system

Abstract

We provide the system. [Solution] A means of collecting data from network storage, The aforementioned data is analyzed using natural language processing technology, and a means for extracting terms is provided. A means for generating definitions for the extracted terms using generative artificial intelligence, A means for automatically generating a knowledge portal that manages links to glossaries and related materials, Means for displaying the aforementioned knowledge portal on a user terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a company, when new graduates, mid-career hires, or outsourced employees start working without knowing company-specific terms and business procedures, there is a problem that work efficiency decreases in the initial stage and it takes time to adapt. This situation particularly has an adverse effect on the speed of a project and the teamwork ability of a team, so a mechanism for enabling early acquisition of knowledge is required.

Means for Solving the Problems

[0005] This invention solves the problem by collecting various documents within a company using data collection means from network storage, analyzing them using natural language processing technology, and extracting terminology. Furthermore, it constructs a knowledge portal that automatically generates definitions for the extracted terms using generative artificial intelligence and manages a glossary and links to related materials. In addition, this knowledge portal is displayed on user terminals, allowing users to easily access information and providing an environment in which new employees can quickly acquire the necessary knowledge.

[0006] "Network storage" refers to a storage system for remotely storing and managing data via the internet or a company's internal network.

[0007] "Natural language processing technology" is an artificial intelligence technology that enables computers to understand, analyze, and generate human language.

[0008] "Term extraction" is the process of identifying specific words or phrases from text data and extracting them based on their importance and frequency.

[0009] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to generate new data based on existing data.

[0010] "Generating a definition" means automatically creating a text that explains the meaning and scope of a given term or concept.

[0011] A "knowledge portal" is a web system or platform that centrally manages knowledge and information within an organization or specific domain, and provides it in a form that is easily accessible to users.

[0012] A "user terminal" is an electronic device that a user directly operates and uses, and includes computers, tablets, and smartphones. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention presents an embodiment of a system that promotes knowledge sharing within a company, particularly enabling new employees and transferees to efficiently acquire business knowledge. The program's processing is described below through the roles of the server, terminal, and user.

[0035] Server Role

[0036] The server first connects to network storage and collects various digital documents within the company. This includes a variety of file formats such as PDF, Word, and Excel. The collected data is stored in a database and prepared for further analysis. The server then uses natural language processing technology to extract specific terms from this data. For the extracted terms, generative artificial intelligence is used to automatically generate definitions, and a glossary is compiled based on these definitions. The generated glossary and related materials are built as a knowledge portal and managed in a way that is accessible to all employees.

[0037] Terminal role

[0038] The terminal provides an interface for users to search and refer to information through this system. When a user searches for a specific term or topic, the terminal interacts with the server to quickly display search results. The terminal is equipped with navigation functions and filtering options to improve usability, allowing users to quickly access terms and materials of interest.

[0039] User roles

[0040] The users are primarily new employees, such as recent graduates and those changing jobs, who learn company terminology and documents through this system. When a user operates a terminal to look up a specific term, the knowledge portal presents the relevant information and related documents. This allows users to acquire company-specific knowledge in a short period of time and contribute to their work as immediate assets.

[0041] Specific example

[0042] For example, if a new employee wants to learn about the contents of an "annual financial report," they can search for the term using their terminal. The server will then provide a page containing definitions and historical documents related to the "annual financial report." This allows the new employee to understand the process and historical background of the "annual financial report" and gain the knowledge to better perform their role.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server connects to network storage and collects digital documents stored company-wide through specified folder paths. This includes folders for specific projects, meeting minutes folders for regular meetings, and shared folders for each department.

[0046] Step 2:

[0047] The server begins analyzing the collected document data. Using natural language processing techniques, it tokenizes words in the text and performs morphological analysis to extract frequently occurring words and phrases. For nouns and technical terms, it records their frequency of appearance.

[0048] Step 3:

[0049] The server then uses generative artificial intelligence on the extracted terms to generate definitions for each term. The generated definitions reflect the context in which the terms are used and include practical examples.

[0050] Step 4:

[0051] The server combines the generated terminology definitions with related documentation information to automatically build a knowledge portal. This portal consists of a page for each term, containing links to related resources and supplementary information.

[0052] Step 5:

[0053] The terminal provides access to a portal interface, allowing users to easily search for the information they need. Users can quickly access relevant terminology definitions and resources by entering keywords.

[0054] Step 6:

[0055] Users can use their devices to browse the knowledge portal and find definitions and related information for terms they are interested in. Furthermore, when their questions are answered, they can submit feedback through the interface, which allows the server to improve the accuracy of the system.

[0056] Step 7:

[0057] The server periodically retrieves new data from network storage, analyzes it, and updates it repeatedly. This keeps the company's knowledge base up-to-date, ensuring that all employees always have access to the latest information.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] A major challenge is the lack of appropriate systems for sharing and efficiently acquiring knowledge within companies. In particular, there is a need for mechanisms that enable new employees and those changing jobs to effectively acquire job-related knowledge in a short period of time.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for collecting information from an information storage device, means for analyzing the information using natural language processing technology and extracting terms, and means for generating definitions for the extracted terms using generative artificial intelligence. This enables the centralization of information within a company, allowing new employees and transferees to acquire knowledge efficiently.

[0063] An "information storage device" is a device used to store and manage various forms of digital data that exist within a network.

[0064] "Natural language processing technology" is a field of technology that enables computers to understand and analyze human language, and is used to extract keywords and context from data.

[0065] "Generative artificial intelligence" refers to artificial intelligence systems that have the ability to generate new information and definitions, and are used to dynamically create content based on user queries.

[0066] A "knowledge sharing platform" is a system designed to share and make accessible the knowledge and information accumulated within a company across the entire organization.

[0067] A "user terminal" is a computer device used by users to search for and refer to information through a knowledge-sharing platform.

[0068] A "user interface" is a mechanism that provides screens and operating methods for users to interact directly with the system.

[0069] This invention relates to a system for streamlining knowledge sharing within a company. Specific embodiments are described below.

[0070] Server Embodiment

[0071] The server connects to an information storage device to collect various forms of digital data (e.g., PDF, Word, Excel) stored within the company. This data is analyzed using natural language processing (NLP) techniques to extract important terms related to business operations. For natural language processing, NLP libraries such as spaCy and NLTK can be used. For the extracted terms, definitions are generated using generative artificial intelligence (e.g., the GPT model). This generation process ensures that natural-sounding sentences are obtained, taking into account past digital data and context. As a result, a glossary is constructed and integrated into a knowledge-sharing platform, streamlining information access throughout the organization.

[0072] Terminal embodiment

[0073] The terminal provides an interface for users to access the knowledge-sharing platform. When a user enters a specific term into the search box, the terminal retrieves information from the server and displays the results on the screen. The user interface includes navigation functions and filtering options, designed to allow users to access information intuitively.

[0074] User Embodiment

[0075] Users are new employees, such as new hires or transferees, who acquire the necessary work-related knowledge in a short period of time through this system. For example, when a user searches for a specific term such as "annual financial report" on the knowledge-sharing platform, the definition of that term and related materials are displayed. This allows users to understand the company's unique business processes and terminology, enabling them to adapt to their work more smoothly.

[0076] Example of a prompt

[0077] "Please provide definitions of terms related to annual financial reporting and related materials."

[0078] This system not only visualizes complex business knowledge and enhances the effectiveness of training new employees, but also contributes to standardizing knowledge among all employees and correcting information disparities.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server collects digital data in various formats (PDF, Word, Excel, etc.) from information storage devices. The input consists of basic metadata such as file paths and format information. The server collects this data via the network, and the collected files and their metadata are output as a result.

[0082] Step 2:

[0083] The server analyzes the collected digital data using natural language processing techniques. In this step, the entire collected document is provided as input, and important keywords are extracted based on frequent terminology patterns and context. As a result of this processing, a list of identified terms is output. During this process, natural language processing libraries are used to segment the text and tag parts of speech.

[0084] Step 3:

[0085] The server uses generative artificial intelligence to generate definitions for the extracted terms. Here, the term list and its surrounding contextual information are used as input. The server prompts the generative AI model with this information to generate contextually relevant, natural, and detailed term definitions. The output results in a new definition list for each term.

[0086] Step 4:

[0087] The server automatically generates a knowledge-sharing platform by aligning the generated definitions and related documents. In this step, the definition list and link collection are entered, and the knowledge base is built. This sets up a template for the knowledge-sharing platform, which is then output as a page accessible to employees.

[0088] Step 5:

[0089] The terminal provides an interface for users to access the knowledge-sharing platform. Here, users enter queries (search terms), and the information returned from the server is visually displayed on the terminal. The interface has a function to instantly display search results, allowing users to quickly find the information they need.

[0090] Step 6:

[0091] Users operate a terminal to look up specific terms and access information provided by the server. The input consists of the user's search terms and interests, while the output displays relevant definitions and materials on the screen. Based on this information, users acquire business knowledge and apply it practically.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] Efficient knowledge sharing within a company is a crucial element for new employees and transferees to acquire company-specific operational knowledge in a short period of time. However, traditional methods present challenges such as difficulty in finding information and inefficient learning processes. In particular, in physical stores, it is difficult for staff to immediately acquire necessary operational knowledge during busy periods, making it challenging to provide an environment where new staff can immediately contribute as valuable assets. Furthermore, the scattering of numerous documents makes it difficult to quickly access new operational procedures and policies.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for collecting information from network storage, means for analyzing the information using natural language processing technology and extracting terms, and means for generating definitions for the extracted terms using generative artificial intelligence. This makes it possible to quickly search for terms and procedures related to store operations and provide procedure manuals and related materials. As a result, new employees and staff can easily acquire business knowledge and be utilized as immediate contributors. Furthermore, by regularly updating the latest information, it becomes possible to continuously provide accurate and useful information.

[0097] "Network storage" refers to a storage medium that stores data and information and is accessible via a network.

[0098] "Natural language processing technology" is a technology used by computers to analyze and understand human language.

[0099] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to create new data and information based on existing data.

[0100] A glossary is a document that compiles important terms and their definitions within a specific field.

[0101] "Related information sources" are materials or links that provide useful additional information on a particular term or topic.

[0102] A "knowledge portal" is a digital platform that aggregates information and knowledge in a way that is easily accessible to users.

[0103] A "user terminal" refers to a device used to access a system or application.

[0104] A "user interface" refers to the screens and operating systems that a user uses to interact with a system.

[0105] A "procedure manual" is a document that describes the steps and methods necessary to perform a specific task or process.

[0106] "References" are additional documents or data used to support specific information or knowledge.

[0107] This invention is a system that enables store staff to efficiently acquire job-related knowledge. The system is realized using a server, user terminals, network storage, and generative artificial intelligence.

[0108] The server first accesses network storage to collect documents related to store operations. This includes procedure manuals and reference materials. The collected data is then processed using natural language processing with programming languages ​​such as Python and their libraries to extract important terms. For example, text analysis is performed using the Python NLTK library.

[0109] Next, the server uses the GPT model of OpenAI (registered trademark), a generative artificial intelligence, to automatically generate definitions for the extracted terms. These generated definitions are compiled into a glossary and registered in the knowledge portal. This knowledge portal is built using a web framework such as Django to make it easily accessible to users.

[0110] The user terminal will be developed using React Native. The user of the terminal will be a store staff member who can access the knowledge portal and search for terms of interest. For example, when a newly assigned staff member searches for "how to prepare for a special sale event," they can instantly view relevant procedures and information on past cases through the terminal.

[0111] This system allows new employees and those whose work locations have changed to quickly adapt to their roles and contribute to store operations as immediate assets. Furthermore, newly added information is regularly updated in the system, ensuring it remains up-to-date.

[0112] As a concrete example, enter the following prompt into the generating AI model: "Generate definition of business chart for sale event preparation." This will generate an overview of efficient preparation methods for sale events and add it to the knowledge portal.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server retrieves digital documents related to store operations from network storage. It receives connection destination storage information and authentication information as input, and retrieves documents in formats such as PDF and text files as output. This operation involves data transfer over the network.

[0116] Step 2:

[0117] The server analyzes the retrieved documents and extracts important terms. The input is the document's text data, and text analysis is performed using natural language processing techniques (e.g., Python's NLTK). The output is a list of the extracted terms.

[0118] Step 3:

[0119] The server uses a generative artificial intelligence model to generate definitions for extracted terms. It receives a list of terms and related prompt sentences as input and automatically generates definitions using OpenAI's GPT, etc. The output is the definition sentence corresponding to each term. Specifically, queries are sent to the model via an API. Prompt sentences such as "Special sale event preparation, business chart, definition generation" are used.

[0120] Step 4:

[0121] The server builds a knowledge portal using the generated definitions and related information. It receives term definitions and linked reference materials as input and automatically generates portal pages using a web framework such as Django. The output is an accessible knowledge portal. Page layout and link configuration are also handled during this process.

