system

Abstract

We provide the system. [Solution] A data storage means for storing files, An analysis means that automatically generates multiple tags by analyzing the contents of the file, A file organization means for organizing and managing files based on generated tags, A search method that identifies relevant files based on ambiguous search queries from users, A filtering method that allows users to specify conditions for filtering search results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When managing a large number of electronic files in an enterprise, the object is to solve the problem that the business efficiency is reduced due to referring to incorrect files of past versions or spending time to find the necessary files. Also, it is an object to enable easy identification of the target file even when the user enters a search keyword incorrectly.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a system including a data storage means for storing files, an analysis means for automatically generating tags by analyzing files, a file organization means for organizing and managing files based on the generated tags, a search means for identifying related files based on ambiguous search queries, and a filtering means for allowing users to specify filtering conditions for search results. The analysis means uses natural language processing technology, and the search means expands the user's search queries using synonyms, enabling accurate file access without stress.

[0006] A "file" refers to a unit of documents or data stored in digital format, a collection of information that can be managed and manipulated electronically.

[0007] "Data storage means" refers to devices or systems that can electronically store and retain information, and that enable reading and writing of the stored data.

[0008] "Analysis means" refers to a function or device that analyzes the content of data, extracts information, and processes it according to a specific purpose.

[0009] A "tag" is an identifier assigned to data or digital files to indicate their content or attributes, making classification and searching easier.

[0010] "File organization methods" refer to functions or systems that classify and arrange files based on specific criteria or rules in order to manage files efficiently.

[0011] "Search means" refers to a function or device used to identify necessary data or files based on conditions or queries specified by the user.

[0012] "Filtering means" refers to a function that provides processing to further narrow down the obtained search results and data, making it easier for users to select the information they want.

[0013] "Natural language processing technology" is a general term for technologies that enable computers to understand, analyze, and process human language.

[0014] "Synonyms" refer to words that are different but have nearly the same or similar meanings, and are used in search queries to broaden the scope of relevant information. [Brief explanation of the drawing]

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

Modes for Carrying Out the Invention

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

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

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

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

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

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

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

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

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

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0036] This invention provides a system for efficiently managing large amounts of digital files and enhancing search functionality. This system primarily improves user convenience through multiple functions implemented on the server.

[0037] First, the user uploads a file from their device to the server. At this point, the file is stored in a data storage device. The server then automatically analyzes the file content, extracts information using natural language processing technology, and generates appropriate tags. This tag information, generated based on the analysis results, is recorded and managed by a file organization device.

[0038] Based on the generated tags, the server categorizes files into specific categories and organizes them chronologically, making it easier for users to quickly find the files they need. This classification information is stored in a database and used when users access files.

[0039] When a user searches for a file, they enter and submit a search query from their terminal. The received query is instantly interpreted by a generative AI model to enable fuzzy searching, and synonyms are used to expand the search. This ensures that relevant files are identified even if the user enters an incorrect query.

[0040] If the search results are extensive, users can further refine them by adding criteria. Filtering methods allow users to narrow down their search by, for example, "a specific project" or "a specific year." The final search results are sent to the user's device, allowing them to access the necessary information.

[0041] In this way, the present invention helps users efficiently find the files they need and significantly improves the efficiency of business processes.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uploads files from their terminal to the server. This transfers the selected files from the user's working folder to the server.

[0045] Step 2:

[0046] The server receives the uploaded file and saves it to a data storage device. This storage location is a designated directory within the server for efficient file management later on.

[0047] Step 3:

[0048] The server analyzes the contents of the stored files, using natural language processing techniques to extract the text from the files. Based on the extracted information, it automatically generates tags that reflect the content.

[0049] Step 4:

[0050] The server assigns the generated tags to the files and uses file organization tools to organize the files chronologically or by category. In this step, the generated tag information is registered in the database.

[0051] Step 5:

[0052] The user enters a search query from their device and sends it to the server. For example, the user might enter a specific project name or report name as the query.

[0053] Step 6:

[0054] The server analyzes the received search query and performs a fuzzy search including synonyms using a generative AI model. This search extracts relevant files even with incorrect input.

[0055] Step 7:

[0056] The server returns the initial search results to the user and provides an interface that allows the user to add filtering conditions as needed.

[0057] Step 8:

[0058] Users can add specific criteria to refine their search results. For example, they can enter conditions such as "2023 reports" to find relevant files.

[0059] Step 9:

[0060] The server processes the search results again, applying the filtering conditions specified by the user, and then provides the final search results to the user.

[0061] Step 10:

[0062] Users can review the final search results displayed on their device and access the desired file. This allows them to efficiently obtain the data necessary for their work.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] In modern information management, efficiently organizing vast amounts of digital information and quickly and accurately retrieving the information users need is a crucial challenge. This requires a flexible search system capable of handling even ambiguous search requests. However, traditional methods have struggled with accurate information classification and efficient searching, requiring significant time and effort.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes a storage device for storing information, an analysis device for analyzing the content of the information and generating multiple identification information using natural language processing technology, and an organization device for classifying and managing the information based on the generated identification information. This enables accurate classification and efficient retrieval of information.

[0068] "Information" refers to various types of digital data and files, which are managed within a system.

[0069] "Storage device" refers to hardware or storage media used to permanently store information.

[0070] An "analysis device" is a device that uses natural language processing and machine learning technologies to analyze information content and generate identification information.

[0071] "Identification information" refers to tags and categories generated to describe the characteristics of information.

[0072] A "sorting device" is a system component used to classify and manage information based on generated identification information.

[0073] A "search request" refers to a query or request that a user enters to retrieve information.

[0074] A "search device" is a system component that functions to identify relevant information in response to a search request.

[0075] A "filtering device" is a system component that has the function of narrowing down search results based on conditions specified by the user.

[0076] This invention provides a system for efficiently managing large amounts of digital information and enhancing search capabilities. Specific embodiments are described below.

[0077] First, users upload information to the server using their devices. This information can take various forms, including documents, images, and audio files. The uploaded information is stored in the server's storage device, which may include, for example, a cloud-based storage service.

[0078] Upon receiving the uploaded information, the server begins analysis via an analysis device. During this process, natural language processing techniques are applied to extract important keywords and phrases from the information. The software used includes machine learning libraries such as TENSORFLOW® and PyTorch. As a result of the analysis, tags are generated as identifying information, and these tags are managed by an organization device.

[0079] Based on the generated identification information, the server categorizes and organizes the information chronologically, etc. This is important for users to quickly search for information later. This organized information is recorded in the database.

[0080] When a user performs a search, they send a search request from their device. The server uses a search device to interpret this request through a generative AI model. For example, a GPT-based generative AI model is used. This allows the server to broaden the search to include similar terms and identify relevant information.

[0081] As a concrete example, consider a case where a user is looking for the "2023 sales report." The user sends a prompt to the server such as "sales report 2023." Based on this prompt, the server considers similar terms, searches for relevant files, and returns the results. In this way, by using the system of the present invention, users can efficiently manage and search for the information they need.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] Users upload information to the server using their own devices. The input is a digital file selected by the user, and the output is that file stored on the server's storage device. Users utilize a dedicated upload interface, uploading files using drag-and-drop or selection buttons.

[0085] Step 2:

[0086] The server retrieves files stored in memory and begins analyzing the information using an analysis device. The input is the stored files, and the output is the identification information generated by the analysis. Specifically, important keywords and concepts are extracted from the file contents using natural language processing techniques. This uses machine learning libraries such as TensorFlow and PyTorch to understand the meaning of the information and generate tags.

[0087] Step 3:

[0088] The server uses an organization device to classify information based on the identification information generated through analysis. The input is the generated identification information and its associated file information, while the output is organized category information. Here, information is assigned to appropriate categories, such as "Reports" and "Contracts," and further organized chronologically. This simplifies information management.

[0089] Step 4:

[0090] When a user wants to search for information, they send a search request from their terminal. The input is the user's search query, and the output is a processed query suitable for searching. The server receives this query via a search device and interprets it using a generative AI model. In this process, the query's words are analyzed, and the search scope is expanded using synonyms and related words.

[0091] Step 5:

[0092] The server uses the processed query to retrieve relevant information from the database. The input is the processed query and the information in the database, and the output is a list of relevant information. Once the search results are obtained, the server refines them according to user-specified criteria. This refinement is based on elements such as date or category.

[0093] Step 6:

[0094] The user views the search results sent from the server on their device. The input is the search results from the server, and the output is the information the user views. The user views the results and accesses the desired data by downloading or referencing the information as needed.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] In data centers and information management services, there is a need to efficiently and quickly manage and search vast amounts of digital files. However, even when using natural language to make vague search requests, accurately identifying highly relevant files is difficult. Furthermore, there is a need for methods that allow users to easily refine search results.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the server includes an information storage means for storing files, an information analysis means for automatically generating multiple classification pieces of information by analyzing the contents of the files, and a search extension means for interpreting search requests and extending searches using a generated AI model. As a result, even when users make ambiguous search requests using natural language, they can efficiently identify highly relevant files and improve the efficiency of their work.

[0100] "Information storage means" refers to devices or functions for efficiently storing and managing digital files.

[0101] "Information analysis means" refers to technology that analyzes the contents of a file and automatically generates classification information based on the contents.

[0102] "Information organization means" refers to a function that efficiently organizes and manages files based on the generated classification information.

[0103] An "information retrieval means" is a function for identifying relevant files based on a search request from a user.

[0104] "Refinement methods" are functions that allow users to limit search results based on specified conditions.

[0105] A "generative AI model" is a technology that uses artificial intelligence to interpret search requests and expand the scope of the search.

[0106] This invention provides a system for effectively managing digital files and quickly searching for specific files. The server stores files using information storage means, analyzes the file contents using information analysis means, and automatically generates classification information. The analysis is performed using natural language processing technology, classifying information to handle even ambiguous search requests. This allows users to efficiently organize and manage their files.

[0107] Furthermore, the generated classification information is managed by an information organization system, allowing users to identify highly relevant files when searching for files via an information retrieval system. The server uses a generation AI model to interpret the user's ambiguous search queries and expand the search, enabling it to quickly find relevant files even if the user enters incorrect information.

[0108] Users can further refine their search results, allowing them to filter information based on specific criteria. For example, it becomes easy for users to display only files from a "specific project" or "specific year."

[0109] For example, if a user wants to find a project report from 2022, they can enter the search query "Project 2022 Report" into their device. The server will then use a generative AI model to find related files, considering synonyms such as "project" and "report." The user can then add further filtering conditions as needed to reach their desired file.

[0110] The following text can be used as an example of a prompt message to input into a generative AI model.

[0111] Please explain in natural language how to search for and display related files using synonyms for the keyword "Project Report 2022."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server stores digital files received from users via terminals into the information storage device. The input is the file itself, and the output is the file stored in the information storage device. The server enables consistent file management.

[0115] Step 2:

[0116] The server analyzes the contents of received files using information analysis tools and automatically generates classification information. The input is the stored file data, and the output is the generated classification information. In this process, the file contents are analyzed and tagged using natural language processing techniques.

[0117] Step 3:

[0118] The server organizes and manages data using information organization tools based on the generated classification information. The input is classification information, and the output is the structure of the organized database. This allows users to easily search and refer to files later.

[0119] Step 4:

[0120] The user enters a search query through a terminal and sends a request to the information retrieval system. The input is the search query, and the output is a list of identified related files. The system receives queries from the terminal and queries the server for appropriate information.

