System

Abstract

A system is provided.SOLUTION: The system includes an input device for inputting information by a user, an analysis device for analyzing the input information, a collection device for collecting information from an external information source, an evaluation device for comparing and evaluating the collected information with the analyzed information, and a notification device for notifying the user of an evaluation result.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 recent years, with the spread of social networking services, the rapid spread of misinformation has become a serious problem. Conventional fact-checking methods require manual verification, which is time-consuming and costly, making it difficult to assess the reliability of information in real time. Furthermore, users have limited means to determine the reliability of information themselves, making it difficult to access accurate information. There is a need for a system that can solve these issues and quickly and efficiently assess the reliability of information. [Means for solving the problem]

[0005] The present invention provides a system that analyzes information entered by a user, collects information from external information sources, and compares and evaluates that information to quickly determine the reliability of that information. The system includes an input device through which a user enters information, an analysis device that analyzes the information, a collection device that collects information from external information sources, an evaluation device that compares and evaluates the collected information with the analyzed information, and a notification device that notifies the user of the evaluation results. The evaluation device also has the function of measuring the degree of agreement between the collected information and the entered information to generate a reliability score, and the analysis device can process information entered in one or more formats: text, image, and video. This allows users to evaluate the reliability of the entered information in real time and make decisions based on accurate information.

[0006] An "input device" is a device that allows a user to input information and is capable of accepting data such as text, images, and videos.

[0007] An "analysis device" is a device that analyzes input information and appropriately understands and processes the content.

[0008] A "collection device" is a device for collecting relevant data from external sources.

[0009] An "evaluation device" is a device that compares and evaluates collected information with analyzed information to determine its reliability.

[0010] The "notification device" is a device for notifying the user of the evaluation results, and has the function of notifying the user visually or audibly.

[0011] The "reliability score" is a numerical representation of the reliability of information measured by measuring the degree of match between collected information and input information.

[0012] "Text" is information data expressed in the form of characters or sentences.

[0013] "Image" refers to still image data that is visually displayed.

[0014] "Video" refers to dynamic visual data consisting of a sequence of image frames.

[0015] "Real-time" means that processing and information provision occurs almost immediately. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention provides a system that allows a user to quickly evaluate the reliability of information. This system is implemented with the following configuration.

[0038] System Configuration

[0039] 1. Input Devices

[0040] Users input information using input devices. Input devices can accept different types of data, such as text, images, videos, etc. For example, a user can use a smartphone or computer to input text information or screenshots of a particular article.

[0041] 2. Analysis device

[0042] The server receives the information entered by the user and analyzes it through an analysis device that has functions such as text analysis, image recognition, and video analysis to understand the content of the entered information and extract important keywords and features.

[0043] 3. Collection Device

[0044] The server collects data from relevant external sources based on the analysis results. The collection device uses scraping technology to obtain the necessary information from websites, databases, etc. For example, it can collect information from news article searches or databases of related research papers.

[0045] 4. Evaluation equipment

[0046] The server compares the information collected by the collection device with the information analyzed by the analysis device and measures the degree of agreement between them using the evaluation device. The evaluation device generates a reliability score based on the degree of agreement. For example, if there is agreement with multiple reliable sources on the same topic, the reliability score will be high.

[0047] 5. Notification device

[0048] The server notifies the user of the reliability score generated by the evaluation device via a notification device, which displays the result to the user visually or audibly, for example, by displaying the reliability score on a smartphone application.

[0049] Processing flow and specific examples

[0050] 1. Enter your information

[0051] The user enters the text information "A specific cure for the new virus has been developed" into the app's input form.

[0052] 2. Analysis of Information

[0053] The server receives the entered text information and uses an analysis device to extract important keywords such as "new virus," "miracle drug," and "development."

[0054] 3. Collection of information

[0055] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[0056] 4. Information Evaluation

[0057] The server compares the collected information with the input information using an evaluation device, measures the degree of match, and generates a reliability score. Among the collected information, there is data that matches with three primary sources, and the degree of match is 60%.

[0058] 5. Notification of Results

[0059] The server displays the generated reliability score (e.g., 60%) to the user via a notification device. The user is notified in the app that "The reliability of this information is 60%."

[0060] In this way, users can quickly assess the reliability of information in real time, which can help limit the spread of misinformation and support informed decision-making.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] A user uses an input device of a terminal to input information such as text, images, videos, etc. For example, the user inputs the text "A specific drug for the new virus has been developed."

[0064] Step 2:

[0065] The device sends the input information to the server as an HTTP request, and the input data is packaged in JSON format or similar.

[0066] Step 3:

[0067] The server receives the HTTP request and passes the input data to the analysis device, which checks the input data format (text, image, video) and performs the appropriate analysis for each.

[0068] Step 4:

[0069] The server's analysis device performs text analysis and extracts important keywords and phrases, such as "new virus," "miracle drug," and "development."

[0070] Step 5:

[0071] The server uses a collection device to collect related data from external sources based on the extracted keywords. The collection device scrapes news sites and databases to obtain related articles and data.

[0072] Step 6:

[0073] The server passes the collected information to the evaluation device, which compares it with the input data to determine the degree of match. The evaluation device counts the number of matching keywords and information and calculates the degree of match.

[0074] Step 7:

[0075] The server's evaluation device generates a reliability score based on the degree of match. For example, if six of the ten pieces of information collected match the input data, the reliability score will be 60%.

[0076] Step 8:

[0077] The server transmits the generated reliability score to a notification device, which notifies the user of the reliability score visually or audibly.

[0078] Step 9:

[0079] The device displays the reliability score received from the notification device to the user, who then sees the result in the app: "The reliability of this information is 60%."

[0080] In this way, users can assess the reliability of input information in real time and make accurate, informed decisions.

[0081] Example 1

[0082] 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."

[0083] In modern society, there is a demand for fast and accurate evaluation of information, but it is difficult to individually evaluate the reliability of many information sources, and the spread of misinformation is becoming a problem. In particular, the amount of information circulating on the Internet is enormous, and there is a need for a means to easily evaluate its reliability. In addition, there is a need for a system that can handle the diverse formats of information and analyze and evaluate them appropriately for each format.

[0084] 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.

[0085] In this invention, the server includes an input means for a user to input information, an analysis means for analyzing the input information, a collection means for collecting information from external information sources, an evaluation means for comparing and evaluating the collected information with the analyzed information, a notification means for notifying the user of the evaluation result, a collection means for scraping related information from external information sources based on keywords extracted by the analysis means, and a means for the evaluation means to measure the degree of coincidence and generate a reliability score. This makes it possible to quickly and accurately evaluate the reliability of information and to handle information in a variety of formats.

[0086] "User" is a person or organization that operates the system to input information and receive results.

[0087] An "input means" is a device or method by which a user provides information to a system, and accepts data in various formats such as text, images, and videos.

[0088] "Analysis means" refers to a device or method for analyzing input information and extracting important keywords and features.

[0089] "Collection methods" are devices and methods for obtaining the required information from external sources, primarily using scraping technology.

[0090] An "evaluation means" is a device or method for comparing collected information with analyzed information, measuring the degree of agreement, and generating a reliability score.

[0091] "Notification means" refers to a device or method for notifying the user of the evaluation results, and displays the results visually or audibly.

[0092] "Keywords" are words or phrases that indicate important and specific information and are extracted by the analysis means from the input information.

[0093] "Scraping" is a technique or method for automatically extracting information from specific web pages.

[0094] The "reliability score" is a numerical index of the reliability of information calculated by the evaluation means based on the degree of agreement between the collected information and the input information.

[0095] MODE FOR CARRYING OUT THE INVENTION

[0096] The present invention relates to a system that enables a user to quickly and accurately evaluate the reliability of information, and is configured as follows.

[0097] System Configuration

[0098] 1. Input Devices

[0099] A device that allows users to input information. Input devices accept data in different formats, such as text, images, and videos. Users can use devices such as smartphones or computers to input text information and screenshots of specific articles.

[0100] 2. Analysis device

[0101] The server receives the input information and analyzes it using an analysis device. The analysis device has functions for text analysis, image recognition, and video analysis, and extracts important keywords and features from the content of the input information. For example, natural language processing (NLP) technology is used to extract keywords from text information.

[0102] 3. Collection Device

[0103] This is a device that allows the server to collect data from external sources based on keywords extracted by the analysis device. The collection device obtains the necessary information by scraping websites and databases. For example, it uses the Google (registered trademark) News API or PubMed API to collect related news articles and research papers.

[0104] 4. Evaluation equipment

[0105] The server compares the information obtained by the collection device with the information analyzed by the analysis device and measures the degree of match. The evaluation device generates a reliability score based on the degree of match. Specifically, it measures the degree of match using algorithms such as cosine similarity and Jaccard index and calculates the reliability score.

[0106] 5. Notification device

[0107] This is a device that notifies the server of the reliability score generated by the evaluation device. The notification device displays the result to the user visually or audibly. For example, the reliability score may be displayed on a smartphone application, notifying the user that "the reliability of this information is 60%."

[0108] Program processing example

[0109] 1. Enter your information

[0110] The user enters the text information "A specific cure for the new virus has been developed" into the app's input form.

[0111] 2. Analysis of Information

[0112] The server receives the entered text information and uses an analysis device to extract important keywords such as "new virus," "miracle drug," and "development."

[0113] 3. Collection of information

[0114] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[0115] 4. Information Evaluation

[0116] The server compares the collected information with the input information using an evaluation device, measures the degree of match, and generates a reliability score. For example, suppose there is data in the collected information that matches three primary sources, and the degree of match is 60%.

[0117] 5. Notification of Results

[0118] The server displays the generated reliability score (e.g., 60%) to the user via a notification device. The user is notified on the app that "the reliability of this information is 60%."

[0119] Examples and prompts

[0120] For example, if a user enters the text "A specific drug for the new virus has been developed," the following prompt sentence will be input to the generative AI model:

[0121] Rate the credibility of the information that "A cure for the new virus has been developed." Calculate a credibility score based on highly trusted sources.

[0122] The system operates based on this prompt, calculates a reliability score, and notifies the user, allowing the user to make a decision based on the accuracy of the information.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1: Enter your information

[0125] A user uses a device to input specific information. The input device accepts data in different formats, such as text, images, and videos. For example, a user inputs the text information "A specific drug for the new virus has been developed" into a smartphone application.

[0126] Input: The user enters the text information "A specific drug for the new virus has been developed" into the terminal.

[0127] Output: Text information is sent to the server.

[0128] Step 2: Analyze the information

[0129] The server receives the text information sent by the user and analyzes it using an analysis device. The analysis device uses natural language processing (NLP) technology to extract important keywords and features from the text information. For example, it can extract keywords such as "new virus," "miracle drug," and "development."

[0130] Input: Text information entered by the user.

[0131] Output: Extracted keywords (e.g., "new virus," "miracle drug," "development").

[0132] What it does: The server uses NLP techniques to analyze the text and extract keywords.

[0133] Step 3: Gather information

[0134] Based on the keywords extracted by the analysis device, the server uses a collection device to collect related information from external sources. The collection device uses web scraping technology to obtain news articles, research papers, etc., for example, using the Google News API or PubMed API.

[0135] Input: Extracted keywords (e.g., "new virus," "miracle drug," "development").

[0136] Output: Collected relevant information (news articles, research papers, etc.).

[0137] What it does: The server performs web scraping to gather relevant information from external sources.

[0138] Step 4: Evaluate the information

[0139] The server compares the collected information with the information entered by the user using an evaluation device to calculate the degree of match. The evaluation device measures the degree of match using algorithms such as cosine similarity or Jaccard index and calculates a reliability score. For example, if the degree of match is 60%, the reliability score is calculated as 60.

[0140] Input: User input and associated collected information.

[0141] Output: Confidence score (e.g. 60%).

[0142] What happens: The server calculates the match and generates a confidence score.

[0143] Step 5: Notification of results

[0144] The server then communicates the generated reliability score to the user via a notification device, which displays the result visually or audibly, for example, "The reliability of this information is 60%" on a smartphone application.

[0145] Input: Confidence score (e.g. 60%).

[0146] Output: A confidence score that is displayed to the user.

[0147] Specific behavior: The server notifies the user of the reliability score through the application.

[0148] Specific examples of processing steps

[0149] 1. Enter your information

[0150] The user types into the app, "A cure for the new virus has been developed."

[0151] 2. Analysis of Information

[0152] The server extracts "new virus," "miracle drug," and "development" from the input information.

[0153] 3. Collection of information

[0154] The server collects news articles and papers.

[0155] 4. Information Evaluation

[0156] The server calculates the match and generates a confidence score.

[0157] 5. Notification of Results

[0158] The server displays to the app, "This information is 60% reliable."

[0159] (Application example 1)

[0160] 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."

[0161] In recent years, the spread of the Internet and smartphones has led to a proliferation of advertising information. This has made it difficult for consumers to select products and services based on their actual reliability. The increasing number of purchasing decisions based on inaccurate advertising information and the spread of false information are increasing the risk of damaging consumer trust. To solve this problem, a system that can quickly and accurately evaluate the reliability of advertising information is needed.

[0162] 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.

[0163] In this invention, the server includes input means for users to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information and the analyzed information, notification means for notifying users of the evaluation results, and advertising information evaluation means for inputting advertising data and evaluating its content, thereby enabling consumers to quickly evaluate the reliability of advertising information and select products and services based on accurate information.

[0164] "User" means a person or entity that uses the system to input information and receive results.

[0165] "Input means" refers to the interface through which users input information into the system, and includes smartphones and computer applications.

[0166] "Analysis means" refers to a method or device for analyzing input information and extracting important keywords and features, and uses natural language processing or image analysis technology.

[0167] "Collection means" refers to the methods and devices used to collect the necessary data from external sources, such as web scraping technology.

[0168] An "evaluation means" is a method or device that compares the collected information with the analyzed information, evaluates the degree of agreement between them, and generates a reliability score.

[0169] The "notification means" is an interface for notifying the user of the evaluation results, and displays the results visually or audibly.

[0170] The "advertising information evaluation means" refers to a method or device for inputting advertising data and analyzing and evaluating its contents.

