application server. It customizes the manual to be concise and easy to understand, especially when the user is in a hurry.

[0316] Application server: Sends the generated manual to the user terminal.

[0317] User terminal: Displays the generated manual to the user and provides the necessary information quickly.

[0318] Prompt Sentence Examples

[0319] Users are in a hurry to find the "test method." Please create a concise and easy-to-understand manual. Users can tell from their facial expressions and tone of voice that they are in a hurry. Please make the content simple and easy to understand.

[0320] In this way, the present invention is a system that flexibly responds to the user's queries and emotions and quickly provides optimal information.

[0321] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0322] Step 1:

[0323] User query input

[0324] A user uses their own device to enter a query containing specific information into the search interface and presses the search button. The device then sends the query, along with the user's facial expressions and tone of voice via the camera and microphone, to the emotion engine. This simultaneously collects the user's emotional data. Input: Query entered by the user. Output: Query data and emotional data. Specifically, the query entered on the browser is sent in text format to the application server.

[0325] Step 2:

[0326] Query Analysis

[0327] The application server analyzes queries received from the user's device. Specifically, it breaks down the query into tokens, one word at a time, and extracts important keywords. At this time, it also uses the emotion data received from the emotion engine as part of the analysis. Input: Query data and emotion data. Output: Keyword list and emotion data. Specifically, it uses a natural language processing tool to tokenize the query and list the main keywords.

[0328] Step 3:

[0329] Database search

[0330] The application server sends a search request to the database server based on the extracted keywords. The database server searches for related data and returns the results to the application server. Input: Keyword list. Output: List of related data. Specifically, it generates an SQL query and sends it to the database server. The database server searches for the relevant records and returns the results.

[0331] Step 4:

[0332] Use of Emotional Data

[0333] The emotion engine analyzes the user's emotional data and sends the results to the application server. The application server uses this emotional data to make further adjustments to the generative AI. Input: Emotional data. Output: Adjustment instructions for the generative AI. Specifically, it decodes the emotional data using facial expression recognition technology and voice analysis tools, and sends the results to the application server.

[0334] Step 5:

[0335] Manual generation using generative AI

[0336] The generative AI generates a new manual based on the data and emotional data received from the application server. It customizes the content according to the user's emotions. Input: List of related data and emotional data. Output: Generated manual. Specifically, it inputs a prompt sentence into the generative AI model, and generates a user-friendly manual based on that.

[0337] Step 6:

[0338] Providing results

[0339] The application server sends the generated manual to the user terminal. The user terminal displays the received manual to the user. Input: Generated manual. Output: Information displayed to the user. Specifically, it generates an HTTP response and sends it to the user terminal. The user terminal displays the manual in a browser or application.

[0340] (Application example 2)

[0341] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0342] In today's world, it is important to quickly and accurately obtain necessary information from a vast amount of documents. However, conventional information retrieval systems have difficulty providing information that meets the user's intentions and are not responsive to the user's emotions. Therefore, there is a need for systems that allow users to quickly obtain the information they need and respond flexibly to the user's emotions.

[0343] The specification processing by specification processing unit 290 of data processing device 12 in application example 2 is realized by the following means. In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related documents based on the query, means for generating a new manual using a generative AI model based on the searched documents, means for recognizing the user's emotions and transmitting the emotion data to an application server, means for requesting the generative AI model to adjust the manual based on the user's emotion data, and means for transmitting the generated manual to a user terminal. This not only enables the user to quickly and accurately obtain specific information, but also makes it possible to provide optimal information according to the user's emotions, thereby improving user satisfaction.

[0344] A "user terminal" is a device that provides an interface for a user to search for specific information.

[0345] An "application server" is a server that receives queries from users, analyzes the queries, and generates search requests to a database server.

[0346] A "database server" is a server that stores a large number of documents and provides related documents in response to queries from an application server.

[0347] "Generative AI model" is a general term for algorithms that generate new manuals based on given documents and query information.

[0348] "User emotion" refers to the emotional state (e.g., stress, relief, hurry, etc.) of the user when searching for information.

[0349] The "emotion engine" is a system component that recognizes the user's emotions and sends the emotion data to the application server.

[0350] A "query" is an inquiry that a user enters to search for specific information.

[0351] "Documents" are collections of information that can be searched, such as business manuals, construction instructions, and past documents.

[0352] A "search tool" is a method or technique for retrieving relevant documents based on a query.

[0353] "Generation means" refers to the method or technology for generating a new manual based on the retrieved documents.

[0354] "Emotion data" is data that represents the user's emotional state and is generated by the emotion engine.

[0355] A "manual" is a guide document created by a generative AI model based on the specific information a user seeks.

[0356] This invention provides a system that allows users to search for specific information and generates an optimal manual based on that information. It also has the ability to recognize the user's emotions and respond accordingly. This system consists of a user terminal, an application server, a database server, a generative AI model, and an emotion engine.

[0357] System Structure

[0358] 1. User Device

[0359] A user terminal is a device that provides an interface for users to search for specific information, such as a web browser or dedicated application for entering queries into a search bar, and also has a camera and microphone for recognizing user emotions.

[0360] 2. Application Server

[0361] The application server receives queries from users, analyzes them, and generates search requests for the database server. It also receives user emotion data from the emotion engine and requests manual adjustments to the generative AI model.

[0362] 3. Database Server

[0363] A database server is a server that stores a large amount of documents and provides related documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[0364] 4. Generative AI Models

[0365] A generative AI model is a general term for an algorithm that generates a new manual based on the provided document, query information, and user emotion data. Generative AI technologies such as GPT-3 (registered trademark) are generally used.

[0366] 5. Emotion Engine

[0367] The emotion engine is a system component that recognizes the user's emotions and sends the emotion data to the application server. Based on this emotion data, a manual that best suits the user's emotions is generated.

[0368] Operation flow

[0369] The user enters a query and their emotional state is recognized. The application server analyzes the query and retrieves relevant documents from the database server. The search results, including the emotional data, are then sent to a generative AI model, which generates an optimal manual. This manual is then sent to the user's device, where it can be viewed by the user.

[0370] Specific examples

[0371] For example, if a user searches for "how to set up a security camera," the system operates as follows:

[0372] 1. User device: The user types in "How to set up a surveillance camera" and presses the search button. The emotion engine determines that the user is in a hurry.

[0373] 2. Application server: Analyzes the query and extracts keywords such as "surveillance camera" and "settings method."

[0374] 3. Database Server: Searches for relevant documents and returns the results to the application server.

[0375] 4. Emotion engine: Sends the user's hurry emotion data to the application server.

[0376] 5. Generative AI model: Based on emotional data, it quickly generates a manual that explains the setup procedure.

[0377] 6. User terminal: Displays the generated manual to help users solve problems quickly.

[0378] Prompt Sentence Examples

[0379] When a user searches for specific information, for example, "How to set up a surveillance camera," the emotion engine analyzes the user's emotions and detects that the user is in a hurry. Based on this, the generative AI model generates a concise manual that can be quickly implemented and sends it to the user's device.

[0380] example:

[0381] (Specific example used within the system)

[0382] "If a user types in 'how to set up a surveillance camera,' and the emotion engine detects that the user is in a hurry, the generated manual will provide concise and specific steps to help the user set up the camera quickly. It will also include specific model names and images of the setting screen to help the user respond quickly."

[0383] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0384] Step 1:

[0385] The user device provides an interface for users to input queries to search for specific information. The user enters a query such as "How to set up a surveillance camera" into the search bar and presses the search button. At this time, the device's camera and microphone are used to transmit the user's facial expressions and voice to the emotion engine. The input data are the query and emotion data, and the output data are the query information and raw data passed to the emotion engine.

[0386] Step 2:

[0387] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. The emotion engine extracts emotional data from the raw data and sends it to the application server. The input data is the user's facial expressions and voice, and the output data is emotional data corresponding to the emotional state. Specifically, it determines the user's emotional state, such as whether they are in a hurry or relaxed.

[0388] Step 3:

[0389] The application server analyzes queries received from users and extracts relevant keywords. The input data is query information and emotion data, and the output data is the extracted keywords. Specifically, it identifies keywords such as "surveillance camera" and "setting method."

[0390] Step 4:

[0391] The application server sends a search request to the database server based on the analysis results. The input data is the extracted keywords, and the output data is a search request to the database server. Specifically, it sends a request to search for documents that match "surveillance camera" and "settings method."

[0392] Step 5:

[0393] The database server searches for relevant documents in response to the received search request and returns the results to the application server. The input data is the search request, and the output data is the relevant documents as search results.

[0394] Step 6:

[0395] The application server requests the generative AI model to generate a manual based on the related documents and emotion data received from the database server. The input data are the related documents and emotion data, and the output data is the data passed to the generative AI model. Specifically, it provides information that serves as a prompt for generating an appropriate manual.

[0396] Step 7:

[0397] The generative AI model generates a new manual based on the provided document and emotion data. The input data is the relevant document, emotion data, and prompt sentences, and the output data is the generated manual. Specifically, if the user is in a hurry, it generates a concise and quick manual.

[0398] Step 8:

[0399] The application server receives the manual generated by the generative AI model and sends it to the user's device. The input data is the generated manual, and the output data is the manual sent to the user's device.

[0400] Step 9:

[0401] The user terminal displays the generated manual to the user, helping the user to quickly acquire information. The input data is the generated manual, and the output data is the displayed manual. Specifically, the user can easily understand "how to set up a surveillance camera" and quickly perform the setting.

[0402] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0403] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0405] [Second embodiment]

[0406] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0407] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0408] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0409] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0410] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0411] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0412] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0413] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0414] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0415] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0416] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0417] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0418] The present invention provides a system for quickly and accurately retrieving necessary information from a large number of scattered documents. This system has the function of allowing users to search for specific information and generate an optimal manual based on that information. Specific aspects of this system are described below.

[0419] System Structure

[0420] The system consists of a user terminal, an application server, and a database server. Furthermore, a generative AI is integrated into the application server.

[0421] 1. User Device

[0422] The user terminal provides an interface for the user to search for specific information, such as a web browser or a dedicated application for entering a query into a search bar.

[0423] 2. Application Server

[0424] The application server is responsible for receiving queries from users, analyzing the queries, and generating search requests to the database server based on the results of the analysis.

[0425] The application server also has the function of requesting the generative AI to generate an optimal manual based on the search results obtained from the database server. This generated manual is ultimately sent to the user's device.

[0426] 3. Database Server

[0427] The database server stores a large number of documents and provides relevant documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[0428] 4. Generative AI

[0429] The generative AI generates new manuals based on the documents and query information sent from the application server, generating specific and easy-to-understand content, including related progress management methods and best practices.

[0430] System Operation

[0431] User query input

[0432] The user enters a query such as "how to manage project progress" into the search bar of the device.

[0433] Query Analysis and Database Search

[0434] The application server analyzes the query received from the user, and extracts keywords such as "project management" and "progress management" from the analysis.

[0435] The application server sends a search request to the database server based on the analysis result.

[0436] The database server searches for relevant documents and returns the results to the application server.

[0437] Manual generation using generative AI

[0438] If the application server determines that the search results do not meet the user's needs, it requests the generative AI to generate a new manual.

[0439] The generative AI generates a new manual based on the provided document and query information, including, for example, detailed instructions on specific progress management methods and tools to be used.

[0440] Providing results

[0441] The application server sends the new manual returned by the generative AI to the user's terminal.

[0442] The user terminal displays the generated manual to the user, and quickly provides the information the user needs.

[0443] Specific examples

[0444] For example, if a user searches for information on "testing methods," the following scenario may occur:

[0445] 1. User: Enter "test method" and press the search button.

[0446] 2. Application server: Analyzes the query and extracts the keywords "test" and "method".

[0447] 3. Database Server: Searches for relevant documents and returns the results.

[0448] 4. Generative AI: Generates manuals for new "test methods" as needed.

[0449] 5. User terminal: Displays the generated manual and makes it viewable by the user.

[0450] This invention speeds up access to necessary information, significantly improving work efficiency, and also contributes to improving productivity by reducing the time users spend searching for information.

[0451] The processing flow will be explained below.

[0452] Step 1:

[0453] The user enters "project management progress management method" into the search interface on the terminal and presses the search button.

[0454] Step 2:

[0455] The terminal transmits the input query "Project management progress management method" to the application server.

[0456] Step 3:

[0457] The application server receives the query and analyzes its meaning using the query analysis module. For example, it extracts keywords such as "project management" and "progress management."

[0458] Step 4:

[0459] The application server sends a search request to the database server based on the extracted keywords.

[0460] Step 5:

[0461] The database server searches for relevant documents and sends the results to the application server.

[0462] Step 6:

[0463] The application server reviews the search results received from the database server and determines whether they meet the user's needs.

[0464] Step 7:

[0465] If the application server determines that the search results are insufficient, it sends the query information and the retrieved documents to the generative AI to request that the generative AI generate a new manual.

[0466] Step 8:

[0467] Based on the provided information, the generative AI generates a new manual for "Project Management Progress Management Methods," including detailed content on progress management procedures, tools to be used, and best practices.

[0468] Step 9:

[0469] The generative AI sends the generated manual to the application server.

[0470] Step 10:

[0471] The application server sends the new manual received from the generative AI to the user's terminal.

[0472] Step 11:

[0473] The terminal displays the generated manual to the user, allowing the user to quickly view the required information.

[0474] Example 1

[0475] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0476] It is difficult to quickly and accurately obtain the necessary information from a large number of scattered documents and provide users with the most appropriate instruction manual. Furthermore, when existing documents are outdated or unclear, there is a lack of a way to determine this and generate appropriate new documents. This makes it difficult for users to quickly access the information they need, resulting in a decline in work efficiency.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0478] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for analyzing the query and extracting keywords, means for searching for related documents based on the query, means for determining the relevance of the documents in the search results, means for generating a new tutorial manual using generative artificial intelligence based on the searched documents, and means for transmitting the generated tutorial manual to a user terminal. This allows the user to quickly and accurately obtain the information they need and always have the latest and most appropriate tutorial manual.

[0479] A "query" is a search term or phrase that a user enters to search for specific information.

[0480] A "user terminal" is a device that a user operates to input queries and receive information, and includes a personal computer, a smartphone, etc.

[0481] The "application server" is a server whose role is to analyze queries received from user terminals, send search requests to the database server, and then request the generative artificial intelligence to generate new instruction manuals.

[0482] A "database server" is a server that stores a large number of documents, searches for related documents based on a query, and returns the search results to the application server.

[0483] "Generative AI" is an AI technology that generates new instruction manuals based on documents and query information sent from an application server.

[0484] "Keywords" are important words or phrases in a query that are used to find relevant documents.

[0485] "Relevance" is a rating that indicates how well a document in the search results meets the requirements of the user's query.