[0122] Step 5:

[0123] The device displays a knowledge portal when accessed by a user. It receives user queries and navigation inputs, and retrieves relevant information from the portal's database. The output is an information page formatted for user readability. Specifically, the user interface is rendered via React Native.

[0124] Step 6:

[0125] Users obtain necessary information from a knowledge portal via their terminals. The input consists of search keywords entered by the user into the terminal, and the output is related information and procedures displayed on the terminal. This information can then be utilized in daily work.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention adds an emotion engine that recognizes user emotions and optimizes interactions, in addition to a system that collects documents and data within a company using network storage, extracts terms by analyzing them using natural language processing technology, and generates definitions for those terms using generative artificial intelligence. The program's processing is described below from the perspectives of the server, terminal, and user.

[0128] Server Role

[0129] The server accesses the company's network storage to collect all relevant documents, such as project materials and meeting minutes. The collected data is analyzed using natural language processing techniques to extract important terms and phrases. Subsequently, generative artificial intelligence is used to generate definitions for the extracted terms, and a knowledge portal is built along with links to related materials. In addition, an emotion engine analyzes user activity logs and comments to evaluate the user's emotional state.

[0130] Terminal role

[0131] The terminal provides an interface for users to access information using this knowledge portal. When a user searches for a term, the terminal displays definitions and related materials retrieved from the server. Furthermore, a sentiment engine analyzes the user's emotions, and if it determines, for example, that the user has questions or complaints, it automatically presents additional reference materials or FAQs.

[0132] User roles

[0133] Users can use the knowledge portal to learn terminology and processes related to their work. This is particularly useful for new employees and those who have transferred to a new department, as it allows them to quickly access definitions of terms and workflows. Furthermore, by gaining new insights based on their emotional response to the information presented by the system and receiving additional support, they can approach their work with greater confidence.

[0134] Specific example

[0135] For example, if a user wants to learn more about the "client contract process," they search for the term on their device. The server provides the definition and relevant documents from the knowledge portal and records which documents the user has accessed. As the user reads through the documents, the sentiment engine predicts areas of uncertainty or potential interest and presents more detailed guides and past success stories. This allows the user to quickly grasp the key points and proceed smoothly with their workflow.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The server accesses network storage and retrieves all relevant documents within the company. This process involves scanning folders and collecting data based on employee access rights.

[0139] Step 2:

[0140] The server passes the retrieved documents to a natural language processing module, which then begins text analysis. This involves extracting nouns and phrases, analyzing co-occurrence relationships, and listing industry-specific terminology.

[0141] Step 3:

[0142] The server uses generative artificial intelligence to generate definitions for the extracted terms. The generated definitions also include usage examples and related categories.

[0143] Step 4:

[0144] The server builds a knowledge portal based on the generated glossary, including links to related resources. This portal is indexed, enabling efficient information retrieval.

[0145] Step 5:

[0146] The terminal receives a search query from the user and sends the query to the server. The server retrieves the appropriate information from the knowledge portal and returns it to the terminal.

[0147] Step 6:

[0148] The device visually displays search results to the user. During this process, an emotion engine analyzes the user's actions and facial expressions to infer their emotional state.

[0149] Step 7:

[0150] The emotion engine adjusts support information based on the inferred user emotions. For example, if a user is feeling frustrated, it automatically displays links to additional step-by-step guides or tutorial videos.

[0151] Step 8:

[0152] Users obtain information through their devices and request further assistance as needed. This allows users to acquire the knowledge they require quickly and efficiently.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0155] In today's information society, vast amounts of information are accumulated within companies, and there is a need for methods to efficiently organize this information and quickly access the necessary information. Furthermore, users require intuitive and rapid information-gathering interfaces to resolve business-related questions and uncertainties. However, traditional methods fail to adequately meet these needs, making technological means essential for optimizing information organization and information delivery in accordance with the user's emotional state.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes means for collecting information from network storage, means for analyzing the information using natural language processing technology and extracting concepts, and means for generating explanations for the extracted concepts using generative artificial intelligence. This makes it possible to efficiently organize information, analyze the user's operation history and statements to evaluate their emotional state, and automatically present additional relevant information when necessary.

[0158] "Network storage" refers to a storage device used to store data that can be accessed over a network.

[0159] "Information" refers to documents and data managed within a company, and includes all kinds of materials related to business operations.

[0160] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract important concepts from information.

[0161] A "concept" refers to important terms and phrases within information extracted using natural language processing technology.

[0162] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates explanatory text and related information based on extracted concepts.

[0163] An "explanation" is a document generated by a generative artificial intelligence system to clarify the meaning and usage of a concept.

[0164] A "server" is a central device that performs the processes of collecting, analyzing, generating, and providing information.

[0165] "Emotional state" refers to the psychological state of a user, inferred from their activity history and statements.

[0166] An "information portal" is a web-based platform for integrating and managing collected information, its accompanying explanations, and related links.

[0167] "Related information" refers to additional materials and support information that are associated with the concept the user searched for.

[0168] This invention is a system designed to streamline and optimize information within a company, utilizing human language analysis and understanding technology and artificial intelligence. The following describes specific embodiments for carrying out this invention.

[0169] The server connects to the company's network storage to collect all relevant information. The hardware used includes large-capacity storage devices on the company network. This information includes project documents, meeting minutes, and other business documents, which are stored by the server in a temporary database.

[0170] The server analyzes the collected information using natural language processing (NLP) techniques. The software used includes open-source NLP libraries and commercial analysis tools. This extracts important concepts and terminology from the information and organizes the resulting data in a structured format.

[0171] Next, the server employs generative artificial intelligence (AI) technology to generate explanations based on the extracted concepts. This AI model, for example, presents background information and examples to help the user deepen their understanding of a particular term. The generative AI model used here is a known natural language generation (NLG) framework.

[0172] The terminal provides an information portal accessible to the user, displaying explanations and related information for the searched concept. The user enters a prompt through the search bar, such as, "Please provide details about the client contract process. Please also provide any relevant documents," and obtains a concept explanation from the server.

[0173] The server also features an emotion engine that analyzes user activity history and comments. This function infers the user's emotional state and adjusts the way information is provided as needed. For example, if the emotion engine determines that a user is dissatisfied with the information, it will automatically display additional reference materials or FAQs on the device.

[0174] This system enables effective information management within a company and optimizes user interaction. Users can rapidly increase their knowledge in their work, resulting in improved work efficiency.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The server connects to the company's network storage and collects information, including project documents and meeting minutes. It receives directory information from the network storage as input and stores the collected information in a temporary database as output. Specifically, its operation involves scanning for necessary data files via configured file paths and transferring them to the database in a predetermined format.

[0178] Step 2:

[0179] The server performs analysis on the collected information using natural language processing (NLP) techniques. It uses information stored in a temporary database as input and identifies important concepts and terms as output. The server activates its NLP engine, extracting frequently occurring words and performing contextual analysis to list and tag business-critical concepts.

[0180] Step 3:

[0181] The server uses a generative AI model to generate explanations for identified concepts. It takes a list of key concepts extracted by NLP analysis as input and outputs a generated explanation for each concept. This process involves prompting the generative AI with background information and usage examples related to the extracted concepts, generating explanations in natural language.

[0182] Step 4:

[0183] The terminal allows users to access an information portal and provides explanations and materials related to the searched concepts. It receives prompt input from the user and displays explanations and related materials retrieved from the server on the screen as output. Specifically, it prepares to display relevant information in an optimized interface based on the user's search query.

[0184] Step 5:

[0185] The server runs an emotion engine that analyzes user operation logs and comments to evaluate the user's emotional state. The input is user interaction data, and the output is the emotion evaluation result at that time. The emotion engine analyzes the frequency and duration of operations and the content of comments to determine the user's current psychological state and perform the emotion evaluation.

[0186] Step 6:

[0187] The device presents additional relevant information and FAQs that the user might find interesting, based on the results of the emotion engine. It receives emotion evaluation data as input and displays hints and additional information on the user screen as output. Specifically, it dynamically places recommended content on the device based on the data received from the engine.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0190] In logistics centers and other industries, there is a need to improve employees' ability to quickly understand and respond to necessary information and procedures. However, current systems present information uniformly and do not provide flexible information tailored to the user's emotional state. This can lead to misunderstandings and decreased work efficiency. To solve this problem, a system is needed that provides information tailored to the individual user's situation, thereby achieving more effective learning and work efficiency.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes means for collecting information from a network storage device, means for analyzing the information using natural language processing technology and extracting terms, means for generating definitions for the extracted terms using generative artificial intelligence, means for automatically generating a knowledge portal that manages links to a glossary and related materials, means for analyzing input data using emotion recognition technology and evaluating the user's emotional state, and means for optimizing the content presented on the knowledge portal according to the user's emotional state. This enables users to receive information in a format that suits their emotional state, thereby promoting understanding of work and enabling efficient work.

[0193] A "network storage device" is a device that stores digital information at a central location and makes it accessible via a network.

[0194] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0195] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate new information and conclusions based on data.

[0196] A "knowledge portal" is an information platform that aggregates information and links and provides them in a format that is easily accessible to users.

[0197] "Emotion recognition technology" is a technology that analyzes user input data to infer a person's emotional state.

[0198] A "user terminal" is a device used by a user to display or manipulate information.

[0199] A "glossary" is a document that systematically organizes terms and their definitions related to a specific field.

[0200] "Related materials" are supplementary sources of information associated with a particular piece of information or topic.

[0201] "Evaluation" in emotion recognition technology refers to the process of observing and judging the user's emotional state.

[0202] "Optimization" is the process of adjusting the way information is presented and its content according to the user's emotional state.

[0203] This invention comprises a system designed to streamline the collection, analysis, and provision of information in a logistics center, and to promote employees' understanding of their work.

[0204] The server is connected to network storage and collects relevant information within the logistics center. The collected information is analyzed using natural language processing technology, and important terms are extracted. In this process, an AI model using a specific algorithm operates to accurately grasp the meaning of the information. Generative artificial intelligence generates appropriate definitions for these terms, and these are aggregated in a knowledge portal.

[0205] Emotion recognition technology analyzes user input behavior through the user's terminal and evaluates the user's emotional state. The emotion recognition engine used here is designed to infer emotions from the user's operation history and voice data. Based on this evaluation result, the information provided is optimized according to the user's state.

[0206] The terminal enables users to access a knowledge portal and displays information customized based on their emotional state. Users can quickly grasp terminology and procedures related to logistics operations using their smart devices. Eye-tracking and voice recognition technologies are employed to create an interface that reduces user burden.

[0207] As a concrete example, consider a scenario where a new employee tries to understand the "inventory management process." The user searches for "inventory management process" through a terminal, and the terminal provides a definition and related materials generated by a generative AI model. While the user reviews the displayed information, an emotion engine senses signs of their interest and confusion, and presents additional FAQs and learning guides. In this way, the user can efficiently acquire information and proceed with their work smoothly.

[0208] An example of a prompt would be, "Please provide details of the inventory management process and related practical guides. Also, please provide additional, sentiment-based information, along with recent success stories." This ensures the system provides optimal information tailored to the user's needs.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The server connects to network storage and collects documents and data within the logistics center. The input is raw data from the network storage, and the output is a dataset ready for analysis. During this process, a data collection script is periodically executed to filter the target data.

[0212] Step 2:

[0213] The server analyzes the collected data using natural language processing techniques and extracts important terms. The input is the collected data, and the output is a list of extracted terms. In this step, an NLP engine is running to perform tokenization and part-of-speech tagging, and specific algorithms are used to identify terms.

[0214] Step 3:

[0215] The server uses generative artificial intelligence to generate definitions for the extracted terms. The input is a list of terms, and the output is a definition for each term. A generative AI model is applied to this process, and the information related to each term is consistently linked by the AI ​​algorithm.

[0216] Step 4:

[0217] The server automatically generates a knowledge portal and manages the glossary and links to related resources. Input is term definitions and related resources, and output is a user-accessible knowledge portal. In this step, a database management system is operational, aggregating information at the portal's front-end.

[0218] Step 5:

[0219] Emotion recognition technology allows the device to analyze user input data and evaluate their emotional state. User input is used as input data, and an emotion evaluation result is generated as output. Emotions are inferred by analyzing the user's voice tone and operation speed using sensors and microphones.

[0220] Step 6:

[0221] Based on the user's emotional state, the device optimizes the content presented in the knowledge portal. Input consists of emotional assessment results and knowledge portal data, while output is a customized information display. FAQs and reference materials are automatically selected and displayed according to the user's needs.

[0222] Step 7:

[0223] Users access a knowledge portal using smart devices to obtain information related to logistics operations. Input is the user's search query, and output is the requested information and reference materials. Users can smoothly access the necessary information through voice commands and touch operations.

[0224] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0225] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0226] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0230] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0231] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0232] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0233] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0234] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0235] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0236] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0237] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0238] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0239] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0240] This invention presents an embodiment of a system that promotes knowledge sharing within a company, particularly enabling new employees and transferees to efficiently acquire business knowledge. The program's processing is described below through the roles of the server, terminal, and user.