[0171] The "reliability score" is an evaluation value generated based on the degree of match between collected information and input information, and indicates the reliability of the information.

[0172] This invention relates to a system for evaluating the reliability of advertising information. This system allows a user to input advertising information, analyzes and evaluates the input information, generates a reliability score, and notifies the user of the result. Specifically, the system is implemented as follows.

[0173] System configuration

[0174] 1. Input Method

[0175] Users use their smartphones or computers to input information such as screenshots and URLs of advertisements into the user interface of an application that accepts information in one or more of the following formats: text, images, and videos.

[0176] 2. Analysis method

[0177] The server receives the entered advertising information and analyzes it using a natural language processing library (e.g., TENSORFLOW (registered trademark) or spaCy). This analysis method extracts keywords and features from the advertising content. For example, it extracts important keywords such as "new ingredients," "effects," and "user reviews."

[0178] 3. Collection Method

[0179] Based on the extracted keywords, the server uses a web scraping library (such as BeautifulSoup or Selenium) to collect related information from external databases and websites. For example, it retrieves academic papers and user reviews on "new ingredients" from the Internet.

[0180] 4. Evaluation Methods

[0181] The collected information is compared with the input advertising information, and a reliability score is generated by evaluating the degree of match. This evaluation is performed using machine learning algorithms (such as scikit-learn). For example, if there is a match with multiple reliable sources, the degree of match will be higher and the reliability score will also be higher.

[0182] 5. Means of notification

[0183] The server notifies the user of the generated reliability score via the smartphone's push notification function. The user can visually check the reliability score (e.g., 85%) on the app.

[0184] Examples and prompts

[0185] Specific examples

[0186] If a user sees an advertisement for a new health supplement and wants to verify its authenticity, they would take the following steps:

[0187] 1. Enter a screenshot or URL of the ad into the app.

[0188] 2. The app analyzes the ad information and extracts key keywords.

[0189] 3. The system collects relevant information based on the keyword and calculates a credibility score.

[0190] 4. The app will notify users by saying, "This ad is 85% trustworthy," allowing them to quickly assess the ad's trustworthiness.

[0191] Prompt Sentence Examples

[0192] When a user uses the app to rate the trustworthiness of a particular ad, they enter:

[0193] "I'd like to learn more about the effects of new ingredient A. Can you tell me how reliable it is?"

[0194] As a result, this invention makes it possible to quickly and accurately evaluate the reliability of advertising information, allowing consumers to select products and services based on reliable information and reducing the influence of inaccurate information.

[0195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0196] Step 1:

[0197] A user uses a device to input a screenshot or URL of an advertisement. This input can be in the form of text, image, or video. For example, a user enters an advertisement URL for a new health supplement into an input form. The input data is sent to the server.

[0198] Step 2:

[0199] The server analyzes the received advertising data using an analysis means. The analysis means uses natural language processing libraries (such as TensorFlow or spaCy) to extract key keywords and features from the input data. In this case, keywords such as "new ingredients" and "effects" are extracted from the text in the advertisement. A keyword list is generated based on the input.

[0200] Step 3:

[0201] The server's collection method collects related information from the web based on the extracted keywords. A web scraping library (such as BeautifulSoup or Selenium) is used to search for related academic papers, review articles, and news to obtain the necessary data. For example, academic papers and user reviews on a "new ingredient" are collected from the internet. A list of collected data is output based on the input keywords.

[0202] Step 4:

[0203] The server compares the collected data with the input ad data using an evaluation method. It uses a machine learning algorithm (such as scikit-learn) to evaluate the degree of match and generate a reliability score. Specifically, it calculates the similarity between the collected information and the data contained in the ad, and if the degree of match is high, it increases the reliability score. A reliability score is generated based on the input data and collected data.

[0204] Step 5:

[0205] The server notifies the user of the generated reliability score using a notification method. The reliability score is displayed on the app using the smartphone's push notification function. For example, the user can visually confirm on the app that "the reliability of this ad is 85%." The reliability score is received and presented to the user as notification data.

[0206] 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.

[0207] The present invention provides a system that allows a user to quickly evaluate the reliability of information, recognizes emotions based on the evaluation, and adjusts the evaluation result. This system is implemented with the following configuration.

[0208] System Configuration

[0209] 1. Input Devices

[0210] A user can input information using an input device. The input device accepts data in different formats, such as text, images, and videos. For example, a user may input text information such as "A specific drug for the new virus has been developed" into the input screen of a smartphone or computer.

[0211] 2. Analysis device

[0212] The server receives the information entered by the user and analyzes it through an analysis device. The analysis device has functions such as text analysis, image recognition, and video analysis, and understands the content of the entered information and extracts important keywords and features. For example, it extracts keywords such as "new virus," "miracle drug," and "development."

[0213] 3. Collection Device

[0214] Based on the analysis results, the server collects data from related external sources. The collection device uses scraping technology to obtain the necessary information from websites, databases, etc., and can collect related news articles and research papers.

[0215] 4. Evaluation equipment

[0216] The server compares the information obtained by the collection device with the information analyzed by the analysis device and measures the degree of match using the evaluation device. The evaluation device generates a reliability score based on the degree of match. For example, if there is a match with multiple reliable sources on the same topic, the reliability score will be high.

[0217] 5. Emotion Engine

[0218] The emotion engine is a device for recognizing user emotions. It has the ability to analyze the text and voice input by the user into an input device and identify emotions. For example, the emotion engine can determine whether the user is excited or confused based on the context and wording used when inputting text.

[0219] 6. Notification device

[0220] The server notifies the user of the reliability score generated by the evaluation device via a notification device. The notification device displays the results to the user visually or audibly. Furthermore, the notification format can be changed according to the user's emotions based on the analysis results of the emotion engine. For example, if the user is excited, the evaluation results will be displayed calmly, while if the user is confused, a notification will be sent that explains the results in a gentle and easy-to-understand manner.

[0221] Processing flow and specific examples

[0222] 1. Enter your information

[0223] The user enters the text information "A specific drug for the new virus has been developed" into the app's input screen.

[0224] 2. Analysis of Information

[0225] The server receives the entered text information and uses an analysis device to extract keywords such as "new virus," "miracle drug," and "development."

[0226] 3. Collection of information

[0227] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[0228] 4. Information Evaluation

[0229] The server compares the collected information with the input data using an evaluation device, measures the degree of match, and generates a reliability score. If six of the ten pieces of collected information match the input data, the reliability score is 60%.

[0230] 5. Emotion Analysis

[0231] The server's emotion engine analyzes the user's input text and identifies the user's emotion, for example, recognizing that the user is excited.

[0232] 6. Notification of Results

[0233] The server generates a reliability score (60%) and displays it to the user via a notification device. The result is displayed in a format that corresponds to the user's emotions based on the analysis results of the emotion engine. For example, an excited user will be presented with calm details, while a confused user will be notified with a gentle explanation.

[0234] In this way, users can evaluate the reliability of input information in real time and receive appropriate feedback based on their emotions, which will help curb the spread of misinformation and support decisions based on accurate information.

[0235] The processing flow will be explained below.

[0236] Step 1:

[0237] A user uses an input device of a terminal to input information such as text, images, videos, etc. For example, the user inputs the text "A specific drug for the new virus has been developed."

[0238] Step 2:

[0239] The device sends the entered information to the server as an HTTP request, which includes all the data entered by the user.

[0240] Step 3:

[0241] The server receives the HTTP request and passes the input data to the analysis device, which checks the format of the information (text, image, video) and performs the appropriate analysis.

[0242] Step 4:

[0243] The server's analysis device performs text analysis and extracts important keywords and phrases, such as "new virus," "miracle drug," and "development."

[0244] Step 5:

[0245] The server uses the extracted keywords to collect related data from external information sources using a collection device, which retrieves related articles and information from news sites and databases.

[0246] Step 6:

[0247] The server passes the collected information to the evaluation device, which then calculates the degree of match between the input data and the collected data. For example, if 6 out of 10 pieces of collected information match the input data, the degree of match is 60%.

[0248] Step 7:

[0249] The server's evaluator generates a reliability score based on the degree of match, which in this case is 60%.

[0250] Step 8:

[0251] The server passes the user's input data to the emotion engine, which then analyzes the user's input for emotion. For example, it recognizes that the user is excited based on the context and style of the input.

[0252] Step 9:

[0253] The server sends the evaluation result and emotion information to the notification device based on the result of the emotion engine. The notification device adjusts the display format of the reliability score according to the user's emotion.

[0254] Step 10:

[0255] The device displays the reliability score and emotional information received from the notification device to the user. For example, if the user is excited, the evaluation result is displayed in a calm and easy-to-understand format, and if the user is confused, the evaluation result is displayed in a gentle and easy-to-understand format.

[0256] In this way, the present invention can provide appropriate feedback to users by combining the reliability evaluation of information with the user's emotions, allowing users to evaluate the reliability of information in real time and make appropriate decisions based on that evaluation.

[0257] Example 2

[0258] 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."

[0259] In modern society, the overload of information has become a problem, making it difficult for users to quickly identify accurate information. Furthermore, it is necessary to consider the user's feelings toward the information, and appropriate feedback may not be provided. This can lead to the spread of misinformation and inappropriate decision-making. Therefore, there is a need for a system that can evaluate the reliability of input information and provide appropriate feedback based on the user's feelings.

[0260] 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.

[0261] In this invention, the server includes input means for a user to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information with the analyzed information, notification means for notifying the user of the evaluation result, and emotion identification means for identifying the emotion of the user. This enables the reliability of the information input by the user to be quickly evaluated and appropriate feedback according to the emotion based on the evaluation result.

[0262] "Input means" refers to a device or interface that allows a user to input information.

[0263] "Analysis means" refers to a device or program that analyzes input information and extracts key keywords and features.

[0264] "Collection tools" are devices or programs used to collect relevant data from external sources.

[0265] The "evaluation means" is a device or program that compares the collected information with the analyzed information to evaluate its reliability.

[0266] "Notification means" refers to a device or interface for notifying the user of the evaluation results.

[0267] The "emotion identification means" is a device or program for identifying an emotion from information input by a user.

[0268] A "reliability score" is a number generated by measuring the degree of match between collected information and input information.

[0269] "Text" is data in a format that includes character information.

[0270] An "image" is a form of data that contains visual information.

[0271] "Video" is a type of data that contains dynamic visual information.

[0272] The "notification format" refers to the display format or method used when the notification means notifies the user.

[0273] The present invention provides a system that quickly evaluates the reliability of information entered by a user and provides emotional feedback based on the evaluation results. Specific embodiments and the hardware and software used are described below.

[0274] System Configuration

[0275] 1. Input Method

[0276] Users input information using input means (devices such as smartphones or computers). The input format can be text, images, videos, or other formats.

[0277] As a specific example, a user enters text information such as "A specific drug for the new virus has been developed" into the input screen of an app.

[0278] 2. Analysis method

[0279] The server receives the information sent by the user and analyzes it using analytical means such as a natural language processing engine or image analysis software.

[0280] As a specific example, keywords such as "new virus," "miracle drug," and "development" are extracted from the input text.

[0281] 3. Collection Method

[0282] Based on the analysis results, the server uses collection methods to collect data from relevant external sources, such as web scraping technology and APIs.

[0283] As a specific example, news articles and research papers related to "new viruses," "miracle drugs," and "developments" are scraped from the web.

[0284] 4. Evaluation Methods

[0285] The server-collected information and the analyzed user-entered information are compared by an evaluation means to measure the degree of match, the evaluation means including an algorithm for generating a reliability score.

[0286] For example, if 6 out of 10 pieces of information collected match the input information, the reliability score is 60%.

[0287] 5. Emotion Identification Measures

[0288] The server uses emotion recognition means to identify emotions from the information and attitudes of the user, often using natural language processing or voice analysis technology.

[0289] As a specific example, it is determined from the input text that the user is excited.

[0290] 6. Means of notification

[0291] The server notifies the user of the result using the notification means based on the reliability score generated by the evaluation means and the emotion identified by the emotion identification means. The notification is performed using a mobile app or a web interface.

[0292] For example, provide results with a 60% confidence score in a calm manner, while providing a gentle explanation for confused users.

[0293] Specific examples

[0294] For example, if a user enters "A miracle cure for the new virus has been developed" into the app's input screen, the server receives this information and uses the analysis means to extract keywords. It then uses the collection means to collect related news articles and research papers from the web. It compares the collected information with the user's input information to generate a reliability score. It then uses the emotion identification means to analyze the user's emotions and notifies the user of the reliability score based on the results.

[0295] This allows users to evaluate the reliability of the information they input in real time and receive appropriate feedback based on their emotions, which can help prevent the spread of misinformation and support decision-making based on accurate information.

[0296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0297] Step 1:

[0298] The user inputs information using the terminal.

[0299] Input: The user enters the text information "A specific drug for the new virus has been developed" into the app's input screen.

[0300] Output: The terminal sends the entered text information to the server.

[0301] Step 2:

[0302] The server receives the text information sent from the terminal.

[0303] Input: Text information sent from the device.

[0304] Output: Passes the input information to a natural language processing engine.

[0305] Step 3:

[0306] The server analyzes the text information using a natural language processing engine.

[0307] Input: Received text information.

[0308] Data processing: Extract keywords such as "new virus," "miracle drug," and "development" from text information.

[0309] Output: Parsed keyword list.

[0310] Step 4:

[0311] The server uses the collection means to collect data from external information sources based on the extracted keyword list.

[0312] Input: Parsed keyword list.

[0313] Data Computing: Scrape relevant information from the web based on keywords.

[0314] Output: A list of collected news articles and research papers.

[0315] Step 5:

[0316] The server compares the collected information with the analyzed user input information using an evaluation means.

[0317] Input: A list of collected information and user-entered information.

[0318] Data calculation: Applying an algorithm to calculate the degree of match.

[0319] Output: Match score.

[0320] Step 6:

[0321] The server generates a reliability score based on the match score obtained by the evaluation means.

[0322] Input: Match score.

[0323] Data calculation: Calculate the reliability score.

[0324] Specific behavior: If 6 out of 10 pieces of information collected match, set the confidence score to 60%.