[0486] A "tutorial manual" is a detailed guide or manual that uses generative artificial intelligence to create new project management and progress management methods.

[0487] The present invention provides a system that allows users to search for specific information and generates optimal instruction manuals based on that information. This system consists of a user terminal, an application server, and a database server. Furthermore, generative artificial intelligence is integrated into the application server.

[0488] System Configuration

[0489] User terminal

[0490] A user device provides an interface for users to search for specific information. Specifically, this corresponds to a PC or smartphone, and provides the user interface via a web browser or dedicated application. For example, a user enters a query such as "project management progress management method" into the search bar.

[0491] Application Server

[0492] The application server is responsible for analyzing queries received from user devices and sending search requests to the database server. Query analysis uses NLP (natural language processing) technology, using libraries such as Python's NLTK and spaCy. It also has the function of requesting a generative AI to generate new tutorials based on search results obtained from the database server. Generative AI uses generative models such as OpenAI's GPT-4.

[0493] As a specific example of query analysis, if a user enters "project management progress management methods," the application server extracts keywords such as "project management" and "progress management." Based on the analysis results, the application server sends a search request to the database server.

[0494] Database Server

[0495] The database server stores a large number of documents and searches for relevant documents based on queries sent from the application server. MySQL or PostgreSQL is used as the database management system (DBMS). For example, it searches past literature and business manuals related to "project management" and returns the results to the application server.

[0496] Generative Artificial Intelligence

[0497] The generative AI generates new tutorials based on the document and query information sent from the application server. It uses OpenAI's GPT-4 and other technologies. The generative AI generates tutorials using the following prompt as input:

[0498] "Generate detailed instruction on how to track project management progress."

[0499] Specific examples

[0500] For example, if a user searches for information on "testing methods," the flow is as follows:

[0501] 1. The user enters "test method" in the device's search bar and presses the search button.

[0502] 2. The application server analyzes the query and extracts the keywords "test" and "method."

[0503] 3. The application server sends a search request to the database server based on the analysis results.

[0504] 4. The database server searches for relevant documents and returns the results to the application server.

[0505] 5. If the application server determines that the search results do not meet the user's needs, it will ask the generative AI to generate a new instruction manual for the "test method," using the prompt "Please generate detailed instructions for the test method."

[0506] 6. The generative AI generates a new tutorial and sends it back to the application server. The generated tutorial includes specific test methods and procedures.

[0507] 7. The application server sends the generated instruction manual to the user terminal.

[0508] 8. The user terminal displays the instruction manual and makes it available for the user to view. For example, the user can view a new instruction manual that describes detailed test methods and use it in their work.

[0509] In this way, this system provides quick access to necessary information, significantly improving work efficiency, and reducing the time spent searching for information, contributing to increased productivity.

[0510] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0511] Step 1: User Query Input

[0512] A user uses a device (PC or smartphone) to input specific information into a search bar. For example, they might input "progress management methods for project management" and press the search button. This query is sent to the application server. The input is "progress management methods for project management," and the output is the query data sent to the application server.

[0513] Step 2: Parsing the query

[0514] The application server analyzes the received query, in particular using NLP techniques to extract important keywords within the query. Libraries used include Python's NLTK and spaCy. For example, the query "How to track progress in project management" is analyzed to extract the keywords "project management" and "progress management." The input is the user's query data, and the output is the extracted keywords.

[0515] Step 3: Database search

[0516] The application server sends a search request to the database server based on the extracted keywords. The database server searches for related documents and returns the results to the application server. MySQL or PostgreSQL is used as the DBMS. For example, searching for past literature or business manuals related to "project management." The input is the extracted keywords, and the output is a list of related documents.

[0517] Step 4: Determine the relevance of search results

[0518] The application server analyzes the returned search results and determines the relevance of the document. This determination is based on criteria such as the document's age, the vagueness of its content, and its specificity to the user's query. The input is the document data of the search results, and the output is the relevance determination result.

[0519] Step 5: Manual generation instructions

[0520] If the degree of compatibility is determined to be low, the application server requests the generative AI to generate a new instruction manual. The specific prompt used is "Please generate a detailed instruction manual on how to manage project progress." The input is the prompt text and related document data, and the output is a generation request.

[0521] Step 6: Receive the generated manual

[0522] The generative AI generates a new tutorial based on the provided document and query information and returns it to the application server. OpenAI's GPT-4 and other technologies are used for generation, and the generated tutorial includes specific progress management methods and explanations of the tools used. The input is the generation request and related data, and the output is the generated tutorial.

[0523] Step 7: Send and view the manual

[0524] The application server sends the generated instruction manual to the user's terminal. The user's terminal displays the received instruction manual to the user, providing the necessary information quickly and accurately. For example, the user can view the new instruction manual that contains detailed progress management methods and use it in their work. The input is the generated instruction manual, and the output is the instruction manual displayed on the user's terminal.

[0525] Through these detailed processing steps, the system enables users to quickly and accurately access the information they need, thereby improving work efficiency.

[0526] (Application example 1)

[0527] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0528] To improve work efficiency at logistics centers, a system that allows quick and accurate access to work procedures and troubleshooting information on-site is required. However, with the current system, it takes time to search for information and obtain manuals, which can lead to work delays and errors. This results in a problem of reduced efficiency of the entire business.

[0529] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0530] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related data based on the query, means for generating a new manual using generative AI based on the searched data, means for transmitting the generated manual to a user terminal, means for receiving specific information by voice input or text input, and means for using a smartphone or head-mounted display as the user terminal. This allows workers at a logistics center to efficiently access work procedures and troubleshooting information, improving the efficiency of overall operations.

[0531] A "user" is a person or robot that uses the system to search for specific information and obtain a manual.

[0532] A "query" is a search request representing specific information entered by a user.

[0533] A "smartphone" is a portable computing device capable of communicating, processing information, and running applications.

[0534] A "head-mounted display" is a device worn on the head that displays visual information.

[0535] A "server" is a central computer that receives and processes user requests.

[0536] An "application server" is a server that is responsible for analyzing queries, searching data, and sending generated manuals.

[0537] A "database server" is a server that stores large amounts of data and provides the data in response to search requests.

[0538] "Generative AI" is artificial intelligence that generates new manuals based on given data and queries.

[0539] A "Manual" is a document that contains specific procedure and troubleshooting information.

[0540] An "interface" is an operation screen or input means for exchanging information between the system and the user.

[0541] This invention is a system for dramatically improving work efficiency in logistics centers. The system is designed to enable workers to quickly and appropriately access work procedures and troubleshooting information using smartphones and head-mounted displays (HMDs).

[0542] System Configuration

[0543] The system is broadly composed of a user terminal, an application server, and a database server. Generative AI is also integrated into the application server.

[0544] User terminal

[0545] User devices provide an interface for workers to search for specific information. User devices include smartphones and head-mounted displays, allowing workers to enter queries via voice or text input.

[0546] Application Server

[0547] The application server receives queries from users and analyzes them. Based on the analysis results, it generates a search request to the database server. The application server also has the function of requesting the generative AI to generate an optimal manual based on the search results obtained from the database server. This generated manual is ultimately sent to the user's device.

[0548] Database Server

[0549] The database server stores large amounts of data and provides relevant data in response to queries from the application server, such as product sorting procedures, troubleshooting methods, and safety measures.

[0550] Generative AI

[0551] The generative AI generates new manuals based on the data and query information sent from the application server, generating specific and easy-to-understand content, such as work procedures and troubleshooting methods.

[0552] System operation explanation

[0553] 1. User query input

[0554] The worker (user) sends queries such as "product sorting procedures" and "how to handle malfunctions" to the system by voice or text input through the interface of their smartphone or HMD.

[0555] 2. Query Analysis and Database Search

[0556] The application server receives the query and analyzes it. From this analysis, it extracts keywords such as "product sorting" and "fault handling" and sends a search request to the database server. The database server searches for related data and returns the results.

[0557] 3. Manual generation using generative AI

[0558] The application server analyzes the search results and sends data and query information to the generative AI as needed. The generative AI generates a new manual based on this data. For example, the following prompt sentence could be sent to the generative AI:

[0559] Example prompt sentence:

[0560] "Generate the most appropriate manual based on the following information: product sorting procedures, safety measures, and troubleshooting methods."

[0561] 4. Providing results

[0562] The application server receives the manual sent back from the generative AI and displays it on the interface of a smartphone or HMD, allowing workers to access the appropriate information in real time.

[0563] Specific examples

[0564] For example, when a worker searches for information on "how to handle a malfunction," the following operations are performed.

[0565] 1. Worker: Send a query to the system by voice or text input, saying "How to handle the malfunction."

[0566] 2. Application server: Analyzes the query and extracts the keyword "failure response."

[0567] 3. Database Server: Searches for relevant data and returns the results to the application server.

[0568] 4. Generative AI: Generates new manuals on how to respond to failures as needed.

[0569] 5. User terminal: Displays the generated manual and makes it available for workers to view.

[0570] This invention will significantly improve operational efficiency at logistics centers, allowing workers to work quickly and accurately.

[0571] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0572] Step 1:

[0573] Entering a query

[0574] A user inputs a query for specific information via voice or text input through the interface of a smartphone or head-mounted display. The input query (e.g., "product sorting procedure") is received by the user device.

[0575] Step 2:

[0576] Submitting a query

[0577] The user terminal sends the input query to the application server, at which point the input data is treated as a query and sent to the application server.

[0578] Step 3:

[0579] Parsing a query

[0580] The application server analyzes the received query using natural language processing (e.g., Spacy) to extract important keywords from the query (e.g., "product sorting"). These keywords form the basis for subsequent data retrieval.

[0581] Step 4:

[0582] Sending a search request to the database

[0583] The application server sends a search request to the database server based on the extracted keywords, which includes the keywords and searches for related data.

[0584] Step 5:

[0585] Database search

[0586] The database server searches for relevant data in the database based on the received search request. As a search result, relevant data (e.g., "documents regarding product sorting procedures") is generated and sent to the application server.

[0587] Step 6:

[0588] Receiving and analyzing search results

[0589] The application server receives the search results sent from the database server and checks their contents. Here, it determines whether the search result data is old or unclear (e.g., checks the update date and time of the data and whether it is unclear).

[0590] Step 7:

[0591] Sending to generative AI

[0592] The application server sends search results and queries to the generative AI as needed. The generative AI receives prompts to generate new manuals based on the given data and query information (e.g., "Generate the optimal manual based on the following information: product sorting procedures, safety measures, and troubleshooting methods").

[0593] Step 8:

[0594] Generate manual

[0595] The generative AI analyzes the prompt and generates an appropriate manual, which includes new information and concrete examples and is designed to meet the user's needs (e.g., "Detailed instructions for product sorting procedures").

[0596] Step 9:

[0597] Sending the generated results

[0598] The generative AI sends the generated manual to the application server, which receives it and prepares it for delivery to the user.

[0599] Step 10:

[0600] Providing manuals

[0601] The application server sends the generated manual to the user terminal, which displays the manual and makes it available for workers to view, allowing workers to access the information they need in real time.

[0602] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0603] The present invention provides a system for quickly and accurately retrieving necessary information from a large number of scattered documents. This system has the functions of allowing users to search for specific information and generating an optimal manual based on that information, as well as the functions of recognizing the user's emotions and providing a response that corresponds to the emotion. Specific embodiments of this system are described below.

[0604] System Structure

[0605] The system consists of a user terminal, an application server, a database server, and an emotion engine. Generative AI is also integrated into the application server.

[0606] 1. User Device

[0607] The user terminal provides an interface for the user to search for specific information, such as a web browser or a dedicated application for entering a query into a search bar.

[0608] It also has sensors and input devices, such as a camera and microphone, to recognize the user's emotions.

[0609] 2. Application Server

[0610] The application server is responsible for receiving queries from users, analyzing the queries, and generating search requests to the database server based on the results of the analysis.

[0611] It also receives user emotional data from the emotion engine and has the ability to request manual adjustments from the generative AI based on this data.

[0612] 3. Database Server

[0613] The database server stores a large number of documents and provides relevant documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[0614] 4. Generative AI

[0615] The generative AI generates a new manual based on the document and query information sent from the application server and the user's emotional data, including detailed content such as progress management procedures, tools to be used, and best practices.

[0616] 5. Emotion Engine

[0617] The emotion engine recognizes the user's emotions and sends the data to the application server, which makes it possible to generate an optimal manual based on the user's emotions.

[0618] System Operation

[0619] User query input

[0620] The user enters "project management progress management methods" into the search interface of the device and presses the search button. At this time, the emotion engine recognizes the user's facial expressions and tone of voice.

[0621] Query Analysis and Database Search

[0622] The application server analyzes the query received from the user, and extracts keywords such as "project management" and "progress management" from the analysis.

[0623] The application server sends a search request to the database server based on the analysis result.

[0624] The database server searches for relevant documents and returns the results to the application server.

[0625] Use of Emotional Data

[0626] The emotion engine analyzes the user's emotion data in real time and sends the results to the application server.

[0627] Based on this emotional data, the application server requests the generative AI to generate the optimal manual based on the query.

[0628] Manual generation using generative AI

[0629] Based on the provided information and the user's emotional data, the generative AI generates a manual on "Project Management Progress Management Methods" that is more user-friendly. Specifically, if the user is feeling stressed, the AI ​​adjusts the manual's writing style and content to be gentle and explanatory.

[0630] Providing results

[0631] The application server sends the new manual returned by the generative AI to the user's terminal.

[0632] The user terminal displays the generated manual to the user, allowing the user to quickly view the required information.

[0633] Specific examples

[0634] For example, when a user searches for information on "testing methods," the following scenario may occur:

[0635] 1. User: Enters "test method" and presses the search button. The emotion engine analyzes the user's facial expressions and voice at this time and detects that the user is in a hurry.

[0636] 2. Application server: Analyzes the query and extracts the keywords "test" and "method".

[0637] 3. Database Server: Searches for relevant documents and returns the results.

[0638] 4. Emotion engine: Sends the user's hurry emotion data to the application server.

[0639] 5. Generative AI: Based on emotional data, it generates a concise and easily understandable manual on the "test method."

[0640] 6. User terminal: Displays the generated manual and helps users quickly obtain information.

[0641] This invention not only speeds up access to necessary information and significantly improves business efficiency, but also provides a flexible system that responds to the user's emotions.

[0642] The processing flow will be explained below.

[0643] Step 1:

[0644] The user enters "project management progress management methods" into the search interface on their device and presses the search button. At this time, the emotion engine recognizes the user's facial expressions and tone of voice in real time.

[0645] Step 2:

[0646] The terminal sends the input query "Project management progress management method" to the application server. At the same time, the emotion engine sends the user's emotion data to the application server.

[0647] Step 3:

[0648] The application server receives the query and analyzes its meaning using the query analysis module. For example, it extracts keywords such as "project management" and "progress management."

