System

Abstract

A system is provided.SOLUTION: A system comprising: means for extracting a keyword from text data input by a user; means for suggesting a repository based on the extracted keyword; and means for storing the text data in the suggested repository.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In today's business environment, vast amounts of information and documents are generated every day. This makes it difficult to quickly search for and access the information needed. Furthermore, there is no unified system for efficiently organizing and managing individual notes and documents, requiring a great deal of time and effort to manage information. This reduces work efficiency and negatively impacts productivity. To address these issues, there is a need for a system that can automatically classify notes and documents, provide efficient search functions, and generate summaries and explanations. [Means for solving the problem]

[0005] The present invention provides a system that extracts keywords from text data entered by a user, suggests a storage location based on the extracted keywords, and saves the text data in the suggested storage location. The system also includes a means for registering URLs of external documents, evaluating the content of the registered URLs to generate associated tags, and saving the URLs together with the generated associated tags. The system also includes a means for searching for related text data and URLs based on a search query entered by the user and presenting the search results to the user. Additionally, the system includes a means for summarizing or explaining the content of the text data and URLs specified by the user and presenting the summary or explanation results to the user, thereby enabling the user to quickly find the information they need from a vast amount of information and improving work efficiency.

[0006] A "user" is an entity that operates a system and inputs, searches, and retrieves information.

[0007] "Text data" refers to unstructured data such as notes and sentences entered by the user.

[0008] "Keywords" are words or phrases extracted from text data that characterize the content of the data.

[0009] A "storage location" is a folder or category for appropriately storing text data.

[0010] An "external document" is an electronic document or material that exists outside the system, and its URL is registered in the system.

[0011] A "URL" is a uniform resource location that specifies the location of an external document.

[0012] "Related tags" are information that represents the contents of external documents or text data, and are used for searching and classifying them.

[0013] A "search query" is a question or condition that a user enters into a system to obtain specific information.

[0014] A "summary" refers to a concise summary of the contents of text data or an external document.

[0015] "Explanation" is a sentence that provides a detailed explanation of the contents of the text data or external document.

[0016] "Generative AI" refers to functions within a system that use artificial intelligence techniques to analyze, classify, search, summarize, and explain text data.

[0017] A "terminal" is a device that is directly operated by a user and provides an interface for interacting with the system.

[0018] A "server" is a computing device that performs the central processing of a system, and is responsible for storing, classifying, searching, and running generative AI data. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention is an information management system that allows users to efficiently manage and search for notes and documents, and receive summaries and explanations. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[0041] Save and categorize your notes

[0042] The user enters a new note. The device analyzes the content of the entered note and extracts keywords. The device then sends the note content, including the extracted keywords, to the server. The server uses generative AI to suggest the optimal storage location. Information about the suggested storage location is sent to the device, and the device notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[0043] Specific examples

[0044] The user enters "Notes about next week's project meeting." The device extracts keywords such as "next week" and "project meeting" and sends them to the server. Using generative AI, the server determines that the "Project-related documents" folder is the most appropriate. The device then suggests to the user, "Do you want to save this note in the Project-related documents folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[0045] Registering an external document URL

[0046] The user enters the URL of an external document. The device sends the URL as is to the server. The server uses generative AI to evaluate the content of the URL and generate relevant tags, allowing the user to easily search for related documents later.

[0047] Specific examples

[0048] The user enters the "URL of the technical specifications." The device sends this to the server, which analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "concept." The generated tags and URL are stored in a database.

[0049] Information Search

[0050] A user enters a search query into their device. The device sends the search query to the server, which uses generative AI to search for related notes and URLs. The search results are sent to the device and displayed to the user.

[0051] Specific examples

[0052] A user searches for "next week's project meeting agenda." The device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "notes about next week's project meeting agenda" and sends it to the device. The device displays the search result to the user.

[0053] Document Summary / Explanation

[0054] The user requests a summary or commentary on a specific note or URL. The device sends the summary / commentary request to the server. The server uses generation AI to generate a summary or commentary for the specified document and sends it to the device. The device then displays the summary / commentary result to the user.

[0055] Specific examples

[0056] The user requests a "summary of the technical specifications." The device sends this request to the server, which then uses a generation AI to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the system design and requirements," and is sent to the device. The device then displays the summary to the user.

[0057] As a result, the present invention allows users to quickly find the information they need from a vast amount of information, greatly improving work efficiency.

[0058] The processing flow will be explained below.

[0059] Save and categorize your notes

[0060] Step 1:

[0061] A user inputs a new note into a terminal. For example, the user inputs "About the agenda for next week's project meeting."

[0062] Step 2:

[0063] The device analyzes the contents of the memo and extracts keywords, such as "next week," "project meeting," and "agenda."

[0064] Step 3:

[0065] The device sends the extracted keywords and memo contents to the server, using JSON format data as the communication protocol.

[0066] Step 4:

[0067] The server passes the received data to the generation AI and instructs it to suggest the best place to save it. The generation AI determines that the "Project-related documents" folder is the best.

[0068] Step 5:

[0069] The server sends the proposed storage location information to the terminal, for example, "Proposed storage location: Project-related documents."

[0070] Step 6:

[0071] The device notifies the user with a suggested save location, for example, by displaying "Would you like to save this note in the 'Project Related Documents' folder?"

[0072] Step 7:

[0073] The user agrees with the proposal, e.g., clicks the "Save" button.

[0074] Step 8:

[0075] Your device will save the note to the specified location, for example, the "Project Related Documents" folder.

[0076] Registering an external document URL

[0077] Step 1:

[0078] The user enters the URL of an external document into the terminal, for example, "https: / / example.com / tech-spec."

[0079] Step 2:

[0080] The device sends the entered URL to the server, using JSON format data as the communication protocol.

[0081] Step 3:

[0082] The server receives the URL and instructs the AI ​​to evaluate the content of the URL. The AI ​​analyzes the content of the URL and generates relevant tags such as "technical specifications," "specifications," and "concepts."

[0083] Step 4:

[0084] The server stores the associated tags and URLs in a centralized database, for example using an SQL query.

[0085] Information Search

[0086] Step 1:

[0087] A user enters a search query into a device, for example, "agenda for next week's project meeting."

[0088] Step 2:

[0089] The device sends a search query to the server, using JSON format data as the communication protocol.

[0090] Step 3:

[0091] The server receives the search query and uses generative AI to search for relevant notes and URLs, for example, based on keywords such as "next week," "project meeting," and "agenda."

[0092] Step 4:

[0093] The server sends the search results to the device, such as a list of found notes or URLs in JSON format.

[0094] Step 5:

[0095] The device displays the search results to the user. For example, a link or URL for "Notes on the agenda for next week's project meeting" appears on the user's screen.

[0096] Document Summary / Explanation

[0097] Step 1:

[0098] The user specifies a specific note or URL on the terminal and requests a summary or explanation. For example, the user requests a "summary of a technical specification."

[0099] Step 2:

[0100] The terminal sends a summary / explanation request to the server. JSON format data is used as the communication protocol.

[0101] Step 3:

[0102] The server passes the summary / explanation request to the generation AI, which generates a summary or explanation of the specified document. For example, the generation AI summarizes a technical specification and generates the content, "This technical specification describes the system design and requirements."

[0103] Step 4:

[0104] The server sends the generated summary and commentary to the device, for example, in JSON format.

[0105] Step 5:

[0106] The terminal displays the summary / explanation result to the user. For example, the following content is displayed on the user's screen: "Summary: This technical specification describes the system design and requirements."

[0107] Example 1

[0108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0109] Efficiently managing large amounts of text data and documents and quickly searching and retrieving necessary information are important challenges in today's information society. However, conventional information management systems face problems such as manual note organization and tagging, and insufficient search accuracy. It is also difficult to quickly understand and summarize the content of external documents. Therefore, there is a need for a system that allows users to efficiently manage and search notes and documents, and receive summaries and explanations.

[0110] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0111] In this invention, the server includes means for extracting keywords from text data entered by a user, means for suggesting a storage location based on the extracted keywords, means for saving the text data in the suggested storage location, means for the user to register URLs of external documents, means for evaluating the content of the registered URLs and generating associated tags, means for searching for associated text data and URLs, and means for generating summaries or explanations of the text data and URLs using a generative AI model, thereby enabling users to efficiently manage notes and documents and quickly search and obtain necessary information.

[0112] "User" refers to a person who uses an information system.

[0113] "Text data" refers to character or document data entered by a user.

[0114] "Keywords" refer to important words or phrases extracted from text data.

[0115] "Storage location" refers to the location of a folder or database where text data or documents are stored.

[0116] "Extraction" refers to the operation of extracting specific keywords or information from text data.

[0117] "Suggestion" refers to a recommendation made by the server to the user based on the extracted data.

[0118] "Saving" refers to the operation of storing text data or documents in a specified location.

[0119] "External documents" refer to documents and files that exist on the Internet.

[0120] "URL" refers to a specific address or link to an external document.

[0121] "Evaluation" refers to the operation of analyzing the content of a registered URL to determine its value and relevance.

[0122] "Tags" refer to keywords or phrases that are associated with a URL or the content of a document.

[0123] "Search" refers to the operation of searching text data or URLs based on a query entered by a user to find relevant information.

[0124] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze, summarize, and make suggestions about text data.

[0125] "Summarization" refers to the operation of concisely summarizing the contents of a document or text data.

[0126] "Explanation" refers to the operation of providing a detailed explanation of the contents of a document or text data.

[0127] The present invention is an information management system that allows users to efficiently manage and search notes and documents, and receive summaries and explanations. This system analyzes text data and URLs of external documents entered by users, suggests appropriate storage locations, and generates related tags, thereby streamlining information management and search. The following describes in detail how this system is implemented.

[0128] In the system configuration, a server and a terminal work together. The server uses a generative AI model to analyze, evaluate, summarize, and explain text data and URLs. The terminal provides a user interface, receives user input, and communicates with the server.

[0129] Save and categorize your notes

[0130] The user enters a new note. The device analyzes the content of the note and extracts keywords using a natural language processing (NLP) library (e.g., spaCy or Gensim). The device then sends the note content, including the extracted keywords, to the server. The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt such as "Suggest a suitable storage location for next week's project meeting" to suggest the best storage location. This prompt is generated via an HTTP POST request.

[0131] For example, if a user types "Notes about next week's project meeting," the device extracts keywords such as "next week" and "project meeting" and sends them to the server. The server determines that the "Project-related documents" folder is the most appropriate location and sends a suggestion to the device. If the user agrees to this suggestion, the note will be saved in the specified folder.

[0132] Registering an external document URL

[0133] The user inputs the URL of an external document. The device sends the input URL as is to the server. The server evaluates the content of the URL using a generative AI model and generates a prompt such as "Please evaluate the URL of the technical specification document," thereby generating relevant tags. The generated tags and URLs are stored in a database (e.g., MySQL or MongoDB).

[0134] As a concrete example, when a user enters the "URL of a technical specification," the device sends it to the server, which analyzes the contents of the URL and generates tags such as "technical specification," "specification," and "concept," which are then stored in a database along with the URL.

[0135] Information Search

[0136] A user enters a search query into a device. The device sends the entered search query to the server. The server uses a generative AI model to search for relevant notes and URLs and finds relevant information from a database by generating prompts such as "Please search for next week's project meeting agenda." The search results are sent to the device and displayed to the user.

[0137] For example, if a user searches for "next week's project meeting agenda," the device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "notes about next week's project meeting agenda" and sends it to the device. The device then displays the search result to the user.

[0138] Document Summary / Explanation

[0139] The user requests a summary or commentary for a specific note or URL. The device sends the summary / commentary request to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and generates a prompt such as "Please generate a summary of the technical specification." The generated summary or commentary is sent to the device and displayed to the user.

[0140] As a concrete example, when a user requests a "summary of the technical specifications," the device sends this request to the server, which then uses a generative AI model to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the system design and requirements," and is sent to the device. The device then displays the summary to the user.

[0141] This allows users to quickly find the information they need from a vast amount of information, significantly improving work efficiency.

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The user inputs a new note into the terminal. Any text is provided to the terminal as input data from the user.

[0145] Step 2:

[0146] The device analyzes the content of the input note and extracts keywords using a natural language processing (NLP) library (e.g., spaCy or Gensim). The input is text data, and the output is a list of extracted keywords. Specifically, the text data is tokenized and important words and phrases are identified.

[0147] Step 3:

[0148] The terminal sends the contents of the memo including the extracted keywords to the server (HTTP POST request). The input is the keyword list and the memo contents, and the output is a request sent to the server.

[0149] Step 4:

[0150] The server inputs the received note content into a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt that suggests the optimal storage location. The input is the note content and a list of keywords, and the output is information about the suggested storage location. Example prompt: "Suggest a suitable storage location for next week's project meeting."

[0151] Step 5:

[0152] The server sends the generated storage location information to the device (HTTP POST request), and the device notifies the user of the storage location suggestion. The input is the storage location information, and the output is a notification to the user. Specifically, a notification is displayed on the device's UI.

[0153] Step 6:

[0154] If the user agrees to the proposal, the device sends the consent information to the server (HTTP POST request). The input is the user's consent, and the output is the transmission of consent data to the server.

[0155] Step 7:

[0156] With the user's consent, the server saves the memo in the specified folder. The saving is done in a database (e.g., MySQL or MongoDB). The input is the memo content and the save location, and the output is the save operation to the database.

[0157] Step 8:

[0158] The user enters the URL of an external document into the terminal. The input is a URL.

[0159] Step 9:

[0160] The terminal sends the entered URL to the server as is (HTTP POST request). The input is a URL, and the output is a request sent to the server.

[0161] Step 10:

[0162] The server analyzes the content of the received URL and uses a generative AI model to generate the associated tag. The input is the URL and the output is the generated tag. Example prompt: "Please rate the URL for the technical specification."

[0163] Step 11:

[0164] The server saves the generated tags and URLs to a database. The input is the tag and URL, and the output is a save operation to the database.

[0165] Step 12:

[0166] A user inputs a search query into a terminal. The input is a search query.

[0167] Step 13:

[0168] The terminal sends the entered search query to the server (HTTP GET request). The input is the search query, and the output is the request sent to the server.

[0169] Step 14:

[0170] The server receives the search query and uses a generative AI model to generate a prompt that searches for related notes and URLs. The input is the search query and the output is a list of related data. Example prompt: "Please find the agenda for next week's project meeting."

[0171] Step 15:

[0172] The server uses the generative AI model to search for relevant notes and URLs from the database and sends the results to the device (HTTP GET request). The input is the search results, and the output is the data sent to the device.

[0173] Step 16:

[0174] The terminal displays the received search results to the user. The input is the search results and the output is the display on the user interface.

[0175] Step 17:

[0176] The user requests a summary or description of a particular note or URL. The input is a summary / description request.

[0177] Step 18:

[0178] The terminal sends a summary / explanation request to the server (HTTP POST request). The input is the request data, and the output is the data sent to the server.

[0179] Step 19:

[0180] The server passes the received request to a generative AI model to generate a summary or commentary of the specified document. The input is a note or URL, and the output is a summary or commentary text. Example prompt: "Generate a summary of a technical specification."

[0181] Step 20:

[0182] The server sends the generated summary and commentary to the terminal (HTTP POST request). The input is the summary / commentary result, and the output is the message sent to the terminal.

[0183] Step 21:

[0184] The terminal displays the received summary / explanation results to the user. The input is the summary / explanation results, and the output is the display on the user interface.

[0185] (Application example 1)

[0186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0187] Robots used in factories require the management of numerous work instructions and maintenance logs, but as this information increases, management becomes cumbersome. A system that can quickly search for necessary information and provide summaries is also needed, but existing systems cannot efficiently achieve this. Therefore, there is a need to streamline information management for factory robots and improve productivity.

[0188] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0189] In this invention, the server includes means for extracting keywords from text data entered by a user, means for suggesting a storage location based on the extracted keywords, means for saving the text data in the suggested storage location, means for searching for files, means for presenting search results to the user, means for generating summaries, and means for presenting the generated summaries to the user, thereby enabling efficient management of robot instructions and logs in a factory, rapid search, and provision of summaries.

[0190] "User" means a person or organization that operates the system and inputs and retrieves information.

[0191] "Text data" refers to information that consists of characters and sentences.

[0192] "Keywords" are important words and phrases extracted from the text data entered by the user.

[0193] A "storage location" is a specific folder or directory where data is stored.

[0194] A "server" is a computer system for storing and processing data.

[0195] A "file" is a unit for recording data or information.

[0196] "Search Results" refers to a collection of relevant information or data discovered based on a user-entered query.

[0197] The "summary" is a short sentence that succinctly summarizes the contents of the input document.

[0198] "URL" means a Uniform Resource Locator that provides a link to a web page or digital document.

[0199] "Related tags" are metadata that indicate keywords or categories related to a document or piece of information.

[0200] "Suggestion" refers to the system suggesting to the user the optimal storage location and processing method.

[0201] This invention is an information management system that efficiently manages, searches, and summarizes work instructions and maintenance logs for factory robots. This system is composed of a server and terminals and performs the following processes.

[0202] 1. System Configuration

[0203] Hardware

[0204] Server: High-performance server (e.g. AWS EC2 instance)

[0205] Terminal: Tablet or computer mounted on a robot in the factory

[0206] Network: Local Area Network (LAN) or Cloud Network

[0207] software

[0208] Server software: Database software (e.g., MySQL), generative AI API (e.g., OpenAI GPT)

[0209] Terminal software: User interface (UI) applications, search engine software

[0210] 2. Save and categorize your notes

[0211] The user inputs new text data (e.g., work instruction notes) into the device. The device extracts important keywords from the input text data. The text data, including the extracted keywords, is then sent to the server. The server uses a generative AI model to suggest the optimal storage location. The device notifies the user of the suggested storage location, and if the user approves, the text data is saved in the specified folder.

[0212] Specific examples

[0213] The user enters "Notes about next week's maintenance work." The device extracts keywords such as "next week" and "maintenance work" and sends them to the server. Using a generative AI model, the server determines that the "Maintenance-related documents" folder is the most appropriate. The device then asks the user, "Do you want to save this note in the Maintenance-related documents folder?" If the user approves, the note is saved in the "Maintenance-related documents" folder.

[0214] 3. Registering the external document URL

[0215] The user enters the URL of an external document. The device sends the URL as is to the server. The server uses a generative AI model to evaluate the URL's content and generate relevant tags, allowing the user to easily search for related documents later.

[0216] Specific examples

[0217] The user enters the URL of the technical specifications for the new machine. The device sends this to the server, which analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "new machine." The generated tags and URLs are stored in a database.

[0218] 4. Information Search

[0219] A user enters a search query into their device. The device sends the search query to the server, which uses a generative AI model to search for relevant text data and URLs. The search results are sent to the device and displayed to the user.

[0220] Specific examples

[0221] A user searches for "Details of next week's maintenance work." The device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "Notes about details of next week's maintenance work" and sends it to the device. The device displays the search result to the user.

[0222] 5. Document Summary / Explanation

[0223] The user requests a summary or commentary for a specific note or URL. The device sends the summary / commentary request to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and sends it to the device. The device then displays the summary / commentary result to the user.

[0224] Specific examples

[0225] The user requests a "summary of the technical specifications for a new machine." The device sends this request to the server, which uses a generative AI model to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the design and requirements for a new machine." and is sent to the device. The device then displays the summary to the user.

[0226] Prompt Sentence Examples

[0227] Write a summary of the following document:

[0228] This technical specification describes the system design and requirements. It includes detailed blueprints and specifications.

[0229] This system enables efficient management of robotic instructions and logs within factories, as well as quick search and provision of summaries.

[0230] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0231] Step 1:

[0232] The user inputs new text data (e.g., notes for a work instruction) into the terminal. The terminal receives this text data and stores it as input data for proceeding to the next step.

[0233] Step 2:

[0234] The device extracts important keywords from the input text data using a text analysis algorithm to identify key words and phrases within the text data, and then transmits the extracted keywords to the server.

[0235] Step 3:

[0236] The server receives the extracted keywords and uses a generative AI model to suggest the best storage location. The generative AI then searches related folders and directories based on the keywords to determine the best storage location. The server then sends the suggested storage location information to the device.

[0237] Step 4:

[0238] The device notifies the user of the storage location proposal received from the server. The user checks and approves the proposed storage location. After this operation is completed, the device proceeds to the next step.

[0239] Step 5:

[0240] The device saves the input text data in a location approved by the user. Specifically, it creates a text file in the specified folder and records the input data in it. This data is later used for searching and generating summaries.

[0241] Step 6:

[0242] The user enters the URL of an external document. The device receives this URL and retains it for the next step.

[0243] Step 7:

[0244] The device sends the entered URL as is to the server. The server receives the URL and evaluates the URL's content using a generative AI model. Based on the evaluation results, the server generates associated tags and stores the URL along with these tags in a database, allowing for easy search of the URL later.

[0245] Step 8:

[0246] The user inputs a search query into the terminal, which receives the query and proceeds to the next step.

[0247] Step 9:

[0248] The device sends a search query to the server, which uses a generative AI model to find relevant text data and URLs. The server searches its database to find results that match the user's search query. The server then sends the search results back to the device and presents them to the user.

[0249] Step 10:

[0250] The user requests a summary or commentary on a particular note or URL. The device receives this request and proceeds to the next step.

[0251] Step 11:

[0252] The device sends a summary / explanation request to the server, which uses the generative AI model to generate a summary or explanation for the specified document. The generated summary or explanation is then sent from the server to the device and displayed to the user through the device.

[0253] Examples:

[0254] If a user requests a "summary of the technical specifications for a new machine," the device sends this request directly to the server, which creates a summary based on the contents of the technical specifications and inputs the following prompt sentence into the generative AI model.

[0255] "Write a summary of the following document: This technical specification describes the system design and requirements. It includes detailed blueprints and specifications."

[0256] The generated summary is "This technical specification describes the design and requirements of a new machine," and is sent to the terminal and presented to the user.

[0257] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0258] The present invention is an information management system that allows users to efficiently manage, search, and retrieve notes and documents, and provides summaries and explanations, while also recognizing and adapting to the user's emotions. Below, the program processing of this system is explained in natural language and detailed with specific examples.