[0241] Server Role

[0242] The server first connects to network storage and collects various digital documents within the company. This includes a variety of file formats such as PDF, Word, and Excel. The collected data is stored in a database and prepared for further analysis. The server then uses natural language processing technology to extract specific terms from this data. For the extracted terms, generative artificial intelligence is used to automatically generate definitions, and a glossary is compiled based on these definitions. The generated glossary and related materials are built as a knowledge portal and managed in a way that is accessible to all employees.

[0243] Terminal role

[0244] The terminal provides an interface for users to search and refer to information through this system. When a user searches for a specific term or topic, the terminal interacts with the server to quickly display search results. The terminal is equipped with navigation functions and filtering options to improve usability, allowing users to quickly access terms and materials of interest.

[0245] User roles

[0246] The users are primarily new employees, such as recent graduates and those changing jobs, who learn company terminology and documents through this system. When a user operates a terminal to look up a specific term, the knowledge portal presents the relevant information and related documents. This allows users to acquire company-specific knowledge in a short period of time and contribute to their work as immediate assets.

[0247] Specific example

[0248] For example, if a new employee wants to learn about the contents of an "annual financial report," they can search for the term using their terminal. The server will then provide a page containing definitions and historical documents related to the "annual financial report." This allows the new employee to understand the process and historical background of the "annual financial report" and gain the knowledge to better perform their role.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The server connects to network storage and collects digital documents stored company-wide through specified folder paths. This includes folders for specific projects, meeting minutes folders for regular meetings, and shared folders for each department.

[0252] Step 2:

[0253] The server begins analyzing the collected document data. Using natural language processing techniques, it tokenizes words in the text and performs morphological analysis to extract frequently occurring words and phrases. For nouns and technical terms, it records their frequency of appearance.

[0254] Step 3:

[0255] The server then uses generative artificial intelligence on the extracted terms to generate definitions for each term. The generated definitions reflect the context in which the terms are used and include practical examples.

[0256] Step 4:

[0257] The server combines the generated terminology definitions with related documentation information to automatically build a knowledge portal. This portal consists of a page for each term, containing links to related resources and supplementary information.

[0258] Step 5:

[0259] The terminal provides access to a portal interface, allowing users to easily search for the information they need. Users can quickly access relevant terminology definitions and resources by entering keywords.

[0260] Step 6:

[0261] Users can use their devices to browse the knowledge portal and find definitions and related information for terms they are interested in. Furthermore, when their questions are answered, they can submit feedback through the interface, which allows the server to improve the accuracy of the system.

[0262] Step 7:

[0263] The server periodically retrieves new data from network storage, analyzes it, and updates it repeatedly. This keeps the company's knowledge base up-to-date, ensuring that all employees always have access to the latest information.

[0264] (Example 1)

[0265] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] A major challenge is the lack of appropriate systems for sharing and efficiently acquiring knowledge within companies. In particular, there is a need for mechanisms that enable new employees and those changing jobs to effectively acquire job-related knowledge in a short period of time.

[0267] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0268] In this invention, the server includes means for collecting information from an information storage device, means for analyzing the information using natural language processing technology and extracting terms, and means for generating definitions for the extracted terms using generative artificial intelligence. This enables the centralization of information within a company, allowing new employees and transferees to acquire knowledge efficiently.

[0269] An "information storage device" is a device used to store and manage various forms of digital data that exist within a network.

[0270] "Natural language processing technology" is a field of technology that enables computers to understand and analyze human language, and is used to extract keywords and context from data.

[0271] "Generative artificial intelligence" refers to artificial intelligence systems that have the ability to generate new information and definitions, and are used to dynamically create content based on user queries.

[0272] A "knowledge sharing platform" is a system designed to share and make accessible the knowledge and information accumulated within a company across the entire organization.

[0273] A "user terminal" is a computer device used by users to search for and refer to information through a knowledge-sharing platform.

[0274] A "user interface" is a mechanism that provides screens and operating methods for users to interact directly with the system.

[0275] This invention relates to a system for streamlining knowledge sharing within a company. Specific embodiments are described below.

[0276] Server Embodiment

[0277] The server connects to an information storage device to collect various forms of digital data (e.g., PDF, Word, Excel) stored within the company. This data is analyzed using natural language processing (NLP) techniques to extract important terms related to business operations. For natural language processing, NLP libraries such as spaCy and NLTK can be used. For the extracted terms, definitions are generated using generative artificial intelligence (e.g., the GPT model). This generation process ensures that natural-sounding sentences are obtained, taking into account past digital data and context. As a result, a glossary is constructed and integrated into a knowledge-sharing platform, streamlining information access throughout the organization.

[0278] Terminal embodiment

[0279] The terminal provides an interface for users to access the knowledge-sharing platform. When a user enters a specific term into the search box, the terminal retrieves information from the server and displays the results on the screen. The user interface includes navigation functions and filtering options, designed to allow users to access information intuitively.

[0280] User Embodiment

[0281] Users are new employees, such as new hires or transferees, who acquire the necessary work-related knowledge in a short period of time through this system. For example, when a user searches for a specific term such as "annual financial report" on the knowledge-sharing platform, the definition of that term and related materials are displayed. This allows users to understand the company's unique business processes and terminology, enabling them to adapt to their work more smoothly.

[0282] Example of a prompt

[0283] "Please provide definitions of terms related to annual financial reporting and related materials."

[0284] This system not only visualizes complex business knowledge and enhances the educational effect for new employees, but also contributes to the equalization of knowledge among all employees and the correction of information gaps.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The server collects digital data in various formats (such as PDF, Word, Excel, etc.) from the information storage device. At this time, the basic metadata related to the file path and format is input. The server collects this through the network, and as a result, the collected files and their metadata are output.

[0288] Step 2:

[0289] The server analyzes the collected digital data using natural language processing technology. In this step, the entire collected document is provided as input, and important keywords are extracted based on the frequency pattern and context of terms. As a result of this process, a list of identified terms is output. At this time, processes such as splitting the text and tagging the part-of-speech are performed using a natural language processing library.

[0290] Step 3:

[0291] The server uses generative artificial intelligence to generate definitions for the extracted terms. Here, the term list and its surrounding context information are used as input. The server inputs this as a prompt to the generative AI model to generate natural and detailed term definitions in line with the context. As a result of the output, a new definition list for each term is obtained.

[0292] Step 4:

[0293] The server automatically generates a knowledge-sharing platform by aligning the generated definitions and related documents. In this step, the definition list and link collection are entered, and the knowledge base is built. This sets up a template for the knowledge-sharing platform, which is then output as a page accessible to employees.

[0294] Step 5:

[0295] The terminal provides an interface for users to access the knowledge-sharing platform. Here, users enter queries (search terms), and the information returned from the server is visually displayed on the terminal. The interface has a function to instantly display search results, allowing users to quickly find the information they need.

[0296] Step 6:

[0297] Users operate a terminal to look up specific terms and access information provided by the server. The input consists of the user's search terms and interests, while the output displays relevant definitions and materials on the screen. Based on this information, users acquire business knowledge and apply it practically.

[0298] (Application Example 1)

[0299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0300] Efficient knowledge sharing within a company is a crucial element for new employees and transferees to acquire company-specific operational knowledge in a short period of time. However, traditional methods present challenges such as difficulty in finding information and inefficient learning processes. In particular, in physical stores, it is difficult for staff to immediately acquire necessary operational knowledge during busy periods, making it challenging to provide an environment where new staff can immediately contribute as valuable assets. Furthermore, the scattering of numerous documents makes it difficult to quickly access new operational procedures and policies.

[0301] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0302] In this invention, the server includes means for collecting information from network storage, means for analyzing the information by natural language processing technology and extracting terms, and means for generating definitions for the extracted terms using generative artificial intelligence. Thereby, it becomes possible to quickly search for terms and procedures related to store operations and provide procedure manuals and related materials. As a result, new employees and staff can easily acquire business knowledge and utilize it as immediate combat power. Also, by regularly updating the latest information, it becomes possible to continuously provide accurate and useful information.

[0303] "Network storage" refers to a storage medium that stores data and information and can be accessed via a network.

[0304] "Natural language processing technology" is a technology used for a computer to analyze and understand human language.

[0305] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to create new data and information based on existing data.

[0306] "Term collection" refers to a document that summarizes important words and their definitions in a specific area.

[0307] "Related information source" refers to materials and links that provide useful additional information for specific terms and topics.

[0308] "Knowledge portal" refers to a digital platform that aggregates information and knowledge in an easily accessible form for users.

[0309] "User terminal" refers to a device used to access a system or application.

[0310] A "user interface" refers to the screens and operating systems that a user uses to interact with a system.

[0311] A "procedure manual" is a document that describes the steps and methods necessary to perform a specific task or process.

[0312] "References" are additional documents or data used to support specific information or knowledge.

[0313] This invention is a system that enables store staff to efficiently acquire job-related knowledge. The system is realized using a server, user terminals, network storage, and generative artificial intelligence.

[0314] The server first accesses network storage to collect documents related to store operations. This includes procedure manuals and reference materials. The collected data is then processed using natural language processing with programming languages ​​such as Python and their libraries to extract important terms. For example, text analysis is performed using the Python NLTK library.

[0315] Next, the server uses the OpenAI GPT model, a generative artificial intelligence, to automatically generate definitions for the extracted terms. These generated definitions are compiled into a glossary and registered in a knowledge portal. This knowledge portal is built using a web framework such as Django to make it easily accessible to users.

[0316] The user terminal will be developed using React Native. The user of the terminal will be a store staff member who can access the knowledge portal and search for terms of interest. For example, when a newly assigned staff member searches for "how to prepare for a special sale event," they can instantly view relevant procedures and information on past cases through the terminal.

[0317] This system allows new employees and those whose work locations have changed to quickly adapt to their roles and contribute to store operations as immediate assets. Furthermore, newly added information is regularly updated in the system, ensuring it remains up-to-date.

[0318] As a concrete example, enter the following prompt into the generating AI model: "Generate definition of business chart for sale event preparation." This will generate an overview of efficient preparation methods for sale events and add it to the knowledge portal.

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The server retrieves digital documents related to store operations from network storage. It receives connection destination storage information and authentication information as input, and retrieves documents in formats such as PDF and text files as output. This operation involves data transfer over the network.

[0322] Step 2:

[0323] The server analyzes the retrieved documents and extracts important terms. The input is the document's text data, and text analysis is performed using natural language processing techniques (e.g., Python's NLTK). The output is a list of the extracted terms.

[0324] Step 3:

[0325] The server uses a generative artificial intelligence model to generate definitions for extracted terms. It receives a list of terms and related prompt sentences as input and automatically generates definitions using OpenAI's GPT, etc. The output is the definition sentence corresponding to each term. Specifically, queries are sent to the model via an API. Prompt sentences such as "Special sale event preparation, business chart, definition generation" are used.

[0326] Step 4:

[0327] The server builds a knowledge portal using the generated definitions and related information. It receives term definitions and linked reference materials as input and automatically generates portal pages using a web framework such as Django. The output is an accessible knowledge portal. Page layout and link configuration are also handled during this process.

[0328] Step 5:

[0329] The device displays a knowledge portal when accessed by a user. It receives user queries and navigation inputs, and retrieves relevant information from the portal's database. The output is an information page formatted for user readability. Specifically, the user interface is rendered via React Native.

[0330] Step 6:

[0331] Users obtain necessary information from a knowledge portal via their terminals. The input consists of search keywords entered by the user into the terminal, and the output is related information and procedures displayed on the terminal. This information can then be utilized in daily work.

[0332] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0333] This invention adds an emotion engine that recognizes user emotions and optimizes interactions, in addition to a system that collects documents and data within a company using network storage, extracts terms by analyzing them using natural language processing technology, and generates definitions for those terms using generative artificial intelligence. The program's processing is described below from the perspectives of the server, terminal, and user.

[0334] Server Role

[0335] The server accesses the company's network storage to collect all relevant documents, such as project materials and meeting minutes. The collected data is analyzed using natural language processing techniques to extract important terms and phrases. Subsequently, generative artificial intelligence is used to generate definitions for the extracted terms, and a knowledge portal is built along with links to related materials. In addition, an emotion engine analyzes user activity logs and comments to evaluate the user's emotional state.

[0336] Terminal role

[0337] The terminal provides an interface for users to access information using this knowledge portal. When a user searches for a term, the terminal displays definitions and related materials retrieved from the server. Furthermore, a sentiment engine analyzes the user's emotions, and if it determines, for example, that the user has questions or complaints, it automatically presents additional reference materials or FAQs.

[0338] User roles

[0339] Users can use the knowledge portal to learn terminology and processes related to their work. This is particularly useful for new employees and those who have transferred to a new department, as it allows them to quickly access definitions of terms and workflows. Furthermore, by gaining new insights based on their emotional response to the information presented by the system and receiving additional support, they can approach their work with greater confidence.