[0649] Step 4:

[0650] The application server sends a search request to the database server based on the extracted keywords.

[0651] Step 5:

[0652] The database server searches for relevant documents and sends the results to the application server. For example, past progress management manuals related to "project management" are retrieved.

[0653] Step 6:

[0654] The application server reviews the search result documents received from the database server to determine whether the retrieved documents meet the user's needs.

[0655] Step 7:

[0656] If the application server determines that the documentation is insufficient, it sends the retrieved documentation and query information to the generative AI and requests it to generate a new manual. At this time, the user's emotional data sent from the emotion engine is also sent.

[0657] Step 8:

[0658] The generative AI generates a new manual for "Project Management Progress Management Methods" based on the provided document, query information, and emotion data. Specifically, if the user is feeling stressed, the AI ​​adjusts the manual's writing style to be gentler and provide more explanations.

[0659] Step 9:

[0660] The generative AI sends the generated manual to the application server.

[0661] Step 10:

[0662] The application server sends the new manual received from the generative AI to the user's terminal.

[0663] Step 11:

[0664] The terminal displays the generated manual to the user, allowing the user to quickly view the information they need. In addition, appropriate information is provided according to the user's emotions, providing a more satisfying experience for the user.

[0665] Example 2

[0666] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0667] In today's business environment, it is extremely important to quickly and accurately retrieve necessary information from a large amount of scattered documents and generate optimal manuals based on that information. However, conventional systems lack the flexibility to respond to user emotions, making it difficult to provide appropriate information, especially when the user is stressed or in a hurry.

[0668] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0669] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related data based on the query, means for generating new information using generative artificial intelligence based on the searched data, means for recognizing and analyzing user emotion data, and means for transmitting the generated information to a user terminal. This makes it possible to generate and provide a flexible and optimal manual that responds to the user's emotions.

[0670] "User" refers to an individual who operates a computer system or electronic device.

[0671] "Query" refers to a question or request that a user enters into a system to retrieve information.

[0672] "Data" refers to a collection of records or facts that comprise information, including digital documents and information stored in databases.

[0673] "Generative AI" refers to a type of AI that has the ability to generate new texts, manuals, etc. based on input information.

[0674] "Emotional Data" refers to data collected and analyzed to identify a user's emotions, including, specifically, a user's facial expressions and tone of voice.

[0675] "User terminal" refers to a hardware device that allows a user to access and operate the system, and includes a computer, smartphone, etc.

[0676] An "application server" is a server that receives and processes user requests and provides information in cooperation with a database.

[0677] A "database server" refers to a server that stores and manages large amounts of data and provides the data as needed and in response to requests.

[0678] "Search Request" means an instruction sent to a system to retrieve specific information.

[0679] "New information" refers to search results and new manuals and explanations generated by generative artificial intelligence.

[0680] This invention is a system that quickly and accurately acquires necessary information from a large amount of scattered data and generates an optimal manual based on the user's emotional data. This system is composed of a user terminal, an application server, a database server, an emotion engine, and generative artificial intelligence. The specific form of this system is described below.

[0681] 1. User Device

[0682] The user device provides an interface for searching for information. This can be achieved through a web browser or a dedicated application. It also has sensors such as a camera and microphone to recognize the user's emotions. The user requests information by entering a specific query into the search bar.

[0683] 2. Application Server

[0684] The application server is responsible for receiving and analyzing queries sent by users. This server breaks down queries into tokens and extracts important keywords. It also receives emotion data from the emotion engine and provides the analysis results to the generative AI.

[0685] 3. Database Server

[0686] The database server searches large amounts of data based on search requests sent from the application server and returns relevant results. The database stores business manuals, technical documents, past cases, etc.

[0687] 4. Generative Artificial Intelligence

[0688] Generative AI generates new manuals based on data and emotional data passed from the application server. This AI has the ability to customize content according to specific contexts and emotions, providing information that is relevant to the user's needs.

[0689] 5. Emotion Engine

[0690] The emotion engine analyzes the user's facial expressions and tone of voice in real time to generate emotion data, which is then sent to the application server to indicate whether the user is feeling stressed or in a hurry.

[0691] Specific examples

[0692] An example will be described in which a user searches for "project management progress management method."

[0693] User: Enters "Project management progress management methods" into the device's search bar and presses the search button. The emotion engine analyzes the user's facial expressions and tone of voice at this time and detects that the user is feeling impatient.

[0694] Application server: Breaks down the query into tokens and extracts keywords such as "project management" and "progress management." It then sends a search request to the database server based on the keywords.

[0695] Database Server: Searches for relevant data and returns the results to the application server.

[0696] Emotion engine: Sends user emotion data to the application server in real time.

[0697] Generative AI: Generates a new "progress management method" manual based on data and emotion data received from the application server. It customizes the manual to be concise and easy to understand, especially when the user is in a hurry.

[0698] Application server: Sends the generated manual to the user terminal.

[0699] User terminal: Displays the generated manual to the user and provides the necessary information quickly.

[0700] Prompt Sentence Examples

[0701] Users are in a hurry to find the "test method." Please create a concise and easy-to-understand manual. Users can tell from their facial expressions and tone of voice that they are in a hurry. Please make the content simple and easy to understand.

[0702] In this way, the present invention is a system that flexibly responds to the user's queries and emotions and quickly provides optimal information.

[0703] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0704] Step 1:

[0705] User query input

[0706] A user uses their own device to enter a query containing specific information into the search interface and presses the search button. The device then sends the query, along with the user's facial expressions and tone of voice via the camera and microphone, to the emotion engine. This simultaneously collects the user's emotional data. Input: Query entered by the user. Output: Query data and emotional data. Specifically, the query entered on the browser is sent in text format to the application server.

[0707] Step 2:

[0708] Query Analysis

[0709] The application server analyzes queries received from the user's device. Specifically, it breaks down the query into tokens, one word at a time, and extracts important keywords. At this time, it also uses the emotion data received from the emotion engine as part of the analysis. Input: Query data and emotion data. Output: Keyword list and emotion data. Specifically, it uses a natural language processing tool to tokenize the query and list the main keywords.

[0710] Step 3:

[0711] Database search

[0712] The application server sends a search request to the database server based on the extracted keywords. The database server searches for related data and returns the results to the application server. Input: Keyword list. Output: List of related data. Specifically, it generates an SQL query and sends it to the database server. The database server searches for the relevant records and returns the results.

[0713] Step 4:

[0714] Use of Emotional Data

[0715] The emotion engine analyzes the user's emotional data and sends the results to the application server. The application server uses this emotional data to make further adjustments to the generative AI. Input: Emotional data. Output: Adjustment instructions for the generative AI. Specifically, it decodes the emotional data using facial expression recognition technology and voice analysis tools, and sends the results to the application server.

[0716] Step 5:

[0717] Manual generation using generative AI

[0718] The generative AI generates a new manual based on the data and emotional data received from the application server. It customizes the content according to the user's emotions. Input: List of related data and emotional data. Output: Generated manual. Specifically, it inputs a prompt sentence into the generative AI model, and generates a user-friendly manual based on that.

[0719] Step 6:

[0720] Providing results

[0721] The application server sends the generated manual to the user terminal. The user terminal displays the received manual to the user. Input: Generated manual. Output: Information displayed to the user. Specifically, it generates an HTTP response and sends it to the user terminal. The user terminal displays the manual in a browser or application.

[0722] (Application example 2)

[0723] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0724] In today's world, it is important to quickly and accurately obtain necessary information from a vast amount of documents. However, conventional information retrieval systems have difficulty providing information that meets the user's intentions and are not responsive to the user's emotions. Therefore, there is a need for systems that allow users to quickly obtain the information they need and respond flexibly to the user's emotions.

[0725] The specification processing by specification processing unit 290 of data processing device 12 in application example 2 is realized by the following means. In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related documents based on the query, means for generating a new manual using a generative AI model based on the searched documents, means for recognizing the user's emotions and transmitting the emotion data to an application server, means for requesting the generative AI model to adjust the manual based on the user's emotion data, and means for transmitting the generated manual to a user terminal. This not only enables the user to quickly and accurately obtain specific information, but also makes it possible to provide optimal information according to the user's emotions, thereby improving user satisfaction.

[0726] A "user terminal" is a device that provides an interface for a user to search for specific information.

[0727] An "application server" is a server that receives queries from users, analyzes the queries, and generates search requests to a database server.

[0728] A "database server" is a server that stores a large number of documents and provides related documents in response to queries from an application server.

[0729] "Generative AI model" is a general term for algorithms that generate new manuals based on given documents and query information.

[0730] "User emotion" refers to the emotional state (e.g., stress, relief, hurry, etc.) of the user when searching for information.

[0731] The "emotion engine" is a system component that recognizes the user's emotions and sends the emotion data to the application server.

[0732] A "query" is an inquiry that a user enters to search for specific information.

[0733] "Documents" are collections of information that can be searched, such as business manuals, construction instructions, and past documents.

[0734] A "search tool" is a method or technique for retrieving relevant documents based on a query.

[0735] "Generation means" refers to the method or technology for generating a new manual based on the retrieved documents.

[0736] "Emotion data" is data that represents the user's emotional state and is generated by the emotion engine.

[0737] A "manual" is a guide document created by a generative AI model based on the specific information a user seeks.

[0738] This invention provides a system that allows users to search for specific information and generates an optimal manual based on that information. It also has the ability to recognize the user's emotions and respond accordingly. This system consists of a user terminal, an application server, a database server, a generative AI model, and an emotion engine.

[0739] System Structure

[0740] 1. User Device

[0741] A user terminal is a device that provides an interface for users to search for specific information, such as a web browser or dedicated application for entering queries into a search bar, and also has a camera and microphone for recognizing user emotions.

[0742] 2. Application Server

[0743] The application server receives queries from users, analyzes them, and generates search requests for the database server. It also receives user emotion data from the emotion engine and requests manual adjustments to the generative AI model.

[0744] 3. Database Server

[0745] A database server is a server that stores a large amount of documents and provides related documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[0746] 4. Generative AI Models

[0747] A generative AI model is a general term for an algorithm that generates a new manual based on the provided document, query information, and user sentiment data. Generative AI technologies such as GPT-3 are generally used.

[0748] 5. Emotion Engine

[0749] The emotion engine is a system component that recognizes the user's emotions and sends the emotion data to the application server. Based on this emotion data, a manual that best suits the user's emotions is generated.

[0750] Operation flow

[0751] The user enters a query and their emotional state is recognized. The application server analyzes the query and retrieves relevant documents from the database server. The search results, including the emotional data, are then sent to a generative AI model, which generates an optimal manual. This manual is then sent to the user's device, where it can be viewed by the user.

[0752] Specific examples

[0753] For example, if a user searches for "how to set up a security camera," the system operates as follows:

[0754] 1. User device: The user types in "How to set up a surveillance camera" and presses the search button. The emotion engine determines that the user is in a hurry.

[0755] 2. Application server: Analyzes the query and extracts keywords such as "surveillance camera" and "settings method."

[0756] 3. Database Server: Searches for relevant documents and returns the results to the application server.

[0757] 4. Emotion engine: Sends the user's hurry emotion data to the application server.

[0758] 5. Generative AI model: Based on emotional data, it quickly generates a manual that explains the setup procedure.

[0759] 6. User terminal: Displays the generated manual to help users solve problems quickly.

[0760] Prompt Sentence Examples

[0761] When a user searches for specific information, for example, "How to set up a surveillance camera," the emotion engine analyzes the user's emotions and detects that the user is in a hurry. Based on this, the generative AI model generates a concise manual that can be quickly implemented and sends it to the user's device.

[0762] example:

[0763] (Specific example used within the system)

[0764] "If a user types in 'how to set up a surveillance camera,' and the emotion engine detects that the user is in a hurry, the generated manual will provide concise and specific steps to help the user set up the camera quickly. It will also include specific model names and images of the setting screen to help the user respond quickly."

[0765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0766] Step 1:

[0767] The user device provides an interface for users to input queries to search for specific information. The user enters a query such as "How to set up a surveillance camera" into the search bar and presses the search button. At this time, the device's camera and microphone are used to transmit the user's facial expressions and voice to the emotion engine. The input data are the query and emotion data, and the output data are the query information and raw data passed to the emotion engine.

[0768] Step 2:

[0769] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. The emotion engine extracts emotional data from the raw data and sends it to the application server. The input data is the user's facial expressions and voice, and the output data is emotional data corresponding to the emotional state. Specifically, it determines the user's emotional state, such as whether they are in a hurry or relaxed.

[0770] Step 3:

[0771] The application server analyzes queries received from users and extracts relevant keywords. The input data is query information and emotion data, and the output data is the extracted keywords. Specifically, it identifies keywords such as "surveillance camera" and "setting method."

[0772] Step 4:

[0773] The application server sends a search request to the database server based on the analysis results. The input data is the extracted keywords, and the output data is a search request to the database server. Specifically, it sends a request to search for documents that match "surveillance camera" and "settings method."

[0774] Step 5:

[0775] The database server searches for relevant documents in response to the received search request and returns the results to the application server. The input data is the search request, and the output data is the relevant documents as search results.

[0776] Step 6:

[0777] The application server requests the generative AI model to generate a manual based on the related documents and emotion data received from the database server. The input data are the related documents and emotion data, and the output data is the data passed to the generative AI model. Specifically, it provides information that serves as a prompt for generating an appropriate manual.

[0778] Step 7:

[0779] The generative AI model generates a new manual based on the provided document and emotion data. The input data is the relevant document, emotion data, and prompt sentences, and the output data is the generated manual. Specifically, if the user is in a hurry, it generates a concise and quick manual.

[0780] Step 8:

[0781] The application server receives the manual generated by the generative AI model and sends it to the user's device. The input data is the generated manual, and the output data is the manual sent to the user's device.

[0782] Step 9:

[0783] The user terminal displays the generated manual to the user, helping the user to quickly acquire information. The input data is the generated manual, and the output data is the displayed manual. Specifically, the user can easily understand "how to set up a surveillance camera" and quickly perform the setting.

[0784] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0785] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0786] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0787] [Third embodiment]

[0788] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0789] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0790] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0791] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0792] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0793] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0794] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0795] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0796] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0797] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0798] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0799] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0800] The present invention provides a system for quickly and accurately retrieving necessary information from a large number of scattered documents. This system has the function of allowing users to search for specific information and generate an optimal manual based on that information. Specific aspects of this system are described below.

[0801] System Structure

[0802] The system consists of a user terminal, an application server, and a database server. Furthermore, a generative AI is integrated into the application server.

[0803] 1. User Device

[0804] The user terminal provides an interface for the user to search for specific information, such as a web browser or a dedicated application for entering a query into a search bar.

[0805] 2. Application Server

[0806] The application server is responsible for receiving queries from users, analyzing the queries, and generating search requests to the database server based on the results of the analysis.

