System

Abstract

To provide a system.SOLUTION: A system includes: means of storing information of a document read by a user in the past; means of collecting behavior data containing at least one of a search query of the user, the content of a Web page being browsed, and a place being searched on a map; means of analyzing the behavior data using a natural langue processing algorithm and grasping the context; means of extracting related information along the context from the database; and means of presenting the extracted related information to the user.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] There is a problem that users forget the contents of the books they have read, and since there is no trigger to remind them of the books they have read, they lose the opportunity to utilize the contents of the books they have read. [Means for solving the problem]

[0005] The system records information about books a user has read in the past and analyzes the context when the user is researching using generative AI, web search, map search, etc. It then provides a system that suggests related phrases, events, places, etc. from books read in the past based on the analyzed context. This gives users an opportunity to recall the contents of the books they have read, increasing the opportunities to utilize the content of the books they have read. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention records information about books a user has read in a database. This database includes information such as the book's title, author, publication date, completion date, and keywords and phrases related to the book's content. When a user conducts research using generative AI, web search, map search, etc., the system analyzes the context. The analysis is based on information obtained from the user's behavior, such as the search query, the content of the web page being viewed, and the location being searched for on a map. The system then suggests related phrases, events, places, etc. from the database based on the analyzed context.

[0029] "Example 2"

[0030] For example, when a user searches for "Rome" on the web, the system analyzes the context and searches a database to see if they have previously read books related to "Rome." If the user has previously read a book about the history of the Roman Empire, the system will suggest phrases, events, and places related to "Rome" from that book. This allows the user to recall the contents of the book and provides an opportunity to gain new knowledge.

[0031] The processing flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: When a user finishes reading a book, the system records the book's information (title, author, publication date, completion date, keywords and phrases related to the content, etc.) in a database.

[0034] Step 2: When a user is researching something using generative AI, web search, or map search, the system analyzes the context (the search query, the content of the web page they are viewing, the location they are searching for on a map, etc.).

[0035] Step 3: The system uses the analyzed context to suggest relevant phrases, events, places, etc. from its database.

[0036] "Example 2"

[0037] Step 1: When a user performs a web search for "Rome," the system analyzes the context.

[0038] Step 2: The system searches the database to see if you have previously read any books related to "Rome."

[0039] Step 3: If you have previously read a book about the history of the Roman Empire, the app will suggest phrases, events, and places related to Rome from that book.

[0040] Example 1

[0041] Next, a description will be given of Example 1 of Form 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."

[0042] Conventional information search systems make it difficult for users to effectively utilize information from books they have read in the past, and are unable to provide information useful for current searches or research activities. Furthermore, they lack the means to analyze user behavior data in real time and provide information based on appropriate context. This prevents users from quickly and accurately obtaining the information they need, reducing the efficiency of information searches.

[0043] 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.

[0044] In this invention, the server includes means for recording information about books that a user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books that have been read in the past based on the analyzed context, means for collecting user behavior data, means for analyzing the context using a natural language processing algorithm based on the collected behavior data, means for searching for related information from a database based on the analyzed context, and means for displaying the searched related information to the user. This makes it possible to utilize the user's past reading history and quickly and accurately provide information that is useful for current searches and research activities.

[0045] A "user" is an individual or group that uses the system to search for information or enter past reading history.

[0046] "Books" refer to information media such as books and e-books that users have read in the past.

[0047] "Means for recording information" is a function that allows users to save information about books they have read in the past in a database.

[0048] "Generative artificial intelligence" refers to algorithms or systems that generate information based on user input and actions.

[0049] An "Internet search" is the act of a user searching for information on the Internet using a web browser.

[0050] A "map search" is the act of a user searching for information about a specific location or area using a map application.

[0051] "Context analysis" is a function that understands the current context based on information such as the user's search query, the content of the web page they are viewing, and the location they are searching for on a map.

[0052] "Suggestion methods" is a function that presents users with related phrases, events, places, etc. from books they have read in the past based on the analyzed context.

[0053] "Means for collecting behavioral data" refers to functions for collecting behavioral data such as user search queries, the content of web pages viewed, and locations searched for on maps.

[0054] A "natural language processing algorithm" is an algorithm that analyzes collected behavioral data and understands the context.

[0055] A "database" is a data storage system that stores information about books that a user has read in the past.

[0056] The "means for searching related information" is a function that searches for information such as related phrases, events, and places from a database based on the analyzed context.

[0057] "Means for displaying information" refers to a function for visually presenting the retrieved related information to the user.

[0058] This invention is a system that records information about books that a user has read in the past, analyzes the context when the user is conducting research using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, places, etc. based on the analyzed context.

[0059] Building a database

[0060] The server builds a database to record information about books that users have read in the past, including book titles, authors, publication dates, completion dates, and keywords or phrases related to the book's content. Users enter this information using a web form, and the server stores the data in the database using SQL queries.

[0061] Contextual Analysis

[0062] The device collects information about the user's search history, such as search queries, the content of web pages viewed, and locations searched for on maps, as well as browsing history, including through browser extensions and applications that collect user behavior data in real time. The server analyzes the collected user behavior data and uses natural language processing (NLP) algorithms to understand the current context.

[0063] Suggestion function

[0064] The server searches for information such as related phrases, events, and places from a database based on the analyzed context. This involves using SQL queries to search for related information in the database. The related information is information that fits the understood context. The device displays the related information received from the server to the user. Specifically, suggestions are displayed to the user through browser pop-ups or application notifications.

[0065] Specific examples

[0066] For example, if a user searches for "tourist attractions in Paris," the following process occurs:

[0067] 1. A user searches for "tourist attractions in Paris."

[0068] 2. The device collects this search query.

[0069] 3. The server analyzes the collected search queries and understands the context related to "Paris."

[0070] 4. The server searches the database for information on books related to "Paris."

[0071] 5. The terminal displays the relevant information received from the server to the user.

[0072] Prompt Sentence Examples

[0073] When a user searches for "tourist attractions in Paris," the server extracts information about books related to Paris from a database and suggests related phrases and events.

[0074] This system utilizes the user's past reading history to quickly and accurately provide information useful for current searches and research activities, allowing users to efficiently obtain the information they need and improving the efficiency of information retrieval.

[0075] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0076] Step 1:

[0077] The user enters information about books they have read in the past.

[0078] Input: A user fills out a web form and enters the book title, author, publication date, completion date, and keywords or phrases related to the book's content.

[0079] Data processing: Send the information entered by the user to the server.

[0080] Output: Book information received by the server.

[0081] What happens: A user fills out a web form and clicks the submit button.

[0082] Step 2:

[0083] The server stores the book information in a database.

[0084] Input: Book information submitted by the user.

[0085] Data processing: The server uses SQL queries to insert the book information into the database.

[0086] Output: Book information stored in a database.

[0087] Specific operation: The server saves the received data in the database using an SQL query.

[0088] Step 3:

[0089] The device collects user behavior data.

[0090] Input: Your search query, the content of the web page you're viewing, or the location you're searching for on a map.

[0091] Data processing: Devices collect behavioral data in real time through browser extensions and applications.

[0092] Output: Collected behavioral data.

[0093] What it does: The device monitors user behavior and collects necessary data.

[0094] Step 4:

[0095] The server analyzes the collected behavioral data.

[0096] Input: Collected behavioral data.

[0097] Data processing: The server uses natural language processing (NLP) algorithms to analyze the behavioral data and understand the current context.

[0098] Output: Parsed context information.

[0099] What happens: The server runs NLP algorithms and analyzes the behavioral data.

[0100] Step 5:

[0101] The server retrieves the relevant information from a database.

[0102] Input: Parsed context information.

[0103] Data processing: The server uses SQL queries to find relevant information in a database.

[0104] Output: The relevant information found.

[0105] What happens: The server runs an SQL query to extract the relevant information from the database.

[0106] Step 6:

[0107] The device displays the relevant information to the user.

[0108] Input: Relevant information received from the server.

[0109] Data processing: The device will display relevant information through browser pop-ups and application notifications.

[0110] Output: Relevant information displayed to the user.

[0111] Specific behavior: The device visually presents the information it receives to the user.

[0112] (Application example 1)

[0113] Next, a description will be given of Application Example 1 of Embodiment 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."

[0114] Conventional systems have difficulty effectively utilizing information from books a user has read in the past, and are unable to provide context-based related information, particularly through generative AI, web searches, and map searches. Furthermore, there is a lack of a way to provide real-time information related to the content a user is viewing, creating a need for improved user convenience. Furthermore, the lack of a function to generate context-related prompts using generative AI has hindered the enrichment of the user's search experience.

[0115] 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.

[0116] In this invention, the server includes: means for recording information about books previously read by a user; means for analyzing the context of the user's research using generative artificial intelligence, web search, map search, etc.; means for suggesting related phrases, events, places, etc. from books previously read based on the analyzed context; means for providing information related to the content currently being viewed by the user in real time; and means for generating context-relevant prompts using generative artificial intelligence. This makes it possible to effectively utilize information about books previously read by the user and provide related information based on the context of generative artificial intelligence, web search, map search, etc. Furthermore, providing information related to the content currently being viewed by the user in real time can improve user convenience. Furthermore, generating context-relevant prompts using generative artificial intelligence can enrich the user's search experience.

[0117] "User" means any individual or entity that uses the System.

[0118] "Information about books read in the past" refers to information about books that the user has previously read, such as the title, author, publication date, completion date, keywords and phrases related to the content, etc.

[0119] "Means for recording" refers to a method or device for storing information about books that a user has read in the past in a database or the like.

[0120] "Generative AI" is AI that has the ability to generate new information and content based on user input and context.

[0121] "Web searching" is the act of finding information on the Internet using a search engine.

[0122] "Map searching" is the act of using a geographic information system to search for information about a particular place or region.

[0123] A "means for analyzing context" is a method or device for understanding the content of a user's search or browsing and analyzing its background and intent.

[0124] A "means for suggesting" is a method or device for suggesting relevant information to a user based on the analyzed context.

[0125] "Real-time provision means" refers to a method or device for instantly providing relevant information to a user while the user is viewing content.

[0126] A "prompt sentence" is a sentence that generative artificial intelligence generates based on the user's context to encourage the next action or information.

[0127] The system for implementing this invention records information about books that a user has read in the past, analyzes the context of generative artificial intelligence, web searches, map searches, etc., and provides related information in real time. A specific embodiment of this system is described below.

[0128] 1. System Program

[0129] The system consists of the following main components:

[0130] Database: SQLite is used to store information about books that users have read in the past.

[0131] Contextual Analysis Module: Uses BeautifulSoup to analyze the content of the web page the user is viewing and extract text.

[0132] Suggestion module: Retrieves information about previously read books from a database and checks whether they contain keywords relevant to the context.

[0133] Generative AI model: Uses the OpenAI® API to generate contextually relevant prompts.

[0134] 2. Program processing explanation

[0135] Hardware and software used

[0136] Hardware: Smartphones, smart glasses

[0137] Software: Python, SQLite, BeautifulSoup, OpenAI API

[0138] Data processing and calculation

[0139] Database connection: We use SQLite to connect to a database that stores information about books that users have read.

[0140] Contextual analysis: Use BeautifulSoup to analyze the content of the web page the user is viewing and extract text.

[0141] Suggestion function: Retrieves information about books you have read in the past from a database and checks whether they contain keywords related to the context.

[0142] Generative AI model: Uses OpenAI APIs to generate contextually relevant prompts.

[0143] 3. Specific Examples

[0144] When a user is reading a news article and the article is about environmental issues, the system suggests information about books about environmental issues that the user has read in the past. For example, the system generates a prompt such as, "I'm reading the latest news article about environmental issues. Please tell me about phrases or events related to environmental issues in books that you have read in the past."

[0145] This system allows users to effectively utilize information from books they have read in the past and provide relevant information in context with generative AI, web searches, map searches, etc. It also improves user convenience by providing information related to the content the user is viewing in real time. Furthermore, it can enrich the user's search experience by using generative AI to generate context-relevant prompts.

[0146] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0147] Step 1:

[0148] Record information about books that a user has read in the past.

[0149] Input: Book title, author, publication date, completion date, keywords or phrases related to the content.

[0150] Processing: The device stores this information in an SQLite database.

[0151] Output: Book information recorded in the database.

[0152] Step 2:

[0153] Users conduct research using generative artificial intelligence, web searches, map searches, etc.

[0154] Input: The search query a user enters or the URL of a web page they are viewing.

[0155] Processing: The device retrieves the user's search query and the content of the web page.

[0156] Output: Search query or web page content.

[0157] Step 3:

[0158] Analyze the context.

[0159] Input: A search query or web page content.

[0160] Processing: The device uses BeautifulSoup to parse the content of the web page and extract the text.

[0161] Output: The extracted text.

[0162] Step 4:

[0163] It suggests related phrases, events, places, etc. from books you have read in the past.

[0164] Input: Extracted text, book information recorded in database.

[0165] Processing: The server retrieves information about books read in the past from the database and checks whether the extracted text contains relevant keywords.

[0166] Output: A list of related phrases, events and places.

[0167] Step 5:

[0168] Provides real-time information related to the content the user is viewing.

[0169] Input: A list of related phrases, events, and places.

[0170] Processing: The terminal displays this information to the user in real time.

[0171] Output: The relevant information displayed to the user.

[0172] Step 6:

[0173] Uses generative artificial intelligence to generate contextually relevant prompts.

[0174] Input: The extracted text.

[0175] Processing: The server uses the OpenAI API to generate a prompt based on the extracted text.

[0176] Output: The generated prompt statement.

[0177] Step 7:

[0178] Provides the user with a generated prompt.

[0179] Input: The generated prompt statement.

[0180] Processing: The terminal displays the generated prompt to the user.

[0181] Output: The prompt text displayed to the user.

[0182] Example 2

[0183] Next, a description will be given of Example 2 of Form 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."

[0184] Modern information retrieval systems face the challenge of making it difficult for users to effectively utilize the content of books they have read in the past. In particular, when users search for a specific topic, they lack a mechanism to refer to their past reading history to provide related information. This results in users being unable to fully utilize their previously acquired knowledge, and they are unable to efficiently acquire new knowledge.

[0185] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for recording information about books that the user has read in the past, a means for analyzing the context when the user is searching for information, a means for suggesting related phrases, events, places, etc. from books that the user has read in the past based on the analyzed context, a means for using a generative AI model for generating suggestions, and a means for sending prompt sentences to the generative AI model. This makes it possible to effectively utilize the contents of books that the user has read in the past and provide information related to the search results.

[0186] A "user" refers to an individual with a history of information searches and book reading.

[0187] "Books" refers to information media such as books and e-books that users have read in the past.

[0188] "Information search" refers to the act of a user researching information through web searches, map searches, etc.

[0189] "Context" refers to the search query and related information surrounding it when a user searches for information.

[0190] "Analysis" refers to the act of understanding a user's search query and context using technologies such as natural language processing and extracting relevant information.

[0191] A "phrase" refers to a specific word or part of a sentence contained in a book.

[0192] "Incident" refers to a specific occurrence or event described in a book.

[0193] "Places" refer to geographic locations or areas mentioned in the book.

[0194] "Suggestion" refers to the act of suggesting information such as related phrases, events, or places to the user.

[0195] A "generative AI model" refers to an artificial intelligence model for generating natural language.

[0196] A "prompt sentence" refers to an instruction sentence input to a generative AI model.

[0197] "Server" refers to the computer system that processes users' search queries and reading history and performs analysis and suggestions.

[0198] This invention is a system that effectively utilizes the contents of books that users have read in the past when searching for information, and provides related information. This system works in cooperation with the server, the terminal, and the user.

[0199] Hardware and software used

[0200] Server: Database management system (e.g., MySQL®), natural language processing engine (e.g., spaCy), generative AI model (e.g., GPT-4®)

[0201] Device: Web browser (e.g., GOOGLE CHROME (registered trademark))

[0202] User: The individual searching for information.

[0203] Specific operation of the system

[0204] 1. Receive the user's search query

[0205] A user opens a web browser and types "Rome" into the search bar.

[0206] The terminal sends this search query to the server.

[0207] 2. Analyze the context of the search query

[0208] The server uses a natural language processing engine (e.g., spaCy) to analyze the context of the search query "Rome."

[0209] Based on the analysis results, related keywords and topics are extracted.

[0210] 3. Search your past reading history

[0211] The server uses a database management system (e.g., MySQL) to retrieve the user's past reading history.

[0212] For example, if the user has previously read a book called "The History of the Roman Empire," the contents of that book are retrieved.

[0213] 4. Suggest related information

[0214] The server extracts phrases, events, and places related to "Rome" from the acquired reading history.

[0215] Use a generative AI model (e.g., GPT-4) to generate appropriate suggestions for the user.

[0216] 5. Present suggestions to the user

[0217] The server sends the generated suggestions to the terminal.

[0218] The device displays suggestions to the user through a web browser.

[0219] Specific examples

[0220] User search query: "Rome"

[0221] Past reading history: "The History of the Roman Empire"

[0222] Suggested content: "The History of the Roman Empire provides detailed information about the founding of the city of Rome and the rule of Caesar. In particular, revisit the chapters on Caesar's reforms and the architecture of the city of Rome."

[0223] Example prompts for generative AI models

[0224] A user is performing a web search for "Rome." In their past reading history, they have a book called "The History of the Roman Empire." Based on the contents of this book, suggest phrases, events, and places related to "Rome" to the user.

[0225] This system allows users to recall the contents of books they have read in the past, providing an opportunity to gain new knowledge.

[0226] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0227] Step 1:

[0228] A user enters a search query. The user opens a web browser, types "Rome" into the search bar, and presses enter, generating the search query "Rome."

[0229] Step 2:

[0230] The terminal sends a search query to the server. The terminal sends the search query "Rome" entered by the user to the server as an HTTP request. The input is the search query "Rome" and the output is an HTTP request to the server.

[0231] Step 3:

[0232] The server analyzes the context of the search query. The server passes the received search query "Rome" to a natural language processing engine (e.g., spaCy) to analyze the context. The input is the search query "Rome," and the output is related keywords and topics as the analysis results. Specifically, the server extracts the meaning of the word "Rome" and related topics.

[0233] Step 4:

[0234] The server searches the database for past reading history. The server uses a database management system (e.g., MySQL) to search for the user's past reading history. The input is the user ID and the analysis result, and the output is information about related books. Specifically, the server executes an SQL query to search for books related to "Rome."

[0235] Step 5:

[0236] The server extracts relevant information and sends a prompt to the generative AI model. The server extracts phrases, events, and places related to "Rome" from the reading history and creates a prompt to send to the generative AI model (e.g., GPT-4). The input is information about the related book, and the output is a prompt. Specifically, the server generates a prompt based on the extracted information.

[0237] Step 6:

[0238] The generative AI model generates suggestions. Based on the received prompt, the generative AI model generates appropriate suggestions for the user. The input is the prompt, and the output is the suggested sentence. Specifically, the generative AI model uses a natural language generation algorithm to create suggested sentences.

[0239] Step 7:

[0240] The server sends the generated suggestions to the device. The server sends the suggestions received from the generative AI model to the device. The input is the suggested sentence, and the output is an HTTP response to the device. Specifically, the server generates an HTTP response and sends it to the device.

[0241] Step 8:

[0242] The terminal displays the suggestions to the user. The terminal displays the received suggestions on a web browser. The input is the suggestion sentence, and the output is the information displayed to the user. Specifically, the terminal uses HTML and JavaScript (registered trademark) to display the suggestions in a format that is easy for the user to view.

[0243] (Application example 2)

[0244] Next, a description will be given of Application Example 2 of Form 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."

[0245] In today's information society, when users research a specific topic, it is important to recall the contents of books they have read in the past and gain new knowledge. However, manually searching through the contents of books users have read in the past to find relevant information is extremely time-consuming. Furthermore, there is a lack of effective ways to link Internet search results with past reading history, making it difficult for users to quickly obtain the information they need. A system that solves these problems is needed.

[0246] 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.

[0247] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for conducting an internet search based on the user's search query and analyzing the obtained search results, and means for generating information to suggest to the user based on the analyzed search results and the user's past reading history. This allows the user to quickly recall the contents of books they have read in the past and acquire new knowledge by linking them to internet search results.

[0248] "User" refers to an individual who uses the system to search for information or view past reading history.

[0249] "Books" refers to written media such as books and e-books that users have read in the past.

[0250] "Means for recording information" refers to a method or device for saving information about books that a user has read in the past in a database or the like.

[0251] "Generative AI" refers to artificial intelligence technology that analyzes users' search queries and contexts and generates relevant information.

[0252] An "internet search" refers to the act of a user searching the web for information about a particular topic.

[0253] "Map search" refers to the act of a user searching the web for a specific location or geographic information.

[0254] "Contextual analysis means" refers to methods or devices for understanding a user's intent and interests from their search queries and behavior, and extracting relevant information.

[0255] "Suggestion means" refers to a method or device for suggesting relevant phrases, events, places, etc. to a user based on analyzed context.

[0256] A "search query" refers to a keyword or phrase that a user enters when searching the Internet.

[0257] "Means for analyzing search results" refers to a method or device for analyzing information obtained through an Internet search and extracting information that is useful to the user.

[0258] "Reading history" refers to information about books a user has read in the past.

[0259] "Means for generating suggested information" refers to a method or device for generating information to suggest to a user based on the analyzed search results and past reading history.

[0260] A system for implementing this invention records information about books a user has read in the past, analyzes the context when the user conducts research using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, places, etc. from books read in the past based on the analyzed context. It also performs an internet search based on the user's search query, analyzes the obtained search results, and generates information to suggest to the user based on the analyzed search results and the user's past reading history.

[0261] Hardware and software used

[0262] Hardware: Smartphones, servers

[0263] Software: Python, BeautifulSoup (for web scraping), SQLite (for database management)

[0264] Data processing and calculation

[0265] 1. Obtaining user search queries: When a user searches for a specific topic on their smartphone, the search query is sent to the system.