[0259] Save and classify notes (emotion engine compatible version)

[0260] When a user enters a new note, the device analyzes the content of the entered note and not only extracts keywords but also recognizes the user's emotions. This emotion recognition is performed using an emotion engine. The device sends the extracted keywords and emotion information to the server. The server passes the received data to a generation AI, which suggests an appropriate storage location. This suggestion is optimized according to the user's emotions. The suggested storage location information is sent to the device, and the device notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[0261] Specific examples

[0262] The user enters "Notes about next week's project meeting." The device uses its emotion engine to recognize that the user is nervous. The emotion information of the keywords "next week," "project meeting," and "nervous" is sent to the server. The server uses generative AI to determine that the "Project-related documents" folder is the best choice. The device suggests to the user, "Do you want to save this note in the 'Project-related documents' folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[0263] Registering an external document URL (emotion engine compatible version)

[0264] When a user enters the URL of an external document, the device sends the URL to the server, which uses a generative AI to evaluate the content of the URL and generate relevant tags, taking into account the user's emotional state. The generated tags and URL are then stored in a database.

[0265] Specific examples

[0266] When a user enters the "URL of a technical specification," the emotion engine recognizes that the user is impatient. The device sends the URL and the "impatient" emotion information to the server. The server uses a generation AI to analyze the contents of the URL and generate tags such as "technical specification," "specifications," and "concept." The generated tags and URL are stored in a database.

[0267] Information Search (Emotion Engine Compatible Version)

[0268] When a user enters a search query, the device recognizes the user's emotion along with the search query. The device then sends this information to the server, which uses generative AI to consider the user's emotion when searching for related notes and URLs. The search results are then sent to the device and presented in a format that best suits the user's emotion.

[0269] Specific examples

[0270] When a user searches for "next week's project meeting agenda," the emotion engine recognizes that the user is confused. The device sends the search query and the "confused" emotion information to the server. The server uses generative AI to prioritize and display notes and documents that are most likely to be useful to the confused user. The device then displays search results optimized for the user.

[0271] Document summary / explanation (emotion engine compatible version)

[0272] When a user requests a summary or commentary for a specific note or URL, the device sends this request and the user's emotional information to the server. The server uses generative AI to generate a summary or commentary for the specified document, optimizing it according to the emotional information. The generated summary or commentary is then sent to the device and presented to the user.

[0273] Specific examples

[0274] When a user requests a "summary of technical specifications," the emotion engine recognizes the user's calmness. The device sends the summary request and the "calm" emotion information to the server. The server uses generative AI to summarize the technical specifications and generate a detailed summary suitable for a calm user. The device then displays this generated summary to the user.

[0275] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[0276] The processing flow will be explained below.

[0277] Save and classify notes (emotion engine compatible version)

[0278] Step 1:

[0279] A user inputs a new note into a terminal. For example, the user inputs "About the agenda for next week's project meeting."

[0280] Step 2:

[0281] The device analyzes the contents of the memo and extracts keywords, such as "next week," "project meeting," and "agenda."

[0282] Step 3:

[0283] The device uses an emotion engine to recognize the user's emotions. For example, the emotion engine determines that the user is nervous.

[0284] Step 4:

[0285] The device sends the extracted keywords and emotion information to the server, which uses JSON format data as the communication protocol.

[0286] Step 5:

[0287] The server passes the received data to the generation AI and instructs it to suggest the best place to save it. For example, the generation AI determines that the "Project-related documents" folder is the best.

[0288] Step 6:

[0289] The server sends the proposed storage location information to the terminal, for example, "Proposed storage location: Project-related documents."

[0290] Step 7:

[0291] The device will notify the user with save location suggestions, for example, "Would you like to save this note in the 'Project Related Documents' folder?"

[0292] Step 8:

[0293] The user agrees with the proposal, e.g., clicks the "Save" button.

[0294] Step 9:

[0295] Your device will save the note to the specified location, for example, the "Project Related Documents" folder.

[0296] Registering an external document URL (emotion engine compatible version)

[0297] Step 1:

[0298] The user enters the URL of an external document into the terminal, for example, "https: / / example.com / tech-spec."

[0299] Step 2:

[0300] The device uses an emotion engine to recognize the user's emotions. For example, the emotion engine determines that the user is anxious.

[0301] Step 3:

[0302] The device sends the input URL and emotion information to the server. JSON format data is used as the communication protocol.

[0303] Step 4:

[0304] The server receives the URL and instructs the AI ​​to evaluate the content of the URL. The AI ​​analyzes the content of the URL and generates relevant tags such as "technical specifications," "specifications," and "concepts."

[0305] Step 5:

[0306] The server stores the associated tags and URLs in a centralized database, for example using an SQL query.

[0307] Information Search (Emotion Engine Compatible Version)

[0308] Step 1:

[0309] A user enters a search query into a device, for example, "agenda for next week's project meeting."

[0310] Step 2:

[0311] The device uses an emotion engine to recognize the user's emotion. For example, the emotion engine determines that the user is confused.

[0312] Step 3:

[0313] The device sends the search query and emotion information to the server, using JSON format data as the communication protocol.

[0314] Step 4:

[0315] The server receives the search query and uses generative AI to find relevant notes and URLs, for example, based on keywords like "next week," "project meeting," or "agenda."

[0316] Step 5:

[0317] The server then prioritizes search results based on emotion and sends them to the device, so for example, a confused user would be shown the most useful notes and documents.

[0318] Step 6:

[0319] The device displays the search results to the user. For example, a link or URL for "Notes on the agenda for next week's project meeting" appears on the user's screen.

[0320] Document summary / explanation (emotion engine compatible version)

[0321] Step 1:

[0322] The user specifies a specific note or URL on the terminal and requests a summary or explanation. For example, the user requests a "summary of a technical specification."

[0323] Step 2:

[0324] The device uses an emotion engine to recognize the user's emotion. For example, the emotion engine determines that the user is calm.

[0325] Step 3:

[0326] The device sends a summary / explanation request and emotion information to the server. JSON format data is used as the communication protocol.

[0327] Step 4:

[0328] The server passes the summary / explanation request to the generation AI, which generates a summary or explanation of the specified document. For example, the generation AI summarizes a technical specification and generates the content, "This technical specification describes the system design and requirements."

[0329] Step 5:

[0330] The server optimizes the generated summaries and commentaries based on emotion information and sends them to the device. For example, a calm user will receive a detailed summary.

[0331] Step 6:

[0332] The terminal displays the summary / explanation result to the user. For example, the following content is displayed on the user's screen: "Summary: This technical specification describes the system design and requirements."

[0333] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[0334] Example 2

[0335] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0336] Conventional information management systems have had the problem of being difficult to present optimal suggestions and search results that take into account the user's emotions when saving and searching text data and URLs entered by the user.In addition, there was no method for suggesting storage locations for information or generating tags based on emotions, so there was a need to improve the user experience.

[0337] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for extracting keywords from text data input by a user, a means for transmitting the extracted keywords and the user's emotional information to the server, a means for the generative AI model to suggest an appropriate storage location taking into account the emotional information, and a means for saving the text data in the suggested storage location. This enables the suggestion of an optimal storage location taking into account the user's emotions and efficient management of text data.

[0338] The system also includes a means for registering URLs of external documents, a means for the generative AI model to evaluate the content of the URL taking into account the user's emotional information and generate related tags, and a means for saving the URL together with the generated related tags, which facilitates tag generation and URL management based on emotional information.

[0339] The system further includes a means for transmitting the search query and emotion information entered by the user to a server, a means for the generative AI model to search for related text data and URLs taking the emotion information into consideration, and a means for presenting search results to the user, thereby realizing efficient search for related information according to the user's emotions and presenting optimal search results.

[0340] "Text data" refers to textual information entered by a user, including notes, documents, messages, and the like.

[0341] A "keyword" is a particularly important word or phrase in text data that summarizes or represents the content of the text data.

[0342] "Emotion information" is data that indicates the user's emotional state, and includes emotions such as tension, impatience, and calmness.

[0343] A "server" refers to a computer system that processes and stores data on a network.

[0344] A "generative AI model" is an artificial intelligence model that performs natural language processing and data analysis, suggesting storage locations and optimizing search results based on user emotions and text data.

[0345] A "storage location" refers to a digital folder or section of a database used to store text data or URLs.

[0346] "External Documents" refers to documents that exist on external websites or other systems and that users register with the system.

[0347] "URL" is an abbreviation for Uniform Resource Locator, and refers to an address for specifying resources on the Internet.

[0348] "Related tags" are keywords or phrases that are associated with text data or URLs to make them easier to search and organize.

[0349] A "search query" is text data that a user enters when searching for information, and refers to a word or phrase that specifies the search target.

[0350] "Search Results" refers to a list of information provided by the system based on a user's search query, including relevant text data and URLs.

[0351] The present invention is an information management system that allows users to efficiently manage, search, and retrieve notes and documents, and provides summaries and explanations, while also recognizing and adapting to the user's emotions. Below, the program processing of this system is explained in natural language and detailed with specific examples.

[0352] Save and classify notes (emotion engine compatible version)

[0353] When a user enters a new note, the device analyzes the content of the entered note and extracts keywords. This analysis uses natural language processing (NLP) technology. The device also uses an emotion engine to recognize the user's emotions. The recognized emotions and extracted keywords are sent from the device to the server. The server uses a generative AI model to suggest the optimal storage location taking into account the emotion information. This suggestion is sent to the device, which notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[0354] Specific examples

[0355] The user enters "Notes about next week's project meeting." The device uses an emotion engine to recognize that the user is nervous. The emotion information of the keywords "next week," "project meeting," and "nervous" is sent to the server. The server uses a generative AI model to suggest the "Project-related documents" folder as a storage location. The device asks the user, "Do you want to save this note in the 'Project-related documents' folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[0356] Prompt Sentence Examples

[0357] Input note: "Notes about next week's project meeting"

[0358] User Emotion: "Tension"

[0359] Folder suggestions by generative AI: "Project-related documents"

[0360] Registering an external document URL (emotion engine compatible version)

[0361] When a user enters the URL of an external document, the device sends the URL to the server, which uses a generative AI model to evaluate the URL's content and generate relevant tags, taking the user's emotional information into account. The generated tags and URL are then stored in a database.

[0362] Specific examples

[0363] When a user enters the "URL of a technical specification," the emotion engine recognizes that the user is impatient. The device sends the URL and the "impatient" emotion information to the server. The server uses a generative AI model to analyze the content of the URL and generate tags such as "technical specification," "specifications," and "concept." The generated tags and URL are stored in a database.

[0364] Prompt Sentence Examples

[0365] Input URL: "http: / / example.com / technical-spec"

[0366] User Emotion: "Impatience"

[0367] Generative AI tags: "Technical specifications," "Specifications," "Concept"

[0368] Information Search (Emotion Engine Compatible Version)

[0369] When a user enters a search query, the device recognizes the user's emotions along with the search query and sends this information to the server. The server uses a generative AI model to search for related notes and URLs while taking the emotional information into account. The search results are sent to the device and presented in a format that best suits the user's emotions.

[0370] Specific examples

[0371] When a user searches for "next week's project meeting agenda," the emotion engine recognizes that the user is confused. The device sends the search query and the "confused" emotion information to the server. The server uses a generative AI model to prioritize the notes and documents that are most likely to be helpful to the confused user. The device then displays search results optimized for the user.

[0372] Prompt Sentence Examples

[0373] Search query: "next week's project meeting agenda"

[0374] User sentiment: "Confused"

[0375] Generative AI search results: the most helpful notes and documents for confused users

[0376] Document summary / explanation (emotion engine compatible version)

[0377] When a user requests a summary or commentary for a specific note or URL, the device sends this request and the user's emotional information to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and optimizes the content according to the emotional information. The generated summary or commentary is sent to the device and presented to the user.

[0378] Specific examples

[0379] When a user requests a "summary of technical specifications," the emotion engine recognizes the user's calm state. The device sends the summary request and the "calm" emotion information to the server. The server uses a generative AI model to summarize the technical specifications and generate a detailed summary appropriate for a calm user. The device then displays this generated summary to the user.

[0380] Prompt Sentence Examples

[0381] Summary request: "Summary of technical specifications"

[0382] User Sentiment: "Calm"

[0383] Generative AI Summarization: Detailed summaries suitable for calm users

[0384] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[0385] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0386] Save and classify notes (emotion engine compatible version)

[0387] Processing Steps

[0388] Step 1:

[0389] The user enters a note.

[0390] Input: Text data (e.g., "Notes about next week's project meeting")

[0391] Action: A user enters a note into a text entry field on a device.

[0392] Output: Text data entered into the terminal

[0393] Step 2:

[0394] The device analyzes the contents of the notes and extracts keywords.

[0395] Input: Text data

[0396] How it works: The device's software uses natural language processing (NLP) to extract key keywords from the note's content.

[0397] Output: Extracted keywords (e.g., "next week" or "project meeting")

[0398] Step 3:

[0399] The terminal uses an emotion engine to recognize the user's emotion.

[0400] Input: Text data, user typing speed, touch strength, and other sensory information

[0401] How it works: The emotion engine analyzes the user's emotional state.

[0402] Output: Emotional information (e.g., "tension")

[0403] Step 4:

[0404] The device transmits the extracted keywords and emotion information to the server.

[0405] Input: Keywords, emotional information

[0406] Operation: The communication module of the terminal sends data to the server as packets.

[0407] Output: Keywords and sentiment information sent to the server

[0408] Step 5:

[0409] The server passes the received data to the generative AI model.

[0410] Input: Keywords, emotional information

[0411] How it works: The server calls the API of the generative AI model and passes the data as an argument.

[0412] Output: The generative AI model begins its analysis

[0413] Step 6:

[0414] A generative AI model analyzes the data and suggests appropriate storage locations.

[0415] Input: Keywords, emotional information

[0416] How it works: A generative AI model suggests storage locations based on keywords and sentiment information.

[0417] Output: Suggested save location (e.g. "Project Related Documents" folder)

[0418] Step 7:

[0419] The server sends the proposal results to the terminal.

[0420] Input: Suggested save location

[0421] Operation: The server sends the proposal results to the device.

[0422] Output: Suggestion results sent to the device

[0423] Step 8:

[0424] The terminal notifies the user of the proposal results.

[0425] Input: Proposal results

[0426] What it does: Displays suggestions on your device in a pop-up notification or dialog box.

[0427] Output: The proposed results presented to the user

[0428] Step 9:

[0429] The user agrees to the proposal.

[0430] Input: Proposal results

[0431] What it does: The user agrees to save the note in the suggested folder.

[0432] Output: User consent

[0433] Step 10:

[0434] Your device will save the note in the suggested folder.

[0435] Input: User consent, memo data

[0436] What it does: The device's file system writes the memo data to the specified location.

[0437] Output: Saved memo data

[0438] Registering an external document URL (emotion engine compatible version)

[0439] Processing Steps

[0440] Step 1:

[0441] The user enters the URL of an external document.

[0442] Input: URL (e.g. "http: / / example.com / technical-spec")

[0443] How it works: A user enters a URL into an input field on their device.

[0444] Output: URL entered in terminal

[0445] Step 2:

[0446] The device sends the URL to the server.

[0447] Input: URL

[0448] Operation: The device's communication module sends the URL to the server.

[0449] Output: URL sent to the server

[0450] Step 3:

[0451] The server passes the URL to the generative AI model.

[0452] Input: URL

[0453] How it works: The server calls the API for the generated AI model and passes the URL.

[0454] Output: The generative AI model begins its analysis

[0455] Step 4:

[0456] A generative AI model evaluates the URL content and generates tags.

[0457] Input: URL

[0458] How it works: A generative AI model analyzes the content of a URL and generates relevant tags.

[0459] Output: Generated tags (e.g. "Technical Specification", "Specification", "Concept")

[0460] Step 5:

[0461] The generative AI model also takes into account the user's emotional information.

[0462] Input: Emotional information (e.g., "impatience")

[0463] How it works: The generative AI model reflects the user's emotional information in tag generation.

[0464] Output: Tag priority based on sentiment information

[0465] Step 6:

[0466] The server stores the generated tag and URL in a database.

[0467] Input: Tag, URL

[0468] How it works: The server calls the database API and saves the tags and URLs.

[0469] Output: Tags and URLs stored in the database

[0470] Information Search (Emotion Engine Compatible Version)

[0471] Processing Steps

[0472] Step 1:

[0473] The user enters a search query.

[0474] Input: Search query (e.g. "agenda for next week's project meeting")

[0475] What happens: A user enters a search query into the search bar.

[0476] Output: Search query entered into the terminal

[0477] Step 2:

[0478] The device sends a search query to the server.

[0479] Input: Search query

[0480] Operation: The communication module of the device sends a search query to the server.

[0481] Output: The search query sent to the server

[0482] Step 3:

[0483] The device recognizes the user's emotions.

[0484] Input: Search query, user reaction data

[0485] How it works: The emotion engine analyzes the user's emotions.

[0486] Output: Emotional information (e.g., "confused")

[0487] Step 4:

[0488] The server uses the generative AI model to search for the data.

[0489] Input: search query, sentiment information

[0490] How it works: The server uses a generative AI model to search data and generate relevant results, taking into account emotional information.

[0491] Output: Relevant data (e.g., project meeting notes and documents)

[0492] Step 5:

[0493] Generative AI models take user sentiment into account to prioritize search results.

[0494] Input: Search results, emotional information

[0495] How it works: Generative AI models optimize search results based on user sentiment.

[0496] Output: Prioritized search results

[0497] Step 6:

[0498] The server sends the optimized search results to the device.

[0499] Input: Optimized search results

[0500] How it works: The server sends optimized search results to the device.

[0501] Output: Device-optimized search results

[0502] Step 7:

[0503] The terminal displays the search results to the user.

[0504] Input: Search results

[0505] How it works: The device's user interface (UI) displays the search results on the screen.

[0506] Output: Search results displayed to the user

[0507] Document summary / explanation (emotion engine compatible version)

[0508] Processing Steps

[0509] Step 1:

[0510] A user requests a summary or commentary on a particular document.

[0511] Input: Abstract Request (e.g., "Technical Specification Abstract")

[0512] Operation: A user enters a request for a document summary into a terminal.

[0513] Output: Summary request entered on terminal

[0514] Step 2:

[0515] The terminal sends a summary request and emotion information to the server.

[0516] Input: Summary request, emotion information

[0517] Operation: The communication module of the terminal sends a summary request and emotion information to the server.

[0518] Output: Summary request and emotion information sent to the server

[0519] Step 3:

[0520] The server asks the generative AI model to generate a summary.

[0521] Input: Summary request, emotion information

[0522] How it works: The server calls the API of the generative AI model and requests a summary.

[0523] Output: Generative AI model begins generating a summary

[0524] Step 4:

[0525] A generative AI model summarizes documents and takes sentiment information into account.

[0526] Input: Document data, emotion information

[0527] How it works: A generative AI model summarizes documents and optimizes content based on user sentiment.

[0528] Output: Optimized summary

[0529] Step 5:

[0530] The server transmits the generated summary and commentary to the terminal.

[0531] Input: Optimized summary

[0532] Operation: The server sends summary data to the terminal.

[0533] Output: Summary data sent to the terminal

[0534] Step 6:

[0535] The device presents a summary and explanation to the user.

[0536] Input: Summary data

[0537] Operation: The terminal's user interface (UI) displays the summary data.

[0538] Output: Summary data displayed to the user

[0539] This will enable the realization of a system that can efficiently manage, search, and summarize information while taking into account the user's emotions.

[0540] (Application example 2)

[0541] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0542] Conventional information management systems provide a means for efficiently managing text data entered by users and URLs of external documents, but lack functionality for presenting appropriate information based on the user's emotions or suggesting storage locations. This results in a uniform user experience, and the inability to provide support optimized for individual emotional states. The present invention aims to solve these problems and provide an information management and optimal presentation method that responds to the user's emotions.

[0543] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for extracting keywords from text data entered by the user, means for suggesting a storage location based on the extracted keywords and emotional information, means for saving the text data in the suggested storage location, means for recognizing the user's emotion, and means for presenting appropriate information according to the recognized emotion. This enables optimal information management and presentation according to the user's emotional state.

[0544] "Text data" is the document format used by users to enter information.

[0545] "Keywords" are important words or phrases extracted from text data.

[0546] "Emotion information" is data that indicates the user's emotional state and is recognized by the emotion engine.

[0547] A "storage location" is a folder or database area where text data and URLs are recorded.

[0548] The "suggestion means" is a function that suggests to the user the most suitable storage location based on the extracted keywords and emotion information.

[0549] An "external document URL" is a link to a document or page on the Internet.

[0550] "Related tags" are keywords related to the content of the registered URL, and are generated to improve searchability and manageability.

[0551] A "search query" is a question or keyword that a user enters to search for information.

[0552] "Search results" are a collection of related information and links presented by the system based on a search query.

[0553] An "information management system" is a system for efficiently storing, retrieving, and presenting user-entered data.

[0554] An "emotion engine" is a technology that recognizes the user's emotions and optimizes the system's operation based on those emotions.

[0555] This invention is an information management system that allows users to efficiently manage, search, summarize, and explain notes and documents, and in particular has the ability to recognize and adapt to the user's emotions. This information management system analyzes text data and URLs of external documents entered by the user and provides appropriate storage locations and additional information along with emotional information.

[0556] Save and classify notes (emotion engine compatible version)

[0557] Program processing overview

[0558] When a user enters a new note, the device analyzes the content and emotional information of the note. The analyzed keywords and emotional information are sent to the server, which uses a generative AI model to suggest the optimal location to save the note. The user is notified of the suggested location, and if the user approves, the note is saved in the designated folder.

[0559] Hardware: Smart glasses, head-mounted displays

[0560] Software: Emotion recognition engine, generative AI model

[0561] Specific examples

[0562] A user inputs "Notes about next week's project meeting," and the emotion engine recognizes that the user is nervous. The keywords "next week," "project meeting," and the emotion information of "nervous" are sent to the server, and the generation AI determines that the "Project-related documents" folder is the most appropriate. The device suggests to the user, "Do you want to save this note in the 'Project-related documents' folder?" and if the user approves, the note is saved.

