System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a specific keyword; means for searching for a plurality of related documents based on the input keyword; means for analyzing a citation relationship of the searched documents, a network between authors, a publication date and time series, a technology series, a publication medium series, a research target series, and a research method series; means for constructing genealogy and series information of the documents based on an analysis result; and means for displaying the constructed genealogy and series information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional literature search systems only search for individual documents based on a single keyword, making it difficult to obtain detailed relationship information between documents, such as citation relationships, author networks, publication date and time series, technology series, publication media series, research subject series, and research method series, all at once. Furthermore, because they lacked the functionality to visually display this relationship information in an easy-to-understand manner, users had the problem of needing a great deal of time and effort to grasp the overall picture of the literature and its progress. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. The present invention is characterized by a system including: means for inputting specific keywords; means for searching for multiple related documents based on the input keywords; means for analyzing the citation relationships, inter-author networks, publication date sequence, technology sequence, publication medium, research subject sequence, and research method sequence of the retrieved documents; means for constructing a genealogy and lineage information of the documents based on the analysis results; and means for displaying the constructed genealogy and lineage information. This system allows users to obtain detailed relationship information of related documents all at once by simply inputting keywords, and display the information in a visually easy-to-understand format.

[0006] A "keyword" is a particular word or phrase that is related to the document for which the user is searching.

[0007] "Literature" refers to sources of information such as academic papers, articles, and books that are consulted for research and investigation.

[0008] "Searching" is the act of locating documents in a database based on specific keywords.

[0009] A "citation relationship" refers to a relationship in which one document references another document.

[0010] An "author network" is a network of relationships that shows how the authors of a paper are connected to each other.

[0011] "Publication date series" is information that organizes the time when documents were published in chronological order.

[0012] A "technology lineage" is information about relationships that show how a particular technology has evolved.

[0013] "By publication medium" is information categorized by the type of journal or conference in which the document was published.

[0014] "Research subject series" refers to information on relationships based on the research subjects covered by the literature.

[0015] "Research methodology series" is information on relationships based on the research methods used in the literature.

[0016] "Genealogy and lineage information" is a collection of information that shows multifaceted connections such as citation relationships between documents, author networks, and technological lineages.

[0017] "Construction" refers to the act of generating genealogy and lineage information based on the analysis results.

[0018] "Display" refers to the act of visually presenting the constructed genealogy and lineage information to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0027] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0040] The system of the present invention begins when a user inputs a specific keyword into a terminal. When the user inputs the keyword into a terminal, the terminal transmits the keyword to a server. The server searches a literature database based on the received keyword and retrieves multiple related documents.

[0041] The server analyzes the citation relationships, author networks, publication date and time series, technology series, publication medium, research subject series, and research method series of the retrieved documents. As a result of the analysis, detailed genealogy and lineage information of the documents is constructed. This allows the server to obtain information that visualizes how the document is related to other documents, which authors are collaborating and how, and the flow of research development.

[0042] The server sends the constructed genealogy and lineage information to the terminal, which then displays it to the user in a format that allows the user to intuitively understand the information, such as a graph, treemap, or network diagram.

[0043] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters the keyword and clicks the search button, the device sends the keyword to the server. The server retrieves literature related to artificial intelligence from a database and analyzes which literature those literature cites and which authors are involved in which research. It also analyzes technological advances and the lineage of related research methods.

[0044] As a result of the analysis, the server generates information such as citation networks, author networks, and development timelines for AI-related literature, and sends it to the terminal, which then visually displays this information to the user, allowing them to intuitively understand the literature and research methods that interest them.

[0045] In this way, the system of the present invention differs from conventional single document searches in that it comprehensively provides information on the relationships between documents and the flow of development, thereby greatly supporting the user's research activities.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user enters a keyword into the search field on the device and clicks the search button.

[0049] Step 2:

[0050] The terminal captures the keyword entered by the user and stores it in the variable keyword.

[0051] Step 3:

[0052] The device generates an HTTP request containing the saved keyword and sends the request to the server.

[0053] Step 4:

[0054] The server analyzes the HTTP request received from the terminal and extracts keywords.

[0055] Step 5:

[0056] The server searches a literature database based on the extracted keywords and retrieves multiple related documents.

[0057] Step 6:

[0058] The server analyzes the citation relationships, author networks, publication date series, technology series, publication media, research subject series, and research method series of the retrieved documents.

[0059] Step 7:

[0060] Based on the analysis results, the server constructs genealogy and lineage information for the literature, such as graphs of citation networks and author collaboration networks.

[0061] Step 8:

[0062] The server converts the constructed genealogy and lineage information into JSON format and sends it to the terminal.

[0063] Step 9:

[0064] The device parses the JSON data received from the server and formats it in a format that is easy for the user to understand, such as a graph, treemap, or network diagram.

[0065] Step 10:

[0066] The device visually displays the formatted information to the user, who can then view the displayed information and take action to obtain more detailed information.

[0067] For example, a user enters the keyword "artificial intelligence." The server retrieves related literature from the database and analyzes the citation relationships and author networks of each literature. As a result, the citation networks between papers and collaborations between authors are visually displayed, allowing the user to grasp the overall picture of research on artificial intelligence.

[0068] Example 1

[0069] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0070] Conventional literature search systems only provide search results for a single document, making it difficult to comprehensively grasp the relationships between documents and the flow of their development. This leaves users unable to intuitively understand the citation relationships between documents, author networks, or the flow of research development, resulting in a decrease in the efficiency of research activities. A system that can solve these problems and support research activities is needed.

[0071] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0072] In this invention, the server includes: means for a user to input specific keywords; means for a terminal to transmit the input keywords to the server; means for the server to search a database for multiple related documents based on the received keywords; means for the server to analyze the citation relationships, author networks, publication date sequence, technology sequence, publication medium, research subject sequence, and research method sequence of the retrieved documents; means for the server to construct genealogy and lineage information of the documents based on the analysis results; means for the server to transmit the constructed genealogy and lineage information to the terminal; and means for the terminal to visually display the transmitted genealogy and lineage information. This allows the user to comprehensively and intuitively understand the relationships between documents and the flow of development.

[0073] A "specific keyword" is a specific word or phrase that a user enters as the subject of a search.

[0074] A "user" is a person or organization that uses the system to conduct a literature search.

[0075] A "terminal" is a device that a user uses to operate the system, specifically a computer device such as a PC or smartphone.

[0076] A "server" is a computer system that searches a literature database based on keywords sent by a user and performs analysis.

[0077] A "literature database" is a data store that stores a large number of research papers and academic books, and is a searchable information source.

[0078] "Searching" is the operation of retrieving relevant documents from a literature database based on specific keywords.

[0079] A "citation relationship" refers to a relationship in which a document refers to or cites another document.

[0080] The "author network" indicates the collaborative relationships between authors, i.e., the publications in which different authors are involved as co-authors.

[0081] The "publication date sequence" indicates the order of the publication dates of documents.

[0082] A "technology lineage" shows how a particular technology has developed and evolved.

[0083] "By publication medium" is used to classify the publication medium (academic journal, conference proceedings, etc.) in which the document was published.

[0084] "Research subject series" refers to the research subject series that different papers address.

[0085] "Research methodology series" refers to the series of research methods used by the document.

[0086] "Analyzing" is the process of examining the contents of a document and related information in detail to derive specific relationships and sequences.

[0087] "Genealogy information" is information that shows an overall picture of related information such as citation relationships between documents and author networks.

[0088] "Series information" is chronological and classification information that shows the flow of development and relationships.

[0089] "Send" refers to the operation of sending information from a terminal to a server or from a server to a terminal.

[0090] "Visual display" refers to the operation of making the information obtained as an analysis result visible to the user in the form of a graph, network diagram, or the like.

[0091] The present invention is a system that allows users to input specific keywords, search for, analyze, and visually display documents related to those keywords, thereby enabling them to comprehensively understand the relationships between documents and the flow of development.

[0092] This system includes the following components:

[0093] 1. User keyword input

[0094] Users use their own devices (PCs, smartphones, etc.) to input specific keywords that are related to the research field or topic that interests them.

[0095] 2. Sending keywords via device

[0096] The terminal sends the input keyword to the server. Specifically, an HTTP request is sent from the terminal to the server. This request includes the keyword entered by the user.

[0097] 3. Searching the literature database using the server

[0098] The server uses the received keywords to search a literature database, which is a data store containing numerous research papers and academic books and can be an SQL database or a cloud-based data store.

[0099] 4. Server-based document analysis

[0100] The server analyzes the literature information obtained as search results. This analysis applies text mining and network analysis techniques. Specifically, analysis is performed on citation relationships, author networks, publication date and time series, technology series, publication media, research subject series, and research method series.

[0101] 5. Server-based genealogy and lineage information construction

[0102] Based on the analysis results, the server constructs detailed genealogy and lineage information for the document, including citation graphs and author network diagrams, which reveal how the document is related to other documents, which authors collaborate and how, and the flow of research development.

[0103] 6. Sending information from the server to the device

[0104] The server generates the constructed genealogy and lineage information as an API response and sends it to the terminal.

[0105] 7. Displaying Information on a Terminal

[0106] The terminal visually displays the received genealogy and lineage information to the user in a variety of formats, including graphs, treemaps, and network diagrams, allowing the user to intuitively grasp the information.

[0107] Specific examples

[0108] For example, if a user enters the keyword "artificial intelligence" to perform a search, the user enters the keyword and clicks the "Search" button, and the device sends the keyword to the server. The server retrieves documents related to "artificial intelligence" from the literature database and analyzes each document's citation relationships, author network, publication timeline, and technology series. As a result of the analysis, the server generates information such as the citation network, author network, and development timeline of documents related to artificial intelligence, and sends this information to the device. The device then visually displays this information to the user, allowing the user to intuitively understand the documents and research methods of interest.

[0109] Prompt Sentence Examples

[0110] Please explain in detail, step by step, the process of the system to analyze the genealogy and lineage information of related documents based on specific keywords and display it visually to the user.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1:

[0113] The user inputs a specific keyword into the terminal.

[0114] Input: Keywords of interest to the user (e.g. "artificial intelligence")

[0115] Output: Keywords displayed on the user's terminal

[0116] Step 2:

[0117] The device sends the entered keyword to the server using an HTTP request.

[0118] Input: Keywords entered by the user into the device

[0119] Data processing / calculation: The terminal generates an HTTP request and includes the keyword as a parameter.

[0120] Output: HTTP request sent to the server

[0121] Step 3:

[0122] The server searches a literature database based on the received keywords.

[0123] Input: Keywords in the HTTP request received by the server

[0124] Data processing / calculation: Searching literature databases using SQL queries or cloud data store APIs to retrieve relevant literature

[0125] Output: List of relevant literature

[0126] Step 4:

[0127] The server analyzes the citation relationships of the documents searched, the network between authors, the publication date sequence, the technology sequence, the publication medium, the research subject sequence, and the research method sequence.

[0128] Input: List of documents obtained as search results

[0129] Data processing / calculation: Using text mining and network analysis tools, we conduct detailed analysis of the relevant information in each document.

[0130] Output: Analysis results (citation relationship graph, author network diagram, etc.)

[0131] Step 5:

[0132] The server constructs genealogy and lineage information for the literature based on the analysis results.

[0133] Input: Literature analysis results

[0134] Data processing / calculation: Based on the analysis results, construct genealogy and series information of literature (such as citation graphs and author network diagrams)

[0135] Output: Genealogy and lineage information

[0136] Step 6:

[0137] The server sends the constructed genealogy and lineage information to the device using API responses.

[0138] Input: Genealogy and lineage information constructed on the server

[0139] Data processing / calculation: Generate the constructed information as an API response in JSON format, etc.

[0140] Output: API response sent to the device

[0141] Step 7:

[0142] The lineage and affiliation information received by the device is visually displayed to the user using a JavaScript library that draws graphs and network diagrams.

[0143] Input: API response data sent from the server

[0144] Data processing / calculation: Converting received data into a visually displayable format and drawing it as a graph or network diagram

[0145] Output: Visual information displayed on the user's device (graphs, treemaps, network diagrams, etc.)

[0146] (Application example 1)

[0147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] Conventional literature search systems simply list search results, making it difficult to intuitively understand the citation relationships between documents, the networks between authors, or the flow of technological developments. Furthermore, it is difficult for users to instantly grasp useful information from a large amount of literature, leading to a decline in research efficiency.

[0149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0150] In this invention, the server includes means for inputting specific keywords, means for searching for multiple related documents based on the input keywords, means for analyzing the citation relationships, author networks, publication date series, technology series, publication medium series, research subject series, and research method series of the retrieved documents, means for constructing a genealogy and lineage information of the documents based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for acquiring data related to the keywords entered by the user and visualizing the data as a citation network. This allows users to intuitively understand the documents and research methods of their interest, improving research efficiency.

[0151] The "means for inputting specific keywords" is an interface device through which a user inputs specific keywords based on a topic of interest.

[0152] The "means for searching for multiple related documents based on the entered keywords" is a function for searching for documents related to the keywords entered by the user from databases and other information sources.

[0153] "Means for analyzing the citation relationships of retrieved documents, the network between authors, the publication date series, the technology series, the publication medium, the research subject series, and the research method series" refers to technical means for analyzing retrieved documents to understand how they are related to other documents, which authors are collaborating and how, and the flow of development of technology and research methods.

[0154] "Means for constructing genealogy and lineage information of documents based on analysis results" is a function for visualizing the relationships between documents and the flow of development based on analyzed information, and constructing genealogy and lineage information.

[0155] The "means for displaying constructed genealogy and lineage information" is an interface device for visually displaying genealogy and lineage information in a format that can be intuitively understood by the user.

[0156] "Means for obtaining data related to keywords entered by users and visualizing that data as a citation network" refers to a technical means for collecting data related to keywords entered by users and visualizing it in the form of graphs, network diagrams, etc., so that users can intuitively grasp the relationships between literature and research methods that interest them.

[0157] The system for implementing this invention mainly consists of a terminal and a server. When a user enters a specific keyword into the terminal, the terminal sends the keyword to the server. The server searches a literature database based on the received keyword and retrieves multiple related documents. It analyzes the citation relationships of the documents, the network between authors, the publication date sequence, the technology sequence, the publication medium, the research subject sequence, and the research method sequence. Based on the analysis results, it constructs genealogy and lineage information of the documents and sends this information to the terminal.

[0158] When a user enters a keyword into their device, data related to that keyword is retrieved and visualized as a citation network. Based on the analysis results, the device visually displays the genealogy and lineage information of the literature. For example, if a user enters the keyword "artificial intelligence," the server retrieves literature data related to artificial intelligence and analyzes the citation relationships and author networks of those literature. The analysis results are displayed in the form of graphs, treemaps, network diagrams, etc., allowing users to visually understand the information.

[0159] This system uses the following hardware and software.

[0160] Hardware: User terminals (computing devices such as smartphones), servers

[0161] Software: Literature database search engine, analysis engine (using networkx as a network analysis library and pyvis as a visualization library), interface software

[0162] This allows users to intuitively understand the relationships and developments between various documents related to a topic of interest.

[0163] Along these lines, the following example prompt sentences can be used:

[0164] example:

[0165] "Write Python code to retrieve data on literature related to keywords entered by the user and visualize that data as a citation network. Use requests, networkx, and pyvis."

[0166] As a result, this invention greatly supports users' research activities, providing a system that allows them to intuitively understand the relationships between documents and the flow of development.

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] The user inputs specific keywords into the terminal. In this step, the user inputs keywords based on the topic of interest, and the keywords are obtained through the terminal interface.

[0170] Input: The keyword entered by the user

[0171] Output: Keywords obtained from the terminal interface

[0172] Step 2:

[0173] The device sends the entered keyword to the server, which then sends the acquired keyword to the server as an HTTP request.

[0174] Input: Keywords obtained in step 1

[0175] Output: Keyword data sent to the server

[0176] Step 3:

[0177] The server searches the literature database based on the received keywords. The server uses the keywords to search the database for multiple related documents.

[0178] Input: Keyword data received from the client

[0179] Output: Related literature data

[0180] Step 4:

[0181] The server analyzes the citation relationships of the retrieved documents, the network between authors, publication date and time series, technology series, publication medium series, research subject series, and research method series.The server uses an analysis engine to process the data based on the retrieved document data in order to understand the complex relationships.

[0182] Input: Literature data obtained in Step 3

[0183] Output: Analysis results of citation relationships between documents, author networks, technology series, etc.

[0184] Step 5:

[0185] The server constructs genealogy and series information for the documents based on the analysis results, and uses the data generated by the analysis engine to create structured data for visualizing the relationships between documents and the flow of development.

[0186] Input: Analysis results generated in step 4

[0187] Output: Genealogy and lineage information for the document

[0188] Step 6:

[0189] The server sends the constructed genealogy and lineage information to the terminal, and returns the generated structured data to the terminal.

[0190] Input: Lineage and genealogy information constructed in Step 5

[0191] Output: Genealogy and lineage information sent to the device

[0192] Step 7:

[0193] The terminal displays the constructed genealogy and lineage information to the user. The terminal visually displays the received data, allowing the user to intuitively understand it.

[0194] Input: Genealogy and lineage information sent to the terminal in step 6

[0195] Output: what is displayed so that the user can understand it visually

[0196] In this way, the processing steps for realizing the application of the invention are concretely explained, allowing users to intuitively understand the relationships and developmental flow of literature related to the topic of interest.

