System

Abstract

A system is provided.SOLUTION: A system comprising means for collecting data from various sources on the Internet, means for inputting the collected data into a generative AI model to generate information interpreted from different viewpoints, means for detecting biases in the generated information and selecting balanced information, means for providing information selected according to a user's search query, and means for improving the generative AI model through feedback from 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] In modern society, there is a huge amount of information available on the Internet, but much of that information is biased, making it difficult for users to obtain unbiased, multifaceted information. In particular, when finding solutions to social problems or complex issues, information from a biased perspective alone does not provide a comprehensive understanding, making it difficult to make appropriate decisions. Furthermore, existing information gathering platforms limit the means by which users can efficiently obtain information from multiple perspectives. This means that users spend a lot of time obtaining information and it is difficult to obtain reliable, multifaceted information. [Means for solving the problem]

[0005] The present invention solves this problem with a system that includes a means for collecting data from various sources on the Internet, a means for inputting the collected data into a generative AI model to generate information interpreted from different perspectives, a means for detecting bias in the generated information and selecting balanced information, a means for providing the selected information in response to a user's search query, and a means for improving the generative AI model through user feedback. Using this system, users can easily obtain multifaceted and balanced information, enabling them to gain a comprehensive understanding of social issues and complex challenges. The system also includes a means for cleansing the collected data and removing noise, and a means for quantifying and classifying bias in the generated information, thereby providing highly accurate information. As a result, users can efficiently collect reliable, multifaceted information.

[0006] An "information source" is a source on the Internet from which data can be obtained, such as a news site, blog, or academic paper database.

[0007] "Data collection means" refers to devices or programs that collect necessary data from sources on the Internet by methods such as scraping or using APIs.

[0008] A "generative AI model" refers to an artificial intelligence model, specifically a natural language processing model, that analyzes collected data and generates information interpreted from different perspectives.

[0009] An "information generation means" is a device or program that uses a generative AI model to create information or summaries from different perspectives based on collected data.

[0010] A "bias detection means" is a device or program that detects whether the generated information contains prejudice or bias, and quantifies and classifies that bias.

[0011] The "information selection means" is a device or program that selects balanced information based on the results of bias detection.

[0012] A "search query" is a keyword or phrase that a user enters when searching for specific information.

[0013] An "information providing means" is a device or program that displays selected information in response to a user's search query.

[0014] A "feedback means" is a device or program that collects feedback such as ratings and comments provided by users and uses it to improve the generative AI model. [Brief explanation of the drawings]

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

[0016] 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.

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

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

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

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

[0036] 1. Overview

[0037] This invention is a system that collects data from various sources on the internet, converts the data into multifaceted information using a generative AI model, and provides it to users. This system uses bias detection means to select balanced information, allowing users to obtain information from reliable, multifaceted perspectives.

[0038] 2. Information gathering methods

[0039] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. It uses Python libraries (e.g., BeautifulSoup, Scrapy) to scrape the data and obtain the required data. The collected data is then stored in a database.

[0040] 3. Generative AI Models

[0041] The server inputs the collected data into a generative AI model (e.g., a natural language processing model) to generate information interpreted from different perspectives. This generative AI model analyzes the data based on the user's search query and provides information from multiple angles, such as economic, scientific, social, and political perspectives.

[0042] 4. Bias detection and filtering

[0043] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. This bias detection algorithm is used to detect information bias and select multifaceted, balanced information. The selected information is balanced and filtered to ensure it is not biased toward any particular viewpoint.

[0044] 5. User Interface

[0045] The terminal (user's device) receives the user's search query, and the server provides information selected based on that query. The search results include information from different perspectives generated by the generative AI model, allowing the user to obtain information from multiple perspectives.

[0046] 6. Feedback Loops

[0047] Users provide feedback on the information provided. This feedback can take the form of comments or ratings, and the server collects and analyzes this feedback. The collected feedback is used as training data for the generative AI model, contributing to improving the model's accuracy.

[0048] Specific examples

[0049] Providing information on climate change

[0050] 1. Information gathering

[0051] The server collects the latest articles related to "climate change" from the Internet and stores them in a database.

[0052] 2. Information generation by generative AI

[0053] The server inputs the collected data into a generative AI model and analyzes the information.

[0054] A generative AI model generates information from the following perspectives, for example:

[0055] Economic perspective: "Climate change could become a drag on economic growth in the long term."

[0056] Science: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years."

[0057] Political perspective: "International conferences are discussing a new agreement to combat climate change."

[0058] 3. Bias detection and filtering

[0059] The server performs bias detection on the generated information and selects balanced articles.

[0060] 4. User Interface

[0061] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[0062] 5. Feedback Loop

[0063] Users provide feedback on the information provided, and the server uses that feedback to improve the generative AI model.

[0064] Embodiments of the present invention enable users to efficiently collect and use multifaceted and balanced information, which can contribute to a comprehensive understanding and solution of social problems.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The server collects data from various sources on the Internet, specifically using Python libraries (such as BeautifulSoup and Scrapy) to retrieve the latest articles from news sites, blogs, and academic paper databases, and stores this data in a database.

[0068] Step 2:

[0069] The server cleanses the collected data, performing text preprocessing to correct grammatical and spelling errors and remove noise such as unnecessary HTML tags and advertisements, thereby ensuring the quality of the data fed into the generative AI model.

[0070] Step 3:

[0071] The server inputs the cleaned data into a generative AI model. For example, it uses a natural language processing model (such as GPT-3) to generate information interpreted from multiple perspectives (economic, scientific, social, political, etc.). This generated information includes analyses and opinions from different angles.

[0072] Step 4:

[0073] The server applies bias-detection algorithms to the generated information, quantifying and categorizing the information's bias—for example, determining whether an article is politically biased or emphasizes an economic perspective.

[0074] Step 5:

[0075] The server performs filtering based on the bias detection results. It selects information whose bias score falls within a certain range and extracts balanced, multifaceted information. This filtering process eliminates information that contains extreme bias.

[0076] Step 6:

[0077] The device receives the user's search query and sends it to the server. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button.

[0078] Step 7:

[0079] The server retrieves selected information based on the user's search query and provides relevant articles to the user, displaying information from multiple perspectives from the generative AI model.

[0080] Step 8:

[0081] The device displays the search results to the user, including articles and summaries for each of the different perspectives generated, allowing the user to access information from multiple angles.

[0082] Step 9:

[0083] Choose how users can provide feedback on the information provided, for example by leaving comments, rating, or suggesting improvements.

[0084] Step 10:

[0085] The server collects and analyzes the feedback provided by users, and this feedback data is used to retrain the generative AI model to improve the accuracy of future information generation.

[0086] Through the above processing steps, the system of the present invention provides users with multifaceted and balanced information, supporting reliable decision-making.

[0087] Example 1

[0088] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0089] Although there is a wide variety of information on the Internet, this information is often biased, making it difficult for users to obtain reliable information from multiple perspectives. Furthermore, there is a demand for improving the quality of collected information and the accuracy of models. A method to solve these issues is needed.

[0090] 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.

[0091] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data using a program and filtering out necessary data, means for inputting the collected data into a large-scale language model and generating information interpreted from different perspectives, means for detecting bias in the generated information and selecting multifaceted information, means for displaying the selected information in response to a user's search query, and means for analyzing user ratings and improving the large-scale language model, thereby enabling users to obtain information from reliable multifaceted perspectives.

[0092] The "Internet" is a global network system that connects computer networks around the world and enables the exchange of information.

[0093] A "source" is an internet location that provides information in digital form, such as a news site, blog, or academic paper database.

[0094] "Data collection" is the process of obtaining the necessary information from the Internet and storing it in a database.

[0095] A "program" is a set of instructions that instruct a computer to perform a specific process.

[0096] "Analysis" is the process of deciphering collected data and extracting and classifying specific information.

[0097] "Filtering" is the process of removing unnecessary information from collected data and selecting only useful data.

[0098] A "large-scale language model" is an artificial intelligence model that learns from large amounts of text data and generates and analyzes natural language.

[0099] "Bias" means that information is biased towards a particular viewpoint or opinion.

[0100] A "query" is a search request to a database or Internet search engine to find specific information.

[0101] "Display" means the visual presentation of information on a computer screen.

[0102] "Rating" refers to the opinions, comments, and rating scores that users give to the information provided.

[0103] This invention is a system that collects data from various sources on the internet, converts that data into multifaceted information using a generative AI model, and provides it to users. This system uses bias detection means to select balanced information, allowing users to obtain information from reliable, multifaceted perspectives.

[0104] Information gathering methods

[0105] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (e.g., BeautifulSoup, Scrapy) to scrape and obtain the required data. The collected data is then stored in a database (e.g., MySQL, PostgreSQL).

[0106] Information generation using generative AI models

[0107] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. This generative AI model uses a large-scale language model (e.g., GPT) to analyze data based on the user's search query and provide information from multiple angles, including economic, scientific, social, and political perspectives.

[0108] Bias Detection and Filtering

[0109] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. The bias detection algorithm is used to detect information bias and select multifaceted, balanced information. The selected information is filtered to ensure it is balanced and not biased toward any particular viewpoint.

[0110] User Interface

[0111] The terminal (user's device) receives the user's search query, and the server provides information selected based on the query. The search results include information from different perspectives generated by the generative AI model, allowing the user to obtain information from multiple perspectives.

[0112] Feedback Loop

[0113] Users provide feedback on the information provided. This feedback can take the form of comments or ratings, and the server collects and analyzes this feedback. The collected feedback is used as training data for the generative AI model, contributing to improving the model's accuracy.

[0114] Specific examples

[0115] Providing information on climate change

[0116] 1. Information gathering

[0117] The server collects the latest articles related to "climate change" from the Internet and stores them in a database.

[0118] 2. Information generation by generative AI

[0119] The server inputs the collected data into a generative AI model and analyzes the information.

[0120] A generative AI model generates information from the following perspectives, for example:

[0121] Economic perspective: "Climate change could become a drag on economic growth in the long term."

[0122] Science: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years."

[0123] Political perspective: "International conferences are discussing a new agreement to combat climate change."

[0124] 3. Bias detection and filtering

[0125] The server performs bias detection on the generated information and selects balanced articles.

[0126] 4. User Interface

[0127] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[0128] 5. Feedback Loop

[0129] Users provide feedback on the information provided, and the server uses that feedback to improve the generative AI model.

[0130] Prompt Sentence Examples

[0131] An example of a prompt to be input to the generative AI model is, "Please summarize the latest economic, scientific, and political perspectives on climate change." Based on this prompt, the generative AI model organizes information from each perspective and provides it to the user.

[0132] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0133] Step 1: Gather information

[0134] The server collects information from news sites, blogs, and academic paper databases on the Internet. Specifically, it uses the Python libraries BeautifulSoup and Scrapy to perform scraping. It receives a list of URLs of target sites as input and obtains the collected text data as output, which is then stored in a database.

[0135] Step 2: Data cleansing

[0136] The server processes the collected data stored in the database and removes unnecessary noise. Specifically, it uses regular expressions and cleaning algorithms to organize the data. It takes the collected raw data as input and produces clean, noise-removed data as output.

[0137] Step 3: Input data into the generative AI model

[0138] The server takes the cleansed data and inputs it into a generative AI model. Specifically, it analyzes the data using a large-scale language model (e.g., GPT). It receives the cleaned data and the user's search query as input and generates information interpreted from different perspectives as output.

[0139] Step 4: Analyze and generate information

[0140] The server uses a generative AI model to generate information based on the user's search query. For example, for the query "climate change," it generates information interpreted from economic, scientific, and political perspectives. It receives the user's query and clean data as input, and obtains information generated from multiple perspectives as output.

[0141] Step 5: Bias detection and filtering

[0142] The server analyzes the generated information to detect bias. Specifically, it uses a bias detection algorithm to quantify the bias of each article and select balanced information. It receives the generated information as input and provides balanced, filtered information as output.

[0143] Step 6: User Interface

[0144] The terminal receives the user's search query, and the server provides selected information. The terminal receives the search query input from the user and displays information filtered from multiple perspectives as output to the user, allowing the user to obtain information from multiple perspectives.

[0145] Step 7: Gather feedback

[0146] The user provides feedback on the provided information. The feedback is in the form of comments or ratings, and the user's ratings are received as input. The feedback data is transferred to the server as output.

[0147] Step 8: Refine the generative AI model

[0148] The server analyzes the collected feedback and uses it as training data for the generative AI model, which improves the model's accuracy. It takes user feedback as input and updates the improved generative AI model as output.

[0149] (Application example 1)

[0150] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0151] Many users today gather a wide range of information online to select products, but information bias and lack of reliability are problems. Even in virtual stores, it is difficult for users to quickly obtain information from multiple perspectives in an unbiased manner. This often leads users to make purchasing decisions based on inaccurate information, which reduces post-purchase satisfaction.

[0152] 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.

[0153] In this invention, the server includes means for collecting data from various information sources on the Internet, means for inputting the collected data into a generative AI model to generate information interpreted from different perspectives, means for detecting bias in the generated information and selecting balanced information, means for providing information selected based on a user's search query when searching for and selecting products in a virtual store, and means for improving the generative AI model through feedback from the user. This enables users to efficiently obtain diversified and balanced information in the virtual store and make purchasing decisions based on reliable information.

[0154] "Diverse information sources on the Internet" refers to multiple types of information sources available on the Internet, such as news sites, blogs, academic paper databases, and social networking sites.

[0155] A "generative AI model" refers to an artificial intelligence model that analyzes collected data and generates information from different perspectives.

[0156] "Bias" refers to a state in which information is biased towards a particular perspective or opinion.

[0157] "Virtual store" refers to a virtual store for selling and purchasing products online.

[0158] "User search query" refers to the keywords or phrases a user uses to search for information.

[0159] "Feedback" refers to the evaluations and comments users make about the information provided or the performance of the system.

[0160] A specific method for implementing a system for carrying out the present invention will now be described.

[0161] 1. Information gathering methods

[0162] The server collects data from various sources on the Internet, such as news sites, blogs, academic paper databases, and social media. Specifically, web scraping is performed using the Python libraries BeautifulSoup and Scrapy. The collected data is then stored in a database.

[0163] 2. Information generation using generative AI models

[0164] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. This generative AI model uses a natural language processing model, such as OpenAI's GPT-3, to provide information from different perspectives (economic, scientific, social, and political) based on the user's search query.

[0165] 3. Bias detection and filtering

[0166] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. It uses a bias detection algorithm to detect information bias and select multifaceted, balanced information.

[0167] 4. User Interface

[0168] The user's device receives the search query, and the server provides information selected based on the query. When searching and selecting products in the virtual store, users can obtain information filtered according to different perspectives.

[0169] 5. Feedback Loop

[0170] Users provide feedback on the information provided. This feedback is accepted in the form of comments and ratings. The server collects and analyzes this feedback and uses it as training data for the generative AI model. This improves the performance of the generative AI model.

[0171] Specific examples

[0172] Scenario: Providing smartphone purchase information

[0173] 1. The server collects the latest information related to smartphones from news sites and blogs on the Internet and stores it in a database.

[0174] 2. The server inputs the collected data into a generative AI model (e.g., GPT-3) to analyze the information. The generative AI model generates information from economic, scientific, social, and political perspectives.

[0175] Example: Economic perspective: "The latest smartphones are 15% more expensive than they were last year."

[0176] Example: Scientific Perspective: "The latest smartphones use new display technology."

[0177] Example: Social perspective: "Many users appreciate the new camera features."

[0178] Example: Political Perspective: "Some countries restrict the import of certain smartphones."

[0179] 3. The server performs bias detection on the generated information and selects balanced articles.

[0180] 4. When a user searches for "smartphone" on their device, information filtered for different perspectives is displayed.

[0181] 5. Users provide feedback on the information provided, and the server collects and analyzes that feedback to improve the generative AI model.

[0182] Prompt Sentence Examples

[0183] "Article: The latest smartphones use new display technology. Explain this information from economic, scientific, social, and political perspectives."

[0184] This system allows users to select products in virtual stores based on reliable, multifaceted information, resulting in highly satisfying purchases.

[0185] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0186] Step 1:

[0187] The server collects data from various sources on the Internet, such as news sites, blogs, and academic paper databases. It uses the Python libraries BeautifulSoup and Scrapy to perform web scraping and stores the retrieved data in a database. The input is a search query, and the output is the articles and papers retrieved by the scraping.

[0188] Step 2:

[0189] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. Specifically, it uses a natural language processing model such as OpenAI's GPT-3. The input is the data collected in step 1, and the output is text generated from economic, scientific, social, and political perspectives. In this stage, a process is carried out in which certain prompt sentences are generated for the model and the results are obtained.

[0190] Step 3:

[0191] The server applies bias detection measures to the generated information. It uses a bias detection algorithm to quantify and classify the bias in the information. The input is the information from different perspectives generated in step 2, and the output is information with quantified and classified bias. This process evaluates the quality of the information and selects fair information.

[0192] Step 4:

[0193] When searching and selecting products in a virtual store, the server provides information selected based on the user's search query. The user's device sends the search query to the server. The input is the user's search query, and the output is balanced information with bias detection. The device displays this information to the user.

[0194] Step 5:

[0195] Users provide feedback on the information provided. This feedback takes the form of comments and ratings, and is received by the server. The input is user feedback, and the output is data for improving the generative AI model. By collecting and analyzing feedback, the training data for the generative AI model is updated, improving the model's performance.

[0196] This allows users to efficiently obtain multifaceted and balanced information in the virtual store, enabling them to make purchasing decisions based on reliable information.

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

[0198] 1. Overview

[0199] This invention is a system that collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. This system combines a means of detecting bias in the generated information and selecting balanced information with an emotion engine that recognizes user emotions. This emotion engine analyzes user feedback, improves the generative AI model, and adjusts the content and display format of the information provided. This enables users to obtain information from reliable, multifaceted perspectives.

[0200] 2. Information gathering methods

[0201] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (such as BeautifulSoup and Scrapy) to perform scraping, obtain the necessary data, and store it in a database.

[0202] 3. Data Cleansing Methods

[0203] The server cleanses and denoises the collected data, including text preprocessing to correct grammatical and spelling errors and remove unnecessary HTML tags, advertisements, and other noise, ensuring the quality of the data fed into the generative AI model.

[0204] 4. Generative AI Models

[0205] The server inputs the cleaned data into a generative AI model (e.g., a natural language processing model) to generate information interpreted from different perspectives. This AI model analyzes the data based on the user's search query and provides information from multiple perspectives (e.g., economic, scientific, social, political, etc.).