[0121] Step 5:

[0122] The server uses a generative AI model to interpret ambiguous search queries and expand upon them. The input is the user's search query, and the output is the interpreted and expanded search query. The AI ​​analyzes and expands the query by utilizing prompts.

[0123] Step 6:

[0124] The server identifies relevant files using information retrieval tools based on the interpreted search query and provides them to the user. The input is an extended search query, and the output is a list of candidate relevant files. It assists the user in accessing the information they need.

[0125] Step 7:

[0126] The user uses a terminal to specify conditions for search results and refines the results through filtering methods. The input consists of the initial search results and range-limiting conditions, while the output is a list of files filtered according to those conditions. Further refinement can be achieved by applying additional conditions.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention is an advanced file management system that incorporates an emotion engine. This system is implemented on a server and operates as follows:

[0129] The user uploads a file from their device to the server. This file is stored in the server's data storage system. Next, the server analyzes the contents of the uploaded file using natural language processing technology and automatically generates appropriate tags associated with the file. These tags clearly represent the file's contents and are useful for later searching and organization.

[0130] Once tags are generated, the server classifies and manages them using its file organization tools. When a user searches for a specific file, they enter and submit a search query on their terminal. This query is a standard string search, but it also includes fuzzy search functionality. Here, a synonym search function comes into play, considering related information beyond the words directly entered by the user.

[0131] A distinctive feature of this invention is the incorporation of an emotion engine. The server analyzes the user's emotional state not only through the search query but also through the input text and the entire interaction. This analysis is performed using emotion analysis technology to evaluate and record the user's current emotions (e.g., stress, anxiety, calmness, etc.) in real time. This system can adjust how search results are presented according to the user's emotions. For example, if it is determined that the user is feeling overwhelmed, the search results will be displayed concisely and with emphasis to support quick decision-making.

[0132] Furthermore, the emotion engine can influence user filtering and the overall system experience. For example, if a relaxed user is observed, the interface may be configured to be more diverse and detailed.

[0133] As a result, this system goes beyond simple file management, providing support tailored to the user's emotional state and realizing a more efficient and meaningful information management environment.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user selects a file on their device and initiates the upload to the server. This action initiates communication from the device to the server.

[0137] Step 2:

[0138] After receiving the uploaded file, the server saves it to a data storage device. The saved file is then ready to be accessed for subsequent processing.

[0139] Step 3:

[0140] The server analyzes the file contents. Using natural language processing technology, it extracts important information from the file's text and automatically generates relevant tags.

[0141] Step 4:

[0142] The server registers the generated tags in a database and organizes the files using a file management system. This ensures that files are managed efficiently and are easily accessible.

[0143] Step 5:

[0144] The user enters a search query from their terminal and sends it to the server. This query triggers the file search.

[0145] Step 6:

[0146] The server processes incoming queries using fuzzy search techniques, including synonym searches, to identify relevant files. This ensures that candidates other than the keywords directly entered by the user are also searched.

[0147] Step 7:

[0148] The server activates the sentiment engine along with the user's search query. It analyzes the user's emotional state based on their query and past interactions.

[0149] Step 8:

[0150] The server constructs search results in a format adapted to the user's emotions, based on the results of the emotion engine. For example, if the user is feeling stressed, the information is presented concisely.

[0151] Step 9:

[0152] The server sends the filtered search results to the user's device. The results are presented with sentiment-based priorities to help the user make quick decisions.

[0153] Step 10:

[0154] Users can review the search results received on their device and access the necessary files to continue their work.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Conventional information management systems faced challenges not only in automatically classifying information and searching for related information, but also in providing information that took into account the user's emotional state. This resulted in inefficient information management and a failure to improve user satisfaction.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes a device for storing information, a device for analyzing the content of the information and automatically generating identification information, and a device for analyzing the user's emotions and adjusting the information results. This enables efficient classification and retrieval of information that takes the user's emotional state into account.

[0160] A "device for storing information" is a device that can retain digital information for a long period of time and retrieve it as needed.

[0161] A "device that analyzes information content and automatically generates identification information" is a device that analyzes the content of information, identifies specific attributes or characteristics, and automatically generates identification information accordingly.

[0162] A "device that selects relevant information based on ambiguous information requests from users" is a device that selects the most relevant information based on unclear information requests provided by users.

[0163] A "device that allows users to specify conditions for limiting information results" is a device that enables users to specify conditions for limiting information results.

[0164] A "device that analyzes the user's emotions and adjusts the information results" is a device that analyzes the user's emotions and uses the analysis results to adjust the way the information results are presented and their content.

[0165] This invention is an advanced information management system that combines an emotion engine, enabling users to efficiently manage information through their terminals and providing information that takes into account the user's emotional state. This system is primarily implemented on a server and performs several important functions.

[0166] The server first functions as a device that stores information uploaded via terminals. This allows digital information to be retained for extended periods and retrieved as needed. General storage devices or cloud storage services can be used for storing the information.

[0167] Next, the server has the function of analyzing the information content and automatically generating identification information. This uses software such as the natural language processing frameworks "NLTK" and "spaCy". By using these technologies, the content of the information is analyzed and related identification information is automatically generated. For example, if information about travel is entered, identification information such as "travel" and "tourism" will be assigned.

[0168] Furthermore, the server provides a function to select relevant information based on the user's ambiguous information requests. This process utilizes a full-text search engine (e.g., ElasticSearch®) to select appropriate information for the user's request. By taking synonyms into consideration, it effectively handles even ambiguous requests.

[0169] Furthermore, the server also functions as a device that analyzes the user's emotions and adjusts the information results accordingly. Using a generative AI model (e.g., OpenAI's GPT model), it analyzes the user's emotions based on their input data and actions, and dynamically adjusts the way information is presented based on the results. For example, if the user is showing signs of anxiety, the server can display necessary information concisely.

[0170] For example, if a user searches for "weekend event information" and the server analyzes the user's mood and determines that they are in a relaxed state, it can display a detailed list of event information, providing a wealth of information.

[0171] An example of a prompt message is when the user instructs the system to "find a recipe," and the server efficiently searches for information tagged with terms such as "dish," "ingredients," and "cooking method" using relevant identification information.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user uploads information to the server using their terminal. Specifically, the user opens a file selection dialog, selects the information they want to upload, and clicks the submit button. This input information (e.g., text or image files) is sent to the server and stored in the data storage device.

[0175] Step 2:

[0176] The server analyzes the content of the stored information and automatically generates identification information. To analyze the content of the information, natural language processing software (e.g., NLTK, spaCy) is used to extract and classify keywords from the input text. As a result of this data processing, identification information related to the information is generated (e.g., tags such as "travel" and "tourism").

[0177] Step 3:

[0178] The server classifies and manages information based on the generated identification information. Using a database management system (e.g., MySQL®), it organizes information into categories based on tags and indexes it for easy searching. This data processing makes information associated with specific categories and tags readily accessible.

[0179] Step 4:

[0180] The user enters a query using a terminal to search for specific information. This query is sent to a server, which analyzes it using a search engine (e.g., Elasticsearch). Based on the input query, the server identifies relevant information and searches for related information, including synonyms. This process outputs an expanded search result for the input query.

[0181] Step 5:

[0182] The server analyzes the user's emotions based on the search results and provides information accordingly. Using a generative AI model (e.g., OpenAI's GPT model), it evaluates the emotional state from the user's search behavior and input. Based on this data analysis, for example, if it's determined that the user is feeling anxious, it provides supportive output by concisely presenting necessary information.

[0183] Step 6:

[0184] The server adjusts the user interface according to the user's emotional state. For example, if the user is assessed as relaxed, it displays more information and provides detailed navigation. This behavior results in an optimized interface output that enhances the user experience.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] Traditional file management systems present information without considering the user's emotional state, which can lead to stress and anxiety hindering the information access experience. Furthermore, they lack the ability to quickly provide appropriate information in response to vague search requests, making it difficult for users to efficiently access the information they need. Therefore, there is a need for a system that presents information in accordance with the user's emotions, supporting a more comfortable and efficient information management experience.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes an information storage means for storing files, an analysis means for automatically generating multiple identifiers by analyzing the contents of the files, and an emotion analysis means for analyzing the user's emotions and adjusting the method of presenting information based on that state. This enables flexible and adaptive information presentation according to the user's emotional state.

[0190] "Information storage means" refers to a device or method for storing and holding files and data.

[0191] "Analysis means" refers to a device or method for analyzing the contents of a file to generate useful information or identifiers.

[0192] An "identifier" is a label or tag that is automatically generated to identify a specific file or data.

[0193] "Information organization means" refers to a device or method for efficiently classifying and managing files based on generated identifiers.

[0194] "Search means" refers to a device or method for identifying relevant information or files based on a user's vague search request.

[0195] A "filtering method" is a device or method for further refining search results based on conditions specified by the user.

[0196] "Emotional analysis means" refers to a device or method for analyzing a user's emotions and adjusting the way information is presented based on those emotions.

[0197] "Emotional analysis technology" is a technology that infers and evaluates a user's emotional state from their input and interactions.

[0198] To implement this invention, an application example called an "emotion-responsive personal assistant robot" is used. The server utilizes a high-performance microphone and camera sensor to process voice commands and search requests received from the user, and stores various files and data using information storage means. Google® Cloud Speech-to-Text API is used for speech recognition, and IBM Watson® Tone Analyzer is utilized for sentiment analysis. Natural language processing is performed using NLTK.

[0199] The server converts user voices into text information and uses sentiment analysis technology to determine the user's emotional state through analysis tools. Based on the analysis results, an identifier is automatically generated, and appropriate information is presented. For example, if the user is determined to be relaxed, a variety of information such as music and news will be provided, while if the user is feeling anxious, only the essential information will be presented concisely. Furthermore, filtering tools are used to resolve ambiguity in search requests and provide precise results based on the user's conditions.

[0200] For example, when a user asks, "Tell me today's schedule," if the server senses the user is in a hurry, it will present only the most important appointments first. Sentiment analysis is performed using prompts such as, "What emotional state is this user in? How should information be presented to help them accomplish their current task?" The information obtained in this way is important for helping users manage their tasks efficiently.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The user provides voice input through a high-performance microphone. This voice data becomes the system's input. The server converts this voice into text using the Google Cloud Speech-to-Text API. The converted voice-to-text output is the system's output.

[0204] Step 2:

[0205] The server passes the converted text data to an analysis tool, which uses IBM Watson Tone Analyzer to analyze the user's emotional state. At this stage, the text data becomes the input, and the analyzed emotional data is output. The emotional state is expressed as "relaxed," "anxious," etc.

[0206] Step 3:

[0207] The server uses NLTK to process text data using natural language processing and searches for related files from information storage. Based on the sentiment analysis results, the search query is refined. The input here consists of sentiment data and text data, and the output is the search results.

[0208] Step 4:

[0209] The server receives a prompt message using a generated AI model: "What emotional state is this user in? How should information be presented to him to best help him complete his current task?" and determines the appropriate method of information presentation. This operation outputs information in a format that corresponds to the user's emotional state.

[0210] Step 5:

[0211] The terminal displays information to the user according to instructions sent from the server. It provides detailed information when the user is relaxed, and only the essentials when the user is anxious. The terminal's input is the appropriate information display format, and the information displayed to the user is the output.