[0807] The application server also has the function of requesting the generative AI to generate an optimal manual based on the search results obtained from the database server. This generated manual is ultimately sent to the user's device.

[0808] 3. Database Server

[0809] The database server stores a large number of documents and provides relevant documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[0810] 4. Generative AI

[0811] The generative AI generates new manuals based on the documents and query information sent from the application server, generating specific and easy-to-understand content, including related progress management methods and best practices.

[0812] System Operation

[0813] User query input

[0814] The user enters a query such as "how to manage project progress" into the search bar of the device.

[0815] Query Analysis and Database Search

[0816] The application server analyzes the query received from the user, and extracts keywords such as "project management" and "progress management" from the analysis.

[0817] The application server sends a search request to the database server based on the analysis result.

[0818] The database server searches for relevant documents and returns the results to the application server.

[0819] Manual generation using generative AI

[0820] If the application server determines that the search results do not meet the user's needs, it requests the generative AI to generate a new manual.

[0821] The generative AI generates a new manual based on the provided document and query information, including, for example, detailed instructions on specific progress management methods and tools to be used.

[0822] Providing results

[0823] The application server sends the new manual returned by the generative AI to the user's terminal.

[0824] The user terminal displays the generated manual to the user, and quickly provides the information the user needs.

[0825] Specific examples

[0826] For example, if a user searches for information on "testing methods," the following scenario may occur:

[0827] 1. User: Enter "test method" and press the search button.

[0828] 2. Application server: Analyzes the query and extracts the keywords "test" and "method".

[0829] 3. Database Server: Searches for relevant documents and returns the results.

[0830] 4. Generative AI: Generates manuals for new "test methods" as needed.

[0831] 5. User terminal: Displays the generated manual and makes it viewable by the user.

[0832] This invention speeds up access to necessary information, significantly improving work efficiency, and also contributes to improving productivity by reducing the time users spend searching for information.

[0833] The processing flow will be explained below.

[0834] Step 1:

[0835] The user enters "project management progress management method" into the search interface on the terminal and presses the search button.

[0836] Step 2:

[0837] The terminal transmits the input query "Project management progress management method" to the application server.

[0838] Step 3:

[0839] The application server receives the query and analyzes its meaning using the query analysis module. For example, it extracts keywords such as "project management" and "progress management."

[0840] Step 4:

[0841] The application server sends a search request to the database server based on the extracted keywords.

[0842] Step 5:

[0843] The database server searches for relevant documents and sends the results to the application server.

[0844] Step 6:

[0845] The application server reviews the search results received from the database server and determines whether they meet the user's needs.

[0846] Step 7:

[0847] If the application server determines that the search results are insufficient, it sends the query information and the retrieved documents to the generative AI to request that the generative AI generate a new manual.

[0848] Step 8:

[0849] Based on the provided information, the generative AI generates a new manual for "Project Management Progress Management Methods," including detailed content on progress management procedures, tools to be used, and best practices.

[0850] Step 9:

[0851] The generative AI sends the generated manual to the application server.

[0852] Step 10:

[0853] The application server sends the new manual received from the generative AI to the user's terminal.

[0854] Step 11:

[0855] The terminal displays the generated manual to the user, allowing the user to quickly view the required information.

[0856] Example 1

[0857] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0858] It is difficult to quickly and accurately obtain the necessary information from a large number of scattered documents and provide users with the most appropriate instruction manual. Furthermore, when existing documents are outdated or unclear, there is a lack of a way to determine this and generate appropriate new documents. This makes it difficult for users to quickly access the information they need, resulting in a decline in work efficiency.

[0859] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0860] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for analyzing the query and extracting keywords, means for searching for related documents based on the query, means for determining the relevance of the documents in the search results, means for generating a new tutorial manual using generative artificial intelligence based on the searched documents, and means for transmitting the generated tutorial manual to a user terminal. This allows the user to quickly and accurately obtain the information they need and always have the latest and most appropriate tutorial manual.

[0861] A "query" is a search term or phrase that a user enters to search for specific information.

[0862] A "user terminal" is a device that a user operates to input queries and receive information, and includes a personal computer, a smartphone, etc.

[0863] The "application server" is a server whose role is to analyze queries received from user terminals, send search requests to the database server, and then request the generative artificial intelligence to generate new instruction manuals.

[0864] A "database server" is a server that stores a large number of documents, searches for related documents based on a query, and returns the search results to the application server.

[0865] "Generative AI" is an AI technology that generates new instruction manuals based on documents and query information sent from an application server.

[0866] "Keywords" are important words or phrases in a query that are used to find relevant documents.

[0867] "Relevance" is a rating that indicates how well a document in the search results meets the requirements of the user's query.

[0868] A "tutorial manual" is a detailed guide or manual that uses generative artificial intelligence to create new project management and progress management methods.

[0869] The present invention provides a system that allows users to search for specific information and generates optimal instruction manuals based on that information. This system consists of a user terminal, an application server, and a database server. Furthermore, generative artificial intelligence is integrated into the application server.

[0870] System Configuration

[0871] User terminal

[0872] A user device provides an interface for users to search for specific information. Specifically, this corresponds to a PC or smartphone, and provides the user interface via a web browser or dedicated application. For example, a user enters a query such as "project management progress management method" into the search bar.

[0873] Application Server

[0874] The application server is responsible for analyzing queries received from user devices and sending search requests to the database server. Query analysis uses NLP (natural language processing) technology, using libraries such as Python's NLTK and spaCy. It also has the function of requesting a generative AI to generate new tutorials based on search results obtained from the database server. Generative AI uses generative models such as OpenAI's GPT-4.

[0875] As a specific example of query analysis, if a user enters "project management progress management methods," the application server extracts keywords such as "project management" and "progress management." Based on the analysis results, the application server sends a search request to the database server.

[0876] Database Server

[0877] The database server stores a large number of documents and searches for relevant documents based on queries sent from the application server. MySQL or PostgreSQL is used as the database management system (DBMS). For example, it searches past literature and business manuals related to "project management" and returns the results to the application server.

[0878] Generative Artificial Intelligence

[0879] The generative AI generates new tutorials based on the document and query information sent from the application server. It uses OpenAI's GPT-4 and other technologies. The generative AI generates tutorials using the following prompt as input:

[0880] "Generate detailed instruction on how to track project management progress."

[0881] Specific examples

[0882] For example, if a user searches for information on "testing methods," the flow is as follows:

[0883] 1. The user enters "test method" in the device's search bar and presses the search button.

[0884] 2. The application server analyzes the query and extracts the keywords "test" and "method."

[0885] 3. The application server sends a search request to the database server based on the analysis results.

[0886] 4. The database server searches for relevant documents and returns the results to the application server.

[0887] 5. If the application server determines that the search results do not meet the user's needs, it will ask the generative AI to generate a new instruction manual for the "test method," using the prompt "Please generate detailed instructions for the test method."

[0888] 6. The generative AI generates a new tutorial and sends it back to the application server. The generated tutorial includes specific test methods and procedures.

[0889] 7. The application server sends the generated instruction manual to the user terminal.

[0890] 8. The user terminal displays the instruction manual and makes it available for the user to view. For example, the user can view a new instruction manual that describes detailed test methods and use it in their work.

[0891] In this way, this system provides quick access to necessary information, significantly improving work efficiency, and reducing the time spent searching for information, contributing to increased productivity.

[0892] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0893] Step 1: User Query Input

[0894] A user uses a device (PC or smartphone) to input specific information into a search bar. For example, they might input "progress management methods for project management" and press the search button. This query is sent to the application server. The input is "progress management methods for project management," and the output is the query data sent to the application server.

[0895] Step 2: Parsing the query

[0896] The application server analyzes the received query, in particular using NLP techniques to extract important keywords within the query. Libraries used include Python's NLTK and spaCy. For example, the query "How to track progress in project management" is analyzed to extract the keywords "project management" and "progress management." The input is the user's query data, and the output is the extracted keywords.

[0897] Step 3: Database search

[0898] The application server sends a search request to the database server based on the extracted keywords. The database server searches for related documents and returns the results to the application server. MySQL or PostgreSQL is used as the DBMS. For example, searching for past literature or business manuals related to "project management." The input is the extracted keywords, and the output is a list of related documents.

[0899] Step 4: Determine the relevance of search results

[0900] The application server analyzes the returned search results and determines the relevance of the document. This determination is based on criteria such as the document's age, the vagueness of its content, and its specificity to the user's query. The input is the document data of the search results, and the output is the relevance determination result.

[0901] Step 5: Manual generation instructions

[0902] If the degree of compatibility is determined to be low, the application server requests the generative AI to generate a new instruction manual. The specific prompt used is "Please generate a detailed instruction manual on how to manage project progress." The input is the prompt text and related document data, and the output is a generation request.

[0903] Step 6: Receive the generated manual

[0904] The generative AI generates a new tutorial based on the provided document and query information and returns it to the application server. OpenAI's GPT-4 and other technologies are used for generation, and the generated tutorial includes specific progress management methods and explanations of the tools used. The input is the generation request and related data, and the output is the generated tutorial.

[0905] Step 7: Send and view the manual

[0906] The application server sends the generated instruction manual to the user's terminal. The user's terminal displays the received instruction manual to the user, providing the necessary information quickly and accurately. For example, the user can view the new instruction manual that contains detailed progress management methods and use it in their work. The input is the generated instruction manual, and the output is the instruction manual displayed on the user's terminal.

[0907] Through these detailed processing steps, the system enables users to quickly and accurately access the information they need, thereby improving work efficiency.

[0908] (Application example 1)

[0909] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0910] To improve work efficiency at logistics centers, a system that allows quick and accurate access to work procedures and troubleshooting information on-site is required. However, with the current system, it takes time to search for information and obtain manuals, which can lead to work delays and errors. This results in a problem of reduced efficiency of the entire business.

[0911] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0912] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related data based on the query, means for generating a new manual using generative AI based on the searched data, means for transmitting the generated manual to a user terminal, means for receiving specific information by voice input or text input, and means for using a smartphone or head-mounted display as the user terminal. This allows workers at a logistics center to efficiently access work procedures and troubleshooting information, improving the efficiency of overall operations.

[0913] A "user" is a person or robot that uses the system to search for specific information and obtain a manual.

[0914] A "query" is a search request representing specific information entered by a user.

[0915] A "smartphone" is a portable computing device capable of communicating, processing information, and running applications.

[0916] A "head-mounted display" is a device worn on the head that displays visual information.

[0917] A "server" is a central computer that receives and processes user requests.

[0918] An "application server" is a server that is responsible for analyzing queries, searching data, and sending generated manuals.

[0919] A "database server" is a server that stores large amounts of data and provides the data in response to search requests.

[0920] "Generative AI" is artificial intelligence that generates new manuals based on given data and queries.

[0921] A "Manual" is a document that contains specific procedure and troubleshooting information.

[0922] An "interface" is an operation screen or input means for exchanging information between the system and the user.

[0923] This invention is a system for dramatically improving work efficiency in logistics centers. The system is designed to enable workers to quickly and appropriately access work procedures and troubleshooting information using smartphones and head-mounted displays (HMDs).

[0924] System Configuration

[0925] The system is broadly composed of a user terminal, an application server, and a database server. Generative AI is also integrated into the application server.

[0926] User terminal

[0927] User devices provide an interface for workers to search for specific information. User devices include smartphones and head-mounted displays, allowing workers to enter queries via voice or text input.

[0928] Application Server

[0929] The application server receives queries from users and analyzes them. Based on the analysis results, it generates a search request to the database server. The application server also has the function of requesting the generative AI to generate an optimal manual based on the search results obtained from the database server. This generated manual is ultimately sent to the user's device.

[0930] Database Server

[0931] The database server stores large amounts of data and provides relevant data in response to queries from the application server, such as product sorting procedures, troubleshooting methods, and safety measures.

[0932] Generative AI

[0933] The generative AI generates new manuals based on the data and query information sent from the application server, generating specific and easy-to-understand content, such as work procedures and troubleshooting methods.

[0934] System operation explanation

[0935] 1. User query input

[0936] The worker (user) sends queries such as "product sorting procedures" and "how to handle malfunctions" to the system by voice or text input through the interface of their smartphone or HMD.

[0937] 2. Query Analysis and Database Search

[0938] The application server receives the query and analyzes it. From this analysis, it extracts keywords such as "product sorting" and "fault handling" and sends a search request to the database server. The database server searches for related data and returns the results.

[0939] 3. Manual generation using generative AI

[0940] The application server analyzes the search results and sends data and query information to the generative AI as needed. The generative AI generates a new manual based on this data. For example, the following prompt sentence could be sent to the generative AI:

[0941] Example prompt sentence:

[0942] "Generate the most appropriate manual based on the following information: product sorting procedures, safety measures, and troubleshooting methods."

[0943] 4. Providing results

[0944] The application server receives the manual sent back from the generative AI and displays it on the interface of a smartphone or HMD, allowing workers to access the appropriate information in real time.

[0945] Specific examples

[0946] For example, when a worker searches for information on "how to handle a malfunction," the following operations are performed.

[0947] 1. Worker: Send a query to the system by voice or text input, saying "How to handle the malfunction."

[0948] 2. Application server: Analyzes the query and extracts the keyword "failure response."

[0949] 3. Database Server: Searches for relevant data and returns the results to the application server.

[0950] 4. Generative AI: Generates new manuals on how to respond to failures as needed.

[0951] 5. User terminal: Displays the generated manual and makes it available for workers to view.

[0952] This invention will significantly improve operational efficiency at logistics centers, allowing workers to work quickly and accurately.

[0953] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0954] Step 1:

[0955] Entering a query

[0956] A user inputs a query for specific information via voice or text input through the interface of a smartphone or head-mounted display. The input query (e.g., "product sorting procedure") is received by the user device.

[0957] Step 2:

[0958] Submitting a query

[0959] The user terminal sends the input query to the application server, at which point the input data is treated as a query and sent to the application server.

[0960] Step 3:

[0961] Parsing a query

[0962] The application server analyzes the received query using natural language processing (e.g., Spacy) to extract important keywords from the query (e.g., "product sorting"). These keywords form the basis for subsequent data retrieval.

[0963] Step 4:

[0964] Sending a search request to the database

[0965] The application server sends a search request to the database server based on the extracted keywords, which includes the keywords and searches for related data.

[0966] Step 5:

[0967] Database search

[0968] The database server searches for relevant data in the database based on the received search request. As a search result, relevant data (e.g., "documents regarding product sorting procedures") is generated and sent to the application server.

[0969] Step 6:

[0970] Receiving and analyzing search results

[0971] The application server receives the search results sent from the database server and checks their contents. Here, it determines whether the search result data is old or unclear (e.g., checks the update date and time of the data and whether it is unclear).