[0340] Specific example

[0341] For example, if a user wants to learn more about the "client contract process," they search for the term on their device. The server provides the definition and relevant documents from the knowledge portal and records which documents the user has accessed. As the user reads through the documents, the sentiment engine predicts areas of uncertainty or potential interest and presents more detailed guides and past success stories. This allows the user to quickly grasp the key points and proceed smoothly with their workflow.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The server accesses network storage and retrieves all relevant documents within the company. This process involves scanning folders and collecting data based on employee access rights.

[0345] Step 2:

[0346] The server passes the retrieved documents to a natural language processing module, which then begins text analysis. This involves extracting nouns and phrases, analyzing co-occurrence relationships, and listing industry-specific terminology.

[0347] Step 3:

[0348] The server uses generative artificial intelligence to generate definitions for the extracted terms. The generated definitions also include usage examples and related categories.

[0349] Step 4:

[0350] The server builds a knowledge portal based on the generated glossary, including links to related resources. This portal is indexed, enabling efficient information retrieval.

[0351] Step 5:

[0352] The terminal receives a search query from the user and sends the query to the server. The server retrieves the appropriate information from the knowledge portal and returns it to the terminal.

[0353] Step 6:

[0354] The device visually displays search results to the user. During this process, an emotion engine analyzes the user's actions and facial expressions to infer their emotional state.

[0355] Step 7:

[0356] The emotion engine adjusts support information based on the inferred user emotions. For example, if a user is feeling frustrated, it automatically displays links to additional step-by-step guides or tutorial videos.

[0357] Step 8:

[0358] Users obtain information through their devices and request further assistance as needed. This allows users to acquire the knowledge they require quickly and efficiently.

[0359] (Example 2)

[0360] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0361] In today's information society, vast amounts of information are accumulated within companies, and there is a need for methods to efficiently organize this information and quickly access the necessary information. Furthermore, users require intuitive and rapid information-gathering interfaces to resolve business-related questions and uncertainties. However, traditional methods fail to adequately meet these needs, making technological means essential for optimizing information organization and information delivery in accordance with the user's emotional state.

[0362] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0363] In this invention, the server includes means for collecting information from network storage, means for analyzing the information using natural language processing technology and extracting concepts, and means for generating explanations for the extracted concepts using generative artificial intelligence. This makes it possible to efficiently organize information, analyze the user's operation history and statements to evaluate their emotional state, and automatically present additional relevant information when necessary.

[0364] "Network storage" refers to a storage device used to store data that can be accessed over a network.

[0365] "Information" refers to documents and data managed within a company, and includes all kinds of materials related to business operations.

[0366] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract important concepts from information.

[0367] A "concept" refers to important terms and phrases within information extracted using natural language processing technology.

[0368] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates explanatory text and related information based on extracted concepts.

[0369] An "explanation" is a document generated by a generative artificial intelligence system to clarify the meaning and usage of a concept.

[0370] A "server" is a central device that performs the processes of collecting, analyzing, generating, and providing information.

[0371] "Emotional state" refers to the psychological state of a user, inferred from their activity history and statements.

[0372] An "information portal" is a web-based platform for integrating and managing collected information, its accompanying explanations, and related links.

[0373] "Related information" refers to additional materials and support information that are associated with the concept the user searched for.

[0374] This invention is a system designed to streamline and optimize information within a company, utilizing human language analysis and understanding technology and artificial intelligence. The following describes specific embodiments for carrying out this invention.

[0375] The server connects to the company's network storage to collect all relevant information. The hardware used includes large-capacity storage devices on the company network. This information includes project documents, meeting minutes, and other business documents, which are stored by the server in a temporary database.

[0376] The server analyzes the collected information using natural language processing (NLP) techniques. The software used includes open-source NLP libraries and commercial analysis tools. This extracts important concepts and terminology from the information and organizes the resulting data in a structured format.

[0377] Next, the server employs generative artificial intelligence (AI) technology to generate explanations based on the extracted concepts. This AI model, for example, presents background information and examples to help the user deepen their understanding of a particular term. The generative AI model used here is a known natural language generation (NLG) framework.

[0378] The terminal provides an information portal accessible to the user, displaying explanations and related information for the searched concept. The user enters a prompt through the search bar, such as, "Please provide details about the client contract process. Please also provide any relevant documents," and obtains a concept explanation from the server.

[0379] The server also features an emotion engine that analyzes user activity history and comments. This function infers the user's emotional state and adjusts the way information is provided as needed. For example, if the emotion engine determines that a user is dissatisfied with the information, it will automatically display additional reference materials or FAQs on the device.

[0380] This system enables effective information management within a company and optimizes user interaction. Users can rapidly increase their knowledge in their work, resulting in improved work efficiency.

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Step 1:

[0383] The server connects to the company's network storage and collects information, including project documents and meeting minutes. It receives directory information from the network storage as input and stores the collected information in a temporary database as output. Specifically, its operation involves scanning for necessary data files via configured file paths and transferring them to the database in a predetermined format.

[0384] Step 2:

[0385] The server performs analysis on the collected information using natural language processing (NLP) techniques. It uses information stored in a temporary database as input and identifies important concepts and terms as output. The server activates its NLP engine, extracting frequently occurring words and performing contextual analysis to list and tag business-critical concepts.

[0386] Step 3:

[0387] The server uses a generative AI model to generate explanations for identified concepts. It takes a list of key concepts extracted by NLP analysis as input and outputs a generated explanation for each concept. This process involves prompting the generative AI with background information and usage examples related to the extracted concepts, generating explanations in natural language.

[0388] Step 4:

[0389] The terminal allows users to access an information portal and provides explanations and materials related to the searched concepts. It receives prompt input from the user and displays explanations and related materials retrieved from the server on the screen as output. Specifically, it prepares to display relevant information in an optimized interface based on the user's search query.

[0390] Step 5:

[0391] The server runs an emotion engine that analyzes user operation logs and comments to evaluate the user's emotional state. The input is user interaction data, and the output is the emotion evaluation result at that time. The emotion engine analyzes the frequency and duration of operations and the content of comments to determine the user's current psychological state and perform the emotion evaluation.

[0392] Step 6:

[0393] The device presents additional relevant information and FAQs that the user might find interesting, based on the results of the emotion engine. It receives emotion evaluation data as input and displays hints and additional information on the user screen as output. Specifically, it dynamically places recommended content on the device based on the data received from the engine.

[0394] (Application Example 2)

[0395] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0396] In logistics centers and other industries, there is a need to improve employees' ability to quickly understand and respond to necessary information and procedures. However, current systems present information uniformly and do not provide flexible information tailored to the user's emotional state. This can lead to misunderstandings and decreased work efficiency. To solve this problem, a system is needed that provides information tailored to the individual user's situation, thereby achieving more effective learning and work efficiency.

[0397] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0398] In this invention, the server includes means for collecting information from a network storage device, means for analyzing the information using natural language processing technology and extracting terms, means for generating definitions for the extracted terms using generative artificial intelligence, means for automatically generating a knowledge portal that manages links to a glossary and related materials, means for analyzing input data using emotion recognition technology and evaluating the user's emotional state, and means for optimizing the content presented on the knowledge portal according to the user's emotional state. This enables users to receive information in a format that suits their emotional state, thereby promoting understanding of work and enabling efficient work.

[0399] A "network storage device" is a device that stores digital information at a central location and makes it accessible via a network.

[0400] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0401] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate new information and conclusions based on data.

[0402] A "knowledge portal" is an information platform that aggregates information and links and provides them in a format that is easily accessible to users.

[0403] "Emotion recognition technology" is a technology that analyzes user input data to infer a person's emotional state.

[0404] A "user terminal" is a device used by a user to display or manipulate information.

[0405] A "glossary" is a document that systematically organizes terms and their definitions related to a specific field.

[0406] "Related materials" are supplementary sources of information associated with a particular piece of information or topic.

[0407] "Evaluation" in emotion recognition technology refers to the process of observing and judging the user's emotional state.

[0408] "Optimization" is the process of adjusting the way information is presented and its content according to the user's emotional state.

[0409] This invention comprises a system designed to streamline the collection, analysis, and provision of information in a logistics center, and to promote employees' understanding of their work.

[0410] The server is connected to network storage and collects relevant information within the logistics center. The collected information is analyzed using natural language processing technology, and important terms are extracted. In this process, an AI model using a specific algorithm operates to accurately grasp the meaning of the information. Generative artificial intelligence generates appropriate definitions for these terms, and these are aggregated in a knowledge portal.

[0411] Emotion recognition technology analyzes user input behavior through the user's terminal and evaluates the user's emotional state. The emotion recognition engine used here is designed to infer emotions from the user's operation history and voice data. Based on this evaluation result, the information provided is optimized according to the user's state.

[0412] The terminal enables users to access a knowledge portal and displays information customized based on their emotional state. Users can quickly grasp terminology and procedures related to logistics operations using their smart devices. Eye-tracking and voice recognition technologies are employed to create an interface that reduces user burden.

[0413] As a concrete example, consider a scenario where a new employee tries to understand the "inventory management process." The user searches for "inventory management process" through a terminal, and the terminal provides a definition and related materials generated by a generative AI model. While the user reviews the displayed information, an emotion engine senses signs of their interest and confusion, and presents additional FAQs and learning guides. In this way, the user can efficiently acquire information and proceed with their work smoothly.

[0414] An example of a prompt would be, "Please provide details of the inventory management process and related practical guides. Also, please provide additional, sentiment-based information, along with recent success stories." This ensures the system provides optimal information tailored to the user's needs.

[0415] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0416] Step 1:

[0417] The server connects to network storage and collects documents and data within the logistics center. The input is raw data from the network storage, and the output is a dataset ready for analysis. During this process, a data collection script is periodically executed to filter the target data.

[0418] Step 2:

[0419] The server analyzes the collected data using natural language processing techniques and extracts important terms. The input is the collected data, and the output is a list of extracted terms. In this step, an NLP engine is running to perform tokenization and part-of-speech tagging, and specific algorithms are used to identify terms.

[0420] Step 3:

[0421] The server uses generative artificial intelligence to generate definitions for the extracted terms. The input is a list of terms, and the output is a definition for each term. A generative AI model is applied to this process, and the information related to each term is consistently linked by the AI ​​algorithm.

[0422] Step 4:

[0423] The server automatically generates a knowledge portal and manages the glossary and links to related resources. Input is term definitions and related resources, and output is a user-accessible knowledge portal. In this step, a database management system is operational, aggregating information at the portal's front-end.

[0424] Step 5:

[0425] Emotion recognition technology allows the device to analyze user input data and evaluate their emotional state. User input is used as input data, and an emotion evaluation result is generated as output. Emotions are inferred by analyzing the user's voice tone and operation speed using sensors and microphones.

[0426] Step 6:

[0427] Based on the user's emotional state, the device optimizes the content presented in the knowledge portal. Input consists of emotional assessment results and knowledge portal data, while output is a customized information display. FAQs and reference materials are automatically selected and displayed according to the user's needs.

[0428] Step 7:

[0429] Users access a knowledge portal using smart devices to obtain information related to logistics operations. Input is the user's search query, and output is the requested information and reference materials. Users can smoothly access the necessary information through voice commands and touch operations.

[0430] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0431] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0432] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0433] [Third Embodiment]

[0434] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0435] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0436] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0437] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0438] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0439] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0440] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0441] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0442] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0443] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0444] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0445] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0446] This invention presents an embodiment of a system that promotes knowledge sharing within a company, particularly enabling new employees and transferees to efficiently acquire business knowledge. The program's processing is described below through the roles of the server, terminal, and user.

[0447] Server Role

[0448] The server first connects to network storage and collects various digital documents within the company. This includes a variety of file formats such as PDF, Word, and Excel. The collected data is stored in a database and prepared for further analysis. The server then uses natural language processing technology to extract specific terms from this data. For the extracted terms, generative artificial intelligence is used to automatically generate definitions, and a glossary is compiled based on these definitions. The generated glossary and related materials are built as a knowledge portal and managed in a way that is accessible to all employees.

[0449] Terminal role

[0450] The terminal provides an interface for users to search and refer to information through this system. When a user searches for a specific term or topic, the terminal interacts with the server to quickly display search results. The terminal is equipped with navigation functions and filtering options to improve usability, allowing users to quickly access terms and materials of interest.

[0451] User roles

[0452] The users are primarily new employees, such as recent graduates and those changing jobs, who learn company terminology and documents through this system. When a user operates a terminal to look up a specific term, the knowledge portal presents the relevant information and related documents. This allows users to acquire company-specific knowledge in a short period of time and contribute to their work as immediate assets.

[0453] Specific example

[0454] For example, if a new employee wants to learn about the contents of an "annual financial report," they can search for the term using their terminal. The server will then provide a page containing definitions and historical documents related to the "annual financial report." This allows the new employee to understand the process and historical background of the "annual financial report" and gain the knowledge to better perform their role.

[0455] The following describes the processing flow.