[0206] 5. Bias detection and filtering

[0207] The server applies a bias detection algorithm to the generated information. The bias detection algorithm quantifies and classifies the bias of the information. For example, it determines whether an article is politically biased or emphasizes an economic perspective. The server selects information whose bias score falls within a certain range, filtering out information containing extreme bias and selecting balanced information.

[0208] 6. User Interface

[0209] The device receives the user's search query and sends it to the server. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button. The server searches for selected information based on the user's search query and provides the user with relevant articles. Search results containing information from multiple perspectives generated by the generative AI model are displayed.

[0210] 7. Emotion Engine

[0211] The server collects user feedback and recognizes emotions using an emotion engine. This emotion engine analyzes emotional data from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user comments, "This information was helpful," the generative AI model is retrained based on that positive emotional data.

[0212] 8. Optimizing information provision

[0213] The server dynamically adjusts the content and display format of information provided based on the user's emotions recognized by the emotion engine. For example, if the user has negative emotions, the server will respond by providing additional information from a different perspective. In this way, the quality of information provided and the user experience are improved.

[0214] Specific examples

[0215] Providing information on climate change

[0216] 1. Information gathering

[0217] The server collects the latest articles on "climate change" from the Internet and stores them in a database.

[0218] 2. Information generation by generative AI

[0219] The server inputs the collected data into a generative AI model to generate information from multiple perspectives.

[0220] Examples: Economic: "Climate change could be a factor that impedes economic growth in the long term." Scientific: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years." Political: "International conferences are discussing new agreements to combat climate change."

[0221] 3. Bias detection and filtering

[0222] The server performs bias detection on the generated information and selects balanced articles.

[0223] 4. User Interface

[0224] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[0225] 5. Leveraging Emotional Engines

[0226] Users provide feedback on articles, saying things like "This information was helpful."

[0227] The server collects this feedback, analyzes it as positive emotional data in the emotion engine, and reflects it in improving the generative AI model.

[0228] Embodiments of the present invention enable users to efficiently collect and use multifaceted and balanced information, supporting reliable decision-making and contributing to a comprehensive understanding and resolution of social problems.

[0229] The processing flow will be explained below.

[0230] Step 1:

[0231] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it performs web scraping using Python libraries (such as BeautifulSoup and Scrapy) to obtain target articles and papers, and stores this data in a database.

[0232] Step 2:

[0233] The server cleanses the collected data. This process removes unnecessary HTML tags and advertisements from the text data and corrects grammar and spelling errors. This normalization process improves the quality of the data that is input into the generative AI model.

[0234] Step 3:

[0235] The server inputs the cleaned data into a generative AI model. For example, a natural language processing model (such as GPT-3) is used to generate information based on the input data, interpreted from different perspectives (economic, scientific, social, political, etc.). This information is then re-stored in a database.

[0236] Step 4:

[0237] The server applies a bias detection algorithm to the generated information. Specifically, it assigns a bias score to each piece of information, quantifying and categorizing its bias. For example, it analyzes how politically biased an article is or how much it emphasizes a scientific perspective.

[0238] Step 5:

[0239] The server filters the information based on the bias score, removing information with extremely high bias and selecting only balanced information. This selected information is then organized in a format suitable for delivery to the user.

[0240] Step 6:

[0241] The device receives the user's search query and sends it to the server. When the user types in "climate change" and presses the send button, the search query is sent to the server.

[0242] Step 7:

[0243] Based on the user's search query, the server searches the database for relevant information and retrieves selected information, including multiple perspectives generated by the generative AI model.

[0244] Step 8:

[0245] The device displays search results to the user, which can be viewed and analyzed, including information from different perspectives, such as economic, scientific, social, and political perspectives.

[0246] Step 9:

[0247] Users provide feedback on the displayed information. For example, they can post comments on an article such as "This was helpful" or "I'd like more detailed information." The sentiment engine also collects the sentiment (positive, negative, neutral) of the feedback.

[0248] Step 10:

[0249] The server collects user feedback and analyzes it with an emotion engine. The collected emotion data is used as training data for the generative AI model, helping to improve the model. For example, it strengthens the method for generating information that generates a lot of positive feedback and improves the method for generating information that generates a lot of negative feedback.

[0250] Step 11:

[0251] The server dynamically adjusts the content and display format of information provided based on the user's emotions recognized by the emotion engine. For example, if the user has negative emotions, it will provide supplementary information with a different perspective or detailed information. In this way, it is possible to provide information that is optimized for each individual user.

[0252] Through this series of processing steps, the system of the present invention provides users with multifaceted and balanced information, supporting reliable decision-making.

[0253] Example 2

[0254] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0255] Conventional information gathering systems have struggled to efficiently collect data from diverse information sources on the Internet, provide information from multiple perspectives, and detect bias. Furthermore, it has been difficult to dynamically improve the accuracy and quality of information provided based on user feedback, failing to contribute to improving the user experience. The present invention aims to solve these problems and provide highly reliable, multifaceted information and an improved user experience.

[0256] 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.

[0257] In this invention, the server includes means for collecting data from various information sources on the Internet, means for cleansing the collected data and removing noise, means for inputting the cleansed data into a generative AI model and generating information interpreted from different perspectives, means for detecting bias in the generated information, quantifying and classifying it, and selecting balanced information, means for receiving a user's search query and providing information corresponding to the query, means for recognizing emotions through user feedback and reflecting the recognition in improving the generative AI model, and means for dynamically adjusting the content and display format of the information provided based on the emotion recognition. This makes it possible to provide highly reliable, multifaceted information and improve the user experience.

[0258] The "Internet" is a large-scale network system that provides information and services worldwide via communication networks.

[0259] "Source" refers to the origin or place that provides data or knowledge, and includes news sites, blogs, academic paper databases, etc.

[0260] "Means of data collection" refers to the techniques and methods used to obtain the required data from sources on the internet, including scraping techniques and API calls.

[0261] "Data cleansing" is the process of removing unnecessary information and noise from collected data to improve the quality of the data.

[0262] "Noise reduction" is the process of removing unnecessary information, such as HTML tags and advertisements, from text data.

[0263] A "generative AI model" is an artificial intelligence model that uses technologies such as natural language processing to generate sentences and data that appear to have been created by humans.

[0264] "Information interpreted from different perspectives" refers to information that is provided based on multiple perspectives, with data analyzed from multiple angles and standpoints.

[0265] "Bias detection measures" refer to techniques and methods for determining whether the information generated is biased toward a particular viewpoint.

[0266] "Quantification and classification" is the process of numerically assessing the bias of information and classifying it into different categories.

[0267] "Balanced information" refers to fair and objective information that is not heavily biased toward any particular viewpoint or bias.

[0268] A "search query" refers to a keyword or phrase that a user enters when searching for specific information.

[0269] "Feedback" refers to responses such as ratings, opinions, and comments provided by users.

[0270] "Means of recognizing emotions" refers to technologies and methods for analyzing emotions such as positive and negative from user feedback.

[0271] "Means for dynamically adjusting the content and display format of information provided" refers to technologies and methods that change the information provided and the way it is displayed in real time based on user sentiment and feedback.

[0272] This invention is a system that collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. This system aims to improve the user experience by combining a means to detect bias in the generated information and select balanced information with an emotion engine that recognizes the user's emotions.

[0273] Information gathering

[0274] The server collects data from news sites, blogs, and academic paper databases on the Internet. This process is performed by scraping using the Python libraries BeautifulSoup and Scrapy. For example, the HTML structure of a news site is analyzed, and the article text and titles are extracted from tags with specific classes and IDs, and then stored in a database.

[0275] Data Cleansing

[0276] The server cleanses and removes noise from the collected data, using Python libraries to correct grammatical and spelling errors, and using regular expressions and BeautifulSoup to remove unnecessary HTML tags and advertisements.

[0277] Information generation using generative AI models

[0278] The server inputs the cleansed data into a generative AI model to generate information interpreted from different perspectives. For example, using GPT-3 as a generative AI model, the following prompt is input: "Please explain climate change from an economic perspective." This will generate information from an economic perspective, a scientific perspective, a political perspective, etc.

[0279] Bias Detection and Filtering

[0280] The server detects, quantifies, and classifies bias in the generated information. Based on this, an algorithm is used to select balanced information. If the generated information is extremely biased, it is filtered out.

[0281] User Interface

[0282] The device receives the user's search query and sends it to the server. For example, if the user enters "climate change" into the search form and presses the search button, the server retrieves information based on the user's search query, generates related articles, and displays information from multiple perspectives generated by the AI ​​model.

[0283] Emotion Engine

[0284] The server collects user feedback and analyzes it using an emotion engine. The emotion engine recognizes positive and negative emotions from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user provides feedback on an article saying, "This information was helpful," the generative AI model can be retrained based on that positive data.

[0285] Optimizing information provision

[0286] The server dynamically adjusts the content and display format of the information provided based on the analysis results of the emotion engine. For example, if a user gives negative feedback, the server can respond by providing additional information from a different perspective, thereby improving the user experience.

[0287] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0288] Step 1:

[0289] The server performs scraping to collect data from news sites, blogs, and academic paper databases on the Internet. The input is a specific list of URLs and search keywords. The server uses BeautifulSoup or Scrapy to analyze the HTML structure and extract article titles, body text, URLs, etc. The output is saved in a database as structured data. For example, searching for tags with specific classes or IDs from the HTML of a news site and extracting article information.

[0290] Step 2:

[0291] The server cleanses the collected data and removes noise. The input is the raw data collected in step 1. Specifically, it uses Python libraries (such as NLTK and SpaCy) to correct grammatical and spelling errors, and uses regular expressions and BeautifulSoup to remove unnecessary HTML tags and advertisements. The output is cleansed text data. For example, <ad>" tags and unnecessary scripts.

[0292] Step 3:

[0293] The server inputs the cleansed data into the generative AI model. The input is the text data cleansed in step 2. The server uses a generative AI model (e.g., GPT-3) and sends a prompt such as "Please explain climate change from an economic perspective" to the generative AI model. The output is text information generated from multiple perspectives. For example, it generates information interpreted from an economic perspective and a scientific perspective.

[0294] Step 4:

[0295] The server detects bias in the generated information, quantifies it, and classifies it. The input is the information generated in step 3. A bias detection algorithm is applied to evaluate and quantify the bias in the information. For example, a political bias score is calculated. The output is information with a bias score assigned. Based on the bias score, balanced information is extracted.

[0296] Step 5:

[0297] The terminal receives the user's search query and sends it to the server. The input is the keyword the user entered into the search form. The terminal receives the input from the user and sends it to the server as a query. The output is the search query forwarded to the server. For example, the query "climate change" is sent.

[0298] Step 6:

[0299] The server searches for selected information based on the user's search query and provides relevant articles. The input is the search query received in step 5 and the information to which a bias score was assigned in step 4. The server searches for information filtered based on the search query and generates search results composed of multiple perspectives. The output is information as search results. For example, it provides articles that include interpretations of "climate change" from economic, scientific, and political perspectives.

[0300] Step 7:

[0301] The terminal displays the search results to the user. The input is the search results generated in step 6. The terminal displays the search results in a user interface, allowing the user to view information by perspective. The output is a visual representation of the search results to the user, for example, separated into tabs for economic perspectives, scientific perspectives, and political perspectives.

[0302] Step 8:

[0303] The device collects feedback from the user and sends it to the server. The input is the user's feedback (e.g., "This information was helpful"). The device receives the user's feedback and sends it to the server. The output is the feedback sent to the server. For example, sending a rating such as "This information was helpful."

[0304] Step 9:

[0305] The server analyzes the feedback from the user and uses an emotion recognition engine. The input is the feedback collected in step 8. The emotion engine recognizes the emotion of the feedback, such as positive or negative. The output is the analyzed emotion data. For example, if the evaluation is positive, the score is output as the analysis result.

[0306] Step 10:

[0307] The server uses emotion recognition to improve the generative AI model. The input is the emotion data obtained in step 9. The generative AI model is retrained using positive feedback, and the model's generation results are adjusted in response to negative feedback. The output is a retrained generative AI model. This improves the quality of the generated information.

[0308] Step 11:

[0309] The server dynamically adjusts the content and display format of the information provided based on emotion recognition. The input is the emotion data from step 9 and the generative AI model retrained in step 10. In response to negative user feedback, additional information from a different perspective is provided and the display format is adjusted accordingly. The output is dynamically adjusted information content and display format, which improves the user experience.

[0310] (Application example 2)

[0311] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0312] Conventional news distribution systems provide users with one-sided articles and do not adequately consider information bias or user sentiment. As a result, users often receive biased information, making it difficult to make reliable decisions. In addition, the collected data is of low quality and noisy, which reduces the accuracy of the generative AI model and reduces the quality of the information provided.

[0313] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data from various information sources on the Internet, means for inputting the collected data into a generative AI model and generating information interpreted from different perspectives, means for detecting bias in the generated information and selecting balanced information, means for providing the selected information in response to a user's search query, means for improving the generative AI model through user feedback, and means for analyzing user sentiment to improve the quality of information provided. This enables users to efficiently collect and use multifaceted and balanced information, supporting reliable decision-making and improving the quality of information provided. Furthermore, cleansing the collected data improves the accuracy of the generative AI model and the quality of the information provided.

[0314] "Diverse information sources on the Internet" refers to multiple types of information sources that can be accessed on the Internet, such as news sites, blogs, and academic paper databases.

[0315] "Data Collection Methods" refers to the use of software and protocols to retrieve and store information from a particular website.

[0316] A "generative AI model" refers to an algorithm that generates and analyzes natural language using technologies such as deep learning.

[0317] "Information interpreted from different perspectives" refers to information on a particular topic that has been analyzed from multiple fields of expertise and perspectives, such as economic, scientific, social, and political perspectives.

[0318] "Means for generating information" refers to technology that analyzes collected data as input and generates new information using a specific algorithm.

[0319] "Means for detecting bias and selecting balanced information" refers to algorithms and techniques for quantifying and evaluating information bias and selecting information with the least bias.

[0320] "Means for providing information selected in response to a user's search query" refers to an interface and system for displaying information filtered based on the search criteria entered by the user.

[0321] "Means for improving generative AI models through user feedback" refers to methods for analyzing feedback such as user ratings and opinions and retraining or adjusting generative AI models based on that feedback.

[0322] "Means of analyzing user emotions to improve the quality of information provided" refers to technology that uses an emotion analysis engine to analyze user feedback and reactions, and then uses the results to improve the content and format of information provided.

[0323] "Data cleansing and denoising" refers to the process of removing unnecessary elements such as grammatical and spelling errors, unnecessary HTML tags, and advertisements from collected data to improve the quality of the data.

[0324] "Means for quantifying and classifying bias" refers to algorithms and techniques for quantifying and classifying information bias as specific numerical values.

[0325] This invention is a system that allows users to efficiently collect and use multifaceted and balanced information. This system collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. The specific configuration and operation of this system are described below.

[0326] First, the server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (BeautifulSoup and Scrapy) to scrape websites, obtain the necessary data, and store it in a database.

[0327] The server then cleanses and denoises the collected data, using text processing libraries such as NLTK and TextBlob to correct grammatical and spelling errors, and remove unnecessary HTML tags and advertisements, ensuring the quality of the data fed into the generative AI model.

[0328] The server inputs the cleaned data into a generative AI model (e.g., a natural language processing model such as GPT-4) to generate information interpreted from different perspectives (e.g., economic, scientific, social, political, etc.). This AI model analyzes the data based on the user's search query and provides information from multiple perspectives.

[0329] The server then applies a bias detection algorithm to the generated information to quantify and classify the bias of the information, selecting information whose bias score falls within a certain range and filtering out information containing extreme bias to select balanced information.

[0330] The device receives a search query from the user. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button. The server searches filtered information based on the user's search query and provides the user with relevant articles. It then displays search results containing information from multiple perspectives generated by the generative AI model.

[0331] The server also collects user feedback and analyzes emotions using an emotion engine. This emotion engine analyzes emotional data from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user comments, "This information was helpful," the generative AI model can be retrained based on that positive emotional data to improve the quality of information provided.

[0332] As a concrete example, let's consider a case where the server collects the latest news on climate change and provides information from multiple perspectives. In this case, the server inputs the following prompt sentence into the generative AI model:

[0333] "Provide different perspectives on the latest news on climate change from economic, scientific, social and political perspectives."

[0334] This allows the generated news articles to help users understand information from multiple perspectives and support reliable decision-making.

[0335] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0336] Step 1:

[0337] The server collects data from various sources on the Internet. Specifically, it uses Python's BeautifulSoup and Scrapy libraries to scrape data from news sites, blogs, and academic paper databases. The collected data is stored in a database on the server. The input is a specific URL or a database query, and the output is the collected text data.

[0338] Step 2:

[0339] The server cleanses the collected data and removes noise. Specifically, it uses text processing libraries such as NLTK and TextBlob to correct grammatical and spelling errors and remove unnecessary HTML tags and advertisements. The input is the text data collected in step 1, and the output is clean text data.

[0340] Step 3:

[0341] The server inputs the cleaned data into a generative AI model to generate information interpreted from different perspectives. For example, it uses a natural language processing model such as GPT-4. The input is the data cleansed in step 2, and the output is multifaceted information interpreted from economic, scientific, social, and political perspectives.

[0342] Step 4:

[0343] The server performs bias detection on the generated information. Specifically, it uses a bias detection algorithm to quantify the bias in the information and select balanced information. The input is the multifaceted information generated in step 3, and the output is filtered information that is judged to have less bias.

[0344] Step 5:

[0345] The terminal receives a search query from the user. The user enters keywords into the search form and presses the search button. The input is the user's search query, and the output is the search query sent to the server.

[0346] Step 6:

[0347] The server searches the filtered information based on the user's search query and selects relevant articles. This information includes information from multiple perspectives generated by the generative AI model. The input is the search query submitted in step 5, and the output is the relevant articles provided to the user.

[0348] Step 7:

[0349] The terminal displays the articles provided by the server to the user. A search result screen containing information from each of the multiple perspectives generated is displayed. The input is the articles selected in step 6, and the output is multifaceted information provided to the user.

[0350] Step 8:

[0351] The user submits feedback on the provided article. Specifically, the user submits a rating or comment such as "This information was helpful." The input is the user's feedback, and the output is the feedback sent to the server.

[0352] Step 9:

[0353] The server analyzes the user feedback using the emotion engine. The analysis results are reflected in improving the generative AI model. The input is the user feedback sent in step 8, and the output is data for adjusting and retraining the generative AI model.