[0197] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0198] The system of the present invention begins when a user inputs a specific keyword into a terminal. When the user inputs the keyword into a terminal, the terminal transmits the keyword to a server. The server searches a literature database based on the received keyword and retrieves multiple related documents.

[0199] The server analyzes the citation relationships, author networks, publication date and time series, technology series, publication medium, research subject series, and research method series of the retrieved documents. As a result of the analysis, detailed genealogy and lineage information of the documents is constructed. This allows the server to obtain information that visualizes how the document is related to other documents, which authors are collaborating and how, and the flow of research development.

[0200] The server sends the constructed genealogy and lineage information to the terminal, which then displays it to the user in a format that allows the user to intuitively understand the information, such as a graph, treemap, or network diagram.

[0201] By incorporating an emotion engine into this system, it is possible to recognize the user's emotional state and dynamically adjust the interface used. The emotion engine analyzes the user's facial expressions, biometric signals, input behavior, etc. when the user enters keywords or browses search results to determine their emotions.

[0202] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters the keyword and clicks the search button, the device sends the keyword to the server. The server retrieves literature related to artificial intelligence from a database and analyzes which literature those literature cites and which authors are involved in which research. It also analyzes technological advances and the lineage of related research methods.

[0203] Based on the analysis results, the server generates information such as citation networks, author networks, and development timelines for AI-related literature, and sends it to the device. The device then visually displays this information to the user. The emotion engine then works to monitor the user's reactions and emotional state. For example, if the user is excited about the information, the system can suggest additional detailed information or related new papers. If the user appears confused, the system can simplify the presentation of the information and adjust it to focus on basic content.

[0204] In this way, unlike conventional single-document searches, the system of the present invention not only provides comprehensive information on the relationships and developmental flow between documents, but also supports more intuitive and effective information search through dynamic interface adjustments based on the user's emotions.

[0205] The processing flow will be explained below.

[0206] Step 1:

[0207] The user enters a keyword into the search field on the device and clicks the search button.

[0208] Step 2:

[0209] The terminal captures the keyword entered by the user and stores it in the variable keyword.

[0210] Step 3:

[0211] The device generates an HTTP request containing the saved keyword and sends the request to the server.

[0212] Step 4:

[0213] The server analyzes the HTTP request received from the terminal and extracts keywords.

[0214] Step 5:

[0215] The server searches a literature database based on the extracted keywords and retrieves multiple related documents.

[0216] Step 6:

[0217] The server analyzes the citation relationships, author networks, publication date series, technology series, publication media, research subject series, and research method series of the retrieved documents.

[0218] Step 7:

[0219] Based on the analysis results, the server constructs genealogy and lineage information for the literature, such as graphs of citation networks and author collaboration networks.

[0220] Step 8:

[0221] The server converts the constructed genealogy and lineage information into JSON format and sends it to the terminal.

[0222] Step 9:

[0223] The device parses the JSON data received from the server and formats it in a format that is easy for the user to understand, such as a graph, treemap, or network diagram.

[0224] Step 10:

[0225] The device visually displays the formatted information to the user, who can then view the displayed information and take action to obtain more detailed information.

[0226] Step 11:

[0227] The device activates an emotion engine while displaying the image and monitors the user's facial expressions and input behavior.

[0228] Step 12:

[0229] The emotion engine estimates the user's emotional state and feeds that information back to the device.

[0230] Step 13:

[0231] The device dynamically changes the content and format of the information displayed based on the emotional feedback, such as presenting more detailed information if the user is excited, or simplifying the information if the user is confused.

[0232] As a concrete example, suppose a user types in the keyword "artificial intelligence" and related literature is displayed. If the emotion engine recognizes the user's excited expression, the system will further display detailed technical explanations and a list of related papers. On the other hand, if the user shows confusion, the system will prioritize displaying concise summaries and introductions to basic concepts.

[0233] Example 2

[0234] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0235] Conventional literature search systems not only allow users to search for a single document, but also make it difficult for them to understand the complex relationships between documents and the development process of research. Furthermore, they are unable to flexibly present information according to the user's emotions and level of understanding, which makes information search inefficient. There is a need to solve this problem and provide a more intuitive and effective information search experience.

[0236] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0237] In this invention, the server includes means for inputting specific keywords, means for searching for multiple related documents based on the input keywords, means for analyzing the citation relationships, author networks, publication date sequence, technology sequence, publication medium, research subject sequence, and research method sequence of the retrieved documents, means for constructing a genealogy and lineage information of the documents based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for analyzing the user's emotional state and dynamically adjusting the interface. This makes it possible to comprehensively grasp the relationships and development process of documents based on the keywords entered by the user and present information according to the user's emotions and level of understanding.

[0238] A "specific keyword" is a word or phrase that a user enters as a search target.

[0239] A "literature database" is a database that stores multiple academic documents and manages them in a searchable format.

[0240] "Citation relationships of documents" is information indicating the relationships in which a document refers to or cites another document.

[0241] "Networks between authors" refers to information that indicates the relationships between authors of multiple documents, including their collaborative research and involvement.

[0242] The "publication date sequence" is information indicating the order or timeline of the dates when documents were published.

[0243] A "technology series" is information that shows the process of research development and evolution in a specific technology field.

[0244] "By publication medium" is information that classifies the publication medium (e.g., academic journal, conference proceedings, etc.) in which each document was published.

[0245] "Research subject series" is information that indicates the series of research subjects or themes that different documents deal with.

[0246] "Research methodology series" is information that indicates the series of research methods and approaches adopted by different documents.

[0247] "Genealogy information" is detailed structural information of relationships based on citation relationships between documents and author networks.

[0248] "Series information" is information that follows a chronological order or classification, such as the publication date and time of a document or the evolution of technology.

[0249] "Emotional state" refers to the user's current emotional or psychological state, and is used to determine the user's reaction and level of understanding.

[0250] "Adjusting the interface" means dynamically changing the format and content of information presentation according to the user's emotional state and reactions.

[0251] The system of this invention begins when a user inputs a specific keyword into a terminal. Specific hardware used by the user is a personal computer, tablet, smartphone, or other device. Software used is a web browser connected to the Internet.

[0252] When a user enters a keyword into the device's search bar, the device sends the keyword to the server. The communication uses the HTTPS protocol, and SSL / TLS is used to ensure data security. For example, if a user enters "artificial intelligence" and clicks the search button, the device sends the keyword to the server.

[0253] The server searches the specified literature database (e.g., academic literature database or patent database) based on the received keywords. Specific software used to perform the search is a literature database API (e.g., PubMed API, IEEE Xplore API).

[0254] The server analyzes the search results obtained from the literature database, including citation network analysis, author network analysis, time series analysis, and technology series analysis, using network graph algorithms (e.g., Dijkstra's algorithm) and machine learning algorithms (e.g., TensorFlow, Scikit-learn).

[0255] For example, the server retrieves documents related to "artificial intelligence" and analyzes their citation relationships and author networks. Based on the results of this analysis, it builds detailed genealogy and developmental sequence information for the documents. This information is stored in JSON format or other suitable data formats.

[0256] The server then sends the constructed genealogy and lineage information to the terminal. Again, the data is securely transmitted using the HTTPS protocol. The terminal receives this information and visually displays it using a data visualization library (e.g., D3.js, Chart.js). This allows users to intuitively understand information such as citation networks, author networks, and the development process of research.

[0257] Furthermore, the emotion engine uses the device's built-in camera and sensor devices to analyze the user's emotional state. Specifically, OpenCV is used to analyze facial expressions, and TensorFlow is used to implement the machine learning model. The interface's information presentation format is dynamically adjusted based on the user's emotional state. For example, if the user is excited about the information, the system will suggest adding more detailed information or related new papers. If the user is confused, the information presentation format will be simplified and focused on basic content.

[0258] Examples of prompts include:

[0259] User: "I'd like to know about the latest research trends in artificial intelligence. What literature is available?"

[0260] AI Model: "Exploring recent research literature on the topic of artificial intelligence. Please wait."

[0261] In this way, the system of the present invention not only comprehensively visualizes the relevance and development process of literature based on the user's keyword input, but also supports more effective information exploration through dynamic interface adjustments according to the user's emotional state.

[0262] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0263] Step 1:

[0264] The user enters a keyword into the device. Specifically, the user enters a specific keyword, such as "artificial intelligence," into the search bar and clicks the search button. The entered keyword is stored in the device's memory.

[0265] Input: The keyword "artificial intelligence" entered by the user.

[0266] Output: The keyword "artificial intelligence" stored by the device.

[0267] Step 2:

[0268] The device sends the entered keyword to the server. The keyword is securely sent to the server using the HTTPS protocol. SSL / TLS is used.

[0269] Input: Keyword saved on device: "artificial intelligence".

[0270] Output: The keyword "artificial intelligence" sent to the server.

[0271] Step 3:

[0272] The server searches a literature database based on keywords. The server uses the search API of the specified literature database (e.g., PubMed API, IEEE Xplore API) to search for relevant literature.

[0273] Input: The keyword "artificial intelligence" received by the server.

[0274] Output: Search results (list of relevant literature) obtained from the literature database.

[0275] Step 4:

[0276] The server retrieves the relevant literature, receives the retrieved data in JSON format, and prepares it for analysis.

[0277] Input: Search results from literature databases.

[0278] Output: Bibliographic data in JSON format.

[0279] Step 5:

[0280] The server analyzes the documents retrieved, using network graph algorithms (e.g., Dijkstra's algorithm) and machine learning algorithms (e.g., TensorFlow, Scikit-learn) to analyze citation relationships, author networks, time series, technology series, etc.

[0281] Input: Bibliographic data in JSON format.

[0282] Output: Analysis results (citation networks, author networks, time series data, etc.).

[0283] Step 6:

[0284] The server constructs genealogy and lineage information based on the analysis results, and the constructed data is stored in a database or memory.

[0285] Input: Analysis results.

[0286] Output: Genealogy and lineage information.

[0287] Step 7:

[0288] The server sends the constructed genealogy and lineage information to the terminal. The data is sent securely to the terminal using the HTTPS protocol.

[0289] Input: Genealogical and lineage information.

[0290] Output: Genealogy and lineage information sent to the device.

[0291] Step 8:

[0292] The device displays the information to the user, using data visualization libraries such as D3.js and Chart.js to visually display the information.

[0293] Input: Genealogy and lineage information sent to the terminal.

[0294] Output: Visualized information (graphs, network diagrams, etc.).

[0295] Step 9:

[0296] The emotion engine analyzes the user's emotional state and dynamically adjusts the interface. It uses the device's built-in camera and sensor devices, as well as OpenCV and TensorFlow to analyze facial expressions and input behavior. It adjusts the information presentation format based on the user's emotional state.

[0297] Input: User's facial expressions, biometric signals, and input behavior.

[0298] Output: Dynamically adjusted interface.

[0299] (Application example 2)

[0300] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] Conventional literature search and content search systems simply display a list of related materials, making it difficult to comprehensively grasp the relationships between materials, such as citation relationships, author networks, and development timelines. Furthermore, they lack the ability to dynamically adjust the interface according to the user's emotional state, and lack the ingenuity to make it easier for users to intuitively understand information. This reduces the efficiency of information search and impairs the user experience.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0303] In this invention, the server includes means for inputting a specific keyword, means for searching for multiple related content based on the input keyword, means for analyzing the citation relationships of the searched content, the network between creators, the publication date sequence, the technology sequence, the publication medium sequence, and the research method sequence, means for constructing a genealogy and lineage information of the content based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for determining the user's emotional state and dynamically adjusting the display format of the analysis results. This not only allows the user to visually grasp the lineage and relevance of content related to keywords of interest, but also enables the user to obtain information using an interface optimal for their emotional state.

[0304] "Specific keywords" are words or phrases that indicate a particular topic or theme that interests a user.

[0305] "Input means" refers to a device or interface that allows a user to input a specific keyword, and specifically includes a keyboard, a touch screen, a voice input device, etc.

[0306] "Multiple related contents" refers to digital information such as videos, articles, audio, literature, etc. that are associated with the entered keywords under certain conditions.

[0307] "Search means" refers to a program or algorithm for retrieving multiple pieces of related content based on a keyword.

[0308] A "citation relationship" refers to a relationship in which a specific piece of content refers to another piece of content as a reference.

[0309] A "network among creators" is a network that shows the relationships and cooperation between individuals and organizations that create content.

[0310] The "release date sequence" indicates the order of the dates and times when the content was released to the public.

[0311] A "technology lineage" indicates how a particular technology or methodology has progressed and evolved.

[0312] "By publication medium" is a classification based on the type of medium in which the content was published (e.g., academic journal, online platform, television broadcast, etc.).

[0313] "Means of analysis" refers to methods and tools for analyzing the citation relationships, networks, time series, etc. of the acquired content.

[0314] "Genealogy and lineage information" refers to detailed information such as citation relationships between content, timelines of development, and creator networks.

[0315] "Display means" refers to a method or device for visually presenting the analysis results to the user, and specifically includes forms such as graphs, treemaps, and network diagrams.

[0316] The "emotional state of the user" indicates the emotion the user feels when viewing a particular piece of content, and refers to states such as joy, excitement, or confusion.

[0317] "Means for determining and dynamically adjusting the display format of the analysis results" refers to a method for analyzing the user's emotional state and changing or optimizing the way information is presented based on the results.

[0318] The system of the present invention starts with the user entering a specific keyword, searches for multiple related pieces of content, and analyzes their citation relationships, networks between creators, publication dates, technology, publication media, and research methodologies to construct a genealogy and series of content and display it to the user. It also has the ability to determine the user's emotional state and dynamically adjust the display format based on the analysis results.

[0319] The system program performs the following processing.

[0320] First, a user inputs a specific keyword using a smartphone or other input device. The device then sends the keyword to a server, which searches for multiple related content. The server then retrieves related digital information, such as videos, articles, and audio, from a database. The hardware used can be a smartphone, tablet, or server computer.

[0321] The server then analyzes the retrieved content's citation relationships, creator networks, publication date and time series, technology series, publication media, and research method series. The analysis includes network analysis using the "networkx" library and data visualization using "matplotlib." This allows for the construction of detailed genealogy and lineage information for the content.

[0322] The server then sends the constructed genealogy and lineage information to the user's device, which then visually displays it. Various visualization techniques, such as treemaps and network diagrams, are used for the display. Specifically, the "matplotlib" library is used to visualize the information.

[0323] Furthermore, the emotion engine works by analyzing the user's facial expressions and behavior while browsing information. The "emotion_detection_module" is used to determine the user's emotional state. Based on this result, the server dynamically adjusts the display format. For example, if the user is excited, the server can suggest additional information or related new content. If the user is confused, the server can simplify the information presentation format and adjust it to focus on the basic content.

[0324] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters a keyword, the server searches for related videos and articles and analyzes their citation relationships and technological advances. It generates a treemap or network diagram of information related to the keyword "artificial intelligence" and displays it on the user's device. If the user is excited by the displayed information, the system will display more detailed information or related new papers. If the user is confused, it will provide simplified information to make it easier to understand.

[0325] Examples of prompts include:

[0326] "Analyze the relationships between videos and articles related to artificial intelligence, and generate a network diagram showing citation relationships, the order in which they were produced, related people, and technological developments."

[0327] In this way, the system of the present invention comprehensively analyzes keyword-based content searches and dynamically presents information according to the user's emotional state, supporting more intuitive and effective information exploration.

[0328] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0329] Step 1:

[0330] A user inputs a specific keyword using a smartphone or other input device.

[0331] Input: The keyword entered by the user.

[0332] Output: Send the keywords to the server.

[0333] Specific operation: The user enters a keyword in the input field and presses the search button. This operation sends the keyword from the device to the server.

[0334] Step 2:

[0335] The server searches a database for multiple related contents based on keywords.

[0336] Input: The keyword sent from the terminal.

[0337] Output: A list of associated content data.

[0338] Specific operation: The server accesses the database and searches for and retrieves digital information such as videos, articles, and audio related to the keywords.

[0339] Step 3:

[0340] The server analyzes the citation relationships of the content it acquires, the network between creators, the publication date and time series, the technology series, the publication media, and the research method series.

[0341] Input: A list of related content data.

[0342] Output: Genealogy and lineage information as analysis results.

[0343] Specific operation: The server performs network analysis using the "networkx" library and prepares the data for visualization using "matplotlib." At the same time, it organizes and analyzes information on citation relationships and technological advances for each piece of content.

[0344] Step 4:

[0345] The server transmits the constructed genealogy and lineage information to the terminal, which then visually displays it.

[0346] Input: Genealogy and lineage information.

[0347] Output: Visualized information displayed on the user's device.

[0348] Specific operation: The server generates the analysis results in the form of a treemap, network diagram, etc. and sends them to the terminal. The terminal receives them and displays them visually using "matplotlib" or similar.

[0349] Step 5:

[0350] The emotion engine analyzes the user's facial expressions and behavior to determine their emotional state.

[0351] Input: User's facial expression data, behavioral data.

[0352] Output: The user's emotional state (excited, confused, etc.).

[0353] Specific operation: Using the "emotion_detection_module" built into the device, data obtained from the camera and microphone is analyzed to determine the user's emotional state in real time.

[0354] Step 6:

[0355] The server dynamically adjusts the display format of the analysis results based on the emotional state.

[0356] Input: The user's emotional state.

[0357] Output: Adjusted display information.