[0972] Step 7:

[0973] Sending to generative AI

[0974] The application server sends search results and queries to the generative AI as needed. The generative AI receives prompts to generate new manuals based on the given data and query information (e.g., "Generate the optimal manual based on the following information: product sorting procedures, safety measures, and troubleshooting methods").

[0975] Step 8:

[0976] Generate manual

[0977] The generative AI analyzes the prompt and generates an appropriate manual, which includes new information and concrete examples and is designed to meet the user's needs (e.g., "Detailed instructions for product sorting procedures").

[0978] Step 9:

[0979] Sending the generated results

[0980] The generative AI sends the generated manual to the application server, which receives it and prepares it for delivery to the user.

[0981] Step 10:

[0982] Providing manuals

[0983] The application server sends the generated manual to the user terminal, which displays the manual and makes it available for workers to view, allowing workers to access the information they need in real time.

[0984] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0985] The present invention provides a system for quickly and accurately retrieving necessary information from a large number of scattered documents. This system has the functions of allowing users to search for specific information and generating an optimal manual based on that information, as well as the functions of recognizing the user's emotions and providing a response that corresponds to the emotion. Specific embodiments of this system are described below.

[0986] System Structure

[0987] The system consists of a user terminal, an application server, a database server, and an emotion engine. Generative AI is also integrated into the application server.

[0988] 1. User Device

[0989] The user terminal provides an interface for the user to search for specific information, such as a web browser or a dedicated application for entering a query into a search bar.

[0990] It also has sensors and input devices, such as a camera and microphone, to recognize the user's emotions.

[0991] 2. Application Server

[0992] The application server is responsible for receiving queries from users, analyzing the queries, and generating search requests to the database server based on the results of the analysis.

[0993] It also receives user emotional data from the emotion engine and has the ability to request manual adjustments from the generative AI based on this data.

[0994] 3. Database Server

[0995] The database server stores a large number of documents and provides relevant documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[0996] 4. Generative AI

[0997] The generative AI generates a new manual based on the document and query information sent from the application server and the user's emotional data, including detailed content such as progress management procedures, tools to be used, and best practices.

[0998] 5. Emotion Engine

[0999] The emotion engine recognizes the user's emotions and sends the data to the application server, which makes it possible to generate an optimal manual based on the user's emotions.

[1000] System Operation

[1001] User query input

[1002] The user enters "project management progress management methods" into the search interface of the device and presses the search button. At this time, the emotion engine recognizes the user's facial expressions and tone of voice.

[1003] Query Analysis and Database Search

[1004] The application server analyzes the query received from the user, and extracts keywords such as "project management" and "progress management" from the analysis.

[1005] The application server sends a search request to the database server based on the analysis result.

[1006] The database server searches for relevant documents and returns the results to the application server.

[1007] Use of Emotional Data

[1008] The emotion engine analyzes the user's emotion data in real time and sends the results to the application server.

[1009] Based on this emotional data, the application server requests the generative AI to generate the optimal manual based on the query.

[1010] Manual generation using generative AI

[1011] Based on the provided information and the user's emotional data, the generative AI generates a manual on "Project Management Progress Management Methods" that is more user-friendly. Specifically, if the user is feeling stressed, the AI ​​adjusts the manual's writing style and content to be gentle and explanatory.

[1012] Providing results

[1013] The application server sends the new manual returned by the generative AI to the user's terminal.

[1014] The user terminal displays the generated manual to the user, allowing the user to quickly view the required information.

[1015] Specific examples

[1016] For example, when a user searches for information on "testing methods," the following scenario may occur:

[1017] 1. User: Enters "test method" and presses the search button. The emotion engine analyzes the user's facial expressions and voice at this time and detects that the user is in a hurry.

[1018] 2. Application server: Analyzes the query and extracts the keywords "test" and "method".

[1019] 3. Database Server: Searches for relevant documents and returns the results.

[1020] 4. Emotion engine: Sends the user's hurry emotion data to the application server.

[1021] 5. Generative AI: Based on emotional data, it generates a concise and easily understandable manual on the "test method."

[1022] 6. User terminal: Displays the generated manual and helps users quickly obtain information.

[1023] This invention not only speeds up access to necessary information and significantly improves business efficiency, but also provides a flexible system that responds to the user's emotions.

[1024] The processing flow will be explained below.

[1025] Step 1:

[1026] The user enters "project management progress management methods" into the search interface on their device and presses the search button. At this time, the emotion engine recognizes the user's facial expressions and tone of voice in real time.

[1027] Step 2:

[1028] The terminal sends the input query "Project management progress management method" to the application server. At the same time, the emotion engine sends the user's emotion data to the application server.

[1029] Step 3:

[1030] The application server receives the query and analyzes its meaning using the query analysis module. For example, it extracts keywords such as "project management" and "progress management."

[1031] Step 4:

[1032] The application server sends a search request to the database server based on the extracted keywords.

[1033] Step 5:

[1034] The database server searches for relevant documents and sends the results to the application server. For example, past progress management manuals related to "project management" are retrieved.

[1035] Step 6:

[1036] The application server reviews the search result documents received from the database server to determine whether the retrieved documents meet the user's needs.

[1037] Step 7:

[1038] If the application server determines that the documentation is insufficient, it sends the retrieved documentation and query information to the generative AI and requests it to generate a new manual. At this time, the user's emotional data sent from the emotion engine is also sent.

[1039] Step 8:

[1040] The generative AI generates a new manual for "Project Management Progress Management Methods" based on the provided document, query information, and emotion data. Specifically, if the user is feeling stressed, the AI ​​adjusts the manual's writing style to be gentler and provide more explanations.

[1041] Step 9:

[1042] The generative AI sends the generated manual to the application server.

[1043] Step 10:

[1044] The application server sends the new manual received from the generative AI to the user's terminal.

[1045] Step 11:

[1046] The terminal displays the generated manual to the user, allowing the user to quickly view the information they need. In addition, appropriate information is provided according to the user's emotions, providing a more satisfying experience for the user.

[1047] Example 2

[1048] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1049] In today's business environment, it is extremely important to quickly and accurately retrieve necessary information from a large amount of scattered documents and generate optimal manuals based on that information. However, conventional systems lack the flexibility to respond to user emotions, making it difficult to provide appropriate information, especially when the user is stressed or in a hurry.

[1050] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1051] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related data based on the query, means for generating new information using generative artificial intelligence based on the searched data, means for recognizing and analyzing user emotion data, and means for transmitting the generated information to a user terminal. This makes it possible to generate and provide a flexible and optimal manual that responds to the user's emotions.

[1052] "User" refers to an individual who operates a computer system or electronic device.

[1053] "Query" refers to a question or request that a user enters into a system to retrieve information.

[1054] "Data" refers to a collection of records or facts that comprise information, including digital documents and information stored in databases.

[1055] "Generative AI" refers to a type of AI that has the ability to generate new texts, manuals, etc. based on input information.

[1056] "Emotional Data" refers to data collected and analyzed to identify a user's emotions, including, specifically, a user's facial expressions and tone of voice.

[1057] "User terminal" refers to a hardware device that allows a user to access and operate the system, and includes a computer, smartphone, etc.

[1058] An "application server" is a server that receives and processes user requests and provides information in cooperation with a database.

[1059] A "database server" refers to a server that stores and manages large amounts of data and provides the data as needed and in response to requests.

[1060] "Search Request" means an instruction sent to a system to retrieve specific information.

[1061] "New information" refers to search results and new manuals and explanations generated by generative artificial intelligence.

[1062] This invention is a system that quickly and accurately acquires necessary information from a large amount of scattered data and generates an optimal manual based on the user's emotional data. This system is composed of a user terminal, an application server, a database server, an emotion engine, and generative artificial intelligence. The specific form of this system is described below.

[1063] 1. User Device

[1064] The user device provides an interface for searching for information. This can be achieved through a web browser or a dedicated application. It also has sensors such as a camera and microphone to recognize the user's emotions. The user requests information by entering a specific query into the search bar.

[1065] 2. Application Server

[1066] The application server is responsible for receiving and analyzing queries sent by users. This server breaks down queries into tokens and extracts important keywords. It also receives emotion data from the emotion engine and provides the analysis results to the generative AI.

[1067] 3. Database Server

[1068] The database server searches large amounts of data based on search requests sent from the application server and returns relevant results. The database stores business manuals, technical documents, past cases, etc.

[1069] 4. Generative Artificial Intelligence

[1070] Generative AI generates new manuals based on data and emotional data passed from the application server. This AI has the ability to customize content according to specific contexts and emotions, providing information that is relevant to the user's needs.

[1071] 5. Emotion Engine

[1072] The emotion engine analyzes the user's facial expressions and tone of voice in real time to generate emotion data, which is then sent to the application server to indicate whether the user is feeling stressed or in a hurry.

[1073] Specific examples

[1074] An example will be described in which a user searches for "project management progress management method."

[1075] User: Enters "Project management progress management methods" into the device's search bar and presses the search button. The emotion engine analyzes the user's facial expressions and tone of voice at this time and detects that the user is feeling impatient.

[1076] Application server: Breaks down the query into tokens and extracts keywords such as "project management" and "progress management." It then sends a search request to the database server based on the keywords.

[1077] Database Server: Searches for relevant data and returns the results to the application server.

[1078] Emotion engine: Sends user emotion data to the application server in real time.

[1079] Generative AI: Generates a new "progress management method" manual based on data and emotion data received from the application server. It customizes the manual to be concise and easy to understand, especially when the user is in a hurry.

[1080] Application server: Sends the generated manual to the user terminal.

[1081] User terminal: Displays the generated manual to the user and provides the necessary information quickly.

[1082] Prompt Sentence Examples

[1083] Users are in a hurry to find the "test method." Please create a concise and easy-to-understand manual. Users can tell from their facial expressions and tone of voice that they are in a hurry. Please make the content simple and easy to understand.

[1084] In this way, the present invention is a system that flexibly responds to the user's queries and emotions and quickly provides optimal information.

[1085] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1086] Step 1:

[1087] User query input

[1088] A user uses their own device to enter a query containing specific information into the search interface and presses the search button. The device then sends the query, along with the user's facial expressions and tone of voice via the camera and microphone, to the emotion engine. This simultaneously collects the user's emotional data. Input: Query entered by the user. Output: Query data and emotional data. Specifically, the query entered on the browser is sent in text format to the application server.

[1089] Step 2:

[1090] Query Analysis

[1091] The application server analyzes queries received from the user's device. Specifically, it breaks down the query into tokens, one word at a time, and extracts important keywords. At this time, it also uses the emotion data received from the emotion engine as part of the analysis. Input: Query data and emotion data. Output: Keyword list and emotion data. Specifically, it uses a natural language processing tool to tokenize the query and list the main keywords.

[1092] Step 3:

[1093] Database search

[1094] The application server sends a search request to the database server based on the extracted keywords. The database server searches for related data and returns the results to the application server. Input: Keyword list. Output: List of related data. Specifically, it generates an SQL query and sends it to the database server. The database server searches for the relevant records and returns the results.

[1095] Step 4:

[1096] Use of Emotional Data

[1097] The emotion engine analyzes the user's emotional data and sends the results to the application server. The application server uses this emotional data to make further adjustments to the generative AI. Input: Emotional data. Output: Adjustment instructions for the generative AI. Specifically, it decodes the emotional data using facial expression recognition technology and voice analysis tools, and sends the results to the application server.

[1098] Step 5:

[1099] Manual generation using generative AI

[1100] The generative AI generates a new manual based on the data and emotional data received from the application server. It customizes the content according to the user's emotions. Input: List of related data and emotional data. Output: Generated manual. Specifically, it inputs a prompt sentence into the generative AI model, and generates a user-friendly manual based on that.

[1101] Step 6:

[1102] Providing results

[1103] The application server sends the generated manual to the user terminal. The user terminal displays the received manual to the user. Input: Generated manual. Output: Information displayed to the user. Specifically, it generates an HTTP response and sends it to the user terminal. The user terminal displays the manual in a browser or application.

[1104] (Application example 2)

[1105] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1106] In today's world, it is important to quickly and accurately obtain necessary information from a vast amount of documents. However, conventional information retrieval systems have difficulty providing information that meets the user's intentions and are not responsive to the user's emotions. Therefore, there is a need for systems that allow users to quickly obtain the information they need and respond flexibly to the user's emotions.

[1107] The specification processing by specification processing unit 290 of data processing device 12 in application example 2 is realized by the following means. In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related documents based on the query, means for generating a new manual using a generative AI model based on the searched documents, means for recognizing the user's emotions and transmitting the emotion data to an application server, means for requesting the generative AI model to adjust the manual based on the user's emotion data, and means for transmitting the generated manual to a user terminal. This not only enables the user to quickly and accurately obtain specific information, but also makes it possible to provide optimal information according to the user's emotions, thereby improving user satisfaction.

[1108] A "user terminal" is a device that provides an interface for a user to search for specific information.

[1109] An "application server" is a server that receives queries from users, analyzes the queries, and generates search requests to a database server.

[1110] A "database server" is a server that stores a large number of documents and provides related documents in response to queries from an application server.

[1111] "Generative AI model" is a general term for algorithms that generate new manuals based on given documents and query information.

[1112] "User emotion" refers to the emotional state (e.g., stress, relief, hurry, etc.) of the user when searching for information.

[1113] The "emotion engine" is a system component that recognizes the user's emotions and sends the emotion data to the application server.

[1114] A "query" is an inquiry that a user enters to search for specific information.

[1115] "Documents" are collections of information that can be searched, such as business manuals, construction instructions, and past documents.

[1116] A "search tool" is a method or technique for retrieving relevant documents based on a query.

[1117] "Generation means" refers to the method or technology for generating a new manual based on the retrieved documents.

[1118] "Emotion data" is data that represents the user's emotional state and is generated by the emotion engine.

[1119] A "manual" is a guide document created by a generative AI model based on the specific information a user seeks.

[1120] This invention provides a system that allows users to search for specific information and generates an optimal manual based on that information. It also has the ability to recognize the user's emotions and respond accordingly. This system consists of a user terminal, an application server, a database server, a generative AI model, and an emotion engine.

[1121] System Structure

[1122] 1. User Device

[1123] A user terminal is a device that provides an interface for users to search for specific information, such as a web browser or dedicated application for entering queries into a search bar, and also has a camera and microphone for recognizing user emotions.

[1124] 2. Application Server

[1125] The application server receives queries from users, analyzes them, and generates search requests for the database server. It also receives user emotion data from the emotion engine and requests manual adjustments to the generative AI model.

[1126] 3. Database Server

[1127] A database server is a server that stores a large amount of documents and provides related documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[1128] 4. Generative AI Models

[1129] A generative AI model is a general term for an algorithm that generates a new manual based on the provided document, query information, and user sentiment data. Generative AI technologies such as GPT-3 are generally used.