[0212] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0213] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0214] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0218] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0219] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0220] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0221] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0222] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0223] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0224] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0225] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0226] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0227] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0228] This invention provides a system for efficiently managing large amounts of digital files and enhancing search functionality. This system primarily improves user convenience through multiple functions implemented on the server.

[0229] First, the user uploads a file from their device to the server. At this point, the file is stored in a data storage device. The server then automatically analyzes the file content, extracts information using natural language processing technology, and generates appropriate tags. This tag information, generated based on the analysis results, is recorded and managed by a file organization device.

[0230] Based on the generated tags, the server categorizes files into specific categories and organizes them chronologically, making it easier for users to quickly find the files they need. This classification information is stored in a database and used when users access files.

[0231] When a user searches for a file, they enter and submit a search query from their terminal. The received query is instantly interpreted by a generative AI model to enable fuzzy searching, and synonyms are used to expand the search. This ensures that relevant files are identified even if the user enters an incorrect query.

[0232] If the search results are extensive, users can further refine them by adding criteria. Filtering methods allow users to narrow down their search by, for example, "a specific project" or "a specific year." The final search results are sent to the user's device, allowing them to access the necessary information.

[0233] In this way, the present invention helps users efficiently find the files they need and significantly improves the efficiency of business processes.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user uploads files from their terminal to the server. This transfers the selected files from the user's working folder to the server.

[0237] Step 2:

[0238] The server receives the uploaded file and saves it to a data storage device. This storage location is a designated directory within the server for efficient file management later on.

[0239] Step 3:

[0240] The server analyzes the contents of the stored files, using natural language processing techniques to extract the text from the files. Based on the extracted information, it automatically generates tags that reflect the content.

[0241] Step 4:

[0242] The server assigns the generated tags to the files and uses file organization tools to organize the files chronologically or by category. In this step, the generated tag information is registered in the database.

[0243] Step 5:

[0244] The user enters a search query from their device and sends it to the server. For example, the user might enter a specific project name or report name as the query.

[0245] Step 6:

[0246] The server analyzes the received search query and performs a fuzzy search including synonyms using a generative AI model. This search extracts relevant files even with incorrect input.

[0247] Step 7:

[0248] The server returns the initial search results to the user and provides an interface that allows the user to add filtering conditions as needed.

[0249] Step 8:

[0250] Users can add specific criteria to refine their search results. For example, they can enter conditions such as "2023 reports" to find relevant files.

[0251] Step 9:

[0252] The server processes the search results again, applying the filtering conditions specified by the user, and then provides the final search results to the user.

[0253] Step 10:

[0254] Users can review the final search results displayed on their device and access the desired file. This allows them to efficiently obtain the data necessary for their work.

[0255] (Example 1)

[0256] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0257] In modern information management, efficiently organizing vast amounts of digital information and quickly and accurately retrieving the information users need is a crucial challenge. This requires a flexible search system capable of handling even ambiguous search requests. However, traditional methods have struggled with accurate information classification and efficient searching, requiring significant time and effort.

[0258] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0259] In this invention, the server includes a storage device for storing information, an analysis device for analyzing the content of the information and generating multiple identification information using natural language processing technology, and an organization device for classifying and managing the information based on the generated identification information. This enables accurate classification and efficient retrieval of information.

[0260] "Information" refers to various types of digital data and files, which are managed within a system.

[0261] "Storage device" refers to hardware or storage media used to permanently store information.

[0262] An "analysis device" is a device that uses natural language processing and machine learning technologies to analyze information content and generate identification information.

[0263] "Identification information" refers to tags and categories generated to describe the characteristics of information.

[0264] A "sorting device" is a system component used to classify and manage information based on generated identification information.

[0265] A "search request" refers to a query or request that a user enters to retrieve information.

[0266] A "search device" is a system component that functions to identify relevant information in response to a search request.

[0267] A "filtering device" is a system component that has the function of narrowing down search results based on conditions specified by the user.

[0268] This invention provides a system for efficiently managing large amounts of digital information and enhancing search capabilities. Specific embodiments are described below.

[0269] First, users upload information to the server using their devices. This information can take various forms, including documents, images, and audio files. The uploaded information is stored in the server's storage device, which may include, for example, a cloud-based storage service.

[0270] Upon receiving the uploaded information, the server begins analysis via an analysis device. During this process, natural language processing techniques are applied to extract important keywords and phrases from the information. The software used includes machine learning libraries such as TensorFlow and PyTorch. As a result of the analysis, tags are generated as identifying information, and these tags are managed by an organization device.

[0271] Based on the generated identification information, the server categorizes and organizes the information chronologically, etc. This is important for users to quickly search for information later. This organized information is recorded in the database.

[0272] When a user performs a search, they send a search request from their device. The server uses a search device to interpret this request through a generative AI model. For example, a GPT-based generative AI model is used. This allows the server to broaden the search to include similar terms and identify relevant information.

[0273] As a concrete example, consider a case where a user is looking for the "2023 sales report." The user sends a prompt to the server such as "sales report 2023." Based on this prompt, the server considers similar terms, searches for relevant files, and returns the results. In this way, by using the system of the present invention, users can efficiently manage and search for the information they need.

[0274] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0275] Step 1:

[0276] Users upload information to the server using their own devices. The input is a digital file selected by the user, and the output is that file stored on the server's storage device. Users utilize a dedicated upload interface, uploading files using drag-and-drop or selection buttons.

[0277] Step 2:

[0278] The server retrieves the files stored in the storage device and starts analyzing the information using the analysis device. The input is the stored file, and the output is the identification information generated by the analysis. Specifically, important keywords and concepts are extracted from the file content using natural language technology. For this, machine learning libraries such as TensorFlow and PyTorch are used to understand the meaning of the information and generate tags.

[0279] Step 3:

[0280] Based on the identification information generated by the analysis, the server classifies the information using the sorting device. The input is the generated identification information and the file information related to it, and the output is the sorted category information. Here, the information is assigned to appropriate categories, such as "report", "contract", etc., and further sorted in chronological order. This facilitates the management of information.

[0281] Step 4:

[0282] When the user wants to search for information, a search request is sent from the terminal. The input is the search query by the user, and the output is the processed query suitable for searching. The server receives this query via the search device and interprets it using the generated AI model. In this process, the query statement is analyzed, and the search scope is expanded using similar words and related words, etc.

[0283] Step 5:

[0284] The server uses the processed query to search for relevant information from the database. The input is the processed query and the information in the database, and the output is a list of relevant information. When the search results are obtained, the server filters them according to the specified conditions of the user. The filtering is performed based on elements such as date and category, etc.

[0285] Step 6:

[0286] The user checks the search results sent from the server on the terminal. The input is the search results from the server, and the output is the information viewed by the user. The user accesses the target data by viewing the results and downloading or referring to the information as needed.

[0287] (Application Example 1)

[0288] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0289] In data centers and information management services, there is a need to efficiently and quickly manage and search a large number of digital files. At this time, there is a problem that it is difficult to accurately identify highly relevant files even when making ambiguous search requests using natural language. In addition, means for allowing users to easily narrow down search results are required.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0291] In this invention, the server includes information storage means for storing files, information analysis means for automatically generating a plurality of classification information by analyzing the contents of the files, and search expansion means for interpreting search requests and expanding searches using a generated AI model. Thereby, even when a user makes an ambiguous search request in natural language, highly relevant files can be efficiently identified, enabling business efficiency improvement.

[0292] The "information storage means" is a device or function for efficiently storing and managing digital files.

[0293] The "information analysis means" is a technology for analyzing the contents of files and automatically generating classification information based on the contents.

[0294] "Information organization means" refers to a function that efficiently organizes and manages files based on the generated classification information.

[0295] An "information retrieval means" is a function for identifying relevant files based on a search request from a user.

[0296] "Refinement methods" are functions that allow users to limit search results based on specified conditions.

[0297] A "generative AI model" is a technology that uses artificial intelligence to interpret search requests and expand the scope of the search.

[0298] This invention provides a system for effectively managing digital files and quickly searching for specific files. The server stores files using information storage means, analyzes the file contents using information analysis means, and automatically generates classification information. The analysis is performed using natural language processing technology, classifying information to handle even ambiguous search requests. This allows users to efficiently organize and manage their files.

[0299] Furthermore, the generated classification information is managed by an information organization system, allowing users to identify highly relevant files when searching for files via an information retrieval system. The server uses a generation AI model to interpret the user's ambiguous search queries and expand the search, enabling it to quickly find relevant files even if the user enters incorrect information.

[0300] Users can further refine their search results, allowing them to filter information based on specific criteria. For example, it becomes easy for users to display only files from a "specific project" or "specific year."

[0301] As a specific example, when a user wants to search for the "project report for 2022", by entering the search query "project 2022 report" through the terminal, the server utilizes the generative AI model to search for relevant files considering synonyms such as "project" and "report". At this time, the user can further add filtering conditions as needed to reach the target file.

[0302] As an example of the prompt text input to the generative AI model, the following text can be used.

[0303] "Please explain in natural language the method of searching for and displaying relevant files by utilizing synonyms of the keyword 'project report for 2022'."

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The server stores the digital file received from the user via the terminal in the information storage means. The input is the file itself, and the output is the file stored in the information storage means. The server enables consistent management of files.

[0307] Step 2:

[0308] The server analyzes the content of the received file using the information analysis means and automatically generates classification information. The input is the stored file data, and the output is the generated classification information. In this process, the file content is analyzed and tagged using natural language processing technology.

[0309] Step 3:

[0310] The server organizes and manages data using information organization tools based on the generated classification information. The input is classification information, and the output is the structure of the organized database. This allows users to easily search and refer to files later.

[0311] Step 4:

[0312] The user enters a search query through a terminal and sends a request to the information retrieval system. The input is the search query, and the output is a list of identified related files. The system receives queries from the terminal and queries the server for appropriate information.

[0313] Step 5:

[0314] The server uses a generative AI model to interpret ambiguous search queries and expand upon them. The input is the user's search query, and the output is the interpreted and expanded search query. The AI ​​analyzes and expands the query by utilizing prompts.

[0315] Step 6:

[0316] The server identifies relevant files using information retrieval tools based on the interpreted search query and provides them to the user. The input is an extended search query, and the output is a list of candidate relevant files. It assists the user in accessing the information they need.

[0317] Step 7:

[0318] The user uses a terminal to specify conditions for search results and refines the results through filtering methods. The input consists of the initial search results and range-limiting conditions, while the output is a list of files filtered according to those conditions. Further refinement can be achieved by applying additional conditions.

[0319] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0320] This invention is an advanced file management system that incorporates an emotion engine. This system is implemented on a server and operates as follows:

[0321] The user uploads a file from their device to the server. This file is stored in the server's data storage system. Next, the server analyzes the contents of the uploaded file using natural language processing technology and automatically generates appropriate tags associated with the file. These tags clearly represent the file's contents and are useful for later searching and organization.

[0322] Once tags are generated, the server classifies and manages them using its file organization tools. When a user searches for a specific file, they enter and submit a search query on their terminal. This query is a standard string search, but it also includes fuzzy search functionality. Here, a synonym search function comes into play, considering related information beyond the words directly entered by the user.