[0358] Specific behavior: Based on feedback from the emotion engine, the server changes the way information is presented, for example, showing additional information or new content if the user is excited, or simplifying the information if the user is confused.

[0359] In this way, the system comprehensively performs content searches based on specific keywords, analyzes their relevance, and dynamically displays information according to the user's emotional state.

[0360] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0361] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0362] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0363] [Second embodiment]

[0364] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0365] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0366] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0367] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0368] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0369] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0370] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0371] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0372] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0373] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0374] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0375] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0376] The system of the present invention begins when a user inputs a specific keyword into a terminal. When the user inputs the keyword into a terminal, the terminal transmits the keyword to a server. The server searches a literature database based on the received keyword and retrieves multiple related documents.

[0377] The server analyzes the citation relationships, author networks, publication date and time series, technology series, publication medium, research subject series, and research method series of the retrieved documents. As a result of the analysis, detailed genealogy and lineage information of the documents is constructed. This allows the server to obtain information that visualizes how the document is related to other documents, which authors are collaborating and how, and the flow of research development.

[0378] The server sends the constructed genealogy and lineage information to the terminal, which then displays it to the user in a format that allows the user to intuitively understand the information, such as a graph, treemap, or network diagram.

[0379] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters the keyword and clicks the search button, the device sends the keyword to the server. The server retrieves literature related to artificial intelligence from a database and analyzes which literature those literature cites and which authors are involved in which research. It also analyzes technological advances and the lineage of related research methods.

[0380] As a result of the analysis, the server generates information such as citation networks, author networks, and development timelines for AI-related literature, and sends it to the terminal, which then visually displays this information to the user, allowing them to intuitively understand the literature and research methods that interest them.

[0381] In this way, the system of the present invention differs from conventional single document searches in that it comprehensively provides information on the relationships between documents and the flow of development, thereby greatly supporting the user's research activities.

[0382] The processing flow will be explained below.

[0383] Step 1:

[0384] The user enters a keyword into the search field on the device and clicks the search button.

[0385] Step 2:

[0386] The terminal captures the keyword entered by the user and stores it in the variable keyword.

[0387] Step 3:

[0388] The device generates an HTTP request containing the saved keyword and sends the request to the server.

[0389] Step 4:

[0390] The server analyzes the HTTP request received from the terminal and extracts keywords.

[0391] Step 5:

[0392] The server searches a literature database based on the extracted keywords and retrieves multiple related documents.

[0393] Step 6:

[0394] The server analyzes the citation relationships, author networks, publication date series, technology series, publication media, research subject series, and research method series of the retrieved documents.

[0395] Step 7:

[0396] Based on the analysis results, the server constructs genealogy and lineage information for the literature, such as graphs of citation networks and author collaboration networks.

[0397] Step 8:

[0398] The server converts the constructed genealogy and lineage information into JSON format and sends it to the terminal.

[0399] Step 9:

[0400] The device parses the JSON data received from the server and formats it in a format that is easy for the user to understand, such as a graph, treemap, or network diagram.

[0401] Step 10:

[0402] The device visually displays the formatted information to the user, who can then view the displayed information and take action to obtain more detailed information.

[0403] For example, a user enters the keyword "artificial intelligence." The server retrieves related literature from the database and analyzes the citation relationships and author networks of each literature. As a result, the citation networks between papers and collaborations between authors are visually displayed, allowing the user to grasp the overall picture of research on artificial intelligence.

[0404] Example 1

[0405] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0406] Conventional literature search systems only provide search results for a single document, making it difficult to comprehensively grasp the relationships between documents and the flow of their development. This leaves users unable to intuitively understand the citation relationships between documents, author networks, or the flow of research development, resulting in a decrease in the efficiency of research activities. A system that can solve these problems and support research activities is needed.

[0407] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0408] In this invention, the server includes: means for a user to input specific keywords; means for a terminal to transmit the input keywords to the server; means for the server to search a database for multiple related documents based on the received keywords; means for the server to analyze the citation relationships, author networks, publication date sequence, technology sequence, publication medium, research subject sequence, and research method sequence of the retrieved documents; means for the server to construct genealogy and lineage information of the documents based on the analysis results; means for the server to transmit the constructed genealogy and lineage information to the terminal; and means for the terminal to visually display the transmitted genealogy and lineage information. This allows the user to comprehensively and intuitively understand the relationships between documents and the flow of development.

[0409] A "specific keyword" is a specific word or phrase that a user enters as the subject of a search.

[0410] A "user" is a person or organization that uses the system to conduct a literature search.

[0411] A "terminal" is a device that a user uses to operate the system, specifically a computer device such as a PC or smartphone.

[0412] A "server" is a computer system that searches a literature database based on keywords sent by a user and performs analysis.

[0413] A "literature database" is a data store that stores a large number of research papers and academic books, and is a searchable information source.

[0414] "Searching" is the operation of retrieving relevant documents from a literature database based on specific keywords.

[0415] A "citation relationship" refers to a relationship in which a document refers to or cites another document.

[0416] The "author network" indicates the collaborative relationships between authors, i.e., the publications in which different authors are involved as co-authors.

[0417] The "publication date sequence" indicates the order of the publication dates of documents.

[0418] A "technology lineage" shows how a particular technology has developed and evolved.

[0419] "By publication medium" is used to classify the publication medium (academic journal, conference proceedings, etc.) in which the document was published.

[0420] "Research subject series" refers to the research subject series that different papers address.

[0421] "Research methodology series" refers to the series of research methods used by the document.

[0422] "Analyzing" is the process of examining the contents of a document and related information in detail to derive specific relationships and sequences.

[0423] "Genealogy information" is information that shows an overall picture of related information such as citation relationships between documents and author networks.

[0424] "Series information" is chronological and classification information that shows the flow of development and relationships.

[0425] "Send" refers to the operation of sending information from a terminal to a server or from a server to a terminal.

[0426] "Visual display" refers to the operation of making the information obtained as an analysis result visible to the user in the form of a graph, network diagram, or the like.

[0427] The present invention is a system that allows users to input specific keywords, search for, analyze, and visually display documents related to those keywords, thereby enabling them to comprehensively understand the relationships between documents and the flow of development.

[0428] This system includes the following components:

[0429] 1. User keyword input

[0430] Users use their own devices (PCs, smartphones, etc.) to input specific keywords that are related to the research field or topic that interests them.

[0431] 2. Sending keywords via device

[0432] The terminal sends the input keyword to the server. Specifically, an HTTP request is sent from the terminal to the server. This request includes the keyword entered by the user.

[0433] 3. Searching the literature database using the server

[0434] The server uses the received keywords to search a literature database, which is a data store containing numerous research papers and academic books and can be an SQL database or a cloud-based data store.

[0435] 4. Server-based document analysis

[0436] The server analyzes the literature information obtained as search results. This analysis applies text mining and network analysis techniques. Specifically, analysis is performed on citation relationships, author networks, publication date and time series, technology series, publication media, research subject series, and research method series.

[0437] 5. Server-based genealogy and lineage information construction

[0438] Based on the analysis results, the server constructs detailed genealogy and lineage information for the document, including citation graphs and author network diagrams, which reveal how the document is related to other documents, which authors collaborate and how, and the flow of research development.

[0439] 6. Sending information from the server to the device

[0440] The server generates the constructed genealogy and lineage information as an API response and sends it to the terminal.

[0441] 7. Displaying Information on a Terminal

[0442] The terminal visually displays the received genealogy and lineage information to the user in a variety of formats, including graphs, treemaps, and network diagrams, allowing the user to intuitively grasp the information.

[0443] Specific examples

[0444] For example, if a user enters the keyword "artificial intelligence" to perform a search, the user enters the keyword and clicks the "Search" button, and the device sends the keyword to the server. The server retrieves documents related to "artificial intelligence" from the literature database and analyzes each document's citation relationships, author network, publication timeline, and technology series. As a result of the analysis, the server generates information such as the citation network, author network, and development timeline of documents related to artificial intelligence, and sends this information to the device. The device then visually displays this information to the user, allowing the user to intuitively understand the documents and research methods of interest.

[0445] Prompt Sentence Examples

[0446] Please explain in detail, step by step, the process of the system to analyze the genealogy and lineage information of related documents based on specific keywords and display it visually to the user.

[0447] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0448] Step 1:

[0449] The user inputs a specific keyword into the terminal.

[0450] Input: Keywords of interest to the user (e.g. "artificial intelligence")

[0451] Output: Keywords displayed on the user's terminal

[0452] Step 2:

[0453] The device sends the entered keyword to the server using an HTTP request.

[0454] Input: Keywords entered by the user into the device

[0455] Data processing / calculation: The terminal generates an HTTP request and includes the keyword as a parameter.

[0456] Output: HTTP request sent to the server

[0457] Step 3:

[0458] The server searches a literature database based on the received keywords.

[0459] Input: Keywords in the HTTP request received by the server

[0460] Data processing / calculation: Searching literature databases using SQL queries or cloud data store APIs to retrieve relevant literature

[0461] Output: List of relevant literature

[0462] Step 4:

[0463] The server analyzes the citation relationships of the documents searched, the network between authors, the publication date sequence, the technology sequence, the publication medium, the research subject sequence, and the research method sequence.

[0464] Input: List of documents obtained as search results

[0465] Data processing / calculation: Using text mining and network analysis tools, we conduct detailed analysis of the relevant information in each document.

[0466] Output: Analysis results (citation relationship graph, author network diagram, etc.)

[0467] Step 5:

[0468] The server constructs genealogy and lineage information for the literature based on the analysis results.

[0469] Input: Literature analysis results

[0470] Data processing / calculation: Based on the analysis results, construct genealogy and series information of literature (such as citation graphs and author network diagrams)

[0471] Output: Genealogy and lineage information

[0472] Step 6:

[0473] The server sends the constructed genealogy and lineage information to the device using API responses.

[0474] Input: Genealogy and lineage information constructed on the server

[0475] Data processing / calculation: Generate the constructed information as an API response in JSON format, etc.

[0476] Output: API response sent to the device

[0477] Step 7:

[0478] The lineage and affiliation information received by the device is visually displayed to the user using a JavaScript library that draws graphs and network diagrams.

[0479] Input: API response data sent from the server

[0480] Data processing / calculation: Converting received data into a visually displayable format and drawing it as a graph or network diagram

[0481] Output: Visual information displayed on the user's device (graphs, treemaps, network diagrams, etc.)

[0482] (Application example 1)

[0483] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0484] Conventional literature search systems simply list search results, making it difficult to intuitively understand the citation relationships between documents, the networks between authors, or the flow of technological developments. Furthermore, it is difficult for users to instantly grasp useful information from a large amount of literature, leading to a decline in research efficiency.

[0485] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0486] In this invention, the server includes means for inputting specific keywords, means for searching for multiple related documents based on the input keywords, means for analyzing the citation relationships, author networks, publication date series, technology series, publication medium series, research subject series, and research method series of the retrieved documents, means for constructing a genealogy and lineage information of the documents based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for acquiring data related to the keywords entered by the user and visualizing the data as a citation network. This allows users to intuitively understand the documents and research methods of their interest, improving research efficiency.

[0487] The "means for inputting specific keywords" is an interface device through which a user inputs specific keywords based on a topic of interest.

[0488] The "means for searching for multiple related documents based on the entered keywords" is a function for searching for documents related to the keywords entered by the user from databases and other information sources.

[0489] "Means for analyzing the citation relationships of retrieved documents, the network between authors, the publication date series, the technology series, the publication medium, the research subject series, and the research method series" refers to technical means for analyzing retrieved documents to understand how they are related to other documents, which authors are collaborating and how, and the flow of development of technology and research methods.

[0490] "Means for constructing genealogy and lineage information of documents based on analysis results" is a function for visualizing the relationships between documents and the flow of development based on analyzed information, and constructing genealogy and lineage information.

[0491] The "means for displaying constructed genealogy and lineage information" is an interface device for visually displaying genealogy and lineage information in a format that can be intuitively understood by the user.

[0492] "Means for obtaining data related to keywords entered by users and visualizing that data as a citation network" refers to a technical means for collecting data related to keywords entered by users and visualizing it in the form of graphs, network diagrams, etc., so that users can intuitively grasp the relationships between literature and research methods that interest them.

[0493] The system for implementing this invention mainly consists of a terminal and a server. When a user enters a specific keyword into the terminal, the terminal sends the keyword to the server. The server searches a literature database based on the received keyword and retrieves multiple related documents. It analyzes the citation relationships of the documents, the network between authors, the publication date sequence, the technology sequence, the publication medium, the research subject sequence, and the research method sequence. Based on the analysis results, it constructs genealogy and lineage information of the documents and sends this information to the terminal.

[0494] When a user enters a keyword into their device, data related to that keyword is retrieved and visualized as a citation network. Based on the analysis results, the device visually displays the genealogy and lineage information of the literature. For example, if a user enters the keyword "artificial intelligence," the server retrieves literature data related to artificial intelligence and analyzes the citation relationships and author networks of those literature. The analysis results are displayed in the form of graphs, treemaps, network diagrams, etc., allowing users to visually understand the information.

[0495] This system uses the following hardware and software.

[0496] Hardware: User terminals (computing devices such as smartphones), servers

[0497] Software: Literature database search engine, analysis engine (using networkx as a network analysis library and pyvis as a visualization library), interface software

[0498] This allows users to intuitively understand the relationships and developments between various documents related to a topic of interest.

[0499] Along these lines, the following example prompt sentences can be used:

[0500] example:

[0501] "Write Python code to retrieve data on literature related to keywords entered by the user and visualize that data as a citation network. Use requests, networkx, and pyvis."

[0502] As a result, this invention greatly supports users' research activities, providing a system that allows them to intuitively understand the relationships between documents and the flow of development.

[0503] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0504] Step 1:

[0505] The user inputs specific keywords into the terminal. In this step, the user inputs keywords based on the topic of interest, and the keywords are obtained through the terminal interface.

[0506] Input: The keyword entered by the user

[0507] Output: Keywords obtained from the terminal interface

[0508] Step 2:

[0509] The device sends the entered keyword to the server, which then sends the acquired keyword to the server as an HTTP request.

[0510] Input: Keywords obtained in step 1

[0511] Output: Keyword data sent to the server

[0512] Step 3:

[0513] The server searches the literature database based on the received keywords. The server uses the keywords to search the database for multiple related documents.

[0514] Input: Keyword data received from the client

[0515] Output: Related literature data

[0516] Step 4:

[0517] The server analyzes the citation relationships of the retrieved documents, the network between authors, publication date and time series, technology series, publication medium series, research subject series, and research method series.The server uses an analysis engine to process the data based on the retrieved document data in order to understand the complex relationships.

[0518] Input: Literature data obtained in Step 3

[0519] Output: Analysis results of citation relationships between documents, author networks, technology series, etc.

[0520] Step 5:

[0521] The server constructs genealogy and series information for the documents based on the analysis results, and uses the data generated by the analysis engine to create structured data for visualizing the relationships between documents and the flow of development.

[0522] Input: Analysis results generated in step 4

[0523] Output: Genealogy and lineage information for the document

[0524] Step 6:

[0525] The server sends the constructed genealogy and lineage information to the terminal, and returns the generated structured data to the terminal.

[0526] Input: Lineage and genealogy information constructed in Step 5

[0527] Output: Genealogy and lineage information sent to the device

[0528] Step 7:

[0529] The terminal displays the constructed genealogy and lineage information to the user. The terminal visually displays the received data, allowing the user to intuitively understand it.

[0530] Input: Genealogy and lineage information sent to the terminal in step 6

[0531] Output: what is displayed so that the user can understand it visually

[0532] In this way, the processing steps for realizing the application of the invention are concretely explained, allowing users to intuitively understand the relationships and developmental flow of literature related to the topic of interest.

[0533] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0534] The system of the present invention begins when a user inputs a specific keyword into a terminal. When the user inputs the keyword into a terminal, the terminal transmits the keyword to a server. The server searches a literature database based on the received keyword and retrieves multiple related documents.

[0535] The server analyzes the citation relationships, author networks, publication date and time series, technology series, publication medium, research subject series, and research method series of the retrieved documents. As a result of the analysis, detailed genealogy and lineage information of the documents is constructed. This allows the server to obtain information that visualizes how the document is related to other documents, which authors are collaborating and how, and the flow of research development.

[0536] The server sends the constructed genealogy and lineage information to the terminal, which then displays it to the user in a format that allows the user to intuitively understand the information, such as a graph, treemap, or network diagram.

[0537] By incorporating an emotion engine into this system, it is possible to recognize the user's emotional state and dynamically adjust the interface used. The emotion engine analyzes the user's facial expressions, biometric signals, input behavior, etc. when the user enters keywords or browses search results to determine their emotions.

[0538] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters the keyword and clicks the search button, the device sends the keyword to the server. The server retrieves literature related to artificial intelligence from a database and analyzes which literature those literature cites and which authors are involved in which research. It also analyzes technological advances and the lineage of related research methods.

[0539] Based on the analysis results, the server generates information such as citation networks, author networks, and development timelines for AI-related literature, and sends it to the device. The device then visually displays this information to the user. The emotion engine then works to monitor the user's reactions and emotional state. For example, if the user is excited about the information, the system can suggest additional detailed information or related new papers. If the user appears confused, the system can simplify the presentation of the information and adjust it to focus on basic content.

[0540] In this way, unlike conventional single-document searches, the system of the present invention not only provides comprehensive information on the relationships and developmental flow between documents, but also supports more intuitive and effective information search through dynamic interface adjustments based on the user's emotions.