[0354] Step 10:

[0355] The server dynamically adjusts the quality of the information provided based on the user's emotional data. For example, if the user has negative emotions, it will respond by providing additional information from a different perspective. The input is the emotional analysis data obtained in step 9, and the output is the adjusted content and display format of the information provided.

[0356] 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.

[0357] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0358] 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.

[0359] [Second embodiment]

[0360] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0371] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0372] 1. Overview

[0373] This invention is a system that collects data from various sources on the internet, converts the data into multifaceted information using a generative AI model, and provides it to users. This system uses bias detection means to select balanced information, allowing users to obtain information from reliable, multifaceted perspectives.

[0374] 2. Information gathering methods

[0375] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. It uses Python libraries (e.g., BeautifulSoup, Scrapy) to scrape the data and obtain the required data. The collected data is then stored in a database.

[0376] 3. Generative AI Models

[0377] The server inputs the collected data into a generative AI model (e.g., a natural language processing model) to generate information interpreted from different perspectives. This generative AI model analyzes the data based on the user's search query and provides information from multiple angles, such as economic, scientific, social, and political perspectives.

[0378] 4. Bias detection and filtering

[0379] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. This bias detection algorithm is used to detect information bias and select multifaceted, balanced information. The selected information is balanced and filtered to ensure it is not biased toward any particular viewpoint.

[0380] 5. User Interface

[0381] The terminal (user's device) receives the user's search query, and the server provides information selected based on that query. The search results include information from different perspectives generated by the generative AI model, allowing the user to obtain information from multiple perspectives.

[0382] 6. Feedback Loops

[0383] Users provide feedback on the information provided. This feedback can take the form of comments or ratings, and the server collects and analyzes this feedback. The collected feedback is used as training data for the generative AI model, contributing to improving the model's accuracy.

[0384] Specific examples

[0385] Providing information on climate change

[0386] 1. Information gathering

[0387] The server collects the latest articles related to "climate change" from the Internet and stores them in a database.

[0388] 2. Information generation by generative AI

[0389] The server inputs the collected data into a generative AI model and analyzes the information.

[0390] A generative AI model generates information from the following perspectives, for example:

[0391] Economic perspective: "Climate change could become a drag on economic growth in the long term."

[0392] Science: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years."

[0393] Political perspective: "International conferences are discussing a new agreement to combat climate change."

[0394] 3. Bias detection and filtering

[0395] The server performs bias detection on the generated information and selects balanced articles.

[0396] 4. User Interface

[0397] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[0398] 5. Feedback Loop

[0399] Users provide feedback on the information provided, and the server uses that feedback to improve the generative AI model.

[0400] Embodiments of the present invention enable users to efficiently collect and use multifaceted and balanced information, which can contribute to a comprehensive understanding and solution of social problems.

[0401] The processing flow will be explained below.

[0402] Step 1:

[0403] The server collects data from various sources on the Internet, specifically using Python libraries (such as BeautifulSoup and Scrapy) to retrieve the latest articles from news sites, blogs, and academic paper databases, and stores this data in a database.

[0404] Step 2:

[0405] The server cleanses the collected data, performing text preprocessing to correct grammatical and spelling errors and remove noise such as unnecessary HTML tags and advertisements, thereby ensuring the quality of the data fed into the generative AI model.

[0406] Step 3:

[0407] The server inputs the cleaned data into a generative AI model. For example, it uses a natural language processing model (such as GPT-3) to generate information interpreted from multiple perspectives (economic, scientific, social, political, etc.). This generated information includes analyses and opinions from different angles.

[0408] Step 4:

[0409] The server applies bias-detection algorithms to the generated information, quantifying and categorizing the information's bias—for example, determining whether an article is politically biased or emphasizes an economic perspective.

[0410] Step 5:

[0411] The server performs filtering based on the bias detection results. It selects information whose bias score falls within a certain range and extracts balanced, multifaceted information. This filtering process eliminates information that contains extreme bias.

[0412] Step 6:

[0413] The device receives the user's search query and sends it to the server. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button.

[0414] Step 7:

[0415] The server retrieves selected information based on the user's search query and provides relevant articles to the user, displaying information from multiple perspectives from the generative AI model.

[0416] Step 8:

[0417] The device displays the search results to the user, including articles and summaries for each of the different perspectives generated, allowing the user to access information from multiple angles.

[0418] Step 9:

[0419] Choose how users can provide feedback on the information provided, for example by leaving comments, rating, or suggesting improvements.

[0420] Step 10:

[0421] The server collects and analyzes the feedback provided by users, and this feedback data is used to retrain the generative AI model to improve the accuracy of future information generation.

[0422] Through the above processing steps, the system of the present invention provides users with multifaceted and balanced information, supporting reliable decision-making.

[0423] Example 1

[0424] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0425] Although there is a wide variety of information on the Internet, this information is often biased, making it difficult for users to obtain reliable information from multiple perspectives. Furthermore, there is a demand for improving the quality of collected information and the accuracy of models. A method to solve these issues is needed.

[0426] 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.

[0427] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data using a program and filtering out necessary data, means for inputting the collected data into a large-scale language model and generating information interpreted from different perspectives, means for detecting bias in the generated information and selecting multifaceted information, means for displaying the selected information in response to a user's search query, and means for analyzing user ratings and improving the large-scale language model, thereby enabling users to obtain information from reliable multifaceted perspectives.

[0428] The "Internet" is a global network system that connects computer networks around the world and enables the exchange of information.

[0429] A "source" is an internet location that provides information in digital form, such as a news site, blog, or academic paper database.

[0430] "Data collection" is the process of obtaining the necessary information from the Internet and storing it in a database.

[0431] A "program" is a set of instructions that instruct a computer to perform a specific process.

[0432] "Analysis" is the process of deciphering collected data and extracting and classifying specific information.

[0433] "Filtering" is the process of removing unnecessary information from collected data and selecting only useful data.

[0434] A "large-scale language model" is an artificial intelligence model that learns from large amounts of text data and generates and analyzes natural language.

[0435] "Bias" means that information is biased towards a particular viewpoint or opinion.

[0436] A "query" is a search request to a database or Internet search engine to find specific information.

[0437] "Display" means the visual presentation of information on a computer screen.

[0438] "Rating" refers to the opinions, comments, and rating scores that users give to the information provided.

[0439] This invention is a system that collects data from various sources on the internet, converts that data into multifaceted information using a generative AI model, and provides it to users. This system uses bias detection means to select balanced information, allowing users to obtain information from reliable, multifaceted perspectives.

[0440] Information gathering methods

[0441] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (e.g., BeautifulSoup, Scrapy) to scrape and obtain the required data. The collected data is then stored in a database (e.g., MySQL, PostgreSQL).

[0442] Information generation using generative AI models

[0443] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. This generative AI model uses a large-scale language model (e.g., GPT) to analyze data based on the user's search query and provide information from multiple angles, including economic, scientific, social, and political perspectives.

[0444] Bias Detection and Filtering

[0445] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. The bias detection algorithm is used to detect information bias and select multifaceted, balanced information. The selected information is filtered to ensure it is balanced and not biased toward any particular viewpoint.

[0446] User Interface

[0447] The terminal (user's device) receives the user's search query, and the server provides information selected based on the query. The search results include information from different perspectives generated by the generative AI model, allowing the user to obtain information from multiple perspectives.

[0448] Feedback Loop

[0449] Users provide feedback on the information provided. This feedback can take the form of comments or ratings, and the server collects and analyzes this feedback. The collected feedback is used as training data for the generative AI model, contributing to improving the model's accuracy.

[0450] Specific examples

[0451] Providing information on climate change

[0452] 1. Information gathering

[0453] The server collects the latest articles related to "climate change" from the Internet and stores them in a database.

[0454] 2. Information generation by generative AI

[0455] The server inputs the collected data into a generative AI model and analyzes the information.

[0456] A generative AI model generates information from the following perspectives, for example:

[0457] Economic perspective: "Climate change could become a drag on economic growth in the long term."

[0458] Science: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years."

[0459] Political perspective: "International conferences are discussing a new agreement to combat climate change."

[0460] 3. Bias detection and filtering

[0461] The server performs bias detection on the generated information and selects balanced articles.

[0462] 4. User Interface

[0463] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[0464] 5. Feedback Loop

[0465] Users provide feedback on the information provided, and the server uses that feedback to improve the generative AI model.

[0466] Prompt Sentence Examples

[0467] An example of a prompt to be input to the generative AI model is, "Please summarize the latest economic, scientific, and political perspectives on climate change." Based on this prompt, the generative AI model organizes information from each perspective and provides it to the user.

[0468] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0469] Step 1: Gather information

[0470] The server collects information from news sites, blogs, and academic paper databases on the Internet. Specifically, it uses the Python libraries BeautifulSoup and Scrapy to perform scraping. It receives a list of URLs of target sites as input and obtains the collected text data as output, which is then stored in a database.

[0471] Step 2: Data cleansing

[0472] The server processes the collected data stored in the database and removes unnecessary noise. Specifically, it uses regular expressions and cleaning algorithms to organize the data. It takes the collected raw data as input and produces clean, noise-removed data as output.

[0473] Step 3: Input data into the generative AI model

[0474] The server takes the cleansed data and inputs it into a generative AI model. Specifically, it analyzes the data using a large-scale language model (e.g., GPT). It receives the cleaned data and the user's search query as input and generates information interpreted from different perspectives as output.

[0475] Step 4: Analyze and generate information

[0476] The server uses a generative AI model to generate information based on the user's search query. For example, for the query "climate change," it generates information interpreted from economic, scientific, and political perspectives. It receives the user's query and clean data as input, and obtains information generated from multiple perspectives as output.

[0477] Step 5: Bias detection and filtering

[0478] The server analyzes the generated information to detect bias. Specifically, it uses a bias detection algorithm to quantify the bias of each article and select balanced information. It receives the generated information as input and provides balanced, filtered information as output.

[0479] Step 6: User Interface

[0480] The terminal receives the user's search query, and the server provides selected information. The terminal receives the search query input from the user and displays information filtered from multiple perspectives as output to the user, allowing the user to obtain information from multiple perspectives.

[0481] Step 7: Gather feedback

[0482] The user provides feedback on the provided information. The feedback is in the form of comments or ratings, and the user's ratings are received as input. The feedback data is transferred to the server as output.

[0483] Step 8: Refine the generative AI model

[0484] The server analyzes the collected feedback and uses it as training data for the generative AI model, which improves the model's accuracy. It takes user feedback as input and updates the improved generative AI model as output.

[0485] (Application example 1)

[0486] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0487] Many users today gather a wide range of information online to select products, but information bias and lack of reliability are problems. Even in virtual stores, it is difficult for users to quickly obtain information from multiple perspectives in an unbiased manner. This often leads users to make purchasing decisions based on inaccurate information, which reduces post-purchase satisfaction.

[0488] 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.

[0489] In this invention, the server includes means for collecting data from various information sources on the Internet, means for inputting the collected data into a generative AI model to generate information interpreted from different perspectives, means for detecting bias in the generated information and selecting balanced information, means for providing information selected based on a user's search query when searching for and selecting products in a virtual store, and means for improving the generative AI model through feedback from the user. This enables users to efficiently obtain diversified and balanced information in the virtual store and make purchasing decisions based on reliable information.

[0490] "Diverse information sources on the Internet" refers to multiple types of information sources available on the Internet, such as news sites, blogs, academic paper databases, and social networking sites.

[0491] A "generative AI model" refers to an artificial intelligence model that analyzes collected data and generates information from different perspectives.

[0492] "Bias" refers to a state in which information is biased towards a particular perspective or opinion.

[0493] "Virtual store" refers to a virtual store for selling and purchasing products online.

[0494] "User search query" refers to the keywords or phrases a user uses to search for information.

[0495] "Feedback" refers to the evaluations and comments users make about the information provided or the performance of the system.

[0496] A specific method for implementing a system for carrying out the present invention will now be described.

[0497] 1. Information gathering methods

[0498] The server collects data from various sources on the Internet, such as news sites, blogs, academic paper databases, and social media. Specifically, web scraping is performed using the Python libraries BeautifulSoup and Scrapy. The collected data is then stored in a database.

[0499] 2. Information generation using generative AI models

[0500] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. This generative AI model uses a natural language processing model, such as OpenAI's GPT-3, to provide information from different perspectives (economic, scientific, social, and political) based on the user's search query.

[0501] 3. Bias detection and filtering

[0502] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. It uses a bias detection algorithm to detect information bias and select multifaceted, balanced information.

[0503] 4. User Interface

[0504] The user's device receives the search query, and the server provides information selected based on the query. When searching and selecting products in the virtual store, users can obtain information filtered according to different perspectives.

[0505] 5. Feedback Loop

[0506] Users provide feedback on the information provided. This feedback is accepted in the form of comments and ratings. The server collects and analyzes this feedback and uses it as training data for the generative AI model. This improves the performance of the generative AI model.

[0507] Specific examples

[0508] Scenario: Providing smartphone purchase information

[0509] 1. The server collects the latest information related to smartphones from news sites and blogs on the Internet and stores it in a database.

[0510] 2. The server inputs the collected data into a generative AI model (e.g., GPT-3) to analyze the information. The generative AI model generates information from economic, scientific, social, and political perspectives.

[0511] Example: Economic perspective: "The latest smartphones are 15% more expensive than they were last year."

[0512] Example: Scientific Perspective: "The latest smartphones use new display technology."

[0513] Example: Social perspective: "Many users appreciate the new camera features."

[0514] Example: Political Perspective: "Some countries restrict the import of certain smartphones."

[0515] 3. The server performs bias detection on the generated information and selects balanced articles.

[0516] 4. When a user searches for "smartphone" on their device, information filtered for different perspectives is displayed.

[0517] 5. Users provide feedback on the information provided, and the server collects and analyzes that feedback to improve the generative AI model.

[0518] Prompt Sentence Examples

[0519] "Article: The latest smartphones use new display technology. Explain this information from economic, scientific, social, and political perspectives."

[0520] This system allows users to select products in virtual stores based on reliable, multifaceted information, resulting in highly satisfying purchases.

[0521] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0522] Step 1:

[0523] The server collects data from various sources on the Internet, such as news sites, blogs, and academic paper databases. It uses the Python libraries BeautifulSoup and Scrapy to perform web scraping and stores the retrieved data in a database. The input is a search query, and the output is the articles and papers retrieved by the scraping.

[0524] Step 2:

[0525] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. Specifically, it uses a natural language processing model such as OpenAI's GPT-3. The input is the data collected in step 1, and the output is text generated from economic, scientific, social, and political perspectives. In this stage, a process is carried out in which certain prompt sentences are generated for the model and the results are obtained.

[0526] Step 3:

[0527] The server applies bias detection measures to the generated information. It uses a bias detection algorithm to quantify and classify the bias in the information. The input is the information from different perspectives generated in step 2, and the output is information with quantified and classified bias. This process evaluates the quality of the information and selects fair information.

[0528] Step 4:

[0529] When searching and selecting products in a virtual store, the server provides information selected based on the user's search query. The user's device sends the search query to the server. The input is the user's search query, and the output is balanced information with bias detection. The device displays this information to the user.

[0530] Step 5:

[0531] Users provide feedback on the information provided. This feedback takes the form of comments and ratings, and is received by the server. The input is user feedback, and the output is data for improving the generative AI model. By collecting and analyzing feedback, the training data for the generative AI model is updated, improving the model's performance.

[0532] This allows users to efficiently obtain multifaceted and balanced information in the virtual store, enabling them to make purchasing decisions based on reliable information.

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

[0534] 1. Overview

[0535] This invention is a system that collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. This system combines a means of detecting bias in the generated information and selecting balanced information with an emotion engine that recognizes user emotions. This emotion engine analyzes user feedback, improves the generative AI model, and adjusts the content and display format of the information provided. This enables users to obtain information from reliable, multifaceted perspectives.

[0536] 2. Information gathering methods

[0537] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (such as BeautifulSoup and Scrapy) to perform scraping, obtain the necessary data, and store it in a database.

[0538] 3. Data Cleansing Methods

[0539] The server cleanses and denoises the collected data, including text preprocessing to correct grammatical and spelling errors and remove unnecessary HTML tags, advertisements, and other noise, ensuring the quality of the data fed into the generative AI model.

[0540] 4. Generative AI Models

[0541] The server inputs the cleaned data into a generative AI model (e.g., a natural language processing model) to generate information interpreted from different perspectives. This AI model analyzes the data based on the user's search query and provides information from multiple perspectives (e.g., economic, scientific, social, political, etc.).

[0542] 5. Bias detection and filtering

[0543] The server applies a bias detection algorithm to the generated information. The bias detection algorithm quantifies and classifies the bias of the information. For example, it determines whether an article is politically biased or emphasizes an economic perspective. The server selects information whose bias score falls within a certain range, filtering out information containing extreme bias and selecting balanced information.

[0544] 6. User Interface

[0545] The device receives the user's search query and sends it to the server. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button. The server searches for selected information based on the user's search query and provides the user with relevant articles. Search results containing information from multiple perspectives generated by the generative AI model are displayed.

[0546] 7. Emotion Engine

[0547] The server collects user feedback and recognizes emotions using an emotion engine. This emotion engine analyzes emotional data from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user comments, "This information was helpful," the generative AI model is retrained based on that positive emotional data.

[0548] 8. Optimizing information provision

[0549] The server dynamically adjusts the content and display format of information provided based on the user's emotions recognized by the emotion engine. For example, if the user has negative emotions, the server will respond by providing additional information from a different perspective. In this way, the quality of information provided and the user experience are improved.

[0550] Specific examples

[0551] Providing information on climate change

[0552] 1. Information gathering

[0553] The server collects the latest articles on "climate change" from the Internet and stores them in a database.

[0554] 2. Information generation by generative AI

[0555] The server inputs the collected data into a generative AI model to generate information from multiple perspectives.

[0556] Examples: Economic: "Climate change could be a factor that impedes economic growth in the long term." Scientific: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years." Political: "International conferences are discussing new agreements to combat climate change."

[0557] 3. Bias detection and filtering

[0558] The server performs bias detection on the generated information and selects balanced articles.

[0559] 4. User Interface

[0560] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[0561] 5. Leveraging Emotional Engines

[0562] Users provide feedback on articles, saying things like "This information was helpful."

[0563] The server collects this feedback, analyzes it as positive emotional data in the emotion engine, and reflects it in improving the generative AI model.