[0266] 2. Internet Search: The server performs an Internet search based on the user's search query and retrieves the search results by scraping web pages using BeautifulSoup.

[0267] 3. Contextual analysis: The server analyzes the content of the search results to understand the user's intent and interests. This analysis is performed using generative artificial intelligence.

[0268] 4. Reading History Search: The server searches the user's past reading history stored in the SQLite database to obtain information about books related to the search query.

[0269] 5. Suggested Reading: The server generates suggested reading information for the user based on the analyzed search results and past reading history, including related phrases, events, and places.

[0270] Specific examples

[0271] For example, if a user searches for "Rome," the server will perform an internet search and analyze the search results. At the same time, it will retrieve information from a database about books on the history of the Roman Empire that the user has read in the past and suggest related phrases, events, and places. This allows the user to recall the contents of books they have read in the past and gain new knowledge.

[0272] Prompt Sentence Examples

[0273] "Build an application that, when a user searches for Rome, suggests related phrases and events from books about the history of the Roman Empire that the user has read."

[0274] The above is an embodiment of the present invention. This system allows users to quickly recall the contents of books they have read in the past and to link them with internet search results to obtain new knowledge.

[0275] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0276] Step 1:

[0277] A user searches on their smartphone for a specific topic.

[0278] Input: The search query entered by the user (e.g., "Rome")

[0279] Output: The search query is sent to the server

[0280] What it does: When a user types "Rome" into the search bar on their smartphone and presses the search button, the query is sent to the server.

[0281] Step 2:

[0282] The server performs an internet search based on the user's search query.

[0283] Input: User's search query (e.g. "Rome")

[0284] Output: HTML data of internet search results

[0285] Specific operation: The server sends a search query to the Google (registered trademark) search API and receives HTML data of the retrieved search results.

[0286] Step 3:

[0287] The search results obtained by the server are analyzed.

[0288] Input: HTML data of internet search results

[0289] Output: Parsed search results text data

[0290] Specific operation: The server uses BeautifulSoup to parse the HTML data and extract the text data of the search results.

[0291] Step 4:

[0292] The server searches the user's past reading history.

[0293] Input: User's search query (e.g. "Rome")

[0294] Output: Relevant reading history data

[0295] Specific operation: The server searches the user's reading history stored in an SQLite database and retrieves information about books related to the search query.

[0296] Step 5:

[0297] The server generates suggestion information based on search results and reading history.

[0298] Input: Analyzed text data of search results, related reading history data

[0299] Output: Suggested information to the user (e.g., related phrases, events, places)

[0300] Specific operation: The server uses generative artificial intelligence to analyze search results and reading history and generate information to suggest to the user.

[0301] Step 6:

[0302] The server provides the generated suggestion information to the user.

[0303] Input: Suggested information

[0304] Output: Suggested information displayed on the user's smartphone

[0305] Specific operation: The server sends the generated suggestion information to the user's smartphone and displays it on the user's screen.

[0306] The above are the specific processing steps of the system for implementing this invention, which allows users to quickly recall the contents of books they have read in the past and link them with internet search results to gain new knowledge.

[0307] 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.

[0308] "Example 1"

[0309] One embodiment of the present invention provides a system that combines an emotion engine that recognizes a user's emotions. This system records information about books the user has read in the past and analyzes the context when the user is researching using generative AI, web search, map search, etc. Then, based on the analyzed context, the system suggests related phrases, events, and places from books that have been read in the past. Furthermore, the emotion engine recognizes the user's emotional state and makes suggestions based on that emotional state. For example, when the user expresses joy, the system suggests phrases, events, and places related to joy from books that have been read in the past.

[0310] "Example 2"

[0311] In another embodiment, a system is provided in which an emotion engine selects related phrases, events, and places based on a user's emotional state. The system records information about books the user has read in the past and analyzes the context when the user is researching using generative AI, web search, map search, etc. Then, based on the analyzed context and the user's emotional state, the system suggests related phrases, events, and places from books that have been read in the past. For example, if the user expresses sadness, the system suggests phrases, events, and places related to sadness from books that have been read in the past.

[0312] The processing flow of each embodiment will be described below.

[0313] "Example 1"

[0314] Step 1: The user reads a book, and the system records the information of the book that the user reads.

[0315] Step 2: The user conducts research using generative AI, web search, map search, etc. At this point, the system analyzes the context of the user's research.

[0316] Step 3: Based on the analyzed context, the system suggests related phrases, events, and places from books you have read in the past.

[0317] Step 4: The emotion engine recognizes the user's emotional state and makes suggestions based on that emotional state.

[0318] "Example 2"

[0319] Step 1: The user reads a book, and the system records the information of the book that the user reads.

[0320] Step 2: The user conducts research using generative AI, web search, map search, etc. At this point, the system analyzes the context of the user's research.

[0321] Step 3: The emotional engine recognizes the user's emotional state and, based on that emotional state, suggests related phrases, events, and places from books they have read.

[0322] Example 1

[0323] Next, a description will be given of Example 1 of Form 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."

[0324] Conventional information search systems have the problem that it is difficult to effectively utilize information from books that users have read in the past, and suggestions do not take into account the user's emotional state, making it difficult to provide the most appropriate information to the user. Furthermore, systems that analyze the user's search behavior and context to provide related information also fail to increase user satisfaction because they do not take into account the user's emotional state.

[0325] 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.

[0326] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is performing an information search or map search, means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for recognizing the user's emotional state, and means for adjusting the suggested content based on the recognized emotional state. This makes it possible to provide optimal information by combining the user's past reading history with their current search behavior and emotional state.

[0327] "User" refers to an individual who uses the system to search for information or record books.

[0328] "Books" refers to literature such as books and e-books that users have read in the past.

[0329] "Means for recording information" refers to a function that allows users to save information about books they have read in the past in a database.

[0330] "Means for analyzing context" refers to a function that analyzes the content of search queries and viewed pages when a user is searching for information or maps, and identifies the user's interests.

[0331] "Suggestion methods" refers to a function that suggests to users related phrases, events, and places from books they have read in the past based on analyzed context.

[0332] "Means for recognizing emotional state" refers to a function that analyzes emotions from the user's facial expressions, voice, etc., and identifies their state.

[0333] "Means for adjusting suggested content" refers to a function for optimizing suggested content based on the perceived emotional state of the user.

[0334] The system of the present invention records information about books that a user has read in the past, analyzes the context when the user is performing an information search or map search, and suggests related phrases, events, and places based on the analyzed context. It also has the ability to recognize the user's emotional state and adjust the suggested content based on that emotional state.

[0335] Hardware and software used

[0336] Hardware: Servers, user devices (PCs, smartphones, tablets, etc.)

[0337] Software: Database management systems (e.g., MySQL, PostgreSQL), natural language processing engines (e.g., Google NLP, IBM Watson®), emotion recognition engines (e.g., Microsoft® Azure® Emotion API)

[0338] Specific operation of the system

[0339] 1. Entering user reading information

[0340] Users enter information about books they have read in the past (title, author, publication date, completion date, keywords and phrases) into the device.

[0341] The terminal temporarily stores the input information and transmits it to the server.

[0342] The server stores the received information in a database management system.

[0343] 2. Collection of user behavior data

[0344] Users generate AI models, perform web searches, and perform map searches.

[0345] The device collects behavioral data, such as the user's search queries, the content of the web pages they are viewing, and the locations they are searching for on a map, and sends it to a server.

[0346] 3. Context analysis

[0347] The server uses a natural language processing engine to analyze the context of the collected behavioral data.

[0348] Based on the analysis results, identify the user's current interests.

[0349] 4. Searching for related information

[0350] The server searches the database for phrases, events, and places related to the parsed context.

[0351] For example, if a user searches for "tourist attractions in Paris," the server will retrieve information related to Paris in the database.

[0352] 5. Emotional state analysis

[0353] The device collects emotional data such as the user's facial expressions and voice and sends it to the server.

[0354] The server uses an emotion recognition engine to analyze the user's emotional state.

[0355] For example, if the user expresses an emotion of happiness, the server records that information.

[0356] 6. Providing suggestions

[0357] The server generates optimal suggestions based on the analyzed context and emotional state.

[0358] For example, if a user searches for tourist attractions in Paris and expresses joy, the server might suggest information such as "Eiffel Tower" or "Louvre Museum."

[0359] The terminal displays the suggestions received from the server to the user.

[0360] Specific examples

[0361] User Action: User performs a web search for "tourist attractions in Paris."

[0362] Contextual analysis: The server analyzes the keywords "Paris" and "tourist attractions" and determines that the user is looking for information about Paris.

[0363] Suggestions: The app searches a database for phrases and events related to Paris in books the user has read in the past, and suggests phrases such as "Eiffel Tower" or "Louvre Museum."

[0364] Sentiment analysis: If the sentiment engine detects that a user is excited, it will suggest attractions or events that will further increase their excitement.

[0365] Prompt Sentence Examples

[0366] Prompt for generative AI model: "What phrases or events related to Paris have you read in books?"

[0367] In this way, the system can combine a user's past reading history with their current behavior and emotional state to provide the most relevant information.

[0368] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0369] Step 1:

[0370] The user enters information about books they have read in the past.

[0371] Input: The user enters the book title, author, publication date, completion date, and keywords or phrases into the device.

[0372] What happens: A user opens the app on their smartphone, taps the "Add New Book" button, and fills out the form with book information.

[0373] Output: Book information temporarily stored on the device.

[0374] Step 2:

[0375] The terminal transmits the input book information to the server.

[0376] Input: Book information temporarily stored on your device.

[0377] Specific operation: When the device presses the "Save" button, the book information is sent to the server.

[0378] Output: Book information sent to the server.

[0379] Step 3:

[0380] The server stores the received book information in a database.

[0381] Input: Book information sent to the server.

[0382] Specific operation: The server stores the book information in a database management system (e.g., MySQL or PostgreSQL).

[0383] Output: Book information stored in a database.

[0384] Step 4:

[0385] Users perform information searches and map searches.

[0386] Input: The search query or page content viewed when a user interacts with a generated AI model, web search, or map search.

[0387] What happens: A user searches for "tourists in Paris" in their browser.

[0388] Output: Search queries and page views collected on the device.

[0389] Step 5:

[0390] The device transmits the collected behavioral data to a server.

[0391] Input: Search queries and page views collected on your device.

[0392] What it does: The device sends the user's search query and the contents of the page they are viewing to the server.

[0393] Output: Behavioral data sent to the server.

[0394] Step 6:

[0395] The server analyzes the context of the behavioral data.

[0396] Input: Behavioral data sent to the server.

[0397] What happens: The server uses a natural language processing engine (e.g., Google NLP or IBM Watson) to analyze the context of the behavioral data.

[0398] Output: Parsed context information.

[0399] Step 7:

[0400] The server retrieves the relevant information from a database.

[0401] Input: Parsed context information.

[0402] What it does: The server searches its database for phrases, events, and places related to the parsed context.

[0403] Output: The relevant information found.

[0404] Step 8:

[0405] The device collects the user's emotional data.

[0406] Input: Emotional data such as the user's facial expressions and voice.

[0407] Specific action: The user smiles through the smartphone camera.

[0408] Output: Emotion data collected on the device.

[0409] Step 9:

[0410] The device transmits the collected emotion data to a server.

[0411] Input: Emotion data collected on the device.

[0412] Specific operation: The device sends emotion data to the server.

[0413] Output: Emotion data sent to the server.

[0414] Step 10:

[0415] The server analyzes the emotion data.

[0416] Input: Emotion data sent to the server.

[0417] Specific operation: The server uses an emotion recognition engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state.

[0418] Output: Parsed emotional state.

[0419] Step 11:

[0420] The server generates suggestions based on the analyzed context and emotional state.

[0421] Input: Parsed contextual information and emotional state.

[0422] What it does: The server generates optimal suggestions based on the context and emotional state analyzed.

[0423] Output: The generated suggestion information.

[0424] Step 12:

[0425] The device displays suggested information to the user.

[0426] Input: The generated suggestion information.

[0427] Specific operation: The device notifies the user of the suggestion information received from the server.

[0428] Output: The suggestions displayed to the user.

[0429] (Application example 1)

[0430] Next, a description will be given of Application Example 1 of Embodiment 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."

[0431] Conventional systems have difficulty effectively utilizing information from books that users have read in the past, and are unable to provide appropriate information tailored to the context, especially when users are using generative artificial intelligence, web searches, map searches, etc. Furthermore, they do not take into account the user's emotional state when making suggestions, which makes it difficult to increase user satisfaction.

[0432] 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.

[0433] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, web search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, emotion analysis means for recognizing the emotional state of the user, and means for making suggestions based on the emotional state of the user. This makes it possible to effectively utilize information about books the user has read in the past and provide appropriate information tailored to the context, and further makes it possible to increase user satisfaction by making suggestions that take the user's emotional state into consideration.

[0434] "User" means any individual or entity that uses the System.

[0435] "Books" refers to books and literature that the user has read in the past.

[0436] "Means for recording information" refers to a device or software that has the function of storing information about books a user has previously read, such as the title, author, publication date, completion date, and keywords or phrases related to the content, in a database.

[0437] "Generative AI" is an AI technology that generates new information and content based on user input.

[0438] "Web search" is the act of searching for information on the Internet using a search engine.

[0439] "Map search" is the act of searching for information about a specific place or region using a geographic information system.

[0440] "Context analysis" refers to technology that analyzes the content of a user's searches or browsing activities to understand their intent and purpose.

[0441] A "suggestion means" is a device or software that has the function of presenting to the user related phrases, events, places, etc. from books the user has read in the past based on the analyzed context.

[0442] "Emotion analysis means" is a technology that recognizes the user's emotional state and makes appropriate suggestions based on that emotion.

[0443] "Emotional state" refers to the emotions such as joy, sadness, anger, etc. that the user is currently feeling.

[0444] The system for implementing this invention records information about books a user has read in the past, and analyzes the context when the user is performing generative artificial intelligence, web searches, map searches, etc. to suggest related phrases, events, and places. It also has the ability to recognize the user's emotional state and make suggestions based on that emotion.

[0445] Hardware and software used

[0446] Hardware: Smartphone

[0447] software:

[0448] SQLite: Database Management

[0449] TextBlob: Natural Language Processing

[0450] SentimentAnalyzer: Sentiment analysis engine

[0451] SearchContextAnalyzer: Contextual Analysis Engine

[0452] Data processing and calculation

[0453] The server first records information about books the user has read in the past in a database, including the book's title, author, publication date, completion date, and keywords or phrases related to the content.

[0454] When a user performs a generative AI search, web search, map search, etc., the server analyzes the context using the SearchContextAnalyzer to extract keywords from the search query, the content of the web page being viewed, the location being searched for on the map, etc.

[0455] Next, the server uses SentimentAnalyzer to recognize the user's emotional state. Sentiment analysis identifies the emotional state the user is currently feeling, such as joy, sadness, or anger.

[0456] Based on the analyzed context and emotional state, the server selects relevant phrases, events, and places from its database and suggests them to the user. The suggestions are filtered according to the user's emotional state. For example, if the user is expressing joy, phrases and events related to joy will be suggested first.

[0457] Specific examples

[0458] If a user searches for "the latest technology in AI-based natural language processing," the server will suggest phrases and events related to "natural language processing" and "AI" from books they have read in the past. Furthermore, if the user expresses joy, the server will prioritize suggesting phrases and events related to joy.

[0459] Prompt Sentence Examples

[0460] "Based on what the user is searching for, suggest related phrases and events from books they've read. Also, if the user's emotional state is joy, prioritize suggesting phrases and events related to joy."

[0461] In this way, it is possible to effectively utilize information about books the user has read in the past and provide appropriate information that is tailored to the context. Furthermore, by making suggestions that take into account the user's emotional state, it is possible to increase user satisfaction.

[0462] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0463] Step 1:

[0464] The server records information about books that users have read in the past in a database. As input, it receives the title, author, publication date, completion date, and keywords or phrases related to the content of the book that the user has read. It stores this information in an SQLite database. As output, it obtains the book information recorded in the database.

[0465] Step 2:

[0466] When a user performs a generative artificial intelligence (AI) search, web search, map search, etc., the device collects contextual information such as the search query, the content of the web page being viewed, and the location being searched for on the map. The device receives the user's search query, the content of the page being viewed, and the location information of the map search as input. The collected contextual information is obtained as output.

[0467] Step 3:

[0468] The server analyzes the collected context information. As input, it receives the context information obtained in step 2. It uses SearchContextAnalyzer to extract keywords from the context information. As output, it obtains the analyzed keywords.

[0469] Step 4:

[0470] The server recognizes the user's emotional state. As input, it receives the user's current behavior and input. It uses SentimentAnalyzer to analyze the user's emotional state. As output, it obtains the user's emotional state (e.g., happy, sad, angry).

[0471] Step 5:

[0472] The server selects relevant phrases, events, and places from the database based on the analyzed contextual information and emotional state. As input, it receives the keywords obtained in step 3 and the emotional state obtained in step 4. It searches and filters relevant book information from the database. As output, it obtains a list of relevant phrases, events, and places.

[0473] Step 6:

[0474] The server suggests selected phrases, events, and places to the user. As input, it receives the relevant information obtained in step 5. It adjusts the suggestions depending on the user's emotional state. As output, it displays the suggested phrases, events, and places to the user.

[0475] The above processing steps make it possible to effectively utilize information about books the user has read in the past and provide appropriate information that matches the context, and further increase user satisfaction by making suggestions that take into account the user's emotional state.

[0476] Example 2

[0477] Next, a description will be given of Example 2 of Form 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."

[0478] Conventional information search systems make it difficult for users to effectively utilize the content of books they have read in the past, and because search results are not linked to the user's past knowledge, it is difficult to acquire new knowledge or reconfirm past knowledge. In addition, they do not provide appropriate information according to the user's emotional state, which leads to a decrease in user satisfaction.

[0479] 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.

[0480] In this invention, the server includes a means for recording information about books the user has read in the past, a means for analyzing the context when the user is using information search or generative artificial intelligence, a means for suggesting related phrases, events, and places from books the user has read in the past based on the analyzed context, and a means for analyzing the user's emotional state and adjusting the suggested content. This makes it possible to effectively utilize the content of books the user has read in the past and provide appropriate information according to the context. Furthermore, providing information according to the user's emotional state can improve user satisfaction.

[0481] "User" refers to an individual or organization that uses the system to search for information or use generative artificial intelligence.

[0482] "Books" refers to information media such as books and e-books that users have read in the past.

[0483] "Means for recording information" refers to a function that allows users to save information about books they have read in the past in a database, etc.

[0484] "Means of context analysis" refers to the ability to analyze inputs made by users when using information search or generative artificial intelligence, and understand their meaning and intent.

[0485] "Suggestion methods" refers to a function that suggests related phrases, events, and places to users from books they have read in the past based on analyzed context.

[0486] "Means for analyzing emotional state" refers to a function for analyzing a user's emotions and adjusting the suggested content based on the results.

[0487] "Generative AI" refers to an AI system that generates responses in natural language based on user input.

[0488] "Information search" refers to the act of a user searching for information through web searches, map searches, etc.

[0489] A "phrase" refers to a series of words or sentences contained within a book.

[0490] "Incident" refers to a specific occurrence or event described in a book.

[0491] "Places" refer to geographical locations or points mentioned in the book.

[0492] MODE FOR CARRYING OUT THE INVENTION

[0493] The present invention is a system that effectively utilizes information from books that a user has read in the past to provide appropriate information according to the context. A specific embodiment of this system will be described below.

[0494] Hardware and software used

[0495] Server: Database management system (e.g., MySQL), natural language processing engine (e.g., SpaCy), sentiment analysis engine (e.g., IBM Watson)

[0496] Device: PC or smartphone used by the user

[0497] Software: Web browser, generative AI model (e.g., OpenAI GPT-4)

[0498] System Overview

[0499] The system analyzes context as users research information using web searches or generative AI, suggesting related phrases, events, and places from previously read books, and can also adjust suggestions based on the user's emotional state.

[0500] Specific actions

[0501] 1. Capturing user behavior

[0502] The device captures search queries and inputs when users use web searches and generative AI.

[0503] Example: When a user searches for "Rome attractions" in a web browser, the device records the search query.

[0504] 2. Context Analysis

[0505] The server analyzes the captured search queries and input content using a natural language processing engine to understand the context.

[0506] Example: The server parses the search query "tourist attractions in Rome" and extracts the keyword "Rome" and the context "tourist attractions."

[0507] 3. Database Search

[0508] The server searches a database of the user's past reading history to identify relevant books.

[0509] Example: The server queries a database to determine that the user has previously read the book "The History of the Roman Empire."

[0510] 4. Extracting relevant information

[0511] The server extracts contextually relevant phrases, events, and places from the identified books.

[0512] Example: The server parses text data from "History of the Roman Empire" and extracts information about tourist attractions such as the "Colosseum" and the "Roman Forum."

[0513] 5. Emotional State Analysis (Optional)

[0514] The server uses an emotion engine to analyze the user's emotional state and adjust the suggestions accordingly.

[0515] Example: If a user indicates an emotion of sadness, the server will use an emotion engine to analyze that emotion and prioritize extracting phrases and events related to sadness.

[0516] 6. Providing Suggestions

[0517] The server sends the extracted information to the user's device and presents suggestions to the user.

[0518] Example: The server extracts information about the Colosseum and the Roman Forum from the History of the Roman Empire and sends it to the user's device, where it is displayed on the user's web browser.

[0519] Examples and prompts

[0520] Examples:

[0521] When a user searches the web for "tourist attractions in Rome," the system suggests information about tourist attractions such as the Colosseum and the Roman Forum from a book they previously read about: "The History of the Roman Empire."

[0522] Example prompt sentence:

[0523] "When a user searches the web for 'tourist attractions in Rome,' suggest relevant tourist attractions from a book they previously read, 'The History of the Roman Empire.'"

[0524] This system allows users to recall the contents of books they have read in the past and provides an opportunity to acquire new knowledge. It also improves user satisfaction by providing information according to the user's emotional state.