[0325] Output: A confidence score.

[0326] Step 7:

[0327] The server uses an emotion identification means to identify emotions from the user's input text.

[0328] Input: The text entered by the user.

[0329] Data processing: Analyze sentiment using natural language processing techniques.

[0330] Output: The user's emotional state (e.g., excited, confused).

[0331] Step 8:

[0332] The server notifies the result based on the confidence score generated by the evaluation means and the emotion determined by the emotion identification means.

[0333] Input: Confidence score and user's emotional state.

[0334] Data calculation: Select the notification format according to the emotional state.

[0335] Specific behavior: Calmly provide details to excited users and gently explain things to confused users.

[0336] Output: The notification format that is displayed to the user.

[0337] (Application example 2)

[0338] 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."

[0339] In today's information society, a huge amount of information is distributed instantly, but much of it is false information, requiring users to quickly select reliable information. To prevent the spread of false information, it is important not only to evaluate the reliability of information but also to provide appropriate feedback based on the user's feelings. However, conventional technology has been unable to provide a system that can adequately solve these issues.

[0340] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes input means for the user to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information and the analyzed information, and notification means including emotion recognition means for notifying the user of the evaluation result and analyzing the user's emotion. This allows the user to evaluate the reliability of the input information in real time and receive appropriate feedback according to their emotion based on the evaluation result.

[0341] "User" means an individual or group of people who use the Content Delivery Service to input and evaluate information.

[0342] "Input means" refers to a device that allows a user to input information in the form of text, images, video, etc.

[0343] "Analysis means" refers to a mechanism within the system that analyzes information input through the input means and extracts important keywords and features.

[0344] "Collection means" refers to the mechanism for collecting relevant information from external sources, for example, using web scraping techniques.

[0345] "Evaluation means" refers to the mechanism within the system that compares the collected information with the analyzed information, measures the degree of agreement, and generates a reliability score.

[0346] "Notification means" refers to a system for communicating to a user the confidence score generated by the evaluation means and the emotion analysis results from the emotion recognition means.

[0347] "Emotion recognition means" refers to a mechanism for analyzing user input information and identifying the user's emotional state.

[0348] The present invention provides a system for evaluating the reliability of information and providing feedback according to the user's feelings. Specific embodiments will be described below.

[0349] System Configuration

[0350] 1. Input Method

[0351] The server is equipped with an input means for users to input information. This input means includes smartphones and computer input devices. Users can input text, images, videos, etc. For example, a user may input text information such as "A specific drug for the new virus has been developed."

[0352] 2. Analysis method

[0353] The server has an analysis means for analyzing the information input through the input means. The analysis means uses a natural language processing library (such as spaCy or NLTK). Important keywords and features are extracted from the text information. For example, keywords such as "new virus," "miracle drug," and "development" are extracted.

[0354] 3. Collection Method

[0355] The server includes a collection unit for collecting related information from external sources based on the keywords extracted by the analysis unit. This collection unit uses a web scraping tool (e.g., BeautifulSoup or Scrapy). Related news articles and research papers can be collected.

[0356] 4. Evaluation Methods

[0357] The server has an evaluation means for comparing the collected information with the analyzed information, measuring the degree of match, and generating a reliability score. If, using the evaluation means, six of the ten pieces of collected information match the input data, the reliability score is calculated as 60%.

[0358] 5. Emotion recognition means

[0359] The server includes an emotion recognition unit for analyzing emotions from user input. Generative AI models (e.g., models using BERT or GPT-3®) are used to identify emotions. Based on the text entered by the user, it can determine whether the user is excited or confused.

[0360] 6. Means of notification

[0361] The server includes a notification means for notifying the user of the reliability score generated by the evaluation means and the analysis result of the emotion recognition means. The notification means includes technology for displaying the results to the user visually or audibly. To provide feedback according to the user's emotional state, a notification system such as Firebase Cloud Messaging is used. For example, an excited user may be notified in a calm manner with detailed information, while a confused user may be notified in a gentle manner with an explanation of the results.

[0362] Specific examples of processing

[0363] Prompt Sentence Examples

[0364] For example, if a user enters "A specific drug for the new virus has been developed" into the app's input screen, the system will process it as follows:

[0365] 1. Enter your information:

[0366] The user inputs the text information "A specific drug for the new virus has been developed" into the input means.

[0367] 2. Analysis of Information:

[0368] The server extracts keywords such as "new virus," "miracle drug," and "development" from the text information.

[0369] 3. Collection of Information:

[0370] Based on the extracted keywords, the server uses collection means to collect related news articles and research papers from the Web.

[0371] 4. Information Evaluation:

[0372] The server compares the collected information with the input data using an evaluation tool and generates a reliability score (e.g., 60%).

[0373] 5. Emotion Analysis:

[0374] The server's emotion recognition means analyzes the user's input text and identifies the user's emotion, for example, recognizing that the user is excited from the input content.

[0375] 6. Notification of Results:

[0376] The reliability score (60%) generated by the server is displayed to the user via a notification method. At this time, the result is displayed in a format that corresponds to the user's emotion based on the analysis results of the emotion recognition method. For example, an excited user will be shown detailed information in a calm manner, while a confused user will be notified in a gentle manner explaining the result.

[0377] In this way, users can evaluate the reliability of input information in real time and make decisions based on reliable information.

[0378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0379] Step 1:

[0380] The user enters information.

[0381] Input: A user uses a smartphone or computer to input the text information, "A cure for the new virus has been developed."

[0382] Specific behavior: The user enters text into the application's input screen and presses the "Send" button.

[0383] Output: The input text information is sent to the server and passed to the analysis means.

[0384] Step 2:

[0385] The server analyzes the entered information.

[0386] Input: Text information sent from your smartphone or computer.

[0387] Data processing: The server uses a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and features from the text information.

[0388] Specific operation: The analysis means on the server extracts keywords such as "new virus," "miracle drug," and "development."

[0389] Output: Extracted keywords.

[0390] Step 3:

[0391] The server collects information from external sources.

[0392] Input: Extracted keywords.

[0393] Data processing: The server uses web scraping tools (e.g., BeautifulSoup or Scrapy) to collect relevant news articles and research papers from the web.

[0394] Specific operations: The server's collection method uses APIs such as Google Search to obtain the content of websites related to keywords such as "new virus," "miracle drug," and "development."

[0395] Output: Collected relevant information (news articles, research papers, etc.).

[0396] Step 4:

[0397] The server evaluates the collected information and input data.

[0398] Input: Collected relevant information and user-entered text information.

[0399] Data Calculation: The server's evaluation means compares the collected information with the analyzed information, measures the degree of agreement, and generates a reliability score.

[0400] Specific behavior: If the server finds that 6 out of 10 pieces of collected information match the user's input text information, it calculates a reliability score of 60%.

[0401] Output: A confidence score (e.g., 60%).

[0402] Step 5:

[0403] The server analyzes the sentiment of the input text information.

[0404] Input: User-entered text information.

[0405] Data computation: The server uses a generative AI model (e.g., BERT or GPT-3) to identify emotions from text information.

[0406] What it does: The sentiment analysis model determines the user's emotions from the text content, recognizing, for example, whether the user is excited or confused.

[0407] Output: The user's emotional state (e.g. excited).

[0408] Step 6:

[0409] The server notifies the user of the results.

[0410] Input: Confidence score and sentiment analysis results.

[0411] Data calculation: The server's notification mechanism generates feedback in the most appropriate format depending on the reliability score and emotional state.

[0412] What it does: The server uses a notification system such as Firebase Cloud Messaging to calmly provide details if the user is excited, or gently explain the outcome to confused users.

[0413] Output: Notification of results (e.g., in a calm display format or a friendly explanation format).

[0414] 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.

[0415] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0416] 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.

[0417] [Second embodiment]

[0418] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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."

[0430] The present invention provides a system that allows a user to quickly evaluate the reliability of information. This system is implemented with the following configuration.

[0431] System Configuration

[0432] 1. Input Devices

[0433] Users input information using input devices. Input devices can accept different types of data, such as text, images, videos, etc. For example, a user can use a smartphone or computer to input text information or screenshots of a particular article.

[0434] 2. Analysis device

[0435] The server receives the information entered by the user and analyzes it through an analysis device that has functions such as text analysis, image recognition, and video analysis to understand the content of the entered information and extract important keywords and features.

[0436] 3. Collection Device

[0437] The server collects data from relevant external sources based on the analysis results. The collection device uses scraping technology to obtain the necessary information from websites, databases, etc. For example, it can collect information from news article searches or databases of related research papers.

[0438] 4. Evaluation equipment

[0439] The server compares the information collected by the collection device with the information analyzed by the analysis device and measures the degree of agreement between them using the evaluation device. The evaluation device generates a reliability score based on the degree of agreement. For example, if there is agreement with multiple reliable sources on the same topic, the reliability score will be high.

[0440] 5. Notification device

[0441] The server notifies the user of the reliability score generated by the evaluation device via a notification device, which displays the result to the user visually or audibly, for example, by displaying the reliability score on a smartphone application.

[0442] Processing flow and specific examples

[0443] 1. Enter your information

[0444] The user enters the text information "A specific cure for the new virus has been developed" into the app's input form.

[0445] 2. Analysis of Information

[0446] The server receives the entered text information and uses an analysis device to extract important keywords such as "new virus," "miracle drug," and "development."

[0447] 3. Collection of information

[0448] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[0449] 4. Information Evaluation

[0450] The server compares the collected information with the input information using an evaluation device, measures the degree of match, and generates a reliability score. Among the collected information, there is data that matches with three primary sources, and the degree of match is 60%.

[0451] 5. Notification of Results

[0452] The server displays the generated reliability score (e.g., 60%) to the user via a notification device. The user is notified in the app that "The reliability of this information is 60%."

[0453] In this way, users can quickly assess the reliability of information in real time, which can help limit the spread of misinformation and support informed decision-making.

[0454] The processing flow will be explained below.

[0455] Step 1:

[0456] A user uses an input device of a terminal to input information such as text, images, videos, etc. For example, the user inputs the text "A specific drug for the new virus has been developed."

[0457] Step 2:

[0458] The device sends the input information to the server as an HTTP request, and the input data is packaged in JSON format or similar.

[0459] Step 3:

[0460] The server receives the HTTP request and passes the input data to the analysis device, which checks the input data format (text, image, video) and performs the appropriate analysis for each.

[0461] Step 4:

[0462] The server's analysis device performs text analysis and extracts important keywords and phrases, such as "new virus," "miracle drug," and "development."

[0463] Step 5:

[0464] The server uses a collection device to collect related data from external sources based on the extracted keywords. The collection device scrapes news sites and databases to obtain related articles and data.

[0465] Step 6:

[0466] The server passes the collected information to the evaluation device, which compares it with the input data to determine the degree of match. The evaluation device counts the number of matching keywords and information and calculates the degree of match.

[0467] Step 7:

[0468] The server's evaluation device generates a reliability score based on the degree of match. For example, if six of the ten pieces of information collected match the input data, the reliability score will be 60%.

[0469] Step 8:

[0470] The server transmits the generated reliability score to a notification device, which notifies the user of the reliability score visually or audibly.

[0471] Step 9:

[0472] The device displays the reliability score received from the notification device to the user, who then sees the result in the app: "The reliability of this information is 60%."

[0473] In this way, users can assess the reliability of input information in real time and make accurate, informed decisions.

[0474] Example 1

[0475] 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."

[0476] In modern society, there is a demand for fast and accurate evaluation of information, but it is difficult to individually evaluate the reliability of many information sources, and the spread of misinformation is becoming a problem. In particular, the amount of information circulating on the Internet is enormous, and there is a need for a means to easily evaluate its reliability. In addition, there is a need for a system that can handle the diverse formats of information and analyze and evaluate them appropriately for each format.

[0477] 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.

[0478] In this invention, the server includes an input means for a user to input information, an analysis means for analyzing the input information, a collection means for collecting information from external information sources, an evaluation means for comparing and evaluating the collected information with the analyzed information, a notification means for notifying the user of the evaluation result, a collection means for scraping related information from external information sources based on keywords extracted by the analysis means, and a means for the evaluation means to measure the degree of coincidence and generate a reliability score. This makes it possible to quickly and accurately evaluate the reliability of information and to handle information in a variety of formats.

[0479] "User" is a person or organization that operates the system to input information and receive results.

[0480] An "input means" is a device or method by which a user provides information to a system, and accepts data in various formats such as text, images, and videos.

[0481] "Analysis means" refers to a device or method for analyzing input information and extracting important keywords and features.

[0482] "Collection methods" are devices and methods for obtaining the required information from external sources, primarily using scraping technology.

[0483] An "evaluation means" is a device or method for comparing collected information with analyzed information, measuring the degree of agreement, and generating a reliability score.

[0484] "Notification means" refers to a device or method for notifying the user of the evaluation results, and displays the results visually or audibly.

[0485] "Keywords" are words or phrases that indicate important and specific information and are extracted by the analysis means from the input information.

[0486] "Scraping" is a technique or method for automatically extracting information from specific web pages.

[0487] The "reliability score" is a numerical index of the reliability of information calculated by the evaluation means based on the degree of agreement between the collected information and the input information.

[0488] MODE FOR CARRYING OUT THE INVENTION

[0489] The present invention relates to a system that enables a user to quickly and accurately evaluate the reliability of information, and is configured as follows.

[0490] System Configuration

[0491] 1. Input Devices

[0492] A device that allows users to input information. Input devices accept data in different formats, such as text, images, and videos. Users can use devices such as smartphones or computers to input text information and screenshots of specific articles.

[0493] 2. Analysis device

[0494] The server receives the input information and analyzes it using an analysis device. The analysis device has functions for text analysis, image recognition, and video analysis, and extracts important keywords and features from the content of the input information. For example, natural language processing (NLP) technology is used to extract keywords from text information.

[0495] 3. Collection Device

[0496] This is a device that allows the server to collect data from external sources based on keywords extracted by the analysis device. The collection device obtains the required information by scraping websites and databases. For example, it uses the Google News API or PubMed API to collect related news articles and research papers.