[0564] Embodiments of the present invention enable users to efficiently collect and use multifaceted and balanced information, supporting reliable decision-making and contributing to a comprehensive understanding and resolution of social problems.

[0565] The processing flow will be explained below.

[0566] Step 1:

[0567] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it performs web scraping using Python libraries (such as BeautifulSoup and Scrapy) to obtain target articles and papers, and stores this data in a database.

[0568] Step 2:

[0569] The server cleanses the collected data. This process removes unnecessary HTML tags and advertisements from the text data and corrects grammar and spelling errors. This normalization process improves the quality of the data that is input into the generative AI model.

[0570] Step 3:

[0571] The server inputs the cleaned data into a generative AI model. For example, a natural language processing model (such as GPT-3) is used to generate information based on the input data, interpreted from different perspectives (economic, scientific, social, political, etc.). This information is then re-stored in a database.

[0572] Step 4:

[0573] The server applies a bias detection algorithm to the generated information. Specifically, it assigns a bias score to each piece of information, quantifying and categorizing its bias. For example, it analyzes how politically biased an article is or how much it emphasizes a scientific perspective.

[0574] Step 5:

[0575] The server filters the information based on the bias score, removing information with extremely high bias and selecting only balanced information. This selected information is then organized in a format suitable for delivery to the user.

[0576] Step 6:

[0577] The device receives the user's search query and sends it to the server. When the user types in "climate change" and presses the send button, the search query is sent to the server.

[0578] Step 7:

[0579] Based on the user's search query, the server searches the database for relevant information and retrieves selected information, including multiple perspectives generated by the generative AI model.

[0580] Step 8:

[0581] The device displays search results to the user, which can be viewed and analyzed, including information from different perspectives, such as economic, scientific, social, and political perspectives.

[0582] Step 9:

[0583] Users provide feedback on the displayed information. For example, they can post comments on an article such as "This was helpful" or "I'd like more detailed information." The sentiment engine also collects the sentiment (positive, negative, neutral) of the feedback.

[0584] Step 10:

[0585] The server collects user feedback and analyzes it with an emotion engine. The collected emotion data is used as training data for the generative AI model, helping to improve the model. For example, it strengthens the method for generating information that generates a lot of positive feedback and improves the method for generating information that generates a lot of negative feedback.

[0586] Step 11:

[0587] The server dynamically adjusts the content and display format of information provided based on the user's emotions recognized by the emotion engine. For example, if the user has negative emotions, it will provide supplementary information with a different perspective or detailed information. In this way, it is possible to provide information that is optimized for each individual user.

[0588] Through this series of processing steps, the system of the present invention provides users with multifaceted and balanced information, supporting reliable decision-making.

[0589] Example 2

[0590] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0591] Conventional information gathering systems have struggled to efficiently collect data from diverse information sources on the Internet, provide information from multiple perspectives, and detect bias. Furthermore, it has been difficult to dynamically improve the accuracy and quality of information provided based on user feedback, failing to contribute to improving the user experience. The present invention aims to solve these problems and provide highly reliable, multifaceted information and an improved user experience.

[0592] 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.

[0593] In this invention, the server includes means for collecting data from various information sources on the Internet, means for cleansing the collected data and removing noise, means for inputting the cleansed data into a generative AI model and generating information interpreted from different perspectives, means for detecting bias in the generated information, quantifying and classifying it, and selecting balanced information, means for receiving a user's search query and providing information corresponding to the query, means for recognizing emotions through user feedback and reflecting the recognition in improving the generative AI model, and means for dynamically adjusting the content and display format of the information provided based on the emotion recognition. This makes it possible to provide highly reliable, multifaceted information and improve the user experience.

[0594] The "Internet" is a large-scale network system that provides information and services worldwide via communication networks.

[0595] "Source" refers to the origin or place that provides data or knowledge, and includes news sites, blogs, academic paper databases, etc.

[0596] "Means of data collection" refers to the techniques and methods used to obtain the required data from sources on the internet, including scraping techniques and API calls.

[0597] "Data cleansing" is the process of removing unnecessary information and noise from collected data to improve the quality of the data.

[0598] "Noise reduction" is the process of removing unnecessary information, such as HTML tags and advertisements, from text data.

[0599] A "generative AI model" is an artificial intelligence model that uses technologies such as natural language processing to generate sentences and data that appear to have been created by humans.

[0600] "Information interpreted from different perspectives" refers to information that is provided based on multiple perspectives, with data analyzed from multiple angles and standpoints.

[0601] "Bias detection measures" refer to techniques and methods for determining whether the information generated is biased toward a particular viewpoint.

[0602] "Quantification and classification" is the process of numerically assessing the bias of information and classifying it into different categories.

[0603] "Balanced information" refers to fair and objective information that is not heavily biased toward any particular viewpoint or bias.

[0604] A "search query" refers to a keyword or phrase that a user enters when searching for specific information.

[0605] "Feedback" refers to responses such as ratings, opinions, and comments provided by users.

[0606] "Means of recognizing emotions" refers to technologies and methods for analyzing emotions such as positive and negative from user feedback.

[0607] "Means for dynamically adjusting the content and display format of information provided" refers to technologies and methods that change the information provided and the way it is displayed in real time based on user sentiment and feedback.

[0608] This invention is a system that collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. This system aims to improve the user experience by combining a means to detect bias in the generated information and select balanced information with an emotion engine that recognizes the user's emotions.

[0609] Information gathering

[0610] The server collects data from news sites, blogs, and academic paper databases on the Internet. This process is performed by scraping using the Python libraries BeautifulSoup and Scrapy. For example, the HTML structure of a news site is analyzed, and the article text and titles are extracted from tags with specific classes and IDs, and then stored in a database.

[0611] Data Cleansing

[0612] The server cleanses and removes noise from the collected data, using Python libraries to correct grammatical and spelling errors, and using regular expressions and BeautifulSoup to remove unnecessary HTML tags and advertisements.

[0613] Information generation using generative AI models

[0614] The server inputs the cleansed data into a generative AI model to generate information interpreted from different perspectives. For example, using GPT-3 as a generative AI model, the following prompt is input: "Please explain climate change from an economic perspective." This will generate information from an economic perspective, a scientific perspective, a political perspective, etc.

[0615] Bias Detection and Filtering

[0616] The server detects, quantifies, and classifies bias in the generated information. Based on this, an algorithm is used to select balanced information. If the generated information is extremely biased, it is filtered out.

[0617] User Interface

[0618] The device receives the user's search query and sends it to the server. For example, if the user enters "climate change" into the search form and presses the search button, the server retrieves information based on the user's search query, generates related articles, and displays information from multiple perspectives generated by the AI ​​model.

[0619] Emotion Engine

[0620] The server collects user feedback and analyzes it using an emotion engine. The emotion engine recognizes positive and negative emotions from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user provides feedback on an article saying, "This information was helpful," the generative AI model can be retrained based on that positive data.

[0621] Optimizing information provision

[0622] The server dynamically adjusts the content and display format of the information provided based on the analysis results of the emotion engine. For example, if a user gives negative feedback, the server can respond by providing additional information from a different perspective, thereby improving the user experience.

[0623] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0624] Step 1:

[0625] The server performs scraping to collect data from news sites, blogs, and academic paper databases on the Internet. The input is a specific list of URLs and search keywords. The server uses BeautifulSoup or Scrapy to analyze the HTML structure and extract article titles, body text, URLs, etc. The output is saved in a database as structured data. For example, searching for tags with specific classes or IDs from the HTML of a news site and extracting article information.

[0626] Step 2:

[0627] The server cleanses the collected data and removes noise. The input is the raw data collected in step 1. Specifically, it uses Python libraries (such as NLTK and SpaCy) to correct grammatical and spelling errors, and uses regular expressions and BeautifulSoup to remove unnecessary HTML tags and advertisements. The output is cleansed text data. For example, <ad>" tags and unnecessary scripts.

[0628] Step 3:

[0629] The server inputs the cleansed data into the generative AI model. The input is the text data cleansed in step 2. The server uses a generative AI model (e.g., GPT-3) and sends a prompt such as "Please explain climate change from an economic perspective" to the generative AI model. The output is text information generated from multiple perspectives. For example, it generates information interpreted from an economic perspective and a scientific perspective.

[0630] Step 4:

[0631] The server detects bias in the generated information, quantifies it, and classifies it. The input is the information generated in step 3. A bias detection algorithm is applied to evaluate and quantify the bias in the information. For example, a political bias score is calculated. The output is information with a bias score assigned. Based on the bias score, balanced information is extracted.

[0632] Step 5:

[0633] The terminal receives the user's search query and sends it to the server. The input is the keyword the user entered into the search form. The terminal receives the input from the user and sends it to the server as a query. The output is the search query forwarded to the server. For example, the query "climate change" is sent.

[0634] Step 6:

[0635] The server searches for selected information based on the user's search query and provides relevant articles. The input is the search query received in step 5 and the information to which a bias score was assigned in step 4. The server searches for information filtered based on the search query and generates search results composed of multiple perspectives. The output is information as search results. For example, it provides articles that include interpretations of "climate change" from economic, scientific, and political perspectives.

[0636] Step 7:

[0637] The terminal displays the search results to the user. The input is the search results generated in step 6. The terminal displays the search results in a user interface, allowing the user to view information by perspective. The output is a visual representation of the search results to the user, for example, separated into tabs for economic perspectives, scientific perspectives, and political perspectives.

[0638] Step 8:

[0639] The device collects feedback from the user and sends it to the server. The input is the user's feedback (e.g., "This information was helpful"). The device receives the user's feedback and sends it to the server. The output is the feedback sent to the server. For example, sending a rating such as "This information was helpful."

[0640] Step 9:

[0641] The server analyzes the feedback from the user and uses an emotion recognition engine. The input is the feedback collected in step 8. The emotion engine recognizes the emotion of the feedback, such as positive or negative. The output is the analyzed emotion data. For example, if the evaluation is positive, the score is output as the analysis result.

[0642] Step 10:

[0643] The server uses emotion recognition to improve the generative AI model. The input is the emotion data obtained in step 9. The generative AI model is retrained using positive feedback, and the model's generation results are adjusted in response to negative feedback. The output is a retrained generative AI model. This improves the quality of the generated information.

[0644] Step 11:

[0645] The server dynamically adjusts the content and display format of the information provided based on emotion recognition. The input is the emotion data from step 9 and the generative AI model retrained in step 10. In response to negative user feedback, additional information from a different perspective is provided and the display format is adjusted accordingly. The output is dynamically adjusted information content and display format, which improves the user experience.

[0646] (Application example 2)

[0647] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0648] Conventional news distribution systems provide users with one-sided articles and do not adequately consider information bias or user sentiment. As a result, users often receive biased information, making it difficult to make reliable decisions. In addition, the collected data is of low quality and noisy, which reduces the accuracy of the generative AI model and reduces the quality of the information provided.

[0649] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data from various information sources on the Internet, means for inputting the collected data into a generative AI model and generating information interpreted from different perspectives, means for detecting bias in the generated information and selecting balanced information, means for providing the selected information in response to a user's search query, means for improving the generative AI model through user feedback, and means for analyzing user sentiment to improve the quality of information provided. This enables users to efficiently collect and use multifaceted and balanced information, supporting reliable decision-making and improving the quality of information provided. Furthermore, cleansing the collected data improves the accuracy of the generative AI model and the quality of the information provided.

[0650] "Diverse information sources on the Internet" refers to multiple types of information sources that can be accessed on the Internet, such as news sites, blogs, and academic paper databases.

[0651] "Data Collection Methods" refers to the use of software and protocols to retrieve and store information from a particular website.

[0652] A "generative AI model" refers to an algorithm that generates and analyzes natural language using technologies such as deep learning.

[0653] "Information interpreted from different perspectives" refers to information on a particular topic that has been analyzed from multiple fields of expertise and perspectives, such as economic, scientific, social, and political perspectives.

[0654] "Means for generating information" refers to technology that analyzes collected data as input and generates new information using a specific algorithm.

[0655] "Means for detecting bias and selecting balanced information" refers to algorithms and techniques for quantifying and evaluating information bias and selecting information with the least bias.

[0656] "Means for providing information selected in response to a user's search query" refers to an interface and system for displaying information filtered based on the search criteria entered by the user.

[0657] "Means for improving generative AI models through user feedback" refers to methods for analyzing feedback such as user ratings and opinions and retraining or adjusting generative AI models based on that feedback.

[0658] "Means of analyzing user emotions to improve the quality of information provided" refers to technology that uses an emotion analysis engine to analyze user feedback and reactions, and then uses the results to improve the content and format of information provided.

[0659] "Data cleansing and denoising" refers to the process of removing unnecessary elements such as grammatical and spelling errors, unnecessary HTML tags, and advertisements from collected data to improve the quality of the data.

[0660] "Means for quantifying and classifying bias" refers to algorithms and techniques for quantifying and classifying information bias as specific numerical values.

[0661] This invention is a system that allows users to efficiently collect and use multifaceted and balanced information. This system collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. The specific configuration and operation of this system are described below.

[0662] First, the server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (BeautifulSoup and Scrapy) to scrape websites, obtain the necessary data, and store it in a database.

[0663] The server then cleanses and denoises the collected data, using text processing libraries such as NLTK and TextBlob to correct grammatical and spelling errors, and remove unnecessary HTML tags and advertisements, ensuring the quality of the data fed into the generative AI model.

[0664] The server inputs the cleaned data into a generative AI model (e.g., a natural language processing model such as GPT-4) to generate information interpreted from different perspectives (e.g., economic, scientific, social, political, etc.). This AI model analyzes the data based on the user's search query and provides information from multiple perspectives.

[0665] The server then applies a bias detection algorithm to the generated information to quantify and classify the bias of the information, selecting information whose bias score falls within a certain range and filtering out information containing extreme bias to select balanced information.

[0666] The device receives a search query from the user. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button. The server searches filtered information based on the user's search query and provides the user with relevant articles. It then displays search results containing information from multiple perspectives generated by the generative AI model.

[0667] The server also collects user feedback and analyzes emotions using an emotion engine. This emotion engine analyzes emotional data from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user comments, "This information was helpful," the generative AI model can be retrained based on that positive emotional data to improve the quality of information provided.

[0668] As a concrete example, let's consider a case where the server collects the latest news on climate change and provides information from multiple perspectives. In this case, the server inputs the following prompt sentence into the generative AI model:

[0669] "Provide different perspectives on the latest news on climate change from economic, scientific, social and political perspectives."

[0670] This allows the generated news articles to help users understand information from multiple perspectives and support reliable decision-making.

[0671] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0672] Step 1:

[0673] The server collects data from various sources on the Internet. Specifically, it uses Python's BeautifulSoup and Scrapy libraries to scrape data from news sites, blogs, and academic paper databases. The collected data is stored in a database on the server. The input is a specific URL or a database query, and the output is the collected text data.

[0674] Step 2:

[0675] The server cleanses the collected data and removes noise. Specifically, it uses text processing libraries such as NLTK and TextBlob to correct grammatical and spelling errors and remove unnecessary HTML tags and advertisements. The input is the text data collected in step 1, and the output is clean text data.

[0676] Step 3:

[0677] The server inputs the cleaned data into a generative AI model to generate information interpreted from different perspectives. For example, it uses a natural language processing model such as GPT-4. The input is the data cleansed in step 2, and the output is multifaceted information interpreted from economic, scientific, social, and political perspectives.

[0678] Step 4:

[0679] The server performs bias detection on the generated information. Specifically, it uses a bias detection algorithm to quantify the bias in the information and select balanced information. The input is the multifaceted information generated in step 3, and the output is filtered information that is judged to have less bias.

[0680] Step 5:

[0681] The terminal receives a search query from the user. The user enters keywords into the search form and presses the search button. The input is the user's search query, and the output is the search query sent to the server.

[0682] Step 6:

[0683] The server searches the filtered information based on the user's search query and selects relevant articles. This information includes information from multiple perspectives generated by the generative AI model. The input is the search query submitted in step 5, and the output is the relevant articles provided to the user.

[0684] Step 7:

[0685] The terminal displays the articles provided by the server to the user. A search result screen containing information from each of the multiple perspectives generated is displayed. The input is the articles selected in step 6, and the output is multifaceted information provided to the user.

[0686] Step 8:

[0687] The user submits feedback on the provided article. Specifically, the user submits a rating or comment such as "This information was helpful." The input is the user's feedback, and the output is the feedback sent to the server.

[0688] Step 9:

[0689] The server analyzes the user feedback using the emotion engine. The analysis results are reflected in improving the generative AI model. The input is the user feedback sent in step 8, and the output is data for adjusting and retraining the generative AI model.

[0690] Step 10:

[0691] The server dynamically adjusts the quality of the information provided based on the user's emotional data. For example, if the user has negative emotions, it will respond by providing additional information from a different perspective. The input is the emotional analysis data obtained in step 9, and the output is the adjusted content and display format of the information provided.

[0692] 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.

[0693] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0694] 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.

[0695] [Third embodiment]

[0696] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0697] 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.

[0698] 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).

[0699] 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.

[0700] 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.

[0701] 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).

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0708] 1. Overview

[0709] This invention is a system that collects data from various sources on the internet, converts the data into multifaceted information using a generative AI model, and provides it to users. This system uses bias detection means to select balanced information, allowing users to obtain information from reliable, multifaceted perspectives.

[0710] 2. Information gathering methods

[0711] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. It uses Python libraries (e.g., BeautifulSoup, Scrapy) to scrape the data and obtain the required data. The collected data is then stored in a database.

[0712] 3. Generative AI Models

[0713] The server inputs the collected data into a generative AI model (e.g., a natural language processing model) to generate information interpreted from different perspectives. This generative AI model analyzes the data based on the user's search query and provides information from multiple angles, such as economic, scientific, social, and political perspectives.

[0714] 4. Bias detection and filtering

[0715] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. This bias detection algorithm is used to detect information bias and select multifaceted, balanced information. The selected information is balanced and filtered to ensure it is not biased toward any particular viewpoint.

[0716] 5. User Interface

[0717] The terminal (user's device) receives the user's search query, and the server provides information selected based on that query. The search results include information from different perspectives generated by the generative AI model, allowing the user to obtain information from multiple perspectives.

[0718] 6. Feedback Loops

[0719] Users provide feedback on the information provided. This feedback can take the form of comments or ratings, and the server collects and analyzes this feedback. The collected feedback is used as training data for the generative AI model, contributing to improving the model's accuracy.