[0456] Step 1:

[0457] The server connects to network storage and collects digital documents stored company-wide through specified folder paths. This includes folders for specific projects, meeting minutes folders for regular meetings, and shared folders for each department.

[0458] Step 2:

[0459] The server begins analyzing the collected document data. Using natural language processing techniques, it tokenizes words in the text and performs morphological analysis to extract frequently occurring words and phrases. For nouns and technical terms, it records their frequency of appearance.

[0460] Step 3:

[0461] The server then uses generative artificial intelligence on the extracted terms to generate definitions for each term. The generated definitions reflect the context in which the terms are used and include practical examples.

[0462] Step 4:

[0463] The server combines the generated terminology definitions with related documentation information to automatically build a knowledge portal. This portal consists of a page for each term, containing links to related resources and supplementary information.

[0464] Step 5:

[0465] The terminal provides access to a portal interface, allowing users to easily search for the information they need. Users can quickly access relevant terminology definitions and resources by entering keywords.

[0466] Step 6:

[0467] Users can use their devices to browse the knowledge portal and find definitions and related information for terms they are interested in. Furthermore, when their questions are answered, they can submit feedback through the interface, which allows the server to improve the accuracy of the system.

[0468] Step 7:

[0469] The server periodically retrieves new data from network storage, analyzes it, and updates it repeatedly. This keeps the company's knowledge base up-to-date, ensuring that all employees always have access to the latest information.

[0470] (Example 1)

[0471] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0472] A major challenge is the lack of appropriate systems for sharing and efficiently acquiring knowledge within companies. In particular, there is a need for mechanisms that enable new employees and those changing jobs to effectively acquire job-related knowledge in a short period of time.

[0473] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0474] In this invention, the server includes means for collecting information from an information storage device, means for analyzing the information using natural language processing technology and extracting terms, and means for generating definitions for the extracted terms using generative artificial intelligence. This enables the centralization of information within a company, allowing new employees and transferees to acquire knowledge efficiently.

[0475] An "information storage device" is a device used to store and manage various forms of digital data that exist within a network.

[0476] "Natural language processing technology" is a field of technology that enables computers to understand and analyze human language, and is used to extract keywords and context from data.

[0477] "Generative artificial intelligence" refers to artificial intelligence systems that have the ability to generate new information and definitions, and are used to dynamically create content based on user queries.

[0478] A "knowledge sharing platform" is a system designed to share and make accessible the knowledge and information accumulated within a company across the entire organization.

[0479] A "user terminal" is a computer device used by users to search for and refer to information through a knowledge-sharing platform.

[0480] A "user interface" is a mechanism that provides screens and operating methods for users to interact directly with the system.

[0481] This invention relates to a system for streamlining knowledge sharing within a company. Specific embodiments are described below.

[0482] Server Embodiment

[0483] The server connects to an information storage device to collect various forms of digital data (e.g., PDF, Word, Excel) stored within the company. This data is analyzed using natural language processing (NLP) techniques to extract important terms related to business operations. For natural language processing, NLP libraries such as spaCy and NLTK can be used. For the extracted terms, definitions are generated using generative artificial intelligence (e.g., the GPT model). This generation process ensures that natural-sounding sentences are obtained, taking into account past digital data and context. As a result, a glossary is constructed and integrated into a knowledge-sharing platform, streamlining information access throughout the organization.

[0484] Terminal embodiment

[0485] The terminal provides an interface for users to access the knowledge-sharing platform. When a user enters a specific term into the search box, the terminal retrieves information from the server and displays the results on the screen. The user interface includes navigation functions and filtering options, designed to allow users to access information intuitively.

[0486] User Embodiment

[0487] Users are new employees, such as new hires or transferees, who acquire the necessary work-related knowledge in a short period of time through this system. For example, when a user searches for a specific term such as "annual financial report" on the knowledge-sharing platform, the definition of that term and related materials are displayed. This allows users to understand the company's unique business processes and terminology, enabling them to adapt to their work more smoothly.

[0488] Example of a prompt

[0489] "Please provide definitions of terms related to annual financial reporting and related materials."

[0490] This system not only visualizes complex business knowledge and enhances the effectiveness of training new employees, but also contributes to standardizing knowledge among all employees and correcting information disparities.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] The server collects digital data in various formats (PDF, Word, Excel, etc.) from information storage devices. The input consists of basic metadata such as file paths and format information. The server collects this data via the network, and the collected files and their metadata are output as a result.

[0494] Step 2:

[0495] The server analyzes the collected digital data using natural language processing techniques. In this step, the entire collected document is provided as input, and important keywords are extracted based on frequent terminology patterns and context. As a result of this processing, a list of identified terms is output. During this process, natural language processing libraries are used to segment the text and tag parts of speech.

[0496] Step 3:

[0497] The server uses generative artificial intelligence to generate definitions for the extracted terms. Here, the term list and its surrounding contextual information are used as input. The server prompts the generative AI model with this information to generate contextually relevant, natural, and detailed term definitions. The output results in a new definition list for each term.

[0498] Step 4:

[0499] The server automatically generates a knowledge-sharing platform by aligning the generated definitions and related documents. In this step, the definition list and link collection are entered, and the knowledge base is built. This sets up a template for the knowledge-sharing platform, which is then output as a page accessible to employees.

[0500] Step 5:

[0501] The terminal provides an interface for users to access the knowledge-sharing platform. Here, users enter queries (search terms), and the information returned from the server is visually displayed on the terminal. The interface has a function to instantly display search results, allowing users to quickly find the information they need.

[0502] Step 6:

[0503] Users operate a terminal to look up specific terms and access information provided by the server. The input consists of the user's search terms and interests, while the output displays relevant definitions and materials on the screen. Based on this information, users acquire business knowledge and apply it practically.

[0504] (Application Example 1)

[0505] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0506] Efficient knowledge sharing within a company is a crucial element for new employees and transferees to acquire company-specific operational knowledge in a short period of time. However, traditional methods present challenges such as difficulty in finding information and inefficient learning processes. In particular, in physical stores, it is difficult for staff to immediately acquire necessary operational knowledge during busy periods, making it challenging to provide an environment where new staff can immediately contribute as valuable assets. Furthermore, the scattering of numerous documents makes it difficult to quickly access new operational procedures and policies.

[0507] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0508] In this invention, the server includes means for collecting information from network storage, means for analyzing the information using natural language processing technology and extracting terms, and means for generating definitions for the extracted terms using generative artificial intelligence. This makes it possible to quickly search for terms and procedures related to store operations and provide procedure manuals and related materials. As a result, new employees and staff can easily acquire business knowledge and be utilized as immediate contributors. Furthermore, by regularly updating the latest information, it becomes possible to continuously provide accurate and useful information.

[0509] "Network storage" refers to a storage medium that stores data and information and is accessible via a network.

[0510] "Natural language processing technology" is a technology used by computers to analyze and understand human language.

[0511] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to create new data and information based on existing data.

[0512] A glossary is a document that compiles important terms and their definitions within a specific field.

[0513] "Related information sources" are materials or links that provide useful additional information on a particular term or topic.

[0514] A "knowledge portal" is a digital platform that aggregates information and knowledge in a way that is easily accessible to users.

[0515] A "user terminal" refers to a device used to access a system or application.

[0516] A "user interface" refers to the screens and operating systems that a user uses to interact with a system.

[0517] A "procedure manual" is a document that describes the steps and methods necessary to perform a specific task or process.

[0518] "References" are additional documents or data used to support specific information or knowledge.

[0519] This invention is a system that enables store staff to efficiently acquire job-related knowledge. The system is realized using a server, user terminals, network storage, and generative artificial intelligence.

[0520] The server first accesses network storage to collect documents related to store operations. This includes procedure manuals and reference materials. The collected data is then processed using natural language processing with programming languages ​​such as Python and their libraries to extract important terms. For example, text analysis is performed using the Python NLTK library.

[0521] Next, the server uses the OpenAI GPT model, a generative artificial intelligence, to automatically generate definitions for the extracted terms. These generated definitions are compiled into a glossary and registered in a knowledge portal. This knowledge portal is built using a web framework such as Django to make it easily accessible to users.

[0522] The user terminal will be developed using React Native. The user of the terminal will be a store staff member who can access the knowledge portal and search for terms of interest. For example, when a newly assigned staff member searches for "how to prepare for a special sale event," they can instantly view relevant procedures and information on past cases through the terminal.

[0523] This system allows new employees and those whose work locations have changed to quickly adapt to their roles and contribute to store operations as immediate assets. Furthermore, newly added information is regularly updated in the system, ensuring it remains up-to-date.

[0524] As a concrete example, enter the following prompt into the generating AI model: "Generate definition of business chart for sale event preparation." This will generate an overview of efficient preparation methods for sale events and add it to the knowledge portal.

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] The server retrieves digital documents related to store operations from network storage. It receives connection destination storage information and authentication information as input, and retrieves documents in formats such as PDF and text files as output. This operation involves data transfer over the network.

[0528] Step 2:

[0529] The server analyzes the retrieved documents and extracts important terms. The input is the document's text data, and text analysis is performed using natural language processing techniques (e.g., Python's NLTK). The output is a list of the extracted terms.

[0530] Step 3:

[0531] The server uses a generative artificial intelligence model to generate definitions for extracted terms. It receives a list of terms and related prompt sentences as input and automatically generates definitions using OpenAI's GPT, etc. The output is the definition sentence corresponding to each term. Specifically, queries are sent to the model via an API. Prompt sentences such as "Special sale event preparation, business chart, definition generation" are used.

[0532] Step 4:

[0533] The server builds a knowledge portal using the generated definitions and related information. It receives term definitions and linked reference materials as input and automatically generates portal pages using a web framework such as Django. The output is an accessible knowledge portal. Page layout and link configuration are also handled during this process.

[0534] Step 5:

[0535] The device displays a knowledge portal when accessed by a user. It receives user queries and navigation inputs, and retrieves relevant information from the portal's database. The output is an information page formatted for user readability. Specifically, the user interface is rendered via React Native.

[0536] Step 6:

[0537] Users obtain necessary information from a knowledge portal via their terminals. The input consists of search keywords entered by the user into the terminal, and the output is related information and procedures displayed on the terminal. This information can then be utilized in daily work.

[0538] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0539] This invention adds an emotion engine that recognizes user emotions and optimizes interactions, in addition to a system that collects documents and data within a company using network storage, extracts terms by analyzing them using natural language processing technology, and generates definitions for those terms using generative artificial intelligence. The program's processing is described below from the perspectives of the server, terminal, and user.

[0540] Server Role

[0541] The server accesses the company's network storage to collect all relevant documents, such as project materials and meeting minutes. The collected data is analyzed using natural language processing techniques to extract important terms and phrases. Subsequently, generative artificial intelligence is used to generate definitions for the extracted terms, and a knowledge portal is built along with links to related materials. In addition, an emotion engine analyzes user activity logs and comments to evaluate the user's emotional state.

[0542] Terminal role

[0543] The terminal provides an interface for users to access information using this knowledge portal. When a user searches for a term, the terminal displays definitions and related materials retrieved from the server. Furthermore, a sentiment engine analyzes the user's emotions, and if it determines, for example, that the user has questions or complaints, it automatically presents additional reference materials or FAQs.

[0544] User roles

[0545] Users can use the knowledge portal to learn terminology and processes related to their work. This is particularly useful for new employees and those who have transferred to a new department, as it allows them to quickly access definitions of terms and workflows. Furthermore, by gaining new insights based on their emotional response to the information presented by the system and receiving additional support, they can approach their work with greater confidence.

[0546] Specific example

[0547] For example, if a user wants to learn more about the "client contract process," they search for the term on their device. The server provides the definition and relevant documents from the knowledge portal and records which documents the user has accessed. As the user reads through the documents, the sentiment engine predicts areas of uncertainty or potential interest and presents more detailed guides and past success stories. This allows the user to quickly grasp the key points and proceed smoothly with their workflow.

[0548] The following describes the processing flow.

[0549] Step 1:

[0550] The server accesses network storage and retrieves all relevant documents within the company. This process involves scanning folders and collecting data based on employee access rights.

[0551] Step 2:

[0552] The server passes the retrieved documents to a natural language processing module, which then begins text analysis. This involves extracting nouns and phrases, analyzing co-occurrence relationships, and listing industry-specific terminology.

[0553] Step 3:

[0554] The server uses generative artificial intelligence to generate definitions for the extracted terms. The generated definitions also include usage examples and related categories.

[0555] Step 4:

[0556] The server builds a knowledge portal based on the generated glossary, including links to related resources. This portal is indexed, enabling efficient information retrieval.

[0557] Step 5:

[0558] The terminal receives a search query from the user and sends the query to the server. The server retrieves the appropriate information from the knowledge portal and returns it to the terminal.

[0559] Step 6:

[0560] The device visually displays search results to the user. During this process, an emotion engine analyzes the user's actions and facial expressions to infer their emotional state.

[0561] Step 7:

[0562] The emotion engine adjusts support information based on the inferred user emotions. For example, if a user is feeling frustrated, it automatically displays links to additional step-by-step guides or tutorial videos.