[0563] Registering an external document URL (emotion engine compatible version)

[0564] Program processing overview

[0565] When a user enters the URL of an external document, the URL and emotional information are sent to the server, which then uses generative AI to analyze the URL's content and emotional information. Based on the analysis results, relevant tags are generated and saved along with the URL.

[0566] Hardware: Smart glasses, head-mounted displays

[0567] Software: Emotion recognition engine, generative AI model

[0568] Specific examples

[0569] The user inputs the "URL of the technical specifications," and the emotion engine recognizes the user's impatience. The URL and the "impatience" emotion information are sent to the server, and the generation AI analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "concept." The generated tags and URL are stored in a database.

[0570] Information Search (Emotion Engine Compatible Version)

[0571] Program processing overview

[0572] When a user enters a search query, the device sends the search query and emotion information to the server, which then uses generative AI to consider the user's emotion when searching for related notes and URLs, and presents search results in a format optimized for the user's emotion.

[0573] Hardware: Smart glasses, head-mounted displays

[0574] Software: Emotion recognition engine, generative AI model

[0575] Specific examples

[0576] A user searches for "next week's project meeting agenda," and the emotion engine recognizes confusion. The search query and the "confused" emotion information are sent to the server, which prioritizes and displays notes and documents that are most likely to be useful to the confused user. The device then displays search results optimized for the user.

[0577] Document summary / explanation (emotion engine compatible version)

[0578] Program processing overview

[0579] When a user requests a summary or commentary of a specific note or URL, the request and emotional information are sent to the server, which uses generative AI to generate a summary or commentary of the specified document, optimizes it for the emotional information, and presents it on the device.

[0580] Hardware: Smart glasses, head-mounted displays

[0581] Software: Emotion recognition engine, generative AI model

[0582] Specific examples

[0583] The user requests a "summary of the technical specifications," and the emotion engine recognizes the emotion of calm. The summary request and the emotion information of "calm" are sent to the server, which then summarizes the technical specifications and generates a detailed summary suitable for a calm user. The terminal displays this summary to the user.

[0584] Prompt Sentence Examples

[0585] "When the user picks up the smartphone, the emotion engine recognizes the user's emotion as 'interest'. At this time, please display detailed information about the related smartphone."

[0586] As described above, the present invention is a system that recognizes a user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing work and shopping more efficiently.

[0587] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0588] Step 1:

[0589] The user inputs a memo. The terminal receives the memo. The input memo is displayed on the system interface. For example, if the user inputs "memo about next week's project meeting," the terminal acquires the text data. Here, the input data is "memo about next week's project meeting."

[0590] Step 2:

[0591] The device passes the text data to a keyword extraction module to analyze the content of the entered notes. The keyword extraction module uses an algorithm to extract important words and phrases. For example, keywords such as "next week" and "project meeting" are extracted. Here, the input data is "Notes about next week's project meeting," and the output data is "next week" and "project meeting."

[0592] Step 3:

[0593] At the same time, the device passes the text data to an emotion recognition engine to analyze the user's emotional state. The emotion recognition engine uses an emotion analysis algorithm to analyze the user's emotions. For example, if the user is recognized as nervous, emotion data is generated. Here, the input data is "Notes about next week's project meeting" and the output data is "nervous."

[0594] Step 4:

[0595] The device sends the extracted keywords and emotion information to the server. The server receives this data and passes it to the generative AI model. The generative AI model analyzes this information and runs an algorithm to suggest an appropriate storage location. For example, the generative AI model may determine that the "Project-related documents" folder is optimal. Here, the input data is "next week," "project meeting," and "tension," and the output data is "Project-related documents."

[0596] Step 5:

[0597] The server returns the generated save location suggestion to the device. The device receives this information and notifies the user. For example, the device suggests to the user, "Do you want to save this memo in the 'Project Related Documents' folder?" Here, the input data is "Project Related Documents" and the output data is a user notification message.

[0598] Step 6:

[0599] If the user agrees with the proposal, the device saves the memo in the specified folder. When the save operation is complete, the device displays a save completion notification to the user. For example, if the user presses the Agree button, the memo is saved in the "Project Related Documents" folder. Here, the input data is the user's consent (clicking the Agree button), and the output data is the memo save operation.

[0600] Step 7:

[0601] Finally, the saved note is recorded in the database, making it available for future searches and summarization requests. It also displays the saved location when the user reviews the note later. For example, the database records that the note was saved in the "Project Related Documents" folder. Here, the input data is the note save operation, and the output data is the database update.

[0602] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0603] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0604] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0605] [Second embodiment]

[0606] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0607] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0608] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0609] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0610] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0611] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0612] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0613] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0614] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0615] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0616] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0617] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0618] The present invention is an information management system that allows users to efficiently manage and search for notes and documents, and receive summaries and explanations. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[0619] Save and categorize your notes

[0620] The user enters a new note. The device analyzes the content of the entered note and extracts keywords. The device then sends the note content, including the extracted keywords, to the server. The server uses generative AI to suggest the optimal storage location. Information about the suggested storage location is sent to the device, and the device notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[0621] Specific examples

[0622] The user enters "Notes about next week's project meeting." The device extracts keywords such as "next week" and "project meeting" and sends them to the server. Using generative AI, the server determines that the "Project-related documents" folder is the most appropriate. The device then suggests to the user, "Do you want to save this note in the Project-related documents folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[0623] Registering an external document URL

[0624] The user enters the URL of an external document. The device sends the URL as is to the server. The server uses generative AI to evaluate the content of the URL and generate relevant tags, allowing the user to easily search for related documents later.

[0625] Specific examples

[0626] The user enters the "URL of the technical specifications." The device sends this to the server, which analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "concept." The generated tags and URL are stored in a database.

[0627] Information Search

[0628] A user enters a search query into their device. The device sends the search query to the server, which uses generative AI to search for related notes and URLs. The search results are sent to the device and displayed to the user.

[0629] Specific examples

[0630] A user searches for "next week's project meeting agenda." The device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "notes about next week's project meeting agenda" and sends it to the device. The device displays the search result to the user.

[0631] Document Summary / Explanation

[0632] The user requests a summary or commentary on a specific note or URL. The device sends the summary / commentary request to the server. The server uses generation AI to generate a summary or commentary for the specified document and sends it to the device. The device then displays the summary / commentary result to the user.

[0633] Specific examples

[0634] The user requests a "summary of the technical specifications." The device sends this request to the server, which then uses a generation AI to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the system design and requirements," and is sent to the device. The device then displays the summary to the user.

[0635] As a result, the present invention allows users to quickly find the information they need from a vast amount of information, greatly improving work efficiency.

[0636] The processing flow will be explained below.

[0637] Save and categorize your notes

[0638] Step 1:

[0639] A user inputs a new note into a terminal. For example, the user inputs "About the agenda for next week's project meeting."

[0640] Step 2:

[0641] The device analyzes the contents of the memo and extracts keywords, such as "next week," "project meeting," and "agenda."

[0642] Step 3:

[0643] The device sends the extracted keywords and memo contents to the server, using JSON format data as the communication protocol.

[0644] Step 4:

[0645] The server passes the received data to the generation AI and instructs it to suggest the best place to save it. The generation AI determines that the "Project-related documents" folder is the best.

[0646] Step 5:

[0647] The server sends the proposed storage location information to the terminal, for example, "Proposed storage location: Project-related documents."

[0648] Step 6:

[0649] The device notifies the user with a suggested save location, for example, by displaying "Would you like to save this note in the 'Project Related Documents' folder?"

[0650] Step 7:

[0651] The user agrees with the proposal, e.g., clicks the "Save" button.

[0652] Step 8:

[0653] Your device will save the note to the specified location, for example, the "Project Related Documents" folder.

[0654] Registering an external document URL

[0655] Step 1:

[0656] The user enters the URL of an external document into the terminal, for example, "https: / / example.com / tech-spec."

[0657] Step 2:

[0658] The device sends the entered URL to the server, using JSON format data as the communication protocol.

[0659] Step 3:

[0660] The server receives the URL and instructs the AI ​​to evaluate the content of the URL. The AI ​​analyzes the content of the URL and generates relevant tags such as "technical specifications," "specifications," and "concepts."

[0661] Step 4:

[0662] The server stores the associated tags and URLs in a centralized database, for example using an SQL query.

[0663] Information Search

[0664] Step 1:

[0665] A user enters a search query into a device, for example, "agenda for next week's project meeting."

[0666] Step 2:

[0667] The device sends a search query to the server, using JSON format data as the communication protocol.

[0668] Step 3:

[0669] The server receives the search query and uses generative AI to search for relevant notes and URLs, for example, based on keywords such as "next week," "project meeting," and "agenda."

[0670] Step 4:

[0671] The server sends the search results to the device, such as a list of found notes or URLs in JSON format.

[0672] Step 5:

[0673] The device displays the search results to the user. For example, a link or URL for "Notes on the agenda for next week's project meeting" appears on the user's screen.

[0674] Document Summary / Explanation

[0675] Step 1:

[0676] The user specifies a specific note or URL on the terminal and requests a summary or explanation. For example, the user requests a "summary of a technical specification."

[0677] Step 2:

[0678] The terminal sends a summary / explanation request to the server. JSON format data is used as the communication protocol.

[0679] Step 3:

[0680] The server passes the summary / explanation request to the generation AI, which generates a summary or explanation of the specified document. For example, the generation AI summarizes a technical specification and generates the content, "This technical specification describes the system design and requirements."

[0681] Step 4:

[0682] The server sends the generated summary and commentary to the device, for example, in JSON format.

[0683] Step 5:

[0684] The terminal displays the summary / explanation result to the user. For example, the following content is displayed on the user's screen: "Summary: This technical specification describes the system design and requirements."

[0685] Example 1

[0686] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0687] Efficiently managing large amounts of text data and documents and quickly searching and retrieving necessary information are important challenges in today's information society. However, conventional information management systems face problems such as manual note organization and tagging, and insufficient search accuracy. It is also difficult to quickly understand and summarize the content of external documents. Therefore, there is a need for a system that allows users to efficiently manage and search notes and documents, and receive summaries and explanations.

[0688] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0689] In this invention, the server includes means for extracting keywords from text data entered by a user, means for suggesting a storage location based on the extracted keywords, means for saving the text data in the suggested storage location, means for the user to register URLs of external documents, means for evaluating the content of the registered URLs and generating associated tags, means for searching for associated text data and URLs, and means for generating summaries or explanations of the text data and URLs using a generative AI model, thereby enabling users to efficiently manage notes and documents and quickly search and obtain necessary information.

[0690] "User" refers to a person who uses an information system.

[0691] "Text data" refers to character or document data entered by a user.

[0692] "Keywords" refer to important words or phrases extracted from text data.

[0693] "Storage location" refers to the location of a folder or database where text data or documents are stored.

[0694] "Extraction" refers to the operation of extracting specific keywords or information from text data.

[0695] "Suggestion" refers to a recommendation made by the server to the user based on the extracted data.

[0696] "Saving" refers to the operation of storing text data or documents in a specified location.

[0697] "External documents" refer to documents and files that exist on the Internet.

[0698] "URL" refers to a specific address or link to an external document.

[0699] "Evaluation" refers to the operation of analyzing the content of a registered URL to determine its value and relevance.

[0700] "Tags" refer to keywords or phrases that are associated with a URL or the content of a document.

[0701] "Search" refers to the operation of searching text data or URLs based on a query entered by a user to find relevant information.

[0702] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze, summarize, and make suggestions about text data.

[0703] "Summarization" refers to the operation of concisely summarizing the contents of a document or text data.

[0704] "Explanation" refers to the operation of providing a detailed explanation of the contents of a document or text data.

[0705] The present invention is an information management system that allows users to efficiently manage and search notes and documents, and receive summaries and explanations. This system analyzes text data and URLs of external documents entered by users, suggests appropriate storage locations, and generates related tags, thereby streamlining information management and search. The following describes in detail how this system is implemented.

[0706] In the system configuration, a server and a terminal work together. The server uses a generative AI model to analyze, evaluate, summarize, and explain text data and URLs. The terminal provides a user interface, receives user input, and communicates with the server.

[0707] Save and categorize your notes

[0708] The user enters a new note. The device analyzes the content of the note and extracts keywords using a natural language processing (NLP) library (e.g., spaCy or Gensim). The device then sends the note content, including the extracted keywords, to the server. The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt such as "Suggest a suitable storage location for next week's project meeting" to suggest the best storage location. This prompt is generated via an HTTP POST request.

[0709] For example, if a user types "Notes about next week's project meeting," the device extracts keywords such as "next week" and "project meeting" and sends them to the server. The server determines that the "Project-related documents" folder is the most appropriate location and sends a suggestion to the device. If the user agrees to this suggestion, the note will be saved in the specified folder.

[0710] Registering an external document URL

[0711] The user inputs the URL of an external document. The device sends the input URL as is to the server. The server evaluates the content of the URL using a generative AI model and generates a prompt such as "Please evaluate the URL of the technical specification document," thereby generating relevant tags. The generated tags and URLs are stored in a database (e.g., MySQL or MongoDB).

[0712] As a concrete example, when a user enters the "URL of a technical specification," the device sends it to the server, which analyzes the contents of the URL and generates tags such as "technical specification," "specification," and "concept," which are then stored in a database along with the URL.

[0713] Information Search

[0714] A user enters a search query into a device. The device sends the entered search query to the server. The server uses a generative AI model to search for relevant notes and URLs and finds relevant information from a database by generating prompts such as "Please search for next week's project meeting agenda." The search results are sent to the device and displayed to the user.

[0715] For example, if a user searches for "next week's project meeting agenda," the device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "notes about next week's project meeting agenda" and sends it to the device. The device then displays the search result to the user.

[0716] Document Summary / Explanation

[0717] The user requests a summary or commentary for a specific note or URL. The device sends the summary / commentary request to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and generates a prompt such as "Please generate a summary of the technical specification." The generated summary or commentary is sent to the device and displayed to the user.

[0718] As a concrete example, when a user requests a "summary of the technical specifications," the device sends this request to the server, which then uses a generative AI model to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the system design and requirements," and is sent to the device. The device then displays the summary to the user.

[0719] This allows users to quickly find the information they need from a vast amount of information, significantly improving work efficiency.

[0720] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0721] Step 1:

[0722] The user inputs a new note into the terminal. Any text is provided to the terminal as input data from the user.

[0723] Step 2:

[0724] The device analyzes the content of the input note and extracts keywords using a natural language processing (NLP) library (e.g., spaCy or Gensim). The input is text data, and the output is a list of extracted keywords. Specifically, the text data is tokenized and important words and phrases are identified.

[0725] Step 3:

[0726] The terminal sends the contents of the memo including the extracted keywords to the server (HTTP POST request). The input is the keyword list and the memo contents, and the output is a request sent to the server.

[0727] Step 4:

[0728] The server inputs the received note content into a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt that suggests the optimal storage location. The input is the note content and a list of keywords, and the output is information about the suggested storage location. Example prompt: "Suggest a suitable storage location for next week's project meeting."

[0729] Step 5:

[0730] The server sends the generated storage location information to the device (HTTP POST request), and the device notifies the user of the storage location suggestion. The input is the storage location information, and the output is a notification to the user. Specifically, a notification is displayed on the device's UI.

[0731] Step 6:

[0732] If the user agrees to the proposal, the device sends the consent information to the server (HTTP POST request). The input is the user's consent, and the output is the transmission of consent data to the server.

[0733] Step 7:

[0734] With the user's consent, the server saves the memo in the specified folder. The saving is done in a database (e.g., MySQL or MongoDB). The input is the memo content and the save location, and the output is the save operation to the database.

[0735] Step 8:

[0736] The user enters the URL of an external document into the terminal. The input is a URL.

[0737] Step 9:

[0738] The terminal sends the entered URL to the server as is (HTTP POST request). The input is a URL, and the output is a request sent to the server.

[0739] Step 10:

[0740] The server analyzes the content of the received URL and uses a generative AI model to generate the associated tag. The input is the URL and the output is the generated tag. Example prompt: "Please rate the URL for the technical specification."

[0741] Step 11:

[0742] The server saves the generated tags and URLs to a database. The input is the tag and URL, and the output is a save operation to the database.

[0743] Step 12:

[0744] A user inputs a search query into a terminal. The input is a search query.

[0745] Step 13:

[0746] The terminal sends the entered search query to the server (HTTP GET request). The input is the search query, and the output is the request sent to the server.

[0747] Step 14:

[0748] The server receives the search query and uses a generative AI model to generate a prompt that searches for related notes and URLs. The input is the search query and the output is a list of related data. Example prompt: "Please find the agenda for next week's project meeting."

[0749] Step 15:

[0750] The server uses the generative AI model to search for relevant notes and URLs from the database and sends the results to the device (HTTP GET request). The input is the search results, and the output is the data sent to the device.

[0751] Step 16:

[0752] The terminal displays the received search results to the user. The input is the search results and the output is the display on the user interface.

[0753] Step 17:

[0754] The user requests a summary or description of a particular note or URL. The input is a summary / description request.

[0755] Step 18:

[0756] The terminal sends a summary / explanation request to the server (HTTP POST request). The input is the request data, and the output is the data sent to the server.

[0757] Step 19:

[0758] The server passes the received request to a generative AI model to generate a summary or commentary of the specified document. The input is a note or URL, and the output is a summary or commentary text. Example prompt: "Generate a summary of a technical specification."

[0759] Step 20:

[0760] The server sends the generated summary and commentary to the terminal (HTTP POST request). The input is the summary / commentary result, and the output is the message sent to the terminal.

[0761] Step 21:

[0762] The terminal displays the received summary / explanation results to the user. The input is the summary / explanation results, and the output is the display on the user interface.

[0763] (Application example 1)

[0764] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0765] Robots used in factories require the management of numerous work instructions and maintenance logs, but as this information increases, management becomes cumbersome. A system that can quickly search for necessary information and provide summaries is also needed, but existing systems cannot efficiently achieve this. Therefore, there is a need to streamline information management for factory robots and improve productivity.

[0766] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0767] In this invention, the server includes means for extracting keywords from text data entered by a user, means for suggesting a storage location based on the extracted keywords, means for saving the text data in the suggested storage location, means for searching for files, means for presenting search results to the user, means for generating summaries, and means for presenting the generated summaries to the user, thereby enabling efficient management of robot instructions and logs in a factory, rapid search, and provision of summaries.

[0768] "User" means a person or organization that operates the system and inputs and retrieves information.

[0769] "Text data" refers to information that consists of characters and sentences.

[0770] "Keywords" are important words and phrases extracted from the text data entered by the user.

[0771] A "storage location" is a specific folder or directory where data is stored.

[0772] A "server" is a computer system for storing and processing data.

[0773] A "file" is a unit for recording data or information.

[0774] "Search Results" refers to a collection of relevant information or data discovered based on a user-entered query.

[0775] The "summary" is a short sentence that succinctly summarizes the contents of the input document.

[0776] "URL" means a Uniform Resource Locator that provides a link to a web page or digital document.

[0777] "Related tags" are metadata that indicate keywords or categories related to a document or piece of information.

[0778] "Suggestion" refers to the system suggesting to the user the optimal storage location and processing method.

[0779] This invention is an information management system that efficiently manages, searches, and summarizes work instructions and maintenance logs for factory robots. This system is composed of a server and terminals and performs the following processes.

[0780] 1. System Configuration

[0781] Hardware

[0782] Server: High-performance server (e.g. AWS EC2 instance)

[0783] Terminal: Tablet or computer mounted on a robot in the factory

[0784] Network: Local Area Network (LAN) or Cloud Network

[0785] software

[0786] Server software: Database software (e.g., MySQL), generative AI API (e.g., OpenAI GPT)

[0787] Terminal software: User interface (UI) applications, search engine software

[0788] 2. Save and categorize your notes

[0789] The user inputs new text data (e.g., work instruction notes) into the device. The device extracts important keywords from the input text data. The text data, including the extracted keywords, is then sent to the server. The server uses a generative AI model to suggest the optimal storage location. The device notifies the user of the suggested storage location, and if the user approves, the text data is saved in the specified folder.

[0790] Specific examples

[0791] The user enters "Notes about next week's maintenance work." The device extracts keywords such as "next week" and "maintenance work" and sends them to the server. Using a generative AI model, the server determines that the "Maintenance-related documents" folder is the most appropriate. The device then asks the user, "Do you want to save this note in the Maintenance-related documents folder?" If the user approves, the note is saved in the "Maintenance-related documents" folder.

[0792] 3. Registering the external document URL

[0793] The user enters the URL of an external document. The device sends the URL as is to the server. The server uses a generative AI model to evaluate the URL's content and generate relevant tags, allowing the user to easily search for related documents later.

[0794] Specific examples

[0795] The user enters the URL of the technical specifications for the new machine. The device sends this to the server, which analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "new machine." The generated tags and URLs are stored in a database.

[0796] 4. Information Search

[0797] A user enters a search query into their device. The device sends the search query to the server, which uses a generative AI model to search for relevant text data and URLs. The search results are sent to the device and displayed to the user.

[0798] Specific examples

[0799] A user searches for "Details of next week's maintenance work." The device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "Notes about details of next week's maintenance work" and sends it to the device. The device displays the search result to the user.

[0800] 5. Document Summary / Explanation

[0801] The user requests a summary or commentary for a specific note or URL. The device sends the summary / commentary request to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and sends it to the device. The device then displays the summary / commentary result to the user.

[0802] Specific examples

[0803] The user requests a "summary of the technical specifications for a new machine." The device sends this request to the server, which uses a generative AI model to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the design and requirements for a new machine." and is sent to the device. The device then displays the summary to the user.

[0804] Prompt Sentence Examples

[0805] Write a summary of the following document:

[0806] This technical specification describes the system design and requirements. It includes detailed blueprints and specifications.

[0807] This system enables efficient management of robotic instructions and logs within factories, as well as quick search and provision of summaries.

[0808] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0809] Step 1:

[0810] The user inputs new text data (e.g., notes for a work instruction) into the terminal. The terminal receives this text data and stores it as input data for proceeding to the next step.

[0811] Step 2:

[0812] The device extracts important keywords from the input text data using a text analysis algorithm to identify key words and phrases within the text data, and then transmits the extracted keywords to the server.