[0497] 4. Evaluation equipment

[0498] The server compares the information obtained by the collection device with the information analyzed by the analysis device and measures the degree of match. The evaluation device generates a reliability score based on the degree of match. Specifically, it measures the degree of match using algorithms such as cosine similarity and Jaccard index and calculates the reliability score.

[0499] 5. Notification device

[0500] This is a device that notifies the server of the reliability score generated by the evaluation device. The notification device displays the result to the user visually or audibly. For example, the reliability score may be displayed on a smartphone application, notifying the user that "the reliability of this information is 60%."

[0501] Program processing example

[0502] 1. Enter your information

[0503] The user enters the text information "A specific cure for the new virus has been developed" into the app's input form.

[0504] 2. Analysis of Information

[0505] The server receives the entered text information and uses an analysis device to extract important keywords such as "new virus," "miracle drug," and "development."

[0506] 3. Collection of information

[0507] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[0508] 4. Information Evaluation

[0509] The server compares the collected information with the input information using an evaluation device, measures the degree of match, and generates a reliability score. For example, suppose there is data in the collected information that matches three primary sources, and the degree of match is 60%.

[0510] 5. Notification of Results

[0511] The server displays the generated reliability score (e.g., 60%) to the user via a notification device. The user is notified on the app that "the reliability of this information is 60%."

[0512] Examples and prompts

[0513] For example, if a user enters the text "A specific drug for the new virus has been developed," the following prompt sentence will be input to the generative AI model:

[0514] Rate the credibility of the information that "A cure for the new virus has been developed." Calculate a credibility score based on highly trusted sources.

[0515] The system operates based on this prompt, calculates a reliability score, and notifies the user, allowing the user to make a decision based on the accuracy of the information.

[0516] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0517] Step 1: Enter your information

[0518] A user uses a device to input specific information. The input device accepts data in different formats, such as text, images, and videos. For example, a user inputs the text information "A specific drug for the new virus has been developed" into a smartphone application.

[0519] Input: The user enters the text information "A specific drug for the new virus has been developed" into the terminal.

[0520] Output: Text information is sent to the server.

[0521] Step 2: Analyze the information

[0522] The server receives the text information sent by the user and analyzes it using an analysis device. The analysis device uses natural language processing (NLP) technology to extract important keywords and features from the text information. For example, it can extract keywords such as "new virus," "miracle drug," and "development."

[0523] Input: Text information entered by the user.

[0524] Output: Extracted keywords (e.g., "new virus," "miracle drug," "development").

[0525] What it does: The server uses NLP techniques to analyze the text and extract keywords.

[0526] Step 3: Gather information

[0527] Based on the keywords extracted by the analysis device, the server uses a collection device to collect related information from external sources. The collection device uses web scraping technology to obtain news articles, research papers, etc., for example, using the Google News API or PubMed API.

[0528] Input: Extracted keywords (e.g., "new virus," "miracle drug," "development").

[0529] Output: Collected relevant information (news articles, research papers, etc.).

[0530] What it does: The server performs web scraping to gather relevant information from external sources.

[0531] Step 4: Evaluate the information

[0532] The server compares the collected information with the information entered by the user using an evaluation device to calculate the degree of match. The evaluation device measures the degree of match using algorithms such as cosine similarity or Jaccard index and calculates a reliability score. For example, if the degree of match is 60%, the reliability score is calculated as 60.

[0533] Input: User input and associated collected information.

[0534] Output: Confidence score (e.g. 60%).

[0535] What happens: The server calculates the match and generates a confidence score.

[0536] Step 5: Notification of results

[0537] The server then communicates the generated reliability score to the user via a notification device, which displays the result visually or audibly, for example, "The reliability of this information is 60%" on a smartphone application.

[0538] Input: Confidence score (e.g. 60%).

[0539] Output: A confidence score that is displayed to the user.

[0540] Specific behavior: The server notifies the user of the reliability score through the application.

[0541] Specific examples of processing steps

[0542] 1. Enter your information

[0543] The user types into the app, "A cure for the new virus has been developed."

[0544] 2. Analysis of Information

[0545] The server extracts "new virus," "miracle drug," and "development" from the input information.

[0546] 3. Collection of information

[0547] The server collects news articles and papers.

[0548] 4. Information Evaluation

[0549] The server calculates the match and generates a confidence score.

[0550] 5. Notification of Results

[0551] The server displays to the app, "This information is 60% reliable."

[0552] (Application example 1)

[0553] 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."

[0554] In recent years, the spread of the Internet and smartphones has led to a proliferation of advertising information. This has made it difficult for consumers to select products and services based on their actual reliability. The increasing number of purchasing decisions based on inaccurate advertising information and the spread of false information are increasing the risk of damaging consumer trust. To solve this problem, a system that can quickly and accurately evaluate the reliability of advertising information is needed.

[0555] 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.

[0556] In this invention, the server includes input means for users to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information and the analyzed information, notification means for notifying users of the evaluation results, and advertising information evaluation means for inputting advertising data and evaluating its content, thereby enabling consumers to quickly evaluate the reliability of advertising information and select products and services based on accurate information.

[0557] "User" means a person or entity that uses the system to input information and receive results.

[0558] "Input means" refers to the interface through which users input information into the system, and includes smartphones and computer applications.

[0559] "Analysis means" refers to a method or device for analyzing input information and extracting important keywords and features, and uses natural language processing or image analysis technology.

[0560] "Collection means" refers to the methods and devices used to collect the necessary data from external sources, such as web scraping technology.

[0561] An "evaluation means" is a method or device that compares the collected information with the analyzed information, evaluates the degree of agreement between them, and generates a reliability score.

[0562] The "notification means" is an interface for notifying the user of the evaluation results, and displays the results visually or audibly.

[0563] The "advertising information evaluation means" refers to a method or device for inputting advertising data and analyzing and evaluating its contents.

[0564] The "reliability score" is an evaluation value generated based on the degree of match between collected information and input information, and indicates the reliability of the information.

[0565] This invention relates to a system for evaluating the reliability of advertising information. This system allows a user to input advertising information, analyzes and evaluates the input information, generates a reliability score, and notifies the user of the result. Specifically, the system is implemented as follows.

[0566] System configuration

[0567] 1. Input Method

[0568] Users use their smartphones or computers to input information such as screenshots and URLs of advertisements into the user interface of an application that accepts information in one or more of the following formats: text, images, and videos.

[0569] 2. Analysis method

[0570] The server receives the entered advertising information and analyzes it using a natural language processing library (e.g., TensorFlow or spaCy). This analysis extracts keywords and features from the advertising content. For example, it extracts important keywords such as "new ingredients," "effectiveness," and "user reviews."

[0571] 3. Collection Method

[0572] Based on the extracted keywords, the server uses a web scraping library (such as BeautifulSoup or Selenium) to collect related information from external databases and websites. For example, it retrieves academic papers and user reviews on "new ingredients" from the Internet.

[0573] 4. Evaluation Methods

[0574] The collected information is compared with the input advertising information, and a reliability score is generated by evaluating the degree of match. This evaluation is performed using machine learning algorithms (such as scikit-learn). For example, if there is a match with multiple reliable sources, the degree of match will be higher and the reliability score will also be higher.

[0575] 5. Means of notification

[0576] The server notifies the user of the generated reliability score via the smartphone's push notification function. The user can visually check the reliability score (e.g., 85%) on the app.

[0577] Examples and prompts

[0578] Specific examples

[0579] If a user sees an advertisement for a new health supplement and wants to verify its authenticity, they would take the following steps:

[0580] 1. Enter a screenshot or URL of the ad into the app.

[0581] 2. The app analyzes the ad information and extracts key keywords.

[0582] 3. The system collects relevant information based on the keyword and calculates a credibility score.

[0583] 4. The app will notify users by saying, "This ad is 85% trustworthy," allowing them to quickly assess the ad's trustworthiness.

[0584] Prompt Sentence Examples

[0585] When a user uses the app to rate the trustworthiness of a particular ad, they enter:

[0586] "I'd like to learn more about the effects of new ingredient A. Can you tell me how reliable it is?"

[0587] As a result, this invention makes it possible to quickly and accurately evaluate the reliability of advertising information, allowing consumers to select products and services based on reliable information and reducing the influence of inaccurate information.

[0588] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0589] Step 1:

[0590] A user uses a device to input a screenshot or URL of an advertisement. This input can be in the form of text, image, or video. For example, a user enters an advertisement URL for a new health supplement into an input form. The input data is sent to the server.

[0591] Step 2:

[0592] The server analyzes the received advertising data using an analysis means. The analysis means uses natural language processing libraries (such as TensorFlow or spaCy) to extract key keywords and features from the input data. In this case, keywords such as "new ingredients" and "effects" are extracted from the text in the advertisement. A keyword list is generated based on the input.

[0593] Step 3:

[0594] The server's collection method collects related information from the web based on the extracted keywords. A web scraping library (such as BeautifulSoup or Selenium) is used to search for related academic papers, review articles, and news to obtain the necessary data. For example, academic papers and user reviews on a "new ingredient" are collected from the internet. A list of collected data is output based on the input keywords.

[0595] Step 4:

[0596] The server compares the collected data with the input ad data using an evaluation method. It uses a machine learning algorithm (such as scikit-learn) to evaluate the degree of match and generate a reliability score. Specifically, it calculates the similarity between the collected information and the data contained in the ad, and if the degree of match is high, it increases the reliability score. A reliability score is generated based on the input data and collected data.

[0597] Step 5:

[0598] The server notifies the user of the generated reliability score using a notification method. The reliability score is displayed on the app using the smartphone's push notification function. For example, the user can visually confirm on the app that "the reliability of this ad is 85%." The reliability score is received and presented to the user as notification data.

[0599] 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.

[0600] The present invention provides a system that allows a user to quickly evaluate the reliability of information, recognizes emotions based on the evaluation, and adjusts the evaluation result. This system is implemented with the following configuration.

[0601] System Configuration

[0602] 1. Input Devices

[0603] A user can input information using an input device. The input device accepts data in different formats, such as text, images, and videos. For example, a user may input text information such as "A specific drug for the new virus has been developed" into the input screen of a smartphone or computer.

[0604] 2. Analysis device

[0605] The server receives the information entered by the user and analyzes it through an analysis device. The analysis device has functions such as text analysis, image recognition, and video analysis, and understands the content of the entered information and extracts important keywords and features. For example, it extracts keywords such as "new virus," "miracle drug," and "development."

[0606] 3. Collection Device

[0607] Based on the analysis results, the server collects data from related external sources. The collection device uses scraping technology to obtain the necessary information from websites, databases, etc., and can collect related news articles and research papers.

[0608] 4. Evaluation equipment

[0609] The server compares the information obtained by the collection device with the information analyzed by the analysis device and measures the degree of match using the evaluation device. The evaluation device generates a reliability score based on the degree of match. For example, if there is a match with multiple reliable sources on the same topic, the reliability score will be high.

[0610] 5. Emotion Engine

[0611] The emotion engine is a device for recognizing user emotions. It has the ability to analyze the text and voice input by the user into an input device and identify emotions. For example, the emotion engine can determine whether the user is excited or confused based on the context and wording used when inputting text.

[0612] 6. Notification device

[0613] The server notifies the user of the reliability score generated by the evaluation device via a notification device. The notification device displays the results to the user visually or audibly. Furthermore, the notification format can be changed according to the user's emotions based on the analysis results of the emotion engine. For example, if the user is excited, the evaluation results will be displayed calmly, while if the user is confused, a notification will be sent that explains the results in a gentle and easy-to-understand manner.

[0614] Processing flow and specific examples

[0615] 1. Enter your information

[0616] The user enters the text information "A specific drug for the new virus has been developed" into the app's input screen.

[0617] 2. Analysis of Information

[0618] The server receives the entered text information and uses an analysis device to extract keywords such as "new virus," "miracle drug," and "development."

[0619] 3. Collection of information

[0620] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[0621] 4. Information Evaluation

[0622] The server compares the collected information with the input data using an evaluation device, measures the degree of match, and generates a reliability score. If six of the ten pieces of collected information match the input data, the reliability score is 60%.

[0623] 5. Emotion Analysis

[0624] The server's emotion engine analyzes the user's input text and identifies the user's emotion, for example, recognizing that the user is excited.

[0625] 6. Notification of Results

[0626] The server generates a reliability score (60%) and displays it to the user via a notification device. The result is displayed in a format that corresponds to the user's emotions based on the analysis results of the emotion engine. For example, an excited user will be presented with calm details, while a confused user will be notified with a gentle explanation.

[0627] In this way, users can evaluate the reliability of input information in real time and receive appropriate feedback based on their emotions, which will help curb the spread of misinformation and support decisions based on accurate information.

[0628] The processing flow will be explained below.

[0629] Step 1:

[0630] A user uses an input device of a terminal to input information such as text, images, videos, etc. For example, the user inputs the text "A specific drug for the new virus has been developed."

[0631] Step 2:

[0632] The device sends the entered information to the server as an HTTP request, which includes all the data entered by the user.

[0633] Step 3:

[0634] The server receives the HTTP request and passes the input data to the analysis device, which checks the format of the information (text, image, video) and performs the appropriate analysis.

[0635] Step 4:

[0636] The server's analysis device performs text analysis and extracts important keywords and phrases, such as "new virus," "miracle drug," and "development."

[0637] Step 5:

[0638] The server uses the extracted keywords to collect related data from external information sources using a collection device, which retrieves related articles and information from news sites and databases.

[0639] Step 6:

[0640] The server passes the collected information to the evaluation device, which then calculates the degree of match between the input data and the collected data. For example, if 6 out of 10 pieces of collected information match the input data, the degree of match is 60%.

[0641] Step 7:

[0642] The server's evaluator generates a reliability score based on the degree of match, which in this case is 60%.

[0643] Step 8:

[0644] The server passes the user's input data to the emotion engine, which then analyzes the user's input for emotion. For example, it recognizes that the user is excited based on the context and style of the input.

[0645] Step 9:

[0646] The server sends the evaluation result and emotion information to the notification device based on the result of the emotion engine. The notification device adjusts the display format of the reliability score according to the user's emotion.