[0720] Specific examples

[0721] Providing information on climate change

[0722] 1. Information gathering

[0723] The server collects the latest articles related to "climate change" from the Internet and stores them in a database.

[0724] 2. Information generation by generative AI

[0725] The server inputs the collected data into a generative AI model and analyzes the information.

[0726] A generative AI model generates information from the following perspectives, for example:

[0727] Economic perspective: "Climate change could become a drag on economic growth in the long term."

[0728] Science: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years."

[0729] Political perspective: "International conferences are discussing a new agreement to combat climate change."

[0730] 3. Bias detection and filtering

[0731] The server performs bias detection on the generated information and selects balanced articles.

[0732] 4. User Interface

[0733] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[0734] 5. Feedback Loop

[0735] Users provide feedback on the information provided, and the server uses that feedback to improve the generative AI model.

[0736] Embodiments of the present invention enable users to efficiently collect and use multifaceted and balanced information, which can contribute to a comprehensive understanding and solution of social problems.

[0737] The processing flow will be explained below.

[0738] Step 1:

[0739] The server collects data from various sources on the Internet, specifically using Python libraries (such as BeautifulSoup and Scrapy) to retrieve the latest articles from news sites, blogs, and academic paper databases, and stores this data in a database.

[0740] Step 2:

[0741] The server cleanses the collected data, performing text preprocessing to correct grammatical and spelling errors and remove noise such as unnecessary HTML tags and advertisements, thereby ensuring the quality of the data fed into the generative AI model.

[0742] Step 3:

[0743] The server inputs the cleaned data into a generative AI model. For example, it uses a natural language processing model (such as GPT-3) to generate information interpreted from multiple perspectives (economic, scientific, social, political, etc.). This generated information includes analyses and opinions from different angles.

[0744] Step 4:

[0745] The server applies bias-detection algorithms to the generated information, quantifying and categorizing the information's bias—for example, determining whether an article is politically biased or emphasizes an economic perspective.

[0746] Step 5:

[0747] The server performs filtering based on the bias detection results. It selects information whose bias score falls within a certain range and extracts balanced, multifaceted information. This filtering process eliminates information that contains extreme bias.

[0748] Step 6:

[0749] The device receives the user's search query and sends it to the server. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button.

[0750] Step 7:

[0751] The server retrieves selected information based on the user's search query and provides relevant articles to the user, displaying information from multiple perspectives from the generative AI model.

[0752] Step 8:

[0753] The device displays the search results to the user, including articles and summaries for each of the different perspectives generated, allowing the user to access information from multiple angles.

[0754] Step 9:

[0755] Choose how users can provide feedback on the information provided, for example by leaving comments, rating, or suggesting improvements.

[0756] Step 10:

[0757] The server collects and analyzes the feedback provided by users, and this feedback data is used to retrain the generative AI model to improve the accuracy of future information generation.

[0758] Through the above processing steps, the system of the present invention provides users with multifaceted and balanced information, supporting reliable decision-making.

[0759] Example 1

[0760] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0761] Although there is a wide variety of information on the Internet, this information is often biased, making it difficult for users to obtain reliable information from multiple perspectives. Furthermore, there is a demand for improving the quality of collected information and the accuracy of models. A method to solve these issues is needed.

[0762] 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.

[0763] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data using a program and filtering out necessary data, means for inputting the collected data into a large-scale language model and generating information interpreted from different perspectives, means for detecting bias in the generated information and selecting multifaceted information, means for displaying the selected information in response to a user's search query, and means for analyzing user ratings and improving the large-scale language model, thereby enabling users to obtain information from reliable multifaceted perspectives.

[0764] The "Internet" is a global network system that connects computer networks around the world and enables the exchange of information.

[0765] A "source" is an internet location that provides information in digital form, such as a news site, blog, or academic paper database.

[0766] "Data collection" is the process of obtaining the necessary information from the Internet and storing it in a database.

[0767] A "program" is a set of instructions that instruct a computer to perform a specific process.

[0768] "Analysis" is the process of deciphering collected data and extracting and classifying specific information.

[0769] "Filtering" is the process of removing unnecessary information from collected data and selecting only useful data.

[0770] A "large-scale language model" is an artificial intelligence model that learns from large amounts of text data and generates and analyzes natural language.

[0771] "Bias" means that information is biased towards a particular viewpoint or opinion.

[0772] A "query" is a search request to a database or Internet search engine to find specific information.

[0773] "Display" means the visual presentation of information on a computer screen.

[0774] "Rating" refers to the opinions, comments, and rating scores that users give to the information provided.

[0775] This invention is a system that collects data from various sources on the internet, converts that data into multifaceted information using a generative AI model, and provides it to users. This system uses bias detection means to select balanced information, allowing users to obtain information from reliable, multifaceted perspectives.

[0776] Information gathering methods

[0777] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (e.g., BeautifulSoup, Scrapy) to scrape and obtain the required data. The collected data is then stored in a database (e.g., MySQL, PostgreSQL).

[0778] Information generation using generative AI models

[0779] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. This generative AI model uses a large-scale language model (e.g., GPT) to analyze data based on the user's search query and provide information from multiple angles, including economic, scientific, social, and political perspectives.

[0780] Bias Detection and Filtering

[0781] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. The bias detection algorithm is used to detect information bias and select multifaceted, balanced information. The selected information is filtered to ensure it is balanced and not biased toward any particular viewpoint.

[0782] User Interface

[0783] The terminal (user's device) receives the user's search query, and the server provides information selected based on the query. The search results include information from different perspectives generated by the generative AI model, allowing the user to obtain information from multiple perspectives.

[0784] Feedback Loop

[0785] Users provide feedback on the information provided. This feedback can take the form of comments or ratings, and the server collects and analyzes this feedback. The collected feedback is used as training data for the generative AI model, contributing to improving the model's accuracy.

[0786] Specific examples

[0787] Providing information on climate change

[0788] 1. Information gathering

[0789] The server collects the latest articles related to "climate change" from the Internet and stores them in a database.

[0790] 2. Information generation by generative AI

[0791] The server inputs the collected data into a generative AI model and analyzes the information.

[0792] A generative AI model generates information from the following perspectives, for example:

[0793] Economic perspective: "Climate change could become a drag on economic growth in the long term."

[0794] Science: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years."

[0795] Political perspective: "International conferences are discussing a new agreement to combat climate change."

[0796] 3. Bias detection and filtering

[0797] The server performs bias detection on the generated information and selects balanced articles.

[0798] 4. User Interface

[0799] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[0800] 5. Feedback Loop

[0801] Users provide feedback on the information provided, and the server uses that feedback to improve the generative AI model.

[0802] Prompt Sentence Examples

[0803] An example of a prompt to be input to the generative AI model is, "Please summarize the latest economic, scientific, and political perspectives on climate change." Based on this prompt, the generative AI model organizes information from each perspective and provides it to the user.

[0804] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0805] Step 1: Gather information

[0806] The server collects information from news sites, blogs, and academic paper databases on the Internet. Specifically, it uses the Python libraries BeautifulSoup and Scrapy to perform scraping. It receives a list of URLs of target sites as input and obtains the collected text data as output, which is then stored in a database.

[0807] Step 2: Data cleansing

[0808] The server processes the collected data stored in the database and removes unnecessary noise. Specifically, it uses regular expressions and cleaning algorithms to organize the data. It takes the collected raw data as input and produces clean, noise-removed data as output.

[0809] Step 3: Input data into the generative AI model

[0810] The server takes the cleansed data and inputs it into a generative AI model. Specifically, it analyzes the data using a large-scale language model (e.g., GPT). It receives the cleaned data and the user's search query as input and generates information interpreted from different perspectives as output.

[0811] Step 4: Analyze and generate information

[0812] The server uses a generative AI model to generate information based on the user's search query. For example, for the query "climate change," it generates information interpreted from economic, scientific, and political perspectives. It receives the user's query and clean data as input, and obtains information generated from multiple perspectives as output.

[0813] Step 5: Bias detection and filtering

[0814] The server analyzes the generated information to detect bias. Specifically, it uses a bias detection algorithm to quantify the bias of each article and select balanced information. It receives the generated information as input and provides balanced, filtered information as output.

[0815] Step 6: User Interface

[0816] The terminal receives the user's search query, and the server provides selected information. The terminal receives the search query input from the user and displays information filtered from multiple perspectives as output to the user, allowing the user to obtain information from multiple perspectives.

[0817] Step 7: Gather feedback

[0818] The user provides feedback on the provided information. The feedback is in the form of comments or ratings, and the user's ratings are received as input. The feedback data is transferred to the server as output.

[0819] Step 8: Refine the generative AI model

[0820] The server analyzes the collected feedback and uses it as training data for the generative AI model, which improves the model's accuracy. It takes user feedback as input and updates the improved generative AI model as output.

[0821] (Application example 1)

[0822] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0823] Many users today gather a wide range of information online to select products, but information bias and lack of reliability are problems. Even in virtual stores, it is difficult for users to quickly obtain information from multiple perspectives in an unbiased manner. This often leads users to make purchasing decisions based on inaccurate information, which reduces post-purchase satisfaction.

[0824] 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.

[0825] In this invention, the server includes means for collecting data from various information sources on the Internet, means for inputting the collected data into a generative AI model to generate information interpreted from different perspectives, means for detecting bias in the generated information and selecting balanced information, means for providing information selected based on a user's search query when searching for and selecting products in a virtual store, and means for improving the generative AI model through feedback from the user. This enables users to efficiently obtain diversified and balanced information in the virtual store and make purchasing decisions based on reliable information.

[0826] "Diverse information sources on the Internet" refers to multiple types of information sources available on the Internet, such as news sites, blogs, academic paper databases, and social networking sites.

[0827] A "generative AI model" refers to an artificial intelligence model that analyzes collected data and generates information from different perspectives.

[0828] "Bias" refers to a state in which information is biased towards a particular perspective or opinion.

[0829] "Virtual store" refers to a virtual store for selling and purchasing products online.

[0830] "User search query" refers to the keywords or phrases a user uses to search for information.

[0831] "Feedback" refers to the evaluations and comments users make about the information provided or the performance of the system.

[0832] A specific method for implementing a system for carrying out the present invention will now be described.

[0833] 1. Information gathering methods

[0834] The server collects data from various sources on the Internet, such as news sites, blogs, academic paper databases, and social media. Specifically, web scraping is performed using the Python libraries BeautifulSoup and Scrapy. The collected data is then stored in a database.

[0835] 2. Information generation using generative AI models

[0836] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. This generative AI model uses a natural language processing model, such as OpenAI's GPT-3, to provide information from different perspectives (economic, scientific, social, and political) based on the user's search query.

[0837] 3. Bias detection and filtering

[0838] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. It uses a bias detection algorithm to detect information bias and select multifaceted, balanced information.

[0839] 4. User Interface

[0840] The user's device receives the search query, and the server provides information selected based on the query. When searching and selecting products in the virtual store, users can obtain information filtered according to different perspectives.

[0841] 5. Feedback Loop

[0842] Users provide feedback on the information provided. This feedback is accepted in the form of comments and ratings. The server collects and analyzes this feedback and uses it as training data for the generative AI model. This improves the performance of the generative AI model.

[0843] Specific examples

[0844] Scenario: Providing smartphone purchase information

[0845] 1. The server collects the latest information related to smartphones from news sites and blogs on the Internet and stores it in a database.

[0846] 2. The server inputs the collected data into a generative AI model (e.g., GPT-3) to analyze the information. The generative AI model generates information from economic, scientific, social, and political perspectives.

[0847] Example: Economic perspective: "The latest smartphones are 15% more expensive than they were last year."

[0848] Example: Scientific Perspective: "The latest smartphones use new display technology."

[0849] Example: Social perspective: "Many users appreciate the new camera features."

[0850] Example: Political Perspective: "Some countries restrict the import of certain smartphones."

[0851] 3. The server performs bias detection on the generated information and selects balanced articles.

[0852] 4. When a user searches for "smartphone" on their device, information filtered for different perspectives is displayed.

[0853] 5. Users provide feedback on the information provided, and the server collects and analyzes that feedback to improve the generative AI model.

[0854] Prompt Sentence Examples

[0855] "Article: The latest smartphones use new display technology. Explain this information from economic, scientific, social, and political perspectives."

[0856] This system allows users to select products in virtual stores based on reliable, multifaceted information, resulting in highly satisfying purchases.

[0857] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0858] Step 1:

[0859] The server collects data from various sources on the Internet, such as news sites, blogs, and academic paper databases. It uses the Python libraries BeautifulSoup and Scrapy to perform web scraping and stores the retrieved data in a database. The input is a search query, and the output is the articles and papers retrieved by the scraping.

[0860] Step 2:

[0861] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. Specifically, it uses a natural language processing model such as OpenAI's GPT-3. The input is the data collected in step 1, and the output is text generated from economic, scientific, social, and political perspectives. In this stage, a process is carried out in which certain prompt sentences are generated for the model and the results are obtained.

[0862] Step 3:

[0863] The server applies bias detection measures to the generated information. It uses a bias detection algorithm to quantify and classify the bias in the information. The input is the information from different perspectives generated in step 2, and the output is information with quantified and classified bias. This process evaluates the quality of the information and selects fair information.

[0864] Step 4:

[0865] When searching and selecting products in a virtual store, the server provides information selected based on the user's search query. The user's device sends the search query to the server. The input is the user's search query, and the output is balanced information with bias detection. The device displays this information to the user.

[0866] Step 5:

[0867] Users provide feedback on the information provided. This feedback takes the form of comments and ratings, and is received by the server. The input is user feedback, and the output is data for improving the generative AI model. By collecting and analyzing feedback, the training data for the generative AI model is updated, improving the model's performance.

[0868] This allows users to efficiently obtain multifaceted and balanced information in the virtual store, enabling them to make purchasing decisions based on reliable information.

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

[0870] 1. Overview

[0871] This invention is a system that collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. This system combines a means of detecting bias in the generated information and selecting balanced information with an emotion engine that recognizes user emotions. This emotion engine analyzes user feedback, improves the generative AI model, and adjusts the content and display format of the information provided. This enables users to obtain information from reliable, multifaceted perspectives.

[0872] 2. Information gathering methods

[0873] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (such as BeautifulSoup and Scrapy) to perform scraping, obtain the necessary data, and store it in a database.

[0874] 3. Data Cleansing Methods

[0875] The server cleanses and denoises the collected data, including text preprocessing to correct grammatical and spelling errors and remove unnecessary HTML tags, advertisements, and other noise, ensuring the quality of the data fed into the generative AI model.

[0876] 4. Generative AI Models

[0877] The server inputs the cleaned data into a generative AI model (e.g., a natural language processing model) to generate information interpreted from different perspectives. This AI model analyzes the data based on the user's search query and provides information from multiple perspectives (e.g., economic, scientific, social, political, etc.).

[0878] 5. Bias detection and filtering

[0879] The server applies a bias detection algorithm to the generated information. The bias detection algorithm quantifies and classifies the bias of the information. For example, it determines whether an article is politically biased or emphasizes an economic perspective. The server selects information whose bias score falls within a certain range, filtering out information containing extreme bias and selecting balanced information.

[0880] 6. User Interface

[0881] The device receives the user's search query and sends it to the server. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button. The server searches for selected information based on the user's search query and provides the user with relevant articles. Search results containing information from multiple perspectives generated by the generative AI model are displayed.

[0882] 7. Emotion Engine

[0883] The server collects user feedback and recognizes emotions using an emotion engine. This emotion engine analyzes emotional data from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user comments, "This information was helpful," the generative AI model is retrained based on that positive emotional data.

[0884] 8. Optimizing information provision

[0885] The server dynamically adjusts the content and display format of information provided based on the user's emotions recognized by the emotion engine. For example, if the user has negative emotions, the server will respond by providing additional information from a different perspective. In this way, the quality of information provided and the user experience are improved.

[0886] Specific examples

[0887] Providing information on climate change

[0888] 1. Information gathering

[0889] The server collects the latest articles on "climate change" from the Internet and stores them in a database.

[0890] 2. Information generation by generative AI

[0891] The server inputs the collected data into a generative AI model to generate information from multiple perspectives.

[0892] Examples: Economic: "Climate change could be a factor that impedes economic growth in the long term." Scientific: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years." Political: "International conferences are discussing new agreements to combat climate change."

[0893] 3. Bias detection and filtering

[0894] The server performs bias detection on the generated information and selects balanced articles.

[0895] 4. User Interface

[0896] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[0897] 5. Leveraging Emotional Engines

[0898] Users provide feedback on articles, saying things like "This information was helpful."

[0899] The server collects this feedback, analyzes it as positive emotional data in the emotion engine, and reflects it in improving the generative AI model.

[0900] Embodiments of the present invention enable users to efficiently collect and use multifaceted and balanced information, supporting reliable decision-making and contributing to a comprehensive understanding and resolution of social problems.

[0901] The processing flow will be explained below.

[0902] Step 1:

[0903] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it performs web scraping using Python libraries (such as BeautifulSoup and Scrapy) to obtain target articles and papers, and stores this data in a database.

[0904] Step 2:

[0905] The server cleanses the collected data. This process removes unnecessary HTML tags and advertisements from the text data and corrects grammar and spelling errors. This normalization process improves the quality of the data that is input into the generative AI model.

[0906] Step 3:

[0907] The server inputs the cleaned data into a generative AI model. For example, a natural language processing model (such as GPT-3) is used to generate information based on the input data, interpreted from different perspectives (economic, scientific, social, political, etc.). This information is then re-stored in a database.

[0908] Step 4:

[0909] The server applies a bias detection algorithm to the generated information. Specifically, it assigns a bias score to each piece of information, quantifying and categorizing its bias. For example, it analyzes how politically biased an article is or how much it emphasizes a scientific perspective.

[0910] Step 5:

[0911] The server filters the information based on the bias score, removing information with extremely high bias and selecting only balanced information. This selected information is then organized in a format suitable for delivery to the user.

[0912] Step 6:

[0913] The device receives the user's search query and sends it to the server. When the user types in "climate change" and presses the send button, the search query is sent to the server.

[0914] Step 7:

[0915] Based on the user's search query, the server searches the database for relevant information and retrieves selected information, including multiple perspectives generated by the generative AI model.

[0916] Step 8:

[0917] The device displays search results to the user, which can be viewed and analyzed, including information from different perspectives, such as economic, scientific, social, and political perspectives.

[0918] Step 9:

[0919] Users provide feedback on the displayed information. For example, they can post comments on an article such as "This was helpful" or "I'd like more detailed information." The sentiment engine also collects the sentiment (positive, negative, neutral) of the feedback.