[0563] Step 8:

[0564] Users obtain information through their devices and request further assistance as needed. This allows users to acquire the knowledge they require quickly and efficiently.

[0565] (Example 2)

[0566] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0567] In today's information society, vast amounts of information are accumulated within companies, and there is a need for methods to efficiently organize this information and quickly access the necessary information. Furthermore, users require intuitive and rapid information-gathering interfaces to resolve business-related questions and uncertainties. However, traditional methods fail to adequately meet these needs, making technological means essential for optimizing information organization and information delivery in accordance with the user's emotional state.

[0568] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0569] In this invention, the server includes means for collecting information from network storage, means for analyzing the information using natural language processing technology and extracting concepts, and means for generating explanations for the extracted concepts using generative artificial intelligence. This makes it possible to efficiently organize information, analyze the user's operation history and statements to evaluate their emotional state, and automatically present additional relevant information when necessary.

[0570] "Network storage" refers to a storage device used to store data that can be accessed over a network.

[0571] "Information" refers to documents and data managed within a company, and includes all kinds of materials related to business operations.

[0572] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract important concepts from information.

[0573] A "concept" refers to important terms and phrases within information extracted using natural language processing technology.

[0574] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates explanatory text and related information based on extracted concepts.

[0575] An "explanation" is a document generated by a generative artificial intelligence system to clarify the meaning and usage of a concept.

[0576] A "server" is a central device that performs the processes of collecting, analyzing, generating, and providing information.

[0577] "Emotional state" refers to the psychological state of a user, inferred from their activity history and statements.

[0578] An "information portal" is a web-based platform for integrating and managing collected information, its accompanying explanations, and related links.

[0579] "Related information" refers to additional materials and support information that are associated with the concept the user searched for.

[0580] This invention is a system designed to streamline and optimize information within a company, utilizing human language analysis and understanding technology and artificial intelligence. The following describes specific embodiments for carrying out this invention.

[0581] The server connects to the company's network storage to collect all relevant information. The hardware used includes large-capacity storage devices on the company network. This information includes project documents, meeting minutes, and other business documents, which are stored by the server in a temporary database.

[0582] The server analyzes the collected information using natural language processing (NLP) techniques. The software used includes open-source NLP libraries and commercial analysis tools. This extracts important concepts and terminology from the information and organizes the resulting data in a structured format.

[0583] Next, the server employs generative artificial intelligence (AI) technology to generate explanations based on the extracted concepts. This AI model, for example, presents background information and examples to help the user deepen their understanding of a particular term. The generative AI model used here is a known natural language generation (NLG) framework.

[0584] The terminal provides an information portal accessible to the user, displaying explanations and related information for the searched concept. The user enters a prompt through the search bar, such as, "Please provide details about the client contract process. Please also provide any relevant documents," and obtains a concept explanation from the server.

[0585] The server also features an emotion engine that analyzes user activity history and comments. This function infers the user's emotional state and adjusts the way information is provided as needed. For example, if the emotion engine determines that a user is dissatisfied with the information, it will automatically display additional reference materials or FAQs on the device.

[0586] This system enables effective information management within a company and optimizes user interaction. Users can rapidly increase their knowledge in their work, resulting in improved work efficiency.

[0587] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0588] Step 1:

[0589] The server connects to the company's network storage and collects information, including project documents and meeting minutes. It receives directory information from the network storage as input and stores the collected information in a temporary database as output. Specifically, its operation involves scanning for necessary data files via configured file paths and transferring them to the database in a predetermined format.

[0590] Step 2:

[0591] The server performs analysis on the collected information using natural language processing (NLP) techniques. It uses information stored in a temporary database as input and identifies important concepts and terms as output. The server activates its NLP engine, extracting frequently occurring words and performing contextual analysis to list and tag business-critical concepts.

[0592] Step 3:

[0593] The server uses a generative AI model to generate explanations for identified concepts. It takes a list of key concepts extracted by NLP analysis as input and outputs a generated explanation for each concept. This process involves prompting the generative AI with background information and usage examples related to the extracted concepts, generating explanations in natural language.

[0594] Step 4:

[0595] The terminal allows users to access an information portal and provides explanations and materials related to the searched concepts. It receives prompt input from the user and displays explanations and related materials retrieved from the server on the screen as output. Specifically, it prepares to display relevant information in an optimized interface based on the user's search query.

[0596] Step 5:

[0597] The server runs an emotion engine that analyzes user operation logs and comments to evaluate the user's emotional state. The input is user interaction data, and the output is the emotion evaluation result at that time. The emotion engine analyzes the frequency and duration of operations and the content of comments to determine the user's current psychological state and perform the emotion evaluation.

[0598] Step 6:

[0599] The device presents additional relevant information and FAQs that the user might find interesting, based on the results of the emotion engine. It receives emotion evaluation data as input and displays hints and additional information on the user screen as output. Specifically, it dynamically places recommended content on the device based on the data received from the engine.

[0600] (Application Example 2)

[0601] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0602] In logistics centers and other industries, there is a need to improve employees' ability to quickly understand and respond to necessary information and procedures. However, current systems present information uniformly and do not provide flexible information tailored to the user's emotional state. This can lead to misunderstandings and decreased work efficiency. To solve this problem, a system is needed that provides information tailored to the individual user's situation, thereby achieving more effective learning and work efficiency.

[0603] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0604] In this invention, the server includes means for collecting information from a network storage device, means for analyzing the information using natural language processing technology and extracting terms, means for generating definitions for the extracted terms using generative artificial intelligence, means for automatically generating a knowledge portal that manages links to a glossary and related materials, means for analyzing input data using emotion recognition technology and evaluating the user's emotional state, and means for optimizing the content presented on the knowledge portal according to the user's emotional state. This enables users to receive information in a format that suits their emotional state, thereby promoting understanding of work and enabling efficient work.

[0605] A "network storage device" is a device that stores digital information at a central location and makes it accessible via a network.

[0606] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0607] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate new information and conclusions based on data.

[0608] A "knowledge portal" is an information platform that aggregates information and links and provides them in a format that is easily accessible to users.

[0609] "Emotion recognition technology" is a technology that analyzes user input data to infer a person's emotional state.

[0610] A "user terminal" is a device used by a user to display or manipulate information.

[0611] A "glossary" is a document that systematically organizes terms and their definitions related to a specific field.

[0612] "Related materials" are supplementary sources of information associated with a particular piece of information or topic.

[0613] "Evaluation" in emotion recognition technology refers to the process of observing and judging the user's emotional state.

[0614] "Optimization" is the process of adjusting the way information is presented and its content according to the user's emotional state.

[0615] This invention comprises a system designed to streamline the collection, analysis, and provision of information in a logistics center, and to promote employees' understanding of their work.

[0616] The server is connected to network storage and collects relevant information within the logistics center. The collected information is analyzed using natural language processing technology, and important terms are extracted. In this process, an AI model using a specific algorithm operates to accurately grasp the meaning of the information. Generative artificial intelligence generates appropriate definitions for these terms, and these are aggregated in a knowledge portal.

[0617] Emotion recognition technology analyzes user input behavior through the user's terminal and evaluates the user's emotional state. The emotion recognition engine used here is designed to infer emotions from the user's operation history and voice data. Based on this evaluation result, the information provided is optimized according to the user's state.

[0618] The terminal enables users to access a knowledge portal and displays information customized based on their emotional state. Users can quickly grasp terminology and procedures related to logistics operations using their smart devices. Eye-tracking and voice recognition technologies are employed to create an interface that reduces user burden.

[0619] As a concrete example, consider a scenario where a new employee tries to understand the "inventory management process." The user searches for "inventory management process" through a terminal, and the terminal provides a definition and related materials generated by a generative AI model. While the user reviews the displayed information, an emotion engine senses signs of their interest and confusion, and presents additional FAQs and learning guides. In this way, the user can efficiently acquire information and proceed with their work smoothly.

[0620] An example of a prompt would be, "Please provide details of the inventory management process and related practical guides. Also, please provide additional, sentiment-based information, along with recent success stories." This ensures the system provides optimal information tailored to the user's needs.

[0621] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0622] Step 1:

[0623] The server connects to network storage and collects documents and data within the logistics center. The input is raw data from the network storage, and the output is a dataset ready for analysis. During this process, a data collection script is periodically executed to filter the target data.

[0624] Step 2:

[0625] The server analyzes the collected data using natural language processing techniques and extracts important terms. The input is the collected data, and the output is a list of extracted terms. In this step, an NLP engine is running to perform tokenization and part-of-speech tagging, and specific algorithms are used to identify terms.

[0626] Step 3:

[0627] The server uses generative artificial intelligence to generate definitions for the extracted terms. The input is a list of terms, and the output is a definition for each term. A generative AI model is applied to this process, and the information related to each term is consistently linked by the AI ​​algorithm.

[0628] Step 4:

[0629] The server automatically generates a knowledge portal and manages the glossary and links to related resources. Input is term definitions and related resources, and output is a user-accessible knowledge portal. In this step, a database management system is operational, aggregating information at the portal's front-end.

[0630] Step 5:

[0631] Emotion recognition technology allows the device to analyze user input data and evaluate their emotional state. User input is used as input data, and an emotion evaluation result is generated as output. Emotions are inferred by analyzing the user's voice tone and operation speed using sensors and microphones.

[0632] Step 6:

[0633] Based on the user's emotional state, the device optimizes the content presented in the knowledge portal. Input consists of emotional assessment results and knowledge portal data, while output is a customized information display. FAQs and reference materials are automatically selected and displayed according to the user's needs.

[0634] Step 7:

[0635] Users access a knowledge portal using smart devices to obtain information related to logistics operations. Input is the user's search query, and output is the requested information and reference materials. Users can smoothly access the necessary information through voice commands and touch operations.

[0636] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0637] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0638] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0639] [Fourth Embodiment]

[0640] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0641] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0642] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0643] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0644] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0645] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0646] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0647] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0648] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0649] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0650] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0651] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0652] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] This invention presents an embodiment of a system that promotes knowledge sharing within a company, particularly enabling new employees and transferees to efficiently acquire business knowledge. The program's processing is described below through the roles of the server, terminal, and user.

[0654] Server Role

[0655] The server first connects to network storage and collects various digital documents within the company. This includes a variety of file formats such as PDF, Word, and Excel. The collected data is stored in a database and prepared for further analysis. The server then uses natural language processing technology to extract specific terms from this data. For the extracted terms, generative artificial intelligence is used to automatically generate definitions, and a glossary is compiled based on these definitions. The generated glossary and related materials are built as a knowledge portal and managed in a way that is accessible to all employees.

[0656] Terminal role

[0657] The terminal provides an interface for users to search and refer to information through this system. When a user searches for a specific term or topic, the terminal interacts with the server to quickly display search results. The terminal is equipped with navigation functions and filtering options to improve usability, allowing users to quickly access terms and materials of interest.

[0658] User roles

[0659] The users are primarily new employees, such as recent graduates and those changing jobs, who learn company terminology and documents through this system. When a user operates a terminal to look up a specific term, the knowledge portal presents the relevant information and related documents. This allows users to acquire company-specific knowledge in a short period of time and contribute to their work as immediate assets.

[0660] Specific example

[0661] For example, if a new employee wants to learn about the contents of an "annual financial report," they can search for the term using their terminal. The server will then provide a page containing definitions and historical documents related to the "annual financial report." This allows the new employee to understand the process and historical background of the "annual financial report" and gain the knowledge to better perform their role.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The server connects to network storage and collects digital documents stored company-wide through specified folder paths. This includes folders for specific projects, meeting minutes folders for regular meetings, and shared folders for each department.

[0665] Step 2:

[0666] The server begins analyzing the collected document data. Using natural language processing techniques, it tokenizes words in the text and performs morphological analysis to extract frequently occurring words and phrases. For nouns and technical terms, it records their frequency of appearance.

[0667] Step 3:

[0668] The server then uses generative artificial intelligence on the extracted terms to generate definitions for each term. The generated definitions reflect the context in which the terms are used and include practical examples.

[0669] Step 4:

[0670] The server combines the generated terminology definitions with related documentation information to automatically build a knowledge portal. This portal consists of a page for each term, containing links to related resources and supplementary information.

[0671] Step 5:

[0672] The terminal provides access to a portal interface, allowing users to easily search for the information they need. Users can quickly access relevant terminology definitions and resources by entering keywords.

[0673] Step 6:

[0674] Users can use their devices to browse the knowledge portal and find definitions and related information for terms they are interested in. Furthermore, when their questions are answered, they can submit feedback through the interface, which allows the server to improve the accuracy of the system.

[0675] Step 7:

[0676] The server periodically retrieves new data from network storage, analyzes it, and updates it repeatedly. This keeps the company's knowledge base up-to-date, ensuring that all employees always have access to the latest information.