[0525] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0526] Step 1:

[0527] Capturing user behavior

[0528] Input: Search queries and inputs users make when searching the web or using generative AI

[0529] What it does: The device captures the search query when a user searches for "Rome attractions" in a web browser.

[0530] Output: Captured search queries and inputs

[0531] Step 2:

[0532] Contextual Analysis

[0533] Input: Captured search queries or inputs

[0534] What happens: The server uses a natural language processing engine (e.g., SpaCy) to parse the search query "tourist attractions in Rome" and extract the keyword "Rome" and the context "tourist attractions."

[0535] Output: Parsed context information (keywords and context)

[0536] Step 3:

[0537] Searching the database

[0538] Input: Parsed context information (keywords and context)

[0539] What happens: The server uses a database management system (e.g., MySQL) to search the user's past reading history and identify relevant books. For example, it verifies that the user has previously read a book called "The History of the Roman Empire."

[0540] Output: Related book information

[0541] Step 4:

[0542] Extracting relevant information

[0543] Input: Related book information

[0544] What it does: The server analyzes the text data of the identified books and extracts contextually relevant phrases, events, and places. For example, it extracts information about tourist attractions such as the Colosseum and the Roman Forum from "The History of the Roman Empire."

[0545] Output: Extracted relevant information (phrases, events, places)

[0546] Step 5:

[0547] Emotional state analysis (optional)

[0548] Input: User emotional state data

[0549] Specific operation: The server uses an emotion analysis engine (e.g., IBM Watson) to analyze the user's emotional state. For example, if the user expresses sadness, the server analyzes the emotion and prioritizes extracting phrases and events related to sadness.

[0550] Output: Adjusted suggestions

[0551] Step 6:

[0552] Providing suggestions

[0553] Input: Extracted related information (phrases, events, locations) and tailored suggestions

[0554] Specific operation: The server sends the extracted information to the user's device and presents suggestions to the user. For example, information about the Colosseum and the Roman Forum extracted from the "History of the Roman Empire" is sent to the user's device and displayed in the web browser.

[0555] Output: Suggested information presented to the user

[0556] (Application example 2)

[0557] Next, a description will be given of Application Example 2 of Form 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."

[0558] Previous content distribution services did not have a system that provided related information by utilizing the content of books that users had previously read. Furthermore, they lacked the ability to adjust suggested content based on the user's emotional state. This resulted in a decrease in the user's search experience and efficiency of information acquisition, and the inability to fully utilize previously acquired knowledge.

[0559] 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.

[0560] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is researching using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for analyzing the user's emotional state, and means for adjusting the suggested content based on the analyzed emotional state. This makes it possible to provide appropriate information according to the user's emotional state while utilizing knowledge from books read in the past.

[0561] "User" refers to an individual who uses the system to search for information or use generative artificial intelligence.

[0562] "Books" refers to information media such as books and e-books that users have read in the past.

[0563] "Means for recording information" refers to a method or device for storing information about books that a user has previously read in a database.

[0564] "Generative AI" refers to AI technology that generates content such as text and images based on user input.

[0565] "Internet search" refers to the act of a user searching for information on the Internet using a web browser.

[0566] "Map search" refers to the act of a user using a map service to search for a specific location or route.

[0567] "Means for analyzing context" refers to a method or device for analyzing keywords a user is searching for or prompts a user is entering.

[0568] "Suggestion means" means a method or device for suggesting relevant phrases, events, or places to a user based on analyzed context or emotional state.

[0569] "Means for analyzing emotional state" refers to a method or apparatus for analyzing emotions from user input or behavior.

[0570] The "means for adjusting the suggested content" refers to a method or device for appropriately changing the content of information provided to the user based on the analyzed emotional state.

[0571] A system for implementing this invention records information about books a user has read in the past, analyzes the context when the user is researching using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, and places based on the analyzed context. It also has the function of analyzing the user's emotional state and adjusting the suggested content based on that emotional state.

[0572] Hardware and Software Configuration

[0573] server:

[0574] Database: A database for storing information about books that users have read in the past.

[0575] Context analysis module: A module for analyzing users' search keywords and prompt sentences.

[0576] Sentiment Analysis Module: A module for analyzing the emotional state of the user.

[0577] Suggestion module: A module for suggesting related phrases, events, and places based on the analysis results.

[0578] Device:

[0579] Smartphone: A device that allows users to use search and generative artificial intelligence.

[0580] Applications: Applications that allow users to use search and generative artificial intelligence.

[0581] Data processing and calculation

[0582] The server records information about books the user has read in a database. When the user uses their smartphone to search the Internet or use generative artificial intelligence, the context analysis module analyzes the search keywords and prompts. The analysis results are sent to the server, and the sentiment analysis module analyzes the user's emotional state. Based on this, the suggestion module selects related phrases, events, and places to suggest to the user.

[0583] Specific examples

[0584] For example, if a user searches for "tourist attractions in Rome," the app will suggest related tourist attractions and historical events based on their previous reading of "The History of the Roman Empire." If the user expresses sadness, it will also suggest sad historical events in Rome.

[0585] Prompt Sentence Examples

[0586] When a user searches for "places to visit in Rome," suggest related places and historical events based on their previous reading of "History of the Roman Empire." If the user expresses sadness, suggest sad historical events in Rome.

[0587] In this way, users can enrich their search experience and leverage their knowledge of previously read books.

[0588] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0589] Step 1:

[0590] A user uses a smartphone to search the Internet or use generative artificial intelligence. The user enters search keywords and prompts. The entered keywords and prompts are sent from the device to a server.

[0591] Step 2:

[0592] The server's context analysis module analyzes the received search keywords and prompts. Specifically, it uses natural language processing technology to understand the meaning and context of the keywords. As a result of the analysis, related topics and themes are extracted. The input is the search keywords and prompts, and the output is the analyzed topics and themes.

[0593] Step 3:

[0594] The server retrieves information about books the user has read in the past from its database. The database records the titles and contents of books the user has read in the past. The input is the user ID, and the output is information about books read in the past.

[0595] Step 4:

[0596] The server's emotion analysis module analyzes the user's emotional state by using an algorithm to infer emotions from the user's input and behavior. The input is the user's search keywords and prompt, and the output is the analyzed emotional state.

[0597] Step 5:

[0598] The server's suggestion module selects related phrases, events, and places based on the results of context analysis, sentiment analysis, and information about previously read books. Specifically, it extracts related information from the contents of previously read books and adjusts the suggested content according to the user's emotional state. The input is the results of context analysis, sentiment analysis, and information about previously read books, and the output is suggested phrases, events, and places.

[0599] Step 6:

[0600] The server sends suggested phrases, events, and locations to the device. The device displays the received suggestions to the user. The user can review the displayed suggestions and explore them further if necessary. The input is the suggested phrases, events, and locations, and the output is the information displayed to the user.

[0601] 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.

[0602] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0603] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

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

[0605] [Second embodiment]

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

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

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

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

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

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

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

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

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

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

[0616] In the smart glasses 214, the 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.

[0617] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0618] "Example 1"

[0619] The system of the present invention records information about books a user has read in a database. This database includes information such as the book's title, author, publication date, completion date, and keywords and phrases related to the book's content. When a user conducts research using generative AI, web search, map search, etc., the system analyzes the context. The analysis is based on information obtained from the user's behavior, such as the search query, the content of the web page being viewed, and the location being searched for on a map. The system then suggests related phrases, events, places, etc. from the database based on the analyzed context.

[0620] "Example 2"

[0621] For example, when a user searches the web for "Rome," the system analyzes the context and searches a database to see if they have previously read books related to "Rome." If the user has previously read a book about the history of the Roman Empire, the system will suggest phrases, events, and places related to "Rome" from that book. This helps users recall the contents of the book and provides an opportunity to gain new knowledge.

[0622] The processing flow of each embodiment will be described below.

[0623] "Example 1"

[0624] Step 1: When a user finishes reading a book, the system records the book's information (title, author, publication date, completion date, keywords and phrases related to the content, etc.) in a database.

[0625] Step 2: When a user is researching something using generative AI, web search, or map search, the system analyzes the context (the search query, the content of the web page they are viewing, the location they are searching for on a map, etc.).

[0626] Step 3: The system uses the analyzed context to suggest relevant phrases, events, places, etc. from its database.

[0627] "Example 2"

[0628] Step 1: When a user performs a web search for "Rome," the system analyzes the context.

[0629] Step 2: The system searches the database to see if you have previously read any books related to "Rome."

[0630] Step 3: If you have previously read a book about the history of the Roman Empire, the app will suggest phrases, events, and places related to Rome from that book.

[0631] Example 1

[0632] Next, a description will be given of Example 1 of Form 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."

[0633] Conventional information search systems make it difficult for users to effectively utilize information from books they have read in the past, and are unable to provide information useful for current searches or research activities. Furthermore, they lack the means to analyze user behavior data in real time and provide information based on appropriate context. This prevents users from quickly and accurately obtaining the information they need, reducing the efficiency of information searches.

[0634] 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.

[0635] In this invention, the server includes means for recording information about books that a user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books that have been read in the past based on the analyzed context, means for collecting user behavior data, means for analyzing the context using a natural language processing algorithm based on the collected behavior data, means for searching for related information from a database based on the analyzed context, and means for displaying the searched related information to the user. This makes it possible to utilize the user's past reading history and quickly and accurately provide information that is useful for current searches and research activities.

[0636] A "user" is an individual or group that uses the system to search for information or enter past reading history.

[0637] "Books" refer to information media such as books and e-books that users have read in the past.

[0638] "Means for recording information" is a function that allows users to save information about books they have read in the past in a database.

[0639] "Generative artificial intelligence" refers to algorithms or systems that generate information based on user input and actions.

[0640] An "Internet search" is the act of a user searching for information on the Internet using a web browser.

[0641] A "map search" is the act of a user searching for information about a specific location or area using a map application.

[0642] "Context analysis" is a function that understands the current context based on information such as the user's search query, the content of the web page they are viewing, and the location they are searching for on a map.

[0643] "Suggestion methods" is a function that presents users with related phrases, events, places, etc. from books they have read in the past based on the analyzed context.

[0644] "Means for collecting behavioral data" refers to functions for collecting behavioral data such as user search queries, the content of web pages viewed, and locations searched for on maps.

[0645] A "natural language processing algorithm" is an algorithm that analyzes collected behavioral data and understands the context.

[0646] A "database" is a data storage system that stores information about books that a user has read in the past.

[0647] The "means for searching related information" is a function that searches for information such as related phrases, events, and places from a database based on the analyzed context.

[0648] "Means for displaying information" refers to a function for visually presenting the retrieved related information to the user.

[0649] This invention is a system that records information about books that a user has read in the past, analyzes the context when the user is conducting research using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, places, etc. based on the analyzed context.

[0650] Building a database

[0651] The server builds a database to record information about books that users have read in the past, including book titles, authors, publication dates, completion dates, and keywords or phrases related to the book's content. Users enter this information using a web form, and the server stores the data in the database using SQL queries.

[0652] Contextual Analysis

[0653] The device collects information such as users' search queries, the content of web pages they view, and locations they search for on maps, including through browser extensions and applications that collect user behavior data in real time. The server analyzes the collected user behavior data and uses natural language processing (NLP) algorithms to understand the current context.

[0654] Suggestion function

[0655] The server searches for related phrases, events, places, and other information from a database based on the analyzed context. This involves using SQL queries to search for related information in the database. The device displays the related information received from the server to the user. Specifically, suggestions are displayed to the user through browser pop-ups or application notifications.

[0656] Specific examples

[0657] For example, if a user searches for "tourist attractions in Paris," the following process occurs:

[0658] 1. A user searches for "tourist attractions in Paris."

[0659] 2. The device collects this search query.

[0660] 3. The server analyzes the collected search queries and understands the context related to "Paris."

[0661] 4. The server searches the database for information on books related to "Paris."

[0662] 5. The terminal displays the relevant information received from the server to the user.

[0663] Prompt Sentence Examples

[0664] When a user searches for "tourist attractions in Paris," the server extracts information about books related to Paris from a database and suggests related phrases and events.

[0665] This system utilizes the user's past reading history to quickly and accurately provide information useful for current searches and research activities, allowing users to efficiently obtain the information they need and improving the efficiency of information retrieval.

[0666] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0667] Step 1:

[0668] The user enters information about books they have read in the past.

[0669] Input: A user fills out a web form and enters the book title, author, publication date, completion date, and keywords or phrases related to the book's content.

[0670] Data processing: Send the information entered by the user to the server.

[0671] Output: Book information received by the server.

[0672] What happens: A user fills out a web form and clicks the submit button.

[0673] Step 2:

[0674] The server stores the book information in a database.

[0675] Input: Book information submitted by the user.

[0676] Data processing: The server uses SQL queries to insert the book information into the database.

[0677] Output: Book information stored in a database.

[0678] Specific operation: The server saves the received data in the database using an SQL query.

[0679] Step 3:

[0680] The device collects user behavior data.

[0681] Input: Your search query, the content of the web page you're viewing, or the location you're searching for on a map.

[0682] Data processing: Devices collect behavioral data in real time through browser extensions and applications.

[0683] Output: Collected behavioral data.

[0684] What it does: The device monitors user behavior and collects necessary data.

[0685] Step 4:

[0686] The server analyzes the collected behavioral data.

[0687] Input: Collected behavioral data.

[0688] Data processing: The server uses natural language processing (NLP) algorithms to analyze the behavioral data and understand the current context.

[0689] Output: Parsed context information.

[0690] What happens: The server runs NLP algorithms and analyzes the behavioral data.

[0691] Step 5:

[0692] The server retrieves the relevant information from a database.

[0693] Input: Parsed context information.

[0694] Data processing: The server uses SQL queries to find relevant information in a database.

[0695] Output: The relevant information found.

[0696] What happens: The server runs an SQL query to extract the relevant information from the database.

[0697] Step 6:

[0698] The device displays the relevant information to the user.

[0699] Input: Relevant information received from the server.

[0700] Data processing: The device will display relevant information through browser pop-ups and application notifications.

[0701] Output: Relevant information displayed to the user.

[0702] Specific behavior: The device visually presents the information it receives to the user.

[0703] (Application example 1)

[0704] Next, a description will be given of Application Example 1 of Form 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."

[0705] Conventional systems have difficulty effectively utilizing information from books a user has read in the past, and are unable to provide context-based related information, particularly through generative AI, web searches, and map searches. Furthermore, there is a lack of a way to provide real-time information related to the content a user is viewing, creating a need for improved user convenience. Furthermore, the lack of a function to generate context-related prompts using generative AI has hindered the enrichment of the user's search experience.

[0706] 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.

[0707] In this invention, the server includes: means for recording information about books previously read by a user; means for analyzing the context of the user's research using generative artificial intelligence, web search, map search, etc.; means for suggesting related phrases, events, places, etc. from books previously read based on the analyzed context; means for providing information related to the content currently being viewed by the user in real time; and means for generating context-relevant prompts using generative artificial intelligence. This makes it possible to effectively utilize information about books previously read by the user and provide related information based on the context of generative artificial intelligence, web search, map search, etc. Furthermore, providing information related to the content currently being viewed by the user in real time can improve user convenience. Furthermore, generating context-relevant prompts using generative artificial intelligence can enrich the user's search experience.

[0708] "User" means any individual or entity that uses the System.

[0709] "Information about books read in the past" refers to information about books that the user has previously read, such as the title, author, publication date, completion date, keywords and phrases related to the content, etc.

[0710] "Means for recording" refers to a method or device for storing information about books that a user has read in the past in a database or the like.

[0711] "Generative AI" is AI that has the ability to generate new information and content based on user input and context.

[0712] "Web searching" is the act of finding information on the Internet using a search engine.

[0713] "Map searching" is the act of using a geographic information system to search for information about a particular place or region.

[0714] A "means for analyzing context" is a method or device for understanding the content of a user's search or browsing and analyzing its background and intent.

[0715] A "means for suggesting" is a method or device for suggesting relevant information to a user based on the analyzed context.

[0716] "Real-time provision means" refers to a method or device for instantly providing relevant information to a user while the user is viewing content.

[0717] A "prompt sentence" is a sentence that generative artificial intelligence generates based on the user's context to encourage the next action or information.

[0718] The system for implementing this invention records information about books that a user has read in the past, analyzes the context of generative artificial intelligence, web searches, map searches, etc., and provides related information in real time. A specific embodiment of this system is described below.

[0719] 1. System Program

[0720] The system consists of the following main components:

[0721] Database: SQLite is used to store information about books that users have read in the past.

[0722] Contextual Analysis Module: Uses BeautifulSoup to analyze the content of the web page the user is viewing and extract text.

[0723] Suggestion module: Retrieves information about previously read books from a database and checks whether they contain keywords relevant to the context.

[0724] Generative AI model: Uses OpenAI APIs to generate contextually relevant prompts.

[0725] 2. Program processing explanation

[0726] Hardware and software used

[0727] Hardware: Smartphones, smart glasses

[0728] Software: Python, SQLite, BeautifulSoup, OpenAI API

[0729] Data processing and calculation

[0730] Database connection: We use SQLite to connect to a database that stores information about books that users have read.

[0731] Contextual analysis: Use BeautifulSoup to analyze the content of the web page the user is viewing and extract text.

[0732] Suggestion function: Retrieves information about books you have read in the past from a database and checks whether they contain keywords related to the context.

[0733] Generative AI model: Uses OpenAI APIs to generate contextually relevant prompts.

[0734] 3. Specific Examples

[0735] When a user is reading a news article and the article is about environmental issues, the system suggests information about books about environmental issues that the user has read in the past. For example, the system generates a prompt such as, "I'm reading the latest news article about environmental issues. Please tell me about phrases or events related to environmental issues in books that you have read in the past."

[0736] This system allows users to effectively utilize information from books they have read in the past and provide relevant information in context with generative AI, web searches, map searches, etc. It also improves user convenience by providing information related to the content the user is viewing in real time. Furthermore, it can enrich the user's search experience by using generative AI to generate context-relevant prompts.

[0737] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0738] Step 1:

[0739] Record information about books that a user has read in the past.

[0740] Input: Book title, author, publication date, completion date, keywords or phrases related to the content.

[0741] Processing: The device stores this information in an SQLite database.

[0742] Output: Book information recorded in the database.

[0743] Step 2:

[0744] Users conduct research using generative artificial intelligence, web searches, map searches, etc.

[0745] Input: The search query a user enters or the URL of a web page they are viewing.

[0746] Processing: The device retrieves the user's search query and the content of the web page.

[0747] Output: Search query or web page content.

[0748] Step 3:

[0749] Analyze the context.

[0750] Input: A search query or web page content.

[0751] Processing: The device uses BeautifulSoup to parse the content of the web page and extract the text.

[0752] Output: The extracted text.

[0753] Step 4:

[0754] It suggests related phrases, events, places, etc. from books you have read in the past.

[0755] Input: Extracted text, book information recorded in database.

[0756] Processing: The server retrieves information about books read in the past from the database and checks whether the extracted text contains relevant keywords.

[0757] Output: A list of related phrases, events and places.

[0758] Step 5:

[0759] Provides real-time information related to the content the user is viewing.

[0760] Input: A list of related phrases, events, and places.

[0761] Processing: The terminal displays this information to the user in real time.

[0762] Output: The relevant information displayed to the user.

[0763] Step 6:

[0764] Uses generative artificial intelligence to generate contextually relevant prompts.

[0765] Input: The extracted text.

[0766] Processing: The server uses the OpenAI API to generate a prompt based on the extracted text.

[0767] Output: The generated prompt statement.

[0768] Step 7:

[0769] Provides the user with a generated prompt.

[0770] Input: The generated prompt statement.

[0771] Processing: The terminal displays the generated prompt to the user.

[0772] Output: The prompt text displayed to the user.

[0773] Example 2

[0774] Next, a description will be given of Example 2 of Form 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."

[0775] Modern information retrieval systems face the challenge of making it difficult for users to effectively utilize the content of books they have read in the past. In particular, when users search for a specific topic, they lack a mechanism to refer to their past reading history to provide related information. This results in users being unable to fully utilize their previously acquired knowledge, and they are unable to efficiently acquire new knowledge.

[0776] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for recording information about books that the user has read in the past, a means for analyzing the context when the user is searching for information, a means for suggesting related phrases, events, places, etc. from books that the user has read in the past based on the analyzed context, a means for using a generative AI model for generating suggestions, and a means for sending prompt sentences to the generative AI model. This makes it possible to effectively utilize the contents of books that the user has read in the past and provide information related to the search results.

[0777] A "user" refers to an individual with a history of information searches and book reading.

[0778] "Books" refers to information media such as books and e-books that users have read in the past.

[0779] "Information search" refers to the act of a user researching information through web searches, map searches, etc.

[0780] "Context" refers to the search query and related information surrounding it when a user searches for information.

[0781] "Analysis" refers to the act of understanding a user's search query and context using technologies such as natural language processing and extracting relevant information.

[0782] A "phrase" refers to a specific word or part of a sentence contained in a book.

[0783] "Incident" refers to a specific occurrence or event described in a book.

[0784] "Places" refer to geographic locations or areas mentioned in the book.

[0785] "Suggestion" refers to the act of suggesting information such as related phrases, events, or places to the user.

[0786] A "generative AI model" refers to an artificial intelligence model for generating natural language.

[0787] A "prompt sentence" refers to an instruction sentence input to a generative AI model.

[0788] "Server" refers to the computer system that processes users' search queries and reading history and performs analysis and suggestions.

[0789] This invention is a system that effectively utilizes the contents of books that users have read in the past when searching for information, and provides related information. This system works in cooperation with the server, the terminal, and the user.

[0790] Hardware and software used

[0791] Server: Database management system (e.g., MySQL), natural language processing engine (e.g., spaCy), generative AI model (e.g., GPT-4)

[0792] Device: Web browser (e.g. Google Chrome)

[0793] User: The individual searching for information.

[0794] Specific operation of the system

[0795] 1. Receive the user's search query

[0796] A user opens a web browser and types "Rome" into the search bar.