[0813] Step 3:

[0814] The server receives the extracted keywords and uses a generative AI model to suggest the best storage location. The generative AI then searches related folders and directories based on the keywords to determine the best storage location. The server then sends the suggested storage location information to the device.

[0815] Step 4:

[0816] The device notifies the user of the storage location proposal received from the server. The user checks and approves the proposed storage location. After this operation is completed, the device proceeds to the next step.

[0817] Step 5:

[0818] The device saves the input text data in a location approved by the user. Specifically, it creates a text file in the specified folder and records the input data in it. This data is later used for searching and generating summaries.

[0819] Step 6:

[0820] The user enters the URL of an external document. The device receives this URL and retains it for the next step.

[0821] Step 7:

[0822] The device sends the entered URL as is to the server. The server receives the URL and evaluates the URL's content using a generative AI model. Based on the evaluation results, the server generates associated tags and stores the URL along with these tags in a database, allowing for easy search of the URL later.

[0823] Step 8:

[0824] The user inputs a search query into the terminal, which receives the query and proceeds to the next step.

[0825] Step 9:

[0826] The device sends a search query to the server, which uses a generative AI model to find relevant text data and URLs. The server searches its database to find results that match the user's search query. The server then sends the search results back to the device and presents them to the user.

[0827] Step 10:

[0828] The user requests a summary or commentary on a particular note or URL. The device receives this request and proceeds to the next step.

[0829] Step 11:

[0830] The device sends a summary / explanation request to the server, which uses the generative AI model to generate a summary or explanation for the specified document. The generated summary or explanation is then sent from the server to the device and displayed to the user through the device.

[0831] Examples:

[0832] If a user requests a "summary of the technical specifications for a new machine," the device sends this request directly to the server, which creates a summary based on the contents of the technical specifications and inputs the following prompt sentence into the generative AI model.

[0833] "Write a summary of the following document: This technical specification describes the system design and requirements. It includes detailed blueprints and specifications."

[0834] The generated summary is "This technical specification describes the design and requirements of a new machine," and is sent to the terminal and presented to the user.

[0835] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0836] The present invention is an information management system that allows users to efficiently manage, search, and retrieve notes and documents, and provides summaries and explanations, while also recognizing and adapting to the user's emotions. Below, the program processing of this system is explained in natural language and detailed with specific examples.

[0837] Save and classify notes (emotion engine compatible version)

[0838] When a user enters a new note, the device analyzes the content of the entered note and not only extracts keywords but also recognizes the user's emotions. This emotion recognition is performed using an emotion engine. The device sends the extracted keywords and emotion information to the server. The server passes the received data to a generation AI, which suggests an appropriate storage location. This suggestion is optimized according to the user's emotions. The suggested storage location information is sent to the device, and the device notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[0839] Specific examples

[0840] The user enters "Notes about next week's project meeting." The device uses its emotion engine to recognize that the user is nervous. The emotion information of the keywords "next week," "project meeting," and "nervous" is sent to the server. The server uses generative AI to determine that the "Project-related documents" folder is the best choice. The device suggests to the user, "Do you want to save this note in the 'Project-related documents' folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[0841] Registering an external document URL (emotion engine compatible version)

[0842] When a user enters the URL of an external document, the device sends the URL to the server, which uses a generative AI to evaluate the content of the URL and generate relevant tags, taking into account the user's emotional state. The generated tags and URL are then stored in a database.

[0843] Specific examples

[0844] When a user enters the "URL of a technical specification," the emotion engine recognizes that the user is impatient. The device sends the URL and the "impatient" emotion information to the server. The server uses a generation AI to analyze the contents of the URL and generate tags such as "technical specification," "specifications," and "concept." The generated tags and URL are stored in a database.

[0845] Information Search (Emotion Engine Compatible Version)

[0846] When a user enters a search query, the device recognizes the user's emotion along with the search query. The device then sends this information to the server, which uses generative AI to consider the user's emotion when searching for related notes and URLs. The search results are then sent to the device and presented in a format that best suits the user's emotion.

[0847] Specific examples

[0848] When a user searches for "next week's project meeting agenda," the emotion engine recognizes that the user is confused. The device sends the search query and the "confused" emotion information to the server. The server uses generative AI to prioritize and display notes and documents that are most likely to be useful to the confused user. The device then displays search results optimized for the user.

[0849] Document summary / explanation (emotion engine compatible version)

[0850] When a user requests a summary or commentary for a specific note or URL, the device sends this request and the user's emotional information to the server. The server uses generative AI to generate a summary or commentary for the specified document, optimizing it according to the emotional information. The generated summary or commentary is then sent to the device and presented to the user.

[0851] Specific examples

[0852] When a user requests a "summary of technical specifications," the emotion engine recognizes the user's calmness. The device sends the summary request and the "calm" emotion information to the server. The server uses generative AI to summarize the technical specifications and generate a detailed summary suitable for a calm user. The device then displays this generated summary to the user.

[0853] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[0854] The processing flow will be explained below.

[0855] Save and classify notes (emotion engine compatible version)

[0856] Step 1:

[0857] A user inputs a new note into a terminal. For example, the user inputs "About the agenda for next week's project meeting."

[0858] Step 2:

[0859] The device analyzes the contents of the memo and extracts keywords, such as "next week," "project meeting," and "agenda."

[0860] Step 3:

[0861] The device uses an emotion engine to recognize the user's emotions. For example, the emotion engine determines that the user is nervous.

[0862] Step 4:

[0863] The device sends the extracted keywords and emotion information to the server, which uses JSON format data as the communication protocol.

[0864] Step 5:

[0865] The server passes the received data to the generation AI and instructs it to suggest the best place to save it. For example, the generation AI determines that the "Project-related documents" folder is the best.

[0866] Step 6:

[0867] The server sends the proposed storage location information to the terminal, for example, "Proposed storage location: Project-related documents."

[0868] Step 7:

[0869] The device will notify the user with save location suggestions, for example, "Would you like to save this note in the 'Project Related Documents' folder?"

[0870] Step 8:

[0871] The user agrees with the proposal, e.g., clicks the "Save" button.

[0872] Step 9:

[0873] Your device will save the note to the specified location, for example, the "Project Related Documents" folder.

[0874] Registering an external document URL (emotion engine compatible version)

[0875] Step 1:

[0876] The user enters the URL of an external document into the terminal, for example, "https: / / example.com / tech-spec."

[0877] Step 2:

[0878] The device uses an emotion engine to recognize the user's emotions. For example, the emotion engine determines that the user is anxious.

[0879] Step 3:

[0880] The device sends the input URL and emotion information to the server. JSON format data is used as the communication protocol.

[0881] Step 4:

[0882] The server receives the URL and instructs the AI ​​to evaluate the content of the URL. The AI ​​analyzes the content of the URL and generates relevant tags such as "technical specifications," "specifications," and "concepts."

[0883] Step 5:

[0884] The server stores the associated tags and URLs in a centralized database, for example using an SQL query.

[0885] Information Search (Emotion Engine Compatible Version)

[0886] Step 1:

[0887] A user enters a search query into a device, for example, "agenda for next week's project meeting."

[0888] Step 2:

[0889] The device uses an emotion engine to recognize the user's emotion. For example, the emotion engine determines that the user is confused.

[0890] Step 3:

[0891] The device sends the search query and emotion information to the server, using JSON format data as the communication protocol.

[0892] Step 4:

[0893] The server receives the search query and uses generative AI to find relevant notes and URLs, for example, based on keywords like "next week," "project meeting," or "agenda."

[0894] Step 5:

[0895] The server then prioritizes search results based on emotion and sends them to the device, so for example, a confused user would be shown the most useful notes and documents.

[0896] Step 6:

[0897] The device displays the search results to the user. For example, a link or URL for "Notes on the agenda for next week's project meeting" appears on the user's screen.

[0898] Document summary / explanation (emotion engine compatible version)

[0899] Step 1:

[0900] The user specifies a specific note or URL on the terminal and requests a summary or explanation. For example, the user requests a "summary of a technical specification."

[0901] Step 2:

[0902] The device uses an emotion engine to recognize the user's emotion. For example, the emotion engine determines that the user is calm.

[0903] Step 3:

[0904] The device sends a summary / explanation request and emotion information to the server. JSON format data is used as the communication protocol.

[0905] Step 4:

[0906] The server passes the summary / explanation request to the generation AI, which generates a summary or explanation of the specified document. For example, the generation AI summarizes a technical specification and generates the content, "This technical specification describes the system design and requirements."

[0907] Step 5:

[0908] The server optimizes the generated summaries and commentaries based on emotion information and sends them to the device. For example, a calm user will receive a detailed summary.

[0909] Step 6:

[0910] The terminal displays the summary / explanation result to the user. For example, the following content is displayed on the user's screen: "Summary: This technical specification describes the system design and requirements."

[0911] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[0912] Example 2

[0913] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0914] Conventional information management systems have had the problem of being difficult to present optimal suggestions and search results that take into account the user's emotions when saving and searching text data and URLs entered by the user.In addition, there was no method for suggesting storage locations for information or generating tags based on emotions, so there was a need to improve the user experience.

[0915] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for extracting keywords from text data input by a user, a means for transmitting the extracted keywords and the user's emotional information to the server, a means for the generative AI model to suggest an appropriate storage location taking into account the emotional information, and a means for saving the text data in the suggested storage location. This enables the suggestion of an optimal storage location taking into account the user's emotions and efficient management of text data.

[0916] The system also includes a means for registering URLs of external documents, a means for the generative AI model to evaluate the content of the URL taking into account the user's emotional information and generate related tags, and a means for saving the URL together with the generated related tags, which facilitates tag generation and URL management based on emotional information.

[0917] The system further includes a means for transmitting the search query and emotion information entered by the user to a server, a means for the generative AI model to search for related text data and URLs taking the emotion information into consideration, and a means for presenting search results to the user, thereby realizing efficient search for related information according to the user's emotions and presenting optimal search results.

[0918] "Text data" refers to textual information entered by a user, including notes, documents, messages, and the like.

[0919] A "keyword" is a particularly important word or phrase in text data that summarizes or represents the content of the text data.

[0920] "Emotion information" is data that indicates the user's emotional state, and includes emotions such as tension, impatience, and calmness.

[0921] A "server" refers to a computer system that processes and stores data on a network.

[0922] A "generative AI model" is an artificial intelligence model that performs natural language processing and data analysis, suggesting storage locations and optimizing search results based on user emotions and text data.

[0923] A "storage location" refers to a digital folder or section of a database used to store text data or URLs.

[0924] "External Documents" refers to documents that exist on external websites or other systems and that users register with the system.

[0925] "URL" is an abbreviation for Uniform Resource Locator, and refers to an address for specifying resources on the Internet.

[0926] "Related tags" are keywords or phrases that are associated with text data or URLs to make them easier to search and organize.

[0927] A "search query" is text data that a user enters when searching for information, and refers to a word or phrase that specifies the search target.

[0928] "Search Results" refers to a list of information provided by the system based on a user's search query, including relevant text data and URLs.

[0929] The present invention is an information management system that allows users to efficiently manage, search, and retrieve notes and documents, and provides summaries and explanations, while also recognizing and adapting to the user's emotions. Below, the program processing of this system is explained in natural language and detailed with specific examples.

[0930] Save and classify notes (emotion engine compatible version)

[0931] When a user enters a new note, the device analyzes the content of the entered note and extracts keywords. This analysis uses natural language processing (NLP) technology. The device also uses an emotion engine to recognize the user's emotions. The recognized emotions and extracted keywords are sent from the device to the server. The server uses a generative AI model to suggest the optimal storage location taking into account the emotion information. This suggestion is sent to the device, which notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[0932] Specific examples

[0933] The user enters "Notes about next week's project meeting." The device uses an emotion engine to recognize that the user is nervous. The emotion information of the keywords "next week," "project meeting," and "nervous" is sent to the server. The server uses a generative AI model to suggest the "Project-related documents" folder as a storage location. The device asks the user, "Do you want to save this note in the 'Project-related documents' folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[0934] Prompt Sentence Examples

[0935] Input note: "Notes about next week's project meeting"

[0936] User Emotion: "Tension"

[0937] Folder suggestions by generative AI: "Project-related documents"

[0938] Registering an external document URL (emotion engine compatible version)

[0939] When a user enters the URL of an external document, the device sends the URL to the server, which uses a generative AI model to evaluate the URL's content and generate relevant tags, taking the user's emotional information into account. The generated tags and URL are then stored in a database.

[0940] Specific examples

[0941] When a user enters the "URL of a technical specification," the emotion engine recognizes that the user is impatient. The device sends the URL and the "impatient" emotion information to the server. The server uses a generative AI model to analyze the content of the URL and generate tags such as "technical specification," "specifications," and "concept." The generated tags and URL are stored in a database.

[0942] Prompt Sentence Examples

[0943] Input URL: "http: / / example.com / technical-spec"

[0944] User Emotion: "Impatience"

[0945] Generative AI tags: "Technical specifications," "Specifications," "Concept"

[0946] Information Search (Emotion Engine Compatible Version)

[0947] When a user enters a search query, the device recognizes the user's emotions along with the search query and sends this information to the server. The server uses a generative AI model to search for related notes and URLs while taking the emotional information into account. The search results are sent to the device and presented in a format that best suits the user's emotions.

[0948] Specific examples

[0949] When a user searches for "next week's project meeting agenda," the emotion engine recognizes that the user is confused. The device sends the search query and the "confused" emotion information to the server. The server uses a generative AI model to prioritize the notes and documents that are most likely to be helpful to the confused user. The device then displays search results optimized for the user.

[0950] Prompt Sentence Examples

[0951] Search query: "next week's project meeting agenda"

[0952] User sentiment: "Confused"

[0953] Generative AI search results: the most helpful notes and documents for confused users

[0954] Document summary / explanation (emotion engine compatible version)

[0955] When a user requests a summary or commentary for a specific note or URL, the device sends this request and the user's emotional information to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and optimizes the content according to the emotional information. The generated summary or commentary is sent to the device and presented to the user.

[0956] Specific examples

[0957] When a user requests a "summary of technical specifications," the emotion engine recognizes the user's calm state. The device sends the summary request and the "calm" emotion information to the server. The server uses a generative AI model to summarize the technical specifications and generate a detailed summary appropriate for a calm user. The device then displays this generated summary to the user.

[0958] Prompt Sentence Examples

[0959] Summary request: "Summary of technical specifications"

[0960] User Sentiment: "Calm"

[0961] Generative AI Summarization: Detailed summaries suitable for calm users

[0962] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[0963] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0964] Save and classify notes (emotion engine compatible version)

[0965] Processing Steps

[0966] Step 1:

[0967] The user enters a note.

[0968] Input: Text data (e.g., "Notes about next week's project meeting")

[0969] Action: A user enters a note into a text entry field on a device.

[0970] Output: Text data entered into the terminal

[0971] Step 2:

[0972] The device analyzes the contents of the notes and extracts keywords.

[0973] Input: Text data

[0974] How it works: The device's software uses natural language processing (NLP) to extract key keywords from the note's content.

[0975] Output: Extracted keywords (e.g., "next week" or "project meeting")

[0976] Step 3:

[0977] The terminal uses an emotion engine to recognize the user's emotion.

[0978] Input: Text data, user typing speed, touch strength, and other sensory information

[0979] How it works: The emotion engine analyzes the user's emotional state.

[0980] Output: Emotional information (e.g., "tension")

[0981] Step 4:

[0982] The device transmits the extracted keywords and emotion information to the server.

[0983] Input: Keywords, emotional information

[0984] Operation: The communication module of the terminal sends data to the server as packets.

[0985] Output: Keywords and sentiment information sent to the server

[0986] Step 5:

[0987] The server passes the received data to the generative AI model.

[0988] Input: Keywords, emotional information

[0989] How it works: The server calls the API of the generative AI model and passes the data as an argument.

[0990] Output: The generative AI model begins its analysis

[0991] Step 6:

[0992] A generative AI model analyzes the data and suggests appropriate storage locations.

[0993] Input: Keywords, emotional information

[0994] How it works: A generative AI model suggests storage locations based on keywords and sentiment information.

[0995] Output: Suggested save location (e.g. "Project Related Documents" folder)

[0996] Step 7:

[0997] The server sends the proposal results to the terminal.

[0998] Input: Suggested save location

[0999] Operation: The server sends the proposal results to the device.

[1000] Output: Suggestion results sent to the device

[1001] Step 8:

[1002] The terminal notifies the user of the proposal results.

[1003] Input: Proposal results

[1004] What it does: Displays suggestions on your device in a pop-up notification or dialog box.

[1005] Output: The proposed results presented to the user

[1006] Step 9:

[1007] The user agrees to the proposal.

[1008] Input: Proposal results

[1009] What it does: The user agrees to save the note in the suggested folder.

[1010] Output: User consent

[1011] Step 10:

[1012] Your device will save the note in the suggested folder.

[1013] Input: User consent, memo data

[1014] What it does: The device's file system writes the memo data to the specified location.

[1015] Output: Saved memo data

[1016] Registering an external document URL (emotion engine compatible version)

[1017] Processing Steps

[1018] Step 1:

[1019] The user enters the URL of an external document.

[1020] Input: URL (e.g. "http: / / example.com / technical-spec")

[1021] How it works: A user enters a URL into an input field on their device.

[1022] Output: URL entered in terminal

[1023] Step 2:

[1024] The device sends the URL to the server.

[1025] Input: URL

[1026] Operation: The device's communication module sends the URL to the server.

[1027] Output: URL sent to the server

[1028] Step 3:

[1029] The server passes the URL to the generative AI model.

[1030] Input: URL

[1031] How it works: The server calls the API for the generated AI model and passes the URL.

[1032] Output: The generative AI model begins its analysis

[1033] Step 4:

[1034] A generative AI model evaluates the URL content and generates tags.

[1035] Input: URL

[1036] How it works: A generative AI model analyzes the content of a URL and generates relevant tags.

[1037] Output: Generated tags (e.g. "Technical Specification", "Specification", "Concept")

[1038] Step 5:

[1039] The generative AI model also takes into account the user's emotional information.

[1040] Input: Emotional information (e.g., "impatience")

[1041] How it works: The generative AI model reflects the user's emotional information in tag generation.

[1042] Output: Tag priority based on sentiment information

[1043] Step 6:

[1044] The server stores the generated tag and URL in a database.

[1045] Input: Tag, URL

[1046] How it works: The server calls the database API and saves the tags and URLs.

[1047] Output: Tags and URLs stored in the database

[1048] Information Search (Emotion Engine Compatible Version)

[1049] Processing Steps

[1050] Step 1:

[1051] The user enters a search query.

[1052] Input: Search query (e.g. "agenda for next week's project meeting")

[1053] What happens: A user enters a search query into the search bar.

[1054] Output: Search query entered into the terminal

[1055] Step 2:

[1056] The device sends a search query to the server.

[1057] Input: Search query

[1058] Operation: The communication module of the device sends a search query to the server.

[1059] Output: The search query sent to the server

[1060] Step 3:

[1061] The device recognizes the user's emotions.

[1062] Input: Search query, user reaction data

[1063] How it works: The emotion engine analyzes the user's emotions.

[1064] Output: Emotional information (e.g., "confused")

[1065] Step 4:

[1066] The server uses the generative AI model to search for the data.

[1067] Input: search query, sentiment information

[1068] How it works: The server uses a generative AI model to search data and generate relevant results, taking into account emotional information.

[1069] Output: Relevant data (e.g., project meeting notes and documents)

[1070] Step 5:

[1071] Generative AI models take user sentiment into account to prioritize search results.

[1072] Input: Search results, emotional information

[1073] How it works: Generative AI models optimize search results based on user sentiment.

[1074] Output: Prioritized search results

[1075] Step 6:

[1076] The server sends the optimized search results to the device.

[1077] Input: Optimized search results

[1078] How it works: The server sends optimized search results to the device.

[1079] Output: Device-optimized search results

[1080] Step 7:

[1081] The terminal displays the search results to the user.

[1082] Input: Search results

[1083] How it works: The device's user interface (UI) displays the search results on the screen.

[1084] Output: Search results displayed to the user

[1085] Document summary / explanation (emotion engine compatible version)

[1086] Processing Steps

[1087] Step 1:

[1088] A user requests a summary or commentary on a particular document.

[1089] Input: Abstract Request (e.g., "Technical Specification Abstract")

[1090] Operation: A user enters a request for a document summary into a terminal.

[1091] Output: Summary request entered on terminal

[1092] Step 2:

[1093] The terminal sends a summary request and emotion information to the server.

[1094] Input: Summary request, emotion information

[1095] Operation: The communication module of the terminal sends a summary request and emotion information to the server.

[1096] Output: Summary request and emotion information sent to the server

[1097] Step 3:

[1098] The server asks the generative AI model to generate a summary.

[1099] Input: Summary request, emotion information

[1100] How it works: The server calls the API of the generative AI model and requests a summary.

[1101] Output: Generative AI model begins generating a summary

[1102] Step 4:

[1103] A generative AI model summarizes documents and takes sentiment information into account.

[1104] Input: Document data, emotion information

[1105] How it works: A generative AI model summarizes documents and optimizes content based on user sentiment.

[1106] Output: Optimized summary

[1107] Step 5:

[1108] The server transmits the generated summary and commentary to the terminal.

[1109] Input: Optimized summary

[1110] Operation: The server sends summary data to the terminal.

[1111] Output: Summary data sent to the terminal

[1112] Step 6:

[1113] The device presents a summary and explanation to the user.

[1114] Input: Summary data

[1115] Operation: The terminal's user interface (UI) displays the summary data.

[1116] Output: Summary data displayed to the user

[1117] This will enable the realization of a system that can efficiently manage, search, and summarize information while taking into account the user's emotions.

[1118] (Application example 2)

[1119] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1120] Conventional information management systems provide a means for efficiently managing text data entered by users and URLs of external documents, but lack functionality for presenting appropriate information based on the user's emotions or suggesting storage locations. This results in a uniform user experience, and the inability to provide support optimized for individual emotional states. The present invention aims to solve these problems and provide an information management and optimal presentation method that responds to the user's emotions.