[0541] The processing flow will be explained below.

[0542] Step 1:

[0543] The user enters a keyword into the search field on the device and clicks the search button.

[0544] Step 2:

[0545] The terminal captures the keyword entered by the user and stores it in the variable keyword.

[0546] Step 3:

[0547] The device generates an HTTP request containing the saved keyword and sends the request to the server.

[0548] Step 4:

[0549] The server analyzes the HTTP request received from the terminal and extracts keywords.

[0550] Step 5:

[0551] The server searches a literature database based on the extracted keywords and retrieves multiple related documents.

[0552] Step 6:

[0553] The server analyzes the citation relationships, author networks, publication date series, technology series, publication media, research subject series, and research method series of the retrieved documents.

[0554] Step 7:

[0555] Based on the analysis results, the server constructs genealogy and lineage information for the literature, such as graphs of citation networks and author collaboration networks.

[0556] Step 8:

[0557] The server converts the constructed genealogy and lineage information into JSON format and sends it to the terminal.

[0558] Step 9:

[0559] The device parses the JSON data received from the server and formats it in a format that is easy for the user to understand, such as a graph, treemap, or network diagram.

[0560] Step 10:

[0561] The device visually displays the formatted information to the user, who can then view the displayed information and take action to obtain more detailed information.

[0562] Step 11:

[0563] The device activates an emotion engine while displaying the image and monitors the user's facial expressions and input behavior.

[0564] Step 12:

[0565] The emotion engine estimates the user's emotional state and feeds that information back to the device.

[0566] Step 13:

[0567] The device dynamically changes the content and format of the information displayed based on the emotional feedback, such as presenting more detailed information if the user is excited, or simplifying the information if the user is confused.

[0568] As a concrete example, suppose a user types in the keyword "artificial intelligence" and related literature is displayed. If the emotion engine recognizes the user's excited expression, the system will further display detailed technical explanations and a list of related papers. On the other hand, if the user shows confusion, the system will prioritize displaying concise summaries and introductions to basic concepts.

[0569] Example 2

[0570] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0571] Conventional literature search systems not only allow users to search for a single document, but also make it difficult for them to understand the complex relationships between documents and the development process of research. Furthermore, they are unable to flexibly present information according to the user's emotions and level of understanding, which makes information search inefficient. There is a need to solve this problem and provide a more intuitive and effective information search experience.

[0572] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0573] In this invention, the server includes means for inputting specific keywords, means for searching for multiple related documents based on the input keywords, means for analyzing the citation relationships, author networks, publication date sequence, technology sequence, publication medium, research subject sequence, and research method sequence of the retrieved documents, means for constructing a genealogy and lineage information of the documents based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for analyzing the user's emotional state and dynamically adjusting the interface. This makes it possible to comprehensively grasp the relationships and development process of documents based on the keywords entered by the user and present information according to the user's emotions and level of understanding.

[0574] A "specific keyword" is a word or phrase that a user enters as a search target.

[0575] A "literature database" is a database that stores multiple academic documents and manages them in a searchable format.

[0576] "Citation relationships of documents" is information indicating the relationships in which a document refers to or cites another document.

[0577] "Networks between authors" refers to information that indicates the relationships between authors of multiple documents, including their collaborative research and involvement.

[0578] The "publication date sequence" is information indicating the order or timeline of the dates when documents were published.

[0579] A "technology series" is information that shows the process of research development and evolution in a specific technology field.

[0580] "By publication medium" is information that classifies the publication medium (e.g., academic journal, conference proceedings, etc.) in which each document was published.

[0581] "Research subject series" is information that indicates the series of research subjects or themes that different documents deal with.

[0582] "Research methodology series" is information that indicates the series of research methods and approaches adopted by different documents.

[0583] "Genealogy information" is detailed structural information of relationships based on citation relationships between documents and author networks.

[0584] "Series information" is information that follows a chronological order or classification, such as the publication date and time of a document or the evolution of technology.

[0585] "Emotional state" refers to the user's current emotional or psychological state, and is used to determine the user's reaction and level of understanding.

[0586] "Adjusting the interface" means dynamically changing the format and content of information presentation according to the user's emotional state and reactions.

[0587] The system of this invention begins when a user inputs a specific keyword into a terminal. Specific hardware used by the user is a personal computer, tablet, smartphone, or other device. Software used is a web browser connected to the Internet.

[0588] When a user enters a keyword into the device's search bar, the device sends the keyword to the server. The communication uses the HTTPS protocol, and SSL / TLS is used to ensure data security. For example, if a user enters "artificial intelligence" and clicks the search button, the device sends the keyword to the server.

[0589] The server searches the specified literature database (e.g., academic literature database or patent database) based on the received keywords. Specific software used to perform the search is a literature database API (e.g., PubMed API, IEEE Xplore API).

[0590] The server analyzes the search results obtained from the literature database, including citation network analysis, author network analysis, time series analysis, and technology series analysis, using network graph algorithms (e.g., Dijkstra's algorithm) and machine learning algorithms (e.g., TensorFlow, Scikit-learn).

[0591] For example, the server retrieves documents related to "artificial intelligence" and analyzes their citation relationships and author networks. Based on the results of this analysis, it builds detailed genealogy and developmental sequence information for the documents. This information is stored in JSON format or other suitable data formats.

[0592] The server then sends the constructed genealogy and lineage information to the terminal. Again, the data is securely transmitted using the HTTPS protocol. The terminal receives this information and visually displays it using a data visualization library (e.g., D3.js, Chart.js). This allows users to intuitively understand information such as citation networks, author networks, and the development process of research.

[0593] Furthermore, the emotion engine uses the device's built-in camera and sensor devices to analyze the user's emotional state. Specifically, OpenCV is used to analyze facial expressions, and TensorFlow is used to implement the machine learning model. The interface's information presentation format is dynamically adjusted based on the user's emotional state. For example, if the user is excited about the information, the system will suggest adding more detailed information or related new papers. If the user is confused, the information presentation format will be simplified and focused on basic content.

[0594] Examples of prompts include:

[0595] User: "I'd like to know about the latest research trends in artificial intelligence. What literature is available?"

[0596] AI Model: "Exploring recent research literature on the topic of artificial intelligence. Please wait."

[0597] In this way, the system of the present invention not only comprehensively visualizes the relevance and development process of literature based on the user's keyword input, but also supports more effective information exploration through dynamic interface adjustments according to the user's emotional state.

[0598] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0599] Step 1:

[0600] The user enters a keyword into the device. Specifically, the user enters a specific keyword, such as "artificial intelligence," into the search bar and clicks the search button. The entered keyword is stored in the device's memory.

[0601] Input: The keyword "artificial intelligence" entered by the user.

[0602] Output: The keyword "artificial intelligence" stored by the device.

[0603] Step 2:

[0604] The device sends the entered keyword to the server. The keyword is securely sent to the server using the HTTPS protocol. SSL / TLS is used.

[0605] Input: Keyword saved on device: "artificial intelligence".

[0606] Output: The keyword "artificial intelligence" sent to the server.

[0607] Step 3:

[0608] The server searches a literature database based on keywords. The server uses the search API of the specified literature database (e.g., PubMed API, IEEE Xplore API) to search for relevant literature.

[0609] Input: The keyword "artificial intelligence" received by the server.

[0610] Output: Search results (list of relevant literature) obtained from the literature database.

[0611] Step 4:

[0612] The server retrieves the relevant literature, receives the retrieved data in JSON format, and prepares it for analysis.

[0613] Input: Search results from literature databases.

[0614] Output: Bibliographic data in JSON format.

[0615] Step 5:

[0616] The server analyzes the documents retrieved, using network graph algorithms (e.g., Dijkstra's algorithm) and machine learning algorithms (e.g., TensorFlow, Scikit-learn) to analyze citation relationships, author networks, time series, technology series, etc.

[0617] Input: Bibliographic data in JSON format.

[0618] Output: Analysis results (citation networks, author networks, time series data, etc.).

[0619] Step 6:

[0620] The server constructs genealogy and lineage information based on the analysis results, and the constructed data is stored in a database or memory.

[0621] Input: Analysis results.

[0622] Output: Genealogy and lineage information.

[0623] Step 7:

[0624] The server sends the constructed genealogy and lineage information to the terminal. The data is sent securely to the terminal using the HTTPS protocol.

[0625] Input: Genealogical and lineage information.

[0626] Output: Genealogy and lineage information sent to the device.

[0627] Step 8:

[0628] The device displays the information to the user, using data visualization libraries such as D3.js and Chart.js to visually display the information.

[0629] Input: Genealogy and lineage information sent to the terminal.

[0630] Output: Visualized information (graphs, network diagrams, etc.).

[0631] Step 9:

[0632] The emotion engine analyzes the user's emotional state and dynamically adjusts the interface. It uses the device's built-in camera and sensor devices, as well as OpenCV and TensorFlow to analyze facial expressions and input behavior. It adjusts the information presentation format based on the user's emotional state.

[0633] Input: User's facial expressions, biometric signals, and input behavior.

[0634] Output: Dynamically adjusted interface.

[0635] (Application example 2)

[0636] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0637] Conventional literature search and content search systems simply display a list of related materials, making it difficult to comprehensively grasp the relationships between materials, such as citation relationships, author networks, and development timelines. Furthermore, they lack the ability to dynamically adjust the interface according to the user's emotional state, and lack the ingenuity to make it easier for users to intuitively understand information. This reduces the efficiency of information search and impairs the user experience.

[0638] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0639] In this invention, the server includes means for inputting a specific keyword, means for searching for multiple related content based on the input keyword, means for analyzing the citation relationships of the searched content, the network between creators, the publication date sequence, the technology sequence, the publication medium sequence, and the research method sequence, means for constructing a genealogy and lineage information of the content based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for determining the user's emotional state and dynamically adjusting the display format of the analysis results. This not only allows the user to visually grasp the lineage and relevance of content related to keywords of interest, but also enables the user to obtain information using an interface optimal for their emotional state.

[0640] "Specific keywords" are words or phrases that indicate a particular topic or theme that interests a user.

[0641] "Input means" refers to a device or interface that allows a user to input a specific keyword, and specifically includes a keyboard, a touch screen, a voice input device, etc.

[0642] "Multiple related contents" refers to digital information such as videos, articles, audio, literature, etc. that are associated with the entered keywords under certain conditions.

[0643] "Search means" refers to a program or algorithm for retrieving multiple pieces of related content based on a keyword.

[0644] A "citation relationship" refers to a relationship in which a specific piece of content refers to another piece of content as a reference.

[0645] A "network among creators" is a network that shows the relationships and cooperation between individuals and organizations that create content.

[0646] The "release date sequence" indicates the order of the dates and times when the content was released to the public.

[0647] A "technology lineage" indicates how a particular technology or methodology has progressed and evolved.

[0648] "By publication medium" is a classification based on the type of medium in which the content was published (e.g., academic journal, online platform, television broadcast, etc.).

[0649] "Means of analysis" refers to methods and tools for analyzing the citation relationships, networks, time series, etc. of the acquired content.

[0650] "Genealogy and lineage information" refers to detailed information such as citation relationships between content, timelines of development, and creator networks.

[0651] "Display means" refers to a method or device for visually presenting the analysis results to the user, and specifically includes forms such as graphs, treemaps, and network diagrams.

[0652] The "emotional state of the user" indicates the emotion the user feels when viewing a particular piece of content, and refers to states such as joy, excitement, or confusion.

[0653] "Means for determining and dynamically adjusting the display format of the analysis results" refers to a method for analyzing the user's emotional state and changing or optimizing the way information is presented based on the results.

[0654] The system of the present invention starts with the user entering a specific keyword, searches for multiple related pieces of content, and analyzes their citation relationships, networks between creators, publication dates, technology, publication media, and research methodologies to construct a genealogy and series of content and display it to the user. It also has the ability to determine the user's emotional state and dynamically adjust the display format based on the analysis results.

[0655] The system program performs the following processing.

[0656] First, a user inputs a specific keyword using a smartphone or other input device. The device then sends the keyword to a server, which searches for multiple related content. The server then retrieves related digital information, such as videos, articles, and audio, from a database. The hardware used can be a smartphone, tablet, or server computer.

[0657] The server then analyzes the retrieved content's citation relationships, creator networks, publication date and time series, technology series, publication media, and research method series. The analysis includes network analysis using the "networkx" library and data visualization using "matplotlib." This allows for the construction of detailed genealogy and lineage information for the content.

[0658] The server then sends the constructed genealogy and lineage information to the user's device, which then visually displays it. Various visualization techniques, such as treemaps and network diagrams, are used for the display. Specifically, the "matplotlib" library is used to visualize the information.

[0659] Furthermore, the emotion engine works by analyzing the user's facial expressions and behavior while browsing information. The "emotion_detection_module" is used to determine the user's emotional state. Based on this result, the server dynamically adjusts the display format. For example, if the user is excited, the server can suggest additional information or related new content. If the user is confused, the server can simplify the information presentation format and adjust it to focus on the basic content.

[0660] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters a keyword, the server searches for related videos and articles and analyzes their citation relationships and technological advances. It generates a treemap or network diagram of information related to the keyword "artificial intelligence" and displays it on the user's device. If the user is excited by the displayed information, the system will display more detailed information or related new papers. If the user is confused, it will provide simplified information to make it easier to understand.

[0661] Examples of prompts include:

[0662] "Analyze the relationships between videos and articles related to artificial intelligence, and generate a network diagram showing citation relationships, the order in which they were produced, related people, and technological developments."

[0663] In this way, the system of the present invention comprehensively analyzes keyword-based content searches and dynamically presents information according to the user's emotional state, supporting more intuitive and effective information exploration.

[0664] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0665] Step 1:

[0666] A user inputs a specific keyword using a smartphone or other input device.

[0667] Input: The keyword entered by the user.

[0668] Output: Send the keywords to the server.

[0669] Specific operation: The user enters a keyword in the input field and presses the search button. This operation sends the keyword from the device to the server.

[0670] Step 2:

[0671] The server searches a database for multiple related contents based on keywords.

[0672] Input: The keyword sent from the terminal.

[0673] Output: A list of associated content data.

[0674] Specific operation: The server accesses the database and searches for and retrieves digital information such as videos, articles, and audio related to the keywords.

[0675] Step 3:

[0676] The server analyzes the citation relationships of the content it acquires, the network between creators, the publication date and time series, the technology series, the publication media, and the research method series.

[0677] Input: A list of related content data.

[0678] Output: Genealogy and lineage information as analysis results.

[0679] Specific operation: The server performs network analysis using the "networkx" library and prepares the data for visualization using "matplotlib." At the same time, it organizes and analyzes information on citation relationships and technological advances for each piece of content.

[0680] Step 4:

[0681] The server transmits the constructed genealogy and lineage information to the terminal, which then visually displays it.

[0682] Input: Genealogy and lineage information.

[0683] Output: Visualized information displayed on the user's device.

[0684] Specific operation: The server generates the analysis results in the form of a treemap, network diagram, etc. and sends them to the terminal. The terminal receives them and displays them visually using "matplotlib" or similar.

[0685] Step 5:

[0686] The emotion engine analyzes the user's facial expressions and behavior to determine their emotional state.

[0687] Input: User's facial expression data, behavioral data.

[0688] Output: The user's emotional state (excited, confused, etc.).

[0689] Specific operation: Using the "emotion_detection_module" built into the device, data obtained from the camera and microphone is analyzed to determine the user's emotional state in real time.

[0690] Step 6:

[0691] The server dynamically adjusts the display format of the analysis results based on the emotional state.

[0692] Input: The user's emotional state.

[0693] Output: Adjusted display information.

[0694] Specific behavior: Based on feedback from the emotion engine, the server changes the way information is presented, for example, showing additional information or new content if the user is excited, or simplifying the information if the user is confused.

[0695] In this way, the system comprehensively performs content searches based on specific keywords, analyzes their relevance, and dynamically displays information according to the user's emotional state.

[0696] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0697] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0698] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0699] [Third embodiment]

[0700] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0701] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0702] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0703] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0704] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0705] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0706] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0707] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0708] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0709] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0710] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0711] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0712] The system of the present invention begins when a user inputs a specific keyword into a terminal. When the user inputs the keyword into a terminal, the terminal transmits the keyword to a server. The server searches a literature database based on the received keyword and retrieves multiple related documents.

[0713] The server analyzes the citation relationships, author networks, publication date and time series, technology series, publication medium, research subject series, and research method series of the retrieved documents. As a result of the analysis, detailed genealogy and lineage information of the documents is constructed. This allows the server to obtain information that visualizes how the document is related to other documents, which authors are collaborating and how, and the flow of research development.

[0714] The server sends the constructed genealogy and lineage information to the terminal, which then displays it to the user in a format that allows the user to intuitively understand the information, such as a graph, treemap, or network diagram.

[0715] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters the keyword and clicks the search button, the device sends the keyword to the server. The server retrieves literature related to artificial intelligence from a database and analyzes which literature those literature cites and which authors are involved in which research. It also analyzes technological advances and the lineage of related research methods.

[0716] As a result of the analysis, the server generates information such as citation networks, author networks, and development timelines for AI-related literature, and sends it to the terminal, which then visually displays this information to the user, allowing them to intuitively understand the literature and research methods that interest them.

[0717] In this way, the system of the present invention differs from conventional single document searches in that it comprehensively provides information on the relationships between documents and the flow of development, thereby greatly supporting the user's research activities.