[0797] The terminal sends this search query to the server.

[0798] 2. Analyze the context of the search query

[0799] The server uses a natural language processing engine (e.g., spaCy) to analyze the context of the search query "Rome."

[0800] Based on the analysis results, related keywords and topics are extracted.

[0801] 3. Search your past reading history

[0802] The server uses a database management system (e.g., MySQL) to retrieve the user's past reading history.

[0803] For example, if the user has previously read a book called "The History of the Roman Empire," the contents of that book are retrieved.

[0804] 4. Suggest related information

[0805] The server extracts phrases, events, and places related to "Rome" from the acquired reading history.

[0806] Use a generative AI model (e.g., GPT-4) to generate appropriate suggestions for the user.

[0807] 5. Present suggestions to the user

[0808] The server sends the generated suggestions to the terminal.

[0809] The device displays suggestions to the user through a web browser.

[0810] Specific examples

[0811] User search query: "Rome"

[0812] Past reading history: "The History of the Roman Empire"

[0813] Suggested content: "The History of the Roman Empire provides detailed information about the founding of the city of Rome and the rule of Caesar. In particular, revisit the chapters on Caesar's reforms and the architecture of the city of Rome."

[0814] Example prompts for generative AI models

[0815] A user is performing a web search for "Rome." In their past reading history, they have a book called "The History of the Roman Empire." Based on the contents of this book, suggest phrases, events, and places related to "Rome" to the user.

[0816] This system allows users to recall the contents of books they have read in the past, providing an opportunity to gain new knowledge.

[0817] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0818] Step 1:

[0819] A user enters a search query. The user opens a web browser, types "Rome" into the search bar, and presses enter, generating the search query "Rome."

[0820] Step 2:

[0821] The terminal sends a search query to the server. The terminal sends the search query "Rome" entered by the user to the server as an HTTP request. The input is the search query "Rome" and the output is an HTTP request to the server.

[0822] Step 3:

[0823] The server analyzes the context of the search query. The server passes the received search query "Rome" to a natural language processing engine (e.g., spaCy) to analyze the context. The input is the search query "Rome," and the output is related keywords and topics as the analysis results. Specifically, the server extracts the meaning of the word "Rome" and related topics.

[0824] Step 4:

[0825] The server searches the database for past reading history. The server uses a database management system (e.g., MySQL) to search for the user's past reading history. The input is the user ID and the analysis result, and the output is information about related books. Specifically, the server executes an SQL query to search for books related to "Rome."

[0826] Step 5:

[0827] The server extracts relevant information and sends a prompt to the generative AI model. The server extracts phrases, events, and places related to "Rome" from the reading history and creates a prompt to send to the generative AI model (e.g., GPT-4). The input is information about the related book, and the output is a prompt. Specifically, the server generates a prompt based on the extracted information.

[0828] Step 6:

[0829] The generative AI model generates suggestions. Based on the received prompt, the generative AI model generates appropriate suggestions for the user. The input is the prompt, and the output is the suggested sentence. Specifically, the generative AI model uses a natural language generation algorithm to create suggested sentences.

[0830] Step 7:

[0831] The server sends the generated suggestions to the device. The server sends the suggestions received from the generative AI model to the device. The input is the suggested sentence, and the output is an HTTP response to the device. Specifically, the server generates an HTTP response and sends it to the device.

[0832] Step 8:

[0833] The device displays the suggestions to the user. The device then displays the received suggestions on a web browser. The input is the suggestion text, and the output is the information displayed to the user. Specifically, the device uses HTML and JavaScript to display the suggestions in a format that is easy for the user to view.

[0834] (Application example 2)

[0835] Next, a description will be given of Application Example 2 of Form 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."

[0836] In today's information society, when users research a specific topic, it is important to recall the contents of books they have read in the past and gain new knowledge. However, manually searching through the contents of books users have read in the past to find relevant information is extremely time-consuming. Furthermore, there is a lack of effective ways to link Internet search results with past reading history, making it difficult for users to quickly obtain the information they need. A system that solves these problems is needed.

[0837] 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.

[0838] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for conducting an internet search based on the user's search query and analyzing the obtained search results, and means for generating information to suggest to the user based on the analyzed search results and the user's past reading history. This allows the user to quickly recall the contents of books they have read in the past and acquire new knowledge by linking them to internet search results.

[0839] "User" refers to an individual who uses the system to search for information or view past reading history.

[0840] "Books" refers to written media such as books and e-books that users have read in the past.

[0841] "Means for recording information" refers to a method or device for saving information about books that a user has read in the past in a database or the like.

[0842] "Generative AI" refers to artificial intelligence technology that analyzes users' search queries and contexts and generates relevant information.

[0843] An "internet search" refers to the act of a user searching the web for information about a particular topic.

[0844] "Map search" refers to the act of a user searching the web for a specific location or geographic information.

[0845] "Contextual analysis means" refers to methods or devices for understanding a user's intent and interests from their search queries and behavior, and extracting relevant information.

[0846] "Suggestion means" refers to a method or device for suggesting relevant phrases, events, places, etc. to a user based on analyzed context.

[0847] A "search query" refers to a keyword or phrase that a user enters when searching the Internet.

[0848] "Means for analyzing search results" refers to a method or device for analyzing information obtained through an Internet search and extracting information that is useful to the user.

[0849] "Reading history" refers to information about books a user has read in the past.

[0850] "Means for generating suggested information" refers to a method or device for generating information to suggest to a user based on the analyzed search results and past reading history.

[0851] A system for implementing this invention records information about books a user has read in the past, analyzes the context when the user conducts research using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, places, etc. from books read in the past based on the analyzed context. It also performs an internet search based on the user's search query, analyzes the obtained search results, and generates information to suggest to the user based on the analyzed search results and the user's past reading history.

[0852] Hardware and software used

[0853] Hardware: Smartphones, servers

[0854] Software: Python, BeautifulSoup (for web scraping), SQLite (for database management)

[0855] Data processing and calculation

[0856] 1. Obtaining user search queries: When a user searches for a specific topic on their smartphone, the search query is sent to the system.

[0857] 2. Internet Search: The server performs an Internet search based on the user's search query and retrieves the search results by scraping web pages using BeautifulSoup.

[0858] 3. Contextual analysis: The server analyzes the content of the search results to understand the user's intent and interests. This analysis is performed using generative artificial intelligence.

[0859] 4. Reading History Search: The server searches the user's past reading history stored in the SQLite database to obtain information about books related to the search query.

[0860] 5. Suggested Reading: The server generates suggested reading information for the user based on the analyzed search results and past reading history, including related phrases, events, and places.

[0861] Specific examples

[0862] For example, if a user searches for "Rome," the server will perform an internet search and analyze the search results. At the same time, it will retrieve information from a database about books on the history of the Roman Empire that the user has read in the past and suggest related phrases, events, and places. This allows the user to recall the contents of books they have read in the past and gain new knowledge.

[0863] Prompt Sentence Examples

[0864] "Build an application that, when a user searches for Rome, suggests related phrases and events from books about the history of the Roman Empire that the user has read."

[0865] The above is an embodiment of the present invention. This system allows users to quickly recall the contents of books they have read in the past and to link them with internet search results to obtain new knowledge.

[0866] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0867] Step 1:

[0868] A user searches on their smartphone for a specific topic.

[0869] Input: The search query entered by the user (e.g., "Rome")

[0870] Output: The search query is sent to the server

[0871] What it does: When a user types "Rome" into the search bar on their smartphone and presses the search button, the query is sent to the server.

[0872] Step 2:

[0873] The server performs an internet search based on the user's search query.

[0874] Input: User's search query (e.g. "Rome")

[0875] Output: HTML data of internet search results

[0876] Specific operation: The server sends a search query to the Google Search API and receives the HTML data of the search results.

[0877] Step 3:

[0878] The search results obtained by the server are analyzed.

[0879] Input: HTML data of internet search results

[0880] Output: Parsed search results text data

[0881] Specific operation: The server uses BeautifulSoup to parse the HTML data and extract the text data of the search results.

[0882] Step 4:

[0883] The server searches the user's past reading history.

[0884] Input: User's search query (e.g. "Rome")

[0885] Output: Relevant reading history data

[0886] Specific operation: The server searches the user's reading history stored in an SQLite database and retrieves information about books related to the search query.

[0887] Step 5:

[0888] The server generates suggestion information based on search results and reading history.

[0889] Input: Analyzed text data of search results, related reading history data

[0890] Output: Suggested information to the user (e.g., related phrases, events, places)

[0891] Specific operation: The server uses generative artificial intelligence to analyze search results and reading history and generate information to suggest to the user.

[0892] Step 6:

[0893] The server provides the generated suggestion information to the user.

[0894] Input: Suggested information

[0895] Output: Suggested information displayed on the user's smartphone

[0896] Specific operation: The server sends the generated suggestion information to the user's smartphone and displays it on the user's screen.

[0897] The above are the specific processing steps of the system for implementing this invention, which allows users to quickly recall the contents of books they have read in the past and link them with internet search results to gain new knowledge.

[0898] 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.

[0899] "Example 1"

[0900] One embodiment of the present invention provides a system that combines an emotion engine that recognizes a user's emotions. This system records information about books the user has read in the past and analyzes the context when the user is researching using generative AI, web search, map search, etc. Then, based on the analyzed context, the system suggests related phrases, events, and places from books that have been read in the past. Furthermore, the emotion engine recognizes the user's emotional state and makes suggestions based on that emotional state. For example, when the user expresses joy, the system suggests phrases, events, and places related to joy from books that have been read in the past.

[0901] "Example 2"

[0902] In another embodiment, a system is provided in which an emotion engine selects related phrases, events, and places based on a user's emotional state. The system records information about books the user has read in the past and analyzes the context when the user is researching using generative AI, web search, map search, etc. Then, based on the analyzed context and the user's emotional state, the system suggests related phrases, events, and places from books that have been read in the past. For example, if the user expresses sadness, the system suggests phrases, events, and places related to sadness from books that have been read in the past.

[0903] The processing flow of each embodiment will be described below.

[0904] "Example 1"

[0905] Step 1: The user reads a book, and the system records the information of the book that the user reads.

[0906] Step 2: The user conducts research using generative AI, web search, map search, etc. At this point, the system analyzes the context of the user's research.

[0907] Step 3: Based on the analyzed context, the system suggests related phrases, events, and places from books you have read in the past.

[0908] Step 4: The emotion engine recognizes the user's emotional state and makes suggestions based on that emotional state.

[0909] "Example 2"

[0910] Step 1: The user reads a book, and the system records the information of the book that the user reads.

[0911] Step 2: The user conducts research using generative AI, web search, map search, etc. At this point, the system analyzes the context of the user's research.

[0912] Step 3: The emotional engine recognizes the user's emotional state and, based on that emotional state, suggests related phrases, events, and places from books they have read.

[0913] Example 1

[0914] Next, a description will be given of Example 1 of Form 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."

[0915] Conventional information search systems have the problem that it is difficult to effectively utilize information from books that users have read in the past, and suggestions do not take into account the user's emotional state, making it difficult to provide the most appropriate information to the user. Furthermore, systems that analyze the user's search behavior and context to provide related information also fail to increase user satisfaction because they do not take into account the user's emotional state.

[0916] 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.

[0917] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is performing an information search or map search, means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for recognizing the user's emotional state, and means for adjusting the suggested content based on the recognized emotional state. This makes it possible to provide optimal information by combining the user's past reading history with their current search behavior and emotional state.

[0918] "User" refers to an individual who uses the system to search for information or record books.

[0919] "Books" refers to literature such as books and e-books that users have read in the past.

[0920] "Means for recording information" refers to a function that allows users to save information about books they have read in the past in a database.

[0921] "Means for analyzing context" refers to a function that analyzes the content of search queries and viewed pages when a user is searching for information or maps, and identifies the user's interests.

[0922] "Suggestion methods" refers to a function that suggests to users related phrases, events, and places from books they have read in the past based on analyzed context.

[0923] "Means for recognizing emotional state" refers to a function that analyzes emotions from the user's facial expressions, voice, etc., and identifies their state.

[0924] "Means for adjusting suggested content" refers to a function for optimizing suggested content based on the perceived emotional state of the user.

[0925] The system of the present invention records information about books that a user has read in the past, analyzes the context when the user is performing an information search or map search, and suggests related phrases, events, and places based on the analyzed context. It also has the ability to recognize the user's emotional state and adjust the suggested content based on that emotional state.

[0926] Hardware and software used

[0927] Hardware: Servers, user devices (PCs, smartphones, tablets, etc.)

[0928] Software: Database management systems (e.g., MySQL, PostgreSQL), natural language processing engines (e.g., Google NLP, IBM Watson), emotion recognition engines (e.g., Microsoft Azure Emotion API)

[0929] Specific operation of the system

[0930] 1. Entering user reading information

[0931] Users enter information about books they have read in the past (title, author, publication date, completion date, keywords and phrases) into the device.

[0932] The terminal temporarily stores the input information and transmits it to the server.

[0933] The server stores the received information in a database management system.

[0934] 2. Collection of user behavior data

[0935] Users generate AI models, perform web searches, and perform map searches.

[0936] The device collects behavioral data, such as the user's search queries, the content of the web pages they are viewing, and the locations they are searching for on a map, and sends it to a server.

[0937] 3. Context analysis

[0938] The server uses a natural language processing engine to analyze the context of the collected behavioral data.

[0939] Based on the analysis results, identify the user's current interests.

[0940] 4. Searching for related information

[0941] The server searches the database for phrases, events, and places related to the parsed context.

[0942] For example, if a user searches for "tourist attractions in Paris," the server will retrieve information related to Paris in the database.

[0943] 5. Emotional state analysis

[0944] The device collects emotional data such as the user's facial expressions and voice and sends it to the server.

[0945] The server uses an emotion recognition engine to analyze the user's emotional state.

[0946] For example, if the user expresses an emotion of happiness, the server records that information.

[0947] 6. Providing suggestions

[0948] The server generates optimal suggestions based on the analyzed context and emotional state.

[0949] For example, if a user searches for tourist attractions in Paris and expresses joy, the server might suggest information such as "Eiffel Tower" or "Louvre Museum."

[0950] The terminal displays the suggestions received from the server to the user.

[0951] Specific examples

[0952] User Action: User performs a web search for "tourist attractions in Paris."

[0953] Contextual analysis: The server analyzes the keywords "Paris" and "tourist attractions" and determines that the user is looking for information about Paris.

[0954] Suggestions: The database is searched for phrases and events related to Paris in books the user has read in the past, and suggestions such as "Eiffel Tower" or "Louvre Museum" are made.

[0955] Sentiment analysis: If the sentiment engine detects that a user is excited, it will suggest attractions or events that will further increase their excitement.

[0956] Prompt Sentence Examples

[0957] Prompt for generative AI model: "What phrases or events related to Paris have you read in books?"

[0958] In this way, the system can combine a user's past reading history with their current behavior and emotional state to provide the most relevant information.

[0959] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0960] Step 1:

[0961] The user enters information about books they have read in the past.

[0962] Input: The user enters the book title, author, publication date, completion date, and keywords or phrases into the device.

[0963] What happens: A user opens the app on their smartphone, taps the "Add New Book" button, and fills out the form with book information.

[0964] Output: Book information temporarily stored on the device.

[0965] Step 2:

[0966] The terminal transmits the input book information to the server.

[0967] Input: Book information temporarily stored on your device.

[0968] Specific operation: When the device presses the "Save" button, the book information is sent to the server.

[0969] Output: Book information sent to the server.

[0970] Step 3:

[0971] The server stores the received book information in a database.

[0972] Input: Book information sent to the server.

[0973] Specific operation: The server stores the book information in a database management system (e.g., MySQL or PostgreSQL).

[0974] Output: Book information stored in a database.

[0975] Step 4:

[0976] Users perform information searches and map searches.

[0977] Input: The search query or page content viewed when a user interacts with a generated AI model, web search, or map search.

[0978] What happens: A user searches for "tourists in Paris" in their browser.

[0979] Output: Search queries and page views collected on the device.

[0980] Step 5:

[0981] The device transmits the collected behavioral data to a server.

[0982] Input: Search queries and page views collected on your device.

[0983] What it does: The device sends the user's search query and the contents of the page they are viewing to the server.

[0984] Output: Behavioral data sent to the server.

[0985] Step 6:

[0986] The server analyzes the context of the behavioral data.

[0987] Input: Behavioral data sent to the server.

[0988] What happens: The server uses a natural language processing engine (e.g., Google NLP or IBM Watson) to analyze the context of the behavioral data.

[0989] Output: Parsed context information.

[0990] Step 7:

[0991] The server retrieves the relevant information from a database.

[0992] Input: Parsed context information.

[0993] What it does: The server searches its database for phrases, events, and places related to the parsed context.

[0994] Output: The relevant information found.

[0995] Step 8:

[0996] The device collects the user's emotional data.

[0997] Input: Emotional data such as the user's facial expressions and voice.

[0998] Specific action: The user smiles through the smartphone camera.

[0999] Output: Emotion data collected on the device.

[1000] Step 9:

[1001] The device transmits the collected emotion data to a server.

[1002] Input: Emotion data collected on the device.

[1003] Specific operation: The device sends emotion data to the server.

[1004] Output: Emotion data sent to the server.

[1005] Step 10:

[1006] The server analyzes the emotion data.

[1007] Input: Emotion data sent to the server.

[1008] Specific operation: The server uses an emotion recognition engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state.

[1009] Output: Parsed emotional state.

[1010] Step 11:

[1011] The server generates suggestions based on the analyzed context and emotional state.

[1012] Input: Parsed contextual information and emotional state.

[1013] What it does: The server generates optimal suggestions based on the context and emotional state analyzed.

[1014] Output: The generated suggestion information.

[1015] Step 12:

[1016] The device displays suggested information to the user.

[1017] Input: The generated suggestion information.

[1018] Specific operation: The device notifies the user of the suggestion information received from the server.

[1019] Output: The suggestions displayed to the user.

[1020] (Application example 1)

[1021] Next, a description will be given of Application Example 1 of Form 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."

[1022] Conventional systems have difficulty effectively utilizing information from books that users have read in the past, and are unable to provide appropriate information tailored to the context, especially when users are using generative artificial intelligence, web searches, map searches, etc. Furthermore, they do not take into account the user's emotional state when making suggestions, which makes it difficult to increase user satisfaction.

[1023] 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.

[1024] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, web search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, emotion analysis means for recognizing the emotional state of the user, and means for making suggestions based on the emotional state of the user. This makes it possible to effectively utilize information about books the user has read in the past and provide appropriate information tailored to the context, and further makes it possible to increase user satisfaction by making suggestions that take the user's emotional state into consideration.

[1025] "User" means any individual or entity that uses the System.

[1026] "Books" refers to books and literature that the user has read in the past.

[1027] "Means for recording information" refers to a device or software that has the function of storing information about books a user has previously read, such as the title, author, publication date, completion date, and keywords or phrases related to the content, in a database.

[1028] "Generative AI" is an AI technology that generates new information and content based on user input.

[1029] "Web search" is the act of searching for information on the Internet using a search engine.

[1030] "Map search" is the act of searching for information about a specific place or region using a geographic information system.

[1031] "Context analysis" refers to technology that analyzes the content of a user's searches or browsing activities to understand their intent and purpose.

[1032] A "suggestion means" is a device or software that has the function of presenting to the user related phrases, events, places, etc. from books the user has read in the past based on the analyzed context.

[1033] "Emotion analysis means" is a technology that recognizes the user's emotional state and makes appropriate suggestions based on that emotion.

[1034] "Emotional state" refers to the emotions such as joy, sadness, anger, etc. that the user is currently feeling.

[1035] The system for implementing this invention records information about books a user has read in the past, and analyzes the context when the user is performing generative artificial intelligence, web searches, map searches, etc. to suggest related phrases, events, and places. It also has the ability to recognize the user's emotional state and make suggestions based on that emotion.

[1036] Hardware and software used

[1037] Hardware: Smartphone

[1038] software:

[1039] SQLite: Database Management

[1040] TextBlob: Natural Language Processing

[1041] SentimentAnalyzer: Sentiment analysis engine

[1042] SearchContextAnalyzer: Contextual Analysis Engine

[1043] Data processing and calculation

[1044] The server first records information about books the user has read in the past in a database, including the book's title, author, publication date, completion date, and keywords or phrases related to the content.

[1045] When a user performs a generative AI search, web search, map search, etc., the server analyzes the context using the SearchContextAnalyzer to extract keywords from the search query, the content of the web page being viewed, the location being searched for on the map, etc.

[1046] Next, the server uses SentimentAnalyzer to recognize the user's emotional state. Sentiment analysis identifies the emotional state the user is currently feeling, such as joy, sadness, or anger.

[1047] Based on the analyzed context and emotional state, the server selects relevant phrases, events, and places from its database and suggests them to the user. The suggestions are filtered according to the user's emotional state. For example, if the user is expressing joy, phrases and events related to joy will be suggested first.

[1048] Specific examples

[1049] If a user searches for "the latest technology in AI-based natural language processing," the server will suggest phrases and events related to "natural language processing" and "AI" from books they have read in the past. Furthermore, if the user expresses joy, the server will prioritize suggesting phrases and events related to joy.

[1050] Prompt Sentence Examples

[1051] "Based on what the user is searching for, suggest related phrases and events from books they've read. Also, if the user's emotional state is joy, prioritize suggesting phrases and events related to joy."

[1052] In this way, it is possible to effectively utilize information about books the user has read in the past and provide appropriate information that is tailored to the context. Furthermore, by making suggestions that take into account the user's emotional state, it is possible to increase user satisfaction.

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] The server records information about books that users have read in the past in a database. As input, it receives the title, author, publication date, completion date, and keywords or phrases related to the content of the book that the user has read. It stores this information in an SQLite database. As output, it obtains the book information recorded in the database.

[1056] Step 2:

[1057] When a user performs a generative artificial intelligence (AI) search, web search, map search, etc., the device collects contextual information such as the search query, the content of the web page being viewed, and the location being searched for on the map. The device receives the user's search query, the content of the page being viewed, and the location information of the map search as input. The collected contextual information is obtained as output.