[0323] A distinctive feature of this invention is the incorporation of an emotion engine. The server analyzes the user's emotional state not only through the search query but also through the input text and the entire interaction. This analysis is performed using emotion analysis technology to evaluate and record the user's current emotions (e.g., stress, anxiety, calmness, etc.) in real time. This system can adjust how search results are presented according to the user's emotions. For example, if it is determined that the user is feeling overwhelmed, the search results will be displayed concisely and with emphasis to support quick decision-making.

[0324] Furthermore, the emotion engine can influence user filtering and the overall system experience. For example, if a relaxed user is observed, the interface may be configured to be more diverse and detailed.

[0325] As a result, this system goes beyond simple file management, providing support tailored to the user's emotional state and realizing a more efficient and meaningful information management environment.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user selects a file on their device and initiates the upload to the server. This action initiates communication from the device to the server.

[0329] Step 2:

[0330] After receiving the uploaded file, the server saves it to a data storage device. The saved file is then ready to be accessed for subsequent processing.

[0331] Step 3:

[0332] The server analyzes the file contents. Using natural language processing technology, it extracts important information from the file's text and automatically generates relevant tags.

[0333] Step 4:

[0334] The server registers the generated tags in a database and organizes the files using a file management system. This ensures that files are managed efficiently and are easily accessible.

[0335] Step 5:

[0336] The user enters a search query from their terminal and sends it to the server. This query triggers the file search.

[0337] Step 6:

[0338] The server processes incoming queries using fuzzy search techniques, including synonym searches, to identify relevant files. This ensures that candidates other than the keywords directly entered by the user are also searched.

[0339] Step 7:

[0340] The server activates the sentiment engine along with the user's search query. It analyzes the user's emotional state based on their query and past interactions.

[0341] Step 8:

[0342] The server constructs search results in a format adapted to the user's emotions, based on the results of the emotion engine. For example, if the user is feeling stressed, the information is presented concisely.

[0343] Step 9:

[0344] The server sends the filtered search results to the user's device. The results are presented with sentiment-based priorities to help the user make quick decisions.

[0345] Step 10:

[0346] Users can review the search results received on their device and access the necessary files to continue their work.

[0347] (Example 2)

[0348] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0349] Conventional information management systems faced challenges not only in automatically classifying information and searching for related information, but also in providing information that took into account the user's emotional state. This resulted in inefficient information management and a failure to improve user satisfaction.

[0350] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0351] In this invention, the server includes a device for storing information, a device for analyzing the content of the information and automatically generating identification information, and a device for analyzing the user's emotions and adjusting the information results. This enables efficient classification and retrieval of information that takes the user's emotional state into account.

[0352] A "device for storing information" is a device that can retain digital information for a long period of time and retrieve it as needed.

[0353] A "device that analyzes information content and automatically generates identification information" is a device that analyzes the content of information, identifies specific attributes or characteristics, and automatically generates identification information accordingly.

[0354] A "device that selects relevant information based on ambiguous information requests from users" is a device that selects the most relevant information based on unclear information requests provided by users.

[0355] A "device that allows users to specify conditions for limiting information results" is a device that enables users to specify conditions for limiting information results.

[0356] A "device that analyzes the user's emotions and adjusts the information results" is a device that analyzes the user's emotions and uses the analysis results to adjust the way the information results are presented and their content.

[0357] This invention is an advanced information management system that combines an emotion engine, enabling users to efficiently manage information through their terminals and providing information that takes into account the user's emotional state. This system is primarily implemented on a server and performs several important functions.

[0358] The server first functions as a device that stores information uploaded via terminals. This allows digital information to be retained for extended periods and retrieved as needed. General storage devices or cloud storage services can be used for storing the information.

[0359] Next, the server has the function of analyzing the information content and automatically generating identification information. This uses software such as the natural language processing frameworks "NLTK" and "spaCy". By using these technologies, the content of the information is analyzed and related identification information is automatically generated. For example, if information about travel is entered, identification information such as "travel" and "tourism" will be assigned.

[0360] Furthermore, the server provides a function to select relevant information based on the user's ambiguous information requests. This process utilizes a full-text search engine (e.g., Elasticsearch) to select appropriate information for the user's request. By taking synonyms into consideration, it effectively handles even ambiguous requests.

[0361] Furthermore, the server also functions as a device that analyzes the user's emotions and adjusts the information results accordingly. Using a generative AI model (e.g., OpenAI's GPT model), it analyzes the user's emotions based on their input data and actions, and dynamically adjusts how information is presented based on the results. For example, if the user is showing signs of anxiety, the server can display necessary information concisely.

[0362] For example, if a user searches for "weekend event information" and the server analyzes the user's mood and determines that they are in a relaxed state, it can display a detailed list of event information, providing a wealth of information.

[0363] An example of a prompt message is when the user instructs the system to "find a recipe," and the server efficiently searches for information tagged with terms such as "dish," "ingredients," and "cooking method" using relevant identification information.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The user uploads information to the server using their terminal. Specifically, the user opens a file selection dialog, selects the information they want to upload, and clicks the submit button. This input information (e.g., text or image files) is sent to the server and stored in the data storage device.

[0367] Step 2:

[0368] The server analyzes the content of the stored information and automatically generates identification information. To analyze the content of the information, natural language processing software (e.g., NLTK, spaCy) is used to extract and classify keywords from the input text. As a result of this data processing, identification information related to the information is generated (e.g., tags such as "travel" and "tourism").

[0369] Step 3:

[0370] The server classifies and manages information based on the generated identification information. Using a database management system (e.g., MySQL), it organizes information into categories based on tags and indexes it for easy searching. This data processing makes information associated with specific categories and tags quickly accessible.

[0371] Step 4:

[0372] The user enters a query using a terminal to search for specific information. This query is sent to a server, which analyzes it using a search engine (e.g., Elasticsearch). Based on the input query, the server identifies relevant information and searches for related information, including synonyms. This process outputs an expanded search result for the input query.

[0373] Step 5:

[0374] The server analyzes the user's emotions based on the search results and provides information accordingly. Using a generative AI model (e.g., OpenAI's GPT model), it evaluates the emotional state from the user's search behavior and input. Based on this data analysis, for example, if it's determined that the user is feeling anxious, it provides supportive output by concisely presenting necessary information.

[0375] Step 6:

[0376] The server adjusts the user interface according to the user's emotional state. For example, if the user is assessed as relaxed, it displays more information and provides detailed navigation. This behavior results in an optimized interface output that enhances the user experience.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0379] Traditional file management systems present information without considering the user's emotional state, which can lead to stress and anxiety hindering the information access experience. Furthermore, they lack the ability to quickly provide appropriate information in response to vague search requests, making it difficult for users to efficiently access the information they need. Therefore, there is a need for a system that presents information in accordance with the user's emotions, supporting a more comfortable and efficient information management experience.

[0380] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0381] In this invention, the server includes an information storage means for storing files, an analysis means for automatically generating multiple identifiers by analyzing the contents of the files, and an emotion analysis means for analyzing the user's emotions and adjusting the method of presenting information based on that state. This enables flexible and adaptive information presentation according to the user's emotional state.

[0382] "Information storage means" refers to a device or method for storing and holding files and data.

[0383] "Analysis means" refers to a device or method for analyzing the contents of a file to generate useful information or identifiers.

[0384] An "identifier" is a label or tag that is automatically generated to identify a specific file or data.

[0385] "Information organization means" refers to a device or method for efficiently classifying and managing files based on generated identifiers.

[0386] "Search means" refers to a device or method for identifying relevant information or files based on a user's vague search request.

[0387] A "filtering method" is a device or method for further refining search results based on conditions specified by the user.

[0388] "Emotional analysis means" refers to a device or method for analyzing a user's emotions and adjusting the way information is presented based on those emotions.

[0389] "Emotional analysis technology" is a technology that infers and evaluates a user's emotional state from their input and interactions.

[0390] To implement this invention, we will use an application example called an "emotion-responsive personal assistant robot." The server utilizes a high-performance microphone and camera sensor to process voice commands and search requests from the user, and stores various files and data using information storage means. Google Cloud Speech-to-Text API is used for speech recognition, and IBM Watson Tone Analyzer is utilized for sentiment analysis. Natural language processing is performed using NLTK.

[0391] The server converts user voices into text information and uses sentiment analysis technology to determine the user's emotional state through analysis tools. Based on the analysis results, an identifier is automatically generated, and appropriate information is presented. For example, if the user is determined to be relaxed, a variety of information such as music and news will be provided, while if the user is feeling anxious, only the essential information will be presented concisely. Furthermore, filtering tools are used to resolve ambiguity in search requests and provide precise results based on the user's conditions.

[0392] For example, when a user asks, "Tell me today's schedule," if the server senses the user is in a hurry, it will present only the most important appointments first. Sentiment analysis is performed using prompts such as, "What emotional state is this user in? How should information be presented to help them accomplish their current task?" The information obtained in this way is important for helping users manage their tasks efficiently.

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The user provides voice input through a high-performance microphone. This voice data becomes the system's input. The server converts this voice into text using the Google Cloud Speech-to-Text API. The converted voice-to-text output is the system's output.

[0396] Step 2:

[0397] The server passes the converted text data to an analysis tool, which uses IBM Watson Tone Analyzer to analyze the user's emotional state. At this stage, the text data becomes the input, and the analyzed emotional data is output. The emotional state is expressed as "relaxed," "anxious," etc.

[0398] Step 3:

[0399] The server uses NLTK to process text data using natural language processing and searches for related files from information storage. Based on the sentiment analysis results, the search query is refined. The input here consists of sentiment data and text data, and the output is the search results.

[0400] Step 4:

[0401] The server receives a prompt message using a generated AI model: "What emotional state is this user in? How should information be presented to him to best help him complete his current task?" and determines the appropriate method of information presentation. This operation outputs information in a format that corresponds to the user's emotional state.

[0402] Step 5:

[0403] The terminal displays information to the user according to instructions sent from the server. It provides detailed information when the user is relaxed, and only the essentials when the user is anxious. The terminal's input is the appropriate information display format, and the information displayed to the user is the output.

[0404] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0405] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0406] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0407] [Third Embodiment]

[0408] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0409] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0410] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0411] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0412] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0413] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0414] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0415] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0416] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0417] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0418] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0419] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0420] This invention provides a system for efficiently managing large amounts of digital files and enhancing search functionality. This system primarily improves user convenience through multiple functions implemented on the server.

[0421] First, the user uploads a file from their device to the server. At this point, the file is stored in a data storage device. The server then automatically analyzes the file content, extracts information using natural language processing technology, and generates appropriate tags. This tag information, generated based on the analysis results, is recorded and managed by a file organization device.

[0422] Based on the generated tags, the server categorizes files into specific categories and organizes them chronologically, making it easier for users to quickly find the files they need. This classification information is stored in a database and used when users access files.

[0423] When a user searches for a file, they enter and submit a search query from their terminal. The received query is instantly interpreted by a generative AI model to enable fuzzy searching, and synonyms are used to expand the search. This ensures that relevant files are identified even if the user enters an incorrect query.

[0424] If the search results are extensive, users can further refine them by adding criteria. Filtering methods allow users to narrow down their search by, for example, "a specific project" or "a specific year." The final search results are sent to the user's device, allowing them to access the necessary information.

[0425] In this way, the present invention helps users efficiently find the files they need and significantly improves the efficiency of business processes.

[0426] The following describes the processing flow.

[0427] Step 1:

[0428] The user uploads files from their terminal to the server. This transfers the selected files from the user's working folder to the server.