[0718] The processing flow will be explained below.

[0719] Step 1:

[0720] The user enters a keyword into the search field on the device and clicks the search button.

[0721] Step 2:

[0722] The terminal captures the keyword entered by the user and stores it in the variable keyword.

[0723] Step 3:

[0724] The device generates an HTTP request containing the saved keyword and sends the request to the server.

[0725] Step 4:

[0726] The server analyzes the HTTP request received from the terminal and extracts keywords.

[0727] Step 5:

[0728] The server searches a literature database based on the extracted keywords and retrieves multiple related documents.

[0729] Step 6:

[0730] The server analyzes the citation relationships, author networks, publication date series, technology series, publication media, research subject series, and research method series of the retrieved documents.

[0731] Step 7:

[0732] Based on the analysis results, the server constructs genealogy and lineage information for the literature, such as graphs of citation networks and author collaboration networks.

[0733] Step 8:

[0734] The server converts the constructed genealogy and lineage information into JSON format and sends it to the terminal.

[0735] Step 9:

[0736] The device parses the JSON data received from the server and formats it in a format that is easy for the user to understand, such as a graph, treemap, or network diagram.

[0737] Step 10:

[0738] The device visually displays the formatted information to the user, who can then view the displayed information and take action to obtain more detailed information.

[0739] For example, a user enters the keyword "artificial intelligence." The server retrieves related literature from the database and analyzes the citation relationships and author networks of each literature. As a result, the citation networks between papers and collaborations between authors are visually displayed, allowing the user to grasp the overall picture of research on artificial intelligence.

[0740] Example 1

[0741] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0742] Conventional literature search systems only provide search results for a single document, making it difficult to comprehensively grasp the relationships between documents and the flow of their development. This leaves users unable to intuitively understand the citation relationships between documents, author networks, or the flow of research development, resulting in a decrease in the efficiency of research activities. A system that can solve these problems and support research activities is needed.

[0743] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0744] In this invention, the server includes: means for a user to input specific keywords; means for a terminal to transmit the input keywords to the server; means for the server to search a database for multiple related documents based on the received keywords; means for the server to analyze the citation relationships, author networks, publication date sequence, technology sequence, publication medium, research subject sequence, and research method sequence of the retrieved documents; means for the server to construct genealogy and lineage information of the documents based on the analysis results; means for the server to transmit the constructed genealogy and lineage information to the terminal; and means for the terminal to visually display the transmitted genealogy and lineage information. This allows the user to comprehensively and intuitively understand the relationships between documents and the flow of development.

[0745] A "specific keyword" is a specific word or phrase that a user enters as the subject of a search.

[0746] A "user" is a person or organization that uses the system to conduct a literature search.

[0747] A "terminal" is a device that a user uses to operate the system, specifically a computer device such as a PC or smartphone.

[0748] A "server" is a computer system that searches a literature database based on keywords sent by a user and performs analysis.

[0749] A "literature database" is a data store that stores a large number of research papers and academic books, and is a searchable information source.

[0750] "Searching" is the operation of retrieving relevant documents from a literature database based on specific keywords.

[0751] A "citation relationship" refers to a relationship in which a document refers to or cites another document.

[0752] The "author network" indicates the collaborative relationships between authors, i.e., the publications in which different authors are involved as co-authors.

[0753] The "publication date sequence" indicates the order of the publication dates of documents.

[0754] A "technology lineage" shows how a particular technology has developed and evolved.

[0755] "By publication medium" is used to classify the publication medium (academic journal, conference proceedings, etc.) in which the document was published.

[0756] "Research subject series" refers to the research subject series that different papers address.

[0757] "Research methodology series" refers to the series of research methods used by the document.

[0758] "Analyzing" is the process of examining the contents of a document and related information in detail to derive specific relationships and sequences.

[0759] "Genealogy information" is information that shows an overall picture of related information such as citation relationships between documents and author networks.

[0760] "Series information" is chronological and classification information that shows the flow of development and relationships.

[0761] "Send" refers to the operation of sending information from a terminal to a server or from a server to a terminal.

[0762] "Visual display" refers to the operation of making the information obtained as an analysis result visible to the user in the form of a graph, network diagram, or the like.

[0763] The present invention is a system that allows users to input specific keywords, search for, analyze, and visually display documents related to those keywords, thereby enabling them to comprehensively understand the relationships between documents and the flow of development.

[0764] This system includes the following components:

[0765] 1. User keyword input

[0766] Users use their own devices (PCs, smartphones, etc.) to input specific keywords that are related to the research field or topic that interests them.

[0767] 2. Sending keywords via device

[0768] The terminal sends the input keyword to the server. Specifically, an HTTP request is sent from the terminal to the server. This request includes the keyword entered by the user.

[0769] 3. Searching the literature database using the server

[0770] The server uses the received keywords to search a literature database, which is a data store containing numerous research papers and academic books and can be an SQL database or a cloud-based data store.

[0771] 4. Server-based document analysis

[0772] The server analyzes the literature information obtained as search results. This analysis applies text mining and network analysis techniques. Specifically, analysis is performed on citation relationships, author networks, publication date and time series, technology series, publication media, research subject series, and research method series.

[0773] 5. Server-based genealogy and lineage information construction

[0774] Based on the analysis results, the server constructs detailed genealogy and lineage information for the document, including citation graphs and author network diagrams, which reveal how the document is related to other documents, which authors collaborate and how, and the flow of research development.

[0775] 6. Sending information from the server to the device

[0776] The server generates the constructed genealogy and lineage information as an API response and sends it to the terminal.

[0777] 7. Displaying Information on a Terminal

[0778] The terminal visually displays the received genealogy and lineage information to the user in a variety of formats, including graphs, treemaps, and network diagrams, allowing the user to intuitively grasp the information.

[0779] Specific examples

[0780] For example, if a user enters the keyword "artificial intelligence" to perform a search, the user enters the keyword and clicks the "Search" button, and the device sends the keyword to the server. The server retrieves documents related to "artificial intelligence" from the literature database and analyzes each document's citation relationships, author network, publication timeline, and technology series. As a result of the analysis, the server generates information such as the citation network, author network, and development timeline of documents related to artificial intelligence, and sends this information to the device. The device then visually displays this information to the user, allowing the user to intuitively understand the documents and research methods of interest.

[0781] Prompt Sentence Examples

[0782] Please explain in detail, step by step, the process of the system to analyze the genealogy and lineage information of related documents based on specific keywords and display it visually to the user.

[0783] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0784] Step 1:

[0785] The user inputs a specific keyword into the terminal.

[0786] Input: Keywords of interest to the user (e.g. "artificial intelligence")

[0787] Output: Keywords displayed on the user's terminal

[0788] Step 2:

[0789] The device sends the entered keyword to the server using an HTTP request.

[0790] Input: Keywords entered by the user into the device

[0791] Data processing / calculation: The terminal generates an HTTP request and includes the keyword as a parameter.

[0792] Output: HTTP request sent to the server

[0793] Step 3:

[0794] The server searches a literature database based on the received keywords.

[0795] Input: Keywords in the HTTP request received by the server

[0796] Data processing / calculation: Searching literature databases using SQL queries or cloud data store APIs to retrieve relevant literature

[0797] Output: List of relevant literature

[0798] Step 4:

[0799] The server analyzes the citation relationships of the documents searched, the network between authors, the publication date sequence, the technology sequence, the publication medium, the research subject sequence, and the research method sequence.

[0800] Input: List of documents obtained as search results

[0801] Data processing / calculation: Using text mining and network analysis tools, we conduct detailed analysis of the relevant information in each document.

[0802] Output: Analysis results (citation relationship graph, author network diagram, etc.)

[0803] Step 5:

[0804] The server constructs genealogy and lineage information for the literature based on the analysis results.

[0805] Input: Literature analysis results

[0806] Data processing / calculation: Based on the analysis results, construct genealogy and series information of literature (such as citation graphs and author network diagrams)

[0807] Output: Genealogy and lineage information

[0808] Step 6:

[0809] The server sends the constructed genealogy and lineage information to the device using API responses.

[0810] Input: Genealogy and lineage information constructed on the server

[0811] Data processing / calculation: Generate the constructed information as an API response in JSON format, etc.

[0812] Output: API response sent to the device

[0813] Step 7:

[0814] The lineage and affiliation information received by the device is visually displayed to the user using a JavaScript library that draws graphs and network diagrams.

[0815] Input: API response data sent from the server

[0816] Data processing / calculation: Converting received data into a visually displayable format and drawing it as a graph or network diagram

[0817] Output: Visual information displayed on the user's device (graphs, treemaps, network diagrams, etc.)

[0818] (Application example 1)

[0819] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0820] Conventional literature search systems simply list search results, making it difficult to intuitively understand the citation relationships between documents, the networks between authors, or the flow of technological developments. Furthermore, it is difficult for users to instantly grasp useful information from a large amount of literature, leading to a decline in research efficiency.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0822] In this invention, the server includes means for inputting specific keywords, means for searching for multiple related documents based on the input keywords, means for analyzing the citation relationships, author networks, publication date series, technology series, publication medium series, research subject series, and research method series of the retrieved documents, means for constructing a genealogy and lineage information of the documents based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for acquiring data related to the keywords entered by the user and visualizing the data as a citation network. This allows users to intuitively understand the documents and research methods of their interest, improving research efficiency.

[0823] The "means for inputting specific keywords" is an interface device through which a user inputs specific keywords based on a topic of interest.

[0824] The "means for searching for multiple related documents based on the entered keywords" is a function for searching for documents related to the keywords entered by the user from databases and other information sources.

[0825] "Means for analyzing the citation relationships of retrieved documents, the network between authors, the publication date series, the technology series, the publication medium, the research subject series, and the research method series" refers to technical means for analyzing retrieved documents to understand how they are related to other documents, which authors are collaborating and how, and the flow of development of technology and research methods.

[0826] "Means for constructing genealogy and lineage information of documents based on analysis results" is a function for visualizing the relationships between documents and the flow of development based on analyzed information, and constructing genealogy and lineage information.

[0827] The "means for displaying constructed genealogy and lineage information" is an interface device for visually displaying genealogy and lineage information in a format that can be intuitively understood by the user.

[0828] "Means for obtaining data related to keywords entered by users and visualizing that data as a citation network" refers to a technical means for collecting data related to keywords entered by users and visualizing it in the form of graphs, network diagrams, etc., so that users can intuitively grasp the relationships between literature and research methods that interest them.

[0829] The system for implementing this invention mainly consists of a terminal and a server. When a user enters a specific keyword into the terminal, the terminal sends the keyword to the server. The server searches a literature database based on the received keyword and retrieves multiple related documents. It analyzes the citation relationships of the documents, the network between authors, the publication date sequence, the technology sequence, the publication medium, the research subject sequence, and the research method sequence. Based on the analysis results, it constructs genealogy and lineage information of the documents and sends this information to the terminal.

[0830] When a user enters a keyword into their device, data related to that keyword is retrieved and visualized as a citation network. Based on the analysis results, the device visually displays the genealogy and lineage information of the literature. For example, if a user enters the keyword "artificial intelligence," the server retrieves literature data related to artificial intelligence and analyzes the citation relationships and author networks of those literature. The analysis results are displayed in the form of graphs, treemaps, network diagrams, etc., allowing users to visually understand the information.

[0831] This system uses the following hardware and software.

[0832] Hardware: User terminals (computing devices such as smartphones), servers

[0833] Software: Literature database search engine, analysis engine (using networkx as a network analysis library and pyvis as a visualization library), interface software

[0834] This allows users to intuitively understand the relationships and developments between various documents related to a topic of interest.

[0835] Along these lines, the following example prompt sentences can be used:

[0836] example:

[0837] "Write Python code to retrieve data on literature related to keywords entered by the user and visualize that data as a citation network. Use requests, networkx, and pyvis."

[0838] As a result, this invention greatly supports users' research activities, providing a system that allows them to intuitively understand the relationships between documents and the flow of development.

[0839] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0840] Step 1:

[0841] The user inputs specific keywords into the terminal. In this step, the user inputs keywords based on the topic of interest, and the keywords are obtained through the terminal interface.

[0842] Input: The keyword entered by the user

[0843] Output: Keywords obtained from the terminal interface

[0844] Step 2:

[0845] The device sends the entered keyword to the server, which then sends the acquired keyword to the server as an HTTP request.

[0846] Input: Keywords obtained in step 1

[0847] Output: Keyword data sent to the server

[0848] Step 3:

[0849] The server searches the literature database based on the received keywords. The server uses the keywords to search the database for multiple related documents.

[0850] Input: Keyword data received from the client

[0851] Output: Related literature data

[0852] Step 4:

[0853] The server analyzes the citation relationships of the retrieved documents, the network between authors, publication date and time series, technology series, publication medium series, research subject series, and research method series.The server uses an analysis engine to process the data based on the retrieved document data in order to understand the complex relationships.

[0854] Input: Literature data obtained in Step 3

[0855] Output: Analysis results of citation relationships between documents, author networks, technology series, etc.

[0856] Step 5:

[0857] The server constructs genealogy and series information for the documents based on the analysis results, and uses the data generated by the analysis engine to create structured data for visualizing the relationships between documents and the flow of development.

[0858] Input: Analysis results generated in step 4

[0859] Output: Genealogy and lineage information for the document

[0860] Step 6:

[0861] The server sends the constructed genealogy and lineage information to the terminal, and returns the generated structured data to the terminal.

[0862] Input: Lineage and genealogy information constructed in Step 5

[0863] Output: Genealogy and lineage information sent to the device

[0864] Step 7:

[0865] The terminal displays the constructed genealogy and lineage information to the user. The terminal visually displays the received data, allowing the user to intuitively understand it.

[0866] Input: Genealogy and lineage information sent to the terminal in step 6

[0867] Output: what is displayed so that the user can understand it visually

[0868] In this way, the processing steps for realizing the application of the invention are concretely explained, allowing users to intuitively understand the relationships and developmental flow of literature related to the topic of interest.

[0869] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0870] The system of the present invention begins when a user inputs a specific keyword into a terminal. When the user inputs the keyword into a terminal, the terminal transmits the keyword to a server. The server searches a literature database based on the received keyword and retrieves multiple related documents.

[0871] The server analyzes the citation relationships, author networks, publication date and time series, technology series, publication medium, research subject series, and research method series of the retrieved documents. As a result of the analysis, detailed genealogy and lineage information of the documents is constructed. This allows the server to obtain information that visualizes how the document is related to other documents, which authors are collaborating and how, and the flow of research development.

[0872] The server sends the constructed genealogy and lineage information to the terminal, which then displays it to the user in a format that allows the user to intuitively understand the information, such as a graph, treemap, or network diagram.

[0873] By incorporating an emotion engine into this system, it is possible to recognize the user's emotional state and dynamically adjust the interface used. The emotion engine analyzes the user's facial expressions, biometric signals, input behavior, etc. when the user enters keywords or browses search results to determine their emotions.

[0874] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters the keyword and clicks the search button, the device sends the keyword to the server. The server retrieves literature related to artificial intelligence from a database and analyzes which literature those literature cites and which authors are involved in which research. It also analyzes technological advances and the lineage of related research methods.

[0875] Based on the analysis results, the server generates information such as citation networks, author networks, and development timelines for AI-related literature, and sends it to the device. The device then visually displays this information to the user. The emotion engine then works to monitor the user's reactions and emotional state. For example, if the user is excited about the information, the system can suggest additional detailed information or related new papers. If the user appears confused, the system can simplify the presentation of the information and adjust it to focus on basic content.

[0876] In this way, unlike conventional single-document searches, the system of the present invention not only provides comprehensive information on the relationships and developmental flow between documents, but also supports more intuitive and effective information search through dynamic interface adjustments based on the user's emotions.

[0877] The processing flow will be explained below.

[0878] Step 1:

[0879] The user enters a keyword into the search field on the device and clicks the search button.

[0880] Step 2:

[0881] The terminal captures the keyword entered by the user and stores it in the variable keyword.

[0882] Step 3:

[0883] The device generates an HTTP request containing the saved keyword and sends the request to the server.

[0884] Step 4:

[0885] The server analyzes the HTTP request received from the terminal and extracts keywords.

[0886] Step 5:

[0887] The server searches a literature database based on the extracted keywords and retrieves multiple related documents.

[0888] Step 6:

[0889] The server analyzes the citation relationships, author networks, publication date series, technology series, publication media, research subject series, and research method series of the retrieved documents.

[0890] Step 7:

[0891] Based on the analysis results, the server constructs genealogy and lineage information for the literature, such as graphs of citation networks and author collaboration networks.

[0892] Step 8:

[0893] The server converts the constructed genealogy and lineage information into JSON format and sends it to the terminal.

[0894] Step 9:

[0895] The device parses the JSON data received from the server and formats it in a format that is easy for the user to understand, such as a graph, treemap, or network diagram.

[0896] Step 10:

[0897] The device visually displays the formatted information to the user, who can then view the displayed information and take action to obtain more detailed information.

[0898] Step 11:

[0899] The device activates an emotion engine while displaying the image and monitors the user's facial expressions and input behavior.

[0900] Step 12:

[0901] The emotion engine estimates the user's emotional state and feeds that information back to the device.

[0902] Step 13:

[0903] The device dynamically changes the content and format of the information displayed based on the emotional feedback, such as presenting more detailed information if the user is excited, or simplifying the information if the user is confused.