[1058] Step 3:

[1059] The server analyzes the collected context information. As input, it receives the context information obtained in step 2. It uses SearchContextAnalyzer to extract keywords from the context information. As output, it obtains the analyzed keywords.

[1060] Step 4:

[1061] The server recognizes the user's emotional state. As input, it receives the user's current behavior and input. It uses SentimentAnalyzer to analyze the user's emotional state. As output, it obtains the user's emotional state (e.g., happy, sad, angry).

[1062] Step 5:

[1063] The server selects relevant phrases, events, and places from the database based on the analyzed contextual information and emotional state. As input, it receives the keywords obtained in step 3 and the emotional state obtained in step 4. It searches and filters relevant book information from the database. As output, it obtains a list of relevant phrases, events, and places.

[1064] Step 6:

[1065] The server suggests selected phrases, events, and places to the user. As input, it receives the relevant information obtained in step 5. It adjusts the suggestions depending on the user's emotional state. As output, it displays the suggested phrases, events, and places to the user.

[1066] The above processing steps make it possible to effectively utilize information about books the user has read in the past and provide appropriate information that matches the context, and further increase user satisfaction by making suggestions that take into account the user's emotional state.

[1067] Example 2

[1068] Next, a description will be given of Example 2 of Form 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."

[1069] Conventional information search systems make it difficult for users to effectively utilize the content of books they have read in the past, and because search results are not linked to the user's past knowledge, it is difficult to acquire new knowledge or reconfirm past knowledge. In addition, they do not provide appropriate information according to the user's emotional state, which leads to a decrease in user satisfaction.

[1070] 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.

[1071] In this invention, the server includes a means for recording information about books the user has read in the past, a means for analyzing the context when the user is using information search or generative artificial intelligence, a means for suggesting related phrases, events, and places from books the user has read in the past based on the analyzed context, and a means for analyzing the user's emotional state and adjusting the suggested content. This makes it possible to effectively utilize the content of books the user has read in the past and provide appropriate information according to the context. Furthermore, providing information according to the user's emotional state can improve user satisfaction.

[1072] "User" refers to an individual or organization that uses the system to search for information or use generative artificial intelligence.

[1073] "Books" refers to information media such as books and e-books that users have read in the past.

[1074] "Means for recording information" refers to a function that allows users to save information about books they have read in the past in a database, etc.

[1075] "Means of context analysis" refers to the ability to analyze inputs made by users when using information search or generative artificial intelligence, and understand their meaning and intent.

[1076] "Suggestion methods" refers to a function that suggests related phrases, events, and places to users from books they have read in the past based on analyzed context.

[1077] "Means for analyzing emotional state" refers to a function for analyzing a user's emotions and adjusting the suggested content based on the results.

[1078] "Generative AI" refers to an AI system that generates responses in natural language based on user input.

[1079] "Information search" refers to the act of a user searching for information through web searches, map searches, etc.

[1080] A "phrase" refers to a series of words or sentences contained within a book.

[1081] "Incident" refers to a specific occurrence or event described in a book.

[1082] "Places" refer to geographical locations or points mentioned in the book.

[1083] MODE FOR CARRYING OUT THE INVENTION

[1084] The present invention is a system that effectively utilizes information from books that a user has read in the past to provide appropriate information according to the context. A specific embodiment of this system will be described below.

[1085] Hardware and software used

[1086] Server: Database management system (e.g., MySQL), natural language processing engine (e.g., SpaCy), sentiment analysis engine (e.g., IBM Watson)

[1087] Device: PC or smartphone used by the user

[1088] Software: Web browser, generative AI model (e.g., OpenAI GPT-4)

[1089] System Overview

[1090] The system analyzes context as users research information using web searches or generative AI, suggesting related phrases, events, and places from previously read books, and can also adjust suggestions based on the user's emotional state.

[1091] Specific actions

[1092] 1. Capturing user behavior

[1093] The device captures search queries and inputs when users use web searches and generative AI.

[1094] Example: When a user searches for "Rome attractions" in a web browser, the device records the search query.

[1095] 2. Context Analysis

[1096] The server analyzes the captured search queries and input content using a natural language processing engine to understand the context.

[1097] Example: The server parses the search query "tourist attractions in Rome" and extracts the keyword "Rome" and the context "tourist attractions."

[1098] 3. Database Search

[1099] The server searches a database of the user's past reading history to identify relevant books.

[1100] Example: The server queries a database to determine that the user has previously read the book "The History of the Roman Empire."

[1101] 4. Extracting relevant information

[1102] The server extracts contextually relevant phrases, events, and places from the identified books.

[1103] Example: The server parses text data from "History of the Roman Empire" and extracts information about tourist attractions such as the "Colosseum" and the "Roman Forum."

[1104] 5. Emotional State Analysis (Optional)

[1105] The server uses an emotion engine to analyze the user's emotional state and adjust the suggestions accordingly.

[1106] Example: If a user indicates an emotion of sadness, the server will use an emotion engine to analyze that emotion and prioritize extracting phrases and events related to sadness.

[1107] 6. Providing Suggestions

[1108] The server sends the extracted information to the user's device and presents suggestions to the user.

[1109] Example: The server extracts information about the Colosseum and the Roman Forum from the History of the Roman Empire and sends it to the user's device, where it is displayed on the user's web browser.

[1110] Examples and prompts

[1111] Examples:

[1112] When a user searches the web for "tourist attractions in Rome," the system suggests information about tourist attractions such as the Colosseum and the Roman Forum from a book they previously read about: "The History of the Roman Empire."

[1113] Example prompt sentence:

[1114] "When a user searches the web for 'tourist attractions in Rome,' suggest relevant tourist attractions from a book they previously read, 'The History of the Roman Empire.'"

[1115] This system allows users to recall the contents of books they have read in the past and provides an opportunity to acquire new knowledge. It also improves user satisfaction by providing information according to the user's emotional state.

[1116] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1117] Step 1:

[1118] Capturing user behavior

[1119] Input: Search queries and inputs users make when searching the web or using generative AI

[1120] What it does: The device captures the search query when a user searches for "Rome attractions" in a web browser.

[1121] Output: Captured search queries and inputs

[1122] Step 2:

[1123] Contextual Analysis

[1124] Input: Captured search queries or inputs

[1125] What happens: The server uses a natural language processing engine (e.g., SpaCy) to parse the search query "tourist attractions in Rome" and extract the keyword "Rome" and the context "tourist attractions."

[1126] Output: Parsed context information (keywords and context)

[1127] Step 3:

[1128] Searching the database

[1129] Input: Parsed context information (keywords and context)

[1130] What happens: The server uses a database management system (e.g., MySQL) to search the user's past reading history and identify relevant books. For example, it verifies that the user has previously read a book called "The History of the Roman Empire."

[1131] Output: Related book information

[1132] Step 4:

[1133] Extracting relevant information

[1134] Input: Related book information

[1135] What it does: The server analyzes the text data of the identified books and extracts contextually relevant phrases, events, and places. For example, it extracts information about tourist attractions such as the Colosseum and the Roman Forum from "The History of the Roman Empire."

[1136] Output: Extracted relevant information (phrases, events, places)

[1137] Step 5:

[1138] Emotional state analysis (optional)

[1139] Input: User emotional state data

[1140] Specific operation: The server uses an emotion analysis engine (e.g., IBM Watson) to analyze the user's emotional state. For example, if the user expresses sadness, the server analyzes the emotion and prioritizes extracting phrases and events related to sadness.

[1141] Output: Adjusted suggestions

[1142] Step 6:

[1143] Providing suggestions

[1144] Input: Extracted related information (phrases, events, locations) and tailored suggestions

[1145] Specific operation: The server sends the extracted information to the user's device and presents suggestions to the user. For example, information about the Colosseum and the Roman Forum extracted from the "History of the Roman Empire" is sent to the user's device and displayed in the web browser.

[1146] Output: Suggested information presented to the user

[1147] (Application example 2)

[1148] Next, a description will be given of Application Example 2 of Form 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."

[1149] Previous content distribution services did not have a system that provided related information by utilizing the content of books that users had previously read. Furthermore, they lacked the ability to adjust suggested content based on the user's emotional state. This resulted in a decrease in the user's search experience and efficiency of information acquisition, and the inability to fully utilize previously acquired knowledge.

[1150] 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.

[1151] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is researching using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for analyzing the user's emotional state, and means for adjusting the suggested content based on the analyzed emotional state. This makes it possible to provide appropriate information according to the user's emotional state while utilizing knowledge from books read in the past.

[1152] "User" refers to an individual who uses the system to search for information or use generative artificial intelligence.

[1153] "Books" refers to information media such as books and e-books that users have read in the past.

[1154] "Means for recording information" refers to a method or device for storing information about books that a user has previously read in a database.

[1155] "Generative AI" refers to AI technology that generates content such as text and images based on user input.

[1156] "Internet search" refers to the act of a user searching for information on the Internet using a web browser.

[1157] "Map search" refers to the act of a user using a map service to search for a specific location or route.

[1158] "Means for analyzing context" refers to a method or device for analyzing keywords a user is searching for or prompts a user is entering.

[1159] "Suggestion means" means a method or device for suggesting relevant phrases, events, or places to a user based on analyzed context or emotional state.

[1160] "Means for analyzing emotional state" refers to a method or apparatus for analyzing emotions from user input or behavior.

[1161] The "means for adjusting the suggested content" refers to a method or device for appropriately changing the content of information provided to the user based on the analyzed emotional state.

[1162] A system for implementing this invention records information about books a user has read in the past, analyzes the context when the user is researching using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, and places based on the analyzed context. It also has the function of analyzing the user's emotional state and adjusting the suggested content based on that emotional state.

[1163] Hardware and Software Configuration

[1164] server:

[1165] Database: A database for storing information about books that users have read in the past.

[1166] Context analysis module: A module for analyzing users' search keywords and prompt sentences.

[1167] Sentiment Analysis Module: A module for analyzing the emotional state of the user.

[1168] Suggestion module: A module for suggesting related phrases, events, and places based on the analysis results.

[1169] Device:

[1170] Smartphone: A device that allows users to use search and generative artificial intelligence.

[1171] Applications: Applications that allow users to use search and generative artificial intelligence.

[1172] Data processing and calculation

[1173] The server records information about books the user has read in a database. When the user uses their smartphone to search the Internet or use generative artificial intelligence, the context analysis module analyzes the search keywords and prompts. The analysis results are sent to the server, and the sentiment analysis module analyzes the user's emotional state. Based on this, the suggestion module selects related phrases, events, and places to suggest to the user.

[1174] Specific examples

[1175] For example, if a user searches for "tourist attractions in Rome," the app will suggest related tourist attractions and historical events based on their previous reading of "The History of the Roman Empire." If the user expresses sadness, it will also suggest sad historical events in Rome.

[1176] Prompt Sentence Examples

[1177] When a user searches for "places to visit in Rome," suggest related places and historical events based on their previous reading of "History of the Roman Empire." If the user expresses sadness, suggest sad historical events in Rome.

[1178] In this way, users can enrich their search experience and leverage their knowledge of previously read books.

[1179] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1180] Step 1:

[1181] A user uses a smartphone to search the Internet or use generative artificial intelligence. The user enters search keywords and prompts. The entered keywords and prompts are sent from the device to a server.

[1182] Step 2:

[1183] The server's context analysis module analyzes the received search keywords and prompts. Specifically, it uses natural language processing technology to understand the meaning and context of the keywords. As a result of the analysis, related topics and themes are extracted. The input is the search keywords and prompts, and the output is the analyzed topics and themes.

[1184] Step 3:

[1185] The server retrieves information about books the user has read in the past from its database. The database records the titles and contents of books the user has read in the past. The input is the user ID, and the output is information about books read in the past.

[1186] Step 4:

[1187] The server's emotion analysis module analyzes the user's emotional state by using an algorithm to infer emotions from the user's input and behavior. The input is the user's search keywords and prompt, and the output is the analyzed emotional state.

[1188] Step 5:

[1189] The server's suggestion module selects related phrases, events, and places based on the results of context analysis, sentiment analysis, and information about previously read books. Specifically, it extracts related information from the contents of previously read books and adjusts the suggested content according to the user's emotional state. The input is the results of context analysis, sentiment analysis, and information about previously read books, and the output is suggested phrases, events, and places.

[1190] Step 6:

[1191] The server sends suggested phrases, events, and locations to the device. The device displays the received suggestions to the user. The user can review the displayed suggestions and explore them further if necessary. The input is the suggested phrases, events, and locations, and the output is the information displayed to the user.

[1192] 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.

[1193] 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> ) 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.

[1194] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1195] 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.

[1196] [Third embodiment]

[1197] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1198] 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.

[1199] 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).

[1200] 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.

[1201] 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.

[1202] 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).

[1203] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1204] 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.

[1205] 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.

[1206] 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.

[1207] 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.

[1208] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1209] "Example 1"

[1210] The system of the present invention records information about books a user has read in a database. This database includes information such as the book's title, author, publication date, completion date, and keywords and phrases related to the book's content. When a user conducts research using generative AI, web search, map search, etc., the system analyzes the context. The analysis is based on information obtained from the user's behavior, such as the search query, the content of the web page being viewed, and the location being searched for on a map. The system then suggests related phrases, events, places, etc. from the database based on the analyzed context.

[1211] "Example 2"

[1212] For example, when a user searches the web for "Rome," the system analyzes the context and searches a database to see if they have previously read books related to "Rome." If the user has previously read a book about the history of the Roman Empire, the system will suggest phrases, events, and places related to "Rome" from that book. This helps users recall the contents of the book and provides an opportunity to gain new knowledge.

[1213] The processing flow of each embodiment will be described below.

[1214] "Example 1"

[1215] Step 1: When a user finishes reading a book, the system records the book's information (title, author, publication date, completion date, keywords and phrases related to the content, etc.) in a database.

[1216] Step 2: When a user is researching something using generative AI, web search, or map search, the system analyzes the context (the search query, the content of the web page they are viewing, the location they are searching for on a map, etc.).

[1217] Step 3: The system uses the analyzed context to suggest relevant phrases, events, places, etc. from its database.

[1218] "Example 2"

[1219] Step 1: When a user performs a web search for "Rome," the system analyzes the context.

[1220] Step 2: The system searches the database to see if you have previously read any books related to "Rome."

[1221] Step 3: If you have previously read a book about the history of the Roman Empire, the app will suggest phrases, events, and places related to Rome from that book.

[1222] Example 1

[1223] Next, a description will be given of Example 1 of Form 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."

[1224] Conventional information search systems make it difficult for users to effectively utilize information from books they have read in the past, and are unable to provide information useful for current searches or research activities. Furthermore, they lack the means to analyze user behavior data in real time and provide information based on appropriate context. This prevents users from quickly and accurately obtaining the information they need, reducing the efficiency of information searches.

[1225] 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.

[1226] In this invention, the server includes means for recording information about books that a user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books that have been read in the past based on the analyzed context, means for collecting user behavior data, means for analyzing the context using a natural language processing algorithm based on the collected behavior data, means for searching for related information from a database based on the analyzed context, and means for displaying the searched related information to the user. This makes it possible to utilize the user's past reading history and quickly and accurately provide information that is useful for current searches and research activities.

[1227] A "user" is an individual or group that uses the system to search for information or enter past reading history.

[1228] "Books" refer to information media such as books and e-books that users have read in the past.

[1229] "Means for recording information" is a function that allows users to save information about books they have read in the past in a database.

[1230] "Generative artificial intelligence" refers to algorithms or systems that generate information based on user input and actions.

[1231] An "Internet search" is the act of a user searching for information on the Internet using a web browser.

[1232] A "map search" is the act of a user searching for information about a specific location or area using a map application.

[1233] "Context analysis" is a function that understands the current context based on information such as the user's search query, the content of the web page they are viewing, and the location they are searching for on a map.

[1234] "Suggestion methods" is a function that presents users with related phrases, events, places, etc. from books they have read in the past based on the analyzed context.

[1235] "Means for collecting behavioral data" refers to functions for collecting behavioral data such as user search queries, the content of web pages viewed, and locations searched for on maps.

[1236] A "natural language processing algorithm" is an algorithm that analyzes collected behavioral data and understands the context.

[1237] A "database" is a data storage system that stores information about books that a user has read in the past.

[1238] The "means for searching related information" is a function that searches for information such as related phrases, events, and places from a database based on the analyzed context.

[1239] "Means for displaying information" refers to a function for visually presenting the retrieved related information to the user.

[1240] This invention is a system that records information about books that a user has read in the past, analyzes the context when the user is conducting research using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, places, etc. based on the analyzed context.

[1241] Building a database

[1242] The server builds a database to record information about books that users have read in the past, including book titles, authors, publication dates, completion dates, and keywords or phrases related to the book's content. Users enter this information using a web form, and the server stores the data in the database using SQL queries.

[1243] Contextual Analysis

[1244] The device collects information such as users' search queries, the content of web pages they view, and locations they search for on maps, including through browser extensions and applications that collect user behavior data in real time. The server analyzes the collected user behavior data and uses natural language processing (NLP) algorithms to understand the current context.

[1245] Suggestion function

[1246] The server searches for related phrases, events, places, and other information from a database based on the analyzed context. This involves using SQL queries to search for related information in the database. The device displays the related information received from the server to the user. Specifically, suggestions are displayed to the user through browser pop-ups or application notifications.

[1247] Specific examples

[1248] For example, if a user searches for "tourist attractions in Paris," the following process occurs:

[1249] 1. A user searches for "tourist attractions in Paris."

[1250] 2. The device collects this search query.

[1251] 3. The server analyzes the collected search queries and understands the context related to "Paris."

[1252] 4. The server searches the database for information on books related to "Paris."

[1253] 5. The terminal displays the relevant information received from the server to the user.

[1254] Prompt Sentence Examples

[1255] When a user searches for "tourist attractions in Paris," the server extracts information about books related to Paris from a database and suggests related phrases and events.

[1256] This system utilizes the user's past reading history to quickly and accurately provide information useful for current searches and research activities, allowing users to efficiently obtain the information they need and improving the efficiency of information retrieval.

[1257] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1258] Step 1:

[1259] The user enters information about books they have read in the past.

[1260] Input: A user fills out a web form and enters the book title, author, publication date, completion date, and keywords or phrases related to the book's content.

[1261] Data processing: Send the information entered by the user to the server.

[1262] Output: Book information received by the server.

[1263] What happens: A user fills out a web form and clicks the submit button.

[1264] Step 2:

[1265] The server stores the book information in a database.

[1266] Input: Book information submitted by the user.

[1267] Data processing: The server uses SQL queries to insert the book information into the database.

[1268] Output: Book information stored in a database.

[1269] Specific operation: The server saves the received data in the database using an SQL query.

[1270] Step 3:

[1271] The device collects user behavior data.

[1272] Input: Your search query, the content of the web page you're viewing, or the location you're searching for on a map.

[1273] Data processing: Devices collect behavioral data in real time through browser extensions and applications.

[1274] Output: Collected behavioral data.

[1275] What it does: The device monitors user behavior and collects necessary data.

[1276] Step 4:

[1277] The server analyzes the collected behavioral data.

[1278] Input: Collected behavioral data.

[1279] Data processing: The server uses natural language processing (NLP) algorithms to analyze the behavioral data and understand the current context.

[1280] Output: Parsed context information.

[1281] What happens: The server runs NLP algorithms and analyzes the behavioral data.

[1282] Step 5:

[1283] The server retrieves the relevant information from a database.

[1284] Input: Parsed context information.

[1285] Data processing: The server uses SQL queries to find relevant information in a database.

[1286] Output: The relevant information found.

[1287] What happens: The server runs an SQL query to extract the relevant information from the database.

[1288] Step 6:

[1289] The device displays the relevant information to the user.

[1290] Input: Relevant information received from the server.

[1291] Data processing: The device will display relevant information through browser pop-ups and application notifications.

[1292] Output: Relevant information displayed to the user.

[1293] Specific behavior: The device visually presents the information it receives to the user.

[1294] (Application example 1)

[1295] Next, a description will be given of Application Example 1 of Form 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."

[1296] Conventional systems have difficulty effectively utilizing information from books a user has read in the past, and are unable to provide context-based related information, particularly through generative AI, web searches, and map searches. Furthermore, there is a lack of a way to provide real-time information related to the content a user is viewing, creating a need for improved user convenience. Furthermore, the lack of a function to generate context-related prompts using generative AI has hindered the enrichment of the user's search experience.

[1297] 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.

[1298] In this invention, the server includes: means for recording information about books previously read by a user; means for analyzing the context of the user's research using generative artificial intelligence, web search, map search, etc.; means for suggesting related phrases, events, places, etc. from books previously read based on the analyzed context; means for providing information related to the content currently being viewed by the user in real time; and means for generating context-relevant prompts using generative artificial intelligence. This makes it possible to effectively utilize information about books previously read by the user and provide related information based on the context of generative artificial intelligence, web search, map search, etc. Furthermore, providing information related to the content currently being viewed by the user in real time can improve user convenience. Furthermore, generating context-relevant prompts using generative artificial intelligence can enrich the user's search experience.

[1299] "User" means any individual or entity that uses the System.

[1300] "Information about books read in the past" refers to information about books that the user has previously read, such as the title, author, publication date, completion date, keywords and phrases related to the content, etc.

[1301] "Means for recording" refers to a method or device for storing information about books that a user has read in the past in a database or the like.

[1302] "Generative AI" is AI that has the ability to generate new information and content based on user input and context.

[1303] "Web searching" is the act of finding information on the Internet using a search engine.

[1304] "Map searching" is the act of using a geographic information system to search for information about a particular place or region.

[1305] A "means for analyzing context" is a method or device for understanding the content of a user's search or browsing and analyzing its background and intent.

[1306] A "means for suggesting" is a method or device for suggesting relevant information to a user based on the analyzed context.