[0920] Step 10:

[0921] The server collects user feedback and analyzes it with an emotion engine. The collected emotion data is used as training data for the generative AI model, helping to improve the model. For example, it strengthens the method for generating information that generates a lot of positive feedback and improves the method for generating information that generates a lot of negative feedback.

[0922] Step 11:

[0923] The server dynamically adjusts the content and display format of information provided based on the user's emotions recognized by the emotion engine. For example, if the user has negative emotions, it will provide supplementary information with a different perspective or detailed information. In this way, it is possible to provide information that is optimized for each individual user.

[0924] Through this series of processing steps, the system of the present invention provides users with multifaceted and balanced information, supporting reliable decision-making.

[0925] Example 2

[0926] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0927] Conventional information gathering systems have struggled to efficiently collect data from diverse information sources on the Internet, provide information from multiple perspectives, and detect bias. Furthermore, it has been difficult to dynamically improve the accuracy and quality of information provided based on user feedback, failing to contribute to improving the user experience. The present invention aims to solve these problems and provide highly reliable, multifaceted information and an improved user experience.

[0928] 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.

[0929] In this invention, the server includes means for collecting data from various information sources on the Internet, means for cleansing the collected data and removing noise, means for inputting the cleansed data into a generative AI model and generating information interpreted from different perspectives, means for detecting bias in the generated information, quantifying and classifying it, and selecting balanced information, means for receiving a user's search query and providing information corresponding to the query, means for recognizing emotions through user feedback and reflecting the recognition in improving the generative AI model, and means for dynamically adjusting the content and display format of the information provided based on the emotion recognition. This makes it possible to provide highly reliable, multifaceted information and improve the user experience.

[0930] The "Internet" is a large-scale network system that provides information and services worldwide via communication networks.

[0931] "Source" refers to the origin or place that provides data or knowledge, and includes news sites, blogs, academic paper databases, etc.

[0932] "Means of data collection" refers to the techniques and methods used to obtain the required data from sources on the internet, including scraping techniques and API calls.

[0933] "Data cleansing" is the process of removing unnecessary information and noise from collected data to improve the quality of the data.

[0934] "Noise reduction" is the process of removing unnecessary information, such as HTML tags and advertisements, from text data.

[0935] A "generative AI model" is an artificial intelligence model that uses technologies such as natural language processing to generate sentences and data that appear to have been created by humans.

[0936] "Information interpreted from different perspectives" refers to information that is provided based on multiple perspectives, with data analyzed from multiple angles and standpoints.

[0937] "Bias detection measures" refer to techniques and methods for determining whether the information generated is biased toward a particular viewpoint.

[0938] "Quantification and classification" is the process of numerically assessing the bias of information and classifying it into different categories.

[0939] "Balanced information" refers to fair and objective information that is not heavily biased toward any particular viewpoint or bias.

[0940] A "search query" refers to a keyword or phrase that a user enters when searching for specific information.

[0941] "Feedback" refers to responses such as ratings, opinions, and comments provided by users.

[0942] "Means of recognizing emotions" refers to technologies and methods for analyzing emotions such as positive and negative from user feedback.

[0943] "Means for dynamically adjusting the content and display format of information provided" refers to technologies and methods that change the information provided and the way it is displayed in real time based on user sentiment and feedback.

[0944] This invention is a system that collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. This system aims to improve the user experience by combining a means to detect bias in the generated information and select balanced information with an emotion engine that recognizes the user's emotions.

[0945] Information gathering

[0946] The server collects data from news sites, blogs, and academic paper databases on the Internet. This process is performed by scraping using the Python libraries BeautifulSoup and Scrapy. For example, the HTML structure of a news site is analyzed, and the article text and titles are extracted from tags with specific classes and IDs, and then stored in a database.

[0947] Data Cleansing

[0948] The server cleanses and removes noise from the collected data, using Python libraries to correct grammatical and spelling errors, and using regular expressions and BeautifulSoup to remove unnecessary HTML tags and advertisements.

[0949] Information generation using generative AI models

[0950] The server inputs the cleansed data into a generative AI model to generate information interpreted from different perspectives. For example, using GPT-3 as a generative AI model, the following prompt is input: "Please explain climate change from an economic perspective." This will generate information from an economic perspective, a scientific perspective, a political perspective, etc.

[0951] Bias Detection and Filtering

[0952] The server detects, quantifies, and classifies bias in the generated information. Based on this, an algorithm is used to select balanced information. If the generated information is extremely biased, it is filtered out.

[0953] User Interface

[0954] The device receives the user's search query and sends it to the server. For example, if the user enters "climate change" into the search form and presses the search button, the server retrieves information based on the user's search query, generates related articles, and displays information from multiple perspectives generated by the AI ​​model.

[0955] Emotion Engine

[0956] The server collects user feedback and analyzes it using an emotion engine. The emotion engine recognizes positive and negative emotions from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user provides feedback on an article saying, "This information was helpful," the generative AI model can be retrained based on that positive data.

[0957] Optimizing information provision

[0958] The server dynamically adjusts the content and display format of the information provided based on the analysis results of the emotion engine. For example, if a user gives negative feedback, the server can respond by providing additional information from a different perspective, thereby improving the user experience.

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

[0960] Step 1:

[0961] The server performs scraping to collect data from news sites, blogs, and academic paper databases on the Internet. The input is a specific list of URLs and search keywords. The server uses BeautifulSoup or Scrapy to analyze the HTML structure and extract article titles, body text, URLs, etc. The output is saved in a database as structured data. For example, searching for tags with specific classes or IDs from the HTML of a news site and extracting article information.

[0962] Step 2:

[0963] The server cleanses the collected data and removes noise. The input is the raw data collected in step 1. Specifically, it uses Python libraries (such as NLTK and SpaCy) to correct grammatical and spelling errors, and uses regular expressions and BeautifulSoup to remove unnecessary HTML tags and advertisements. The output is cleansed text data. For example, <ad>" tags and unnecessary scripts.

[0964] Step 3:

[0965] The server inputs the cleansed data into the generative AI model. The input is the text data cleansed in step 2. The server uses a generative AI model (e.g., GPT-3) and sends a prompt such as "Please explain climate change from an economic perspective" to the generative AI model. The output is text information generated from multiple perspectives. For example, it generates information interpreted from an economic perspective and a scientific perspective.

[0966] Step 4:

[0967] The server detects bias in the generated information, quantifies it, and classifies it. The input is the information generated in step 3. A bias detection algorithm is applied to evaluate and quantify the bias in the information. For example, a political bias score is calculated. The output is information with a bias score assigned. Based on the bias score, balanced information is extracted.

[0968] Step 5:

[0969] The terminal receives the user's search query and sends it to the server. The input is the keyword the user entered into the search form. The terminal receives the input from the user and sends it to the server as a query. The output is the search query forwarded to the server. For example, the query "climate change" is sent.

[0970] Step 6:

[0971] The server searches for selected information based on the user's search query and provides relevant articles. The input is the search query received in step 5 and the information to which a bias score was assigned in step 4. The server searches for information filtered based on the search query and generates search results composed of multiple perspectives. The output is information as search results. For example, it provides articles that include interpretations of "climate change" from economic, scientific, and political perspectives.

[0972] Step 7:

[0973] The terminal displays the search results to the user. The input is the search results generated in step 6. The terminal displays the search results in a user interface, allowing the user to view information by perspective. The output is a visual representation of the search results to the user, for example, separated into tabs for economic perspectives, scientific perspectives, and political perspectives.

[0974] Step 8:

[0975] The device collects feedback from the user and sends it to the server. The input is the user's feedback (e.g., "This information was helpful"). The device receives the user's feedback and sends it to the server. The output is the feedback sent to the server. For example, sending a rating such as "This information was helpful."

[0976] Step 9:

[0977] The server analyzes the feedback from the user and uses an emotion recognition engine. The input is the feedback collected in step 8. The emotion engine recognizes the emotion of the feedback, such as positive or negative. The output is the analyzed emotion data. For example, if the evaluation is positive, the score is output as the analysis result.

[0978] Step 10:

[0979] The server uses emotion recognition to improve the generative AI model. The input is the emotion data obtained in step 9. The generative AI model is retrained using positive feedback, and the model's generation results are adjusted in response to negative feedback. The output is a retrained generative AI model. This improves the quality of the generated information.

[0980] Step 11:

[0981] The server dynamically adjusts the content and display format of the information provided based on emotion recognition. The input is the emotion data from step 9 and the generative AI model retrained in step 10. In response to negative user feedback, additional information from a different perspective is provided and the display format is adjusted accordingly. The output is dynamically adjusted information content and display format, which improves the user experience.

[0982] (Application example 2)

[0983] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0984] Conventional news distribution systems provide users with one-sided articles and do not adequately consider information bias or user sentiment. As a result, users often receive biased information, making it difficult to make reliable decisions. In addition, the collected data is of low quality and noisy, which reduces the accuracy of the generative AI model and reduces the quality of the information provided.

[0985] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data from various information sources on the Internet, means for inputting the collected data into a generative AI model and generating information interpreted from different perspectives, means for detecting bias in the generated information and selecting balanced information, means for providing the selected information in response to a user's search query, means for improving the generative AI model through user feedback, and means for analyzing user sentiment to improve the quality of information provided. This enables users to efficiently collect and use multifaceted and balanced information, supporting reliable decision-making and improving the quality of information provided. Furthermore, cleansing the collected data improves the accuracy of the generative AI model and the quality of the information provided.

[0986] "Diverse information sources on the Internet" refers to multiple types of information sources that can be accessed on the Internet, such as news sites, blogs, and academic paper databases.

[0987] "Data Collection Methods" refers to the use of software and protocols to retrieve and store information from a particular website.

[0988] A "generative AI model" refers to an algorithm that generates and analyzes natural language using technologies such as deep learning.

[0989] "Information interpreted from different perspectives" refers to information on a particular topic that has been analyzed from multiple fields of expertise and perspectives, such as economic, scientific, social, and political perspectives.

[0990] "Means for generating information" refers to technology that analyzes collected data as input and generates new information using a specific algorithm.

[0991] "Means for detecting bias and selecting balanced information" refers to algorithms and techniques for quantifying and evaluating information bias and selecting information with the least bias.

[0992] "Means for providing information selected in response to a user's search query" refers to an interface and system for displaying information filtered based on the search criteria entered by the user.

[0993] "Means for improving generative AI models through user feedback" refers to methods for analyzing feedback such as user ratings and opinions and retraining or adjusting generative AI models based on that feedback.

[0994] "Means of analyzing user emotions to improve the quality of information provided" refers to technology that uses an emotion analysis engine to analyze user feedback and reactions, and then uses the results to improve the content and format of information provided.

[0995] "Data cleansing and denoising" refers to the process of removing unnecessary elements such as grammatical and spelling errors, unnecessary HTML tags, and advertisements from collected data to improve the quality of the data.

[0996] "Means for quantifying and classifying bias" refers to algorithms and techniques for quantifying and classifying information bias as specific numerical values.

[0997] This invention is a system that allows users to efficiently collect and use multifaceted and balanced information. This system collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. The specific configuration and operation of this system are described below.

[0998] First, the server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (BeautifulSoup and Scrapy) to scrape websites, obtain the necessary data, and store it in a database.

[0999] The server then cleanses and denoises the collected data, using text processing libraries such as NLTK and TextBlob to correct grammatical and spelling errors, and remove unnecessary HTML tags and advertisements, ensuring the quality of the data fed into the generative AI model.

[1000] The server inputs the cleaned data into a generative AI model (e.g., a natural language processing model such as GPT-4) to generate information interpreted from different perspectives (e.g., economic, scientific, social, political, etc.). This AI model analyzes the data based on the user's search query and provides information from multiple perspectives.

[1001] The server then applies a bias detection algorithm to the generated information to quantify and classify the bias of the information, selecting information whose bias score falls within a certain range and filtering out information containing extreme bias to select balanced information.

[1002] The device receives a search query from the user. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button. The server searches filtered information based on the user's search query and provides the user with relevant articles. It then displays search results containing information from multiple perspectives generated by the generative AI model.

[1003] The server also collects user feedback and analyzes emotions using an emotion engine. This emotion engine analyzes emotional data from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user comments, "This information was helpful," the generative AI model can be retrained based on that positive emotional data to improve the quality of information provided.

[1004] As a concrete example, let's consider a case where the server collects the latest news on climate change and provides information from multiple perspectives. In this case, the server inputs the following prompt sentence into the generative AI model:

[1005] "Provide different perspectives on the latest news on climate change from economic, scientific, social and political perspectives."

[1006] This allows the generated news articles to help users understand information from multiple perspectives and support reliable decision-making.

[1007] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1008] Step 1:

[1009] The server collects data from various sources on the Internet. Specifically, it uses Python's BeautifulSoup and Scrapy libraries to scrape data from news sites, blogs, and academic paper databases. The collected data is stored in a database on the server. The input is a specific URL or a database query, and the output is the collected text data.

[1010] Step 2:

[1011] The server cleanses the collected data and removes noise. Specifically, it uses text processing libraries such as NLTK and TextBlob to correct grammatical and spelling errors and remove unnecessary HTML tags and advertisements. The input is the text data collected in step 1, and the output is clean text data.

[1012] Step 3:

[1013] The server inputs the cleaned data into a generative AI model to generate information interpreted from different perspectives. For example, it uses a natural language processing model such as GPT-4. The input is the data cleansed in step 2, and the output is multifaceted information interpreted from economic, scientific, social, and political perspectives.

[1014] Step 4:

[1015] The server performs bias detection on the generated information. Specifically, it uses a bias detection algorithm to quantify the bias in the information and select balanced information. The input is the multifaceted information generated in step 3, and the output is filtered information that is judged to have less bias.

[1016] Step 5:

[1017] The terminal receives a search query from the user. The user enters keywords into the search form and presses the search button. The input is the user's search query, and the output is the search query sent to the server.

[1018] Step 6:

[1019] The server searches the filtered information based on the user's search query and selects relevant articles. This information includes information from multiple perspectives generated by the generative AI model. The input is the search query submitted in step 5, and the output is the relevant articles provided to the user.

[1020] Step 7:

[1021] The terminal displays the articles provided by the server to the user. A search result screen containing information from each of the multiple perspectives generated is displayed. The input is the articles selected in step 6, and the output is multifaceted information provided to the user.

[1022] Step 8:

[1023] The user submits feedback on the provided article. Specifically, the user submits a rating or comment such as "This information was helpful." The input is the user's feedback, and the output is the feedback sent to the server.

[1024] Step 9:

[1025] The server analyzes the user feedback using the emotion engine. The analysis results are reflected in improving the generative AI model. The input is the user feedback sent in step 8, and the output is data for adjusting and retraining the generative AI model.

[1026] Step 10:

[1027] The server dynamically adjusts the quality of the information provided based on the user's emotional data. For example, if the user has negative emotions, it will respond by providing additional information from a different perspective. The input is the emotional analysis data obtained in step 9, and the output is the adjusted content and display format of the information provided.

[1028] 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.

[1029] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1030] 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.

[1031] [Fourth embodiment]

[1032] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1033] 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.

[1034] 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).

[1035] 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.

[1036] 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.

[1037] 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).

[1038] 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.

[1039] 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.

[1040] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1041] 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.

[1042] 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.

[1043] 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.

[1044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1045] 1. Overview

[1046] This invention is a system that collects data from various sources on the internet, converts the data into multifaceted information using a generative AI model, and provides it to users. This system uses bias detection means to select balanced information, allowing users to obtain information from reliable, multifaceted perspectives.

[1047] 2. Information gathering methods

[1048] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. It uses Python libraries (e.g., BeautifulSoup, Scrapy) to scrape the data and obtain the required data. The collected data is then stored in a database.

[1049] 3. Generative AI Models

[1050] The server inputs the collected data into a generative AI model (e.g., a natural language processing model) to generate information interpreted from different perspectives. This generative AI model analyzes the data based on the user's search query and provides information from multiple angles, such as economic, scientific, social, and political perspectives.

[1051] 4. Bias detection and filtering

[1052] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. This bias detection algorithm is used to detect information bias and select multifaceted, balanced information. The selected information is balanced and filtered to ensure it is not biased toward any particular viewpoint.

[1053] 5. User Interface

[1054] The terminal (user's device) receives the user's search query, and the server provides information selected based on that query. The search results include information from different perspectives generated by the generative AI model, allowing the user to obtain information from multiple perspectives.

[1055] 6. Feedback Loops

[1056] Users provide feedback on the information provided. This feedback can take the form of comments or ratings, and the server collects and analyzes this feedback. The collected feedback is used as training data for the generative AI model, contributing to improving the model's accuracy.

[1057] Specific examples

[1058] Providing information on climate change

[1059] 1. Information gathering

[1060] The server collects the latest articles related to "climate change" from the Internet and stores them in a database.

[1061] 2. Information generation by generative AI

[1062] The server inputs the collected data into a generative AI model and analyzes the information.

[1063] A generative AI model generates information from the following perspectives, for example:

[1064] Economic perspective: "Climate change could become a drag on economic growth in the long term."

[1065] Science: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years."

[1066] Political perspective: "International conferences are discussing a new agreement to combat climate change."

[1067] 3. Bias detection and filtering

[1068] The server performs bias detection on the generated information and selects balanced articles.

[1069] 4. User Interface

[1070] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[1071] 5. Feedback Loop

[1072] Users provide feedback on the information provided, and the server uses that feedback to improve the generative AI model.

[1073] Embodiments of the present invention enable users to efficiently collect and use multifaceted and balanced information, which can contribute to a comprehensive understanding and solution of social problems.

[1074] The processing flow will be explained below.

[1075] Step 1:

[1076] The server collects data from various sources on the Internet, specifically using Python libraries (such as BeautifulSoup and Scrapy) to retrieve the latest articles from news sites, blogs, and academic paper databases, and stores this data in a database.

[1077] Step 2:

[1078] The server cleanses the collected data, performing text preprocessing to correct grammatical and spelling errors and remove noise such as unnecessary HTML tags and advertisements, thereby ensuring the quality of the data fed into the generative AI model.

[1079] Step 3:

[1080] The server inputs the cleaned data into a generative AI model. For example, it uses a natural language processing model (such as GPT-3) to generate information interpreted from multiple perspectives (economic, scientific, social, political, etc.). This generated information includes analyses and opinions from different angles.