[0904] As a concrete example, suppose a user types in the keyword "artificial intelligence" and related literature is displayed. If the emotion engine recognizes the user's excited expression, the system will further display detailed technical explanations and a list of related papers. On the other hand, if the user shows confusion, the system will prioritize displaying concise summaries and introductions to basic concepts.

[0905] Example 2

[0906] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0907] Conventional literature search systems not only allow users to search for a single document, but also make it difficult for them to understand the complex relationships between documents and the development process of research. Furthermore, they are unable to flexibly present information according to the user's emotions and level of understanding, which makes information search inefficient. There is a need to solve this problem and provide a more intuitive and effective information search experience.

[0908] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0909] In this invention, the server includes means for inputting specific keywords, means for searching for multiple related documents based on the input keywords, means for analyzing the citation relationships, author networks, publication date sequence, technology sequence, publication medium, research subject sequence, and research method sequence of the retrieved documents, means for constructing a genealogy and lineage information of the documents based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for analyzing the user's emotional state and dynamically adjusting the interface. This makes it possible to comprehensively grasp the relationships and development process of documents based on the keywords entered by the user and present information according to the user's emotions and level of understanding.

[0910] A "specific keyword" is a word or phrase that a user enters as a search target.

[0911] A "literature database" is a database that stores multiple academic documents and manages them in a searchable format.

[0912] "Citation relationships of documents" is information indicating the relationships in which a document refers to or cites another document.

[0913] "Networks between authors" refers to information that indicates the relationships between authors of multiple documents, including their collaborative research and involvement.

[0914] The "publication date sequence" is information indicating the order or timeline of the dates when documents were published.

[0915] A "technology series" is information that shows the process of research development and evolution in a specific technology field.

[0916] "By publication medium" is information that classifies the publication medium (e.g., academic journal, conference proceedings, etc.) in which each document was published.

[0917] "Research subject series" is information that indicates the series of research subjects or themes that different documents deal with.

[0918] "Research methodology series" is information that indicates the series of research methods and approaches adopted by different documents.

[0919] "Genealogy information" is detailed structural information of relationships based on citation relationships between documents and author networks.

[0920] "Series information" is information that follows a chronological order or classification, such as the publication date and time of a document or the evolution of technology.

[0921] "Emotional state" refers to the user's current emotional or psychological state, and is used to determine the user's reaction and level of understanding.

[0922] "Adjusting the interface" means dynamically changing the format and content of information presentation according to the user's emotional state and reactions.

[0923] The system of this invention begins when a user inputs a specific keyword into a terminal. Specific hardware used by the user is a personal computer, tablet, smartphone, or other device. Software used is a web browser connected to the Internet.

[0924] When a user enters a keyword into the device's search bar, the device sends the keyword to the server. The communication uses the HTTPS protocol, and SSL / TLS is used to ensure data security. For example, if a user enters "artificial intelligence" and clicks the search button, the device sends the keyword to the server.

[0925] The server searches the specified literature database (e.g., academic literature database or patent database) based on the received keywords. Specific software used to perform the search is a literature database API (e.g., PubMed API, IEEE Xplore API).

[0926] The server analyzes the search results obtained from the literature database, including citation network analysis, author network analysis, time series analysis, and technology series analysis, using network graph algorithms (e.g., Dijkstra's algorithm) and machine learning algorithms (e.g., TensorFlow, Scikit-learn).

[0927] For example, the server retrieves documents related to "artificial intelligence" and analyzes their citation relationships and author networks. Based on the results of this analysis, it builds detailed genealogy and developmental sequence information for the documents. This information is stored in JSON format or other suitable data formats.

[0928] The server then sends the constructed genealogy and lineage information to the terminal. Again, the data is securely transmitted using the HTTPS protocol. The terminal receives this information and visually displays it using a data visualization library (e.g., D3.js, Chart.js). This allows users to intuitively understand information such as citation networks, author networks, and the development process of research.

[0929] Furthermore, the emotion engine uses the device's built-in camera and sensor devices to analyze the user's emotional state. Specifically, OpenCV is used to analyze facial expressions, and TensorFlow is used to implement the machine learning model. The interface's information presentation format is dynamically adjusted based on the user's emotional state. For example, if the user is excited about the information, the system will suggest adding more detailed information or related new papers. If the user is confused, the information presentation format will be simplified and focused on basic content.

[0930] Examples of prompts include:

[0931] User: "I'd like to know about the latest research trends in artificial intelligence. What literature is available?"

[0932] AI Model: "Exploring recent research literature on the topic of artificial intelligence. Please wait."

[0933] In this way, the system of the present invention not only comprehensively visualizes the relevance and development process of literature based on the user's keyword input, but also supports more effective information exploration through dynamic interface adjustments according to the user's emotional state.

[0934] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0935] Step 1:

[0936] The user enters a keyword into the device. Specifically, the user enters a specific keyword, such as "artificial intelligence," into the search bar and clicks the search button. The entered keyword is stored in the device's memory.

[0937] Input: The keyword "artificial intelligence" entered by the user.

[0938] Output: The keyword "artificial intelligence" stored by the device.

[0939] Step 2:

[0940] The device sends the entered keyword to the server. The keyword is securely sent to the server using the HTTPS protocol. SSL / TLS is used.

[0941] Input: Keyword saved on device: "artificial intelligence".

[0942] Output: The keyword "artificial intelligence" sent to the server.

[0943] Step 3:

[0944] The server searches a literature database based on keywords. The server uses the search API of the specified literature database (e.g., PubMed API, IEEE Xplore API) to search for relevant literature.

[0945] Input: The keyword "artificial intelligence" received by the server.

[0946] Output: Search results (list of relevant literature) obtained from the literature database.

[0947] Step 4:

[0948] The server retrieves the relevant literature, receives the retrieved data in JSON format, and prepares it for analysis.

[0949] Input: Search results from literature databases.

[0950] Output: Bibliographic data in JSON format.

[0951] Step 5:

[0952] The server analyzes the documents retrieved, using network graph algorithms (e.g., Dijkstra's algorithm) and machine learning algorithms (e.g., TensorFlow, Scikit-learn) to analyze citation relationships, author networks, time series, technology series, etc.

[0953] Input: Bibliographic data in JSON format.

[0954] Output: Analysis results (citation networks, author networks, time series data, etc.).

[0955] Step 6:

[0956] The server constructs genealogy and lineage information based on the analysis results, and the constructed data is stored in a database or memory.

[0957] Input: Analysis results.

[0958] Output: Genealogy and lineage information.

[0959] Step 7:

[0960] The server sends the constructed genealogy and lineage information to the terminal. The data is sent securely to the terminal using the HTTPS protocol.

[0961] Input: Genealogical and lineage information.

[0962] Output: Genealogy and lineage information sent to the device.

[0963] Step 8:

[0964] The device displays the information to the user, using data visualization libraries such as D3.js and Chart.js to visually display the information.

[0965] Input: Genealogy and lineage information sent to the terminal.

[0966] Output: Visualized information (graphs, network diagrams, etc.).

[0967] Step 9:

[0968] The emotion engine analyzes the user's emotional state and dynamically adjusts the interface. It uses the device's built-in camera and sensor devices, as well as OpenCV and TensorFlow to analyze facial expressions and input behavior. It adjusts the information presentation format based on the user's emotional state.

[0969] Input: User's facial expressions, biometric signals, and input behavior.

[0970] Output: Dynamically adjusted interface.

[0971] (Application example 2)

[0972] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0973] Conventional literature search and content search systems simply display a list of related materials, making it difficult to comprehensively grasp the relationships between materials, such as citation relationships, author networks, and development timelines. Furthermore, they lack the ability to dynamically adjust the interface according to the user's emotional state, and lack the ingenuity to make it easier for users to intuitively understand information. This reduces the efficiency of information search and impairs the user experience.

[0974] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0975] In this invention, the server includes means for inputting a specific keyword, means for searching for multiple related content based on the input keyword, means for analyzing the citation relationships of the searched content, the network between creators, the publication date sequence, the technology sequence, the publication medium sequence, and the research method sequence, means for constructing a genealogy and lineage information of the content based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for determining the user's emotional state and dynamically adjusting the display format of the analysis results. This not only allows the user to visually grasp the lineage and relevance of content related to keywords of interest, but also enables the user to obtain information using an interface optimal for their emotional state.

[0976] "Specific keywords" are words or phrases that indicate a particular topic or theme that interests a user.

[0977] "Input means" refers to a device or interface that allows a user to input a specific keyword, and specifically includes a keyboard, a touch screen, a voice input device, etc.

[0978] "Multiple related contents" refers to digital information such as videos, articles, audio, literature, etc. that are associated with the entered keywords under certain conditions.

[0979] "Search means" refers to a program or algorithm for retrieving multiple pieces of related content based on a keyword.

[0980] A "citation relationship" refers to a relationship in which a specific piece of content refers to another piece of content as a reference.

[0981] A "network among creators" is a network that shows the relationships and cooperation between individuals and organizations that create content.

[0982] The "release date sequence" indicates the order of the dates and times when the content was released to the public.

[0983] A "technology lineage" indicates how a particular technology or methodology has progressed and evolved.

[0984] "By publication medium" is a classification based on the type of medium in which the content was published (e.g., academic journal, online platform, television broadcast, etc.).

[0985] "Means of analysis" refers to methods and tools for analyzing the citation relationships, networks, time series, etc. of the acquired content.

[0986] "Genealogy and lineage information" refers to detailed information such as citation relationships between content, timelines of development, and creator networks.

[0987] "Display means" refers to a method or device for visually presenting the analysis results to the user, and specifically includes forms such as graphs, treemaps, and network diagrams.

[0988] The "emotional state of the user" indicates the emotion the user feels when viewing a particular piece of content, and refers to states such as joy, excitement, or confusion.

[0989] "Means for determining and dynamically adjusting the display format of the analysis results" refers to a method for analyzing the user's emotional state and changing or optimizing the way information is presented based on the results.

[0990] The system of the present invention starts with the user entering a specific keyword, searches for multiple related pieces of content, and analyzes their citation relationships, networks between creators, publication dates, technology, publication media, and research methodologies to construct a genealogy and series of content and display it to the user. It also has the ability to determine the user's emotional state and dynamically adjust the display format based on the analysis results.

[0991] The system program performs the following processing.

[0992] First, a user inputs a specific keyword using a smartphone or other input device. The device then sends the keyword to a server, which searches for multiple related content. The server then retrieves related digital information, such as videos, articles, and audio, from a database. The hardware used can be a smartphone, tablet, or server computer.

[0993] The server then analyzes the retrieved content's citation relationships, creator networks, publication date and time series, technology series, publication media, and research method series. The analysis includes network analysis using the "networkx" library and data visualization using "matplotlib." This allows for the construction of detailed genealogy and lineage information for the content.

[0994] The server then sends the constructed genealogy and lineage information to the user's device, which then visually displays it. Various visualization techniques, such as treemaps and network diagrams, are used for the display. Specifically, the "matplotlib" library is used to visualize the information.

[0995] Furthermore, the emotion engine works by analyzing the user's facial expressions and behavior while browsing information. The "emotion_detection_module" is used to determine the user's emotional state. Based on this result, the server dynamically adjusts the display format. For example, if the user is excited, the server can suggest additional information or related new content. If the user is confused, the server can simplify the information presentation format and adjust it to focus on the basic content.

[0996] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters a keyword, the server searches for related videos and articles and analyzes their citation relationships and technological advances. It generates a treemap or network diagram of information related to the keyword "artificial intelligence" and displays it on the user's device. If the user is excited by the displayed information, the system will display more detailed information or related new papers. If the user is confused, it will provide simplified information to make it easier to understand.

[0997] Examples of prompts include:

[0998] "Analyze the relationships between videos and articles related to artificial intelligence, and generate a network diagram showing citation relationships, the order in which they were produced, related people, and technological developments."

[0999] In this way, the system of the present invention comprehensively analyzes keyword-based content searches and dynamically presents information according to the user's emotional state, supporting more intuitive and effective information exploration.

[1000] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1001] Step 1:

[1002] A user inputs a specific keyword using a smartphone or other input device.

[1003] Input: The keyword entered by the user.

[1004] Output: Send the keywords to the server.

[1005] Specific operation: The user enters a keyword in the input field and presses the search button. This operation sends the keyword from the device to the server.

[1006] Step 2:

[1007] The server searches a database for multiple related contents based on keywords.

[1008] Input: The keyword sent from the terminal.

[1009] Output: A list of associated content data.

[1010] Specific operation: The server accesses the database and searches for and retrieves digital information such as videos, articles, and audio related to the keywords.

[1011] Step 3:

[1012] The server analyzes the citation relationships of the content it acquires, the network between creators, the publication date and time series, the technology series, the publication media, and the research method series.

[1013] Input: A list of related content data.

[1014] Output: Genealogy and lineage information as analysis results.

[1015] Specific operation: The server performs network analysis using the "networkx" library and prepares the data for visualization using "matplotlib." At the same time, it organizes and analyzes information on citation relationships and technological advances for each piece of content.

[1016] Step 4:

[1017] The server transmits the constructed genealogy and lineage information to the terminal, which then visually displays it.

[1018] Input: Genealogy and lineage information.

[1019] Output: Visualized information displayed on the user's device.

[1020] Specific operation: The server generates the analysis results in the form of a treemap, network diagram, etc. and sends them to the terminal. The terminal receives them and displays them visually using "matplotlib" or similar.

[1021] Step 5:

[1022] The emotion engine analyzes the user's facial expressions and behavior to determine their emotional state.

[1023] Input: User's facial expression data, behavioral data.

[1024] Output: The user's emotional state (excited, confused, etc.).

[1025] Specific operation: Using the "emotion_detection_module" built into the device, data obtained from the camera and microphone is analyzed to determine the user's emotional state in real time.

[1026] Step 6:

[1027] The server dynamically adjusts the display format of the analysis results based on the emotional state.

[1028] Input: The user's emotional state.

[1029] Output: Adjusted display information.

[1030] Specific behavior: Based on feedback from the emotion engine, the server changes the way information is presented, for example, showing additional information or new content if the user is excited, or simplifying the information if the user is confused.

[1031] In this way, the system comprehensively performs content searches based on specific keywords, analyzes their relevance, and dynamically displays information according to the user's emotional state.

[1032] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1033] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1034] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1035] [Fourth embodiment]

[1036] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1037] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1038] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1039] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1040] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1041] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1042] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1043] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1044] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1045] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1046] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1047] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1048] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1049] The system of the present invention begins when a user inputs a specific keyword into a terminal. When the user inputs the keyword into a terminal, the terminal transmits the keyword to a server. The server searches a literature database based on the received keyword and retrieves multiple related documents.

[1050] The server analyzes the citation relationships, author networks, publication date and time series, technology series, publication medium, research subject series, and research method series of the retrieved documents. As a result of the analysis, detailed genealogy and lineage information of the documents is constructed. This allows the server to obtain information that visualizes how the document is related to other documents, which authors are collaborating and how, and the flow of research development.

[1051] The server sends the constructed genealogy and lineage information to the terminal, which then displays it to the user in a format that allows the user to intuitively understand the information, such as a graph, treemap, or network diagram.

[1052] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters the keyword and clicks the search button, the device sends the keyword to the server. The server retrieves literature related to artificial intelligence from a database and analyzes which literature those literature cites and which authors are involved in which research. It also analyzes technological advances and the lineage of related research methods.

[1053] As a result of the analysis, the server generates information such as citation networks, author networks, and development timelines for AI-related literature, and sends it to the terminal, which then visually displays this information to the user, allowing them to intuitively understand the literature and research methods that interest them.

[1054] In this way, the system of the present invention differs from conventional single document searches in that it comprehensively provides information on the relationships between documents and the flow of development, thereby greatly supporting the user's research activities.

[1055] The processing flow will be explained below.

[1056] Step 1:

[1057] The user enters a keyword into the search field on the device and clicks the search button.

[1058] Step 2:

[1059] The terminal captures the keyword entered by the user and stores it in the variable keyword.

[1060] Step 3:

[1061] The device generates an HTTP request containing the saved keyword and sends the request to the server.

[1062] Step 4:

[1063] The server analyzes the HTTP request received from the terminal and extracts keywords.

[1064] Step 5:

[1065] The server searches a literature database based on the extracted keywords and retrieves multiple related documents.

[1066] Step 6:

[1067] The server analyzes the citation relationships, author networks, publication date series, technology series, publication media, research subject series, and research method series of the retrieved documents.

[1068] Step 7:

[1069] Based on the analysis results, the server constructs genealogy and lineage information for the literature, such as graphs of citation networks and author collaboration networks.

[1070] Step 8:

[1071] The server converts the constructed genealogy and lineage information into JSON format and sends it to the terminal.

[1072] Step 9:

[1073] The device parses the JSON data received from the server and formats it in a format that is easy for the user to understand, such as a graph, treemap, or network diagram.

[1074] Step 10:

[1075] The device visually displays the formatted information to the user, who can then view the displayed information and take action to obtain more detailed information.

[1076] For example, a user enters the keyword "artificial intelligence." The server retrieves related literature from the database and analyzes the citation relationships and author networks of each literature. As a result, the citation networks between papers and collaborations between authors are visually displayed, allowing the user to grasp the overall picture of research on artificial intelligence.

[1077] Example 1

[1078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1079] Conventional literature search systems only provide search results for a single document, making it difficult to comprehensively grasp the relationships between documents and the flow of their development. This leaves users unable to intuitively understand the citation relationships between documents, author networks, or the flow of research development, resulting in a decrease in the efficiency of research activities. A system that can solve these problems and support research activities is needed.