[1307] "Real-time provision means" refers to a method or device for instantly providing relevant information to a user while the user is viewing content.

[1308] A "prompt sentence" is a sentence that generative artificial intelligence generates based on the user's context to encourage the next action or information.

[1309] The system for implementing this invention records information about books that a user has read in the past, analyzes the context of generative artificial intelligence, web searches, map searches, etc., and provides related information in real time. A specific embodiment of this system is described below.

[1310] 1. System Program

[1311] The system consists of the following main components:

[1312] Database: SQLite is used to store information about books that users have read in the past.

[1313] Contextual Analysis Module: Uses BeautifulSoup to analyze the content of the web page the user is viewing and extract text.

[1314] Suggestion module: Retrieves information about previously read books from a database and checks whether they contain keywords relevant to the context.

[1315] Generative AI model: Uses OpenAI APIs to generate contextually relevant prompts.

[1316] 2. Program processing explanation

[1317] Hardware and software used

[1318] Hardware: Smartphones, smart glasses

[1319] Software: Python, SQLite, BeautifulSoup, OpenAI API

[1320] Data processing and calculation

[1321] Database connection: We use SQLite to connect to a database that stores information about books that users have read.

[1322] Contextual analysis: Use BeautifulSoup to analyze the content of the web page the user is viewing and extract text.

[1323] Suggestion function: Retrieves information about books you have read in the past from a database and checks whether they contain keywords related to the context.

[1324] Generative AI model: Uses OpenAI APIs to generate contextually relevant prompts.

[1325] 3. Specific Examples

[1326] When a user is reading a news article and the article is about environmental issues, the system suggests information about books about environmental issues that the user has read in the past. For example, the system generates a prompt such as, "I'm reading the latest news article about environmental issues. Please tell me about phrases or events related to environmental issues in books that you have read in the past."

[1327] This system allows users to effectively utilize information from books they have read in the past and provide relevant information in context with generative AI, web searches, map searches, etc. It also improves user convenience by providing information related to the content the user is viewing in real time. Furthermore, it can enrich the user's search experience by using generative AI to generate context-relevant prompts.

[1328] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1329] Step 1:

[1330] Record information about books that a user has read in the past.

[1331] Input: Book title, author, publication date, completion date, keywords or phrases related to the content.

[1332] Processing: The device stores this information in an SQLite database.

[1333] Output: Book information recorded in the database.

[1334] Step 2:

[1335] Users conduct research using generative artificial intelligence, web searches, map searches, etc.

[1336] Input: The search query a user enters or the URL of a web page they are viewing.

[1337] Processing: The device retrieves the user's search query and the content of the web page.

[1338] Output: Search query or web page content.

[1339] Step 3:

[1340] Analyze the context.

[1341] Input: A search query or web page content.

[1342] Processing: The device uses BeautifulSoup to parse the content of the web page and extract the text.

[1343] Output: The extracted text.

[1344] Step 4:

[1345] It suggests related phrases, events, places, etc. from books you have read in the past.

[1346] Input: Extracted text, book information recorded in database.

[1347] Processing: The server retrieves information about books read in the past from the database and checks whether the extracted text contains relevant keywords.

[1348] Output: A list of related phrases, events and places.

[1349] Step 5:

[1350] Provides real-time information related to the content the user is viewing.

[1351] Input: A list of related phrases, events, and places.

[1352] Processing: The terminal displays this information to the user in real time.

[1353] Output: The relevant information displayed to the user.

[1354] Step 6:

[1355] Uses generative artificial intelligence to generate contextually relevant prompts.

[1356] Input: The extracted text.

[1357] Processing: The server uses the OpenAI API to generate a prompt based on the extracted text.

[1358] Output: The generated prompt statement.

[1359] Step 7:

[1360] Provides the user with a generated prompt.

[1361] Input: The generated prompt statement.

[1362] Processing: The terminal displays the generated prompt to the user.

[1363] Output: The prompt text displayed to the user.

[1364] Example 2

[1365] Next, a description will be given of Example 2 of Form 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."

[1366] Modern information retrieval systems face the challenge of making it difficult for users to effectively utilize the content of books they have read in the past. In particular, when users search for a specific topic, they lack a mechanism to refer to their past reading history to provide related information. This results in users being unable to fully utilize their previously acquired knowledge, and they are unable to efficiently acquire new knowledge.

[1367] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for recording information about books that the user has read in the past, a means for analyzing the context when the user is searching for information, a means for suggesting related phrases, events, places, etc. from books that the user has read in the past based on the analyzed context, a means for using a generative AI model for generating suggestions, and a means for sending prompt sentences to the generative AI model. This makes it possible to effectively utilize the contents of books that the user has read in the past and provide information related to the search results.

[1368] A "user" refers to an individual with a history of information searches and book reading.

[1369] "Books" refers to information media such as books and e-books that users have read in the past.

[1370] "Information search" refers to the act of a user researching information through web searches, map searches, etc.

[1371] "Context" refers to the search query and related information surrounding it when a user searches for information.

[1372] "Analysis" refers to the act of understanding a user's search query and context using technologies such as natural language processing and extracting relevant information.

[1373] A "phrase" refers to a specific word or part of a sentence contained in a book.

[1374] "Incident" refers to a specific occurrence or event described in a book.

[1375] "Places" refer to geographic locations or areas mentioned in the book.

[1376] "Suggestion" refers to the act of suggesting information such as related phrases, events, or places to the user.

[1377] A "generative AI model" refers to an artificial intelligence model for generating natural language.

[1378] A "prompt sentence" refers to an instruction sentence input to a generative AI model.

[1379] "Server" refers to the computer system that processes users' search queries and reading history and performs analysis and suggestions.

[1380] This invention is a system that effectively utilizes the contents of books that users have read in the past when searching for information, and provides related information. This system works in cooperation with the server, the terminal, and the user.

[1381] Hardware and software used

[1382] Server: Database management system (e.g., MySQL), natural language processing engine (e.g., spaCy), generative AI model (e.g., GPT-4)

[1383] Device: Web browser (e.g. Google Chrome)

[1384] User: The individual searching for information.

[1385] Specific operation of the system

[1386] 1. Receive the user's search query

[1387] A user opens a web browser and types "Rome" into the search bar.

[1388] The terminal sends this search query to the server.

[1389] 2. Analyze the context of the search query

[1390] The server uses a natural language processing engine (e.g., spaCy) to analyze the context of the search query "Rome."

[1391] Based on the analysis results, related keywords and topics are extracted.

[1392] 3. Search your past reading history

[1393] The server uses a database management system (e.g., MySQL) to retrieve the user's past reading history.

[1394] For example, if the user has previously read a book called "The History of the Roman Empire," the contents of that book are retrieved.

[1395] 4. Suggest related information

[1396] The server extracts phrases, events, and places related to "Rome" from the acquired reading history.

[1397] Use a generative AI model (e.g., GPT-4) to generate appropriate suggestions for the user.

[1398] 5. Present suggestions to the user

[1399] The server sends the generated suggestions to the terminal.

[1400] The device displays suggestions to the user through a web browser.

[1401] Specific examples

[1402] User search query: "Rome"

[1403] Past reading history: "The History of the Roman Empire"

[1404] Suggested content: "The History of the Roman Empire provides detailed information about the founding of the city of Rome and the rule of Caesar. In particular, revisit the chapters on Caesar's reforms and the architecture of the city of Rome."

[1405] Example prompts for generative AI models

[1406] A user is performing a web search for "Rome." In their past reading history, they have a book called "The History of the Roman Empire." Based on the contents of this book, suggest phrases, events, and places related to "Rome" to the user.

[1407] This system allows users to recall the contents of books they have read in the past, providing an opportunity to gain new knowledge.

[1408] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1409] Step 1:

[1410] A user enters a search query. The user opens a web browser, types "Rome" into the search bar, and presses enter, generating the search query "Rome."

[1411] Step 2:

[1412] The terminal sends a search query to the server. The terminal sends the search query "Rome" entered by the user to the server as an HTTP request. The input is the search query "Rome" and the output is an HTTP request to the server.

[1413] Step 3:

[1414] The server analyzes the context of the search query. The server passes the received search query "Rome" to a natural language processing engine (e.g., spaCy) to analyze the context. The input is the search query "Rome," and the output is related keywords and topics as the analysis results. Specifically, the server extracts the meaning of the word "Rome" and related topics.

[1415] Step 4:

[1416] The server searches the database for past reading history. The server uses a database management system (e.g., MySQL) to search for the user's past reading history. The input is the user ID and the analysis result, and the output is information about related books. Specifically, the server executes an SQL query to search for books related to "Rome."

[1417] Step 5:

[1418] The server extracts relevant information and sends a prompt to the generative AI model. The server extracts phrases, events, and places related to "Rome" from the reading history and creates a prompt to send to the generative AI model (e.g., GPT-4). The input is information about the related book, and the output is a prompt. Specifically, the server generates a prompt based on the extracted information.

[1419] Step 6:

[1420] The generative AI model generates suggestions. Based on the received prompt, the generative AI model generates appropriate suggestions for the user. The input is the prompt, and the output is the suggested sentence. Specifically, the generative AI model uses a natural language generation algorithm to create suggested sentences.

[1421] Step 7:

[1422] The server sends the generated suggestions to the device. The server sends the suggestions received from the generative AI model to the device. The input is the suggested sentence, and the output is an HTTP response to the device. Specifically, the server generates an HTTP response and sends it to the device.

[1423] Step 8:

[1424] The device displays the suggestions to the user. The device then displays the received suggestions on a web browser. The input is the suggestion text, and the output is the information displayed to the user. Specifically, the device uses HTML and JavaScript to display the suggestions in a format that is easy for the user to view.

[1425] (Application example 2)

[1426] Next, a description will be given of Application Example 2 of Form 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."

[1427] In today's information society, when users research a specific topic, it is important to recall the contents of books they have read in the past and gain new knowledge. However, manually searching through the contents of books users have read in the past to find relevant information is extremely time-consuming. Furthermore, there is a lack of effective ways to link Internet search results with past reading history, making it difficult for users to quickly obtain the information they need. A system that solves these problems is needed.

[1428] 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.

[1429] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for conducting an internet search based on the user's search query and analyzing the obtained search results, and means for generating information to suggest to the user based on the analyzed search results and the user's past reading history. This allows the user to quickly recall the contents of books they have read in the past and acquire new knowledge by linking them to internet search results.

[1430] "User" refers to an individual who uses the system to search for information or view past reading history.

[1431] "Books" refers to written media such as books and e-books that users have read in the past.

[1432] "Means for recording information" refers to a method or device for saving information about books that a user has read in the past in a database or the like.

[1433] "Generative AI" refers to artificial intelligence technology that analyzes users' search queries and contexts and generates relevant information.

[1434] An "internet search" refers to the act of a user searching the web for information about a particular topic.

[1435] "Map search" refers to the act of a user searching the web for a specific location or geographic information.

[1436] "Contextual analysis means" refers to methods or devices for understanding a user's intent and interests from their search queries and behavior, and extracting relevant information.

[1437] "Suggestion means" refers to a method or device for suggesting relevant phrases, events, places, etc. to a user based on analyzed context.

[1438] A "search query" refers to a keyword or phrase that a user enters when searching the Internet.

[1439] "Means for analyzing search results" refers to a method or device for analyzing information obtained through an Internet search and extracting information that is useful to the user.

[1440] "Reading history" refers to information about books a user has read in the past.

[1441] "Means for generating suggested information" refers to a method or device for generating information to suggest to a user based on the analyzed search results and past reading history.

[1442] A system for implementing this invention records information about books a user has read in the past, analyzes the context when the user conducts research using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, places, etc. from books read in the past based on the analyzed context. It also performs an internet search based on the user's search query, analyzes the obtained search results, and generates information to suggest to the user based on the analyzed search results and the user's past reading history.

[1443] Hardware and software used

[1444] Hardware: Smartphones, servers

[1445] Software: Python, BeautifulSoup (for web scraping), SQLite (for database management)

[1446] Data processing and calculation

[1447] 1. Obtaining user search queries: When a user searches for a specific topic on their smartphone, the search query is sent to the system.

[1448] 2. Internet Search: The server performs an Internet search based on the user's search query and retrieves the search results by scraping web pages using BeautifulSoup.

[1449] 3. Contextual analysis: The server analyzes the content of the search results to understand the user's intent and interests. This analysis is performed using generative artificial intelligence.

[1450] 4. Reading History Search: The server searches the user's past reading history stored in the SQLite database to obtain information about books related to the search query.

[1451] 5. Suggested Reading: The server generates suggested reading information for the user based on the analyzed search results and past reading history, including related phrases, events, and places.

[1452] Specific examples

[1453] For example, if a user searches for "Rome," the server will perform an internet search and analyze the search results. At the same time, it will retrieve information from a database about books on the history of the Roman Empire that the user has read in the past and suggest related phrases, events, and places. This allows the user to recall the contents of books they have read in the past and gain new knowledge.

[1454] Prompt Sentence Examples

[1455] "Build an application that, when a user searches for Rome, suggests related phrases and events from books about the history of the Roman Empire that the user has read."

[1456] The above is an embodiment of the present invention. This system allows users to quickly recall the contents of books they have read in the past and to link them with internet search results to obtain new knowledge.

[1457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1458] Step 1:

[1459] A user searches on their smartphone for a specific topic.

[1460] Input: The search query entered by the user (e.g., "Rome")

[1461] Output: The search query is sent to the server

[1462] What it does: When a user types "Rome" into the search bar on their smartphone and presses the search button, the query is sent to the server.

[1463] Step 2:

[1464] The server performs an internet search based on the user's search query.

[1465] Input: User's search query (e.g. "Rome")

[1466] Output: HTML data of internet search results

[1467] Specific operation: The server sends a search query to the Google Search API and receives the HTML data of the search results.

[1468] Step 3:

[1469] The search results obtained by the server are analyzed.

[1470] Input: HTML data of internet search results

[1471] Output: Parsed search results text data

[1472] Specific operation: The server uses BeautifulSoup to parse the HTML data and extract the text data of the search results.

[1473] Step 4:

[1474] The server searches the user's past reading history.

[1475] Input: User's search query (e.g. "Rome")

[1476] Output: Relevant reading history data

[1477] Specific operation: The server searches the user's reading history stored in an SQLite database and retrieves information about books related to the search query.

[1478] Step 5:

[1479] The server generates suggestion information based on search results and reading history.

[1480] Input: Analyzed text data of search results, related reading history data

[1481] Output: Suggested information to the user (e.g., related phrases, events, places)

[1482] Specific operation: The server uses generative artificial intelligence to analyze search results and reading history and generate information to suggest to the user.

[1483] Step 6:

[1484] The server provides the generated suggestion information to the user.

[1485] Input: Suggested information

[1486] Output: Suggested information displayed on the user's smartphone

[1487] Specific operation: The server sends the generated suggestion information to the user's smartphone and displays it on the user's screen.

[1488] The above are the specific processing steps of the system for implementing this invention, which allows users to quickly recall the contents of books they have read in the past and link them with internet search results to gain new knowledge.

[1489] 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.

[1490] "Example 1"

[1491] One embodiment of the present invention provides a system that combines an emotion engine that recognizes a user's emotions. This system records information about books the user has read in the past and analyzes the context when the user is researching using generative AI, web search, map search, etc. Then, based on the analyzed context, the system suggests related phrases, events, and places from books that have been read in the past. Furthermore, the emotion engine recognizes the user's emotional state and makes suggestions based on that emotional state. For example, when the user expresses joy, the system suggests phrases, events, and places related to joy from books that have been read in the past.

[1492] "Example 2"

[1493] In another embodiment, a system is provided in which an emotion engine selects related phrases, events, and places based on a user's emotional state. The system records information about books the user has read in the past and analyzes the context when the user is researching using generative AI, web search, map search, etc. Then, based on the analyzed context and the user's emotional state, the system suggests related phrases, events, and places from books that have been read in the past. For example, if the user expresses sadness, the system suggests phrases, events, and places related to sadness from books that have been read in the past.

[1494] The processing flow of each embodiment will be described below.

[1495] "Example 1"

[1496] Step 1: The user reads a book, and the system records the information of the book that the user reads.

[1497] Step 2: The user conducts research using generative AI, web search, map search, etc. At this point, the system analyzes the context of the user's research.

[1498] Step 3: Based on the analyzed context, the system suggests related phrases, events, and places from books you have read in the past.

[1499] Step 4: The emotion engine recognizes the user's emotional state and makes suggestions based on that emotional state.

[1500] "Example 2"

[1501] Step 1: The user reads a book, and the system records the information of the book that the user reads.

[1502] Step 2: The user conducts research using generative AI, web search, map search, etc. At this point, the system analyzes the context of the user's research.

[1503] Step 3: The emotional engine recognizes the user's emotional state and, based on that emotional state, suggests related phrases, events, and places from books they have read.

[1504] Example 1

[1505] Next, a description will be given of Example 1 of Form 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."

[1506] Conventional information search systems have the problem that it is difficult to effectively utilize information from books that users have read in the past, and suggestions do not take into account the user's emotional state, making it difficult to provide the most appropriate information to the user. Furthermore, systems that analyze the user's search behavior and context to provide related information also fail to increase user satisfaction because they do not take into account the user's emotional state.

[1507] 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.

[1508] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is performing an information search or map search, means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for recognizing the user's emotional state, and means for adjusting the suggested content based on the recognized emotional state. This makes it possible to provide optimal information by combining the user's past reading history with their current search behavior and emotional state.

[1509] "User" refers to an individual who uses the system to search for information or record books.

[1510] "Books" refers to literature such as books and e-books that users have read in the past.

[1511] "Means for recording information" refers to a function that allows users to save information about books they have read in the past in a database.

[1512] "Means for analyzing context" refers to a function that analyzes the content of search queries and viewed pages when a user is searching for information or maps, and identifies the user's interests.

[1513] "Suggestion methods" refers to a function that suggests to users related phrases, events, and places from books they have read in the past based on analyzed context.

[1514] "Means for recognizing emotional state" refers to a function that analyzes emotions from the user's facial expressions, voice, etc., and identifies their state.

[1515] "Means for adjusting suggested content" refers to a function for optimizing suggested content based on the perceived emotional state of the user.

[1516] The system of the present invention records information about books that a user has read in the past, analyzes the context when the user is performing an information search or map search, and suggests related phrases, events, and places based on the analyzed context. It also has the ability to recognize the user's emotional state and adjust the suggested content based on that emotional state.

[1517] Hardware and software used

[1518] Hardware: Servers, user devices (PCs, smartphones, tablets, etc.)

[1519] Software: Database management systems (e.g., MySQL, PostgreSQL), natural language processing engines (e.g., Google NLP, IBM Watson), emotion recognition engines (e.g., Microsoft Azure Emotion API)

[1520] Specific operation of the system

[1521] 1. Entering user reading information

[1522] Users enter information about books they have read in the past (title, author, publication date, completion date, keywords and phrases) into the device.

[1523] The terminal temporarily stores the input information and transmits it to the server.

[1524] The server stores the received information in a database management system.

[1525] 2. Collection of user behavior data

[1526] Users generate AI models, perform web searches, and perform map searches.

[1527] The device collects behavioral data, such as the user's search queries, the content of the web pages they are viewing, and the locations they are searching for on a map, and sends it to a server.

[1528] 3. Context analysis

[1529] The server uses a natural language processing engine to analyze the context of the collected behavioral data.

[1530] Based on the analysis results, identify the user's current interests.

[1531] 4. Searching for related information

[1532] The server searches the database for phrases, events, and places related to the parsed context.

[1533] For example, if a user searches for "tourist attractions in Paris," the server will retrieve information related to Paris in the database.

[1534] 5. Emotional state analysis

[1535] The device collects emotional data such as the user's facial expressions and voice and sends it to the server.

[1536] The server uses an emotion recognition engine to analyze the user's emotional state.

[1537] For example, if the user expresses an emotion of happiness, the server records that information.

[1538] 6. Providing suggestions

[1539] The server generates optimal suggestions based on the analyzed context and emotional state.

[1540] For example, if a user searches for tourist attractions in Paris and expresses joy, the server might suggest information such as "Eiffel Tower" or "Louvre Museum."

[1541] The terminal displays the suggestions received from the server to the user.

[1542] Specific examples

[1543] User Action: User performs a web search for "tourist attractions in Paris."

[1544] Contextual analysis: The server analyzes the keywords "Paris" and "tourist attractions" and determines that the user is looking for information about Paris.

[1545] Suggestions: The database is searched for phrases and events related to Paris in books the user has read in the past, and suggestions such as "Eiffel Tower" or "Louvre Museum" are made.

[1546] Sentiment analysis: If the sentiment engine detects that a user is excited, it will suggest attractions or events that will further increase their excitement.

[1547] Prompt Sentence Examples

[1548] Prompt for generative AI model: "What phrases or events related to Paris have you read in books?"

[1549] In this way, the system can combine a user's past reading history with their current behavior and emotional state to provide the most relevant information.

[1550] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1551] Step 1:

[1552] The user enters information about books they have read in the past.

[1553] Input: The user enters the book title, author, publication date, completion date, and keywords or phrases into the device.

[1554] What happens: A user opens the app on their smartphone, taps the "Add New Book" button, and fills out the form with book information.

[1555] Output: Book information temporarily stored on the device.

[1556] Step 2:

[1557] The terminal transmits the input book information to the server.

[1558] Input: Book information temporarily stored on your device.

[1559] Specific operation: When the device presses the "Save" button, the book information is sent to the server.

[1560] Output: Book information sent to the server.

[1561] Step 3:

[1562] The server stores the received book information in a database.

[1563] Input: Book information sent to the server.

[1564] Specific operation: The server stores the book information in a database management system (e.g., MySQL or PostgreSQL).

[1565] Output: Book information stored in a database.

[1566] Step 4:

[1567] Users perform information searches and map searches.

[1568] Input: The search query or page content viewed when a user interacts with a generated AI model, web search, or map search.

[1569] What happens: A user searches for "tourists in Paris" in their browser.

[1570] Output: Search queries and page views collected on the device.