[0647] Step 10:

[0648] The device displays the reliability score and emotional information received from the notification device to the user. For example, if the user is excited, the evaluation result is displayed in a calm and easy-to-understand format, and if the user is confused, the evaluation result is displayed in a gentle and easy-to-understand format.

[0649] In this way, the present invention can provide appropriate feedback to users by combining the reliability evaluation of information with the user's emotions, allowing users to evaluate the reliability of information in real time and make appropriate decisions based on that evaluation.

[0650] Example 2

[0651] 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."

[0652] In modern society, the overload of information has become a problem, making it difficult for users to quickly identify accurate information. Furthermore, it is necessary to consider the user's feelings toward the information, and appropriate feedback may not be provided. This can lead to the spread of misinformation and inappropriate decision-making. Therefore, there is a need for a system that can evaluate the reliability of input information and provide appropriate feedback based on the user's feelings.

[0653] 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.

[0654] In this invention, the server includes input means for a user to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information with the analyzed information, notification means for notifying the user of the evaluation result, and emotion identification means for identifying the emotion of the user. This enables the reliability of the information input by the user to be quickly evaluated and appropriate feedback according to the emotion based on the evaluation result.

[0655] "Input means" refers to a device or interface that allows a user to input information.

[0656] "Analysis means" refers to a device or program that analyzes input information and extracts key keywords and features.

[0657] "Collection tools" are devices or programs used to collect relevant data from external sources.

[0658] The "evaluation means" is a device or program that compares the collected information with the analyzed information to evaluate its reliability.

[0659] "Notification means" refers to a device or interface for notifying the user of the evaluation results.

[0660] The "emotion identification means" is a device or program for identifying an emotion from information input by a user.

[0661] A "reliability score" is a number generated by measuring the degree of match between collected information and input information.

[0662] "Text" is data in a format that includes character information.

[0663] An "image" is a form of data that contains visual information.

[0664] "Video" is a type of data that contains dynamic visual information.

[0665] The "notification format" refers to the display format or method used when the notification means notifies the user.

[0666] The present invention provides a system that quickly evaluates the reliability of information entered by a user and provides emotional feedback based on the evaluation results. Specific embodiments and the hardware and software used are described below.

[0667] System Configuration

[0668] 1. Input Method

[0669] Users input information using input means (devices such as smartphones or computers). The input format can be text, images, videos, or other formats.

[0670] As a specific example, a user enters text information such as "A specific drug for the new virus has been developed" into the input screen of an app.

[0671] 2. Analysis method

[0672] The server receives the information sent by the user and analyzes it using analytical means such as a natural language processing engine or image analysis software.

[0673] As a specific example, keywords such as "new virus," "miracle drug," and "development" are extracted from the input text.

[0674] 3. Collection Method

[0675] Based on the analysis results, the server uses collection methods to collect data from relevant external sources, such as web scraping technology and APIs.

[0676] As a specific example, news articles and research papers related to "new viruses," "miracle drugs," and "developments" are scraped from the web.

[0677] 4. Evaluation Methods

[0678] The server-collected information and the analyzed user-entered information are compared by an evaluation means to measure the degree of match, the evaluation means including an algorithm for generating a reliability score.

[0679] For example, if 6 out of 10 pieces of information collected match the input information, the reliability score is 60%.

[0680] 5. Emotion Identification Measures

[0681] The server uses emotion recognition means to identify emotions from the information and attitudes of the user, often using natural language processing or voice analysis technology.

[0682] As a specific example, it is determined from the input text that the user is excited.

[0683] 6. Means of notification

[0684] The server notifies the user of the result using the notification means based on the reliability score generated by the evaluation means and the emotion identified by the emotion identification means. The notification is performed using a mobile app or a web interface.

[0685] For example, provide results with a 60% confidence score in a calm manner, while providing a gentle explanation for confused users.

[0686] Specific examples

[0687] For example, if a user enters "A miracle cure for the new virus has been developed" into the app's input screen, the server receives this information and uses the analysis means to extract keywords. It then uses the collection means to collect related news articles and research papers from the web. It compares the collected information with the user's input information to generate a reliability score. It then uses the emotion identification means to analyze the user's emotions and notifies the user of the reliability score based on the results.

[0688] This allows users to evaluate the reliability of the information they input in real time and receive appropriate feedback based on their emotions, which can help prevent the spread of misinformation and support decision-making based on accurate information.

[0689] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0690] Step 1:

[0691] The user inputs information using the terminal.

[0692] Input: The user enters the text information "A specific drug for the new virus has been developed" into the app's input screen.

[0693] Output: The terminal sends the entered text information to the server.

[0694] Step 2:

[0695] The server receives the text information sent from the terminal.

[0696] Input: Text information sent from the device.

[0697] Output: Passes the input information to a natural language processing engine.

[0698] Step 3:

[0699] The server analyzes the text information using a natural language processing engine.

[0700] Input: Received text information.

[0701] Data processing: Extract keywords such as "new virus," "miracle drug," and "development" from text information.

[0702] Output: Parsed keyword list.

[0703] Step 4:

[0704] The server uses the collection means to collect data from external information sources based on the extracted keyword list.

[0705] Input: Parsed keyword list.

[0706] Data Computing: Scrape relevant information from the web based on keywords.

[0707] Output: A list of collected news articles and research papers.

[0708] Step 5:

[0709] The server compares the collected information with the analyzed user input information using an evaluation means.

[0710] Input: A list of collected information and user-entered information.

[0711] Data calculation: Applying an algorithm to calculate the degree of match.

[0712] Output: Match score.

[0713] Step 6:

[0714] The server generates a reliability score based on the match score obtained by the evaluation means.

[0715] Input: Match score.

[0716] Data calculation: Calculate the reliability score.

[0717] Specific behavior: If 6 out of 10 pieces of information collected match, set the confidence score to 60%.

[0718] Output: A confidence score.

[0719] Step 7:

[0720] The server uses an emotion identification means to identify emotions from the user's input text.

[0721] Input: The text entered by the user.

[0722] Data processing: Analyze sentiment using natural language processing techniques.

[0723] Output: The user's emotional state (e.g., excited, confused).

[0724] Step 8:

[0725] The server notifies the result based on the confidence score generated by the evaluation means and the emotion determined by the emotion identification means.

[0726] Input: Confidence score and user's emotional state.

[0727] Data calculation: Select the notification format according to the emotional state.

[0728] Specific behavior: Calmly provide details to excited users and gently explain things to confused users.

[0729] Output: The notification format that is displayed to the user.

[0730] (Application example 2)

[0731] 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."

[0732] In today's information society, a huge amount of information is distributed instantly, but much of it is false information, requiring users to quickly select reliable information. To prevent the spread of false information, it is important not only to evaluate the reliability of information but also to provide appropriate feedback based on the user's feelings. However, conventional technology has been unable to provide a system that can adequately solve these issues.

[0733] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes input means for the user to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information and the analyzed information, and notification means including emotion recognition means for notifying the user of the evaluation result and analyzing the user's emotion. This allows the user to evaluate the reliability of the input information in real time and receive appropriate feedback according to their emotion based on the evaluation result.

[0734] "User" means an individual or group of people who use the Content Delivery Service to input and evaluate information.

[0735] "Input means" refers to a device that allows a user to input information in the form of text, images, video, etc.

[0736] "Analysis means" refers to a mechanism within the system that analyzes information input through the input means and extracts important keywords and features.

[0737] "Collection means" refers to the mechanism for collecting relevant information from external sources, for example, using web scraping techniques.

[0738] "Evaluation means" refers to the mechanism within the system that compares the collected information with the analyzed information, measures the degree of agreement, and generates a reliability score.

[0739] "Notification means" refers to a system for communicating to a user the confidence score generated by the evaluation means and the emotion analysis results from the emotion recognition means.

[0740] "Emotion recognition means" refers to a mechanism for analyzing user input information and identifying the user's emotional state.

[0741] The present invention provides a system for evaluating the reliability of information and providing feedback according to the user's feelings. Specific embodiments will be described below.

[0742] System Configuration

[0743] 1. Input Method

[0744] The server is equipped with an input means for users to input information. This input means includes smartphones and computer input devices. Users can input text, images, videos, etc. For example, a user may input text information such as "A specific drug for the new virus has been developed."

[0745] 2. Analysis method

[0746] The server has an analysis means for analyzing the information input through the input means. The analysis means uses a natural language processing library (such as spaCy or NLTK). Important keywords and features are extracted from the text information. For example, keywords such as "new virus," "miracle drug," and "development" are extracted.

[0747] 3. Collection Method

[0748] The server includes a collection unit for collecting related information from external sources based on the keywords extracted by the analysis unit. This collection unit uses a web scraping tool (e.g., BeautifulSoup or Scrapy). Related news articles and research papers can be collected.

[0749] 4. Evaluation Methods

[0750] The server has an evaluation means for comparing the collected information with the analyzed information, measuring the degree of match, and generating a reliability score. If, using the evaluation means, six of the ten pieces of collected information match the input data, the reliability score is calculated as 60%.

[0751] 5. Emotion recognition means

[0752] The server is equipped with an emotion recognition unit to analyze emotions from user input. Here, emotions are identified using a generative AI model (e.g., a model using BERT or GPT-3). From the text entered by the user, it can determine whether the user is excited or confused.

[0753] 6. Means of notification

[0754] The server includes a notification means for notifying the user of the reliability score generated by the evaluation means and the analysis result of the emotion recognition means. The notification means includes technology for displaying the results to the user visually or audibly. To provide feedback according to the user's emotional state, a notification system such as Firebase Cloud Messaging is used. For example, an excited user may be notified in a calm manner with detailed information, while a confused user may be notified in a gentle manner with an explanation of the results.

[0755] Specific examples of processing

[0756] Prompt Sentence Examples

[0757] For example, if a user enters "A specific drug for the new virus has been developed" into the app's input screen, the system will process it as follows:

[0758] 1. Enter your information:

[0759] The user inputs the text information "A specific drug for the new virus has been developed" into the input means.

[0760] 2. Analysis of Information:

[0761] The server extracts keywords such as "new virus," "miracle drug," and "development" from the text information.

[0762] 3. Collection of Information:

[0763] Based on the extracted keywords, the server uses collection means to collect related news articles and research papers from the Web.

[0764] 4. Information Evaluation:

[0765] The server compares the collected information with the input data using an evaluation tool and generates a reliability score (e.g., 60%).

[0766] 5. Emotion Analysis:

[0767] The server's emotion recognition means analyzes the user's input text and identifies the user's emotion, for example, recognizing that the user is excited from the input content.

[0768] 6. Notification of Results:

[0769] The reliability score (60%) generated by the server is displayed to the user via a notification method. At this time, the result is displayed in a format that corresponds to the user's emotion based on the analysis results of the emotion recognition method. For example, an excited user will be shown detailed information in a calm manner, while a confused user will be notified in a gentle manner explaining the result.

[0770] In this way, users can evaluate the reliability of input information in real time and make decisions based on reliable information.

[0771] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0772] Step 1:

[0773] The user enters information.

[0774] Input: A user uses a smartphone or computer to input the text information, "A cure for the new virus has been developed."

[0775] Specific behavior: The user enters text into the application's input screen and presses the "Send" button.

[0776] Output: The input text information is sent to the server and passed to the analysis means.

[0777] Step 2:

[0778] The server analyzes the entered information.

[0779] Input: Text information sent from your smartphone or computer.

[0780] Data processing: The server uses a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and features from the text information.

[0781] Specific operation: The analysis means on the server extracts keywords such as "new virus," "miracle drug," and "development."

[0782] Output: Extracted keywords.

[0783] Step 3:

[0784] The server collects information from external sources.

[0785] Input: Extracted keywords.

[0786] Data processing: The server uses web scraping tools (e.g., BeautifulSoup or Scrapy) to collect relevant news articles and research papers from the web.

[0787] Specific operations: The server's collection method uses APIs such as Google Search to obtain the content of websites related to keywords such as "new virus," "miracle drug," and "development."

[0788] Output: Collected relevant information (news articles, research papers, etc.).

[0789] Step 4:

[0790] The server evaluates the collected information and input data.

[0791] Input: Collected relevant information and user-entered text information.

[0792] Data Calculation: The server's evaluation means compares the collected information with the analyzed information, measures the degree of agreement, and generates a reliability score.

[0793] Specific behavior: If the server finds that 6 out of 10 pieces of collected information match the user's input text information, it calculates a reliability score of 60%.

[0794] Output: A confidence score (e.g., 60%).

[0795] Step 5:

[0796] The server analyzes the sentiment of the input text information.

[0797] Input: User-entered text information.

[0798] Data computation: The server uses a generative AI model (e.g., BERT or GPT-3) to identify emotions from text information.

[0799] What it does: The sentiment analysis model determines the user's emotions from the text content, recognizing, for example, whether the user is excited or confused.

[0800] Output: The user's emotional state (e.g. excited).

[0801] Step 6:

[0802] The server notifies the user of the results.

[0803] Input: Confidence score and sentiment analysis results.

[0804] Data calculation: The server's notification mechanism generates feedback in the most appropriate format depending on the reliability score and emotional state.

[0805] What it does: The server uses a notification system such as Firebase Cloud Messaging to calmly provide details if the user is excited, or gently explain the outcome to confused users.

[0806] Output: Notification of results (e.g., in a calm display format or a friendly explanation format).

[0807] 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.

[0808] 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.

[0809] 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.

[0810] [Third embodiment]

[0811] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0812] 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.

[0813] 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).

[0814] 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.

[0815] 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.

[0816] 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).

[0817] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] The present invention provides a system that allows a user to quickly evaluate the reliability of information. This system is implemented with the following configuration.

[0824] System Configuration

[0825] 1. Input Devices

[0826] Users input information using input devices. Input devices can accept different types of data, such as text, images, videos, etc. For example, a user can use a smartphone or computer to input text information or screenshots of a particular article.

[0827] 2. Analysis device

[0828] The server receives the information entered by the user and analyzes it through an analysis device that has functions such as text analysis, image recognition, and video analysis to understand the content of the entered information and extract important keywords and features.