[0429] Step 2:

[0430] The server receives the uploaded file and saves it to a data storage device. This storage location is a designated directory within the server for efficient file management later on.

[0431] Step 3:

[0432] The server analyzes the contents of the stored files, using natural language processing techniques to extract the text from the files. Based on the extracted information, it automatically generates tags that reflect the content.

[0433] Step 4:

[0434] The server assigns the generated tags to the files and uses file organization tools to organize the files chronologically or by category. In this step, the generated tag information is registered in the database.

[0435] Step 5:

[0436] The user enters a search query from their device and sends it to the server. For example, the user might enter a specific project name or report name as the query.

[0437] Step 6:

[0438] The server analyzes the received search query and performs a fuzzy search including synonyms using a generative AI model. This search extracts relevant files even with incorrect input.

[0439] Step 7:

[0440] The server returns the initial search results to the user and provides an interface that allows the user to add filtering conditions as needed.

[0441] Step 8:

[0442] Users can add specific criteria to refine their search results. For example, they can enter conditions such as "2023 reports" to find relevant files.

[0443] Step 9:

[0444] The server processes the search results again, applying the filtering conditions specified by the user, and then provides the final search results to the user.

[0445] Step 10:

[0446] Users can review the final search results displayed on their device and access the desired file. This allows them to efficiently obtain the data necessary for their work.

[0447] (Example 1)

[0448] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0449] In modern information management, efficiently organizing vast amounts of digital information and quickly and accurately retrieving the information users need is a crucial challenge. This requires a flexible search system capable of handling even ambiguous search requests. However, traditional methods have struggled with accurate information classification and efficient searching, requiring significant time and effort.

[0450] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0451] In this invention, the server includes a storage device for storing information, an analysis device for analyzing the content of the information and generating multiple identification information using natural language processing technology, and an organization device for classifying and managing the information based on the generated identification information. This enables accurate classification and efficient retrieval of information.

[0452] "Information" refers to various types of digital data and files, which are managed within a system.

[0453] "Storage device" refers to hardware or storage media used to permanently store information.

[0454] An "analysis device" is a device that uses natural language processing and machine learning technologies to analyze information content and generate identification information.

[0455] "Identification information" refers to tags and categories generated to describe the characteristics of information.

[0456] A "sorting device" is a system component used to classify and manage information based on generated identification information.

[0457] A "search request" refers to a query or request that a user enters to retrieve information.

[0458] A "search device" is a system component that functions to identify relevant information in response to a search request.

[0459] A "filtering device" is a system component that has the function of narrowing down search results based on conditions specified by the user.

[0460] This invention provides a system for efficiently managing large amounts of digital information and enhancing search capabilities. Specific embodiments are described below.

[0461] First, users upload information to the server using their devices. This information can take various forms, including documents, images, and audio files. The uploaded information is stored in the server's storage device, which may include, for example, a cloud-based storage service.

[0462] Upon receiving the uploaded information, the server begins analysis via an analysis device. During this process, natural language processing techniques are applied to extract important keywords and phrases from the information. The software used includes machine learning libraries such as TensorFlow and PyTorch. As a result of the analysis, tags are generated as identifying information, and these tags are managed by an organization device.

[0463] Based on the generated identification information, the server categorizes and organizes the information chronologically, etc. This is important for users to quickly search for information later. This organized information is recorded in the database.

[0464] When a user performs a search, they send a search request from their device. The server uses a search device to interpret this request through a generative AI model. For example, a GPT-based generative AI model is used. This allows the server to broaden the search to include similar terms and identify relevant information.

[0465] As a concrete example, consider a case where a user is looking for the "2023 sales report." The user sends a prompt to the server such as "sales report 2023." Based on this prompt, the server considers similar terms, searches for relevant files, and returns the results. In this way, by using the system of the present invention, users can efficiently manage and search for the information they need.

[0466] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0467] Step 1:

[0468] Users upload information to the server using their own devices. The input is a digital file selected by the user, and the output is that file stored on the server's storage device. Users utilize a dedicated upload interface, uploading files using drag-and-drop or selection buttons.

[0469] Step 2:

[0470] The server retrieves files stored in memory and begins analyzing the information using an analysis device. The input is the stored files, and the output is the identification information generated by the analysis. Specifically, important keywords and concepts are extracted from the file contents using natural language processing techniques. This uses machine learning libraries such as TensorFlow and PyTorch to understand the meaning of the information and generate tags.

[0471] Step 3:

[0472] The server uses an organization device to classify information based on the identification information generated through analysis. The input is the generated identification information and its associated file information, while the output is organized category information. Here, information is assigned to appropriate categories, such as "Reports" and "Contracts," and further organized chronologically. This simplifies information management.

[0473] Step 4:

[0474] When a user wants to search for information, they send a search request from their terminal. The input is the user's search query, and the output is a processed query suitable for searching. The server receives this query via a search device and interprets it using a generative AI model. In this process, the query's words are analyzed, and the search scope is expanded using synonyms and related words.

[0475] Step 5:

[0476] The server uses the processed query to retrieve relevant information from the database. The input is the processed query and the information in the database, and the output is a list of relevant information. Once the search results are obtained, the server refines them according to user-specified criteria. This refinement is based on elements such as date or category.

[0477] Step 6:

[0478] The user views the search results sent from the server on their device. The input is the search results from the server, and the output is the information the user views. The user views the results and accesses the desired data by downloading or referencing the information as needed.

[0479] (Application Example 1)

[0480] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0481] In data centers and information management services, there is a need to efficiently and quickly manage and search vast amounts of digital files. However, even when using natural language to make vague search requests, accurately identifying highly relevant files is difficult. Furthermore, there is a need for methods that allow users to easily refine search results.

[0482] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0483] In this invention, the server includes an information storage means for storing files, an information analysis means for automatically generating multiple classification pieces of information by analyzing the contents of the files, and a search extension means for interpreting search requests and extending searches using a generated AI model. As a result, even when users make ambiguous search requests using natural language, they can efficiently identify highly relevant files and improve the efficiency of their work.

[0484] "Information storage means" refers to devices or functions for efficiently storing and managing digital files.

[0485] "Information analysis means" refers to technology that analyzes the contents of a file and automatically generates classification information based on the contents.

[0486] "Information organization means" refers to a function that efficiently organizes and manages files based on the generated classification information.

[0487] An "information retrieval means" is a function for identifying relevant files based on a search request from a user.

[0488] "Refinement methods" are functions that allow users to limit search results based on specified conditions.

[0489] A "generative AI model" is a technology that uses artificial intelligence to interpret search requests and expand the scope of the search.

[0490] This invention provides a system for effectively managing digital files and quickly searching for specific files. The server stores files using information storage means, analyzes the file contents using information analysis means, and automatically generates classification information. The analysis is performed using natural language processing technology, classifying information to handle even ambiguous search requests. This allows users to efficiently organize and manage their files.

[0491] Furthermore, the generated classification information is managed by an information organization system, allowing users to identify highly relevant files when searching for files via an information retrieval system. The server uses a generation AI model to interpret the user's ambiguous search queries and expand the search, enabling it to quickly find relevant files even if the user enters incorrect information.

[0492] Users can further refine their search results, allowing them to filter information based on specific criteria. For example, it becomes easy for users to display only files from a "specific project" or "specific year."

[0493] For example, if a user wants to find a project report from 2022, they can enter the search query "Project 2022 Report" into their device. The server will then use a generative AI model to find related files, considering synonyms such as "project" and "report." The user can then add further filtering conditions as needed to reach their desired file.

[0494] The following text can be used as an example of a prompt message to input into a generative AI model.

[0495] Please explain in natural language how to search for and display related files using synonyms for the keyword "Project Report 2022."

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The server stores digital files received from users via terminals into the information storage device. The input is the file itself, and the output is the file stored in the information storage device. The server enables consistent file management.

[0499] Step 2:

[0500] The server analyzes the contents of received files using information analysis tools and automatically generates classification information. The input is the stored file data, and the output is the generated classification information. In this process, the file contents are analyzed and tagged using natural language processing techniques.

[0501] Step 3:

[0502] The server organizes and manages data using information organization tools based on the generated classification information. The input is classification information, and the output is the structure of the organized database. This allows users to easily search and refer to files later.

[0503] Step 4:

[0504] The user enters a search query through a terminal and sends a request to the information retrieval system. The input is the search query, and the output is a list of identified related files. The system receives queries from the terminal and queries the server for appropriate information.

[0505] Step 5:

[0506] The server uses a generative AI model to interpret ambiguous search queries and expand upon them. The input is the user's search query, and the output is the interpreted and expanded search query. The AI ​​analyzes and expands the query by utilizing prompts.

[0507] Step 6:

[0508] The server identifies relevant files using information retrieval tools based on the interpreted search query and provides them to the user. The input is an extended search query, and the output is a list of candidate relevant files. It assists the user in accessing the information they need.

[0509] Step 7:

[0510] The user uses a terminal to specify conditions for search results and refines the results through filtering methods. The input consists of the initial search results and range-limiting conditions, while the output is a list of files filtered according to those conditions. Further refinement can be achieved by applying additional conditions.

[0511] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0512] This invention is an advanced file management system that incorporates an emotion engine. This system is implemented on a server and operates as follows:

[0513] The user uploads a file from their device to the server. This file is stored in the server's data storage system. Next, the server analyzes the contents of the uploaded file using natural language processing technology and automatically generates appropriate tags associated with the file. These tags clearly represent the file's contents and are useful for later searching and organization.

[0514] Once tags are generated, the server classifies and manages them using its file organization tools. When a user searches for a specific file, they enter and submit a search query on their terminal. This query is a standard string search, but it also includes fuzzy search functionality. Here, a synonym search function comes into play, considering related information beyond the words directly entered by the user.

[0515] A distinctive feature of this invention is the incorporation of an emotion engine. The server analyzes the user's emotional state not only through the search query but also through the input text and the entire interaction. This analysis is performed using emotion analysis technology to evaluate and record the user's current emotions (e.g., stress, anxiety, calmness, etc.) in real time. This system can adjust how search results are presented according to the user's emotions. For example, if it is determined that the user is feeling overwhelmed, the search results will be displayed concisely and with emphasis to support quick decision-making.

[0516] Furthermore, the emotion engine can influence user filtering and the overall system experience. For example, if a relaxed user is observed, the interface may be configured to be more diverse and detailed.

[0517] As a result, this system goes beyond simple file management, providing support tailored to the user's emotional state and realizing a more efficient and meaningful information management environment.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The user selects a file on their device and initiates the upload to the server. This action initiates communication from the device to the server.

[0521] Step 2:

[0522] After receiving the uploaded file, the server saves it to a data storage device. The saved file is then ready to be accessed for subsequent processing.

[0523] Step 3:

[0524] The server analyzes the file contents. Using natural language processing technology, it extracts important information from the file's text and automatically generates relevant tags.

[0525] Step 4:

[0526] The server registers the generated tags in a database and organizes the files using a file management system. This ensures that files are managed efficiently and are easily accessible.

[0527] Step 5:

[0528] The user enters a search query from their terminal and sends it to the server. This query triggers the file search.

[0529] Step 6:

[0530] The server processes incoming queries using fuzzy search techniques, including synonym searches, to identify relevant files. This ensures that candidates other than the keywords directly entered by the user are also searched.