[1130] 5. Emotion Engine

[1131] The emotion engine is a system component that recognizes the user's emotions and sends the emotion data to the application server. Based on this emotion data, a manual that best suits the user's emotions is generated.

[1132] Operation flow

[1133] The user enters a query and their emotional state is recognized. The application server analyzes the query and retrieves relevant documents from the database server. The search results, including the emotional data, are then sent to a generative AI model, which generates an optimal manual. This manual is then sent to the user's device, where it can be viewed by the user.

[1134] Specific examples

[1135] For example, if a user searches for "how to set up a security camera," the system operates as follows:

[1136] 1. User device: The user types in "How to set up a surveillance camera" and presses the search button. The emotion engine determines that the user is in a hurry.

[1137] 2. Application server: Analyzes the query and extracts keywords such as "surveillance camera" and "settings method."

[1138] 3. Database Server: Searches for relevant documents and returns the results to the application server.

[1139] 4. Emotion engine: Sends the user's hurry emotion data to the application server.

[1140] 5. Generative AI model: Based on emotional data, it quickly generates a manual that explains the setup procedure.

[1141] 6. User terminal: Displays the generated manual to help users solve problems quickly.

[1142] Prompt Sentence Examples

[1143] When a user searches for specific information, for example, "How to set up a surveillance camera," the emotion engine analyzes the user's emotions and detects that the user is in a hurry. Based on this, the generative AI model generates a concise manual that can be quickly implemented and sends it to the user's device.

[1144] example:

[1145] (Specific example used within the system)

[1146] "If a user types in 'how to set up a surveillance camera,' and the emotion engine detects that the user is in a hurry, the generated manual will provide concise and specific steps to help the user set up the camera quickly. It will also include specific model names and images of the setting screen to help the user respond quickly."

[1147] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1148] Step 1:

[1149] The user device provides an interface for users to input queries to search for specific information. The user enters a query such as "How to set up a surveillance camera" into the search bar and presses the search button. At this time, the device's camera and microphone are used to transmit the user's facial expressions and voice to the emotion engine. The input data are the query and emotion data, and the output data are the query information and raw data passed to the emotion engine.

[1150] Step 2:

[1151] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. The emotion engine extracts emotional data from the raw data and sends it to the application server. The input data is the user's facial expressions and voice, and the output data is emotional data corresponding to the emotional state. Specifically, it determines the user's emotional state, such as whether they are in a hurry or relaxed.

[1152] Step 3:

[1153] The application server analyzes queries received from users and extracts relevant keywords. The input data is query information and emotion data, and the output data is the extracted keywords. Specifically, it identifies keywords such as "surveillance camera" and "setting method."

[1154] Step 4:

[1155] The application server sends a search request to the database server based on the analysis results. The input data is the extracted keywords, and the output data is a search request to the database server. Specifically, it sends a request to search for documents that match "surveillance camera" and "settings method."

[1156] Step 5:

[1157] The database server searches for relevant documents in response to the received search request and returns the results to the application server. The input data is the search request, and the output data is the relevant documents as search results.

[1158] Step 6:

[1159] The application server requests the generative AI model to generate a manual based on the related documents and emotion data received from the database server. The input data are the related documents and emotion data, and the output data is the data passed to the generative AI model. Specifically, it provides information that serves as a prompt for generating an appropriate manual.

[1160] Step 7:

[1161] The generative AI model generates a new manual based on the provided document and emotion data. The input data is the relevant document, emotion data, and prompt sentences, and the output data is the generated manual. Specifically, if the user is in a hurry, it generates a concise and quick manual.

[1162] Step 8:

[1163] The application server receives the manual generated by the generative AI model and sends it to the user's device. The input data is the generated manual, and the output data is the manual sent to the user's device.

[1164] Step 9:

[1165] The user terminal displays the generated manual to the user, helping the user to quickly acquire information. The input data is the generated manual, and the output data is the displayed manual. Specifically, the user can easily understand "how to set up a surveillance camera" and quickly perform the setting.

[1166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1167] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1168] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1169] [Fourth embodiment]

[1170] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1171] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1174] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1177] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1179] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1181] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1182] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1183] The present invention provides a system for quickly and accurately retrieving necessary information from a large number of scattered documents. This system has the function of allowing users to search for specific information and generate an optimal manual based on that information. Specific aspects of this system are described below.

[1184] System Structure

[1185] The system consists of a user terminal, an application server, and a database server. Furthermore, a generative AI is integrated into the application server.

[1186] 1. User Device

[1187] The user terminal provides an interface for the user to search for specific information, such as a web browser or a dedicated application for entering a query into a search bar.

[1188] 2. Application Server

[1189] The application server is responsible for receiving queries from users, analyzing the queries, and generating search requests to the database server based on the results of the analysis.

[1190] The application server also has the function of requesting the generative AI to generate an optimal manual based on the search results obtained from the database server. This generated manual is ultimately sent to the user's device.

[1191] 3. Database Server

[1192] The database server stores a large number of documents and provides relevant documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[1193] 4. Generative AI

[1194] The generative AI generates new manuals based on the documents and query information sent from the application server, generating specific and easy-to-understand content, including related progress management methods and best practices.

[1195] System Operation

[1196] User query input

[1197] The user enters a query such as "how to manage project progress" into the search bar of the device.

[1198] Query Analysis and Database Search

[1199] The application server analyzes the query received from the user, and extracts keywords such as "project management" and "progress management" from the analysis.

[1200] The application server sends a search request to the database server based on the analysis result.

[1201] The database server searches for relevant documents and returns the results to the application server.

[1202] Manual generation using generative AI

[1203] If the application server determines that the search results do not meet the user's needs, it requests the generative AI to generate a new manual.

[1204] The generative AI generates a new manual based on the provided document and query information, including, for example, detailed instructions on specific progress management methods and tools to be used.

[1205] Providing results

[1206] The application server sends the new manual returned by the generative AI to the user's terminal.

[1207] The user terminal displays the generated manual to the user, and quickly provides the information the user needs.

[1208] Specific examples

[1209] For example, if a user searches for information on "testing methods," the following scenario may occur:

[1210] 1. User: Enter "test method" and press the search button.

[1211] 2. Application server: Analyzes the query and extracts the keywords "test" and "method".

[1212] 3. Database Server: Searches for relevant documents and returns the results.

[1213] 4. Generative AI: Generates manuals for new "test methods" as needed.

[1214] 5. User terminal: Displays the generated manual and makes it viewable by the user.

[1215] This invention speeds up access to necessary information, significantly improving work efficiency, and also contributes to improving productivity by reducing the time users spend searching for information.

[1216] The processing flow will be explained below.

[1217] Step 1:

[1218] The user enters "project management progress management method" into the search interface on the terminal and presses the search button.

[1219] Step 2:

[1220] The terminal transmits the input query "Project management progress management method" to the application server.

[1221] Step 3:

[1222] The application server receives the query and analyzes its meaning using the query analysis module. For example, it extracts keywords such as "project management" and "progress management."

[1223] Step 4:

[1224] The application server sends a search request to the database server based on the extracted keywords.

[1225] Step 5:

[1226] The database server searches for relevant documents and sends the results to the application server.

[1227] Step 6:

[1228] The application server reviews the search results received from the database server and determines whether they meet the user's needs.

[1229] Step 7:

[1230] If the application server determines that the search results are insufficient, it sends the query information and the retrieved documents to the generative AI to request that the generative AI generate a new manual.

[1231] Step 8:

[1232] Based on the provided information, the generative AI generates a new manual for "Project Management Progress Management Methods," including detailed content on progress management procedures, tools to be used, and best practices.

[1233] Step 9:

[1234] The generative AI sends the generated manual to the application server.

[1235] Step 10:

[1236] The application server sends the new manual received from the generative AI to the user's terminal.

[1237] Step 11:

[1238] The terminal displays the generated manual to the user, allowing the user to quickly view the required information.

[1239] Example 1

[1240] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1241] It is difficult to quickly and accurately obtain the necessary information from a large number of scattered documents and provide users with the most appropriate instruction manual. Furthermore, when existing documents are outdated or unclear, there is a lack of a way to determine this and generate appropriate new documents. This makes it difficult for users to quickly access the information they need, resulting in a decline in work efficiency.

[1242] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1243] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for analyzing the query and extracting keywords, means for searching for related documents based on the query, means for determining the relevance of the documents in the search results, means for generating a new tutorial manual using generative artificial intelligence based on the searched documents, and means for transmitting the generated tutorial manual to a user terminal. This allows the user to quickly and accurately obtain the information they need and always have the latest and most appropriate tutorial manual.

[1244] A "query" is a search term or phrase that a user enters to search for specific information.

[1245] A "user terminal" is a device that a user operates to input queries and receive information, and includes a personal computer, a smartphone, etc.

[1246] The "application server" is a server whose role is to analyze queries received from user terminals, send search requests to the database server, and then request the generative artificial intelligence to generate new instruction manuals.

[1247] A "database server" is a server that stores a large number of documents, searches for related documents based on a query, and returns the search results to the application server.

[1248] "Generative AI" is an AI technology that generates new instruction manuals based on documents and query information sent from an application server.

[1249] "Keywords" are important words or phrases in a query that are used to find relevant documents.

[1250] "Relevance" is a rating that indicates how well a document in the search results meets the requirements of the user's query.

[1251] A "tutorial manual" is a detailed guide or manual that uses generative artificial intelligence to create new project management and progress management methods.

[1252] The present invention provides a system that allows users to search for specific information and generates optimal instruction manuals based on that information. This system consists of a user terminal, an application server, and a database server. Furthermore, generative artificial intelligence is integrated into the application server.

[1253] System Configuration

[1254] User terminal

[1255] A user device provides an interface for users to search for specific information. Specifically, this corresponds to a PC or smartphone, and provides the user interface via a web browser or dedicated application. For example, a user enters a query such as "project management progress management method" into the search bar.

[1256] Application Server

[1257] The application server is responsible for analyzing queries received from user devices and sending search requests to the database server. Query analysis uses NLP (natural language processing) technology, using libraries such as Python's NLTK and spaCy. It also has the function of requesting a generative AI to generate new tutorials based on search results obtained from the database server. Generative AI uses generative models such as OpenAI's GPT-4.

[1258] As a specific example of query analysis, if a user enters "project management progress management methods," the application server extracts keywords such as "project management" and "progress management." Based on the analysis results, the application server sends a search request to the database server.

[1259] Database Server

[1260] The database server stores a large number of documents and searches for relevant documents based on queries sent from the application server. MySQL or PostgreSQL is used as the database management system (DBMS). For example, it searches past literature and business manuals related to "project management" and returns the results to the application server.

[1261] Generative Artificial Intelligence

[1262] The generative AI generates new tutorials based on the document and query information sent from the application server. It uses OpenAI's GPT-4 and other technologies. The generative AI generates tutorials using the following prompt as input:

[1263] "Generate detailed instruction on how to track project management progress."

[1264] Specific examples

[1265] For example, if a user searches for information on "testing methods," the flow is as follows:

[1266] 1. The user enters "test method" in the device's search bar and presses the search button.

[1267] 2. The application server analyzes the query and extracts the keywords "test" and "method."

[1268] 3. The application server sends a search request to the database server based on the analysis results.

[1269] 4. The database server searches for relevant documents and returns the results to the application server.

[1270] 5. If the application server determines that the search results do not meet the user's needs, it will ask the generative AI to generate a new instruction manual for the "test method," using the prompt "Please generate detailed instructions for the test method."

[1271] 6. The generative AI generates a new tutorial and sends it back to the application server. The generated tutorial includes specific test methods and procedures.

[1272] 7. The application server sends the generated instruction manual to the user terminal.

[1273] 8. The user terminal displays the instruction manual and makes it available for the user to view. For example, the user can view a new instruction manual that describes detailed test methods and use it in their work.

[1274] In this way, this system provides quick access to necessary information, significantly improving work efficiency, and reducing the time spent searching for information, contributing to increased productivity.

[1275] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1276] Step 1: User Query Input

[1277] A user uses a device (PC or smartphone) to input specific information into a search bar. For example, they might input "progress management methods for project management" and press the search button. This query is sent to the application server. The input is "progress management methods for project management," and the output is the query data sent to the application server.

[1278] Step 2: Parsing the query

[1279] The application server analyzes the received query, in particular using NLP techniques to extract important keywords within the query. Libraries used include Python's NLTK and spaCy. For example, the query "How to track progress in project management" is analyzed to extract the keywords "project management" and "progress management." The input is the user's query data, and the output is the extracted keywords.

[1280] Step 3: Database search

[1281] The application server sends a search request to the database server based on the extracted keywords. The database server searches for related documents and returns the results to the application server. MySQL or PostgreSQL is used as the DBMS. For example, searching for past literature or business manuals related to "project management." The input is the extracted keywords, and the output is a list of related documents.

[1282] Step 4: Determine the relevance of search results

[1283] The application server analyzes the returned search results and determines the relevance of the document. This determination is based on criteria such as the document's age, the vagueness of its content, and its specificity to the user's query. The input is the document data of the search results, and the output is the relevance determination result.

[1284] Step 5: Manual generation instructions

[1285] If the degree of compatibility is determined to be low, the application server requests the generative AI to generate a new instruction manual. The specific prompt used is "Please generate a detailed instruction manual on how to manage project progress." The input is the prompt text and related document data, and the output is a generation request.

[1286] Step 6: Receive the generated manual

[1287] The generative AI generates a new tutorial based on the provided document and query information and returns it to the application server. OpenAI's GPT-4 and other technologies are used for generation, and the generated tutorial includes specific progress management methods and explanations of the tools used. The input is the generation request and related data, and the output is the generated tutorial.

[1288] Step 7: Send and view the manual

[1289] The application server sends the generated instruction manual to the user's terminal. The user's terminal displays the received instruction manual to the user, providing the necessary information quickly and accurately. For example, the user can view the new instruction manual that contains detailed progress management methods and use it in their work. The input is the generated instruction manual, and the output is the instruction manual displayed on the user's terminal.

[1290] Through these detailed processing steps, the system enables users to quickly and accurately access the information they need, thereby improving work efficiency.

[1291] (Application example 1)

[1292] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1293] To improve work efficiency at logistics centers, a system that allows quick and accurate access to work procedures and troubleshooting information on-site is required. However, with the current system, it takes time to search for information and obtain manuals, which can lead to work delays and errors. This results in a problem of reduced efficiency of the entire business.

[1294] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1295] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related data based on the query, means for generating a new manual using generative AI based on the searched data, means for transmitting the generated manual to a user terminal, means for receiving specific information by voice input or text input, and means for using a smartphone or head-mounted display as the user terminal. This allows workers at a logistics center to efficiently access work procedures and troubleshooting information, improving the efficiency of overall operations.

[1296] A "user" is a person or robot that uses the system to search for specific information and obtain a manual.