[0677] (Example 1)

[0678] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] A major challenge is the lack of appropriate systems for sharing and efficiently acquiring knowledge within companies. In particular, there is a need for mechanisms that enable new employees and those changing jobs to effectively acquire job-related knowledge in a short period of time.

[0680] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0681] In this invention, the server includes means for collecting information from an information storage device, means for analyzing the information using natural language processing technology and extracting terms, and means for generating definitions for the extracted terms using generative artificial intelligence. This enables the centralization of information within a company, allowing new employees and transferees to acquire knowledge efficiently.

[0682] An "information storage device" is a device used to store and manage various forms of digital data that exist within a network.

[0683] "Natural language processing technology" is a field of technology that enables computers to understand and analyze human language, and is used to extract keywords and context from data.

[0684] "Generative artificial intelligence" refers to artificial intelligence systems that have the ability to generate new information and definitions, and are used to dynamically create content based on user queries.

[0685] A "knowledge sharing platform" is a system designed to share and make accessible the knowledge and information accumulated within a company across the entire organization.

[0686] A "user terminal" is a computer device used by users to search for and refer to information through a knowledge-sharing platform.

[0687] A "user interface" is a mechanism that provides screens and operating methods for users to interact directly with the system.

[0688] This invention relates to a system for streamlining knowledge sharing within a company. Specific embodiments are described below.

[0689] Server Embodiment

[0690] The server connects to an information storage device to collect various forms of digital data (e.g., PDF, Word, Excel) stored within the company. This data is analyzed using natural language processing (NLP) techniques to extract important terms related to business operations. For natural language processing, NLP libraries such as spaCy and NLTK can be used. For the extracted terms, definitions are generated using generative artificial intelligence (e.g., the GPT model). This generation process ensures that natural-sounding sentences are obtained, taking into account past digital data and context. As a result, a glossary is constructed and integrated into a knowledge-sharing platform, streamlining information access throughout the organization.

[0691] Terminal embodiment

[0692] The terminal provides an interface for users to access the knowledge-sharing platform. When a user enters a specific term into the search box, the terminal retrieves information from the server and displays the results on the screen. The user interface includes navigation functions and filtering options, designed to allow users to access information intuitively.

[0693] User Embodiment

[0694] Users are new employees, such as new hires or transferees, who acquire the necessary work-related knowledge in a short period of time through this system. For example, when a user searches for a specific term such as "annual financial report" on the knowledge-sharing platform, the definition of that term and related materials are displayed. This allows users to understand the company's unique business processes and terminology, enabling them to adapt to their work more smoothly.

[0695] Example of a prompt

[0696] "Please provide definitions of terms related to annual financial reporting and related materials."

[0697] This system not only visualizes complex business knowledge and enhances the effectiveness of training new employees, but also contributes to standardizing knowledge among all employees and correcting information disparities.

[0698] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0699] Step 1:

[0700] The server collects digital data in various formats (PDF, Word, Excel, etc.) from information storage devices. The input consists of basic metadata such as file paths and format information. The server collects this data via the network, and the collected files and their metadata are output as a result.

[0701] Step 2:

[0702] The server analyzes the collected digital data using natural language processing techniques. In this step, the entire collected document is provided as input, and important keywords are extracted based on frequent terminology patterns and context. As a result of this processing, a list of identified terms is output. During this process, natural language processing libraries are used to segment the text and tag parts of speech.

[0703] Step 3:

[0704] The server uses generative artificial intelligence to generate definitions for the extracted terms. Here, the term list and its surrounding contextual information are used as input. The server prompts the generative AI model with this information to generate contextually relevant, natural, and detailed term definitions. The output results in a new definition list for each term.

[0705] Step 4:

[0706] The server automatically generates a knowledge-sharing platform by aligning the generated definitions and related documents. In this step, the definition list and link collection are entered, and the knowledge base is built. This sets up a template for the knowledge-sharing platform, which is then output as a page accessible to employees.

[0707] Step 5:

[0708] The terminal provides an interface for users to access the knowledge-sharing platform. Here, users enter queries (search terms), and the information returned from the server is visually displayed on the terminal. The interface has a function to instantly display search results, allowing users to quickly find the information they need.

[0709] Step 6:

[0710] Users operate a terminal to look up specific terms and access information provided by the server. The input consists of the user's search terms and interests, while the output displays relevant definitions and materials on the screen. Based on this information, users acquire business knowledge and apply it practically.

[0711] (Application Example 1)

[0712] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] Efficient knowledge sharing within a company is a crucial element for new employees and transferees to acquire company-specific operational knowledge in a short period of time. However, traditional methods present challenges such as difficulty in finding information and inefficient learning processes. In particular, in physical stores, it is difficult for staff to immediately acquire necessary operational knowledge during busy periods, making it challenging to provide an environment where new staff can immediately contribute as valuable assets. Furthermore, the scattering of numerous documents makes it difficult to quickly access new operational procedures and policies.

[0714] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0715] In this invention, the server includes means for collecting information from network storage, means for analyzing the information using natural language processing technology and extracting terms, and means for generating definitions for the extracted terms using generative artificial intelligence. This makes it possible to quickly search for terms and procedures related to store operations and provide procedure manuals and related materials. As a result, new employees and staff can easily acquire business knowledge and be utilized as immediate contributors. Furthermore, by regularly updating the latest information, it becomes possible to continuously provide accurate and useful information.

[0716] "Network storage" refers to a storage medium that stores data and information and is accessible via a network.

[0717] "Natural language processing technology" is a technology used by computers to analyze and understand human language.

[0718] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to create new data and information based on existing data.

[0719] A glossary is a document that compiles important terms and their definitions within a specific field.

[0720] "Related information sources" are materials or links that provide useful additional information on a particular term or topic.

[0721] A "knowledge portal" is a digital platform that aggregates information and knowledge in a way that is easily accessible to users.

[0722] A "user terminal" refers to a device used to access a system or application.

[0723] A "user interface" refers to the screens and operating systems that a user uses to interact with a system.

[0724] A "procedure manual" is a document that describes the steps and methods necessary to perform a specific task or process.

[0725] "References" are additional documents or data used to support specific information or knowledge.

[0726] This invention is a system that enables store staff to efficiently acquire job-related knowledge. The system is realized using a server, user terminals, network storage, and generative artificial intelligence.

[0727] The server first accesses network storage to collect documents related to store operations. This includes procedure manuals and reference materials. The collected data is then processed using natural language processing with programming languages ​​such as Python and their libraries to extract important terms. For example, text analysis is performed using the Python NLTK library.

[0728] Next, the server uses the OpenAI GPT model, a generative artificial intelligence, to automatically generate definitions for the extracted terms. These generated definitions are compiled into a glossary and registered in a knowledge portal. This knowledge portal is built using a web framework such as Django to make it easily accessible to users.

[0729] The user terminal will be developed using React Native. The user of the terminal will be a store staff member who can access the knowledge portal and search for terms of interest. For example, when a newly assigned staff member searches for "how to prepare for a special sale event," they can instantly view relevant procedures and information on past cases through the terminal.

[0730] This system allows new employees and those whose work locations have changed to quickly adapt to their roles and contribute to store operations as immediate assets. Furthermore, newly added information is regularly updated in the system, ensuring it remains up-to-date.

[0731] As a concrete example, enter the following prompt into the generating AI model: "Generate definition of business chart for sale event preparation." This will generate an overview of efficient preparation methods for sale events and add it to the knowledge portal.

[0732] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0733] Step 1:

[0734] The server retrieves digital documents related to store operations from network storage. It receives connection destination storage information and authentication information as input, and retrieves documents in formats such as PDF and text files as output. This operation involves data transfer over the network.

[0735] Step 2:

[0736] The server analyzes the retrieved documents and extracts important terms. The input is the document's text data, and text analysis is performed using natural language processing techniques (e.g., Python's NLTK). The output is a list of the extracted terms.

[0737] Step 3:

[0738] The server uses a generative artificial intelligence model to generate definitions for extracted terms. It receives a list of terms and related prompt sentences as input and automatically generates definitions using OpenAI's GPT, etc. The output is the definition sentence corresponding to each term. Specifically, queries are sent to the model via an API. Prompt sentences such as "Special sale event preparation, business chart, definition generation" are used.

[0739] Step 4:

[0740] The server builds a knowledge portal using the generated definitions and related information. It receives term definitions and linked reference materials as input and automatically generates portal pages using a web framework such as Django. The output is an accessible knowledge portal. Page layout and link configuration are also handled during this process.

[0741] Step 5:

[0742] The device displays a knowledge portal when accessed by a user. It receives user queries and navigation inputs, and retrieves relevant information from the portal's database. The output is an information page formatted for user readability. Specifically, the user interface is rendered via React Native.

[0743] Step 6:

[0744] Users obtain necessary information from a knowledge portal via their terminals. The input consists of search keywords entered by the user into the terminal, and the output is related information and procedures displayed on the terminal. This information can then be utilized in daily work.

[0745] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0746] This invention adds an emotion engine that recognizes user emotions and optimizes interactions, in addition to a system that collects documents and data within a company using network storage, extracts terms by analyzing them using natural language processing technology, and generates definitions for those terms using generative artificial intelligence. The program's processing is described below from the perspectives of the server, terminal, and user.

[0747] Server Role

[0748] The server accesses the company's network storage to collect all relevant documents, such as project materials and meeting minutes. The collected data is analyzed using natural language processing techniques to extract important terms and phrases. Subsequently, generative artificial intelligence is used to generate definitions for the extracted terms, and a knowledge portal is built along with links to related materials. In addition, an emotion engine analyzes user activity logs and comments to evaluate the user's emotional state.

[0749] Terminal role

[0750] The terminal provides an interface for users to access information using this knowledge portal. When a user searches for a term, the terminal displays definitions and related materials retrieved from the server. Furthermore, a sentiment engine analyzes the user's emotions, and if it determines, for example, that the user has questions or complaints, it automatically presents additional reference materials or FAQs.

[0751] User roles

[0752] Users can use the knowledge portal to learn terminology and processes related to their work. This is particularly useful for new employees and those who have transferred to a new department, as it allows them to quickly access definitions of terms and workflows. Furthermore, by gaining new insights based on their emotional response to the information presented by the system and receiving additional support, they can approach their work with greater confidence.

[0753] Specific example

[0754] For example, if a user wants to learn more about the "client contract process," they search for the term on their device. The server provides the definition and relevant documents from the knowledge portal and records which documents the user has accessed. As the user reads through the documents, the sentiment engine predicts areas of uncertainty or potential interest and presents more detailed guides and past success stories. This allows the user to quickly grasp the key points and proceed smoothly with their workflow.

[0755] The following describes the processing flow.

[0756] Step 1:

[0757] The server accesses network storage and retrieves all relevant documents within the company. This process involves scanning folders and collecting data based on employee access rights.

[0758] Step 2:

[0759] The server passes the retrieved documents to a natural language processing module, which then begins text analysis. This involves extracting nouns and phrases, analyzing co-occurrence relationships, and listing industry-specific terminology.

[0760] Step 3:

[0761] The server uses generative artificial intelligence to generate definitions for the extracted terms. The generated definitions also include usage examples and related categories.

[0762] Step 4:

[0763] The server builds a knowledge portal based on the generated glossary, including links to related resources. This portal is indexed, enabling efficient information retrieval.

[0764] Step 5:

[0765] The terminal receives a search query from the user and sends the query to the server. The server retrieves the appropriate information from the knowledge portal and returns it to the terminal.

[0766] Step 6:

[0767] The device visually displays search results to the user. During this process, an emotion engine analyzes the user's actions and facial expressions to infer their emotional state.

[0768] Step 7:

[0769] The emotion engine adjusts support information based on the inferred user emotions. For example, if a user is feeling frustrated, it automatically displays links to additional step-by-step guides or tutorial videos.

[0770] Step 8:

[0771] Users obtain information through their devices and request further assistance as needed. This allows users to acquire the knowledge they require quickly and efficiently.

[0772] (Example 2)

[0773] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0774] In today's information society, vast amounts of information are accumulated within companies, and there is a need for methods to efficiently organize this information and quickly access the necessary information. Furthermore, users require intuitive and rapid information-gathering interfaces to resolve business-related questions and uncertainties. However, traditional methods fail to adequately meet these needs, making technological means essential for optimizing information organization and information delivery in accordance with the user's emotional state.

[0775] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0776] In this invention, the server includes means for collecting information from network storage, means for analyzing the information using natural language processing technology and extracting concepts, and means for generating explanations for the extracted concepts using generative artificial intelligence. This makes it possible to efficiently organize information, analyze the user's operation history and statements to evaluate their emotional state, and automatically present additional relevant information when necessary.

[0777] "Network storage" refers to a storage device used to store data that can be accessed over a network.

[0778] "Information" refers to documents and data managed within a company, and includes all kinds of materials related to business operations.

[0779] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract important concepts from information.

[0780] A "concept" refers to important terms and phrases within information extracted using natural language processing technology.

[0781] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates explanatory text and related information based on extracted concepts.