[0531] Step 7:

[0532] The server activates the sentiment engine along with the user's search query. It analyzes the user's emotional state based on their query and past interactions.

[0533] Step 8:

[0534] The server constructs search results in a format adapted to the user's emotions, based on the results of the emotion engine. For example, if the user is feeling stressed, the information is presented concisely.

[0535] Step 9:

[0536] The server sends the filtered search results to the user's device. The results are presented with sentiment-based priorities to help the user make quick decisions.

[0537] Step 10:

[0538] Users can review the search results received on their device and access the necessary files to continue their work.

[0539] (Example 2)

[0540] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0541] Conventional information management systems faced challenges not only in automatically classifying information and searching for related information, but also in providing information that took into account the user's emotional state. This resulted in inefficient information management and a failure to improve user satisfaction.

[0542] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0543] In this invention, the server includes a device for storing information, a device for analyzing the content of the information and automatically generating identification information, and a device for analyzing the user's emotions and adjusting the information results. This enables efficient classification and retrieval of information that takes the user's emotional state into account.

[0544] A "device for storing information" is a device that can retain digital information for a long period of time and retrieve it as needed.

[0545] A "device that analyzes information content and automatically generates identification information" is a device that analyzes the content of information, identifies specific attributes or characteristics, and automatically generates identification information accordingly.

[0546] A "device that selects relevant information based on ambiguous information requests from users" is a device that selects the most relevant information based on unclear information requests provided by users.

[0547] A "device that allows users to specify conditions for limiting information results" is a device that enables users to specify conditions for limiting information results.

[0548] A "device that analyzes the user's emotions and adjusts the information results" is a device that analyzes the user's emotions and uses the analysis results to adjust the way the information results are presented and their content.

[0549] This invention is an advanced information management system that combines an emotion engine, enabling users to efficiently manage information through their terminals and providing information that takes into account the user's emotional state. This system is primarily implemented on a server and performs several important functions.

[0550] The server first functions as a device that stores information uploaded via terminals. This allows digital information to be retained for extended periods and retrieved as needed. General storage devices or cloud storage services can be used for storing the information.

[0551] Next, the server has the function of analyzing the information content and automatically generating identification information. This uses software such as the natural language processing frameworks "NLTK" and "spaCy". By using these technologies, the content of the information is analyzed and related identification information is automatically generated. For example, if information about travel is entered, identification information such as "travel" and "tourism" will be assigned.

[0552] Furthermore, the server provides a function to select relevant information based on the user's ambiguous information requests. This process utilizes a full-text search engine (e.g., Elasticsearch) to select appropriate information for the user's request. By taking synonyms into consideration, it effectively handles even ambiguous requests.

[0553] Furthermore, the server also functions as a device that analyzes the user's emotions and adjusts the information results accordingly. Using a generative AI model (e.g., OpenAI's GPT model), it analyzes the user's emotions based on their input data and actions, and dynamically adjusts how information is presented based on the results. For example, if the user is showing signs of anxiety, the server can display necessary information concisely.

[0554] For example, if a user searches for "weekend event information" and the server analyzes the user's mood and determines that they are in a relaxed state, it can display a detailed list of event information, providing a wealth of information.

[0555] An example of a prompt message is when the user instructs the system to "find a recipe," and the server efficiently searches for information tagged with terms such as "dish," "ingredients," and "cooking method" using relevant identification information.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The user uploads information to the server using their terminal. Specifically, the user opens a file selection dialog, selects the information they want to upload, and clicks the submit button. This input information (e.g., text or image files) is sent to the server and stored in the data storage device.

[0559] Step 2:

[0560] The server analyzes the content of the stored information and automatically generates identification information. To analyze the content of the information, natural language processing software (e.g., NLTK, spaCy) is used to extract and classify keywords from the input text. As a result of this data processing, identification information related to the information is generated (e.g., tags such as "travel" and "tourism").

[0561] Step 3:

[0562] The server classifies and manages information based on the generated identification information. Using a database management system (e.g., MySQL), it organizes information into categories based on tags and indexes it for easy searching. This data processing makes information associated with specific categories and tags quickly accessible.

[0563] Step 4:

[0564] The user enters a query using a terminal to search for specific information. This query is sent to a server, which analyzes it using a search engine (e.g., Elasticsearch). Based on the input query, the server identifies relevant information and searches for related information, including synonyms. This process outputs an expanded search result for the input query.

[0565] Step 5:

[0566] The server analyzes the user's emotions based on the search results and provides information accordingly. Using a generative AI model (e.g., OpenAI's GPT model), it evaluates the emotional state from the user's search behavior and input. Based on this data analysis, for example, if it's determined that the user is feeling anxious, it provides supportive output by concisely presenting necessary information.

[0567] Step 6:

[0568] The server adjusts the user interface according to the user's emotional state. For example, if the user is assessed as relaxed, it displays more information and provides detailed navigation. This behavior results in an optimized interface output that enhances the user experience.

[0569] (Application Example 2)

[0570] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0571] Traditional file management systems present information without considering the user's emotional state, which can lead to stress and anxiety hindering the information access experience. Furthermore, they lack the ability to quickly provide appropriate information in response to vague search requests, making it difficult for users to efficiently access the information they need. Therefore, there is a need for a system that presents information in accordance with the user's emotions, supporting a more comfortable and efficient information management experience.

[0572] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0573] In this invention, the server includes an information storage means for storing files, an analysis means for automatically generating multiple identifiers by analyzing the contents of the files, and an emotion analysis means for analyzing the user's emotions and adjusting the method of presenting information based on that state. This enables flexible and adaptive information presentation according to the user's emotional state.

[0574] "Information storage means" refers to a device or method for storing and holding files and data.

[0575] "Analysis means" refers to a device or method for analyzing the contents of a file to generate useful information or identifiers.

[0576] An "identifier" is a label or tag that is automatically generated to identify a specific file or data.

[0577] "Information organization means" refers to a device or method for efficiently classifying and managing files based on generated identifiers.

[0578] "Search means" refers to a device or method for identifying relevant information or files based on a user's vague search request.

[0579] A "filtering method" is a device or method for further refining search results based on conditions specified by the user.

[0580] "Emotional analysis means" refers to a device or method for analyzing a user's emotions and adjusting the way information is presented based on those emotions.

[0581] "Emotional analysis technology" is a technology that infers and evaluates a user's emotional state from their input and interactions.

[0582] To implement this invention, we will use an application example called an "emotion-responsive personal assistant robot." The server utilizes a high-performance microphone and camera sensor to process voice commands and search requests from the user, and stores various files and data using information storage means. Google Cloud Speech-to-Text API is used for speech recognition, and IBM Watson Tone Analyzer is utilized for sentiment analysis. Natural language processing is performed using NLTK.

[0583] The server converts user voices into text information and uses sentiment analysis technology to determine the user's emotional state through analysis tools. Based on the analysis results, an identifier is automatically generated, and appropriate information is presented. For example, if the user is determined to be relaxed, a variety of information such as music and news will be provided, while if the user is feeling anxious, only the essential information will be presented concisely. Furthermore, filtering tools are used to resolve ambiguity in search requests and provide precise results based on the user's conditions.

[0584] For example, when a user asks, "Tell me today's schedule," if the server senses the user is in a hurry, it will present only the most important appointments first. Sentiment analysis is performed using prompts such as, "What emotional state is this user in? How should information be presented to help them accomplish their current task?" The information obtained in this way is important for helping users manage their tasks efficiently.

[0585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0586] Step 1:

[0587] The user provides voice input through a high-performance microphone. This voice data becomes the system's input. The server converts this voice into text using the Google Cloud Speech-to-Text API. The converted voice-to-text output is the system's output.

[0588] Step 2:

[0589] The server passes the converted text data to an analysis tool, which uses IBM Watson Tone Analyzer to analyze the user's emotional state. At this stage, the text data becomes the input, and the analyzed emotional data is output. The emotional state is expressed as "relaxed," "anxious," etc.

[0590] Step 3:

[0591] The server uses NLTK to process text data using natural language processing and searches for related files from information storage. Based on the sentiment analysis results, the search query is refined. The input here consists of sentiment data and text data, and the output is the search results.

[0592] Step 4:

[0593] The server receives a prompt message using a generated AI model: "What emotional state is this user in? How should information be presented to him to best help him complete his current task?" and determines the appropriate method of information presentation. This operation outputs information in a format that corresponds to the user's emotional state.

[0594] Step 5:

[0595] The terminal displays information to the user according to instructions sent from the server. It provides detailed information when the user is relaxed, and only the essentials when the user is anxious. The terminal's input is the appropriate information display format, and the information displayed to the user is the output.

[0596] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0597] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0598] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0599] [Fourth Embodiment]

[0600] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0601] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0602] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0603] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0604] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0605] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0606] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0607] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0608] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0609] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0610] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0611] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0612] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0613] This invention provides a system for efficiently managing large amounts of digital files and enhancing search functionality. This system primarily improves user convenience through multiple functions implemented on the server.

[0614] First, the user uploads a file from their device to the server. At this point, the file is stored in a data storage device. The server then automatically analyzes the file content, extracts information using natural language processing technology, and generates appropriate tags. This tag information, generated based on the analysis results, is recorded and managed by a file organization device.

[0615] Based on the generated tags, the server categorizes files into specific categories and organizes them chronologically, making it easier for users to quickly find the files they need. This classification information is stored in a database and used when users access files.

[0616] When a user searches for a file, they enter and submit a search query from their terminal. The received query is instantly interpreted by a generative AI model to enable fuzzy searching, and synonyms are used to expand the search. This ensures that relevant files are identified even if the user enters an incorrect query.

[0617] If the search results are extensive, users can further refine them by adding criteria. Filtering methods allow users to narrow down their search by, for example, "a specific project" or "a specific year." The final search results are sent to the user's device, allowing them to access the necessary information.

[0618] In this way, the present invention helps users efficiently find the files they need and significantly improves the efficiency of business processes.

[0619] The following describes the processing flow.

[0620] Step 1:

[0621] The user uploads files from their terminal to the server. This transfers the selected files from the user's working folder to the server.

[0622] Step 2:

[0623] The server receives the uploaded file and saves it to a data storage device. This storage location is a designated directory within the server for efficient file management later on.

[0624] Step 3:

[0625] The server analyzes the contents of the stored files, using natural language processing techniques to extract the text from the files. Based on the extracted information, it automatically generates tags that reflect the content.

[0626] Step 4:

[0627] The server assigns the generated tags to the files and uses file organization tools to organize the files chronologically or by category. In this step, the generated tag information is registered in the database.

[0628] Step 5:

[0629] The user enters a search query from their device and sends it to the server. For example, the user might enter a specific project name or report name as the query.

[0630] Step 6:

[0631] The server analyzes the received search query and performs a fuzzy search including synonyms using a generative AI model. This search extracts relevant files even with incorrect input.

[0632] Step 7:

[0633] The server returns the initial search results to the user and provides an interface that allows the user to add filtering conditions as needed.

[0634] Step 8:

[0635] Users can add specific criteria to refine their search results. For example, they can enter conditions such as "2023 reports" to find relevant files.

[0636] Step 9:

[0637] The server processes the search results again, applying the filtering conditions specified by the user, and then provides the final search results to the user.

[0638] Step 10:

[0639] Users can review the final search results displayed on their device and access the desired file. This allows them to efficiently obtain the data necessary for their work.