[1297] A "query" is a search request representing specific information entered by a user.

[1298] A "smartphone" is a portable computing device capable of communicating, processing information, and running applications.

[1299] A "head-mounted display" is a device worn on the head that displays visual information.

[1300] A "server" is a central computer that receives and processes user requests.

[1301] An "application server" is a server that is responsible for analyzing queries, searching data, and sending generated manuals.

[1302] A "database server" is a server that stores large amounts of data and provides the data in response to search requests.

[1303] "Generative AI" is artificial intelligence that generates new manuals based on given data and queries.

[1304] A "Manual" is a document that contains specific procedure and troubleshooting information.

[1305] An "interface" is an operation screen or input means for exchanging information between the system and the user.

[1306] This invention is a system for dramatically improving work efficiency in logistics centers. The system is designed to enable workers to quickly and appropriately access work procedures and troubleshooting information using smartphones and head-mounted displays (HMDs).

[1307] System Configuration

[1308] The system is broadly composed of a user terminal, an application server, and a database server. Generative AI is also integrated into the application server.

[1309] User terminal

[1310] User devices provide an interface for workers to search for specific information. User devices include smartphones and head-mounted displays, allowing workers to enter queries via voice or text input.

[1311] Application Server

[1312] The application server receives queries from users and analyzes them. Based on the analysis results, it generates a search request to the database server. The application server also has the function of requesting the generative AI to generate an optimal manual based on the search results obtained from the database server. This generated manual is ultimately sent to the user's device.

[1313] Database Server

[1314] The database server stores large amounts of data and provides relevant data in response to queries from the application server, such as product sorting procedures, troubleshooting methods, and safety measures.

[1315] Generative AI

[1316] The generative AI generates new manuals based on the data and query information sent from the application server, generating specific and easy-to-understand content, such as work procedures and troubleshooting methods.

[1317] System operation explanation

[1318] 1. User query input

[1319] The worker (user) sends queries such as "product sorting procedures" and "how to handle malfunctions" to the system by voice or text input through the interface of their smartphone or HMD.

[1320] 2. Query Analysis and Database Search

[1321] The application server receives the query and analyzes it. From this analysis, it extracts keywords such as "product sorting" and "fault handling" and sends a search request to the database server. The database server searches for related data and returns the results.

[1322] 3. Manual generation using generative AI

[1323] The application server analyzes the search results and sends data and query information to the generative AI as needed. The generative AI generates a new manual based on this data. For example, the following prompt sentence could be sent to the generative AI:

[1324] Example prompt sentence:

[1325] "Generate the most appropriate manual based on the following information: product sorting procedures, safety measures, and troubleshooting methods."

[1326] 4. Providing results

[1327] The application server receives the manual sent back from the generative AI and displays it on the interface of a smartphone or HMD, allowing workers to access the appropriate information in real time.

[1328] Specific examples

[1329] For example, when a worker searches for information on "how to handle a malfunction," the following operations are performed.

[1330] 1. Worker: Send a query to the system by voice or text input, saying "How to handle the malfunction."

[1331] 2. Application server: Analyzes the query and extracts the keyword "failure response."

[1332] 3. Database Server: Searches for relevant data and returns the results to the application server.

[1333] 4. Generative AI: Generates new manuals on how to respond to failures as needed.

[1334] 5. User terminal: Displays the generated manual and makes it available for workers to view.

[1335] This invention will significantly improve operational efficiency at logistics centers, allowing workers to work quickly and accurately.

[1336] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1337] Step 1:

[1338] Entering a query

[1339] A user inputs a query for specific information via voice or text input through the interface of a smartphone or head-mounted display. The input query (e.g., "product sorting procedure") is received by the user device.

[1340] Step 2:

[1341] Submitting a query

[1342] The user terminal sends the input query to the application server, at which point the input data is treated as a query and sent to the application server.

[1343] Step 3:

[1344] Parsing a query

[1345] The application server analyzes the received query using natural language processing (e.g., Spacy) to extract important keywords from the query (e.g., "product sorting"). These keywords form the basis for subsequent data retrieval.

[1346] Step 4:

[1347] Sending a search request to the database

[1348] The application server sends a search request to the database server based on the extracted keywords, which includes the keywords and searches for related data.

[1349] Step 5:

[1350] Database search

[1351] The database server searches for relevant data in the database based on the received search request. As a search result, relevant data (e.g., "documents regarding product sorting procedures") is generated and sent to the application server.

[1352] Step 6:

[1353] Receiving and analyzing search results

[1354] The application server receives the search results sent from the database server and checks their contents. Here, it determines whether the search result data is old or unclear (e.g., checks the update date and time of the data and whether it is unclear).

[1355] Step 7:

[1356] Sending to generative AI

[1357] The application server sends search results and queries to the generative AI as needed. The generative AI receives prompts to generate new manuals based on the given data and query information (e.g., "Generate the optimal manual based on the following information: product sorting procedures, safety measures, and troubleshooting methods").

[1358] Step 8:

[1359] Generate manual

[1360] The generative AI analyzes the prompt and generates an appropriate manual, which includes new information and concrete examples and is designed to meet the user's needs (e.g., "Detailed instructions for product sorting procedures").

[1361] Step 9:

[1362] Sending the generated results

[1363] The generative AI sends the generated manual to the application server, which receives it and prepares it for delivery to the user.

[1364] Step 10:

[1365] Providing manuals

[1366] The application server sends the generated manual to the user terminal, which displays the manual and makes it available for workers to view, allowing workers to access the information they need in real time.

[1367] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1368] The present invention provides a system for quickly and accurately retrieving necessary information from a large number of scattered documents. This system has the functions of allowing users to search for specific information and generating an optimal manual based on that information, as well as the functions of recognizing the user's emotions and providing a response that corresponds to the emotion. Specific embodiments of this system are described below.

[1369] System Structure

[1370] The system consists of a user terminal, an application server, a database server, and an emotion engine. Generative AI is also integrated into the application server.

[1371] 1. User Device

[1372] The user terminal provides an interface for the user to search for specific information, such as a web browser or a dedicated application for entering a query into a search bar.

[1373] It also has sensors and input devices, such as a camera and microphone, to recognize the user's emotions.

[1374] 2. Application Server

[1375] The application server is responsible for receiving queries from users, analyzing the queries, and generating search requests to the database server based on the results of the analysis.

[1376] It also receives user emotional data from the emotion engine and has the ability to request manual adjustments from the generative AI based on this data.

[1377] 3. Database Server

[1378] The database server stores a large number of documents and provides relevant documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[1379] 4. Generative AI

[1380] The generative AI generates a new manual based on the document and query information sent from the application server and the user's emotional data, including detailed content such as progress management procedures, tools to be used, and best practices.

[1381] 5. Emotion Engine

[1382] The emotion engine recognizes the user's emotions and sends the data to the application server, which makes it possible to generate an optimal manual based on the user's emotions.

[1383] System Operation

[1384] User query input

[1385] The user enters "project management progress management methods" into the search interface of the device and presses the search button. At this time, the emotion engine recognizes the user's facial expressions and tone of voice.

[1386] Query Analysis and Database Search

[1387] The application server analyzes the query received from the user, and extracts keywords such as "project management" and "progress management" from the analysis.

[1388] The application server sends a search request to the database server based on the analysis result.

[1389] The database server searches for relevant documents and returns the results to the application server.

[1390] Use of Emotional Data

[1391] The emotion engine analyzes the user's emotion data in real time and sends the results to the application server.

[1392] Based on this emotional data, the application server requests the generative AI to generate the optimal manual based on the query.

[1393] Manual generation using generative AI

[1394] Based on the provided information and the user's emotional data, the generative AI generates a manual on "Project Management Progress Management Methods" that is more user-friendly. Specifically, if the user is feeling stressed, the AI ​​adjusts the manual's writing style and content to be gentle and explanatory.

[1395] Providing results

[1396] The application server sends the new manual returned by the generative AI to the user's terminal.

[1397] The user terminal displays the generated manual to the user, allowing the user to quickly view the required information.

[1398] Specific examples

[1399] For example, when a user searches for information on "testing methods," the following scenario may occur:

[1400] 1. User: Enters "test method" and presses the search button. The emotion engine analyzes the user's facial expressions and voice at this time and detects that the user is in a hurry.

[1401] 2. Application server: Analyzes the query and extracts the keywords "test" and "method".

[1402] 3. Database Server: Searches for relevant documents and returns the results.

[1403] 4. Emotion engine: Sends the user's hurry emotion data to the application server.

[1404] 5. Generative AI: Based on emotional data, it generates a concise and easily understandable manual on the "test method."

[1405] 6. User terminal: Displays the generated manual and helps users quickly obtain information.

[1406] This invention not only speeds up access to necessary information and significantly improves business efficiency, but also provides a flexible system that responds to the user's emotions.

[1407] The processing flow will be explained below.

[1408] Step 1:

[1409] The user enters "project management progress management methods" into the search interface on their device and presses the search button. At this time, the emotion engine recognizes the user's facial expressions and tone of voice in real time.

[1410] Step 2:

[1411] The terminal sends the input query "Project management progress management method" to the application server. At the same time, the emotion engine sends the user's emotion data to the application server.

[1412] Step 3:

[1413] The application server receives the query and analyzes its meaning using the query analysis module. For example, it extracts keywords such as "project management" and "progress management."

[1414] Step 4:

[1415] The application server sends a search request to the database server based on the extracted keywords.

[1416] Step 5:

[1417] The database server searches for relevant documents and sends the results to the application server. For example, past progress management manuals related to "project management" are retrieved.

[1418] Step 6:

[1419] The application server reviews the search result documents received from the database server to determine whether the retrieved documents meet the user's needs.

[1420] Step 7:

[1421] If the application server determines that the documentation is insufficient, it sends the retrieved documentation and query information to the generative AI and requests it to generate a new manual. At this time, the user's emotional data sent from the emotion engine is also sent.

[1422] Step 8:

[1423] The generative AI generates a new manual for "Project Management Progress Management Methods" based on the provided document, query information, and emotion data. Specifically, if the user is feeling stressed, the AI ​​adjusts the manual's writing style to be gentler and provide more explanations.

[1424] Step 9:

[1425] The generative AI sends the generated manual to the application server.

[1426] Step 10:

[1427] The application server sends the new manual received from the generative AI to the user's terminal.

[1428] Step 11:

[1429] The terminal displays the generated manual to the user, allowing the user to quickly view the information they need. In addition, appropriate information is provided according to the user's emotions, providing a more satisfying experience for the user.

[1430] Example 2

[1431] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1432] In today's business environment, it is extremely important to quickly and accurately retrieve necessary information from a large amount of scattered documents and generate optimal manuals based on that information. However, conventional systems lack the flexibility to respond to user emotions, making it difficult to provide appropriate information, especially when the user is stressed or in a hurry.

[1433] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1434] In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related data based on the query, means for generating new information using generative artificial intelligence based on the searched data, means for recognizing and analyzing user emotion data, and means for transmitting the generated information to a user terminal. This makes it possible to generate and provide a flexible and optimal manual that responds to the user's emotions.

[1435] "User" refers to an individual who operates a computer system or electronic device.

[1436] "Query" refers to a question or request that a user enters into a system to retrieve information.

[1437] "Data" refers to a collection of records or facts that comprise information, including digital documents and information stored in databases.

[1438] "Generative AI" refers to a type of AI that has the ability to generate new texts, manuals, etc. based on input information.

[1439] "Emotional Data" refers to data collected and analyzed to identify a user's emotions, including, specifically, a user's facial expressions and tone of voice.

[1440] "User terminal" refers to a hardware device that allows a user to access and operate the system, and includes a computer, smartphone, etc.

[1441] An "application server" is a server that receives and processes user requests and provides information in cooperation with a database.

[1442] A "database server" refers to a server that stores and manages large amounts of data and provides the data as needed and in response to requests.

[1443] "Search Request" means an instruction sent to a system to retrieve specific information.

[1444] "New information" refers to search results and new manuals and explanations generated by generative artificial intelligence.

[1445] This invention is a system that quickly and accurately acquires necessary information from a large amount of scattered data and generates an optimal manual based on the user's emotional data. This system is composed of a user terminal, an application server, a database server, an emotion engine, and generative artificial intelligence. The specific form of this system is described below.

[1446] 1. User Device

[1447] The user device provides an interface for searching for information. This can be achieved through a web browser or a dedicated application. It also has sensors such as a camera and microphone to recognize the user's emotions. The user requests information by entering a specific query into the search bar.

[1448] 2. Application Server

[1449] The application server is responsible for receiving and analyzing queries sent by users. This server breaks down queries into tokens and extracts important keywords. It also receives emotion data from the emotion engine and provides the analysis results to the generative AI.

[1450] 3. Database Server

[1451] The database server searches large amounts of data based on search requests sent from the application server and returns relevant results. The database stores business manuals, technical documents, past cases, etc.

[1452] 4. Generative Artificial Intelligence

[1453] Generative AI generates new manuals based on data and emotional data passed from the application server. This AI has the ability to customize content according to specific contexts and emotions, providing information that is relevant to the user's needs.

[1454] 5. Emotion Engine

[1455] The emotion engine analyzes the user's facial expressions and tone of voice in real time to generate emotion data, which is then sent to the application server to indicate whether the user is feeling stressed or in a hurry.

[1456] Specific examples

[1457] An example will be described in which a user searches for "project management progress management method."

[1458] User: Enters "Project management progress management methods" into the device's search bar and presses the search button. The emotion engine analyzes the user's facial expressions and tone of voice at this time and detects that the user is feeling impatient.

[1459] Application server: Breaks down the query into tokens and extracts keywords such as "project management" and "progress management." It then sends a search request to the database server based on the keywords.

[1460] Database Server: Searches for relevant data and returns the results to the application server.

[1461] Emotion engine: Sends user emotion data to the application server in real time.

[1462] Generative AI: Generates a new "progress management method" manual based on data and emotion data received from the application server. It customizes the manual to be concise and easy to understand, especially when the user is in a hurry.

[1463] Application server: Sends the generated manual to the user terminal.

[1464] User terminal: Displays the generated manual to the user and provides the necessary information quickly.

[1465] Prompt Sentence Examples

[1466] Users are in a hurry to find the "test method." Please create a concise and easy-to-understand manual. Users can tell from their facial expressions and tone of voice that they are in a hurry. Please make the content simple and easy to understand.

[1467] In this way, the present invention is a system that flexibly responds to the user's queries and emotions and quickly provides optimal information.

[1468] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1469] Step 1:

[1470] User query input

[1471] A user uses their own device to enter a query containing specific information into the search interface and presses the search button. The device then sends the query, along with the user's facial expressions and tone of voice via the camera and microphone, to the emotion engine. This simultaneously collects the user's emotional data. Input: Query entered by the user. Output: Query data and emotional data. Specifically, the query entered on the browser is sent in text format to the application server.