[0829] 3. Collection Device

[0830] The server collects data from relevant external sources based on the analysis results. The collection device uses scraping technology to obtain the necessary information from websites, databases, etc. For example, it can collect information from news article searches or databases of related research papers.

[0831] 4. Evaluation equipment

[0832] The server compares the information collected by the collection device with the information analyzed by the analysis device and measures the degree of agreement between them using the evaluation device. The evaluation device generates a reliability score based on the degree of agreement. For example, if there is agreement with multiple reliable sources on the same topic, the reliability score will be high.

[0833] 5. Notification device

[0834] The server notifies the user of the reliability score generated by the evaluation device via a notification device, which displays the result to the user visually or audibly, for example, by displaying the reliability score on a smartphone application.

[0835] Processing flow and specific examples

[0836] 1. Enter your information

[0837] The user enters the text information "A specific cure for the new virus has been developed" into the app's input form.

[0838] 2. Analysis of Information

[0839] The server receives the entered text information and uses an analysis device to extract important keywords such as "new virus," "miracle drug," and "development."

[0840] 3. Collection of information

[0841] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[0842] 4. Information Evaluation

[0843] The server compares the collected information with the input information using an evaluation device, measures the degree of match, and generates a reliability score. Among the collected information, there is data that matches with three primary sources, and the degree of match is 60%.

[0844] 5. Notification of Results

[0845] The server displays the generated reliability score (e.g., 60%) to the user via a notification device. The user is notified in the app that "The reliability of this information is 60%."

[0846] In this way, users can quickly assess the reliability of information in real time, which can help limit the spread of misinformation and support informed decision-making.

[0847] The processing flow will be explained below.

[0848] Step 1:

[0849] A user uses an input device of a terminal to input information such as text, images, videos, etc. For example, the user inputs the text "A specific drug for the new virus has been developed."

[0850] Step 2:

[0851] The device sends the input information to the server as an HTTP request, and the input data is packaged in JSON format or similar.

[0852] Step 3:

[0853] The server receives the HTTP request and passes the input data to the analysis device, which checks the input data format (text, image, video) and performs the appropriate analysis for each.

[0854] Step 4:

[0855] The server's analysis device performs text analysis and extracts important keywords and phrases, such as "new virus," "miracle drug," and "development."

[0856] Step 5:

[0857] The server uses a collection device to collect related data from external sources based on the extracted keywords. The collection device scrapes news sites and databases to obtain related articles and data.

[0858] Step 6:

[0859] The server passes the collected information to the evaluation device, which compares it with the input data to determine the degree of match. The evaluation device counts the number of matching keywords and information and calculates the degree of match.

[0860] Step 7:

[0861] The server's evaluation device generates a reliability score based on the degree of match. For example, if six of the ten pieces of information collected match the input data, the reliability score will be 60%.

[0862] Step 8:

[0863] The server transmits the generated reliability score to a notification device, which notifies the user of the reliability score visually or audibly.

[0864] Step 9:

[0865] The device displays the reliability score received from the notification device to the user, who then sees the result in the app: "The reliability of this information is 60%."

[0866] In this way, users can assess the reliability of input information in real time and make accurate, informed decisions.

[0867] Example 1

[0868] 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."

[0869] In modern society, there is a demand for fast and accurate evaluation of information, but it is difficult to individually evaluate the reliability of many information sources, and the spread of misinformation is becoming a problem. In particular, the amount of information circulating on the Internet is enormous, and there is a need for a means to easily evaluate its reliability. In addition, there is a need for a system that can handle the diverse formats of information and analyze and evaluate them appropriately for each format.

[0870] 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.

[0871] In this invention, the server includes an input means for a user to input information, an analysis means for analyzing the input information, a collection means for collecting information from external information sources, an evaluation means for comparing and evaluating the collected information with the analyzed information, a notification means for notifying the user of the evaluation result, a collection means for scraping related information from external information sources based on keywords extracted by the analysis means, and a means for the evaluation means to measure the degree of coincidence and generate a reliability score. This makes it possible to quickly and accurately evaluate the reliability of information and to handle information in a variety of formats.

[0872] "User" is a person or organization that operates the system to input information and receive results.

[0873] An "input means" is a device or method by which a user provides information to a system, and accepts data in various formats such as text, images, and videos.

[0874] "Analysis means" refers to a device or method for analyzing input information and extracting important keywords and features.

[0875] "Collection methods" are devices and methods for obtaining the required information from external sources, primarily using scraping technology.

[0876] An "evaluation means" is a device or method for comparing collected information with analyzed information, measuring the degree of agreement, and generating a reliability score.

[0877] "Notification means" refers to a device or method for notifying the user of the evaluation results, and displays the results visually or audibly.

[0878] "Keywords" are words or phrases that indicate important and specific information and are extracted by the analysis means from the input information.

[0879] "Scraping" is a technique or method for automatically extracting information from specific web pages.

[0880] The "reliability score" is a numerical index of the reliability of information calculated by the evaluation means based on the degree of agreement between the collected information and the input information.

[0881] MODE FOR CARRYING OUT THE INVENTION

[0882] The present invention relates to a system that enables a user to quickly and accurately evaluate the reliability of information, and is configured as follows.

[0883] System Configuration

[0884] 1. Input Devices

[0885] A device that allows users to input information. Input devices accept data in different formats, such as text, images, and videos. Users can use devices such as smartphones or computers to input text information and screenshots of specific articles.

[0886] 2. Analysis device

[0887] The server receives the input information and analyzes it using an analysis device. The analysis device has functions for text analysis, image recognition, and video analysis, and extracts important keywords and features from the content of the input information. For example, natural language processing (NLP) technology is used to extract keywords from text information.

[0888] 3. Collection Device

[0889] This is a device that allows the server to collect data from external sources based on keywords extracted by the analysis device. The collection device obtains the required information by scraping websites and databases. For example, it uses the Google News API or PubMed API to collect related news articles and research papers.

[0890] 4. Evaluation equipment

[0891] The server compares the information obtained by the collection device with the information analyzed by the analysis device and measures the degree of match. The evaluation device generates a reliability score based on the degree of match. Specifically, it measures the degree of match using algorithms such as cosine similarity and Jaccard index and calculates the reliability score.

[0892] 5. Notification device

[0893] This is a device that notifies the server of the reliability score generated by the evaluation device. The notification device displays the result to the user visually or audibly. For example, the reliability score may be displayed on a smartphone application, notifying the user that "the reliability of this information is 60%."

[0894] Program processing example

[0895] 1. Enter your information

[0896] The user enters the text information "A specific cure for the new virus has been developed" into the app's input form.

[0897] 2. Analysis of Information

[0898] The server receives the entered text information and uses an analysis device to extract important keywords such as "new virus," "miracle drug," and "development."

[0899] 3. Collection of information

[0900] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[0901] 4. Information Evaluation

[0902] The server compares the collected information with the input information using an evaluation device, measures the degree of match, and generates a reliability score. For example, suppose there is data in the collected information that matches three primary sources, and the degree of match is 60%.

[0903] 5. Notification of Results

[0904] The server displays the generated reliability score (e.g., 60%) to the user via a notification device. The user is notified on the app that "the reliability of this information is 60%."

[0905] Examples and prompts

[0906] For example, if a user enters the text "A specific drug for the new virus has been developed," the following prompt sentence will be input to the generative AI model:

[0907] Rate the credibility of the information that "A cure for the new virus has been developed." Calculate a credibility score based on highly trusted sources.

[0908] The system operates based on this prompt, calculates a reliability score, and notifies the user, allowing the user to make a decision based on the accuracy of the information.

[0909] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0910] Step 1: Enter your information

[0911] A user uses a device to input specific information. The input device accepts data in different formats, such as text, images, and videos. For example, a user inputs the text information "A specific drug for the new virus has been developed" into a smartphone application.

[0912] Input: The user enters the text information "A specific drug for the new virus has been developed" into the terminal.

[0913] Output: Text information is sent to the server.

[0914] Step 2: Analyze the information

[0915] The server receives the text information sent by the user and analyzes it using an analysis device. The analysis device uses natural language processing (NLP) technology to extract important keywords and features from the text information. For example, it can extract keywords such as "new virus," "miracle drug," and "development."

[0916] Input: Text information entered by the user.

[0917] Output: Extracted keywords (e.g., "new virus," "miracle drug," "development").

[0918] What it does: The server uses NLP techniques to analyze the text and extract keywords.

[0919] Step 3: Gather information

[0920] Based on the keywords extracted by the analysis device, the server uses a collection device to collect related information from external sources. The collection device uses web scraping technology to obtain news articles, research papers, etc., for example, using the Google News API or PubMed API.

[0921] Input: Extracted keywords (e.g., "new virus," "miracle drug," "development").

[0922] Output: Collected relevant information (news articles, research papers, etc.).

[0923] What it does: The server performs web scraping to gather relevant information from external sources.

[0924] Step 4: Evaluate the information

[0925] The server compares the collected information with the information entered by the user using an evaluation device to calculate the degree of match. The evaluation device measures the degree of match using algorithms such as cosine similarity or Jaccard index and calculates a reliability score. For example, if the degree of match is 60%, the reliability score is calculated as 60.

[0926] Input: User input and associated collected information.

[0927] Output: Confidence score (e.g. 60%).

[0928] What happens: The server calculates the match and generates a confidence score.

[0929] Step 5: Notification of results

[0930] The server then communicates the generated reliability score to the user via a notification device, which displays the result visually or audibly, for example, "The reliability of this information is 60%" on a smartphone application.

[0931] Input: Confidence score (e.g. 60%).

[0932] Output: A confidence score that is displayed to the user.

[0933] Specific behavior: The server notifies the user of the reliability score through the application.

[0934] Specific examples of processing steps

[0935] 1. Enter your information

[0936] The user types into the app, "A cure for the new virus has been developed."

[0937] 2. Analysis of Information

[0938] The server extracts "new virus," "miracle drug," and "development" from the input information.

[0939] 3. Collection of information

[0940] The server collects news articles and papers.

[0941] 4. Information Evaluation

[0942] The server calculates the match and generates a confidence score.

[0943] 5. Notification of Results

[0944] The server displays to the app, "This information is 60% reliable."

[0945] (Application example 1)

[0946] 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."

[0947] In recent years, the spread of the Internet and smartphones has led to a proliferation of advertising information. This has made it difficult for consumers to select products and services based on their actual reliability. The increasing number of purchasing decisions based on inaccurate advertising information and the spread of false information are increasing the risk of damaging consumer trust. To solve this problem, a system that can quickly and accurately evaluate the reliability of advertising information is needed.

[0948] 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.

[0949] In this invention, the server includes input means for users to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information and the analyzed information, notification means for notifying users of the evaluation results, and advertising information evaluation means for inputting advertising data and evaluating its content, thereby enabling consumers to quickly evaluate the reliability of advertising information and select products and services based on accurate information.

[0950] "User" means a person or entity that uses the system to input information and receive results.

[0951] "Input means" refers to the interface through which users input information into the system, and includes smartphones and computer applications.

[0952] "Analysis means" refers to a method or device for analyzing input information and extracting important keywords and features, and uses natural language processing or image analysis technology.

[0953] "Collection means" refers to the methods and devices used to collect the necessary data from external sources, such as web scraping technology.

[0954] An "evaluation means" is a method or device that compares the collected information with the analyzed information, evaluates the degree of agreement between them, and generates a reliability score.

[0955] The "notification means" is an interface for notifying the user of the evaluation results, and displays the results visually or audibly.

[0956] The "advertising information evaluation means" refers to a method or device for inputting advertising data and analyzing and evaluating its contents.

[0957] The "reliability score" is an evaluation value generated based on the degree of match between collected information and input information, and indicates the reliability of the information.

[0958] This invention relates to a system for evaluating the reliability of advertising information. This system allows a user to input advertising information, analyzes and evaluates the input information, generates a reliability score, and notifies the user of the result. Specifically, the system is implemented as follows.

[0959] System configuration

[0960] 1. Input Method

[0961] Users use their smartphones or computers to input information such as screenshots and URLs of advertisements into the user interface of an application that accepts information in one or more of the following formats: text, images, and videos.

[0962] 2. Analysis method

[0963] The server receives the entered advertising information and analyzes it using a natural language processing library (e.g., TensorFlow or spaCy). This analysis extracts keywords and features from the advertising content. For example, it extracts important keywords such as "new ingredients," "effectiveness," and "user reviews."

[0964] 3. Collection Method

[0965] Based on the extracted keywords, the server uses a web scraping library (such as BeautifulSoup or Selenium) to collect related information from external databases and websites. For example, it retrieves academic papers and user reviews on "new ingredients" from the Internet.

[0966] 4. Evaluation Methods

[0967] The collected information is compared with the input advertising information, and a reliability score is generated by evaluating the degree of match. This evaluation is performed using machine learning algorithms (such as scikit-learn). For example, if there is a match with multiple reliable sources, the degree of match will be higher and the reliability score will also be higher.

[0968] 5. Means of notification

[0969] The server notifies the user of the generated reliability score via the smartphone's push notification function. The user can visually check the reliability score (e.g., 85%) on the app.

[0970] Examples and prompts

[0971] Specific examples

[0972] If a user sees an advertisement for a new health supplement and wants to verify its authenticity, they would take the following steps:

[0973] 1. Enter a screenshot or URL of the ad into the app.

[0974] 2. The app analyzes the ad information and extracts key keywords.

[0975] 3. The system collects relevant information based on the keyword and calculates a credibility score.

[0976] 4. The app will notify users by saying, "This ad is 85% trustworthy," allowing them to quickly assess the ad's trustworthiness.

[0977] Prompt Sentence Examples

[0978] When a user uses the app to rate the trustworthiness of a particular ad, they enter:

[0979] "I'd like to learn more about the effects of new ingredient A. Can you tell me how reliable it is?"

[0980] As a result, this invention makes it possible to quickly and accurately evaluate the reliability of advertising information, allowing consumers to select products and services based on reliable information and reducing the influence of inaccurate information.

[0981] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0982] Step 1:

[0983] A user uses a device to input a screenshot or URL of an advertisement. This input can be in the form of text, image, or video. For example, a user enters an advertisement URL for a new health supplement into an input form. The input data is sent to the server.