[1571] Step 5:

[1572] The device transmits the collected behavioral data to a server.

[1573] Input: Search queries and page views collected on your device.

[1574] What it does: The device sends the user's search query and the contents of the page they are viewing to the server.

[1575] Output: Behavioral data sent to the server.

[1576] Step 6:

[1577] The server analyzes the context of the behavioral data.

[1578] Input: Behavioral data sent to the server.

[1579] What happens: The server uses a natural language processing engine (e.g., Google NLP or IBM Watson) to analyze the context of the behavioral data.

[1580] Output: Parsed context information.

[1581] Step 7:

[1582] The server retrieves the relevant information from a database.

[1583] Input: Parsed context information.

[1584] What it does: The server searches its database for phrases, events, and places related to the parsed context.

[1585] Output: The relevant information found.

[1586] Step 8:

[1587] The device collects the user's emotional data.

[1588] Input: Emotional data such as the user's facial expressions and voice.

[1589] Specific action: The user smiles through the smartphone camera.

[1590] Output: Emotion data collected on the device.

[1591] Step 9:

[1592] The device transmits the collected emotion data to a server.

[1593] Input: Emotion data collected on the device.

[1594] Specific operation: The device sends emotion data to the server.

[1595] Output: Emotion data sent to the server.

[1596] Step 10:

[1597] The server analyzes the emotion data.

[1598] Input: Emotion data sent to the server.

[1599] Specific operation: The server uses an emotion recognition engine (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state.

[1600] Output: Parsed emotional state.

[1601] Step 11:

[1602] The server generates suggestions based on the analyzed context and emotional state.

[1603] Input: Parsed contextual information and emotional state.

[1604] What it does: The server generates optimal suggestions based on the context and emotional state analyzed.

[1605] Output: The generated suggestion information.

[1606] Step 12:

[1607] The device displays suggested information to the user.

[1608] Input: The generated suggestion information.

[1609] Specific operation: The device notifies the user of the suggestion information received from the server.

[1610] Output: The suggestions displayed to the user.

[1611] (Application example 1)

[1612] Next, a description will be given of Application Example 1 of Form 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."

[1613] Conventional systems have difficulty effectively utilizing information from books that users have read in the past, and are unable to provide appropriate information tailored to the context, especially when users are using generative artificial intelligence, web searches, map searches, etc. Furthermore, they do not take into account the user's emotional state when making suggestions, which makes it difficult to increase user satisfaction.

[1614] 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.

[1615] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, web search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, emotion analysis means for recognizing the emotional state of the user, and means for making suggestions based on the emotional state of the user. This makes it possible to effectively utilize information about books the user has read in the past and provide appropriate information tailored to the context, and further makes it possible to increase user satisfaction by making suggestions that take the user's emotional state into consideration.

[1616] "User" means any individual or entity that uses the System.

[1617] "Books" refers to books and literature that the user has read in the past.

[1618] "Means for recording information" refers to a device or software that has the function of storing information about books a user has previously read, such as the title, author, publication date, completion date, and keywords or phrases related to the content, in a database.

[1619] "Generative AI" is an AI technology that generates new information and content based on user input.

[1620] "Web search" is the act of searching for information on the Internet using a search engine.

[1621] "Map search" is the act of searching for information about a specific place or region using a geographic information system.

[1622] "Context analysis" refers to technology that analyzes the content of a user's searches or browsing activities to understand their intent and purpose.

[1623] A "suggestion means" is a device or software that has the function of presenting to the user related phrases, events, places, etc. from books the user has read in the past based on the analyzed context.

[1624] "Emotion analysis means" is a technology that recognizes the user's emotional state and makes appropriate suggestions based on that emotion.

[1625] "Emotional state" refers to the emotions such as joy, sadness, anger, etc. that the user is currently feeling.

[1626] The system for implementing this invention records information about books a user has read in the past, and analyzes the context when the user is performing generative artificial intelligence, web searches, map searches, etc. to suggest related phrases, events, and places. It also has the ability to recognize the user's emotional state and make suggestions based on that emotion.

[1627] Hardware and software used

[1628] Hardware: Smartphone

[1629] software:

[1630] SQLite: Database Management

[1631] TextBlob: Natural Language Processing

[1632] SentimentAnalyzer: Sentiment analysis engine

[1633] SearchContextAnalyzer: Contextual Analysis Engine

[1634] Data processing and calculation

[1635] The server first records information about books the user has read in the past in a database, including the book's title, author, publication date, completion date, and keywords or phrases related to the content.

[1636] When a user performs a generative AI search, web search, map search, etc., the server analyzes the context using the SearchContextAnalyzer to extract keywords from the search query, the content of the web page being viewed, the location being searched for on the map, etc.

[1637] Next, the server uses SentimentAnalyzer to recognize the user's emotional state. Sentiment analysis identifies the emotional state the user is currently feeling, such as joy, sadness, or anger.

[1638] Based on the analyzed context and emotional state, the server selects relevant phrases, events, and places from its database and suggests them to the user. The suggestions are filtered according to the user's emotional state. For example, if the user is expressing joy, phrases and events related to joy will be suggested first.

[1639] Specific examples

[1640] If a user searches for "the latest technology in AI-based natural language processing," the server will suggest phrases and events related to "natural language processing" and "AI" from books they have read in the past. Furthermore, if the user expresses joy, the server will prioritize suggesting phrases and events related to joy.

[1641] Prompt Sentence Examples

[1642] "Based on what the user is searching for, suggest related phrases and events from books they've read. Also, if the user's emotional state is joy, prioritize suggesting phrases and events related to joy."

[1643] In this way, it is possible to effectively utilize information about books the user has read in the past and provide appropriate information that is tailored to the context. Furthermore, by making suggestions that take into account the user's emotional state, it is possible to increase user satisfaction.

[1644] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1645] Step 1:

[1646] The server records information about books that users have read in the past in a database. As input, it receives the title, author, publication date, completion date, and keywords or phrases related to the content of the book that the user has read. It stores this information in an SQLite database. As output, it obtains the book information recorded in the database.

[1647] Step 2:

[1648] When a user performs a generative artificial intelligence (AI) search, web search, map search, etc., the device collects contextual information such as the search query, the content of the web page being viewed, and the location being searched for on the map. The device receives the user's search query, the content of the page being viewed, and the location information of the map search as input. The collected contextual information is obtained as output.

[1649] Step 3:

[1650] The server analyzes the collected context information. As input, it receives the context information obtained in step 2. It uses SearchContextAnalyzer to extract keywords from the context information. As output, it obtains the analyzed keywords.

[1651] Step 4:

[1652] The server recognizes the user's emotional state. As input, it receives the user's current behavior and input. It uses SentimentAnalyzer to analyze the user's emotional state. As output, it obtains the user's emotional state (e.g., happy, sad, angry).

[1653] Step 5:

[1654] The server selects relevant phrases, events, and places from the database based on the analyzed contextual information and emotional state. As input, it receives the keywords obtained in step 3 and the emotional state obtained in step 4. It searches and filters relevant book information from the database. As output, it obtains a list of relevant phrases, events, and places.

[1655] Step 6:

[1656] The server suggests selected phrases, events, and places to the user. As input, it receives the relevant information obtained in step 5. It adjusts the suggestions depending on the user's emotional state. As output, it displays the suggested phrases, events, and places to the user.

[1657] The above processing steps make it possible to effectively utilize information about books the user has read in the past and provide appropriate information that matches the context, and further increase user satisfaction by making suggestions that take into account the user's emotional state.

[1658] Example 2

[1659] Next, a description will be given of Example 2 of Form 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."

[1660] Conventional information search systems make it difficult for users to effectively utilize the content of books they have read in the past, and because search results are not linked to the user's past knowledge, it is difficult to acquire new knowledge or reconfirm past knowledge. In addition, they do not provide appropriate information according to the user's emotional state, which leads to a decrease in user satisfaction.

[1661] 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.

[1662] In this invention, the server includes a means for recording information about books the user has read in the past, a means for analyzing the context when the user is using information search or generative artificial intelligence, a means for suggesting related phrases, events, and places from books the user has read in the past based on the analyzed context, and a means for analyzing the user's emotional state and adjusting the suggested content. This makes it possible to effectively utilize the content of books the user has read in the past and provide appropriate information according to the context. Furthermore, providing information according to the user's emotional state can improve user satisfaction.

[1663] "User" refers to an individual or organization that uses the system to search for information or use generative artificial intelligence.

[1664] "Books" refers to information media such as books and e-books that users have read in the past.

[1665] "Means for recording information" refers to a function that allows users to save information about books they have read in the past in a database, etc.

[1666] "Means of context analysis" refers to the ability to analyze inputs made by users when using information search or generative artificial intelligence, and understand their meaning and intent.

[1667] "Suggestion methods" refers to a function that suggests related phrases, events, and places to users from books they have read in the past based on analyzed context.

[1668] "Means for analyzing emotional state" refers to a function for analyzing a user's emotions and adjusting the suggested content based on the results.

[1669] "Generative AI" refers to an AI system that generates responses in natural language based on user input.

[1670] "Information search" refers to the act of a user searching for information through web searches, map searches, etc.

[1671] A "phrase" refers to a series of words or sentences contained within a book.

[1672] "Incident" refers to a specific occurrence or event described in a book.

[1673] "Places" refer to geographical locations or points mentioned in the book.

[1674] MODE FOR CARRYING OUT THE INVENTION

[1675] The present invention is a system that effectively utilizes information from books that a user has read in the past to provide appropriate information according to the context. A specific embodiment of this system will be described below.

[1676] Hardware and software used

[1677] Server: Database management system (e.g., MySQL), natural language processing engine (e.g., SpaCy), sentiment analysis engine (e.g., IBM Watson)

[1678] Device: PC or smartphone used by the user

[1679] Software: Web browser, generative AI model (e.g., OpenAI GPT-4)

[1680] System Overview

[1681] The system analyzes context as users research information using web searches or generative AI, suggesting related phrases, events, and places from previously read books, and can also adjust suggestions based on the user's emotional state.

[1682] Specific actions

[1683] 1. Capturing user behavior

[1684] The device captures search queries and inputs when users use web searches and generative AI.

[1685] Example: When a user searches for "Rome attractions" in a web browser, the device records the search query.

[1686] 2. Context Analysis

[1687] The server analyzes the captured search queries and input content using a natural language processing engine to understand the context.

[1688] Example: The server parses the search query "tourist attractions in Rome" and extracts the keyword "Rome" and the context "tourist attractions."

[1689] 3. Database Search

[1690] The server searches a database of the user's past reading history to identify relevant books.

[1691] Example: The server queries a database to determine that the user has previously read the book "The History of the Roman Empire."

[1692] 4. Extracting relevant information

[1693] The server extracts contextually relevant phrases, events, and places from the identified books.

[1694] Example: The server parses text data from "History of the Roman Empire" and extracts information about tourist attractions such as the "Colosseum" and the "Roman Forum."

[1695] 5. Emotional State Analysis (Optional)

[1696] The server uses an emotion engine to analyze the user's emotional state and adjust the suggestions accordingly.

[1697] Example: If a user indicates an emotion of sadness, the server will use an emotion engine to analyze that emotion and prioritize extracting phrases and events related to sadness.

[1698] 6. Providing Suggestions

[1699] The server sends the extracted information to the user's device and presents suggestions to the user.

[1700] Example: The server extracts information about the Colosseum and the Roman Forum from the History of the Roman Empire and sends it to the user's device, where it is displayed on the user's web browser.

[1701] Examples and prompts

[1702] Examples:

[1703] When a user searches the web for "tourist attractions in Rome," the system suggests information about tourist attractions such as the Colosseum and the Roman Forum from a book they previously read about: "The History of the Roman Empire."

[1704] Example prompt sentence:

[1705] "When a user searches the web for 'tourist attractions in Rome,' suggest relevant tourist attractions from a book they previously read, 'The History of the Roman Empire.'"

[1706] This system allows users to recall the contents of books they have read in the past and provides an opportunity to acquire new knowledge. It also improves user satisfaction by providing information according to the user's emotional state.

[1707] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1708] Step 1:

[1709] Capturing user behavior

[1710] Input: Search queries and inputs users make when searching the web or using generative AI

[1711] What it does: The device captures the search query when a user searches for "Rome attractions" in a web browser.

[1712] Output: Captured search queries and inputs

[1713] Step 2:

[1714] Contextual Analysis

[1715] Input: Captured search queries or inputs

[1716] What happens: The server uses a natural language processing engine (e.g., SpaCy) to parse the search query "tourist attractions in Rome" and extract the keyword "Rome" and the context "tourist attractions."

[1717] Output: Parsed context information (keywords and context)

[1718] Step 3:

[1719] Searching the database

[1720] Input: Parsed context information (keywords and context)

[1721] What happens: The server uses a database management system (e.g., MySQL) to search the user's past reading history and identify relevant books. For example, it verifies that the user has previously read a book called "The History of the Roman Empire."

[1722] Output: Related book information

[1723] Step 4:

[1724] Extracting relevant information

[1725] Input: Related book information

[1726] What it does: The server analyzes the text data of the identified books and extracts contextually relevant phrases, events, and places. For example, it extracts information about tourist attractions such as the Colosseum and the Roman Forum from "The History of the Roman Empire."

[1727] Output: Extracted relevant information (phrases, events, places)

[1728] Step 5:

[1729] Emotional state analysis (optional)

[1730] Input: User emotional state data

[1731] Specific operation: The server uses an emotion analysis engine (e.g., IBM Watson) to analyze the user's emotional state. For example, if the user expresses sadness, the server analyzes the emotion and prioritizes extracting phrases and events related to sadness.

[1732] Output: Adjusted suggestions

[1733] Step 6:

[1734] Providing suggestions

[1735] Input: Extracted related information (phrases, events, locations) and tailored suggestions

[1736] Specific operation: The server sends the extracted information to the user's device and presents suggestions to the user. For example, information about the Colosseum and the Roman Forum extracted from the "History of the Roman Empire" is sent to the user's device and displayed in the web browser.

[1737] Output: Suggested information presented to the user

[1738] (Application example 2)

[1739] Next, a description will be given of Application Example 2 of Form 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."

[1740] Previous content distribution services did not have a system that provided related information by utilizing the content of books that users had previously read. Furthermore, they lacked the ability to adjust suggested content based on the user's emotional state. This resulted in a decrease in the user's search experience and efficiency of information acquisition, and the inability to fully utilize previously acquired knowledge.

[1741] 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.

[1742] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is researching using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for analyzing the user's emotional state, and means for adjusting the suggested content based on the analyzed emotional state. This makes it possible to provide appropriate information according to the user's emotional state while utilizing knowledge from books read in the past.

[1743] "User" refers to an individual who uses the system to search for information or use generative artificial intelligence.

[1744] "Books" refers to information media such as books and e-books that users have read in the past.

[1745] "Means for recording information" refers to a method or device for storing information about books that a user has previously read in a database.

[1746] "Generative AI" refers to AI technology that generates content such as text and images based on user input.

[1747] "Internet search" refers to the act of a user searching for information on the Internet using a web browser.

[1748] "Map search" refers to the act of a user using a map service to search for a specific location or route.

[1749] "Means for analyzing context" refers to a method or device for analyzing keywords a user is searching for or prompts a user is entering.

[1750] "Suggestion means" means a method or device for suggesting relevant phrases, events, or places to a user based on analyzed context or emotional state.

[1751] "Means for analyzing emotional state" refers to a method or apparatus for analyzing emotions from user input or behavior.

[1752] The "means for adjusting the suggested content" refers to a method or device for appropriately changing the content of information provided to the user based on the analyzed emotional state.

[1753] A system for implementing this invention records information about books a user has read in the past, analyzes the context when the user is researching using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, and places based on the analyzed context. It also has the function of analyzing the user's emotional state and adjusting the suggested content based on that emotional state.

[1754] Hardware and Software Configuration

[1755] server:

[1756] Database: A database for storing information about books that users have read in the past.

[1757] Context analysis module: A module for analyzing users' search keywords and prompt sentences.

[1758] Sentiment Analysis Module: A module for analyzing the emotional state of the user.

[1759] Suggestion module: A module for suggesting related phrases, events, and places based on the analysis results.

[1760] Device:

[1761] Smartphone: A device that allows users to use search and generative artificial intelligence.

[1762] Applications: Applications that allow users to use search and generative artificial intelligence.

[1763] Data processing and calculation

[1764] The server records information about books the user has read in a database. When the user uses their smartphone to search the Internet or use generative artificial intelligence, the context analysis module analyzes the search keywords and prompts. The analysis results are sent to the server, and the sentiment analysis module analyzes the user's emotional state. Based on this, the suggestion module selects related phrases, events, and places to suggest to the user.

[1765] Specific examples

[1766] For example, if a user searches for "tourist attractions in Rome," the app will suggest related tourist attractions and historical events based on their previous reading of "The History of the Roman Empire." If the user expresses sadness, it will also suggest sad historical events in Rome.

[1767] Prompt Sentence Examples

[1768] When a user searches for "places to visit in Rome," suggest related places and historical events based on their previous reading of "History of the Roman Empire." If the user expresses sadness, suggest sad historical events in Rome.

[1769] In this way, users can enrich their search experience and leverage their knowledge of previously read books.

[1770] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1771] Step 1:

[1772] A user uses a smartphone to search the Internet or use generative artificial intelligence. The user enters search keywords and prompts. The entered keywords and prompts are sent from the device to a server.

[1773] Step 2:

[1774] The server's context analysis module analyzes the received search keywords and prompts. Specifically, it uses natural language processing technology to understand the meaning and context of the keywords. As a result of the analysis, related topics and themes are extracted. The input is the search keywords and prompts, and the output is the analyzed topics and themes.

[1775] Step 3:

[1776] The server retrieves information about books the user has read in the past from its database. The database records the titles and contents of books the user has read in the past. The input is the user ID, and the output is information about books read in the past.

[1777] Step 4:

[1778] The server's emotion analysis module analyzes the user's emotional state by using an algorithm to infer emotions from the user's input and behavior. The input is the user's search keywords and prompt, and the output is the analyzed emotional state.

[1779] Step 5:

[1780] The server's suggestion module selects related phrases, events, and places based on the results of context analysis, sentiment analysis, and information about previously read books. Specifically, it extracts related information from the contents of previously read books and adjusts the suggested content according to the user's emotional state. The input is the results of context analysis, sentiment analysis, and information about previously read books, and the output is suggested phrases, events, and places.

[1781] Step 6:

[1782] The server sends suggested phrases, events, and locations to the device. The device displays the received suggestions to the user. The user can review the displayed suggestions and explore them further if necessary. The input is the suggested phrases, events, and locations, and the output is the information displayed to the user.

[1783] 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.

[1784] 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> ) 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.

[1785] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1786] 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.

[1787] [Fourth embodiment]

[1788] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1789] 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.

[1790] 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).

[1791] 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.

[1792] 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.

[1793] 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).

[1794] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1795] 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.

[1796] FIG. 8 shows an example of the main functions of the data processing device 12 and the robot 414.

[1797] 8, in the data processing device 12, a specific process is performed by a processor 28. A specific process program 56 is stored in the storage 32.

[1798] 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.

[1799] 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.

[1800] 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.

[1801] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1802] "Example 1"

[1803] The system of the present invention records information about books a user has read in a database. This database includes information such as the book's title, author, publication date, completion date, and keywords and phrases related to the book's content. When a user conducts research using generative AI, web search, map search, etc., the system analyzes the context. The analysis is based on information obtained from the user's behavior, such as the search query, the content of the web page being viewed, and the location being searched for on a map. The system then suggests related phrases, events, places, etc. from the database based on the analyzed context.

[1804] "Example 2"

[1805] For example, when a user searches the web for "Rome," the system analyzes the context and searches a database to see if they have previously read books related to "Rome." If the user has previously read a book about the history of the Roman Empire, the system will suggest phrases, events, and places related to "Rome" from that book. This helps users recall the contents of the book and provides an opportunity to gain new knowledge.

[1806] The processing flow of each embodiment will be described below.

[1807] "Example 1"

[1808] Step 1: When a user finishes reading a book, the system records the book's information (title, author, publication date, completion date, keywords and phrases related to the content, etc.) in a database.

[1809] Step 2: When a user is researching something using generative AI, web search, or map search, the system analyzes the context (the search query, the content of the web page they are viewing, the location they are searching for on a map, etc.).

[1810] Step 3: The system uses the analyzed context to suggest relevant phrases, events, places, etc. from its database.

[1811] "Example 2"

[1812] Step 1: When a user performs a web search for "Rome," the system analyzes the context.

[1813] Step 2: The system searches the database to see if you have previously read any books related to "Rome."

[1814] Step 3: If you have previously read a book about the history of the Roman Empire, the app will suggest phrases, events, and places related to Rome from that book.

[1815] Example 1

[1816] Next, a description will be given of Example 1 of Form 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."

[1817] Conventional information search systems make it difficult for users to effectively utilize information from books they have read in the past, and are unable to provide information useful for current searches or research activities. Furthermore, they lack the means to analyze user behavior data in real time and provide information based on appropriate context. This prevents users from quickly and accurately obtaining the information they need, reducing the efficiency of information searches.

[1818] 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.

[1819] In this invention, the server includes means for recording information about books that a user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books that have been read in the past based on the analyzed context, means for collecting user behavior data, means for analyzing the context using a natural language processing algorithm based on the collected behavior data, means for searching for related information from a database based on the analyzed context, and means for displaying the searched related information to the user. This makes it possible to utilize the user's past reading history and quickly and accurately provide information that is useful for current searches and research activities.

[1820] A "user" is an individual or group that uses the system to search for information or enter past reading history.

[1821] "Books" refer to information media such as books and e-books that users have read in the past.

[1822] "Means for recording information" is a function that allows users to save information about books they have read in the past in a database.

[1823] "Generative artificial intelligence" refers to algorithms or systems that generate information based on user input and actions.

[1824] An "Internet search" is the act of a user searching for information on the Internet using a web browser.