[1080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1081] In this invention, the server includes: means for a user to input specific keywords; means for a terminal to transmit the input keywords to the server; means for the server to search a database for multiple related documents based on the received keywords; means for the server to analyze the citation relationships, author networks, publication date sequence, technology sequence, publication medium, research subject sequence, and research method sequence of the retrieved documents; means for the server to construct genealogy and lineage information of the documents based on the analysis results; means for the server to transmit the constructed genealogy and lineage information to the terminal; and means for the terminal to visually display the transmitted genealogy and lineage information. This allows the user to comprehensively and intuitively understand the relationships between documents and the flow of development.

[1082] A "specific keyword" is a specific word or phrase that a user enters as the subject of a search.

[1083] A "user" is a person or organization that uses the system to conduct a literature search.

[1084] A "terminal" is a device that a user uses to operate the system, specifically a computer device such as a PC or smartphone.

[1085] A "server" is a computer system that searches a literature database based on keywords sent by a user and performs analysis.

[1086] A "literature database" is a data store that stores a large number of research papers and academic books, and is a searchable information source.

[1087] "Searching" is the operation of retrieving relevant documents from a literature database based on specific keywords.

[1088] A "citation relationship" refers to a relationship in which a document refers to or cites another document.

[1089] The "author network" indicates the collaborative relationships between authors, i.e., the publications in which different authors are involved as co-authors.

[1090] The "publication date sequence" indicates the order of the publication dates of documents.

[1091] A "technology lineage" shows how a particular technology has developed and evolved.

[1092] "By publication medium" is used to classify the publication medium (academic journal, conference proceedings, etc.) in which the document was published.

[1093] "Research subject series" refers to the research subject series that different papers address.

[1094] "Research methodology series" refers to the series of research methods used by the document.

[1095] "Analyzing" is the process of examining the contents of a document and related information in detail to derive specific relationships and sequences.

[1096] "Genealogy information" is information that shows an overall picture of related information such as citation relationships between documents and author networks.

[1097] "Series information" is chronological and classification information that shows the flow of development and relationships.

[1098] "Send" refers to the operation of sending information from a terminal to a server or from a server to a terminal.

[1099] "Visual display" refers to the operation of making the information obtained as an analysis result visible to the user in the form of a graph, network diagram, or the like.

[1100] The present invention is a system that allows users to input specific keywords, search for, analyze, and visually display documents related to those keywords, thereby enabling them to comprehensively understand the relationships between documents and the flow of development.

[1101] This system includes the following components:

[1102] 1. User keyword input

[1103] Users use their own devices (PCs, smartphones, etc.) to input specific keywords that are related to the research field or topic that interests them.

[1104] 2. Sending keywords via device

[1105] The terminal sends the input keyword to the server. Specifically, an HTTP request is sent from the terminal to the server. This request includes the keyword entered by the user.

[1106] 3. Searching the literature database using the server

[1107] The server uses the received keywords to search a literature database, which is a data store containing numerous research papers and academic books and can be an SQL database or a cloud-based data store.

[1108] 4. Server-based document analysis

[1109] The server analyzes the literature information obtained as search results. This analysis applies text mining and network analysis techniques. Specifically, analysis is performed on citation relationships, author networks, publication date and time series, technology series, publication media, research subject series, and research method series.

[1110] 5. Server-based genealogy and lineage information construction

[1111] Based on the analysis results, the server constructs detailed genealogy and lineage information for the document, including citation graphs and author network diagrams, which reveal how the document is related to other documents, which authors collaborate and how, and the flow of research development.

[1112] 6. Sending information from the server to the device

[1113] The server generates the constructed genealogy and lineage information as an API response and sends it to the terminal.

[1114] 7. Displaying Information on a Terminal

[1115] The terminal visually displays the received genealogy and lineage information to the user in a variety of formats, including graphs, treemaps, and network diagrams, allowing the user to intuitively grasp the information.

[1116] Specific examples

[1117] For example, if a user enters the keyword "artificial intelligence" to perform a search, the user enters the keyword and clicks the "Search" button, and the device sends the keyword to the server. The server retrieves documents related to "artificial intelligence" from the literature database and analyzes each document's citation relationships, author network, publication timeline, and technology series. As a result of the analysis, the server generates information such as the citation network, author network, and development timeline of documents related to artificial intelligence, and sends this information to the device. The device then visually displays this information to the user, allowing the user to intuitively understand the documents and research methods of interest.

[1118] Prompt Sentence Examples

[1119] Please explain in detail, step by step, the process of the system to analyze the genealogy and lineage information of related documents based on specific keywords and display it visually to the user.

[1120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1121] Step 1:

[1122] The user inputs a specific keyword into the terminal.

[1123] Input: Keywords of interest to the user (e.g. "artificial intelligence")

[1124] Output: Keywords displayed on the user's terminal

[1125] Step 2:

[1126] The device sends the entered keyword to the server using an HTTP request.

[1127] Input: Keywords entered by the user into the device

[1128] Data processing / calculation: The terminal generates an HTTP request and includes the keyword as a parameter.

[1129] Output: HTTP request sent to the server

[1130] Step 3:

[1131] The server searches a literature database based on the received keywords.

[1132] Input: Keywords in the HTTP request received by the server

[1133] Data processing / calculation: Searching literature databases using SQL queries or cloud data store APIs to retrieve relevant literature

[1134] Output: List of relevant literature

[1135] Step 4:

[1136] The server analyzes the citation relationships of the documents searched, the network between authors, the publication date sequence, the technology sequence, the publication medium, the research subject sequence, and the research method sequence.

[1137] Input: List of documents obtained as search results

[1138] Data processing / calculation: Using text mining and network analysis tools, we conduct detailed analysis of the relevant information in each document.

[1139] Output: Analysis results (citation relationship graph, author network diagram, etc.)

[1140] Step 5:

[1141] The server constructs genealogy and lineage information for the literature based on the analysis results.

[1142] Input: Literature analysis results

[1143] Data processing / calculation: Based on the analysis results, construct genealogy and series information of literature (such as citation graphs and author network diagrams)

[1144] Output: Genealogy and lineage information

[1145] Step 6:

[1146] The server sends the constructed genealogy and lineage information to the device using API responses.

[1147] Input: Genealogy and lineage information constructed on the server

[1148] Data processing / calculation: Generate the constructed information as an API response in JSON format, etc.

[1149] Output: API response sent to the device

[1150] Step 7:

[1151] The lineage and affiliation information received by the device is visually displayed to the user using a JavaScript library that draws graphs and network diagrams.

[1152] Input: API response data sent from the server

[1153] Data processing / calculation: Converting received data into a visually displayable format and drawing it as a graph or network diagram

[1154] Output: Visual information displayed on the user's device (graphs, treemaps, network diagrams, etc.)

[1155] (Application example 1)

[1156] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1157] Conventional literature search systems simply list search results, making it difficult to intuitively understand the citation relationships between documents, the networks between authors, or the flow of technological developments. Furthermore, it is difficult for users to instantly grasp useful information from a large amount of literature, leading to a decline in research efficiency.

[1158] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1159] In this invention, the server includes means for inputting specific keywords, means for searching for multiple related documents based on the input keywords, means for analyzing the citation relationships, author networks, publication date series, technology series, publication medium series, research subject series, and research method series of the retrieved documents, means for constructing a genealogy and lineage information of the documents based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for acquiring data related to the keywords entered by the user and visualizing the data as a citation network. This allows users to intuitively understand the documents and research methods of their interest, improving research efficiency.

[1160] The "means for inputting specific keywords" is an interface device through which a user inputs specific keywords based on a topic of interest.

[1161] The "means for searching for multiple related documents based on the entered keywords" is a function for searching for documents related to the keywords entered by the user from databases and other information sources.

[1162] "Means for analyzing the citation relationships of retrieved documents, the network between authors, the publication date series, the technology series, the publication medium, the research subject series, and the research method series" refers to technical means for analyzing retrieved documents to understand how they are related to other documents, which authors are collaborating and how, and the flow of development of technology and research methods.

[1163] "Means for constructing genealogy and lineage information of documents based on analysis results" is a function for visualizing the relationships between documents and the flow of development based on analyzed information, and constructing genealogy and lineage information.

[1164] The "means for displaying constructed genealogy and lineage information" is an interface device for visually displaying genealogy and lineage information in a format that can be intuitively understood by the user.

[1165] "Means for obtaining data related to keywords entered by users and visualizing that data as a citation network" refers to a technical means for collecting data related to keywords entered by users and visualizing it in the form of graphs, network diagrams, etc., so that users can intuitively grasp the relationships between literature and research methods that interest them.

[1166] The system for implementing this invention mainly consists of a terminal and a server. When a user enters a specific keyword into the terminal, the terminal sends the keyword to the server. The server searches a literature database based on the received keyword and retrieves multiple related documents. It analyzes the citation relationships of the documents, the network between authors, the publication date sequence, the technology sequence, the publication medium, the research subject sequence, and the research method sequence. Based on the analysis results, it constructs genealogy and lineage information of the documents and sends this information to the terminal.

[1167] When a user enters a keyword into their device, data related to that keyword is retrieved and visualized as a citation network. Based on the analysis results, the device visually displays the genealogy and lineage information of the literature. For example, if a user enters the keyword "artificial intelligence," the server retrieves literature data related to artificial intelligence and analyzes the citation relationships and author networks of those literature. The analysis results are displayed in the form of graphs, treemaps, network diagrams, etc., allowing users to visually understand the information.

[1168] This system uses the following hardware and software.

[1169] Hardware: User terminals (computing devices such as smartphones), servers

[1170] Software: Literature database search engine, analysis engine (using networkx as a network analysis library and pyvis as a visualization library), interface software

[1171] This allows users to intuitively understand the relationships and developments between various documents related to a topic of interest.

[1172] Along these lines, the following example prompt sentences can be used:

[1173] example:

[1174] "Write Python code to retrieve data on literature related to keywords entered by the user and visualize that data as a citation network. Use requests, networkx, and pyvis."

[1175] As a result, this invention greatly supports users' research activities, providing a system that allows them to intuitively understand the relationships between documents and the flow of development.

[1176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1177] Step 1:

[1178] The user inputs specific keywords into the terminal. In this step, the user inputs keywords based on the topic of interest, and the keywords are obtained through the terminal interface.

[1179] Input: The keyword entered by the user

[1180] Output: Keywords obtained from the terminal interface

[1181] Step 2:

[1182] The device sends the entered keyword to the server, which then sends the acquired keyword to the server as an HTTP request.

[1183] Input: Keywords obtained in step 1

[1184] Output: Keyword data sent to the server

[1185] Step 3:

[1186] The server searches the literature database based on the received keywords. The server uses the keywords to search the database for multiple related documents.

[1187] Input: Keyword data received from the client

[1188] Output: Related literature data

[1189] Step 4:

[1190] The server analyzes the citation relationships of the retrieved documents, the network between authors, publication date and time series, technology series, publication medium series, research subject series, and research method series.The server uses an analysis engine to process the data based on the retrieved document data in order to understand the complex relationships.

[1191] Input: Literature data obtained in Step 3

[1192] Output: Analysis results of citation relationships between documents, author networks, technology series, etc.

[1193] Step 5:

[1194] The server constructs genealogy and series information for the documents based on the analysis results, and uses the data generated by the analysis engine to create structured data for visualizing the relationships between documents and the flow of development.

[1195] Input: Analysis results generated in step 4

[1196] Output: Genealogy and lineage information for the document

[1197] Step 6:

[1198] The server sends the constructed genealogy and lineage information to the terminal, and returns the generated structured data to the terminal.

[1199] Input: Lineage and genealogy information constructed in Step 5

[1200] Output: Genealogy and lineage information sent to the device

[1201] Step 7:

[1202] The terminal displays the constructed genealogy and lineage information to the user. The terminal visually displays the received data, allowing the user to intuitively understand it.

[1203] Input: Genealogy and lineage information sent to the terminal in step 6

[1204] Output: what is displayed so that the user can understand it visually

[1205] In this way, the processing steps for realizing the application of the invention are concretely explained, allowing users to intuitively understand the relationships and developmental flow of literature related to the topic of interest.

[1206] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1207] The system of the present invention begins when a user inputs a specific keyword into a terminal. When the user inputs the keyword into a terminal, the terminal transmits the keyword to a server. The server searches a literature database based on the received keyword and retrieves multiple related documents.

[1208] The server analyzes the citation relationships, author networks, publication date and time series, technology series, publication medium, research subject series, and research method series of the retrieved documents. As a result of the analysis, detailed genealogy and lineage information of the documents is constructed. This allows the server to obtain information that visualizes how the document is related to other documents, which authors are collaborating and how, and the flow of research development.

[1209] The server sends the constructed genealogy and lineage information to the terminal, which then displays it to the user in a format that allows the user to intuitively understand the information, such as a graph, treemap, or network diagram.

[1210] By incorporating an emotion engine into this system, it is possible to recognize the user's emotional state and dynamically adjust the interface used. The emotion engine analyzes the user's facial expressions, biometric signals, input behavior, etc. when the user enters keywords or browses search results to determine their emotions.

[1211] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters the keyword and clicks the search button, the device sends the keyword to the server. The server retrieves literature related to artificial intelligence from a database and analyzes which literature those literature cites and which authors are involved in which research. It also analyzes technological advances and the lineage of related research methods.

[1212] Based on the analysis results, the server generates information such as citation networks, author networks, and development timelines for AI-related literature, and sends it to the device. The device then visually displays this information to the user. The emotion engine then works to monitor the user's reactions and emotional state. For example, if the user is excited about the information, the system can suggest additional detailed information or related new papers. If the user appears confused, the system can simplify the presentation of the information and adjust it to focus on basic content.

[1213] In this way, unlike conventional single-document searches, the system of the present invention not only provides comprehensive information on the relationships and developmental flow between documents, but also supports more intuitive and effective information search through dynamic interface adjustments based on the user's emotions.

[1214] The processing flow will be explained below.

[1215] Step 1:

[1216] The user enters a keyword into the search field on the device and clicks the search button.

[1217] Step 2:

[1218] The terminal captures the keyword entered by the user and stores it in the variable keyword.

[1219] Step 3:

[1220] The device generates an HTTP request containing the saved keyword and sends the request to the server.

[1221] Step 4:

[1222] The server analyzes the HTTP request received from the terminal and extracts keywords.

[1223] Step 5:

[1224] The server searches a literature database based on the extracted keywords and retrieves multiple related documents.

[1225] Step 6:

[1226] The server analyzes the citation relationships, author networks, publication date series, technology series, publication media, research subject series, and research method series of the retrieved documents.

[1227] Step 7:

[1228] Based on the analysis results, the server constructs genealogy and lineage information for the literature, such as graphs of citation networks and author collaboration networks.

[1229] Step 8:

[1230] The server converts the constructed genealogy and lineage information into JSON format and sends it to the terminal.

[1231] Step 9:

[1232] The device parses the JSON data received from the server and formats it in a format that is easy for the user to understand, such as a graph, treemap, or network diagram.

[1233] Step 10:

[1234] The device visually displays the formatted information to the user, who can then view the displayed information and take action to obtain more detailed information.

[1235] Step 11:

[1236] The device activates an emotion engine while displaying the image and monitors the user's facial expressions and input behavior.

[1237] Step 12:

[1238] The emotion engine estimates the user's emotional state and feeds that information back to the device.

[1239] Step 13:

[1240] The device dynamically changes the content and format of the information displayed based on the emotional feedback, such as presenting more detailed information if the user is excited, or simplifying the information if the user is confused.

[1241] As a concrete example, suppose a user types in the keyword "artificial intelligence" and related literature is displayed. If the emotion engine recognizes the user's excited expression, the system will further display detailed technical explanations and a list of related papers. On the other hand, if the user shows confusion, the system will prioritize displaying concise summaries and introductions to basic concepts.

[1242] Example 2

[1243] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1244] Conventional literature search systems not only allow users to search for a single document, but also make it difficult for them to understand the complex relationships between documents and the development process of research. Furthermore, they are unable to flexibly present information according to the user's emotions and level of understanding, which makes information search inefficient. There is a need to solve this problem and provide a more intuitive and effective information search experience.

[1245] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1246] In this invention, the server includes means for inputting specific keywords, means for searching for multiple related documents based on the input keywords, means for analyzing the citation relationships, author networks, publication date sequence, technology sequence, publication medium, research subject sequence, and research method sequence of the retrieved documents, means for constructing a genealogy and lineage information of the documents based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for analyzing the user's emotional state and dynamically adjusting the interface. This makes it possible to comprehensively grasp the relationships and development process of documents based on the keywords entered by the user and present information according to the user's emotions and level of understanding.

[1247] A "specific keyword" is a word or phrase that a user enters as a search target.

[1248] A "literature database" is a database that stores multiple academic documents and manages them in a searchable format.

[1249] "Citation relationships of documents" is information indicating the relationships in which a document refers to or cites another document.

[1250] "Networks between authors" refers to information that indicates the relationships between authors of multiple documents, including their collaborative research and involvement.

[1251] The "publication date sequence" is information indicating the order or timeline of the dates when documents were published.

[1252] A "technology series" is information that shows the process of research development and evolution in a specific technology field.

[1253] "By publication medium" is information that classifies the publication medium (e.g., academic journal, conference proceedings, etc.) in which each document was published.