[0984] Step 2:

[0985] The server analyzes the received advertising data using an analysis means. The analysis means uses natural language processing libraries (such as TensorFlow or spaCy) to extract key keywords and features from the input data. In this case, keywords such as "new ingredients" and "effects" are extracted from the text in the advertisement. A keyword list is generated based on the input.

[0986] Step 3:

[0987] The server's collection method collects related information from the web based on the extracted keywords. A web scraping library (such as BeautifulSoup or Selenium) is used to search for related academic papers, review articles, and news to obtain the necessary data. For example, academic papers and user reviews on a "new ingredient" are collected from the internet. A list of collected data is output based on the input keywords.

[0988] Step 4:

[0989] The server compares the collected data with the input ad data using an evaluation method. It uses a machine learning algorithm (such as scikit-learn) to evaluate the degree of match and generate a reliability score. Specifically, it calculates the similarity between the collected information and the data contained in the ad, and if the degree of match is high, it increases the reliability score. A reliability score is generated based on the input data and collected data.

[0990] Step 5:

[0991] The server notifies the user of the generated reliability score using a notification method. The reliability score is displayed on the app using the smartphone's push notification function. For example, the user can visually confirm on the app that "the reliability of this ad is 85%." The reliability score is received and presented to the user as notification data.

[0992] 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.

[0993] The present invention provides a system that allows a user to quickly evaluate the reliability of information, recognizes emotions based on the evaluation, and adjusts the evaluation result. This system is implemented with the following configuration.

[0994] System Configuration

[0995] 1. Input Devices

[0996] A user can input information using an input device. The input device accepts data in different formats, such as text, images, and videos. For example, a user may input text information such as "A specific drug for the new virus has been developed" into the input screen of a smartphone or computer.

[0997] 2. Analysis device

[0998] The server receives the information entered by the user and analyzes it through an analysis device. The analysis device has functions such as text analysis, image recognition, and video analysis, and understands the content of the entered information and extracts important keywords and features. For example, it extracts keywords such as "new virus," "miracle drug," and "development."

[0999] 3. Collection Device

[1000] Based on the analysis results, the server collects data from related external sources. The collection device uses scraping technology to obtain the necessary information from websites, databases, etc., and can collect related news articles and research papers.

[1001] 4. Evaluation equipment

[1002] The server compares the information obtained by the collection device with the information analyzed by the analysis device and measures the degree of match using the evaluation device. The evaluation device generates a reliability score based on the degree of match. For example, if there is a match with multiple reliable sources on the same topic, the reliability score will be high.

[1003] 5. Emotion Engine

[1004] The emotion engine is a device for recognizing user emotions. It has the ability to analyze the text and voice input by the user into an input device and identify emotions. For example, the emotion engine can determine whether the user is excited or confused based on the context and wording used when inputting text.

[1005] 6. Notification device

[1006] The server notifies the user of the reliability score generated by the evaluation device via a notification device. The notification device displays the results to the user visually or audibly. Furthermore, the notification format can be changed according to the user's emotions based on the analysis results of the emotion engine. For example, if the user is excited, the evaluation results will be displayed calmly, while if the user is confused, a notification will be sent that explains the results in a gentle and easy-to-understand manner.

[1007] Processing flow and specific examples

[1008] 1. Enter your information

[1009] The user enters the text information "A specific drug for the new virus has been developed" into the app's input screen.

[1010] 2. Analysis of Information

[1011] The server receives the entered text information and uses an analysis device to extract keywords such as "new virus," "miracle drug," and "development."

[1012] 3. Collection of information

[1013] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[1014] 4. Information Evaluation

[1015] The server compares the collected information with the input data using an evaluation device, measures the degree of match, and generates a reliability score. If six of the ten pieces of collected information match the input data, the reliability score is 60%.

[1016] 5. Emotion Analysis

[1017] The server's emotion engine analyzes the user's input text and identifies the user's emotion, for example, recognizing that the user is excited.

[1018] 6. Notification of Results

[1019] The server generates a reliability score (60%) and displays it to the user via a notification device. The result is displayed in a format that corresponds to the user's emotions based on the analysis results of the emotion engine. For example, an excited user will be presented with calm details, while a confused user will be notified with a gentle explanation.

[1020] In this way, users can evaluate the reliability of input information in real time and receive appropriate feedback based on their emotions, which will help curb the spread of misinformation and support decisions based on accurate information.

[1021] The processing flow will be explained below.

[1022] Step 1:

[1023] A user uses an input device of a terminal to input information such as text, images, videos, etc. For example, the user inputs the text "A specific drug for the new virus has been developed."

[1024] Step 2:

[1025] The device sends the entered information to the server as an HTTP request, which includes all the data entered by the user.

[1026] Step 3:

[1027] The server receives the HTTP request and passes the input data to the analysis device, which checks the format of the information (text, image, video) and performs the appropriate analysis.

[1028] Step 4:

[1029] The server's analysis device performs text analysis and extracts important keywords and phrases, such as "new virus," "miracle drug," and "development."

[1030] Step 5:

[1031] The server uses the extracted keywords to collect related data from external information sources using a collection device, which retrieves related articles and information from news sites and databases.

[1032] Step 6:

[1033] The server passes the collected information to the evaluation device, which then calculates the degree of match between the input data and the collected data. For example, if 6 out of 10 pieces of collected information match the input data, the degree of match is 60%.

[1034] Step 7:

[1035] The server's evaluator generates a reliability score based on the degree of match, which in this case is 60%.

[1036] Step 8:

[1037] The server passes the user's input data to the emotion engine, which then analyzes the user's input for emotion. For example, it recognizes that the user is excited based on the context and style of the input.

[1038] Step 9:

[1039] The server sends the evaluation result and emotion information to the notification device based on the result of the emotion engine. The notification device adjusts the display format of the reliability score according to the user's emotion.

[1040] Step 10:

[1041] The device displays the reliability score and emotional information received from the notification device to the user. For example, if the user is excited, the evaluation result is displayed in a calm and easy-to-understand format, and if the user is confused, the evaluation result is displayed in a gentle and easy-to-understand format.

[1042] In this way, the present invention can provide appropriate feedback to users by combining the reliability evaluation of information with the user's emotions, allowing users to evaluate the reliability of information in real time and make appropriate decisions based on that evaluation.

[1043] Example 2

[1044] 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."

[1045] In modern society, the overload of information has become a problem, making it difficult for users to quickly identify accurate information. Furthermore, it is necessary to consider the user's feelings toward the information, and appropriate feedback may not be provided. This can lead to the spread of misinformation and inappropriate decision-making. Therefore, there is a need for a system that can evaluate the reliability of input information and provide appropriate feedback based on the user's feelings.

[1046] 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.

[1047] In this invention, the server includes input means for a user to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information with the analyzed information, notification means for notifying the user of the evaluation result, and emotion identification means for identifying the emotion of the user. This enables the reliability of the information input by the user to be quickly evaluated and appropriate feedback according to the emotion based on the evaluation result.

[1048] "Input means" refers to a device or interface that allows a user to input information.

[1049] "Analysis means" refers to a device or program that analyzes input information and extracts key keywords and features.

[1050] "Collection tools" are devices or programs used to collect relevant data from external sources.

[1051] The "evaluation means" is a device or program that compares the collected information with the analyzed information to evaluate its reliability.

[1052] "Notification means" refers to a device or interface for notifying the user of the evaluation results.

[1053] The "emotion identification means" is a device or program for identifying an emotion from information input by a user.

[1054] A "reliability score" is a number generated by measuring the degree of match between collected information and input information.

[1055] "Text" is data in a format that includes character information.

[1056] An "image" is a form of data that contains visual information.

[1057] "Video" is a type of data that contains dynamic visual information.

[1058] The "notification format" refers to the display format or method used when the notification means notifies the user.

[1059] The present invention provides a system that quickly evaluates the reliability of information entered by a user and provides emotional feedback based on the evaluation results. Specific embodiments and the hardware and software used are described below.

[1060] System Configuration

[1061] 1. Input Method

[1062] Users input information using input means (devices such as smartphones or computers). The input format can be text, images, videos, or other formats.

[1063] As a specific example, a user enters text information such as "A specific drug for the new virus has been developed" into the input screen of an app.

[1064] 2. Analysis method

[1065] The server receives the information sent by the user and analyzes it using analytical means such as a natural language processing engine or image analysis software.

[1066] As a specific example, keywords such as "new virus," "miracle drug," and "development" are extracted from the input text.

[1067] 3. Collection Method

[1068] Based on the analysis results, the server uses collection methods to collect data from relevant external sources, such as web scraping technology and APIs.

[1069] As a specific example, news articles and research papers related to "new viruses," "miracle drugs," and "developments" are scraped from the web.

[1070] 4. Evaluation Methods

[1071] The server-collected information and the analyzed user-entered information are compared by an evaluation means to measure the degree of match, the evaluation means including an algorithm for generating a reliability score.

[1072] For example, if 6 out of 10 pieces of information collected match the input information, the reliability score is 60%.

[1073] 5. Emotion Identification Measures

[1074] The server uses emotion recognition means to identify emotions from the information and attitudes of the user, often using natural language processing or voice analysis technology.

[1075] As a specific example, it is determined from the input text that the user is excited.

[1076] 6. Means of notification

[1077] The server notifies the user of the result using the notification means based on the reliability score generated by the evaluation means and the emotion identified by the emotion identification means. The notification is performed using a mobile app or a web interface.

[1078] For example, provide results with a 60% confidence score in a calm manner, while providing a gentle explanation for confused users.

[1079] Specific examples

[1080] For example, if a user enters "A miracle cure for the new virus has been developed" into the app's input screen, the server receives this information and uses the analysis means to extract keywords. It then uses the collection means to collect related news articles and research papers from the web. It compares the collected information with the user's input information to generate a reliability score. It then uses the emotion identification means to analyze the user's emotions and notifies the user of the reliability score based on the results.

[1081] This allows users to evaluate the reliability of the information they input in real time and receive appropriate feedback based on their emotions, which can help prevent the spread of misinformation and support decision-making based on accurate information.

[1082] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1083] Step 1:

[1084] The user inputs information using the terminal.

[1085] Input: The user enters the text information "A specific drug for the new virus has been developed" into the app's input screen.

[1086] Output: The terminal sends the entered text information to the server.

[1087] Step 2:

[1088] The server receives the text information sent from the terminal.

[1089] Input: Text information sent from the device.

[1090] Output: Passes the input information to a natural language processing engine.

[1091] Step 3:

[1092] The server analyzes the text information using a natural language processing engine.

[1093] Input: Received text information.

[1094] Data processing: Extract keywords such as "new virus," "miracle drug," and "development" from text information.

[1095] Output: Parsed keyword list.

[1096] Step 4:

[1097] The server uses the collection means to collect data from external information sources based on the extracted keyword list.

[1098] Input: Parsed keyword list.

[1099] Data Computing: Scrape relevant information from the web based on keywords.

[1100] Output: A list of collected news articles and research papers.

[1101] Step 5:

[1102] The server compares the collected information with the analyzed user input information using an evaluation means.

[1103] Input: A list of collected information and user-entered information.

[1104] Data calculation: Applying an algorithm to calculate the degree of match.

[1105] Output: Match score.

[1106] Step 6:

[1107] The server generates a reliability score based on the match score obtained by the evaluation means.

[1108] Input: Match score.

[1109] Data calculation: Calculate the reliability score.

[1110] Specific behavior: If 6 out of 10 pieces of information collected match, set the confidence score to 60%.

[1111] Output: A confidence score.

[1112] Step 7:

[1113] The server uses an emotion identification means to identify emotions from the user's input text.

[1114] Input: The text entered by the user.

[1115] Data processing: Analyze sentiment using natural language processing techniques.

[1116] Output: The user's emotional state (e.g., excited, confused).

[1117] Step 8:

[1118] The server notifies the result based on the confidence score generated by the evaluation means and the emotion determined by the emotion identification means.

[1119] Input: Confidence score and user's emotional state.

[1120] Data calculation: Select the notification format according to the emotional state.

[1121] Specific behavior: Calmly provide details to excited users and gently explain things to confused users.

[1122] Output: The notification format that is displayed to the user.

[1123] (Application example 2)

[1124] 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."

[1125] In today's information society, a huge amount of information is distributed instantly, but much of it is false information, requiring users to quickly select reliable information. To prevent the spread of false information, it is important not only to evaluate the reliability of information but also to provide appropriate feedback based on the user's feelings. However, conventional technology has been unable to provide a system that can adequately solve these issues.

[1126] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes input means for the user to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information and the analyzed information, and notification means including emotion recognition means for notifying the user of the evaluation result and analyzing the user's emotion. This allows the user to evaluate the reliability of the input information in real time and receive appropriate feedback according to their emotion based on the evaluation result.

[1127] "User" means an individual or group of people who use the Content Delivery Service to input and evaluate information.

[1128] "Input means" refers to a device that allows a user to input information in the form of text, images, video, etc.

[1129] "Analysis means" refers to a mechanism within the system that analyzes information input through the input means and extracts important keywords and features.

[1130] "Collection means" refers to the mechanism for collecting relevant information from external sources, for example, using web scraping techniques.

[1131] "Evaluation means" refers to the mechanism within the system that compares the collected information with the analyzed information, measures the degree of agreement, and generates a reliability score.

[1132] "Notification means" refers to a system for communicating to a user the confidence score generated by the evaluation means and the emotion analysis results from the emotion recognition means.

[1133] "Emotion recognition means" refers to a mechanism for analyzing user input information and identifying the user's emotional state.

[1134] The present invention provides a system for evaluating the reliability of information and providing feedback according to the user's feelings. Specific embodiments will be described below.

[1135] System Configuration

[1136] 1. Input Method

[1137] The server is equipped with an input means for users to input information. This input means includes smartphones and computer input devices. Users can input text, images, videos, etc. For example, a user may input text information such as "A specific drug for the new virus has been developed."

[1138] 2. Analysis method

[1139] The server has an analysis means for analyzing the information input through the input means. The analysis means uses a natural language processing library (such as spaCy or NLTK). Important keywords and features are extracted from the text information. For example, keywords such as "new virus," "miracle drug," and "development" are extracted.