[1121] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for extracting keywords from text data entered by the user, means for suggesting a storage location based on the extracted keywords and emotional information, means for saving the text data in the suggested storage location, means for recognizing the user's emotion, and means for presenting appropriate information according to the recognized emotion. This enables optimal information management and presentation according to the user's emotional state.

[1122] "Text data" is the document format used by users to enter information.

[1123] "Keywords" are important words or phrases extracted from text data.

[1124] "Emotion information" is data that indicates the user's emotional state and is recognized by the emotion engine.

[1125] A "storage location" is a folder or database area where text data and URLs are recorded.

[1126] The "suggestion means" is a function that suggests to the user the most suitable storage location based on the extracted keywords and emotion information.

[1127] An "external document URL" is a link to a document or page on the Internet.

[1128] "Related tags" are keywords related to the content of the registered URL, and are generated to improve searchability and manageability.

[1129] A "search query" is a question or keyword that a user enters to search for information.

[1130] "Search results" are a collection of related information and links presented by the system based on a search query.

[1131] An "information management system" is a system for efficiently storing, retrieving, and presenting user-entered data.

[1132] An "emotion engine" is a technology that recognizes the user's emotions and optimizes the system's operation based on those emotions.

[1133] This invention is an information management system that allows users to efficiently manage, search, summarize, and explain notes and documents, and in particular has the ability to recognize and adapt to the user's emotions. This information management system analyzes text data and URLs of external documents entered by the user and provides appropriate storage locations and additional information along with emotional information.

[1134] Save and classify notes (emotion engine compatible version)

[1135] Program processing overview

[1136] When a user enters a new note, the device analyzes the content and emotional information of the note. The analyzed keywords and emotional information are sent to the server, which uses a generative AI model to suggest the optimal location to save the note. The user is notified of the suggested location, and if the user approves, the note is saved in the designated folder.

[1137] Hardware: Smart glasses, head-mounted displays

[1138] Software: Emotion recognition engine, generative AI model

[1139] Specific examples

[1140] A user inputs "Notes about next week's project meeting," and the emotion engine recognizes that the user is nervous. The keywords "next week," "project meeting," and the emotion information of "nervous" are sent to the server, and the generation AI determines that the "Project-related documents" folder is the most appropriate. The device suggests to the user, "Do you want to save this note in the 'Project-related documents' folder?" and if the user approves, the note is saved.

[1141] Registering an external document URL (emotion engine compatible version)

[1142] Program processing overview

[1143] When a user enters the URL of an external document, the URL and emotional information are sent to the server, which then uses generative AI to analyze the URL's content and emotional information. Based on the analysis results, relevant tags are generated and saved along with the URL.

[1144] Hardware: Smart glasses, head-mounted displays

[1145] Software: Emotion recognition engine, generative AI model

[1146] Specific examples

[1147] The user inputs the "URL of the technical specifications," and the emotion engine recognizes the user's impatience. The URL and the "impatience" emotion information are sent to the server, and the generation AI analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "concept." The generated tags and URL are stored in a database.

[1148] Information Search (Emotion Engine Compatible Version)

[1149] Program processing overview

[1150] When a user enters a search query, the device sends the search query and emotion information to the server, which then uses generative AI to consider the user's emotion when searching for related notes and URLs, and presents search results in a format optimized for the user's emotion.

[1151] Hardware: Smart glasses, head-mounted displays

[1152] Software: Emotion recognition engine, generative AI model

[1153] Specific examples

[1154] A user searches for "next week's project meeting agenda," and the emotion engine recognizes confusion. The search query and the "confused" emotion information are sent to the server, which prioritizes and displays notes and documents that are most likely to be useful to the confused user. The device then displays search results optimized for the user.

[1155] Document summary / explanation (emotion engine compatible version)

[1156] Program processing overview

[1157] When a user requests a summary or commentary of a specific note or URL, the request and emotional information are sent to the server, which uses generative AI to generate a summary or commentary of the specified document, optimizes it for the emotional information, and presents it on the device.

[1158] Hardware: Smart glasses, head-mounted displays

[1159] Software: Emotion recognition engine, generative AI model

[1160] Specific examples

[1161] The user requests a "summary of the technical specifications," and the emotion engine recognizes the emotion of calm. The summary request and the emotion information of "calm" are sent to the server, which then summarizes the technical specifications and generates a detailed summary suitable for a calm user. The terminal displays this summary to the user.

[1162] Prompt Sentence Examples

[1163] "When the user picks up the smartphone, the emotion engine recognizes the user's emotion as 'interest'. At this time, please display detailed information about the related smartphone."

[1164] As described above, the present invention is a system that recognizes a user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing work and shopping more efficiently.

[1165] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1166] Step 1:

[1167] The user inputs a memo. The terminal receives the memo. The input memo is displayed on the system interface. For example, if the user inputs "memo about next week's project meeting," the terminal acquires the text data. Here, the input data is "memo about next week's project meeting."

[1168] Step 2:

[1169] The device passes the text data to a keyword extraction module to analyze the content of the entered notes. The keyword extraction module uses an algorithm to extract important words and phrases. For example, keywords such as "next week" and "project meeting" are extracted. Here, the input data is "Notes about next week's project meeting," and the output data is "next week" and "project meeting."

[1170] Step 3:

[1171] At the same time, the device passes the text data to an emotion recognition engine to analyze the user's emotional state. The emotion recognition engine uses an emotion analysis algorithm to analyze the user's emotions. For example, if the user is recognized as nervous, emotion data is generated. Here, the input data is "Notes about next week's project meeting" and the output data is "nervous."

[1172] Step 4:

[1173] The device sends the extracted keywords and emotion information to the server. The server receives this data and passes it to the generative AI model. The generative AI model analyzes this information and runs an algorithm to suggest an appropriate storage location. For example, the generative AI model may determine that the "Project-related documents" folder is optimal. Here, the input data is "next week," "project meeting," and "tension," and the output data is "Project-related documents."

[1174] Step 5:

[1175] The server returns the generated save location suggestion to the device. The device receives this information and notifies the user. For example, the device suggests to the user, "Do you want to save this memo in the 'Project Related Documents' folder?" Here, the input data is "Project Related Documents" and the output data is a user notification message.

[1176] Step 6:

[1177] If the user agrees with the proposal, the device saves the memo in the specified folder. When the save operation is complete, the device displays a save completion notification to the user. For example, if the user presses the Agree button, the memo is saved in the "Project Related Documents" folder. Here, the input data is the user's consent (clicking the Agree button), and the output data is the memo save operation.

[1178] Step 7:

[1179] Finally, the saved note is recorded in the database, making it available for future searches and summarization requests. It also displays the saved location when the user reviews the note later. For example, the database records that the note was saved in the "Project Related Documents" folder. Here, the input data is the note save operation, and the output data is the database update.

[1180] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1181] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1182] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1183] [Third embodiment]

[1184] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1185] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1186] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1187] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1188] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1189] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1190] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1191] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1192] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1193] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1194] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1195] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1196] The present invention is an information management system that allows users to efficiently manage and search for notes and documents, and receive summaries and explanations. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[1197] Save and categorize your notes

[1198] The user enters a new note. The device analyzes the content of the entered note and extracts keywords. The device then sends the note content, including the extracted keywords, to the server. The server uses generative AI to suggest the optimal storage location. Information about the suggested storage location is sent to the device, and the device notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[1199] Specific examples

[1200] The user enters "Notes about next week's project meeting." The device extracts keywords such as "next week" and "project meeting" and sends them to the server. Using generative AI, the server determines that the "Project-related documents" folder is the most appropriate. The device then suggests to the user, "Do you want to save this note in the Project-related documents folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[1201] Registering an external document URL

[1202] The user enters the URL of an external document. The device sends the URL as is to the server. The server uses generative AI to evaluate the content of the URL and generate relevant tags, allowing the user to easily search for related documents later.

[1203] Specific examples

[1204] The user enters the "URL of the technical specifications." The device sends this to the server, which analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "concept." The generated tags and URL are stored in a database.

[1205] Information Search

[1206] A user enters a search query into their device. The device sends the search query to the server, which uses generative AI to search for related notes and URLs. The search results are sent to the device and displayed to the user.

[1207] Specific examples

[1208] A user searches for "next week's project meeting agenda." The device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "notes about next week's project meeting agenda" and sends it to the device. The device displays the search result to the user.

[1209] Document Summary / Explanation

[1210] The user requests a summary or commentary on a specific note or URL. The device sends the summary / commentary request to the server. The server uses generation AI to generate a summary or commentary for the specified document and sends it to the device. The device then displays the summary / commentary result to the user.

[1211] Specific examples

[1212] The user requests a "summary of the technical specifications." The device sends this request to the server, which then uses a generation AI to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the system design and requirements," and is sent to the device. The device then displays the summary to the user.

[1213] As a result, the present invention allows users to quickly find the information they need from a vast amount of information, greatly improving work efficiency.

[1214] The processing flow will be explained below.

[1215] Save and categorize your notes

[1216] Step 1:

[1217] A user inputs a new note into a terminal. For example, the user inputs "About the agenda for next week's project meeting."

[1218] Step 2:

[1219] The device analyzes the contents of the memo and extracts keywords, such as "next week," "project meeting," and "agenda."

[1220] Step 3:

[1221] The device sends the extracted keywords and memo contents to the server, using JSON format data as the communication protocol.

[1222] Step 4:

[1223] The server passes the received data to the generation AI and instructs it to suggest the best place to save it. The generation AI determines that the "Project-related documents" folder is the best.

[1224] Step 5:

[1225] The server sends the proposed storage location information to the terminal, for example, "Proposed storage location: Project-related documents."

[1226] Step 6:

[1227] The device notifies the user with a suggested save location, for example, by displaying "Would you like to save this note in the 'Project Related Documents' folder?"

[1228] Step 7:

[1229] The user agrees with the proposal, e.g., clicks the "Save" button.

[1230] Step 8:

[1231] Your device will save the note to the specified location, for example, the "Project Related Documents" folder.

[1232] Registering an external document URL

[1233] Step 1:

[1234] The user enters the URL of an external document into the terminal, for example, "https: / / example.com / tech-spec."

[1235] Step 2:

[1236] The device sends the entered URL to the server, using JSON format data as the communication protocol.

[1237] Step 3:

[1238] The server receives the URL and instructs the AI ​​to evaluate the content of the URL. The AI ​​analyzes the content of the URL and generates relevant tags such as "technical specifications," "specifications," and "concepts."

[1239] Step 4:

[1240] The server stores the associated tags and URLs in a centralized database, for example using an SQL query.

[1241] Information Search

[1242] Step 1:

[1243] A user enters a search query into a device, for example, "agenda for next week's project meeting."

[1244] Step 2:

[1245] The device sends a search query to the server, using JSON format data as the communication protocol.

[1246] Step 3:

[1247] The server receives the search query and uses generative AI to search for relevant notes and URLs, for example, based on keywords such as "next week," "project meeting," and "agenda."

[1248] Step 4:

[1249] The server sends the search results to the device, such as a list of found notes or URLs in JSON format.

[1250] Step 5:

[1251] The device displays the search results to the user. For example, a link or URL for "Notes on the agenda for next week's project meeting" appears on the user's screen.

[1252] Document Summary / Explanation

[1253] Step 1:

[1254] The user specifies a specific note or URL on the terminal and requests a summary or explanation. For example, the user requests a "summary of a technical specification."

[1255] Step 2:

[1256] The terminal sends a summary / explanation request to the server. JSON format data is used as the communication protocol.

[1257] Step 3:

[1258] The server passes the summary / explanation request to the generation AI, which generates a summary or explanation of the specified document. For example, the generation AI summarizes a technical specification and generates the content, "This technical specification describes the system design and requirements."

[1259] Step 4:

[1260] The server sends the generated summary and commentary to the device, for example, in JSON format.

[1261] Step 5:

[1262] The terminal displays the summary / explanation result to the user. For example, the following content is displayed on the user's screen: "Summary: This technical specification describes the system design and requirements."

[1263] Example 1

[1264] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1265] Efficiently managing large amounts of text data and documents and quickly searching and retrieving necessary information are important challenges in today's information society. However, conventional information management systems face problems such as manual note organization and tagging, and insufficient search accuracy. It is also difficult to quickly understand and summarize the content of external documents. Therefore, there is a need for a system that allows users to efficiently manage and search notes and documents, and receive summaries and explanations.

[1266] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1267] In this invention, the server includes means for extracting keywords from text data entered by a user, means for suggesting a storage location based on the extracted keywords, means for saving the text data in the suggested storage location, means for the user to register URLs of external documents, means for evaluating the content of the registered URLs and generating associated tags, means for searching for associated text data and URLs, and means for generating summaries or explanations of the text data and URLs using a generative AI model, thereby enabling users to efficiently manage notes and documents and quickly search and obtain necessary information.

[1268] "User" refers to a person who uses an information system.

[1269] "Text data" refers to character or document data entered by a user.

[1270] "Keywords" refer to important words or phrases extracted from text data.

[1271] "Storage location" refers to the location of a folder or database where text data or documents are stored.

[1272] "Extraction" refers to the operation of extracting specific keywords or information from text data.

[1273] "Suggestion" refers to a recommendation made by the server to the user based on the extracted data.

[1274] "Saving" refers to the operation of storing text data or documents in a specified location.

[1275] "External documents" refer to documents and files that exist on the Internet.

[1276] "URL" refers to a specific address or link to an external document.

[1277] "Evaluation" refers to the operation of analyzing the content of a registered URL to determine its value and relevance.

[1278] "Tags" refer to keywords or phrases that are associated with a URL or the content of a document.

[1279] "Search" refers to the operation of searching text data or URLs based on a query entered by a user to find relevant information.

[1280] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze, summarize, and make suggestions about text data.

[1281] "Summarization" refers to the operation of concisely summarizing the contents of a document or text data.

[1282] "Explanation" refers to the operation of providing a detailed explanation of the contents of a document or text data.

[1283] The present invention is an information management system that allows users to efficiently manage and search notes and documents, and receive summaries and explanations. This system analyzes text data and URLs of external documents entered by users, suggests appropriate storage locations, and generates related tags, thereby streamlining information management and search. The following describes in detail how this system is implemented.

[1284] In the system configuration, a server and a terminal work together. The server uses a generative AI model to analyze, evaluate, summarize, and explain text data and URLs. The terminal provides a user interface, receives user input, and communicates with the server.

[1285] Save and categorize your notes

[1286] The user enters a new note. The device analyzes the content of the note and extracts keywords using a natural language processing (NLP) library (e.g., spaCy or Gensim). The device then sends the note content, including the extracted keywords, to the server. The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt such as "Suggest a suitable storage location for next week's project meeting" to suggest the best storage location. This prompt is generated via an HTTP POST request.

[1287] For example, if a user types "Notes about next week's project meeting," the device extracts keywords such as "next week" and "project meeting" and sends them to the server. The server determines that the "Project-related documents" folder is the most appropriate location and sends a suggestion to the device. If the user agrees to this suggestion, the note will be saved in the specified folder.

[1288] Registering an external document URL

[1289] The user inputs the URL of an external document. The device sends the input URL as is to the server. The server evaluates the content of the URL using a generative AI model and generates a prompt such as "Please evaluate the URL of the technical specification document," thereby generating relevant tags. The generated tags and URLs are stored in a database (e.g., MySQL or MongoDB).

[1290] As a concrete example, when a user enters the "URL of a technical specification," the device sends it to the server, which analyzes the contents of the URL and generates tags such as "technical specification," "specification," and "concept," which are then stored in a database along with the URL.

[1291] Information Search

[1292] A user enters a search query into a device. The device sends the entered search query to the server. The server uses a generative AI model to search for relevant notes and URLs and finds relevant information from a database by generating prompts such as "Please search for next week's project meeting agenda." The search results are sent to the device and displayed to the user.

[1293] For example, if a user searches for "next week's project meeting agenda," the device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "notes about next week's project meeting agenda" and sends it to the device. The device then displays the search result to the user.

[1294] Document Summary / Explanation

[1295] The user requests a summary or commentary for a specific note or URL. The device sends the summary / commentary request to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and generates a prompt such as "Please generate a summary of the technical specification." The generated summary or commentary is sent to the device and displayed to the user.

[1296] As a concrete example, when a user requests a "summary of the technical specifications," the device sends this request to the server, which then uses a generative AI model to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the system design and requirements," and is sent to the device. The device then displays the summary to the user.

[1297] This allows users to quickly find the information they need from a vast amount of information, significantly improving work efficiency.

[1298] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1299] Step 1:

[1300] The user inputs a new note into the terminal. Any text is provided to the terminal as input data from the user.

[1301] Step 2:

[1302] The device analyzes the content of the input note and extracts keywords using a natural language processing (NLP) library (e.g., spaCy or Gensim). The input is text data, and the output is a list of extracted keywords. Specifically, the text data is tokenized and important words and phrases are identified.

[1303] Step 3:

[1304] The terminal sends the contents of the memo including the extracted keywords to the server (HTTP POST request). The input is the keyword list and the memo contents, and the output is a request sent to the server.

[1305] Step 4:

[1306] The server inputs the received note content into a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt that suggests the optimal storage location. The input is the note content and a list of keywords, and the output is information about the suggested storage location. Example prompt: "Suggest a suitable storage location for next week's project meeting."

[1307] Step 5:

[1308] The server sends the generated storage location information to the device (HTTP POST request), and the device notifies the user of the storage location suggestion. The input is the storage location information, and the output is a notification to the user. Specifically, a notification is displayed on the device's UI.

[1309] Step 6:

[1310] If the user agrees to the proposal, the device sends the consent information to the server (HTTP POST request). The input is the user's consent, and the output is the transmission of consent data to the server.

[1311] Step 7:

[1312] With the user's consent, the server saves the memo in the specified folder. The saving is done in a database (e.g., MySQL or MongoDB). The input is the memo content and the save location, and the output is the save operation to the database.

[1313] Step 8:

[1314] The user enters the URL of an external document into the terminal. The input is a URL.

[1315] Step 9:

[1316] The terminal sends the entered URL to the server as is (HTTP POST request). The input is a URL, and the output is a request sent to the server.

[1317] Step 10:

[1318] The server analyzes the content of the received URL and uses a generative AI model to generate the associated tag. The input is the URL and the output is the generated tag. Example prompt: "Please rate the URL for the technical specification."

[1319] Step 11:

[1320] The server saves the generated tags and URLs to a database. The input is the tag and URL, and the output is a save operation to the database.

[1321] Step 12:

[1322] A user inputs a search query into a terminal. The input is a search query.

[1323] Step 13:

[1324] The terminal sends the entered search query to the server (HTTP GET request). The input is the search query, and the output is the request sent to the server.

[1325] Step 14:

[1326] The server receives the search query and uses a generative AI model to generate a prompt that searches for related notes and URLs. The input is the search query and the output is a list of related data. Example prompt: "Please find the agenda for next week's project meeting."

[1327] Step 15:

[1328] The server uses the generative AI model to search for relevant notes and URLs from the database and sends the results to the device (HTTP GET request). The input is the search results, and the output is the data sent to the device.

[1329] Step 16:

[1330] The terminal displays the received search results to the user. The input is the search results and the output is the display on the user interface.

[1331] Step 17:

[1332] The user requests a summary or description of a particular note or URL. The input is a summary / description request.

[1333] Step 18:

[1334] The terminal sends a summary / explanation request to the server (HTTP POST request). The input is the request data, and the output is the data sent to the server.

[1335] Step 19:

[1336] The server passes the received request to a generative AI model to generate a summary or commentary of the specified document. The input is a note or URL, and the output is a summary or commentary text. Example prompt: "Generate a summary of a technical specification."

[1337] Step 20:

[1338] The server sends the generated summary and commentary to the terminal (HTTP POST request). The input is the summary / commentary result, and the output is the message sent to the terminal.

[1339] Step 21:

[1340] The terminal displays the received summary / explanation results to the user. The input is the summary / explanation results, and the output is the display on the user interface.

[1341] (Application example 1)

[1342] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1343] Robots used in factories require the management of numerous work instructions and maintenance logs, but as this information increases, management becomes cumbersome. A system that can quickly search for necessary information and provide summaries is also needed, but existing systems cannot efficiently achieve this. Therefore, there is a need to streamline information management for factory robots and improve productivity.

[1344] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1345] In this invention, the server includes means for extracting keywords from text data entered by a user, means for suggesting a storage location based on the extracted keywords, means for saving the text data in the suggested storage location, means for searching for files, means for presenting search results to the user, means for generating summaries, and means for presenting the generated summaries to the user, thereby enabling efficient management of robot instructions and logs in a factory, rapid search, and provision of summaries.

[1346] "User" means a person or organization that operates the system and inputs and retrieves information.

[1347] "Text data" refers to information that consists of characters and sentences.

[1348] "Keywords" are important words and phrases extracted from the text data entered by the user.

[1349] A "storage location" is a specific folder or directory where data is stored.

[1350] A "server" is a computer system for storing and processing data.

[1351] A "file" is a unit for recording data or information.

[1352] "Search Results" refers to a collection of relevant information or data discovered based on a user-entered query.

[1353] The "summary" is a short sentence that succinctly summarizes the contents of the input document.

[1354] "URL" means a Uniform Resource Locator that provides a link to a web page or digital document.

[1355] "Related tags" are metadata that indicate keywords or categories related to a document or piece of information.

[1356] "Suggestion" refers to the system suggesting to the user the optimal storage location and processing method.

[1357] This invention is an information management system that efficiently manages, searches, and summarizes work instructions and maintenance logs for factory robots. This system is composed of a server and terminals and performs the following processes.

[1358] 1. System Configuration

[1359] Hardware

[1360] Server: High-performance server (e.g. AWS EC2 instance)

[1361] Terminal: Tablet or computer mounted on a robot in the factory

[1362] Network: Local Area Network (LAN) or Cloud Network

[1363] software

[1364] Server software: Database software (e.g., MySQL), generative AI API (e.g., OpenAI GPT)

[1365] Terminal software: User interface (UI) applications, search engine software

[1366] 2. Save and categorize your notes

[1367] The user inputs new text data (e.g., work instruction notes) into the device. The device extracts important keywords from the input text data. The text data, including the extracted keywords, is then sent to the server. The server uses a generative AI model to suggest the optimal storage location. The device notifies the user of the suggested storage location, and if the user approves, the text data is saved in the specified folder.