[0640] (Example 1)

[0641] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0642] In modern information management, efficiently organizing vast amounts of digital information and quickly and accurately retrieving the information users need is a crucial challenge. This requires a flexible search system capable of handling even ambiguous search requests. However, traditional methods have struggled with accurate information classification and efficient searching, requiring significant time and effort.

[0643] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0644] In this invention, the server includes a storage device for storing information, an analysis device for analyzing the content of the information and generating multiple identification information using natural language processing technology, and an organization device for classifying and managing the information based on the generated identification information. This enables accurate classification and efficient retrieval of information.

[0645] "Information" refers to various types of digital data and files, which are managed within a system.

[0646] "Storage device" refers to hardware or storage media used to permanently store information.

[0647] An "analysis device" is a device that uses natural language processing and machine learning technologies to analyze information content and generate identification information.

[0648] "Identification information" refers to tags and categories generated to describe the characteristics of information.

[0649] A "sorting device" is a system component used to classify and manage information based on generated identification information.

[0650] A "search request" refers to a query or request that a user enters to retrieve information.

[0651] A "search device" is a system component that functions to identify relevant information in response to a search request.

[0652] A "filtering device" is a system component that has the function of narrowing down search results based on conditions specified by the user.

[0653] This invention provides a system for efficiently managing large amounts of digital information and enhancing search capabilities. Specific embodiments are described below.

[0654] First, users upload information to the server using their devices. This information can take various forms, including documents, images, and audio files. The uploaded information is stored in the server's storage device, which may include, for example, a cloud-based storage service.

[0655] Upon receiving the uploaded information, the server begins analysis via an analysis device. During this process, natural language processing techniques are applied to extract important keywords and phrases from the information. The software used includes machine learning libraries such as TensorFlow and PyTorch. As a result of the analysis, tags are generated as identifying information, and these tags are managed by an organization device.

[0656] Based on the generated identification information, the server categorizes and organizes the information chronologically, etc. This is important for users to quickly search for information later. This organized information is recorded in the database.

[0657] When a user performs a search, they send a search request from their device. The server uses a search device to interpret this request through a generative AI model. For example, a GPT-based generative AI model is used. This allows the server to broaden the search to include similar terms and identify relevant information.

[0658] As a concrete example, consider a case where a user is looking for the "2023 sales report." The user sends a prompt to the server such as "sales report 2023." Based on this prompt, the server considers similar terms, searches for relevant files, and returns the results. In this way, by using the system of the present invention, users can efficiently manage and search for the information they need.

[0659] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0660] Step 1:

[0661] Users upload information to the server using their own devices. The input is a digital file selected by the user, and the output is that file stored on the server's storage device. Users utilize a dedicated upload interface, uploading files using drag-and-drop or selection buttons.

[0662] Step 2:

[0663] The server retrieves files stored in memory and begins analyzing the information using an analysis device. The input is the stored files, and the output is the identification information generated by the analysis. Specifically, important keywords and concepts are extracted from the file contents using natural language processing techniques. This uses machine learning libraries such as TensorFlow and PyTorch to understand the meaning of the information and generate tags.

[0664] Step 3:

[0665] The server uses an organization device to classify information based on the identification information generated through analysis. The input is the generated identification information and its associated file information, while the output is organized category information. Here, information is assigned to appropriate categories, such as "Reports" and "Contracts," and further organized chronologically. This simplifies information management.

[0666] Step 4:

[0667] When a user wants to search for information, they send a search request from their terminal. The input is the user's search query, and the output is a processed query suitable for searching. The server receives this query via a search device and interprets it using a generative AI model. In this process, the query's words are analyzed, and the search scope is expanded using synonyms and related words.

[0668] Step 5:

[0669] The server uses the processed query to retrieve relevant information from the database. The input is the processed query and the information in the database, and the output is a list of relevant information. Once the search results are obtained, the server refines them according to user-specified criteria. This refinement is based on elements such as date or category.

[0670] Step 6:

[0671] The user views the search results sent from the server on their device. The input is the search results from the server, and the output is the information the user views. The user views the results and accesses the desired data by downloading or referencing the information as needed.

[0672] (Application Example 1)

[0673] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] In data centers and information management services, there is a need to efficiently and quickly manage and search vast amounts of digital files. However, even when using natural language to make vague search requests, accurately identifying highly relevant files is difficult. Furthermore, there is a need for methods that allow users to easily refine search results.

[0675] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0676] In this invention, the server includes an information storage means for storing files, an information analysis means for automatically generating multiple classification pieces of information by analyzing the contents of the files, and a search extension means for interpreting search requests and extending searches using a generated AI model. As a result, even when users make ambiguous search requests using natural language, they can efficiently identify highly relevant files and improve the efficiency of their work.

[0677] "Information storage means" refers to devices or functions for efficiently storing and managing digital files.

[0678] "Information analysis means" refers to technology that analyzes the contents of a file and automatically generates classification information based on the contents.

[0679] "Information organization means" refers to a function that efficiently organizes and manages files based on the generated classification information.

[0680] An "information retrieval means" is a function for identifying relevant files based on a search request from a user.

[0681] "Refinement methods" are functions that allow users to limit search results based on specified conditions.

[0682] A "generative AI model" is a technology that uses artificial intelligence to interpret search requests and expand the scope of the search.

[0683] This invention provides a system for effectively managing digital files and quickly searching for specific files. The server stores files using information storage means, analyzes the file contents using information analysis means, and automatically generates classification information. The analysis is performed using natural language processing technology, classifying information to handle even ambiguous search requests. This allows users to efficiently organize and manage their files.

[0684] Furthermore, the generated classification information is managed by an information organization system, allowing users to identify highly relevant files when searching for files via an information retrieval system. The server uses a generation AI model to interpret the user's ambiguous search queries and expand the search, enabling it to quickly find relevant files even if the user enters incorrect information.

[0685] Users can further refine their search results, allowing them to filter information based on specific criteria. For example, it becomes easy for users to display only files from a "specific project" or "specific year."

[0686] For example, if a user wants to find a project report from 2022, they can enter the search query "Project 2022 Report" into their device. The server will then use a generative AI model to find related files, considering synonyms such as "project" and "report." The user can then add further filtering conditions as needed to reach their desired file.

[0687] The following text can be used as an example of a prompt message to input into a generative AI model.

[0688] Please explain in natural language how to search for and display related files using synonyms for the keyword "Project Report 2022."

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The server stores digital files received from users via terminals into the information storage device. The input is the file itself, and the output is the file stored in the information storage device. The server enables consistent file management.

[0692] Step 2:

[0693] The server analyzes the contents of received files using information analysis tools and automatically generates classification information. The input is the stored file data, and the output is the generated classification information. In this process, the file contents are analyzed and tagged using natural language processing techniques.

[0694] Step 3:

[0695] The server organizes and manages data using information organization tools based on the generated classification information. The input is classification information, and the output is the structure of the organized database. This allows users to easily search and refer to files later.

[0696] Step 4:

[0697] The user enters a search query through a terminal and sends a request to the information retrieval system. The input is the search query, and the output is a list of identified related files. The system receives queries from the terminal and queries the server for appropriate information.

[0698] Step 5:

[0699] The server uses a generative AI model to interpret ambiguous search queries and expand upon them. The input is the user's search query, and the output is the interpreted and expanded search query. The AI ​​analyzes and expands the query by utilizing prompts.

[0700] Step 6:

[0701] The server identifies relevant files using information retrieval tools based on the interpreted search query and provides them to the user. The input is an extended search query, and the output is a list of candidate relevant files. It assists the user in accessing the information they need.

[0702] Step 7:

[0703] The user uses a terminal to specify conditions for search results and refines the results through filtering methods. The input consists of the initial search results and range-limiting conditions, while the output is a list of files filtered according to those conditions. Further refinement can be achieved by applying additional conditions.

[0704] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0705] This invention is an advanced file management system that incorporates an emotion engine. This system is implemented on a server and operates as follows:

[0706] The user uploads a file from their device to the server. This file is stored in the server's data storage system. Next, the server analyzes the contents of the uploaded file using natural language processing technology and automatically generates appropriate tags associated with the file. These tags clearly represent the file's contents and are useful for later searching and organization.

[0707] Once tags are generated, the server classifies and manages them using its file organization tools. When a user searches for a specific file, they enter and submit a search query on their terminal. This query is a standard string search, but it also includes fuzzy search functionality. Here, a synonym search function comes into play, considering related information beyond the words directly entered by the user.

[0708] A distinctive feature of this invention is the incorporation of an emotion engine. The server analyzes the user's emotional state not only through the search query but also through the input text and the entire interaction. This analysis is performed using emotion analysis technology to evaluate and record the user's current emotions (e.g., stress, anxiety, calmness, etc.) in real time. This system can adjust how search results are presented according to the user's emotions. For example, if it is determined that the user is feeling overwhelmed, the search results will be displayed concisely and with emphasis to support quick decision-making.

[0709] Furthermore, the emotion engine can influence user filtering and the overall system experience. For example, if a relaxed user is observed, the interface may be configured to be more diverse and detailed.

[0710] As a result, this system goes beyond simple file management, providing support tailored to the user's emotional state and realizing a more efficient and meaningful information management environment.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The user selects a file on their device and initiates the upload to the server. This action initiates communication from the device to the server.

[0714] Step 2:

[0715] After receiving the uploaded file, the server saves it to a data storage device. The saved file is then ready to be accessed for subsequent processing.

[0716] Step 3:

[0717] The server analyzes the file contents. Using natural language processing technology, it extracts important information from the file's text and automatically generates relevant tags.

[0718] Step 4:

[0719] The server registers the generated tags in a database and organizes the files using a file management system. This ensures that files are managed efficiently and are easily accessible.

[0720] Step 5:

[0721] The user enters a search query from their terminal and sends it to the server. This query triggers the file search.

[0722] Step 6:

[0723] The server processes incoming queries using fuzzy search techniques, including synonym searches, to identify relevant files. This ensures that candidates other than the keywords directly entered by the user are also searched.

[0724] Step 7:

[0725] The server activates the sentiment engine along with the user's search query. It analyzes the user's emotional state based on their query and past interactions.

[0726] Step 8:

[0727] The server constructs search results in a format adapted to the user's emotions, based on the results of the emotion engine. For example, if the user is feeling stressed, the information is presented concisely.

[0728] Step 9:

[0729] The server sends the filtered search results to the user's device. The results are presented with sentiment-based priorities to help the user make quick decisions.

[0730] Step 10:

[0731] Users can review the search results received on their device and access the necessary files to continue their work.

[0732] (Example 2)

[0733] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] Conventional information management systems faced challenges not only in automatically classifying information and searching for related information, but also in providing information that took into account the user's emotional state. This resulted in inefficient information management and a failure to improve user satisfaction.

[0735] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0736] In this invention, the server includes a device for storing information, a device for analyzing the content of the information and automatically generating identification information, and a device for analyzing the user's emotions and adjusting the information results. This enables efficient classification and retrieval of information that takes the user's emotional state into account.

[0737] A "device for storing information" is a device that can retain digital information for a long period of time and retrieve it as needed.

[0738] A "device that analyzes information content and automatically generates identification information" is a device that analyzes the content of information, identifies specific attributes or characteristics, and automatically generates identification information accordingly.