[0782] An "explanation" is a document generated by a generative artificial intelligence system to clarify the meaning and usage of a concept.

[0783] A "server" is a central device that performs the processes of collecting, analyzing, generating, and providing information.

[0784] "Emotional state" refers to the psychological state of a user, inferred from their activity history and statements.

[0785] An "information portal" is a web-based platform for integrating and managing collected information, its accompanying explanations, and related links.

[0786] "Related information" refers to additional materials and support information that are associated with the concept the user searched for.

[0787] This invention is a system designed to streamline and optimize information within a company, utilizing human language analysis and understanding technology and artificial intelligence. The following describes specific embodiments for carrying out this invention.

[0788] The server connects to the company's network storage to collect all relevant information. The hardware used includes large-capacity storage devices on the company network. This information includes project documents, meeting minutes, and other business documents, which are stored by the server in a temporary database.

[0789] The server analyzes the collected information using natural language processing (NLP) techniques. The software used includes open-source NLP libraries and commercial analysis tools. This extracts important concepts and terminology from the information and organizes the resulting data in a structured format.

[0790] Next, the server employs generative artificial intelligence (AI) technology to generate explanations based on the extracted concepts. This AI model, for example, presents background information and examples to help the user deepen their understanding of a particular term. The generative AI model used here is a known natural language generation (NLG) framework.

[0791] The terminal provides an information portal accessible to the user, displaying explanations and related information for the searched concept. The user enters a prompt through the search bar, such as, "Please provide details about the client contract process. Please also provide any relevant documents," and obtains a concept explanation from the server.

[0792] The server also features an emotion engine that analyzes user activity history and comments. This function infers the user's emotional state and adjusts the way information is provided as needed. For example, if the emotion engine determines that a user is dissatisfied with the information, it will automatically display additional reference materials or FAQs on the device.

[0793] This system enables effective information management within a company and optimizes user interaction. Users can rapidly increase their knowledge in their work, resulting in improved work efficiency.

[0794] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0795] Step 1:

[0796] The server connects to the company's network storage and collects information, including project documents and meeting minutes. It receives directory information from the network storage as input and stores the collected information in a temporary database as output. Specifically, its operation involves scanning for necessary data files via configured file paths and transferring them to the database in a predetermined format.

[0797] Step 2:

[0798] The server performs analysis on the collected information using natural language processing (NLP) techniques. It uses information stored in a temporary database as input and identifies important concepts and terms as output. The server activates its NLP engine, extracting frequently occurring words and performing contextual analysis to list and tag business-critical concepts.

[0799] Step 3:

[0800] The server uses a generative AI model to generate explanations for identified concepts. It takes a list of key concepts extracted by NLP analysis as input and outputs a generated explanation for each concept. This process involves prompting the generative AI with background information and usage examples related to the extracted concepts, generating explanations in natural language.

[0801] Step 4:

[0802] The terminal allows users to access an information portal and provides explanations and materials related to the searched concepts. It receives prompt input from the user and displays explanations and related materials retrieved from the server on the screen as output. Specifically, it prepares to display relevant information in an optimized interface based on the user's search query.

[0803] Step 5:

[0804] The server runs an emotion engine that analyzes user operation logs and comments to evaluate the user's emotional state. The input is user interaction data, and the output is the emotion evaluation result at that time. The emotion engine analyzes the frequency and duration of operations and the content of comments to determine the user's current psychological state and perform the emotion evaluation.

[0805] Step 6:

[0806] The device presents additional relevant information and FAQs that the user might find interesting, based on the results of the emotion engine. It receives emotion evaluation data as input and displays hints and additional information on the user screen as output. Specifically, it dynamically places recommended content on the device based on the data received from the engine.

[0807] (Application Example 2)

[0808] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0809] In logistics centers and other industries, there is a need to improve employees' ability to quickly understand and respond to necessary information and procedures. However, current systems present information uniformly and do not provide flexible information tailored to the user's emotional state. This can lead to misunderstandings and decreased work efficiency. To solve this problem, a system is needed that provides information tailored to the individual user's situation, thereby achieving more effective learning and work efficiency.

[0810] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0811] In this invention, the server includes means for collecting information from a network storage device, means for analyzing the information using natural language processing technology and extracting terms, means for generating definitions for the extracted terms using generative artificial intelligence, means for automatically generating a knowledge portal that manages links to a glossary and related materials, means for analyzing input data using emotion recognition technology and evaluating the user's emotional state, and means for optimizing the content presented on the knowledge portal according to the user's emotional state. This enables users to receive information in a format that suits their emotional state, thereby promoting understanding of work and enabling efficient work.

[0812] A "network storage device" is a device that stores digital information at a central location and makes it accessible via a network.

[0813] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0814] "Generative artificial intelligence" refers to artificial intelligence technology that has the ability to generate new information and conclusions based on data.

[0815] A "knowledge portal" is an information platform that aggregates information and links and provides them in a format that is easily accessible to users.

[0816] "Emotion recognition technology" is a technology that analyzes user input data to infer a person's emotional state.

[0817] A "user terminal" is a device used by a user to display or manipulate information.

[0818] A "glossary" is a document that systematically organizes terms and their definitions related to a specific field.

[0819] "Related materials" are supplementary sources of information associated with a particular piece of information or topic.

[0820] "Evaluation" in emotion recognition technology refers to the process of observing and judging the user's emotional state.

[0821] "Optimization" is the process of adjusting the way information is presented and its content according to the user's emotional state.

[0822] This invention comprises a system designed to streamline the collection, analysis, and provision of information in a logistics center, and to promote employees' understanding of their work.

[0823] The server is connected to network storage and collects relevant information within the logistics center. The collected information is analyzed using natural language processing technology, and important terms are extracted. In this process, an AI model using a specific algorithm operates to accurately grasp the meaning of the information. Generative artificial intelligence generates appropriate definitions for these terms, and these are aggregated in a knowledge portal.

[0824] Emotion recognition technology analyzes user input behavior through the user's terminal and evaluates the user's emotional state. The emotion recognition engine used here is designed to infer emotions from the user's operation history and voice data. Based on this evaluation result, the information provided is optimized according to the user's state.

[0825] The terminal enables users to access a knowledge portal and displays information customized based on their emotional state. Users can quickly grasp terminology and procedures related to logistics operations using their smart devices. Eye-tracking and voice recognition technologies are employed to create an interface that reduces user burden.

[0826] As a concrete example, consider a scenario where a new employee tries to understand the "inventory management process." The user searches for "inventory management process" through a terminal, and the terminal provides a definition and related materials generated by a generative AI model. While the user reviews the displayed information, an emotion engine senses signs of their interest and confusion, and presents additional FAQs and learning guides. In this way, the user can efficiently acquire information and proceed with their work smoothly.

[0827] An example of a prompt would be, "Please provide details of the inventory management process and related practical guides. Also, please provide additional, sentiment-based information, along with recent success stories." This ensures the system provides optimal information tailored to the user's needs.

[0828] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0829] Step 1:

[0830] The server connects to network storage and collects documents and data within the logistics center. The input is raw data from the network storage, and the output is a dataset ready for analysis. During this process, a data collection script is periodically executed to filter the target data.

[0831] Step 2:

[0832] The server analyzes the collected data using natural language processing techniques and extracts important terms. The input is the collected data, and the output is a list of extracted terms. In this step, an NLP engine is running to perform tokenization and part-of-speech tagging, and specific algorithms are used to identify terms.

[0833] Step 3:

[0834] The server uses generative artificial intelligence to generate definitions for the extracted terms. The input is a list of terms, and the output is a definition for each term. A generative AI model is applied to this process, and the information related to each term is consistently linked by the AI ​​algorithm.

[0835] Step 4:

[0836] The server automatically generates a knowledge portal and manages the glossary and links to related resources. Input is term definitions and related resources, and output is a user-accessible knowledge portal. In this step, a database management system is operational, aggregating information at the portal's front-end.

[0837] Step 5:

[0838] Emotion recognition technology allows the device to analyze user input data and evaluate their emotional state. User input is used as input data, and an emotion evaluation result is generated as output. Emotions are inferred by analyzing the user's voice tone and operation speed using sensors and microphones.

[0839] Step 6:

[0840] Based on the user's emotional state, the device optimizes the content presented in the knowledge portal. Input consists of emotional assessment results and knowledge portal data, while output is a customized information display. FAQs and reference materials are automatically selected and displayed according to the user's needs.

[0841] Step 7:

[0842] Users access a knowledge portal using smart devices to obtain information related to logistics operations. Input is the user's search query, and output is the requested information and reference materials. Users can smoothly access the necessary information through voice commands and touch operations.

[0843] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0844] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0845] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0846] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0847] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0848] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0849] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0850] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0851] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0852] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0853] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0854] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0855] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0856] 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.

[0857] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0858] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0859] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0860] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0861] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0862] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0863] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0864] The following is further disclosed regarding the embodiments described above.

[0865] (Claim 1)

[0866] A means of collecting data from network storage,

[0867] The aforementioned data is analyzed using natural language processing technology, and a means for extracting terms is provided.

[0868] A means for generating definitions for the extracted terms using generative artificial intelligence,

[0869] A means for automatically generating a knowledge portal that manages links to glossaries and related materials,

[0870] Means for displaying the aforementioned knowledge portal on a user terminal,

[0871] A system that includes this.

[0872] (Claim 2)

[0873] The system according to claim 1, further comprising means for visualizing the definitions of the aforementioned terms and related examples on a user interface.

[0874] (Claim 3)

[0875] The system according to claim 1, further comprising means for periodically collecting new data and updating the glossary and knowledge portal.

[0876] "Example 1"

[0877] (Claim 1)

[0878] Means for collecting information from information storage devices,

[0879] A means for analyzing the aforementioned information using natural language processing technology and extracting terms,

[0880] A means for generating definitions for the extracted terms using generative artificial intelligence,

[0881] A means for automatically generating a knowledge-sharing platform that manages glossaries and links to related materials,

[0882] The means for displaying the aforementioned knowledge sharing platform on a user terminal,

[0883] A mechanism that includes this.

[0884] (Claim 2)

[0885] The mechanism according to claim 1, further comprising means for visualizing the definitions of the aforementioned terms and related examples on a user interface.

[0886] (Claim 3)

[0887] The mechanism according to claim 1, further comprising means for periodically collecting new information and updating the glossary and knowledge-sharing platform.

[0888] "Application Example 1"

[0889] (Claim 1)

[0890] A means of collecting information from network storage,

[0891] A means for analyzing the aforementioned information using natural language processing technology and extracting terms,

[0892] A means for generating definitions for the extracted terms using generative artificial intelligence,

[0893] A means for automatically generating a knowledge portal that manages glossaries and links to related information sources,

[0894] Means for displaying the aforementioned knowledge portal on a user terminal,

[0895] A means of providing information including procedural manuals and reference materials related to store operations,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising means for visualizing the definitions of the terms and related procedures on a user interface.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising means for periodically collecting new information and updating the glossary and knowledge portal.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] A means of collecting information from network storage,

[0904] A means for analyzing the aforementioned information using natural language processing technology and extracting concepts,

[0905] A means for generating an explanation for the extracted concept using generative artificial intelligence,

[0906] A means for automatically generating an information portal that manages links to information collections and related information,

[0907] Means for displaying the information portal on the user device,

[0908] A means of evaluating a user's emotional state by analyzing their operation history and statements,

[0909] Means for presenting additional relevant information based on the aforementioned evaluation,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising means for visualizing the above-mentioned concept and related information on a display device.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising means for periodically collecting new information and updating the information collection and information portal.

[0915] "Application example 2 of combining emotional engines"

[0916] (Claim 1)

[0917] Means for collecting information from network storage devices,

[0918] A means for analyzing the aforementioned information using natural language processing technology and extracting terms,

[0919] A means for generating definitions for the extracted terms using generative artificial intelligence,

[0920] A means for automatically generating a knowledge portal that manages links to glossaries and related materials,

[0921] A means for displaying the aforementioned knowledge portal on a user terminal,

[0922] A means of analyzing input data using emotion recognition technology and evaluating the user's emotional state,

[0923] A means to optimize the content presented in the knowledge portal according to the user's emotional state,

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, further comprising means for visualizing the definitions of the aforementioned terms and related examples on a user interface.

[0927] (Claim 3)

[0928] The system according to claim 1, further comprising means for periodically collecting new information and updating the glossary and knowledge portal. [Explanation of Symbols]

[0929] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data from network storage, The aforementioned data is analyzed using natural language processing technology, and a means for extracting terms is provided. A means for generating definitions for the extracted terms using generative artificial intelligence, A means for automatically generating a knowledge portal that manages links to glossaries and related materials, Means for displaying the aforementioned knowledge portal on a user terminal, A system that includes this.

2. The system according to claim 1, further comprising means for visualizing the definitions of the aforementioned terms and related examples on a user interface.

3. The system according to claim 1, further comprising means for periodically collecting new data and updating the glossary and knowledge portal.

Images