[1472] Step 2:

[1473] Query Analysis

[1474] The application server analyzes queries received from the user's device. Specifically, it breaks down the query into tokens, one word at a time, and extracts important keywords. At this time, it also uses the emotion data received from the emotion engine as part of the analysis. Input: Query data and emotion data. Output: Keyword list and emotion data. Specifically, it uses a natural language processing tool to tokenize the query and list the main keywords.

[1475] Step 3:

[1476] Database search

[1477] The application server sends a search request to the database server based on the extracted keywords. The database server searches for related data and returns the results to the application server. Input: Keyword list. Output: List of related data. Specifically, it generates an SQL query and sends it to the database server. The database server searches for the relevant records and returns the results.

[1478] Step 4:

[1479] Use of Emotional Data

[1480] The emotion engine analyzes the user's emotional data and sends the results to the application server. The application server uses this emotional data to make further adjustments to the generative AI. Input: Emotional data. Output: Adjustment instructions for the generative AI. Specifically, it decodes the emotional data using facial expression recognition technology and voice analysis tools, and sends the results to the application server.

[1481] Step 5:

[1482] Manual generation using generative AI

[1483] The generative AI generates a new manual based on the data and emotional data received from the application server. It customizes the content according to the user's emotions. Input: List of related data and emotional data. Output: Generated manual. Specifically, it inputs a prompt sentence into the generative AI model, and generates a user-friendly manual based on that.

[1484] Step 6:

[1485] Providing results

[1486] The application server sends the generated manual to the user terminal. The user terminal displays the received manual to the user. Input: Generated manual. Output: Information displayed to the user. Specifically, it generates an HTTP response and sends it to the user terminal. The user terminal displays the manual in a browser or application.

[1487] (Application example 2)

[1488] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1489] In today's world, it is important to quickly and accurately obtain necessary information from a vast amount of documents. However, conventional information retrieval systems have difficulty providing information that meets the user's intentions and are not responsive to the user's emotions. Therefore, there is a need for systems that allow users to quickly obtain the information they need and respond flexibly to the user's emotions.

[1490] The specification processing by specification processing unit 290 of data processing device 12 in application example 2 is realized by the following means. In this invention, the server includes means for receiving and processing a query containing specific information from a user, means for searching for related documents based on the query, means for generating a new manual using a generative AI model based on the searched documents, means for recognizing the user's emotions and transmitting the emotion data to an application server, means for requesting the generative AI model to adjust the manual based on the user's emotion data, and means for transmitting the generated manual to a user terminal. This not only enables the user to quickly and accurately obtain specific information, but also makes it possible to provide optimal information according to the user's emotions, thereby improving user satisfaction.

[1491] A "user terminal" is a device that provides an interface for a user to search for specific information.

[1492] An "application server" is a server that receives queries from users, analyzes the queries, and generates search requests to a database server.

[1493] A "database server" is a server that stores a large number of documents and provides related documents in response to queries from an application server.

[1494] "Generative AI model" is a general term for algorithms that generate new manuals based on given documents and query information.

[1495] "User emotion" refers to the emotional state (e.g., stress, relief, hurry, etc.) of the user when searching for information.

[1496] The "emotion engine" is a system component that recognizes the user's emotions and sends the emotion data to the application server.

[1497] A "query" is an inquiry that a user enters to search for specific information.

[1498] "Documents" are collections of information that can be searched, such as business manuals, construction instructions, and past documents.

[1499] A "search tool" is a method or technique for retrieving relevant documents based on a query.

[1500] "Generation means" refers to the method or technology for generating a new manual based on the retrieved documents.

[1501] "Emotion data" is data that represents the user's emotional state and is generated by the emotion engine.

[1502] A "manual" is a guide document created by a generative AI model based on the specific information a user seeks.

[1503] This invention provides a system that allows users to search for specific information and generates an optimal manual based on that information. It also has the ability to recognize the user's emotions and respond accordingly. This system consists of a user terminal, an application server, a database server, a generative AI model, and an emotion engine.

[1504] System Structure

[1505] 1. User Device

[1506] A user terminal is a device that provides an interface for users to search for specific information, such as a web browser or dedicated application for entering queries into a search bar, and also has a camera and microphone for recognizing user emotions.

[1507] 2. Application Server

[1508] The application server receives queries from users, analyzes them, and generates search requests for the database server. It also receives user emotion data from the emotion engine and requests manual adjustments to the generative AI model.

[1509] 3. Database Server

[1510] A database server is a server that stores a large amount of documents and provides related documents in response to queries from the application server, such as business manuals, construction procedures, and past documents.

[1511] 4. Generative AI Models

[1512] A generative AI model is a general term for an algorithm that generates a new manual based on the provided document, query information, and user sentiment data. Generative AI technologies such as GPT-3 are generally used.

[1513] 5. Emotion Engine

[1514] The emotion engine is a system component that recognizes the user's emotions and sends the emotion data to the application server. Based on this emotion data, a manual that best suits the user's emotions is generated.

[1515] Operation flow

[1516] The user enters a query and their emotional state is recognized. The application server analyzes the query and retrieves relevant documents from the database server. The search results, including the emotional data, are then sent to a generative AI model, which generates an optimal manual. This manual is then sent to the user's device, where it can be viewed by the user.

[1517] Specific examples

[1518] For example, if a user searches for "how to set up a security camera," the system operates as follows:

[1519] 1. User device: The user types in "How to set up a surveillance camera" and presses the search button. The emotion engine determines that the user is in a hurry.

[1520] 2. Application server: Analyzes the query and extracts keywords such as "surveillance camera" and "settings method."

[1521] 3. Database Server: Searches for relevant documents and returns the results to the application server.

[1522] 4. Emotion engine: Sends the user's hurry emotion data to the application server.

[1523] 5. Generative AI model: Based on emotional data, it quickly generates a manual that explains the setup procedure.

[1524] 6. User terminal: Displays the generated manual to help users solve problems quickly.

[1525] Prompt Sentence Examples

[1526] When a user searches for specific information, for example, "How to set up a surveillance camera," the emotion engine analyzes the user's emotions and detects that the user is in a hurry. Based on this, the generative AI model generates a concise manual that can be quickly implemented and sends it to the user's device.

[1527] example:

[1528] (Specific example used within the system)

[1529] "If a user types in 'how to set up a surveillance camera,' and the emotion engine detects that the user is in a hurry, the generated manual will provide concise and specific steps to help the user set up the camera quickly. It will also include specific model names and images of the setting screen to help the user respond quickly."

[1530] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1531] Step 1:

[1532] The user device provides an interface for users to input queries to search for specific information. The user enters a query such as "How to set up a surveillance camera" into the search bar and presses the search button. At this time, the device's camera and microphone are used to transmit the user's facial expressions and voice to the emotion engine. The input data are the query and emotion data, and the output data are the query information and raw data passed to the emotion engine.

[1533] Step 2:

[1534] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. The emotion engine extracts emotional data from the raw data and sends it to the application server. The input data is the user's facial expressions and voice, and the output data is emotional data corresponding to the emotional state. Specifically, it determines the user's emotional state, such as whether they are in a hurry or relaxed.

[1535] Step 3:

[1536] The application server analyzes queries received from users and extracts relevant keywords. The input data is query information and emotion data, and the output data is the extracted keywords. Specifically, it identifies keywords such as "surveillance camera" and "setting method."

[1537] Step 4:

[1538] The application server sends a search request to the database server based on the analysis results. The input data is the extracted keywords, and the output data is a search request to the database server. Specifically, it sends a request to search for documents that match "surveillance camera" and "settings method."

[1539] Step 5:

[1540] The database server searches for relevant documents in response to the received search request and returns the results to the application server. The input data is the search request, and the output data is the relevant documents as search results.

[1541] Step 6:

[1542] The application server requests the generative AI model to generate a manual based on the related documents and emotion data received from the database server. The input data are the related documents and emotion data, and the output data is the data passed to the generative AI model. Specifically, it provides information that serves as a prompt for generating an appropriate manual.

[1543] Step 7:

[1544] The generative AI model generates a new manual based on the provided document and emotion data. The input data is the relevant document, emotion data, and prompt sentences, and the output data is the generated manual. Specifically, if the user is in a hurry, it generates a concise and quick manual.

[1545] Step 8:

[1546] The application server receives the manual generated by the generative AI model and sends it to the user's device. The input data is the generated manual, and the output data is the manual sent to the user's device.

[1547] Step 9:

[1548] The user terminal displays the generated manual to the user, helping the user to quickly acquire information. The input data is the generated manual, and the output data is the displayed manual. Specifically, the user can easily understand "how to set up a surveillance camera" and quickly perform the setting.

[1549] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1550] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1551] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1552] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1553] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1554] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1555] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1556] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1557] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1558] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1559] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1560] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1561] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1562] 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.

[1563] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1564] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1565] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1566] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1567] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1568] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1569] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1570] The following is further disclosed regarding the above embodiment.

[1571] (Claim 1)

[1572] means for receiving and processing queries containing specific information from a user;

[1573] a means for retrieving relevant documents based on a query;

[1574] A method for generating new manuals using generative AI based on the searched documents, and

[1575] means for transmitting the generated manual to a user terminal;

[1576] A system including:

[1577] (Claim 2)

[1578] A means for parsing the query and extracting keywords;

[1579] a means for determining the age or ambiguity of documents in the search results;

[1580] 10. The system of claim 1, further comprising means for sending the document and the query to the generative AI based on the determination.

[1581] (Claim 3)

[1582] means for sending a query from a user terminal to an application server;

[1583] means for sending a search request from the application server to the database server;

[1584] means for transmitting search results from the database server to the application server;

[1585] A means for transmitting the generated manual from the generative AI to the application server;

[1586] 2. The system according to claim 1, further comprising means for transmitting the generated manual from the application server to the user terminal.

[1587] "Example 1"

[1588] (Claim 1)

[1589] means for receiving and processing queries containing specific information from a user;

[1590] A means for parsing the query and extracting keywords;

[1591] a means for retrieving relevant documents based on a query;

[1592] a means for determining the relevance of documents in the search results;

[1593] A means for generating new instruction manuals based on the retrieved documents using generative artificial intelligence;

[1594] means for transmitting the generated instruction manual to a user terminal;

[1595] A system including:

[1596] (Claim 2)

[1597] 10. The system of claim 1, further comprising means for sending the document and the query to the generative artificial intelligence based on the determination.

[1598] (Claim 3)

[1599] means for sending a query from a user terminal to an application server;

[1600] means for sending a search request from the application server to the database server;

[1601] means for transmitting search results from the database server to the application server;

[1602] A means for transmitting the generated instruction manual from the generative artificial intelligence to an application server;

[1603] 10. The system of claim 1, further comprising means for transmitting the generated instruction manual from the application server to the user terminal.

[1604] "Application Example 1"

[1605] (Claim 1)

[1606] means for receiving and processing queries containing specific information from a user;

[1607] a means for retrieving relevant data based on a query;

[1608] A method for generating new manuals using generative AI based on the searched data, and

[1609] means for transmitting the generated manual to a user terminal;

[1610] a means for receiving specific information by voice input or text input;

[1611] A means for using a smartphone or a head-mounted display as a user terminal;

[1612] A system including:

[1613] (Claim 2)

[1614] A means for parsing the query and extracting keywords;

[1615] A means of determining the age or ambiguity of search results data;

[1616] 10. The system of claim 1, further comprising means for transmitting data and queries to the generative AI based on the determination.

[1617] (Claim 3)

[1618] means for sending a query from a user terminal to an application server;

[1619] means for sending a search request from the application server to the database server;

[1620] means for transmitting search results from the database server to the application server;

[1621] A means for transmitting the generated manual from the generative AI to the application server;

[1622] 10. The system according to claim 1, further comprising means for displaying the generated manual on an interface of a user terminal.

[1623] "Example 2: Combining Emotion Engines"

[1624] (Claim 1)

[1625] means for receiving and processing queries containing specific information from a user;

[1626] a means for retrieving relevant data based on a query;

[1627] A means for generating new information based on the searched data using generative artificial intelligence;

[1628] means for recognizing and analyzing user emotion data;

[1629] means for transmitting the generated information to a user terminal;

[1630] A system including:

[1631] (Claim 2)

[1632] A means for parsing the query and extracting keywords;

[1633] A means of determining the freshness and clarity of the data in the search results;

[1634] 10. The system of claim 1, further comprising means for transmitting the data and the query to the generative artificial intelligence based on the determination.

[1635] (Claim 3)

[1636] means for sending a query from a user terminal to an application server;

[1637] means for sending a search request from the application server to the database server;

[1638] means for transmitting search results from the database server to the application server;

[1639] A means for transmitting information generated from the generative artificial intelligence to an application server;

[1640] 10. The system of claim 1, further comprising means for transmitting the generated information from the application server to the user terminal.

[1641] "Application example 2 when combining emotion engines"

[1642] (Claim 1)

[1643] means for receiving and processing queries containing specific information from a user;

[1644] a means for retrieving relevant documents based on a query;

[1645] A means for generating new manuals using a generative AI model based on the retrieved documents;

[1646] means for recognizing a user's emotion and transmitting the emotion data to an application server;

[1647] A means to request manual adjustments to the generative AI model based on the user's emotional data; and

[1648] means for transmitting the generated manual to a user terminal;

[1649] A system including:

[1650] (Claim 2)

[1651] A means for parsing the query and extracting keywords;

[1652] a means for determining the age or ambiguity of documents in the search results;

[1653] a means for the emotion engine to analyze the user's emotion;

[1654] 10. The system of claim 1, further comprising means for sending the document and the query to a generative AI model based on the analysis results.

[1655] (Claim 3)

[1656] means for sending a query from a user terminal to an application server;

[1657] means for sending a search request from the application server to the database server;

[1658] means for transmitting search results from the database server to the application server;

[1659] A means for generating a manual by sending emotion data and documents from an application server to a generative AI model;

[1660] A means for transmitting the generated manual from the generative AI model to an application server;

[1661] 2. The system according to claim 1, further comprising means for transmitting the generated manual from the application server to the user terminal. [Explanation of symbols]

[1662] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving and processing queries containing specific information from a user; a means for retrieving relevant documents based on a query; A method for generating new manuals using generative AI based on the searched documents, and means for transmitting the generated manual to a user terminal; A system including:

2. A means for parsing the query and extracting keywords; a means for determining the age or ambiguity of documents in the search results; The system of claim 1 , further comprising means for sending the document and the query to the generative AI based on the determination.

3. means for sending a query from a user terminal to an application server; means for sending a search request from the application server to the database server; means for transmitting search results from the database server to the application server; A means for transmitting the generated manual from the generative AI to the application server; 2. The system according to claim 1, further comprising means for transmitting the generated manual from the application server to the user terminal.

Images