[1368] Specific examples

[1369] The user enters "Notes about next week's maintenance work." The device extracts keywords such as "next week" and "maintenance work" and sends them to the server. Using a generative AI model, the server determines that the "Maintenance-related documents" folder is the most appropriate. The device then asks the user, "Do you want to save this note in the Maintenance-related documents folder?" If the user approves, the note is saved in the "Maintenance-related documents" folder.

[1370] 3. Registering the external document URL

[1371] The user enters the URL of an external document. The device sends the URL as is to the server. The server uses a generative AI model to evaluate the URL's content and generate relevant tags, allowing the user to easily search for related documents later.

[1372] Specific examples

[1373] The user enters the URL of the technical specifications for the new machine. The device sends this to the server, which analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "new machine." The generated tags and URLs are stored in a database.

[1374] 4. Information Search

[1375] A user enters a search query into their device. The device sends the search query to the server, which uses a generative AI model to search for relevant text data and URLs. The search results are sent to the device and displayed to the user.

[1376] Specific examples

[1377] A user searches for "Details of next week's maintenance work." The device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "Notes about details of next week's maintenance work" and sends it to the device. The device displays the search result to the user.

[1378] 5. Document Summary / Explanation

[1379] The user requests a summary or commentary for a specific note or URL. The device sends the summary / commentary request to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and sends it to the device. The device then displays the summary / commentary result to the user.

[1380] Specific examples

[1381] The user requests a "summary of the technical specifications for a new machine." The device sends this request to the server, which uses a generative AI model to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the design and requirements for a new machine." and is sent to the device. The device then displays the summary to the user.

[1382] Prompt Sentence Examples

[1383] Write a summary of the following document:

[1384] This technical specification describes the system design and requirements. It includes detailed blueprints and specifications.

[1385] This system enables efficient management of robotic instructions and logs within factories, as well as quick search and provision of summaries.

[1386] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1387] Step 1:

[1388] The user inputs new text data (e.g., notes for a work instruction) into the terminal. The terminal receives this text data and stores it as input data for proceeding to the next step.

[1389] Step 2:

[1390] The device extracts important keywords from the input text data using a text analysis algorithm to identify key words and phrases within the text data, and then transmits the extracted keywords to the server.

[1391] Step 3:

[1392] The server receives the extracted keywords and uses a generative AI model to suggest the best storage location. The generative AI then searches related folders and directories based on the keywords to determine the best storage location. The server then sends the suggested storage location information to the device.

[1393] Step 4:

[1394] The device notifies the user of the storage location proposal received from the server. The user checks and approves the proposed storage location. After this operation is completed, the device proceeds to the next step.

[1395] Step 5:

[1396] The device saves the input text data in a location approved by the user. Specifically, it creates a text file in the specified folder and records the input data in it. This data is later used for searching and generating summaries.

[1397] Step 6:

[1398] The user enters the URL of an external document. The device receives this URL and retains it for the next step.

[1399] Step 7:

[1400] The device sends the entered URL as is to the server. The server receives the URL and evaluates the URL's content using a generative AI model. Based on the evaluation results, the server generates associated tags and stores the URL along with these tags in a database, allowing for easy search of the URL later.

[1401] Step 8:

[1402] The user inputs a search query into the terminal, which receives the query and proceeds to the next step.

[1403] Step 9:

[1404] The device sends a search query to the server, which uses a generative AI model to find relevant text data and URLs. The server searches its database to find results that match the user's search query. The server then sends the search results back to the device and presents them to the user.

[1405] Step 10:

[1406] The user requests a summary or commentary on a particular note or URL. The device receives this request and proceeds to the next step.

[1407] Step 11:

[1408] The device sends a summary / explanation request to the server, which uses the generative AI model to generate a summary or explanation for the specified document. The generated summary or explanation is then sent from the server to the device and displayed to the user through the device.

[1409] Examples:

[1410] If a user requests a "summary of the technical specifications for a new machine," the device sends this request directly to the server, which creates a summary based on the contents of the technical specifications and inputs the following prompt sentence into the generative AI model.

[1411] "Write a summary of the following document: This technical specification describes the system design and requirements. It includes detailed blueprints and specifications."

[1412] The generated summary is "This technical specification describes the design and requirements of a new machine," and is sent to the terminal and presented to the user.

[1413] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1414] The present invention is an information management system that allows users to efficiently manage, search, and retrieve notes and documents, and provides summaries and explanations, while also recognizing and adapting to the user's emotions. Below, the program processing of this system is explained in natural language and detailed with specific examples.

[1415] Save and classify notes (emotion engine compatible version)

[1416] When a user enters a new note, the device analyzes the content of the entered note and not only extracts keywords but also recognizes the user's emotions. This emotion recognition is performed using an emotion engine. The device sends the extracted keywords and emotion information to the server. The server passes the received data to a generation AI, which suggests an appropriate storage location. This suggestion is optimized according to the user's emotions. The suggested storage location information is sent to the device, and the device notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[1417] Specific examples

[1418] The user enters "Notes about next week's project meeting." The device uses its emotion engine to recognize that the user is nervous. The emotion information of the keywords "next week," "project meeting," and "nervous" is sent to the server. The server uses generative AI to determine that the "Project-related documents" folder is the best choice. The device suggests to the user, "Do you want to save this note in the 'Project-related documents' folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[1419] Registering an external document URL (emotion engine compatible version)

[1420] When a user enters the URL of an external document, the device sends the URL to the server, which uses a generative AI to evaluate the content of the URL and generate relevant tags, taking into account the user's emotional state. The generated tags and URL are then stored in a database.

[1421] Specific examples

[1422] When a user enters the "URL of a technical specification," the emotion engine recognizes that the user is impatient. The device sends the URL and the "impatient" emotion information to the server. The server uses a generation AI to analyze the contents of the URL and generate tags such as "technical specification," "specifications," and "concept." The generated tags and URL are stored in a database.

[1423] Information Search (Emotion Engine Compatible Version)

[1424] When a user enters a search query, the device recognizes the user's emotion along with the search query. The device then sends this information to the server, which uses generative AI to consider the user's emotion when searching for related notes and URLs. The search results are then sent to the device and presented in a format that best suits the user's emotion.

[1425] Specific examples

[1426] When a user searches for "next week's project meeting agenda," the emotion engine recognizes that the user is confused. The device sends the search query and the "confused" emotion information to the server. The server uses generative AI to prioritize and display notes and documents that are most likely to be useful to the confused user. The device then displays search results optimized for the user.

[1427] Document summary / explanation (emotion engine compatible version)

[1428] When a user requests a summary or commentary for a specific note or URL, the device sends this request and the user's emotional information to the server. The server uses generative AI to generate a summary or commentary for the specified document, optimizing it according to the emotional information. The generated summary or commentary is then sent to the device and presented to the user.

[1429] Specific examples

[1430] When a user requests a "summary of technical specifications," the emotion engine recognizes the user's calmness. The device sends the summary request and the "calm" emotion information to the server. The server uses generative AI to summarize the technical specifications and generate a detailed summary suitable for a calm user. The device then displays this generated summary to the user.

[1431] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[1432] The processing flow will be explained below.

[1433] Save and classify notes (emotion engine compatible version)

[1434] Step 1:

[1435] A user inputs a new note into a terminal. For example, the user inputs "About the agenda for next week's project meeting."

[1436] Step 2:

[1437] The device analyzes the contents of the memo and extracts keywords, such as "next week," "project meeting," and "agenda."

[1438] Step 3:

[1439] The device uses an emotion engine to recognize the user's emotions. For example, the emotion engine determines that the user is nervous.

[1440] Step 4:

[1441] The device sends the extracted keywords and emotion information to the server, which uses JSON format data as the communication protocol.

[1442] Step 5:

[1443] The server passes the received data to the generation AI and instructs it to suggest the best place to save it. For example, the generation AI determines that the "Project-related documents" folder is the best.

[1444] Step 6:

[1445] The server sends the proposed storage location information to the terminal, for example, "Proposed storage location: Project-related documents."

[1446] Step 7:

[1447] The device will notify the user with save location suggestions, for example, "Would you like to save this note in the 'Project Related Documents' folder?"

[1448] Step 8:

[1449] The user agrees with the proposal, e.g., clicks the "Save" button.

[1450] Step 9:

[1451] Your device will save the note to the specified location, for example, the "Project Related Documents" folder.

[1452] Registering an external document URL (emotion engine compatible version)

[1453] Step 1:

[1454] The user enters the URL of an external document into the terminal, for example, "https: / / example.com / tech-spec."

[1455] Step 2:

[1456] The device uses an emotion engine to recognize the user's emotions. For example, the emotion engine determines that the user is anxious.

[1457] Step 3:

[1458] The device sends the input URL and emotion information to the server. JSON format data is used as the communication protocol.

[1459] Step 4:

[1460] The server receives the URL and instructs the AI ​​to evaluate the content of the URL. The AI ​​analyzes the content of the URL and generates relevant tags such as "technical specifications," "specifications," and "concepts."

[1461] Step 5:

[1462] The server stores the associated tags and URLs in a centralized database, for example using an SQL query.

[1463] Information Search (Emotion Engine Compatible Version)

[1464] Step 1:

[1465] A user enters a search query into a device, for example, "agenda for next week's project meeting."

[1466] Step 2:

[1467] The device uses an emotion engine to recognize the user's emotion. For example, the emotion engine determines that the user is confused.

[1468] Step 3:

[1469] The device sends the search query and emotion information to the server, using JSON format data as the communication protocol.

[1470] Step 4:

[1471] The server receives the search query and uses generative AI to find relevant notes and URLs, for example, based on keywords like "next week," "project meeting," or "agenda."

[1472] Step 5:

[1473] The server then prioritizes search results based on emotion and sends them to the device, so for example, a confused user would be shown the most useful notes and documents.

[1474] Step 6:

[1475] The device displays the search results to the user. For example, a link or URL for "Notes on the agenda for next week's project meeting" appears on the user's screen.

[1476] Document summary / explanation (emotion engine compatible version)

[1477] Step 1:

[1478] The user specifies a specific note or URL on the terminal and requests a summary or explanation. For example, the user requests a "summary of a technical specification."

[1479] Step 2:

[1480] The device uses an emotion engine to recognize the user's emotion. For example, the emotion engine determines that the user is calm.

[1481] Step 3:

[1482] The device sends a summary / explanation request and emotion information to the server. JSON format data is used as the communication protocol.

[1483] Step 4:

[1484] The server passes the summary / explanation request to the generation AI, which generates a summary or explanation of the specified document. For example, the generation AI summarizes a technical specification and generates the content, "This technical specification describes the system design and requirements."

[1485] Step 5:

[1486] The server optimizes the generated summaries and commentaries based on emotion information and sends them to the device. For example, a calm user will receive a detailed summary.

[1487] Step 6:

[1488] The terminal displays the summary / explanation result to the user. For example, the following content is displayed on the user's screen: "Summary: This technical specification describes the system design and requirements."

[1489] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[1490] Example 2

[1491] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1492] Conventional information management systems have had the problem of being difficult to present optimal suggestions and search results that take into account the user's emotions when saving and searching text data and URLs entered by the user.In addition, there was no method for suggesting storage locations for information or generating tags based on emotions, so there was a need to improve the user experience.

[1493] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for extracting keywords from text data input by a user, a means for transmitting the extracted keywords and the user's emotional information to the server, a means for the generative AI model to suggest an appropriate storage location taking into account the emotional information, and a means for saving the text data in the suggested storage location. This enables the suggestion of an optimal storage location taking into account the user's emotions and efficient management of text data.

[1494] The system also includes a means for registering URLs of external documents, a means for the generative AI model to evaluate the content of the URL taking into account the user's emotional information and generate related tags, and a means for saving the URL together with the generated related tags, which facilitates tag generation and URL management based on emotional information.

[1495] The system further includes a means for transmitting the search query and emotion information entered by the user to a server, a means for the generative AI model to search for related text data and URLs taking the emotion information into consideration, and a means for presenting search results to the user, thereby realizing efficient search for related information according to the user's emotions and presenting optimal search results.

[1496] "Text data" refers to textual information entered by a user, including notes, documents, messages, and the like.

[1497] A "keyword" is a particularly important word or phrase in text data that summarizes or represents the content of the text data.

[1498] "Emotion information" is data that indicates the user's emotional state, and includes emotions such as tension, impatience, and calmness.

[1499] A "server" refers to a computer system that processes and stores data on a network.

[1500] A "generative AI model" is an artificial intelligence model that performs natural language processing and data analysis, suggesting storage locations and optimizing search results based on user emotions and text data.

[1501] A "storage location" refers to a digital folder or section of a database used to store text data or URLs.

[1502] "External Documents" refers to documents that exist on external websites or other systems and that users register with the system.

[1503] "URL" is an abbreviation for Uniform Resource Locator, and refers to an address for specifying resources on the Internet.

[1504] "Related tags" are keywords or phrases that are associated with text data or URLs to make them easier to search and organize.

[1505] A "search query" is text data that a user enters when searching for information, and refers to a word or phrase that specifies the search target.

[1506] "Search Results" refers to a list of information provided by the system based on a user's search query, including relevant text data and URLs.

[1507] The present invention is an information management system that allows users to efficiently manage, search, and retrieve notes and documents, and provides summaries and explanations, while also recognizing and adapting to the user's emotions. Below, the program processing of this system is explained in natural language and detailed with specific examples.

[1508] Save and classify notes (emotion engine compatible version)

[1509] When a user enters a new note, the device analyzes the content of the entered note and extracts keywords. This analysis uses natural language processing (NLP) technology. The device also uses an emotion engine to recognize the user's emotions. The recognized emotions and extracted keywords are sent from the device to the server. The server uses a generative AI model to suggest the optimal storage location taking into account the emotion information. This suggestion is sent to the device, which notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[1510] Specific examples

[1511] The user enters "Notes about next week's project meeting." The device uses an emotion engine to recognize that the user is nervous. The emotion information of the keywords "next week," "project meeting," and "nervous" is sent to the server. The server uses a generative AI model to suggest the "Project-related documents" folder as a storage location. The device asks the user, "Do you want to save this note in the 'Project-related documents' folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[1512] Prompt Sentence Examples

[1513] Input note: "Notes about next week's project meeting"

[1514] User Emotion: "Tension"

[1515] Folder suggestions by generative AI: "Project-related documents"

[1516] Registering an external document URL (emotion engine compatible version)

[1517] When a user enters the URL of an external document, the device sends the URL to the server, which uses a generative AI model to evaluate the URL's content and generate relevant tags, taking the user's emotional information into account. The generated tags and URL are then stored in a database.

[1518] Specific examples

[1519] When a user enters the "URL of a technical specification," the emotion engine recognizes that the user is impatient. The device sends the URL and the "impatient" emotion information to the server. The server uses a generative AI model to analyze the content of the URL and generate tags such as "technical specification," "specifications," and "concept." The generated tags and URL are stored in a database.

[1520] Prompt Sentence Examples

[1521] Input URL: "http: / / example.com / technical-spec"

[1522] User Emotion: "Impatience"

[1523] Generative AI tags: "Technical specifications," "Specifications," "Concept"

[1524] Information Search (Emotion Engine Compatible Version)

[1525] When a user enters a search query, the device recognizes the user's emotions along with the search query and sends this information to the server. The server uses a generative AI model to search for related notes and URLs while taking the emotional information into account. The search results are sent to the device and presented in a format that best suits the user's emotions.

[1526] Specific examples

[1527] When a user searches for "next week's project meeting agenda," the emotion engine recognizes that the user is confused. The device sends the search query and the "confused" emotion information to the server. The server uses a generative AI model to prioritize the notes and documents that are most likely to be helpful to the confused user. The device then displays search results optimized for the user.

[1528] Prompt Sentence Examples

[1529] Search query: "next week's project meeting agenda"

[1530] User sentiment: "Confused"

[1531] Generative AI search results: the most helpful notes and documents for confused users

[1532] Document summary / explanation (emotion engine compatible version)

[1533] When a user requests a summary or commentary for a specific note or URL, the device sends this request and the user's emotional information to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and optimizes the content according to the emotional information. The generated summary or commentary is sent to the device and presented to the user.

[1534] Specific examples

[1535] When a user requests a "summary of technical specifications," the emotion engine recognizes the user's calm state. The device sends the summary request and the "calm" emotion information to the server. The server uses a generative AI model to summarize the technical specifications and generate a detailed summary appropriate for a calm user. The device then displays this generated summary to the user.

[1536] Prompt Sentence Examples

[1537] Summary request: "Summary of technical specifications"

[1538] User Sentiment: "Calm"

[1539] Generative AI Summarization: Detailed summaries suitable for calm users

[1540] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[1541] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1542] Save and classify notes (emotion engine compatible version)

[1543] Processing Steps

[1544] Step 1:

[1545] The user enters a note.

[1546] Input: Text data (e.g., "Notes about next week's project meeting")

[1547] Action: A user enters a note into a text entry field on a device.

[1548] Output: Text data entered into the terminal

[1549] Step 2:

[1550] The device analyzes the contents of the notes and extracts keywords.

[1551] Input: Text data

[1552] How it works: The device's software uses natural language processing (NLP) to extract key keywords from the note's content.

[1553] Output: Extracted keywords (e.g., "next week" or "project meeting")

[1554] Step 3:

[1555] The terminal uses an emotion engine to recognize the user's emotion.

[1556] Input: Text data, user typing speed, touch strength, and other sensory information

[1557] How it works: The emotion engine analyzes the user's emotional state.

[1558] Output: Emotional information (e.g., "tension")

[1559] Step 4:

[1560] The device transmits the extracted keywords and emotion information to the server.

[1561] Input: Keywords, emotional information

[1562] Operation: The communication module of the terminal sends data to the server as packets.

[1563] Output: Keywords and sentiment information sent to the server

[1564] Step 5:

[1565] The server passes the received data to the generative AI model.

[1566] Input: Keywords, emotional information

[1567] How it works: The server calls the API of the generative AI model and passes the data as an argument.

[1568] Output: The generative AI model begins its analysis

[1569] Step 6:

[1570] A generative AI model analyzes the data and suggests appropriate storage locations.

[1571] Input: Keywords, emotional information

[1572] How it works: A generative AI model suggests storage locations based on keywords and sentiment information.

[1573] Output: Suggested save location (e.g. "Project Related Documents" folder)

[1574] Step 7:

[1575] The server sends the proposal results to the terminal.

[1576] Input: Suggested save location

[1577] Operation: The server sends the proposal results to the device.

[1578] Output: Suggestion results sent to the device

[1579] Step 8:

[1580] The terminal notifies the user of the proposal results.

[1581] Input: Proposal results

[1582] What it does: Displays suggestions on your device in a pop-up notification or dialog box.

[1583] Output: The proposed results presented to the user

[1584] Step 9:

[1585] The user agrees to the proposal.

[1586] Input: Proposal results

[1587] What it does: The user agrees to save the note in the suggested folder.

[1588] Output: User consent

[1589] Step 10:

[1590] Your device will save the note in the suggested folder.

[1591] Input: User consent, memo data

[1592] What it does: The device's file system writes the memo data to the specified location.

[1593] Output: Saved memo data

[1594] Registering an external document URL (emotion engine compatible version)

[1595] Processing Steps

[1596] Step 1:

[1597] The user enters the URL of an external document.

[1598] Input: URL (e.g. "http: / / example.com / technical-spec")

[1599] How it works: A user enters a URL into an input field on their device.

[1600] Output: URL entered in terminal

[1601] Step 2:

[1602] The device sends the URL to the server.

[1603] Input: URL

[1604] Operation: The device's communication module sends the URL to the server.

[1605] Output: URL sent to the server

[1606] Step 3:

[1607] The server passes the URL to the generative AI model.

[1608] Input: URL

[1609] How it works: The server calls the API for the generated AI model and passes the URL.

[1610] Output: The generative AI model begins its analysis

[1611] Step 4:

[1612] A generative AI model evaluates the URL content and generates tags.

[1613] Input: URL

[1614] How it works: A generative AI model analyzes the content of a URL and generates relevant tags.

[1615] Output: Generated tags (e.g. "Technical Specification", "Specification", "Concept")

[1616] Step 5:

[1617] The generative AI model also takes into account the user's emotional information.

[1618] Input: Emotional information (e.g., "impatience")

[1619] How it works: The generative AI model reflects the user's emotional information in tag generation.

[1620] Output: Tag priority based on sentiment information

[1621] Step 6:

[1622] The server stores the generated tag and URL in a database.

[1623] Input: Tag, URL

[1624] How it works: The server calls the database API and saves the tags and URLs.

[1625] Output: Tags and URLs stored in the database

[1626] Information Search (Emotion Engine Compatible Version)

[1627] Processing Steps

[1628] Step 1:

[1629] The user enters a search query.

[1630] Input: Search query (e.g. "agenda for next week's project meeting")

[1631] What happens: A user enters a search query into the search bar.

[1632] Output: Search query entered into the terminal

[1633] Step 2:

[1634] The device sends a search query to the server.

[1635] Input: Search query

[1636] Operation: The communication module of the device sends a search query to the server.

[1637] Output: The search query sent to the server

[1638] Step 3:

[1639] The device recognizes the user's emotions.

[1640] Input: Search query, user reaction data

[1641] How it works: The emotion engine analyzes the user's emotions.

[1642] Output: Emotional information (e.g., "confused")

[1643] Step 4:

[1644] The server uses the generative AI model to search for the data.

[1645] Input: search query, sentiment information

[1646] How it works: The server uses a generative AI model to search data and generate relevant results, taking into account emotional information.

[1647] Output: Relevant data (e.g., project meeting notes and documents)

[1648] Step 5:

[1649] Generative AI models take user sentiment into account to prioritize search results.

[1650] Input: Search results, emotional information

[1651] How it works: Generative AI models optimize search results based on user sentiment.

[1652] Output: Prioritized search results

[1653] Step 6:

[1654] The server sends the optimized search results to the device.

[1655] Input: Optimized search results

[1656] How it works: The server sends optimized search results to the device.

[1657] Output: Device-optimized search results

[1658] Step 7:

[1659] The terminal displays the search results to the user.