[0739] A "device that selects relevant information based on ambiguous information requests from users" is a device that selects the most relevant information based on unclear information requests provided by users.

[0740] A "device that allows users to specify conditions for limiting information results" is a device that enables users to specify conditions for limiting information results.

[0741] A "device that analyzes the user's emotions and adjusts the information results" is a device that analyzes the user's emotions and uses the analysis results to adjust the way the information results are presented and their content.

[0742] This invention is an advanced information management system that combines an emotion engine, enabling users to efficiently manage information through their terminals and providing information that takes into account the user's emotional state. This system is primarily implemented on a server and performs several important functions.

[0743] The server first functions as a device that stores information uploaded via terminals. This allows digital information to be retained for extended periods and retrieved as needed. General storage devices or cloud storage services can be used for storing the information.

[0744] Next, the server has the function of analyzing the information content and automatically generating identification information. This uses software such as the natural language processing frameworks "NLTK" and "spaCy". By using these technologies, the content of the information is analyzed and related identification information is automatically generated. For example, if information about travel is entered, identification information such as "travel" and "tourism" will be assigned.

[0745] Furthermore, the server provides a function to select relevant information based on the user's ambiguous information requests. This process utilizes a full-text search engine (e.g., Elasticsearch) to select appropriate information for the user's request. By taking synonyms into consideration, it effectively handles even ambiguous requests.

[0746] Furthermore, the server also functions as a device that analyzes the user's emotions and adjusts the information results accordingly. Using a generative AI model (e.g., OpenAI's GPT model), it analyzes the user's emotions based on their input data and actions, and dynamically adjusts how information is presented based on the results. For example, if the user is showing signs of anxiety, the server can display necessary information concisely.

[0747] For example, if a user searches for "weekend event information" and the server analyzes the user's mood and determines that they are in a relaxed state, it can display a detailed list of event information, providing a wealth of information.

[0748] An example of a prompt message is when the user instructs the system to "find a recipe," and the server efficiently searches for information tagged with terms such as "dish," "ingredients," and "cooking method" using relevant identification information.

[0749] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0750] Step 1:

[0751] The user uploads information to the server using their terminal. Specifically, the user opens a file selection dialog, selects the information they want to upload, and clicks the submit button. This input information (e.g., text or image files) is sent to the server and stored in the data storage device.

[0752] Step 2:

[0753] The server analyzes the content of the stored information and automatically generates identification information. To analyze the content of the information, natural language processing software (e.g., NLTK, spaCy) is used to extract and classify keywords from the input text. As a result of this data processing, identification information related to the information is generated (e.g., tags such as "travel" and "tourism").

[0754] Step 3:

[0755] The server classifies and manages information based on the generated identification information. Using a database management system (e.g., MySQL), it organizes information into categories based on tags and indexes it for easy searching. This data processing makes information associated with specific categories and tags quickly accessible.

[0756] Step 4:

[0757] The user enters a query using a terminal to search for specific information. This query is sent to a server, which analyzes it using a search engine (e.g., Elasticsearch). Based on the input query, the server identifies relevant information and searches for related information, including synonyms. This process outputs an expanded search result for the input query.

[0758] Step 5:

[0759] The server analyzes the user's emotions based on the search results and provides information accordingly. Using a generative AI model (e.g., OpenAI's GPT model), it evaluates the emotional state from the user's search behavior and input. Based on this data analysis, for example, if it's determined that the user is feeling anxious, it provides supportive output by concisely presenting necessary information.

[0760] Step 6:

[0761] The server adjusts the user interface according to the user's emotional state. For example, if the user is assessed as relaxed, it displays more information and provides detailed navigation. This behavior results in an optimized interface output that enhances the user experience.

[0762] (Application Example 2)

[0763] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0764] Traditional file management systems present information without considering the user's emotional state, which can lead to stress and anxiety hindering the information access experience. Furthermore, they lack the ability to quickly provide appropriate information in response to vague search requests, making it difficult for users to efficiently access the information they need. Therefore, there is a need for a system that presents information in accordance with the user's emotions, supporting a more comfortable and efficient information management experience.

[0765] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0766] In this invention, the server includes an information storage means for storing files, an analysis means for automatically generating multiple identifiers by analyzing the contents of the files, and an emotion analysis means for analyzing the user's emotions and adjusting the method of presenting information based on that state. This enables flexible and adaptive information presentation according to the user's emotional state.

[0767] "Information storage means" refers to a device or method for storing and holding files and data.

[0768] "Analysis means" refers to a device or method for analyzing the contents of a file to generate useful information or identifiers.

[0769] An "identifier" is a label or tag that is automatically generated to identify a specific file or data.

[0770] "Information organization means" refers to a device or method for efficiently classifying and managing files based on generated identifiers.

[0771] "Search means" refers to a device or method for identifying relevant information or files based on a user's vague search request.

[0772] A "filtering method" is a device or method for further refining search results based on conditions specified by the user.

[0773] "Emotional analysis means" refers to a device or method for analyzing a user's emotions and adjusting the way information is presented based on those emotions.

[0774] "Emotional analysis technology" is a technology that infers and evaluates a user's emotional state from their input and interactions.

[0775] To implement this invention, we will use an application example called an "emotion-responsive personal assistant robot." The server utilizes a high-performance microphone and camera sensor to process voice commands and search requests from the user, and stores various files and data using information storage means. Google Cloud Speech-to-Text API is used for speech recognition, and IBM Watson Tone Analyzer is utilized for sentiment analysis. Natural language processing is performed using NLTK.

[0776] The server converts user voices into text information and uses sentiment analysis technology to determine the user's emotional state through analysis tools. Based on the analysis results, an identifier is automatically generated, and appropriate information is presented. For example, if the user is determined to be relaxed, a variety of information such as music and news will be provided, while if the user is feeling anxious, only the essential information will be presented concisely. Furthermore, filtering tools are used to resolve ambiguity in search requests and provide precise results based on the user's conditions.

[0777] For example, when a user asks, "Tell me today's schedule," if the server senses the user is in a hurry, it will present only the most important appointments first. Sentiment analysis is performed using prompts such as, "What emotional state is this user in? How should information be presented to help them accomplish their current task?" The information obtained in this way is important for helping users manage their tasks efficiently.

[0778] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0779] Step 1:

[0780] The user provides voice input through a high-performance microphone. This voice data becomes the system's input. The server converts this voice into text using the Google Cloud Speech-to-Text API. The converted voice-to-text output is the system's output.

[0781] Step 2:

[0782] The server passes the converted text data to an analysis tool, which uses IBM Watson Tone Analyzer to analyze the user's emotional state. At this stage, the text data becomes the input, and the analyzed emotional data is output. The emotional state is expressed as "relaxed," "anxious," etc.

[0783] Step 3:

[0784] The server uses NLTK to process text data using natural language processing and searches for related files from information storage. Based on the sentiment analysis results, the search query is refined. The input here consists of sentiment data and text data, and the output is the search results.

[0785] Step 4:

[0786] The server receives a prompt message using a generated AI model: "What emotional state is this user in? How should information be presented to him to best help him complete his current task?" and determines the appropriate method of information presentation. This operation outputs information in a format that corresponds to the user's emotional state.

[0787] Step 5:

[0788] The terminal displays information to the user according to instructions sent from the server. It provides detailed information when the user is relaxed, and only the essentials when the user is anxious. The terminal's input is the appropriate information display format, and the information displayed to the user is the output.

[0789] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0790] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0791] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0792] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0793] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0794] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0795] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0796] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0797] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0798] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0799] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0800] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0801] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0802] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0803] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0804] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0805] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0806] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0807] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0808] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0809] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0810] The following is further disclosed regarding the embodiments described above.

[0811] (Claim 1)

[0812] A data storage means for storing files,

[0813] An analysis means that automatically generates multiple tags by analyzing the contents of the file,

[0814] A file organization means for organizing and managing files based on generated tags,

[0815] A search method that identifies relevant files based on ambiguous search queries from users,

[0816] A filtering method that allows users to specify conditions for filtering search results,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the analysis means uses natural language processing technology to understand the file contents and generate tags.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the search means expands the user's search query using synonyms and identifies related files.

[0822] "Example 1"

[0823] (Claim 1)

[0824] A storage device for storing information,

[0825] An analysis device that analyzes the content of the information and generates multiple identification information using natural language technology,

[0826] A sorting device that classifies and manages information based on the generated identification information,

[0827] A search device that identifies relevant information based on ambiguous search requests from users,

[0828] A filtering device that allows users to specify conditions for narrowing down search results,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, wherein the analysis device processes the information content using machine learning technology and generates identification information.

[0832] (Claim 3)

[0833] The system according to claim 1, wherein the search device expands the user's search request by utilizing similar words and identifies relevant information.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] Information storage means for storing files,

[0837] An information analysis means that automatically generates multiple classification information by analyzing the contents of the file,

[0838] Information organization means for organizing and managing the files based on the generated classification information,

[0839] An information retrieval method that identifies relevant files based on ambiguous search requests from users,

[0840] A filtering method that allows users to specify conditions for narrowing down search results,

[0841] A search enhancement means that interprets search requests and extends searches using a generative AI model,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, wherein the information analysis means recognizes the contents of a file using natural language understanding technology and generates classification information.

[0845] (Claim 3)

[0846] The system according to claim 1, wherein the information retrieval means expands the user's search request using synonyms and identifies related files.

[0847] "Example 2 of combining an emotion engine"

[0848] (Claim 1)

[0849] A device for storing information,

[0850] A device that analyzes the content of the information and automatically generates multiple identification pieces of information,

[0851] A device for classifying and storing information based on the generated identification information,

[0852] A device that selects relevant information based on ambiguous information requests from users,

[0853] A device that allows the user to specify conditions for limiting the information results,

[0854] A device that analyzes the user's emotions and adjusts the information results,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the analysis device analyzes the information content using language processing technology and generates identification information.

[0858] (Claim 3)

[0859] The system according to claim 1, wherein the selection device expands the user's information request using synonyms and selects relevant information.

[0860] "Application example 2 when combining with an emotional engine"

[0861] (Claim 1)

[0862] Information storage means for storing files,

[0863] An analysis means that automatically generates multiple identifiers by analyzing the contents of the file,

[0864] Information organization means for organizing and managing the files based on the generated identifier,

[0865] A search method that identifies relevant files based on ambiguous search requests from users,

[0866] A filtering method that allows users to specify conditions for narrowing down search results,

[0867] An emotion analysis means that analyzes the user's emotions and adjusts the method of presenting information based on that state,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, wherein the analysis means uses natural language processing technology to understand the file contents and generate an identifier, and the emotion analysis means uses emotion analysis technology to determine the emotional state of the user.

[0871] (Claim 3)

[0872] The system according to claim 1, wherein the search means expands the user's search request using synonyms and identifies related files, and the sentiment analysis means dynamically adjusts the display of search results according to the user's emotions. [Explanation of symbols]

[0873] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data storage means for storing files, An analysis means that automatically generates multiple tags by analyzing the contents of the file, A file organization means for organizing and managing files based on generated tags, A search method that identifies relevant files based on ambiguous search queries from users, A filtering method that allows users to specify conditions for filtering search results, A system that includes this.

2. The system according to claim 1, wherein the analysis means uses natural language processing technology to understand the file contents and generate tags.

3. The system according to claim 1, wherein the search means expands the user's search query using synonyms and identifies related files.

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