[1081] Step 4:

[1082] The server applies bias-detection algorithms to the generated information, quantifying and categorizing the information's bias—for example, determining whether an article is politically biased or emphasizes an economic perspective.

[1083] Step 5:

[1084] The server performs filtering based on the bias detection results. It selects information whose bias score falls within a certain range and extracts balanced, multifaceted information. This filtering process eliminates information that contains extreme bias.

[1085] Step 6:

[1086] The device receives the user's search query and sends it to the server. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button.

[1087] Step 7:

[1088] The server retrieves selected information based on the user's search query and provides relevant articles to the user, displaying information from multiple perspectives from the generative AI model.

[1089] Step 8:

[1090] The device displays the search results to the user, including articles and summaries for each of the different perspectives generated, allowing the user to access information from multiple angles.

[1091] Step 9:

[1092] Choose how users can provide feedback on the information provided, for example by leaving comments, rating, or suggesting improvements.

[1093] Step 10:

[1094] The server collects and analyzes the feedback provided by users, and this feedback data is used to retrain the generative AI model to improve the accuracy of future information generation.

[1095] Through the above processing steps, the system of the present invention provides users with multifaceted and balanced information, supporting reliable decision-making.

[1096] Example 1

[1097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1098] Although there is a wide variety of information on the Internet, this information is often biased, making it difficult for users to obtain reliable information from multiple perspectives. Furthermore, there is a demand for improving the quality of collected information and the accuracy of models. A method to solve these issues is needed.

[1099] 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.

[1100] In this invention, the server includes means for collecting data from multiple information sources on the Internet, means for analyzing the collected data using a program and filtering out necessary data, means for inputting the collected data into a large-scale language model and generating information interpreted from different perspectives, means for detecting bias in the generated information and selecting multifaceted information, means for displaying the selected information in response to a user's search query, and means for analyzing user ratings and improving the large-scale language model, thereby enabling users to obtain information from reliable multifaceted perspectives.

[1101] The "Internet" is a global network system that connects computer networks around the world and enables the exchange of information.

[1102] A "source" is an internet location that provides information in digital form, such as a news site, blog, or academic paper database.

[1103] "Data collection" is the process of obtaining the necessary information from the Internet and storing it in a database.

[1104] A "program" is a set of instructions that instruct a computer to perform a specific process.

[1105] "Analysis" is the process of deciphering collected data and extracting and classifying specific information.

[1106] "Filtering" is the process of removing unnecessary information from collected data and selecting only useful data.

[1107] A "large-scale language model" is an artificial intelligence model that learns from large amounts of text data and generates and analyzes natural language.

[1108] "Bias" means that information is biased towards a particular viewpoint or opinion.

[1109] A "query" is a search request to a database or Internet search engine to find specific information.

[1110] "Display" means the visual presentation of information on a computer screen.

[1111] "Rating" refers to the opinions, comments, and rating scores that users give to the information provided.

[1112] This invention is a system that collects data from various sources on the internet, converts that data into multifaceted information using a generative AI model, and provides it to users. This system uses bias detection means to select balanced information, allowing users to obtain information from reliable, multifaceted perspectives.

[1113] Information gathering methods

[1114] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (e.g., BeautifulSoup, Scrapy) to scrape and obtain the required data. The collected data is then stored in a database (e.g., MySQL, PostgreSQL).

[1115] Information generation using generative AI models

[1116] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. This generative AI model uses a large-scale language model (e.g., GPT) to analyze data based on the user's search query and provide information from multiple angles, including economic, scientific, social, and political perspectives.

[1117] Bias Detection and Filtering

[1118] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. The bias detection algorithm is used to detect information bias and select multifaceted, balanced information. The selected information is filtered to ensure it is balanced and not biased toward any particular viewpoint.

[1119] User Interface

[1120] The terminal (user's device) receives the user's search query, and the server provides information selected based on the query. The search results include information from different perspectives generated by the generative AI model, allowing the user to obtain information from multiple perspectives.

[1121] Feedback Loop

[1122] Users provide feedback on the information provided. This feedback can take the form of comments or ratings, and the server collects and analyzes this feedback. The collected feedback is used as training data for the generative AI model, contributing to improving the model's accuracy.

[1123] Specific examples

[1124] Providing information on climate change

[1125] 1. Information gathering

[1126] The server collects the latest articles related to "climate change" from the Internet and stores them in a database.

[1127] 2. Information generation by generative AI

[1128] The server inputs the collected data into a generative AI model and analyzes the information.

[1129] A generative AI model generates information from the following perspectives, for example:

[1130] Economic perspective: "Climate change could become a drag on economic growth in the long term."

[1131] Science: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years."

[1132] Political perspective: "International conferences are discussing a new agreement to combat climate change."

[1133] 3. Bias detection and filtering

[1134] The server performs bias detection on the generated information and selects balanced articles.

[1135] 4. User Interface

[1136] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[1137] 5. Feedback Loop

[1138] Users provide feedback on the information provided, and the server uses that feedback to improve the generative AI model.

[1139] Prompt Sentence Examples

[1140] An example of a prompt to be input to the generative AI model is, "Please summarize the latest economic, scientific, and political perspectives on climate change." Based on this prompt, the generative AI model organizes information from each perspective and provides it to the user.

[1141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1142] Step 1: Gather information

[1143] The server collects information from news sites, blogs, and academic paper databases on the Internet. Specifically, it uses the Python libraries BeautifulSoup and Scrapy to perform scraping. It receives a list of URLs of target sites as input and obtains the collected text data as output, which is then stored in a database.

[1144] Step 2: Data cleansing

[1145] The server processes the collected data stored in the database and removes unnecessary noise. Specifically, it uses regular expressions and cleaning algorithms to organize the data. It takes the collected raw data as input and produces clean, noise-removed data as output.

[1146] Step 3: Input data into the generative AI model

[1147] The server takes the cleansed data and inputs it into a generative AI model. Specifically, it analyzes the data using a large-scale language model (e.g., GPT). It receives the cleaned data and the user's search query as input and generates information interpreted from different perspectives as output.

[1148] Step 4: Analyze and generate information

[1149] The server uses a generative AI model to generate information based on the user's search query. For example, for the query "climate change," it generates information interpreted from economic, scientific, and political perspectives. It receives the user's query and clean data as input, and obtains information generated from multiple perspectives as output.

[1150] Step 5: Bias detection and filtering

[1151] The server analyzes the generated information to detect bias. Specifically, it uses a bias detection algorithm to quantify the bias of each article and select balanced information. It receives the generated information as input and provides balanced, filtered information as output.

[1152] Step 6: User Interface

[1153] The terminal receives the user's search query, and the server provides selected information. The terminal receives the search query input from the user and displays information filtered from multiple perspectives as output to the user, allowing the user to obtain information from multiple perspectives.

[1154] Step 7: Gather feedback

[1155] The user provides feedback on the provided information. The feedback is in the form of comments or ratings, and the user's ratings are received as input. The feedback data is transferred to the server as output.

[1156] Step 8: Refine the generative AI model

[1157] The server analyzes the collected feedback and uses it as training data for the generative AI model, which improves the model's accuracy. It takes user feedback as input and updates the improved generative AI model as output.

[1158] (Application example 1)

[1159] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1160] Many users today gather a wide range of information online to select products, but information bias and lack of reliability are problems. Even in virtual stores, it is difficult for users to quickly obtain information from multiple perspectives in an unbiased manner. This often leads users to make purchasing decisions based on inaccurate information, which reduces post-purchase satisfaction.

[1161] 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.

[1162] In this invention, the server includes means for collecting data from various information sources on the Internet, means for inputting the collected data into a generative AI model to generate information interpreted from different perspectives, means for detecting bias in the generated information and selecting balanced information, means for providing information selected based on a user's search query when searching for and selecting products in a virtual store, and means for improving the generative AI model through feedback from the user. This enables users to efficiently obtain diversified and balanced information in the virtual store and make purchasing decisions based on reliable information.

[1163] "Diverse information sources on the Internet" refers to multiple types of information sources available on the Internet, such as news sites, blogs, academic paper databases, and social networking sites.

[1164] A "generative AI model" refers to an artificial intelligence model that analyzes collected data and generates information from different perspectives.

[1165] "Bias" refers to a state in which information is biased towards a particular perspective or opinion.

[1166] "Virtual store" refers to a virtual store for selling and purchasing products online.

[1167] "User search query" refers to the keywords or phrases a user uses to search for information.

[1168] "Feedback" refers to the evaluations and comments users make about the information provided or the performance of the system.

[1169] A specific method for implementing a system for carrying out the present invention will now be described.

[1170] 1. Information gathering methods

[1171] The server collects data from various sources on the Internet, such as news sites, blogs, academic paper databases, and social media. Specifically, web scraping is performed using the Python libraries BeautifulSoup and Scrapy. The collected data is then stored in a database.

[1172] 2. Information generation using generative AI models

[1173] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. This generative AI model uses a natural language processing model, such as OpenAI's GPT-3, to provide information from different perspectives (economic, scientific, social, and political) based on the user's search query.

[1174] 3. Bias detection and filtering

[1175] The server applies bias detection measures to the information generated by the generative AI model, quantifying and classifying the bias of each article. It uses a bias detection algorithm to detect information bias and select multifaceted, balanced information.

[1176] 4. User Interface

[1177] The user's device receives the search query, and the server provides information selected based on the query. When searching and selecting products in the virtual store, users can obtain information filtered according to different perspectives.

[1178] 5. Feedback Loop

[1179] Users provide feedback on the information provided. This feedback is accepted in the form of comments and ratings. The server collects and analyzes this feedback and uses it as training data for the generative AI model. This improves the performance of the generative AI model.

[1180] Specific examples

[1181] Scenario: Providing smartphone purchase information

[1182] 1. The server collects the latest information related to smartphones from news sites and blogs on the Internet and stores it in a database.

[1183] 2. The server inputs the collected data into a generative AI model (e.g., GPT-3) to analyze the information. The generative AI model generates information from economic, scientific, social, and political perspectives.

[1184] Example: Economic perspective: "The latest smartphones are 15% more expensive than they were last year."

[1185] Example: Scientific Perspective: "The latest smartphones use new display technology."

[1186] Example: Social perspective: "Many users appreciate the new camera features."

[1187] Example: Political Perspective: "Some countries restrict the import of certain smartphones."

[1188] 3. The server performs bias detection on the generated information and selects balanced articles.

[1189] 4. When a user searches for "smartphone" on their device, information filtered for different perspectives is displayed.

[1190] 5. Users provide feedback on the information provided, and the server collects and analyzes that feedback to improve the generative AI model.

[1191] Prompt Sentence Examples

[1192] "Article: The latest smartphones use new display technology. Explain this information from economic, scientific, social, and political perspectives."

[1193] This system allows users to select products in virtual stores based on reliable, multifaceted information, resulting in highly satisfying purchases.

[1194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1195] Step 1:

[1196] The server collects data from various sources on the Internet, such as news sites, blogs, and academic paper databases. It uses the Python libraries BeautifulSoup and Scrapy to perform web scraping and stores the retrieved data in a database. The input is a search query, and the output is the articles and papers retrieved by the scraping.

[1197] Step 2:

[1198] The server inputs the collected data into a generative AI model to generate information interpreted from different perspectives. Specifically, it uses a natural language processing model such as OpenAI's GPT-3. The input is the data collected in step 1, and the output is text generated from economic, scientific, social, and political perspectives. In this stage, a process is carried out in which certain prompt sentences are generated for the model and the results are obtained.

[1199] Step 3:

[1200] The server applies bias detection measures to the generated information. It uses a bias detection algorithm to quantify and classify the bias in the information. The input is the information from different perspectives generated in step 2, and the output is information with quantified and classified bias. This process evaluates the quality of the information and selects fair information.

[1201] Step 4:

[1202] When searching and selecting products in a virtual store, the server provides information selected based on the user's search query. The user's device sends the search query to the server. The input is the user's search query, and the output is balanced information with bias detection. The device displays this information to the user.

[1203] Step 5:

[1204] Users provide feedback on the information provided. This feedback takes the form of comments and ratings, and is received by the server. The input is user feedback, and the output is data for improving the generative AI model. By collecting and analyzing feedback, the training data for the generative AI model is updated, improving the model's performance.

[1205] This allows users to efficiently obtain multifaceted and balanced information in the virtual store, enabling them to make purchasing decisions based on reliable information.

[1206] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1207] 1. Overview

[1208] This invention is a system that collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. This system combines a means of detecting bias in the generated information and selecting balanced information with an emotion engine that recognizes user emotions. This emotion engine analyzes user feedback, improves the generative AI model, and adjusts the content and display format of the information provided. This enables users to obtain information from reliable, multifaceted perspectives.

[1209] 2. Information gathering methods

[1210] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (such as BeautifulSoup and Scrapy) to perform scraping, obtain the necessary data, and store it in a database.

[1211] 3. Data Cleansing Methods

[1212] The server cleanses and denoises the collected data, including text preprocessing to correct grammatical and spelling errors and remove unnecessary HTML tags, advertisements, and other noise, ensuring the quality of the data fed into the generative AI model.

[1213] 4. Generative AI Models

[1214] The server inputs the cleaned data into a generative AI model (e.g., a natural language processing model) to generate information interpreted from different perspectives. This AI model analyzes the data based on the user's search query and provides information from multiple perspectives (e.g., economic, scientific, social, political, etc.).

[1215] 5. Bias detection and filtering

[1216] The server applies a bias detection algorithm to the generated information. The bias detection algorithm quantifies and classifies the bias of the information. For example, it determines whether an article is politically biased or emphasizes an economic perspective. The server selects information whose bias score falls within a certain range, filtering out information containing extreme bias and selecting balanced information.

[1217] 6. User Interface

[1218] The device receives the user's search query and sends it to the server. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button. The server searches for selected information based on the user's search query and provides the user with relevant articles. Search results containing information from multiple perspectives generated by the generative AI model are displayed.

[1219] 7. Emotion Engine

[1220] The server collects user feedback and recognizes emotions using an emotion engine. This emotion engine analyzes emotional data from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user comments, "This information was helpful," the generative AI model is retrained based on that positive emotional data.

[1221] 8. Optimizing information provision

[1222] The server dynamically adjusts the content and display format of information provided based on the user's emotions recognized by the emotion engine. For example, if the user has negative emotions, the server will respond by providing additional information from a different perspective. In this way, the quality of information provided and the user experience are improved.

[1223] Specific examples

[1224] Providing information on climate change

[1225] 1. Information gathering

[1226] The server collects the latest articles on "climate change" from the Internet and stores them in a database.

[1227] 2. Information generation by generative AI

[1228] The server inputs the collected data into a generative AI model to generate information from multiple perspectives.

[1229] Examples: Economic: "Climate change could be a factor that impedes economic growth in the long term." Scientific: "New research shows that greenhouse gas emissions have skyrocketed over the past 50 years." Political: "International conferences are discussing new agreements to combat climate change."

[1230] 3. Bias detection and filtering

[1231] The server performs bias detection on the generated information and selects balanced articles.

[1232] 4. User Interface

[1233] When a user searches for "climate change," the device displays information filtered according to different perspectives.

[1234] 5. Leveraging Emotional Engines

[1235] Users provide feedback on articles, saying things like "This information was helpful."

[1236] The server collects this feedback, analyzes it as positive emotional data in the emotion engine, and reflects it in improving the generative AI model.

[1237] Embodiments of the present invention enable users to efficiently collect and use multifaceted and balanced information, supporting reliable decision-making and contributing to a comprehensive understanding and resolution of social problems.

[1238] The processing flow will be explained below.

[1239] Step 1:

[1240] The server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it performs web scraping using Python libraries (such as BeautifulSoup and Scrapy) to obtain target articles and papers, and stores this data in a database.

[1241] Step 2:

[1242] The server cleanses the collected data. This process removes unnecessary HTML tags and advertisements from the text data and corrects grammar and spelling errors. This normalization process improves the quality of the data that is input into the generative AI model.

[1243] Step 3:

[1244] The server inputs the cleaned data into a generative AI model. For example, a natural language processing model (such as GPT-3) is used to generate information based on the input data, interpreted from different perspectives (economic, scientific, social, political, etc.). This information is then re-stored in a database.

[1245] Step 4:

[1246] The server applies a bias detection algorithm to the generated information. Specifically, it assigns a bias score to each piece of information, quantifying and categorizing its bias. For example, it analyzes how politically biased an article is or how much it emphasizes a scientific perspective.

[1247] Step 5:

[1248] The server filters the information based on the bias score, removing information with extremely high bias and selecting only balanced information. This selected information is then organized in a format suitable for delivery to the user.

[1249] Step 6:

[1250] The device receives the user's search query and sends it to the server. When the user types in "climate change" and presses the send button, the search query is sent to the server.

[1251] Step 7:

[1252] Based on the user's search query, the server searches the database for relevant information and retrieves selected information, including multiple perspectives generated by the generative AI model.

[1253] Step 8:

[1254] The device displays search results to the user, which can be viewed and analyzed, including information from different perspectives, such as economic, scientific, social, and political perspectives.

[1255] Step 9:

[1256] Users provide feedback on the displayed information. For example, they can post comments on an article such as "This was helpful" or "I'd like more detailed information." The sentiment engine also collects the sentiment (positive, negative, neutral) of the feedback.

[1257] Step 10:

[1258] The server collects user feedback and analyzes it with an emotion engine. The collected emotion data is used as training data for the generative AI model, helping to improve the model. For example, it strengthens the method for generating information that generates a lot of positive feedback and improves the method for generating information that generates a lot of negative feedback.

[1259] Step 11:

[1260] The server dynamically adjusts the content and display format of information provided based on the user's emotions recognized by the emotion engine. For example, if the user has negative emotions, it will provide supplementary information with a different perspective or detailed information. In this way, it is possible to provide information that is optimized for each individual user.

[1261] Through this series of processing steps, the system of the present invention provides users with multifaceted and balanced information, supporting reliable decision-making.

[1262] Example 2

[1263] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1264] Conventional information gathering systems have struggled to efficiently collect data from diverse information sources on the Internet, provide information from multiple perspectives, and detect bias. Furthermore, it has been difficult to dynamically improve the accuracy and quality of information provided based on user feedback, failing to contribute to improving the user experience. The present invention aims to solve these problems and provide highly reliable, multifaceted information and an improved user experience.

[1265] 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.