[1660] Input: Search results

[1661] How it works: The device's user interface (UI) displays the search results on the screen.

[1662] Output: Search results displayed to the user

[1663] Document summary / explanation (emotion engine compatible version)

[1664] Processing Steps

[1665] Step 1:

[1666] A user requests a summary or commentary on a particular document.

[1667] Input: Abstract Request (e.g., "Technical Specification Abstract")

[1668] Operation: A user enters a request for a document summary into a terminal.

[1669] Output: Summary request entered on terminal

[1670] Step 2:

[1671] The terminal sends a summary request and emotion information to the server.

[1672] Input: Summary request, emotion information

[1673] Operation: The communication module of the terminal sends a summary request and emotion information to the server.

[1674] Output: Summary request and emotion information sent to the server

[1675] Step 3:

[1676] The server asks the generative AI model to generate a summary.

[1677] Input: Summary request, emotion information

[1678] How it works: The server calls the API of the generative AI model and requests a summary.

[1679] Output: Generative AI model begins generating a summary

[1680] Step 4:

[1681] A generative AI model summarizes documents and takes sentiment information into account.

[1682] Input: Document data, emotion information

[1683] How it works: A generative AI model summarizes documents and optimizes content based on user sentiment.

[1684] Output: Optimized summary

[1685] Step 5:

[1686] The server transmits the generated summary and commentary to the terminal.

[1687] Input: Optimized summary

[1688] Operation: The server sends summary data to the terminal.

[1689] Output: Summary data sent to the terminal

[1690] Step 6:

[1691] The device presents a summary and explanation to the user.

[1692] Input: Summary data

[1693] Operation: The terminal's user interface (UI) displays the summary data.

[1694] Output: Summary data displayed to the user

[1695] This will enable the realization of a system that can efficiently manage, search, and summarize information while taking into account the user's emotions.

[1696] (Application example 2)

[1697] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1698] Conventional information management systems provide a means for efficiently managing text data entered by users and URLs of external documents, but lack functionality for presenting appropriate information based on the user's emotions or suggesting storage locations. This results in a uniform user experience, and the inability to provide support optimized for individual emotional states. The present invention aims to solve these problems and provide an information management and optimal presentation method that responds to the user's emotions.

[1699] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for extracting keywords from text data entered by the user, means for suggesting a storage location based on the extracted keywords and emotional information, means for saving the text data in the suggested storage location, means for recognizing the user's emotion, and means for presenting appropriate information according to the recognized emotion. This enables optimal information management and presentation according to the user's emotional state.

[1700] "Text data" is the document format used by users to enter information.

[1701] "Keywords" are important words or phrases extracted from text data.

[1702] "Emotion information" is data that indicates the user's emotional state and is recognized by the emotion engine.

[1703] A "storage location" is a folder or database area where text data and URLs are recorded.

[1704] The "suggestion means" is a function that suggests to the user the most suitable storage location based on the extracted keywords and emotion information.

[1705] An "external document URL" is a link to a document or page on the Internet.

[1706] "Related tags" are keywords related to the content of the registered URL, and are generated to improve searchability and manageability.

[1707] A "search query" is a question or keyword that a user enters to search for information.

[1708] "Search results" are a collection of related information and links presented by the system based on a search query.

[1709] An "information management system" is a system for efficiently storing, retrieving, and presenting user-entered data.

[1710] An "emotion engine" is a technology that recognizes the user's emotions and optimizes the system's operation based on those emotions.

[1711] This invention is an information management system that allows users to efficiently manage, search, summarize, and explain notes and documents, and in particular has the ability to recognize and adapt to the user's emotions. This information management system analyzes text data and URLs of external documents entered by the user and provides appropriate storage locations and additional information along with emotional information.

[1712] Save and classify notes (emotion engine compatible version)

[1713] Program processing overview

[1714] When a user enters a new note, the device analyzes the content and emotional information of the note. The analyzed keywords and emotional information are sent to the server, which uses a generative AI model to suggest the optimal location to save the note. The user is notified of the suggested location, and if the user approves, the note is saved in the designated folder.

[1715] Hardware: Smart glasses, head-mounted displays

[1716] Software: Emotion recognition engine, generative AI model

[1717] Specific examples

[1718] A user inputs "Notes about next week's project meeting," and the emotion engine recognizes that the user is nervous. The keywords "next week," "project meeting," and the emotion information of "nervous" are sent to the server, and the generation AI determines that the "Project-related documents" folder is the most appropriate. The device suggests to the user, "Do you want to save this note in the 'Project-related documents' folder?" and if the user approves, the note is saved.

[1719] Registering an external document URL (emotion engine compatible version)

[1720] Program processing overview

[1721] When a user enters the URL of an external document, the URL and emotional information are sent to the server, which then uses generative AI to analyze the URL's content and emotional information. Based on the analysis results, relevant tags are generated and saved along with the URL.

[1722] Hardware: Smart glasses, head-mounted displays

[1723] Software: Emotion recognition engine, generative AI model

[1724] Specific examples

[1725] The user inputs the "URL of the technical specifications," and the emotion engine recognizes the user's impatience. The URL and the "impatience" emotion information are sent to the server, and the generation AI analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "concept." The generated tags and URL are stored in a database.

[1726] Information Search (Emotion Engine Compatible Version)

[1727] Program processing overview

[1728] When a user enters a search query, the device sends the search query and emotion information to the server, which then uses generative AI to consider the user's emotion when searching for related notes and URLs, and presents search results in a format optimized for the user's emotion.

[1729] Hardware: Smart glasses, head-mounted displays

[1730] Software: Emotion recognition engine, generative AI model

[1731] Specific examples

[1732] A user searches for "next week's project meeting agenda," and the emotion engine recognizes confusion. The search query and the "confused" emotion information are sent to the server, which prioritizes and displays notes and documents that are most likely to be useful to the confused user. The device then displays search results optimized for the user.

[1733] Document summary / explanation (emotion engine compatible version)

[1734] Program processing overview

[1735] When a user requests a summary or commentary of a specific note or URL, the request and emotional information are sent to the server, which uses generative AI to generate a summary or commentary of the specified document, optimizes it for the emotional information, and presents it on the device.

[1736] Hardware: Smart glasses, head-mounted displays

[1737] Software: Emotion recognition engine, generative AI model

[1738] Specific examples

[1739] The user requests a "summary of the technical specifications," and the emotion engine recognizes the emotion of calm. The summary request and the emotion information of "calm" are sent to the server, which then summarizes the technical specifications and generates a detailed summary suitable for a calm user. The terminal displays this summary to the user.

[1740] Prompt Sentence Examples

[1741] "When the user picks up the smartphone, the emotion engine recognizes the user's emotion as 'interest'. At this time, please display detailed information about the related smartphone."

[1742] As described above, the present invention is a system that recognizes a user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing work and shopping more efficiently.

[1743] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1744] Step 1:

[1745] The user inputs a memo. The terminal receives the memo. The input memo is displayed on the system interface. For example, if the user inputs "memo about next week's project meeting," the terminal acquires the text data. Here, the input data is "memo about next week's project meeting."

[1746] Step 2:

[1747] The device passes the text data to a keyword extraction module to analyze the content of the entered notes. The keyword extraction module uses an algorithm to extract important words and phrases. For example, keywords such as "next week" and "project meeting" are extracted. Here, the input data is "Notes about next week's project meeting," and the output data is "next week" and "project meeting."

[1748] Step 3:

[1749] At the same time, the device passes the text data to an emotion recognition engine to analyze the user's emotional state. The emotion recognition engine uses an emotion analysis algorithm to analyze the user's emotions. For example, if the user is recognized as nervous, emotion data is generated. Here, the input data is "Notes about next week's project meeting" and the output data is "nervous."

[1750] Step 4:

[1751] The device sends the extracted keywords and emotion information to the server. The server receives this data and passes it to the generative AI model. The generative AI model analyzes this information and runs an algorithm to suggest an appropriate storage location. For example, the generative AI model may determine that the "Project-related documents" folder is optimal. Here, the input data is "next week," "project meeting," and "tension," and the output data is "Project-related documents."

[1752] Step 5:

[1753] The server returns the generated save location suggestion to the device. The device receives this information and notifies the user. For example, the device suggests to the user, "Do you want to save this memo in the 'Project Related Documents' folder?" Here, the input data is "Project Related Documents" and the output data is a user notification message.

[1754] Step 6:

[1755] If the user agrees with the proposal, the device saves the memo in the specified folder. When the save operation is complete, the device displays a save completion notification to the user. For example, if the user presses the Agree button, the memo is saved in the "Project Related Documents" folder. Here, the input data is the user's consent (clicking the Agree button), and the output data is the memo save operation.

[1756] Step 7:

[1757] Finally, the saved note is recorded in the database, making it available for future searches and summarization requests. It also displays the saved location when the user reviews the note later. For example, the database records that the note was saved in the "Project Related Documents" folder. Here, the input data is the note save operation, and the output data is the database update.

[1758] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1759] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1760] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1761] [Fourth embodiment]

[1762] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1763] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1764] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1765] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1766] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1767] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1768] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1769] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1770] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1771] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1772] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1773] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1774] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1775] The present invention is an information management system that allows users to efficiently manage and search for notes and documents, and receive summaries and explanations. Below, we will explain the program processing of this system in natural language and provide detailed examples.

[1776] Save and categorize your notes

[1777] The user enters a new note. The device analyzes the content of the entered note and extracts keywords. The device then sends the note content, including the extracted keywords, to the server. The server uses generative AI to suggest the optimal storage location. Information about the suggested storage location is sent to the device, and the device notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[1778] Specific examples

[1779] The user enters "Notes about next week's project meeting." The device extracts keywords such as "next week" and "project meeting" and sends them to the server. Using generative AI, the server determines that the "Project-related documents" folder is the most appropriate. The device then suggests to the user, "Do you want to save this note in the Project-related documents folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[1780] Registering an external document URL

[1781] The user enters the URL of an external document. The device sends the URL as is to the server. The server uses generative AI to evaluate the content of the URL and generate relevant tags, allowing the user to easily search for related documents later.

[1782] Specific examples

[1783] The user enters the "URL of the technical specifications." The device sends this to the server, which analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "concept." The generated tags and URL are stored in a database.

[1784] Information Search

[1785] A user enters a search query into their device. The device sends the search query to the server, which uses generative AI to search for related notes and URLs. The search results are sent to the device and displayed to the user.

[1786] Specific examples

[1787] A user searches for "next week's project meeting agenda." The device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "notes about next week's project meeting agenda" and sends it to the device. The device displays the search result to the user.

[1788] Document Summary / Explanation

[1789] The user requests a summary or commentary on a specific note or URL. The device sends the summary / commentary request to the server. The server uses generation AI to generate a summary or commentary for the specified document and sends it to the device. The device then displays the summary / commentary result to the user.

[1790] Specific examples

[1791] The user requests a "summary of the technical specifications." The device sends this request to the server, which then uses a generation AI to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the system design and requirements," and is sent to the device. The device then displays the summary to the user.

[1792] As a result, the present invention allows users to quickly find the information they need from a vast amount of information, greatly improving work efficiency.

[1793] The processing flow will be explained below.

[1794] Save and categorize your notes

[1795] Step 1:

[1796] A user inputs a new note into a terminal. For example, the user inputs "About the agenda for next week's project meeting."

[1797] Step 2:

[1798] The device analyzes the contents of the memo and extracts keywords, such as "next week," "project meeting," and "agenda."

[1799] Step 3:

[1800] The device sends the extracted keywords and memo contents to the server, using JSON format data as the communication protocol.

[1801] Step 4:

[1802] The server passes the received data to the generation AI and instructs it to suggest the best place to save it. The generation AI determines that the "Project-related documents" folder is the best.

[1803] Step 5:

[1804] The server sends the proposed storage location information to the terminal, for example, "Proposed storage location: Project-related documents."

[1805] Step 6:

[1806] The device notifies the user with a suggested save location, for example, by displaying "Would you like to save this note in the 'Project Related Documents' folder?"

[1807] Step 7:

[1808] The user agrees with the proposal, e.g., clicks the "Save" button.

[1809] Step 8:

[1810] Your device will save the note to the specified location, for example, the "Project Related Documents" folder.

[1811] Registering an external document URL

[1812] Step 1:

[1813] The user enters the URL of an external document into the terminal, for example, "https: / / example.com / tech-spec."

[1814] Step 2:

[1815] The device sends the entered URL to the server, using JSON format data as the communication protocol.

[1816] Step 3:

[1817] The server receives the URL and instructs the AI ​​to evaluate the content of the URL. The AI ​​analyzes the content of the URL and generates relevant tags such as "technical specifications," "specifications," and "concepts."

[1818] Step 4:

[1819] The server stores the associated tags and URLs in a centralized database, for example using an SQL query.

[1820] Information Search

[1821] Step 1:

[1822] A user enters a search query into a device, for example, "agenda for next week's project meeting."

[1823] Step 2:

[1824] The device sends a search query to the server, using JSON format data as the communication protocol.

[1825] Step 3:

[1826] The server receives the search query and uses generative AI to search for relevant notes and URLs, for example, based on keywords such as "next week," "project meeting," and "agenda."

[1827] Step 4:

[1828] The server sends the search results to the device, such as a list of found notes or URLs in JSON format.

[1829] Step 5:

[1830] The device displays the search results to the user. For example, a link or URL for "Notes on the agenda for next week's project meeting" appears on the user's screen.

[1831] Document Summary / Explanation

[1832] Step 1:

[1833] The user specifies a specific note or URL on the terminal and requests a summary or explanation. For example, the user requests a "summary of a technical specification."

[1834] Step 2:

[1835] The terminal sends a summary / explanation request to the server. JSON format data is used as the communication protocol.

[1836] Step 3:

[1837] The server passes the summary / explanation request to the generation AI, which generates a summary or explanation of the specified document. For example, the generation AI summarizes a technical specification and generates the content, "This technical specification describes the system design and requirements."

[1838] Step 4:

[1839] The server sends the generated summary and commentary to the device, for example, in JSON format.

[1840] Step 5:

[1841] The terminal displays the summary / explanation result to the user. For example, the following content is displayed on the user's screen: "Summary: This technical specification describes the system design and requirements."

[1842] Example 1

[1843] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1844] Efficiently managing large amounts of text data and documents and quickly searching and retrieving necessary information are important challenges in today's information society. However, conventional information management systems face problems such as manual note organization and tagging, and insufficient search accuracy. It is also difficult to quickly understand and summarize the content of external documents. Therefore, there is a need for a system that allows users to efficiently manage and search notes and documents, and receive summaries and explanations.

[1845] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1846] In this invention, the server includes means for extracting keywords from text data entered by a user, means for suggesting a storage location based on the extracted keywords, means for saving the text data in the suggested storage location, means for the user to register URLs of external documents, means for evaluating the content of the registered URLs and generating associated tags, means for searching for associated text data and URLs, and means for generating summaries or explanations of the text data and URLs using a generative AI model, thereby enabling users to efficiently manage notes and documents and quickly search and obtain necessary information.

[1847] "User" refers to a person who uses an information system.

[1848] "Text data" refers to character or document data entered by a user.

[1849] "Keywords" refer to important words or phrases extracted from text data.

[1850] "Storage location" refers to the location of a folder or database where text data or documents are stored.

[1851] "Extraction" refers to the operation of extracting specific keywords or information from text data.

[1852] "Suggestion" refers to a recommendation made by the server to the user based on the extracted data.

[1853] "Saving" refers to the operation of storing text data or documents in a specified location.

[1854] "External documents" refer to documents and files that exist on the Internet.

[1855] "URL" refers to a specific address or link to an external document.

[1856] "Evaluation" refers to the operation of analyzing the content of a registered URL to determine its value and relevance.

[1857] "Tags" refer to keywords or phrases that are associated with a URL or the content of a document.

[1858] "Search" refers to the operation of searching text data or URLs based on a query entered by a user to find relevant information.

[1859] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze, summarize, and make suggestions about text data.

[1860] "Summarization" refers to the operation of concisely summarizing the contents of a document or text data.

[1861] "Explanation" refers to the operation of providing a detailed explanation of the contents of a document or text data.

[1862] The present invention is an information management system that allows users to efficiently manage and search notes and documents, and receive summaries and explanations. This system analyzes text data and URLs of external documents entered by users, suggests appropriate storage locations, and generates related tags, thereby streamlining information management and search. The following describes in detail how this system is implemented.

[1863] In the system configuration, a server and a terminal work together. The server uses a generative AI model to analyze, evaluate, summarize, and explain text data and URLs. The terminal provides a user interface, receives user input, and communicates with the server.

[1864] Save and categorize your notes

[1865] The user enters a new note. The device analyzes the content of the note and extracts keywords using a natural language processing (NLP) library (e.g., spaCy or Gensim). The device then sends the note content, including the extracted keywords, to the server. The server uses a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt such as "Suggest a suitable storage location for next week's project meeting" to suggest the best storage location. This prompt is generated via an HTTP POST request.

[1866] For example, if a user types "Notes about next week's project meeting," the device extracts keywords such as "next week" and "project meeting" and sends them to the server. The server determines that the "Project-related documents" folder is the most appropriate location and sends a suggestion to the device. If the user agrees to this suggestion, the note will be saved in the specified folder.

[1867] Registering an external document URL

[1868] The user inputs the URL of an external document. The device sends the input URL as is to the server. The server evaluates the content of the URL using a generative AI model and generates a prompt such as "Please evaluate the URL of the technical specification document," thereby generating relevant tags. The generated tags and URLs are stored in a database (e.g., MySQL or MongoDB).

[1869] As a concrete example, when a user enters the "URL of a technical specification," the device sends it to the server, which analyzes the contents of the URL and generates tags such as "technical specification," "specification," and "concept," which are then stored in a database along with the URL.

[1870] Information Search

[1871] A user enters a search query into a device. The device sends the entered search query to the server. The server uses a generative AI model to search for relevant notes and URLs and finds relevant information from a database by generating prompts such as "Please search for next week's project meeting agenda." The search results are sent to the device and displayed to the user.

[1872] For example, if a user searches for "next week's project meeting agenda," the device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "notes about next week's project meeting agenda" and sends it to the device. The device then displays the search result to the user.

[1873] Document Summary / Explanation

[1874] The user requests a summary or commentary for a specific note or URL. The device sends the summary / commentary request to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and generates a prompt such as "Please generate a summary of the technical specification." The generated summary or commentary is sent to the device and displayed to the user.

[1875] As a concrete example, when a user requests a "summary of the technical specifications," the device sends this request to the server, which then uses a generative AI model to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the system design and requirements," and is sent to the device. The device then displays the summary to the user.

[1876] This allows users to quickly find the information they need from a vast amount of information, significantly improving work efficiency.

[1877] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1878] Step 1:

[1879] The user inputs a new note into the terminal. Any text is provided to the terminal as input data from the user.

[1880] Step 2:

[1881] The device analyzes the content of the input note and extracts keywords using a natural language processing (NLP) library (e.g., spaCy or Gensim). The input is text data, and the output is a list of extracted keywords. Specifically, the text data is tokenized and important words and phrases are identified.

[1882] Step 3:

[1883] The terminal sends the contents of the memo including the extracted keywords to the server (HTTP POST request). The input is the keyword list and the memo contents, and the output is a request sent to the server.

[1884] Step 4:

[1885] The server inputs the received note content into a generative AI model (e.g., OpenAI's GPT-3) to generate a prompt that suggests the optimal storage location. The input is the note content and a list of keywords, and the output is information about the suggested storage location. Example prompt: "Suggest a suitable storage location for next week's project meeting."

[1886] Step 5:

[1887] The server sends the generated storage location information to the device (HTTP POST request), and the device notifies the user of the storage location suggestion. The input is the storage location information, and the output is a notification to the user. Specifically, a notification is displayed on the device's UI.

[1888] Step 6:

[1889] If the user agrees to the proposal, the device sends the consent information to the server (HTTP POST request). The input is the user's consent, and the output is the transmission of consent data to the server.

[1890] Step 7:

[1891] With the user's consent, the server saves the memo in the specified folder. The saving is done in a database (e.g., MySQL or MongoDB). The input is the memo content and the save location, and the output is the save operation to the database.

[1892] Step 8:

[1893] The user enters the URL of an external document into the terminal. The input is a URL.

[1894] Step 9:

[1895] The terminal sends the entered URL to the server as is (HTTP POST request). The input is a URL, and the output is a request sent to the server.

[1896] Step 10:

[1897] The server analyzes the content of the received URL and uses a generative AI model to generate the associated tag. The input is the URL and the output is the generated tag. Example prompt: "Please rate the URL for the technical specification."

[1898] Step 11:

[1899] The server saves the generated tags and URLs to a database. The input is the tag and URL, and the output is a save operation to the database.

[1900] Step 12:

[1901] A user inputs a search query into a terminal. The input is a search query.

[1902] Step 13:

[1903] The terminal sends the entered search query to the server (HTTP GET request). The input is the search query, and the output is the request sent to the server.

[1904] Step 14:

[1905] The server receives the search query and uses a generative AI model to generate a prompt that searches for related notes and URLs. The input is the search query and the output is a list of related data. Example prompt: "Please find the agenda for next week's project meeting."

[1906] Step 15:

[1907] The server uses the generative AI model to search for relevant notes and URLs from the database and sends the results to the device (HTTP GET request). The input is the search results, and the output is the data sent to the device.

[1908] Step 16:

[1909] The terminal displays the received search results to the user. The input is the search results and the output is the display on the user interface.

[1910] Step 17:

[1911] The user requests a summary or description of a particular note or URL. The input is a summary / description request.

[1912] Step 18:

[1913] The terminal sends a summary / explanation request to the server (HTTP POST request). The input is the request data, and the output is the data sent to the server.

[1914] Step 19:

[1915] The server passes the received request to a generative AI model to generate a summary or commentary of the specified document. The input is a note or URL, and the output is a summary or commentary text. Example prompt: "Generate a summary of a technical specification."

[1916] Step 20:

[1917] The server sends the generated summary and commentary to the terminal (HTTP POST request). The input is the summary / commentary result, and the output is the message sent to the terminal.

[1918] Step 21:

[1919] The terminal displays the received summary / explanation results to the user. The input is the summary / explanation results, and the output is the display on the user interface.