[1254] "Research subject series" is information that indicates the series of research subjects or themes that different documents deal with.

[1255] "Research methodology series" is information that indicates the series of research methods and approaches adopted by different documents.

[1256] "Genealogy information" is detailed structural information of relationships based on citation relationships between documents and author networks.

[1257] "Series information" is information that follows a chronological order or classification, such as the publication date and time of a document or the evolution of technology.

[1258] "Emotional state" refers to the user's current emotional or psychological state, and is used to determine the user's reaction and level of understanding.

[1259] "Adjusting the interface" means dynamically changing the format and content of information presentation according to the user's emotional state and reactions.

[1260] The system of this invention begins when a user inputs a specific keyword into a terminal. Specific hardware used by the user is a personal computer, tablet, smartphone, or other device. Software used is a web browser connected to the Internet.

[1261] When a user enters a keyword into the device's search bar, the device sends the keyword to the server. The communication uses the HTTPS protocol, and SSL / TLS is used to ensure data security. For example, if a user enters "artificial intelligence" and clicks the search button, the device sends the keyword to the server.

[1262] The server searches the specified literature database (e.g., academic literature database or patent database) based on the received keywords. Specific software used to perform the search is a literature database API (e.g., PubMed API, IEEE Xplore API).

[1263] The server analyzes the search results obtained from the literature database, including citation network analysis, author network analysis, time series analysis, and technology series analysis, using network graph algorithms (e.g., Dijkstra's algorithm) and machine learning algorithms (e.g., TensorFlow, Scikit-learn).

[1264] For example, the server retrieves documents related to "artificial intelligence" and analyzes their citation relationships and author networks. Based on the results of this analysis, it builds detailed genealogy and developmental sequence information for the documents. This information is stored in JSON format or other suitable data formats.

[1265] The server then sends the constructed genealogy and lineage information to the terminal. Again, the data is securely transmitted using the HTTPS protocol. The terminal receives this information and visually displays it using a data visualization library (e.g., D3.js, Chart.js). This allows users to intuitively understand information such as citation networks, author networks, and the development process of research.

[1266] Furthermore, the emotion engine uses the device's built-in camera and sensor devices to analyze the user's emotional state. Specifically, OpenCV is used to analyze facial expressions, and TensorFlow is used to implement the machine learning model. The interface's information presentation format is dynamically adjusted based on the user's emotional state. For example, if the user is excited about the information, the system will suggest adding more detailed information or related new papers. If the user is confused, the information presentation format will be simplified and focused on basic content.

[1267] Examples of prompts include:

[1268] User: "I'd like to know about the latest research trends in artificial intelligence. What literature is available?"

[1269] AI Model: "Exploring recent research literature on the topic of artificial intelligence. Please wait."

[1270] In this way, the system of the present invention not only comprehensively visualizes the relevance and development process of literature based on the user's keyword input, but also supports more effective information exploration through dynamic interface adjustments according to the user's emotional state.

[1271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1272] Step 1:

[1273] The user enters a keyword into the device. Specifically, the user enters a specific keyword, such as "artificial intelligence," into the search bar and clicks the search button. The entered keyword is stored in the device's memory.

[1274] Input: The keyword "artificial intelligence" entered by the user.

[1275] Output: The keyword "artificial intelligence" stored by the device.

[1276] Step 2:

[1277] The device sends the entered keyword to the server. The keyword is securely sent to the server using the HTTPS protocol. SSL / TLS is used.

[1278] Input: Keyword saved on device: "artificial intelligence".

[1279] Output: The keyword "artificial intelligence" sent to the server.

[1280] Step 3:

[1281] The server searches a literature database based on keywords. The server uses the search API of the specified literature database (e.g., PubMed API, IEEE Xplore API) to search for relevant literature.

[1282] Input: The keyword "artificial intelligence" received by the server.

[1283] Output: Search results (list of relevant literature) obtained from the literature database.

[1284] Step 4:

[1285] The server retrieves the relevant literature, receives the retrieved data in JSON format, and prepares it for analysis.

[1286] Input: Search results from literature databases.

[1287] Output: Bibliographic data in JSON format.

[1288] Step 5:

[1289] The server analyzes the documents retrieved, using network graph algorithms (e.g., Dijkstra's algorithm) and machine learning algorithms (e.g., TensorFlow, Scikit-learn) to analyze citation relationships, author networks, time series, technology series, etc.

[1290] Input: Bibliographic data in JSON format.

[1291] Output: Analysis results (citation networks, author networks, time series data, etc.).

[1292] Step 6:

[1293] The server constructs genealogy and lineage information based on the analysis results, and the constructed data is stored in a database or memory.

[1294] Input: Analysis results.

[1295] Output: Genealogy and lineage information.

[1296] Step 7:

[1297] The server sends the constructed genealogy and lineage information to the terminal. The data is sent securely to the terminal using the HTTPS protocol.

[1298] Input: Genealogical and lineage information.

[1299] Output: Genealogy and lineage information sent to the device.

[1300] Step 8:

[1301] The device displays the information to the user, using data visualization libraries such as D3.js and Chart.js to visually display the information.

[1302] Input: Genealogy and lineage information sent to the terminal.

[1303] Output: Visualized information (graphs, network diagrams, etc.).

[1304] Step 9:

[1305] The emotion engine analyzes the user's emotional state and dynamically adjusts the interface. It uses the device's built-in camera and sensor devices, as well as OpenCV and TensorFlow to analyze facial expressions and input behavior. It adjusts the information presentation format based on the user's emotional state.

[1306] Input: User's facial expressions, biometric signals, and input behavior.

[1307] Output: Dynamically adjusted interface.

[1308] (Application example 2)

[1309] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1310] Conventional literature search and content search systems simply display a list of related materials, making it difficult to comprehensively grasp the relationships between materials, such as citation relationships, author networks, and development timelines. Furthermore, they lack the ability to dynamically adjust the interface according to the user's emotional state, and lack the ingenuity to make it easier for users to intuitively understand information. This reduces the efficiency of information search and impairs the user experience.

[1311] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1312] In this invention, the server includes means for inputting a specific keyword, means for searching for multiple related content based on the input keyword, means for analyzing the citation relationships of the searched content, the network between creators, the publication date sequence, the technology sequence, the publication medium sequence, and the research method sequence, means for constructing a genealogy and lineage information of the content based on the analysis results, means for displaying the constructed genealogy and lineage information, and means for determining the user's emotional state and dynamically adjusting the display format of the analysis results. This not only allows the user to visually grasp the lineage and relevance of content related to keywords of interest, but also enables the user to obtain information using an interface optimal for their emotional state.

[1313] "Specific keywords" are words or phrases that indicate a particular topic or theme that interests a user.

[1314] "Input means" refers to a device or interface that allows a user to input a specific keyword, and specifically includes a keyboard, a touch screen, a voice input device, etc.

[1315] "Multiple related contents" refers to digital information such as videos, articles, audio, literature, etc. that are associated with the entered keywords under certain conditions.

[1316] "Search means" refers to a program or algorithm for retrieving multiple pieces of related content based on a keyword.

[1317] A "citation relationship" refers to a relationship in which a specific piece of content refers to another piece of content as a reference.

[1318] A "network among creators" is a network that shows the relationships and cooperation between individuals and organizations that create content.

[1319] The "release date sequence" indicates the order of the dates and times when the content was released to the public.

[1320] A "technology lineage" indicates how a particular technology or methodology has progressed and evolved.

[1321] "By publication medium" is a classification based on the type of medium in which the content was published (e.g., academic journal, online platform, television broadcast, etc.).

[1322] "Means of analysis" refers to methods and tools for analyzing the citation relationships, networks, time series, etc. of the acquired content.

[1323] "Genealogy and lineage information" refers to detailed information such as citation relationships between content, timelines of development, and creator networks.

[1324] "Display means" refers to a method or device for visually presenting the analysis results to the user, and specifically includes forms such as graphs, treemaps, and network diagrams.

[1325] The "emotional state of the user" indicates the emotion the user feels when viewing a particular piece of content, and refers to states such as joy, excitement, or confusion.

[1326] "Means for determining and dynamically adjusting the display format of the analysis results" refers to a method for analyzing the user's emotional state and changing or optimizing the way information is presented based on the results.

[1327] The system of the present invention starts with the user entering a specific keyword, searches for multiple related pieces of content, and analyzes their citation relationships, networks between creators, publication dates, technology, publication media, and research methodologies to construct a genealogy and series of content and display it to the user. It also has the ability to determine the user's emotional state and dynamically adjust the display format based on the analysis results.

[1328] The system program performs the following processing.

[1329] First, a user inputs a specific keyword using a smartphone or other input device. The device then sends the keyword to a server, which searches for multiple related content. The server then retrieves related digital information, such as videos, articles, and audio, from a database. The hardware used can be a smartphone, tablet, or server computer.

[1330] The server then analyzes the retrieved content's citation relationships, creator networks, publication date and time series, technology series, publication media, and research method series. The analysis includes network analysis using the "networkx" library and data visualization using "matplotlib." This allows for the construction of detailed genealogy and lineage information for the content.

[1331] The server then sends the constructed genealogy and lineage information to the user's device, which then visually displays it. Various visualization techniques, such as treemaps and network diagrams, are used for the display. Specifically, the "matplotlib" library is used to visualize the information.

[1332] Furthermore, the emotion engine works by analyzing the user's facial expressions and behavior while browsing information. The "emotion_detection_module" is used to determine the user's emotional state. Based on this result, the server dynamically adjusts the display format. For example, if the user is excited, the server can suggest additional information or related new content. If the user is confused, the server can simplify the information presentation format and adjust it to focus on the basic content.

[1333] As a concrete example, consider the case where a user enters the keyword "artificial intelligence." When the user enters a keyword, the server searches for related videos and articles and analyzes their citation relationships and technological advances. It generates a treemap or network diagram of information related to the keyword "artificial intelligence" and displays it on the user's device. If the user is excited by the displayed information, the system will display more detailed information or related new papers. If the user is confused, it will provide simplified information to make it easier to understand.

[1334] Examples of prompts include:

[1335] "Analyze the relationships between videos and articles related to artificial intelligence, and generate a network diagram showing citation relationships, the order in which they were produced, related people, and technological developments."

[1336] In this way, the system of the present invention comprehensively analyzes keyword-based content searches and dynamically presents information according to the user's emotional state, supporting more intuitive and effective information exploration.

[1337] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1338] Step 1:

[1339] A user inputs a specific keyword using a smartphone or other input device.

[1340] Input: The keyword entered by the user.

[1341] Output: Send the keywords to the server.

[1342] Specific operation: The user enters a keyword in the input field and presses the search button. This operation sends the keyword from the device to the server.

[1343] Step 2:

[1344] The server searches a database for multiple related contents based on keywords.

[1345] Input: The keyword sent from the terminal.

[1346] Output: A list of associated content data.

[1347] Specific operation: The server accesses the database and searches for and retrieves digital information such as videos, articles, and audio related to the keywords.

[1348] Step 3:

[1349] The server analyzes the citation relationships of the content it acquires, the network between creators, the publication date and time series, the technology series, the publication media, and the research method series.

[1350] Input: A list of related content data.

[1351] Output: Genealogy and lineage information as analysis results.

[1352] Specific operation: The server performs network analysis using the "networkx" library and prepares the data for visualization using "matplotlib." At the same time, it organizes and analyzes information on citation relationships and technological advances for each piece of content.

[1353] Step 4:

[1354] The server transmits the constructed genealogy and lineage information to the terminal, which then visually displays it.

[1355] Input: Genealogy and lineage information.

[1356] Output: Visualized information displayed on the user's device.

[1357] Specific operation: The server generates the analysis results in the form of a treemap, network diagram, etc. and sends them to the terminal. The terminal receives them and displays them visually using "matplotlib" or similar.

[1358] Step 5:

[1359] The emotion engine analyzes the user's facial expressions and behavior to determine their emotional state.

[1360] Input: User's facial expression data, behavioral data.

[1361] Output: The user's emotional state (excited, confused, etc.).

[1362] Specific operation: Using the "emotion_detection_module" built into the device, data obtained from the camera and microphone is analyzed to determine the user's emotional state in real time.

[1363] Step 6:

[1364] The server dynamically adjusts the display format of the analysis results based on the emotional state.

[1365] Input: The user's emotional state.

[1366] Output: Adjusted display information.

[1367] Specific behavior: Based on feedback from the emotion engine, the server changes the way information is presented, for example, showing additional information or new content if the user is excited, or simplifying the information if the user is confused.

[1368] In this way, the system comprehensively performs content searches based on specific keywords, analyzes their relevance, and dynamically displays information according to the user's emotional state.

[1369] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1370] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1371] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1372] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1373] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1374] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1375] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1376] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, motorcycles, and other devices, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1377] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1378] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1379] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1380] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1381] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1382] 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.

[1383] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1384] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1385] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1386] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1387] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1388] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1389] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1390] The following is further disclosed regarding the above embodiment.

[1391] (Claim 1)

[1392] a means for inputting specific keywords;

[1393] A means for searching for multiple related documents based on input keywords;

[1394] A means for analyzing the citation relationships of the retrieved documents, the network between authors, the publication date series, the technology series, the publication medium, the research subject series, and the research method series;

[1395] A means for constructing genealogy and lineage information of documents based on the analysis results;

[1396] a means for displaying the constructed genealogy and lineage information;

[1397] A system including:

[1398] (Claim 2)

[1399] 10. The system of claim 1, wherein the system searches a database of documents related to a particular keyword.

[1400] (Claim 3)

[1401] 10. The system of claim 1, wherein the constructed genealogy and lineage information is output in a defined format.

[1402] "Example 1"

[1403] (Claim 1)

[1404] a means for a user to input a specific keyword;

[1405] A means for transmitting the input keyword to a server by the terminal;

[1406] A means for searching a database for a plurality of related documents based on the received keywords by the server;

[1407] A means for analyzing the citation relationships, author networks, publication date series, technology series, publication media, research subject series, and research method series of the documents searched by the server;

[1408] A means for the server to construct genealogy and lineage information of documents based on the analysis results;

[1409] A server transmits the constructed genealogy and lineage information to a terminal;

[1410] means for the terminal to visually display the transmitted genealogy and lineage information;

[1411] A system including:

[1412] (Claim 2)

[1413] 10. The system of claim 1, wherein the system searches a database of documents related to a particular keyword.

[1414] (Claim 3)

[1415] 10. The system of claim 1, wherein the constructed genealogy and lineage information is output in a defined format.

[1416] "Application Example 1"

[1417] (Claim 1)

[1418] a means for inputting specific keywords;

[1419] A means for searching for multiple related documents based on input keywords;

[1420] A means for analyzing the citation relationships of the retrieved documents, the network between authors, the publication date series, the technology series, the publication medium, the research subject series, and the research method series;

[1421] A means for constructing genealogy and lineage information of documents based on the analysis results;

[1422] a means for displaying the constructed genealogy and lineage information;

[1423] A means for acquiring data related to keywords entered by a user and visualizing the data as a citation network;

[1424] A system including:

[1425] (Claim 2)

[1426] 10. The system of claim 1, wherein the system searches a database of documents related to a particular keyword.

[1427] (Claim 3)

[1428] 10. The system of claim 1, wherein the constructed genealogy and lineage information is output in a defined format.

[1429] "Example 2: Combining Emotion Engines"

[1430] (Claim 1)

[1431] a means for inputting specific keywords;

[1432] A means for searching for multiple related documents based on input keywords;

[1433] A means for analyzing the citation relationships of the retrieved documents, the network between authors, the publication date series, the technology series, the publication medium, the research subject series, and the research method series;

[1434] A means for constructing genealogy and lineage information of documents based on the analysis results;

[1435] a means for displaying the constructed genealogy and lineage information;

[1436] means for analyzing a user's emotional state and dynamically adjusting the interface;

[1437] A system including:

[1438] (Claim 2)

[1439] 10. The system of claim 1, wherein the system searches a database of documents related to a particular keyword.

[1440] (Claim 3)

[1441] 10. The system of claim 1, wherein the constructed genealogy and lineage information is output in a defined format.

[1442] "Application example 2 when combining emotion engines"

[1443] (Claim 1)

[1444] a means for inputting specific keywords;

[1445] A means for searching for multiple related content based on an input keyword;

[1446] A means for analyzing the citation relationships of the searched content, the network between creators, the publication date series, the technology series, the publication medium series, and the research method series;

[1447] means for constructing genealogy and sequence information of the content based on the analysis results;

[1448] a means for displaying the constructed genealogy and lineage information;

[1449] means for determining the emotional state of a user and dynamically adjusting the display format of the analysis results;

[1450] A system including:

[1451] (Claim 2)

[1452] 10. The system of claim 1, wherein the system searches a database for content related to a particular keyword.

[1453] (Claim 3)

[1454] 10. The system of claim 1, wherein the constructed genealogy and lineage information is output in a defined format. [Explanation of symbols]

[1455] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting specific keywords; A means for searching for multiple related documents based on input keywords; A means for analyzing the citation relationships of the retrieved documents, the network between authors, the publication date series, the technology series, the publication medium, the research subject series, and the research method series; A means for constructing genealogy and lineage information of documents based on the analysis results; a means for displaying the constructed genealogy and lineage information; A system including:

2. 10. The system of claim 1, wherein the system searches a database of documents related to a particular keyword.

3. 10. The system of claim 1, wherein the system outputs the constructed genealogy and lineage information in a defined format.

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