[1825] A "map search" is the act of a user searching for information about a specific location or area using a map application.

[1826] "Context analysis" is a function that understands the current context based on information such as the user's search query, the content of the web page they are viewing, and the location they are searching for on a map.

[1827] "Suggestion methods" is a function that presents users with related phrases, events, places, etc. from books they have read in the past based on the analyzed context.

[1828] "Means for collecting behavioral data" refers to functions for collecting behavioral data such as user search queries, the content of web pages viewed, and locations searched for on maps.

[1829] A "natural language processing algorithm" is an algorithm that analyzes collected behavioral data and understands the context.

[1830] A "database" is a data storage system that stores information about books that a user has read in the past.

[1831] The "means for searching related information" is a function that searches for information such as related phrases, events, and places from a database based on the analyzed context.

[1832] "Means for displaying information" refers to a function for visually presenting the retrieved related information to the user.

[1833] This invention is a system that records information about books that a user has read in the past, analyzes the context when the user is conducting research using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, places, etc. based on the analyzed context.

[1834] Building a database

[1835] The server builds a database to record information about books that users have read in the past, including book titles, authors, publication dates, completion dates, and keywords or phrases related to the book's content. Users enter this information using a web form, and the server stores the data in the database using SQL queries.

[1836] Contextual Analysis

[1837] The device collects information such as users' search queries, the content of web pages they view, and locations they search for on maps, including through browser extensions and applications that collect user behavior data in real time. The server analyzes the collected user behavior data and uses natural language processing (NLP) algorithms to understand the current context.

[1838] Suggestion function

[1839] The server searches for related phrases, events, places, and other information from a database based on the analyzed context. This involves using SQL queries to search for related information in the database. The device displays the related information received from the server to the user. Specifically, suggestions are displayed to the user through browser pop-ups or application notifications.

[1840] Specific examples

[1841] For example, if a user searches for "tourist attractions in Paris," the following process occurs:

[1842] 1. A user searches for "tourist attractions in Paris."

[1843] 2. The device collects this search query.

[1844] 3. The server analyzes the collected search queries and understands the context related to "Paris."

[1845] 4. The server searches the database for information on books related to "Paris."

[1846] 5. The terminal displays the relevant information received from the server to the user.

[1847] Prompt Sentence Examples

[1848] When a user searches for "tourist attractions in Paris," the server extracts information about books related to Paris from a database and suggests related phrases and events.

[1849] This system utilizes the user's past reading history to quickly and accurately provide information useful for current searches and research activities, allowing users to efficiently obtain the information they need and improving the efficiency of information retrieval.

[1850] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1851] Step 1:

[1852] The user enters information about books they have read in the past.

[1853] Input: A user fills out a web form and enters the book title, author, publication date, completion date, and keywords or phrases related to the book's content.

[1854] Data processing: Send the information entered by the user to the server.

[1855] Output: Book information received by the server.

[1856] What happens: A user fills out a web form and clicks the submit button.

[1857] Step 2:

[1858] The server stores the book information in a database.

[1859] Input: Book information submitted by the user.

[1860] Data processing: The server uses SQL queries to insert the book information into the database.

[1861] Output: Book information stored in a database.

[1862] Specific operation: The server saves the received data in the database using an SQL query.

[1863] Step 3:

[1864] The device collects user behavior data.

[1865] Input: Your search query, the content of the web page you're viewing, or the location you're searching for on a map.

[1866] Data processing: Devices collect behavioral data in real time through browser extensions and applications.

[1867] Output: Collected behavioral data.

[1868] What it does: The device monitors user behavior and collects necessary data.

[1869] Step 4:

[1870] The server analyzes the collected behavioral data.

[1871] Input: Collected behavioral data.

[1872] Data processing: The server uses natural language processing (NLP) algorithms to analyze the behavioral data and understand the current context.

[1873] Output: Parsed context information.

[1874] What happens: The server runs NLP algorithms and analyzes the behavioral data.

[1875] Step 5:

[1876] The server retrieves the relevant information from a database.

[1877] Input: Parsed context information.

[1878] Data processing: The server uses SQL queries to find relevant information in a database.

[1879] Output: The relevant information found.

[1880] What happens: The server runs an SQL query to extract the relevant information from the database.

[1881] Step 6:

[1882] The device displays the relevant information to the user.

[1883] Input: Relevant information received from the server.

[1884] Data processing: The device will display relevant information through browser pop-ups and application notifications.

[1885] Output: Relevant information displayed to the user.

[1886] Specific behavior: The device visually presents the information it receives to the user.

[1887] (Application example 1)

[1888] Next, a description will be given of Application Example 1 of Embodiment 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."

[1889] Conventional systems have difficulty effectively utilizing information from books a user has read in the past, and are unable to provide context-based related information, particularly through generative AI, web searches, and map searches. Furthermore, there is a lack of a way to provide real-time information related to the content a user is viewing, creating a need for improved user convenience. Furthermore, the lack of a function to generate context-relevant prompts using generative AI has hindered the enrichment of the user's search experience.

[1890] 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.

[1891] In this invention, the server includes: means for recording information about books previously read by a user; means for analyzing the context of the user's research using generative artificial intelligence, web search, map search, etc.; means for suggesting related phrases, events, places, etc. from books previously read based on the analyzed context; means for providing information related to the content currently being viewed by the user in real time; and means for generating context-relevant prompts using generative artificial intelligence. This makes it possible to effectively utilize information about books previously read by the user and provide related information based on the context of generative artificial intelligence, web search, map search, etc. Furthermore, providing information related to the content currently being viewed by the user in real time can improve user convenience. Furthermore, generating context-relevant prompts using generative artificial intelligence can enrich the user's search experience.

[1892] "User" means any individual or entity that uses the System.

[1893] "Information about books read in the past" refers to information about books that the user has previously read, such as the title, author, publication date, completion date, keywords and phrases related to the content, etc.

[1894] "Means for recording" refers to a method or device for storing information about books that a user has read in the past in a database or the like.

[1895] "Generative AI" is AI that has the ability to generate new information and content based on user input and context.

[1896] "Web searching" is the act of finding information on the Internet using a search engine.

[1897] "Map searching" is the act of using a geographic information system to search for information about a particular place or region.

[1898] A "means for analyzing context" is a method or device for understanding the content of a user's search or browsing and analyzing its background and intent.

[1899] A "means for suggesting" is a method or device for suggesting relevant information to a user based on the analyzed context.

[1900] "Real-time provision means" refers to a method or device for instantly providing relevant information to a user while the user is viewing content.

[1901] A "prompt sentence" is a sentence that generative artificial intelligence generates based on the user's context to encourage the next action or information.

[1902] The system for implementing this invention records information about books that a user has read in the past, analyzes the context of generative artificial intelligence, web searches, map searches, etc., and provides related information in real time. A specific embodiment of this system is described below.

[1903] 1. System Program

[1904] The system consists of the following main components:

[1905] Database: SQLite is used to store information about books that users have read in the past.

[1906] Contextual Analysis Module: Uses BeautifulSoup to analyze the content of the web page the user is viewing and extract text.

[1907] Suggestion module: Retrieves information about previously read books from a database and checks whether they contain keywords relevant to the context.

[1908] Generative AI model: Uses OpenAI APIs to generate contextually relevant prompts.

[1909] 2. Program processing explanation

[1910] Hardware and software used

[1911] Hardware: Smartphones, smart glasses

[1912] Software: Python, SQLite, BeautifulSoup, OpenAI API

[1913] Data processing and calculation

[1914] Database connection: We use SQLite to connect to a database that stores information about books that users have read.

[1915] Contextual analysis: Use BeautifulSoup to analyze the content of the web page the user is viewing and extract text.

[1916] Suggestion function: Retrieves information about books you have read in the past from a database and checks whether they contain keywords related to the context.

[1917] Generative AI model: Uses OpenAI APIs to generate contextually relevant prompts.

[1918] 3. Specific Examples

[1919] When a user is reading a news article and the article is about environmental issues, the system suggests information about books about environmental issues that the user has read in the past. For example, the system generates a prompt such as, "I'm reading the latest news article about environmental issues. Please tell me about phrases or events related to environmental issues in books that you have read in the past."

[1920] This system allows users to effectively utilize information from books they have read in the past and provide relevant information in context with generative AI, web searches, map searches, etc. It also improves user convenience by providing information related to the content the user is viewing in real time. Furthermore, it can enrich the user's search experience by using generative AI to generate context-relevant prompts.

[1921] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1922] Step 1:

[1923] Record information about books that a user has read in the past.

[1924] Input: Book title, author, publication date, completion date, keywords or phrases related to the content.

[1925] Processing: The device stores this information in an SQLite database.

[1926] Output: Book information recorded in the database.

[1927] Step 2:

[1928] Users conduct research using generative artificial intelligence, web searches, map searches, etc.

[1929] Input: The search query a user enters or the URL of a web page they are viewing.

[1930] Processing: The device retrieves the user's search query and the content of the web page.

[1931] Output: Search query or web page content.

[1932] Step 3:

[1933] Analyze the context.

[1934] Input: A search query or web page content.

[1935] Processing: The device uses BeautifulSoup to parse the content of the web page and extract the text.

[1936] Output: The extracted text.

[1937] Step 4:

[1938] It suggests related phrases, events, places, etc. from books you have read in the past.

[1939] Input: Extracted text, book information recorded in database.

[1940] Processing: The server retrieves information about books read in the past from the database and checks whether the extracted text contains relevant keywords.

[1941] Output: A list of related phrases, events and places.

[1942] Step 5:

[1943] Provides real-time information related to the content the user is viewing.

[1944] Input: A list of related phrases, events, and places.

[1945] Processing: The terminal displays this information to the user in real time.

[1946] Output: The relevant information displayed to the user.

[1947] Step 6:

[1948] Uses generative artificial intelligence to generate contextually relevant prompts.

[1949] Input: The extracted text.

[1950] Processing: The server uses the OpenAI API to generate a prompt based on the extracted text.

[1951] Output: The generated prompt statement.

[1952] Step 7:

[1953] Provides the user with a generated prompt.

[1954] Input: The generated prompt statement.

[1955] Processing: The terminal displays the generated prompt to the user.

[1956] Output: The prompt text displayed to the user.

[1957] Example 2

[1958] Next, a description will be given of Example 2 of Form 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."

[1959] Modern information retrieval systems face the challenge of making it difficult for users to effectively utilize the content of books they have read in the past. In particular, when users search for a specific topic, they lack a mechanism to refer to their past reading history to provide related information. This results in users being unable to fully utilize their previously acquired knowledge, and they are unable to efficiently acquire new knowledge.

[1960] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for recording information about books that the user has read in the past, a means for analyzing the context when the user is searching for information, a means for suggesting related phrases, events, places, etc. from books that the user has read in the past based on the analyzed context, a means for using a generative AI model for generating suggestions, and a means for sending prompt sentences to the generative AI model. This makes it possible to effectively utilize the contents of books that the user has read in the past and provide information related to the search results.

[1961] A "user" refers to an individual with a history of information searches and book reading.

[1962] "Books" refers to information media such as books and e-books that users have read in the past.

[1963] "Information search" refers to the act of a user researching information through web searches, map searches, etc.

[1964] "Context" refers to the search query and related information surrounding it when a user searches for information.

[1965] "Analysis" refers to the act of understanding a user's search query and context using technologies such as natural language processing and extracting relevant information.

[1966] A "phrase" refers to a specific word or part of a sentence contained in a book.

[1967] "Incident" refers to a specific occurrence or event described in a book.

[1968] "Places" refer to geographic locations or areas mentioned in the book.

[1969] "Suggestion" refers to the act of suggesting information such as related phrases, events, or places to the user.

[1970] A "generative AI model" refers to an artificial intelligence model for generating natural language.

[1971] A "prompt sentence" refers to an instruction sentence input to a generative AI model.

[1972] "Server" refers to the computer system that processes users' search queries and reading history and performs analysis and suggestions.

[1973] This invention is a system that effectively utilizes the contents of books that users have read in the past when searching for information, and provides related information. This system works in cooperation with the server, the terminal, and the user.

[1974] Hardware and software used

[1975] Server: Database management system (e.g., MySQL), natural language processing engine (e.g., spaCy), generative AI model (e.g., GPT-4)

[1976] Device: Web browser (e.g. Google Chrome)

[1977] User: The individual searching for information.

[1978] Specific operation of the system

[1979] 1. Receive the user's search query

[1980] A user opens a web browser and types "Rome" into the search bar.

[1981] The terminal sends this search query to the server.

[1982] 2. Analyze the context of the search query

[1983] The server uses a natural language processing engine (e.g., spaCy) to analyze the context of the search query "Rome."

[1984] Based on the analysis results, related keywords and topics are extracted.

[1985] 3. Search your past reading history

[1986] The server uses a database management system (e.g., MySQL) to retrieve the user's past reading history.

[1987] For example, if the user has previously read a book called "The History of the Roman Empire," the contents of that book are retrieved.

[1988] 4. Suggest related information

[1989] The server extracts phrases, events, and places related to "Rome" from the acquired reading history.

[1990] Use a generative AI model (e.g., GPT-4) to generate appropriate suggestions for the user.

[1991] 5. Present suggestions to the user

[1992] The server sends the generated suggestions to the terminal.

[1993] The device displays suggestions to the user through a web browser.

[1994] Specific examples

[1995] User search query: "Rome"

[1996] Past reading history: "The History of the Roman Empire"

[1997] Suggested content: "The History of the Roman Empire provides detailed information about the founding of the city of Rome and the rule of Caesar. In particular, revisit the chapters on Caesar's reforms and the architecture of the city of Rome."

[1998] Example prompts for generative AI models

[1999] A user is performing a web search for "Rome." In their past reading history, they have a book called "The History of the Roman Empire." Based on the contents of this book, suggest phrases, events, and places related to "Rome" to the user.

[2000] This system allows users to recall the contents of books they have read in the past and provides an opportunity to gain new knowledge.

[2001] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2002] Step 1:

[2003] A user enters a search query. The user opens a web browser, types "Rome" into the search bar, and presses enter, generating the search query "Rome."

[2004] Step 2:

[2005] The terminal sends a search query to the server. The terminal sends the search query "Rome" entered by the user to the server as an HTTP request. The input is the search query "Rome" and the output is an HTTP request to the server.

[2006] Step 3:

[2007] The server analyzes the context of the search query. The server passes the received search query "Rome" to a natural language processing engine (e.g., spaCy) to analyze the context. The input is the search query "Rome," and the output is related keywords and topics as the analysis results. Specifically, the server extracts the meaning of the word "Rome" and related topics.

[2008] Step 4:

[2009] The server searches the database for past reading history. The server uses a database management system (e.g., MySQL) to search for the user's past reading history. The input is the user ID and the analysis result, and the output is information about related books. Specifically, the server executes an SQL query to search for books related to "Rome."

[2010] Step 5:

[2011] The server extracts relevant information and sends a prompt to the generative AI model. The server extracts phrases, events, and places related to "Rome" from the reading history and creates a prompt to send to the generative AI model (e.g., GPT-4). The input is information about the related book, and the output is a prompt. Specifically, the server generates a prompt based on the extracted information.

[2012] Step 6:

[2013] The generative AI model generates suggestions. Based on the received prompt, the generative AI model generates appropriate suggestions for the user. The input is the prompt, and the output is the suggested sentence. Specifically, the generative AI model uses a natural language generation algorithm to create suggested sentences.

[2014] Step 7:

[2015] The server sends the generated suggestions to the device. The server sends the suggestions received from the generative AI model to the device. The input is the suggested sentence, and the output is an HTTP response to the device. Specifically, the server generates an HTTP response and sends it to the device.

[2016] Step 8:

[2017] The device displays the suggestions to the user. The device then displays the received suggestions on a web browser. The input is the suggestion text, and the output is the information displayed to the user. Specifically, the device uses HTML and JavaScript to display the suggestions in a format that is easy for the user to view.

[2018] (Application example 2)

[2019] Next, a description will be given of Application Example 2 of Form 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."

[2020] In today's information society, when users research a specific topic, it is important to recall the contents of books they have read in the past and gain new knowledge. However, manually searching through the contents of books users have read in the past to find relevant information is extremely time-consuming. Furthermore, there is a lack of effective ways to link Internet search results with past reading history, making it difficult for users to quickly obtain the information they need. A system that solves these problems is needed.

[2021] 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.

[2022] In this invention, the server includes means for recording information about books the user has read in the past, means for analyzing the context when the user is conducting research using generative artificial intelligence, internet search, map search, etc., means for suggesting related phrases, events, places, etc. from books read in the past based on the analyzed context, means for conducting an internet search based on the user's search query and analyzing the obtained search results, and means for generating information to suggest to the user based on the analyzed search results and the user's past reading history. This allows the user to quickly recall the contents of books they have read in the past and acquire new knowledge by linking them to internet search results.

[2023] "User" refers to an individual who uses the system to search for information or view past reading history.

[2024] "Books" refers to written media such as books and e-books that users have read in the past.

[2025] "Means for recording information" refers to a method or device for saving information about books that a user has read in the past in a database or the like.

[2026] "Generative AI" refers to artificial intelligence technology that analyzes users' search queries and contexts and generates relevant information.

[2027] An "internet search" refers to the act of a user searching the web for information about a particular topic.

[2028] "Map search" refers to the act of a user searching the web for a specific location or geographic information.

[2029] "Contextual analysis means" refers to methods or devices for understanding a user's intent and interests from their search queries and behavior, and extracting relevant information.

[2030] "Suggestion means" refers to a method or device for suggesting relevant phrases, events, places, etc. to a user based on analyzed context.

[2031] A "search query" refers to a keyword or phrase that a user enters when searching the Internet.

[2032] "Means for analyzing search results" refers to a method or device for analyzing information obtained through an Internet search and extracting information that is useful to the user.

[2033] "Reading history" refers to information about books a user has read in the past.

[2034] "Means for generating suggested information" refers to a method or device for generating information to suggest to a user based on the analyzed search results and past reading history.

[2035] A system for implementing this invention records information about books a user has read in the past, analyzes the context when the user conducts research using generative artificial intelligence, internet searches, map searches, etc., and suggests related phrases, events, places, etc. from books read in the past based on the analyzed context. It also performs an internet search based on the user's search query, analyzes the obtained search results, and generates information to suggest to the user based on the analyzed search results and the user's past reading history.

[2036] Hardware and software used

[2037] Hardware: Smartphones, servers

[2038] Software: Python, BeautifulSoup (for web scraping), SQLite (for database management)

[2039] Data processing and calculation

[2040] 1. Obtaining user search queries: When a user searches for a specific topic on their smartphone, the search query is sent to the system.

[2041] 2. Internet Search: The server performs an Internet search based on the user's search query and retrieves the search results by scraping web pages using BeautifulSoup.

[2042] 3. Contextual analysis: The server analyzes the content of the search results to understand the user's intent and interests. This analysis is performed using generative artificial intelligence.

[2043] 4. Reading History Search: The server searches the user's past reading history stored in the SQLite database to obtain information about books related to the search query.

[2044] 5. Suggested Reading: The server generates suggested reading information for the user based on the analyzed search results and past reading history, including related phrases, events, and places.

[2045] Specific examples

[2046] For example, if a user searches for "Rome," the server will perform an internet search and analyze the search results. At the same time, it will retrieve information from a database about books on the history of the Roman Empire that the user has read in the past and suggest related phrases, events, and places. This allows the user to recall the contents of books they have read in the past and gain new knowledge.

[2047] Prompt Sentence Examples

[2048] "Build an application that, when a user searches for Rome, suggests related phrases and events from books about the history of the Roman Empire that the user has read."

[2049] The above is an embodiment of the present invention. This system allows users to quickly recall the contents of books they have read in the past and to link them with internet search results to obtain new knowledge.

[2050] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2051] Step 1:

[2052] A user searches on their smartphone for a specific topic.

[2053] Input: The search query entered by the user (e.g., "Rome")

[2054] Output: The search query is sent to the server

[2055] What it does: When a user types "Rome" into the search bar on their smartphone and presses the search button, the query is sent to the server.

[2056] Step 2:

[2057] The server performs an internet search based on the user's search query.

[2058] Input: User's search query (e.g. "Rome")

[2059] Output: HTML data of internet search results

[2060] Specific operation: The server sends a search query to the Google Search API and receives the HTML data of the search results.

[2061] Step 3:

[2062] The search results obtained by the server are analyzed.

[2063] Input: HTML data of internet search results

[2064] Output: Parsed search results text data

[2065] Specific operation: The server uses BeautifulSoup to parse the HTML data and extract the text data of the search results.

[2066] Step 4:

[2067] The server searches the user's past reading history.

[2068] Input: User's search query (e.g. "Rome")

[2069] Output: Relevant reading history data

[2070] Specific operation: The server searches the user's reading history stored in an SQLite database and retrieves information about books related to the search query.

[2071] Step 5:

[2072] The server generates suggestion information based on search results and reading history.

[2073] Input: Analyzed text data of search results, related reading history data

[2074] Output: Suggested information to the user (e.g., related phrases, events, places)

[2075] Specific operation: The server uses generative artificial intelligence to analy...

Claims

1. A means for storing information about books previously read by users in a database; means for collecting behavioral data including at least one of the user's search queries, the content of web pages viewed, and locations searched on a map; means for analyzing said behavioral data using natural language processing algorithms to understand the context; means for extracting said contextually relevant information from said database; means for presenting the extracted related information to the user; A system including:

2. A means for acquiring a search query when a user performs a web search; A means of analyzing the search queries obtained and understanding the context; A means for acquiring contents of books related to the context from a database in which information on books previously read by the user is stored; A means for extracting contents related to the context from the related books; means for generating suggestions for the user based on the extracted content; A system including:

3. A means for acquiring a search query when a user performs a web search; means for conducting an internet search based on the search query; means for analyzing search results from the Internet search and extracting text data of the search results; A means for obtaining the contents of books extracted from a database storing information on books previously read by the user based on the search query; Generate suggestions for the user based on the text data of the search results and the contents of the books. system.

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