[1266] In this invention, the server includes means for collecting data from various information sources on the Internet, means for cleansing the collected data and removing noise, means for inputting the cleansed data into a generative AI model and generating information interpreted from different perspectives, means for detecting bias in the generated information, quantifying and classifying it, and selecting balanced information, means for receiving a user's search query and providing information corresponding to the query, means for recognizing emotions through user feedback and reflecting the recognition in improving the generative AI model, and means for dynamically adjusting the content and display format of the information provided based on the emotion recognition. This makes it possible to provide highly reliable, multifaceted information and improve the user experience.

[1267] The "Internet" is a large-scale network system that provides information and services worldwide via communication networks.

[1268] "Source" refers to the origin or place that provides data or knowledge, and includes news sites, blogs, academic paper databases, etc.

[1269] "Means of data collection" refers to the techniques and methods used to obtain the required data from sources on the internet, including scraping techniques and API calls.

[1270] "Data cleansing" is the process of removing unnecessary information and noise from collected data to improve the quality of the data.

[1271] "Noise reduction" is the process of removing unnecessary information, such as HTML tags and advertisements, from text data.

[1272] A "generative AI model" is an artificial intelligence model that uses technologies such as natural language processing to generate sentences and data that appear to have been created by humans.

[1273] "Information interpreted from different perspectives" refers to information that is provided based on multiple perspectives, with data analyzed from multiple angles and standpoints.

[1274] "Bias detection measures" refer to techniques and methods for determining whether the information generated is biased toward a particular viewpoint.

[1275] "Quantification and classification" is the process of numerically assessing the bias of information and classifying it into different categories.

[1276] "Balanced information" refers to fair and objective information that is not heavily biased toward any particular viewpoint or bias.

[1277] A "search query" refers to a keyword or phrase that a user enters when searching for specific information.

[1278] "Feedback" refers to responses such as ratings, opinions, and comments provided by users.

[1279] "Means of recognizing emotions" refers to technologies and methods for analyzing emotions such as positive and negative from user feedback.

[1280] "Means for dynamically adjusting the content and display format of information provided" refers to technologies and methods that change the information provided and the way it is displayed in real time based on user sentiment and feedback.

[1281] This invention is a system that collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. This system aims to improve the user experience by combining a means to detect bias in the generated information and select balanced information with an emotion engine that recognizes the user's emotions.

[1282] Information gathering

[1283] The server collects data from news sites, blogs, and academic paper databases on the Internet. This process is performed by scraping using the Python libraries BeautifulSoup and Scrapy. For example, the HTML structure of a news site is analyzed, and the article text and titles are extracted from tags with specific classes and IDs, and then stored in a database.

[1284] Data Cleansing

[1285] The server cleanses and removes noise from the collected data, using Python libraries to correct grammatical and spelling errors, and using regular expressions and BeautifulSoup to remove unnecessary HTML tags and advertisements.

[1286] Information generation using generative AI models

[1287] The server inputs the cleansed data into a generative AI model to generate information interpreted from different perspectives. For example, using GPT-3 as a generative AI model, the following prompt is input: "Please explain climate change from an economic perspective." This will generate information from an economic perspective, a scientific perspective, a political perspective, etc.

[1288] Bias Detection and Filtering

[1289] The server detects, quantifies, and classifies bias in the generated information. Based on this, an algorithm is used to select balanced information. If the generated information is extremely biased, it is filtered out.

[1290] User Interface

[1291] The device receives the user's search query and sends it to the server. For example, if the user enters "climate change" into the search form and presses the search button, the server retrieves information based on the user's search query, generates related articles, and displays information from multiple perspectives generated by the AI ​​model.

[1292] Emotion Engine

[1293] The server collects user feedback and analyzes it using an emotion engine. The emotion engine recognizes positive and negative emotions from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user provides feedback on an article saying, "This information was helpful," the generative AI model can be retrained based on that positive data.

[1294] Optimizing information provision

[1295] The server dynamically adjusts the content and display format of the information provided based on the analysis results of the emotion engine. For example, if a user gives negative feedback, the server can respond by providing additional information from a different perspective, thereby improving the user experience.

[1296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1297] Step 1:

[1298] The server performs scraping to collect data from news sites, blogs, and academic paper databases on the Internet. The input is a specific list of URLs and search keywords. The server uses BeautifulSoup or Scrapy to analyze the HTML structure and extract article titles, body text, URLs, etc. The output is saved in a database as structured data. For example, searching for tags with specific classes or IDs from the HTML of a news site and extracting article information.

[1299] Step 2:

[1300] The server cleanses the collected data and removes noise. The input is the raw data collected in step 1. Specifically, it uses Python libraries (such as NLTK and SpaCy) to correct grammatical and spelling errors, and uses regular expressions and BeautifulSoup to remove unnecessary HTML tags and advertisements. The output is cleansed text data. For example, <ad>" tags and unnecessary scripts.

[1301] Step 3:

[1302] The server inputs the cleansed data into the generative AI model. The input is the text data cleansed in step 2. The server uses a generative AI model (e.g., GPT-3) and sends a prompt such as "Please explain climate change from an economic perspective" to the generative AI model. The output is text information generated from multiple perspectives. For example, it generates information interpreted from an economic perspective and a scientific perspective.

[1303] Step 4:

[1304] The server detects bias in the generated information, quantifies it, and classifies it. The input is the information generated in step 3. A bias detection algorithm is applied to evaluate and quantify the bias in the information. For example, a political bias score is calculated. The output is information with a bias score assigned. Based on the bias score, balanced information is extracted.

[1305] Step 5:

[1306] The terminal receives the user's search query and sends it to the server. The input is the keyword the user entered into the search form. The terminal receives the input from the user and sends it to the server as a query. The output is the search query forwarded to the server. For example, the query "climate change" is sent.

[1307] Step 6:

[1308] The server searches for selected information based on the user's search query and provides relevant articles. The input is the search query received in step 5 and the information to which a bias score was assigned in step 4. The server searches for information filtered based on the search query and generates search results composed of multiple perspectives. The output is information as search results. For example, it provides articles that include interpretations of "climate change" from economic, scientific, and political perspectives.

[1309] Step 7:

[1310] The terminal displays the search results to the user. The input is the search results generated in step 6. The terminal displays the search results in a user interface, allowing the user to view information by perspective. The output is a visual representation of the search results to the user, for example, separated into tabs for economic perspectives, scientific perspectives, and political perspectives.

[1311] Step 8:

[1312] The device collects feedback from the user and sends it to the server. The input is the user's feedback (e.g., "This information was helpful"). The device receives the user's feedback and sends it to the server. The output is the feedback sent to the server. For example, sending a rating such as "This information was helpful."

[1313] Step 9:

[1314] The server analyzes the feedback from the user and uses an emotion recognition engine. The input is the feedback collected in step 8. The emotion engine recognizes the emotion of the feedback, such as positive or negative. The output is the analyzed emotion data. For example, if the evaluation is positive, the score is output as the analysis result.

[1315] Step 10:

[1316] The server uses emotion recognition to improve the generative AI model. The input is the emotion data obtained in step 9. The generative AI model is retrained using positive feedback, and the model's generation results are adjusted in response to negative feedback. The output is a retrained generative AI model. This improves the quality of the generated information.

[1317] Step 11:

[1318] The server dynamically adjusts the content and display format of the information provided based on emotion recognition. The input is the emotion data from step 9 and the generative AI model retrained in step 10. In response to negative user feedback, additional information from a different perspective is provided and the display format is adjusted accordingly. The output is dynamically adjusted information content and display format, which improves the user experience.

[1319] (Application example 2)

[1320] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1321] Conventional news distribution systems provide users with one-sided articles and do not adequately consider information bias or user sentiment. As a result, users often receive biased information, making it difficult to make reliable decisions. In addition, the collected data is of low quality and noisy, which reduces the accuracy of the generative AI model and reduces the quality of the information provided.

[1322] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting data from various information sources on the Internet, means for inputting the collected data into a generative AI model and generating information interpreted from different perspectives, means for detecting bias in the generated information and selecting balanced information, means for providing the selected information in response to a user's search query, means for improving the generative AI model through user feedback, and means for analyzing user sentiment to improve the quality of information provided. This enables users to efficiently collect and use multifaceted and balanced information, supporting reliable decision-making and improving the quality of information provided. Furthermore, cleansing the collected data improves the accuracy of the generative AI model and the quality of the information provided.

[1323] "Diverse information sources on the Internet" refers to multiple types of information sources that can be accessed on the Internet, such as news sites, blogs, and academic paper databases.

[1324] "Data Collection Methods" refers to the use of software and protocols to retrieve and store information from a particular website.

[1325] A "generative AI model" refers to an algorithm that generates and analyzes natural language using technologies such as deep learning.

[1326] "Information interpreted from different perspectives" refers to information on a particular topic that has been analyzed from multiple fields of expertise and perspectives, such as economic, scientific, social, and political perspectives.

[1327] "Means for generating information" refers to technology that analyzes collected data as input and generates new information using a specific algorithm.

[1328] "Means for detecting bias and selecting balanced information" refers to algorithms and techniques for quantifying and evaluating information bias and selecting information with the least bias.

[1329] "Means for providing information selected in response to a user's search query" refers to an interface and system for displaying information filtered based on the search criteria entered by the user.

[1330] "Means for improving generative AI models through user feedback" refers to methods for analyzing feedback such as user ratings and opinions and retraining or adjusting generative AI models based on that feedback.

[1331] "Means of analyzing user emotions to improve the quality of information provided" refers to technology that uses an emotion analysis engine to analyze user feedback and reactions, and then uses the results to improve the content and format of information provided.

[1332] "Data cleansing and denoising" refers to the process of removing unnecessary elements such as grammatical and spelling errors, unnecessary HTML tags, and advertisements from collected data to improve the quality of the data.

[1333] "Means for quantifying and classifying bias" refers to algorithms and techniques for quantifying and classifying information bias as specific numerical values.

[1334] This invention is a system that allows users to efficiently collect and use multifaceted and balanced information. This system collects data from various sources on the Internet and generates and provides multifaceted information using a generative AI model. The specific configuration and operation of this system are described below.

[1335] First, the server collects data from sources on the Internet, such as news sites, blogs, and academic paper databases. Specifically, it uses Python libraries (BeautifulSoup and Scrapy) to scrape websites, obtain the necessary data, and store it in a database.

[1336] The server then cleanses and denoises the collected data, using text processing libraries such as NLTK and TextBlob to correct grammatical and spelling errors, and remove unnecessary HTML tags and advertisements, ensuring the quality of the data fed into the generative AI model.

[1337] The server inputs the cleaned data into a generative AI model (e.g., a natural language processing model such as GPT-4) to generate information interpreted from different perspectives (e.g., economic, scientific, social, political, etc.). This AI model analyzes the data based on the user's search query and provides information from multiple perspectives.

[1338] The server then applies a bias detection algorithm to the generated information to quantify and classify the bias of the information, selecting information whose bias score falls within a certain range and filtering out information containing extreme bias to select balanced information.

[1339] The device receives a search query from the user. Specifically, the user enters a keyword such as "climate change" into the search form and presses the search button. The server searches filtered information based on the user's search query and provides the user with relevant articles. It then displays search results containing information from multiple perspectives generated by the generative AI model.

[1340] The server also collects user feedback and analyzes emotions using an emotion engine. This emotion engine analyzes emotional data from user input, ratings, and comments, and reflects the results in improving the generative AI model. For example, if a user comments, "This information was helpful," the generative AI model can be retrained based on that positive emotional data to improve the quality of information provided.

[1341] As a concrete example, let's consider a case where the server collects the latest news on climate change and provides information from multiple perspectives. In this case, the server inputs the following prompt sentence into the generative AI model:

[1342] "Provide different perspectives on the latest news on climate change from economic, scientific, social and political perspectives."

[1343] This allows the generated news articles to help users understand information from multiple perspectives and support reliable decision-making.

[1344] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1345] Step 1:

[1346] The server collects data from various sources on the Internet. Specifically, it uses Python's BeautifulSoup and Scrapy libraries to scrape data from news sites, blogs, and academic paper databases. The collected data is stored in a database on the server. The input is a specific URL or a database query, and the output is the collected text data.

[1347] Step 2:

[1348] The server cleanses the collected data and removes noise. Specifically, it uses text processing libraries such as NLTK and TextBlob to correct grammatical and spelling errors and remove unnecessary HTML tags and advertisements. The input is the text data collected in step 1, and the output is clean text data.

[1349] Step 3:

[1350] The server inputs the cleaned data into a generative AI model to generate information interpreted from different perspectives. For example, it uses a natural language processing model such as GPT-4. The input is the data cleansed in step 2, and the output is multifaceted information interpreted from economic, scientific, social, and political perspectives.

[1351] Step 4:

[1352] The server performs bias detection on the generated information. Specifically, it uses a bias detection algorithm to quantify the bias in the information and select balanced information. The input is the multifaceted information generated in step 3, and the output is filtered information that is judged to have less bias.

[1353] Step 5:

[1354] The terminal receives a search query from the user. The user enters keywords into the search form and presses the search button. The input is the user's search query, and the output is the search query sent to the server.

[1355] Step 6:

[1356] The server searches the filtered information based on the user's search query and selects relevant articles. This information includes information from multiple perspectives generated by the generative AI model. The input is the search query submitted in step 5, and the output is the relevant articles provided to the user.

[1357] Step 7:

[1358] The terminal displays the articles provided by the server to the user. A search result screen containing information from each of the multiple perspectives generated is displayed. The input is the articles selected in step 6, and the output is multifaceted information provided to the user.

[1359] Step 8:

[1360] The user submits feedback on the provided article. Specifically, the user submits a rating or comment such as "This information was helpful." The input is the user's feedback, and the output is the feedback sent to the server.

[1361] Step 9:

[1362] The server analyzes the user feedback using the emotion engine. The analysis results are reflected in improving the generative AI model. The input is the user feedback sent in step 8, and the output is data for adjusting and retraining the generative AI model.

[1363] Step 10:

[1364] The server dynamically adjusts the quality of the information provided based on the user's emotional data. For example, if the user has negative emotions, it will respond by providing additional information from a different perspective. The input is the emotional analysis data obtained in step 9, and the output is the adjusted content and display format of the information provided.

[1365] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1366] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1367] 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 robot 414.

[1368] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1369] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1370] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1371] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1372] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1373] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1374] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1375] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1376] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1377] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1378] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1379] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1380] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1381] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1382] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1383] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1384] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1385] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1386] The following is further disclosed regarding the above embodiment.

[1387] (Claim 1)

[1388] A means of collecting data from various sources on the Internet;

[1389] A means of inputting collected data into a generative AI model to generate information interpreted from different perspectives;

[1390] A means for detecting bias in the generated information and selecting balanced information;

[1391] a means for providing selected information in response to a user's search query;

[1392] A means to improve generative AI models through user feedback; and

[1393] A system including:

[1394] (Claim 2)

[1395] 10. The system of claim 1, further comprising means for cleansing and removing noise from the collected data.

[1396] (Claim 3)

[1397] 10. The system of claim 1, further comprising means for quantifying and classifying bias in the generated information.

[1398] "Example 1"

[1399] (Claim 1)

[1400] a means of collecting data from multiple sources on the Internet;

[1401] A means for programmatically analyzing the collected data and filtering out necessary data;

[1402] A means for inputting the collected data into a large-scale language model to generate information interpreted from different perspectives;

[1403] A means for detecting bias in the generated information and selecting multifaceted information;

[1404] a means for displaying selected information in response to a user's search query;

[1405] A means of analyzing user feedback and improving large-scale language models;

[1406] A system including:

[1407] (Claim 2)

[1408] 10. The system of claim 1, further comprising means for organizing the collected data and removing unnecessary information.

[1409] (Claim 3)

[1410] 10. The system of claim 1, further comprising means for quantifying and classifying bias in the generated information.

[1411] "Application Example 1"

[1412] (Claim 1)

[1413] A means of collecting data from various sources on the Internet;

[1414] A means of inputting collected data into a generative AI model to generate information interpreted from different perspectives;

[1415] A means for detecting bias in the generated information and selecting balanced information;

[1416] A means for providing selected information based on a user's search query when searching and selecting products in a virtual store;

[1417] A means to improve generative AI models through user feedback; and

[1418] A system including:

[1419] (Claim 2)

[1420] 10. The system of claim 1, further comprising means for cleansing and removing noise from the collected data.

[1421] (Claim 3)

[1422] 10. The system of claim 1, further comprising means for quantifying and classifying bias in the generated information.

[1423] "Example 2: Combining Emotion Engines"

[1424] (Claim 1)

[1425] A means of collecting data from various sources on the Internet;

[1426] A means of cleansing and denoising the collected data;

[1427] A means of inputting the cleansed data into a generative AI model to generate information interpreted from different perspectives;

[1428] A means to detect, quantify and classify bias in the information generated and select balanced information;

[1429] means for receiving a user's search query and providing information responsive to the query;

[1430] A means to recognize emotions through user feedback and reflect them in improving generative AI models.

[1431] A means for dynamically adjusting the content and display format of information provided based on emotion recognition;

[1432] A system including:

[1433] (Claim 2)

[1434] 10. The system of claim 1,

[1435] The system further comprising means for scraping the collected data.

[1436] (Claim 3)

[1437] 10. The system of claim 1,

[1438] The system further includes means for classifying the generated information into different perspectives and providing information based on each of the perspectives.

[1439] "Application example 2 when combining emotion engines"

[1440] (Claim 1)

[1441] A means of collecting data from various sources on the Internet;

[1442] A means of inputting collected data into a generative AI model to generate information interpreted from different perspectives;

[1443] A means for detecting bias in the generated information and selecting balanced information;

[1444] a means for providing selected information in response to a user's search query;

[1445] A means to improve generative AI models through user feedback; and

[1446] A means of analyzing user sentiment to improve the quality of information provided;

[1447] A system including:

[1448] (Claim 2)

[1449] 10. The system of claim 1, further comprising means for cleansing and removing noise from the collected data.

[1450] (Claim 3)

[1451] 10. The system of claim 1, further comprising means for quantifying and classifying bias in the generated information. [Explanation of symbols]

[1452] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / ad> < / url:> < / ad> < / url:> < / ad> < / url:> < / ad>

Claims

1. A means of collecting data from various sources on the Internet; A means of inputting collected data into a generative AI model to generate information interpreted from different perspectives; A means for detecting bias in the generated information and selecting balanced information; a means for providing selected information in response to a user's search query; A means to improve generative AI models through user feedback; and A system including:

2. The system of claim 1 further comprising means for cleansing and removing noise from the collected data.

3. The system of claim 1 further comprising means for quantifying and classifying bias in the generated information.

Images