[1920] (Application example 1)

[1921] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1922] Robots used in factories require the management of numerous work instructions and maintenance logs, but as this information increases, management becomes cumbersome. A system that can quickly search for necessary information and provide summaries is also needed, but existing systems cannot efficiently achieve this. Therefore, there is a need to streamline information management for factory robots and improve productivity.

[1923] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1924] In this invention, the server includes means for extracting keywords from text data entered by a user, means for suggesting a storage location based on the extracted keywords, means for saving the text data in the suggested storage location, means for searching for files, means for presenting search results to the user, means for generating summaries, and means for presenting the generated summaries to the user, thereby enabling efficient management of robot instructions and logs in a factory, rapid search, and provision of summaries.

[1925] "User" means a person or organization that operates the system and inputs and retrieves information.

[1926] "Text data" refers to information that consists of characters and sentences.

[1927] "Keywords" are important words and phrases extracted from the text data entered by the user.

[1928] A "storage location" is a specific folder or directory where data is stored.

[1929] A "server" is a computer system for storing and processing data.

[1930] A "file" is a unit for recording data or information.

[1931] "Search Results" refers to a collection of relevant information or data discovered based on a user-entered query.

[1932] The "summary" is a short sentence that succinctly summarizes the contents of the input document.

[1933] "URL" means a Uniform Resource Locator that provides a link to a web page or digital document.

[1934] "Related tags" are metadata that indicate keywords or categories related to a document or piece of information.

[1935] "Suggestion" refers to the system suggesting to the user the optimal storage location and processing method.

[1936] This invention is an information management system that efficiently manages, searches, and summarizes work instructions and maintenance logs for factory robots. This system is composed of a server and terminals and performs the following processes.

[1937] 1. System Configuration

[1938] Hardware

[1939] Server: High-performance server (e.g. AWS EC2 instance)

[1940] Terminal: Tablet or computer mounted on a robot in the factory

[1941] Network: Local Area Network (LAN) or Cloud Network

[1942] software

[1943] Server software: Database software (e.g., MySQL), generative AI API (e.g., OpenAI GPT)

[1944] Terminal software: User interface (UI) applications, search engine software

[1945] 2. Save and categorize your notes

[1946] The user inputs new text data (e.g., work instruction notes) into the device. The device extracts important keywords from the input text data. The text data, including the extracted keywords, is then sent to the server. The server uses a generative AI model to suggest the optimal storage location. The device notifies the user of the suggested storage location, and if the user approves, the text data is saved in the specified folder.

[1947] Specific examples

[1948] The user enters "Notes about next week's maintenance work." The device extracts keywords such as "next week" and "maintenance work" and sends them to the server. Using a generative AI model, the server determines that the "Maintenance-related documents" folder is the most appropriate. The device then asks the user, "Do you want to save this note in the Maintenance-related documents folder?" If the user approves, the note is saved in the "Maintenance-related documents" folder.

[1949] 3. Registering the external document URL

[1950] The user enters the URL of an external document. The device sends the URL as is to the server. The server uses a generative AI model to evaluate the URL's content and generate relevant tags, allowing the user to easily search for related documents later.

[1951] Specific examples

[1952] The user enters the URL of the technical specifications for the new machine. The device sends this to the server, which analyzes the contents of the URL and generates tags such as "technical specifications," "specifications," and "new machine." The generated tags and URLs are stored in a database.

[1953] 4. Information Search

[1954] A user enters a search query into their device. The device sends the search query to the server, which uses a generative AI model to search for relevant text data and URLs. The search results are sent to the device and displayed to the user.

[1955] Specific examples

[1956] A user searches for "Details of next week's maintenance work." The device sends the search query to the server, which searches the database for relevant notes and documents. As a search result, the server finds "Notes about details of next week's maintenance work" and sends it to the device. The device displays the search result to the user.

[1957] 5. Document Summary / Explanation

[1958] The user requests a summary or commentary for a specific note or URL. The device sends the summary / commentary request to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and sends it to the device. The device then displays the summary / commentary result to the user.

[1959] Specific examples

[1960] The user requests a "summary of the technical specifications for a new machine." The device sends this request to the server, which uses a generative AI model to concisely summarize the technical specifications. The generated summary is labeled "This technical specification describes the design and requirements for a new machine." and is sent to the device. The device then displays the summary to the user.

[1961] Prompt Sentence Examples

[1962] Write a summary of the following document:

[1963] This technical specification describes the system design and requirements. It includes detailed blueprints and specifications.

[1964] This system enables efficient management of robotic instructions and logs within factories, as well as quick search and provision of summaries.

[1965] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1966] Step 1:

[1967] The user inputs new text data (e.g., notes for a work instruction) into the terminal. The terminal receives this text data and stores it as input data for proceeding to the next step.

[1968] Step 2:

[1969] The device extracts important keywords from the input text data using a text analysis algorithm to identify key words and phrases within the text data, and then transmits the extracted keywords to the server.

[1970] Step 3:

[1971] The server receives the extracted keywords and uses a generative AI model to suggest the best storage location. The generative AI then searches related folders and directories based on the keywords to determine the best storage location. The server then sends the suggested storage location information to the device.

[1972] Step 4:

[1973] The device notifies the user of the storage location proposal received from the server. The user checks and approves the proposed storage location. After this operation is completed, the device proceeds to the next step.

[1974] Step 5:

[1975] The device saves the input text data in a location approved by the user. Specifically, it creates a text file in the specified folder and records the input data in it. This data is later used for searching and generating summaries.

[1976] Step 6:

[1977] The user enters the URL of an external document. The device receives this URL and retains it for the next step.

[1978] Step 7:

[1979] The device sends the entered URL as is to the server. The server receives the URL and evaluates the URL's content using a generative AI model. Based on the evaluation results, the server generates associated tags and stores the URL along with these tags in a database, allowing for easy search of the URL later.

[1980] Step 8:

[1981] The user inputs a search query into the terminal, which receives the query and proceeds to the next step.

[1982] Step 9:

[1983] The device sends a search query to the server, which uses a generative AI model to find relevant text data and URLs. The server searches its database to find results that match the user's search query. The server then sends the search results back to the device and presents them to the user.

[1984] Step 10:

[1985] The user requests a summary or commentary on a particular note or URL. The device receives this request and proceeds to the next step.

[1986] Step 11:

[1987] The device sends a summary / explanation request to the server, which uses the generative AI model to generate a summary or explanation for the specified document. The generated summary or explanation is then sent from the server to the device and displayed to the user through the device.

[1988] Examples:

[1989] If a user requests a "summary of the technical specifications for a new machine," the device sends this request directly to the server, which creates a summary based on the contents of the technical specifications and inputs the following prompt sentence into the generative AI model.

[1990] "Write a summary of the following document: This technical specification describes the system design and requirements. It includes detailed blueprints and specifications."

[1991] The generated summary is "This technical specification describes the design and requirements of a new machine," and is sent to the terminal and presented to the user.

[1992] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1993] The present invention is an information management system that allows users to efficiently manage, search, and retrieve notes and documents, and provides summaries and explanations, while also recognizing and adapting to the user's emotions. Below, the program processing of this system is explained in natural language and detailed with specific examples.

[1994] Save and classify notes (emotion engine compatible version)

[1995] When a user enters a new note, the device analyzes the content of the entered note and not only extracts keywords but also recognizes the user's emotions. This emotion recognition is performed using an emotion engine. The device sends the extracted keywords and emotion information to the server. The server passes the received data to a generation AI, which suggests an appropriate storage location. This suggestion is optimized according to the user's emotions. The suggested storage location information is sent to the device, and the device notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[1996] Specific examples

[1997] The user enters "Notes about next week's project meeting." The device uses its emotion engine to recognize that the user is nervous. The emotion information of the keywords "next week," "project meeting," and "nervous" is sent to the server. The server uses generative AI to determine that the "Project-related documents" folder is the best choice. The device suggests to the user, "Do you want to save this note in the 'Project-related documents' folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[1998] Registering an external document URL (emotion engine compatible version)

[1999] When a user enters the URL of an external document, the device sends the URL to the server, which uses a generative AI to evaluate the content of the URL and generate relevant tags, taking into account the user's emotional state. The generated tags and URL are then stored in a database.

[2000] Specific examples

[2001] When a user enters the "URL of a technical specification," the emotion engine recognizes that the user is impatient. The device sends the URL and the "impatient" emotion information to the server. The server uses a generation AI to analyze the contents of the URL and generate tags such as "technical specification," "specifications," and "concept." The generated tags and URL are stored in a database.

[2002] Information Search (Emotion Engine Compatible Version)

[2003] When a user enters a search query, the device recognizes the user's emotion along with the search query. The device then sends this information to the server, which uses generative AI to consider the user's emotion when searching for related notes and URLs. The search results are then sent to the device and presented in a format that best suits the user's emotion.

[2004] Specific examples

[2005] When a user searches for "next week's project meeting agenda," the emotion engine recognizes that the user is confused. The device sends the search query and the "confused" emotion information to the server. The server uses generative AI to prioritize and display notes and documents that are most likely to be useful to the confused user. The device then displays search results optimized for the user.

[2006] Document summary / explanation (emotion engine compatible version)

[2007] When a user requests a summary or commentary for a specific note or URL, the device sends this request and the user's emotional information to the server. The server uses generative AI to generate a summary or commentary for the specified document, optimizing it according to the emotional information. The generated summary or commentary is then sent to the device and presented to the user.

[2008] Specific examples

[2009] When a user requests a "summary of technical specifications," the emotion engine recognizes the user's calmness. The device sends the summary request and the "calm" emotion information to the server. The server uses generative AI to summarize the technical specifications and generate a detailed summary suitable for a calm user. The device then displays this generated summary to the user.

[2010] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[2011] The processing flow will be explained below.

[2012] Save and classify notes (emotion engine compatible version)

[2013] Step 1:

[2014] A user inputs a new note into a terminal. For example, the user inputs "About the agenda for next week's project meeting."

[2015] Step 2:

[2016] The device analyzes the contents of the memo and extracts keywords, such as "next week," "project meeting," and "agenda."

[2017] Step 3:

[2018] The device uses an emotion engine to recognize the user's emotions. For example, the emotion engine determines that the user is nervous.

[2019] Step 4:

[2020] The device sends the extracted keywords and emotion information to the server, which uses JSON format data as the communication protocol.

[2021] Step 5:

[2022] The server passes the received data to the generation AI and instructs it to suggest the best place to save it. For example, the generation AI determines that the "Project-related documents" folder is the best.

[2023] Step 6:

[2024] The server sends the proposed storage location information to the terminal, for example, "Proposed storage location: Project-related documents."

[2025] Step 7:

[2026] The device will notify the user with save location suggestions, for example, "Would you like to save this note in the 'Project Related Documents' folder?"

[2027] Step 8:

[2028] The user agrees with the proposal, e.g., clicks the "Save" button.

[2029] Step 9:

[2030] Your device will save the note to the specified location, for example, the "Project Related Documents" folder.

[2031] Registering an external document URL (emotion engine compatible version)

[2032] Step 1:

[2033] The user enters the URL of an external document into the terminal, for example, "https: / / example.com / tech-spec."

[2034] Step 2:

[2035] The device uses an emotion engine to recognize the user's emotions. For example, the emotion engine determines that the user is anxious.

[2036] Step 3:

[2037] The device sends the input URL and emotion information to the server. JSON format data is used as the communication protocol.

[2038] Step 4:

[2039] The server receives the URL and instructs the AI ​​to evaluate the content of the URL. The AI ​​analyzes the content of the URL and generates relevant tags such as "technical specifications," "specifications," and "concepts."

[2040] Step 5:

[2041] The server stores the associated tags and URLs in a centralized database, for example using an SQL query.

[2042] Information Search (Emotion Engine Compatible Version)

[2043] Step 1:

[2044] A user enters a search query into a device, for example, "agenda for next week's project meeting."

[2045] Step 2:

[2046] The device uses an emotion engine to recognize the user's emotion. For example, the emotion engine determines that the user is confused.

[2047] Step 3:

[2048] The device sends the search query and emotion information to the server, using JSON format data as the communication protocol.

[2049] Step 4:

[2050] The server receives the search query and uses generative AI to find relevant notes and URLs, for example, based on keywords like "next week," "project meeting," or "agenda."

[2051] Step 5:

[2052] The server then prioritizes search results based on emotion and sends them to the device, so for example, a confused user would be shown the most useful notes and documents.

[2053] Step 6:

[2054] The device displays the search results to the user. For example, a link or URL for "Notes on the agenda for next week's project meeting" appears on the user's screen.

[2055] Document summary / explanation (emotion engine compatible version)

[2056] Step 1:

[2057] The user specifies a specific note or URL on the terminal and requests a summary or explanation. For example, the user requests a "summary of a technical specification."

[2058] Step 2:

[2059] The device uses an emotion engine to recognize the user's emotion. For example, the emotion engine determines that the user is calm.

[2060] Step 3:

[2061] The device sends a summary / explanation request and emotion information to the server. JSON format data is used as the communication protocol.

[2062] Step 4:

[2063] The server passes the summary / explanation request to the generation AI, which generates a summary or explanation of the specified document. For example, the generation AI summarizes a technical specification and generates the content, "This technical specification describes the system design and requirements."

[2064] Step 5:

[2065] The server optimizes the generated summaries and commentaries based on emotion information and sends them to the device. For example, a calm user will receive a detailed summary.

[2066] Step 6:

[2067] The terminal displays the summary / explanation result to the user. For example, the following content is displayed on the user's screen: "Summary: This technical specification describes the system design and requirements."

[2068] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[2069] Example 2

[2070] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2071] Conventional information management systems have had the problem of being difficult to present optimal suggestions and search results that take into account the user's emotions when saving and searching text data and URLs entered by the user.In addition, there was no method for suggesting storage locations for information or generating tags based on emotions, so there was a need to improve the user experience.

[2072] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for extracting keywords from text data input by a user, a means for transmitting the extracted keywords and the user's emotional information to the server, a means for the generative AI model to suggest an appropriate storage location taking into account the emotional information, and a means for saving the text data in the suggested storage location. This enables the suggestion of an optimal storage location taking into account the user's emotions and efficient management of text data.

[2073] The system also includes a means for registering URLs of external documents, a means for the generative AI model to evaluate the content of the URL taking into account the user's emotional information and generate related tags, and a means for saving the URL together with the generated related tags, which facilitates tag generation and URL management based on emotional information.

[2074] The system further includes a means for transmitting the search query and emotion information entered by the user to a server, a means for the generative AI model to search for related text data and URLs taking the emotion information into consideration, and a means for presenting search results to the user, thereby realizing efficient search for related information according to the user's emotions and presenting optimal search results.

[2075] "Text data" refers to textual information entered by a user, including notes, documents, messages, and the like.

[2076] A "keyword" is a particularly important word or phrase in text data that summarizes or represents the content of the text data.

[2077] "Emotion information" is data that indicates the user's emotional state, and includes emotions such as tension, impatience, and calmness.

[2078] A "server" refers to a computer system that processes and stores data on a network.

[2079] A "generative AI model" is an artificial intelligence model that performs natural language processing and data analysis, suggesting storage locations and optimizing search results based on user emotions and text data.

[2080] A "storage location" refers to a digital folder or section of a database used to store text data or URLs.

[2081] "External Documents" refers to documents that exist on external websites or other systems and that users register with the system.

[2082] "URL" is an abbreviation for Uniform Resource Locator, and refers to an address for specifying resources on the Internet.

[2083] "Related tags" are keywords or phrases that are associated with text data or URLs to make them easier to search and organize.

[2084] A "search query" is text data that a user enters when searching for information, and refers to a word or phrase that specifies the search target.

[2085] "Search Results" refers to a list of information provided by the system based on a user's search query, including relevant text data and URLs.

[2086] The present invention is an information management system that allows users to efficiently manage, search, and retrieve notes and documents, and provides summaries and explanations, while also recognizing and adapting to the user's emotions. Below, the program processing of this system is explained in natural language and detailed with specific examples.

[2087] Save and classify notes (emotion engine compatible version)

[2088] When a user enters a new note, the device analyzes the content of the entered note and extracts keywords. This analysis uses natural language processing (NLP) technology. The device also uses an emotion engine to recognize the user's emotions. The recognized emotions and extracted keywords are sent from the device to the server. The server uses a generative AI model to suggest the optimal storage location taking into account the emotion information. This suggestion is sent to the device, which notifies the user. If the user agrees to the suggestion, the device saves the note in the specified folder.

[2089] Specific examples

[2090] The user enters "Notes about next week's project meeting." The device uses an emotion engine to recognize that the user is nervous. The emotion information of the keywords "next week," "project meeting," and "nervous" is sent to the server. The server uses a generative AI model to suggest the "Project-related documents" folder as a storage location. The device asks the user, "Do you want to save this note in the 'Project-related documents' folder?" If the user approves, the note is saved in the "Project-related documents" folder.

[2091] Prompt Sentence Examples

[2092] Input note: "Notes about next week's project meeting"

[2093] User Emotion: "Tension"

[2094] Folder suggestions by generative AI: "Project-related documents"

[2095] Registering an external document URL (emotion engine compatible version)

[2096] When a user enters the URL of an external document, the device sends the URL to the server, which uses a generative AI model to evaluate the URL's content and generate relevant tags, taking the user's emotional information into account. The generated tags and URL are then stored in a database.

[2097] Specific examples

[2098] When a user enters the "URL of a technical specification," the emotion engine recognizes that the user is impatient. The device sends the URL and the "impatient" emotion information to the server. The server uses a generative AI model to analyze the content of the URL and generate tags such as "technical specification," "specifications," and "concept." The generated tags and URL are stored in a database.

[2099] Prompt Sentence Examples

[2100] Input URL: "http: / / example.com / technical-spec"

[2101] User Emotion: "Impatience"

[2102] Generative AI tags: "Technical specifications," "Specifications," "Concept"

[2103] Information Search (Emotion Engine Compatible Version)

[2104] When a user enters a search query, the device recognizes the user's emotions along with the search query and sends this information to the server. The server uses a generative AI model to search for related notes and URLs while taking the emotional information into account. The search results are sent to the device and presented in a format that best suits the user's emotions.

[2105] Specific examples

[2106] When a user searches for "next week's project meeting agenda," the emotion engine recognizes that the user is confused. The device sends the search query and the "confused" emotion information to the server. The server uses a generative AI model to prioritize the notes and documents that are most likely to be helpful to the confused user. The device then displays search results optimized for the user.

[2107] Prompt Sentence Examples

[2108] Search query: "next week's project meeting agenda"

[2109] User sentiment: "Confused"

[2110] Generative AI search results: the most helpful notes and documents for confused users

[2111] Document summary / explanation (emotion engine compatible version)

[2112] When a user requests a summary or commentary for a specific note or URL, the device sends this request and the user's emotional information to the server. The server uses a generative AI model to generate a summary or commentary for the specified document and optimizes the content according to the emotional information. The generated summary or commentary is sent to the device and presented to the user.

[2113] Specific examples

[2114] When a user requests a "summary of technical specifications," the emotion engine recognizes the user's calm state. The device sends the summary request and the "calm" emotion information to the server. The server uses a generative AI model to summarize the technical specifications and generate a detailed summary appropriate for a calm user. The device then displays this generated summary to the user.

[2115] Prompt Sentence Examples

[2116] Summary request: "Summary of technical specifications"

[2117] User Sentiment: "Calm"

[2118] Generative AI Summarization: Detailed summaries suitable for calm users

[2119] In this way, the present invention provides a system that recognizes the user's emotions and optimizes the management and presentation of information based on those emotions, thereby supporting the user in performing their work more efficiently.

[2120] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2121] Save and classify notes (emotion engine compatible version)

[2122] Processing Steps

[2123] Step 1:

[2124] The user enters a note.

[2125] Input: Text data (e.g., "Notes about next week's project meeting")

[2126] Action: A user enters a note into a text entry field on a device.

[2127] Output: Text data entered into the terminal

[2128] Step 2:

[2129] The device analyzes the contents of the notes and extracts keywords.

[2130] Input: Text data

[2131] How it works: The device's software uses natural language processing (NLP) to extract key keywords from the note's content.

[2132] Output: Extracted keywords (e.g., "next week" or "project meeting")

[2133] Step 3:

[2134] The terminal uses an emotion engine to recognize the user's emotion.

[2135] Input: Text data, user typing speed, touch strength, and other sensory information

[2136] How it works: The emotion engine analyzes the user's emotional state.

[2137] Output: Emotional information (e.g., "tension")

[2138] Step 4:

[2139] The device transmits the extracted keywords and emotion information to the server.

[2140] Input: Keywords, emotional information

[2141] Operation: The communication module of the terminal sends data to the server as packets.

[2142] Output: Keywords and sentiment information sent to the server

[2143] Step 5:

[2144] The server passes the received data to the generative AI model.

[2145] Input: Keywords, emotional information

[2146] How it works: The server calls the API of the generative AI model and passes the data as an argument.

[2147] Output: The generative AI model begins its analysis

[2148] Step 6:

[2149] A generative AI model analyzes the data and suggests appropriate storage locations.

[2150] Input: Keywords, emotional information

[2151] How it works: A generative AI model suggests storage locations based on keywords and sentiment information.

[2152] Output: Suggested save location (e.g. "Project Related Documents" folder)

[2153] Step 7:

[2154] The server sends the proposal results to the terminal.

[2155] Input: Suggested save location

[2156] Operation: The server sends the proposal results to the device.

[2157] Output: Suggestion results sent to the device

[2158] Step 8:

[2159] The terminal notifies the user o...

Claims

1. A means for extracting keywords from text data input by a user; a means for suggesting storage locations based on the extracted keywords; means for storing the text data in the suggested storage location; A system including:

2. A means to register the URL of an external document, A means for evaluating the content of the registered URLs and generating related tags; A means to save the URL along with the associated generated tags; The system of claim 1 , comprising:

3. A means for searching for relevant text data and URLs based on a user-entered search query; means for presenting search results to a user; The system of claim 1 , comprising:

4. A means for summarizing or explaining the contents of user-specified text data or URLs; means for presenting the summary or commentary results to the user; The system of claim 1 , comprising:

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