[1140] 3. Collection Method

[1141] The server includes a collection unit for collecting related information from external sources based on the keywords extracted by the analysis unit. This collection unit uses a web scraping tool (e.g., BeautifulSoup or Scrapy). Related news articles and research papers can be collected.

[1142] 4. Evaluation Methods

[1143] The server has an evaluation means for comparing the collected information with the analyzed information, measuring the degree of match, and generating a reliability score. If, using the evaluation means, six of the ten pieces of collected information match the input data, the reliability score is calculated as 60%.

[1144] 5. Emotion recognition means

[1145] The server is equipped with an emotion recognition unit to analyze emotions from user input. Here, emotions are identified using a generative AI model (e.g., a model using BERT or GPT-3). From the text entered by the user, it can determine whether the user is excited or confused.

[1146] 6. Means of notification

[1147] The server includes a notification means for notifying the user of the reliability score generated by the evaluation means and the analysis result of the emotion recognition means. The notification means includes technology for displaying the results to the user visually or audibly. To provide feedback according to the user's emotional state, a notification system such as Firebase Cloud Messaging is used. For example, an excited user may be notified in a calm manner with detailed information, while a confused user may be notified in a gentle manner with an explanation of the results.

[1148] Specific examples of processing

[1149] Prompt Sentence Examples

[1150] For example, if a user enters "A specific drug for the new virus has been developed" into the app's input screen, the system will process it as follows:

[1151] 1. Enter your information:

[1152] The user inputs the text information "A specific drug for the new virus has been developed" into the input means.

[1153] 2. Analysis of Information:

[1154] The server extracts keywords such as "new virus," "miracle drug," and "development" from the text information.

[1155] 3. Collection of Information:

[1156] Based on the extracted keywords, the server uses collection means to collect related news articles and research papers from the Web.

[1157] 4. Information Evaluation:

[1158] The server compares the collected information with the input data using an evaluation tool and generates a reliability score (e.g., 60%).

[1159] 5. Emotion Analysis:

[1160] The server's emotion recognition means analyzes the user's input text and identifies the user's emotion, for example, recognizing that the user is excited from the input content.

[1161] 6. Notification of Results:

[1162] The reliability score (60%) generated by the server is displayed to the user via a notification method. At this time, the result is displayed in a format that corresponds to the user's emotion based on the analysis results of the emotion recognition method. For example, an excited user will be shown detailed information in a calm manner, while a confused user will be notified in a gentle manner explaining the result.

[1163] In this way, users can evaluate the reliability of input information in real time and make decisions based on reliable information.

[1164] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1165] Step 1:

[1166] The user enters information.

[1167] Input: A user uses a smartphone or computer to input the text information, "A cure for the new virus has been developed."

[1168] Specific behavior: The user enters text into the application's input screen and presses the "Send" button.

[1169] Output: The input text information is sent to the server and passed to the analysis means.

[1170] Step 2:

[1171] The server analyzes the entered information.

[1172] Input: Text information sent from your smartphone or computer.

[1173] Data processing: The server uses a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and features from the text information.

[1174] Specific operation: The analysis means on the server extracts keywords such as "new virus," "miracle drug," and "development."

[1175] Output: Extracted keywords.

[1176] Step 3:

[1177] The server collects information from external sources.

[1178] Input: Extracted keywords.

[1179] Data processing: The server uses web scraping tools (e.g., BeautifulSoup or Scrapy) to collect relevant news articles and research papers from the web.

[1180] Specific operations: The server's collection method uses APIs such as Google Search to obtain the content of websites related to keywords such as "new virus," "miracle drug," and "development."

[1181] Output: Collected relevant information (news articles, research papers, etc.).

[1182] Step 4:

[1183] The server evaluates the collected information and input data.

[1184] Input: Collected relevant information and user-entered text information.

[1185] Data Calculation: The server's evaluation means compares the collected information with the analyzed information, measures the degree of agreement, and generates a reliability score.

[1186] Specific behavior: If the server finds that 6 out of 10 pieces of collected information match the user's input text information, it calculates a reliability score of 60%.

[1187] Output: A confidence score (e.g., 60%).

[1188] Step 5:

[1189] The server analyzes the sentiment of the input text information.

[1190] Input: User-entered text information.

[1191] Data computation: The server uses a generative AI model (e.g., BERT or GPT-3) to identify emotions from text information.

[1192] What it does: The sentiment analysis model determines the user's emotions from the text content, recognizing, for example, whether the user is excited or confused.

[1193] Output: The user's emotional state (e.g. excited).

[1194] Step 6:

[1195] The server notifies the user of the results.

[1196] Input: Confidence score and sentiment analysis results.

[1197] Data calculation: The server's notification mechanism generates feedback in the most appropriate format depending on the reliability score and emotional state.

[1198] What it does: The server uses a notification system such as Firebase Cloud Messaging to calmly provide details if the user is excited, or gently explain the outcome to confused users.

[1199] Output: Notification of results (e.g., in a calm display format or a friendly explanation format).

[1200] 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.

[1201] 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.

[1202] 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.

[1203] [Fourth embodiment]

[1204] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1205] 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.

[1206] 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).

[1207] 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.

[1208] 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.

[1209] 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).

[1210] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] 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."

[1217] The present invention provides a system that allows a user to quickly evaluate the reliability of information. This system is implemented with the following configuration.

[1218] System Configuration

[1219] 1. Input Devices

[1220] Users input information using input devices. Input devices can accept different types of data, such as text, images, videos, etc. For example, a user can use a smartphone or computer to input text information or screenshots of a particular article.

[1221] 2. Analysis device

[1222] The server receives the information entered by the user and analyzes it through an analysis device that has functions such as text analysis, image recognition, and video analysis to understand the content of the entered information and extract important keywords and features.

[1223] 3. Collection Device

[1224] The server collects data from relevant external sources based on the analysis results. The collection device uses scraping technology to obtain the necessary information from websites, databases, etc. For example, it can collect information from news article searches or databases of related research papers.

[1225] 4. Evaluation equipment

[1226] The server compares the information collected by the collection device with the information analyzed by the analysis device and measures the degree of agreement between them using the evaluation device. The evaluation device generates a reliability score based on the degree of agreement. For example, if there is agreement with multiple reliable sources on the same topic, the reliability score will be high.

[1227] 5. Notification device

[1228] The server notifies the user of the reliability score generated by the evaluation device via a notification device, which displays the result to the user visually or audibly, for example, by displaying the reliability score on a smartphone application.

[1229] Processing flow and specific examples

[1230] 1. Enter your information

[1231] The user enters the text information "A specific cure for the new virus has been developed" into the app's input form.

[1232] 2. Analysis of Information

[1233] The server receives the entered text information and uses an analysis device to extract important keywords such as "new virus," "miracle drug," and "development."

[1234] 3. Collection of information

[1235] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[1236] 4. Information Evaluation

[1237] The server compares the collected information with the input information using an evaluation device, measures the degree of match, and generates a reliability score. Among the collected information, there is data that matches with three primary sources, and the degree of match is 60%.

[1238] 5. Notification of Results

[1239] The server displays the generated reliability score (e.g., 60%) to the user via a notification device. The user is notified in the app that "The reliability of this information is 60%."

[1240] In this way, users can quickly assess the reliability of information in real time, which can help limit the spread of misinformation and support informed decision-making.

[1241] The processing flow will be explained below.

[1242] Step 1:

[1243] A user uses an input device of a terminal to input information such as text, images, videos, etc. For example, the user inputs the text "A specific drug for the new virus has been developed."

[1244] Step 2:

[1245] The device sends the input information to the server as an HTTP request, and the input data is packaged in JSON format or similar.

[1246] Step 3:

[1247] The server receives the HTTP request and passes the input data to the analysis device, which checks the input data format (text, image, video) and performs the appropriate analysis for each.

[1248] Step 4:

[1249] The server's analysis device performs text analysis and extracts important keywords and phrases, such as "new virus," "miracle drug," and "development."

[1250] Step 5:

[1251] The server uses a collection device to collect related data from external sources based on the extracted keywords. The collection device scrapes news sites and databases to obtain related articles and data.

[1252] Step 6:

[1253] The server passes the collected information to the evaluation device, which compares it with the input data to determine the degree of match. The evaluation device counts the number of matching keywords and information and calculates the degree of match.

[1254] Step 7:

[1255] The server's evaluation device generates a reliability score based on the degree of match. For example, if six of the ten pieces of information collected match the input data, the reliability score will be 60%.

[1256] Step 8:

[1257] The server transmits the generated reliability score to a notification device, which notifies the user of the reliability score visually or audibly.

[1258] Step 9:

[1259] The device displays the reliability score received from the notification device to the user, who then sees the result in the app: "The reliability of this information is 60%."

[1260] In this way, users can assess the reliability of input information in real time and make accurate, informed decisions.

[1261] Example 1

[1262] 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."

[1263] In modern society, there is a demand for fast and accurate evaluation of information, but it is difficult to individually evaluate the reliability of many information sources, and the spread of misinformation is becoming a problem. In particular, the amount of information circulating on the Internet is enormous, and there is a need for a means to easily evaluate its reliability. In addition, there is a need for a system that can handle the diverse formats of information and analyze and evaluate them appropriately for each format.

[1264] 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.

[1265] In this invention, the server includes an input means for a user to input information, an analysis means for analyzing the input information, a collection means for collecting information from external information sources, an evaluation means for comparing and evaluating the collected information with the analyzed information, a notification means for notifying the user of the evaluation result, a collection means for scraping related information from external information sources based on keywords extracted by the analysis means, and a means for the evaluation means to measure the degree of coincidence and generate a reliability score. This makes it possible to quickly and accurately evaluate the reliability of information and to handle information in a variety of formats.

[1266] "User" is a person or organization that operates the system to input information and receive results.

[1267] An "input means" is a device or method by which a user provides information to a system, and accepts data in various formats such as text, images, and videos.

[1268] "Analysis means" refers to a device or method for analyzing input information and extracting important keywords and features.

[1269] "Collection methods" are devices and methods for obtaining the required information from external sources, primarily using scraping technology.

[1270] An "evaluation means" is a device or method for comparing collected information with analyzed information, measuring the degree of agreement, and generating a reliability score.

[1271] "Notification means" refers to a device or method for notifying the user of the evaluation results, and displays the results visually or audibly.

[1272] "Keywords" are words or phrases that indicate important and specific information and are extracted by the analysis means from the input information.

[1273] "Scraping" is a technique or method for automatically extracting information from specific web pages.

[1274] The "reliability score" is a numerical index of the reliability of information calculated by the evaluation means based on the degree of agreement between the collected information and the input information.

[1275] MODE FOR CARRYING OUT THE INVENTION

[1276] The present invention relates to a system that enables a user to quickly and accurately evaluate the reliability of information, and is configured as follows.

[1277] System Configuration

[1278] 1. Input Devices

[1279] A device that allows users to input information. Input devices accept data in different formats, such as text, images, and videos. Users can use devices such as smartphones or computers to input text information and screenshots of specific articles.

[1280] 2. Analysis device

[1281] The server receives the input information and analyzes it using an analysis device. The analysis device has functions for text analysis, image recognition, and video analysis, and extracts important keywords and features from the content of the input information. For example, natural language processing (NLP) technology is used to extract keywords from text information.

[1282] 3. Collection Device

[1283] This is a device that allows the server to collect data from external sources based on keywords extracted by the analysis device. The collection device obtains the required information by scraping websites and databases. For example, it uses the Google News API or PubMed API to collect related news articles and research papers.

[1284] 4. Evaluation equipment

[1285] The server compares the information obtained by the collection device with the information analyzed by the analysis device and measures the degree of match. The evaluation device generates a reliability score based on the degree of match. Specifically, it measures the degree of match using algorithms such as cosine similarity and Jaccard index and calculates the reliability score.

[1286] 5. Notification device

[1287] This is a device that notifies the server of the reliability score generated by the evaluation device. The notification device displays the result to the user visually or audibly. For example, the reliability score may be displayed on a smartphone application, notifying the user that "the reliability of this information is 60%."

[1288] Program processing example

[1289] 1. Enter your information

[1290] The user enters the text information "A specific cure for the new virus has been developed" into the app's input form.

[1291] 2. Analysis of Information

[1292] The server receives the entered text information and uses an analysis device to extract important keywords such as "new virus," "miracle drug," and "development."

[1293] 3. Collection of information

[1294] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[1295] 4. Information Evaluation

[1296] The server compares the collected information with the input information using an evaluation device, measures the degree of match, and generates a reliability score. For example, suppose there is data in the collected information that matches three primary sources, and the degree of match is 60%.

[1297] 5. Notification of Results

[1298] The server displays the generated reliability score (e.g., 60%) to the user via a notification device. The user is notified on the app that "the reliability of this information is 60%."

[1299] Examples and prompts

[1300] For example, if a user enters the text "A specific drug for the new virus has been developed," the following prompt sentence will be input to the generative AI model:

[1301] Rate the credibility of the information that "A cure for the new virus has been developed." Calculate a credibility score based on highly trusted sources.

[1302] The system operates based on this prompt, calculates a reliability score, and notifies the user, allowing the user to make a decision based on the accuracy of the information.

[1303] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1304] Step 1: Enter your information

[1305] A user uses a device to input specific information. The input device accepts data in different formats, such as text, images, and videos. For example, a user inputs the text information "A specific drug for the new virus has been developed" into a smartphone application.

[1306] Input: The user enters the text information "A specific drug for the new virus has been developed" into the terminal.

[1307] Output: Text information is sent to the server.

[1308] Step 2: Analyze the information

[1309] The server receives the text information sent by the user and analyzes it using an analysis device. The analysis device uses natural language processing (NLP) technology to extract important keywords and features from the text information. For example, it can extract keywords such as "new virus," "miracle drug," and "development."

[1310] Input: Text information entered by the user.

[1311] Output: Extracted keywords (e.g., "new virus," "miracle drug," "development").

[1312] What it does: The server uses NLP techniques to analyze the text and extract keywords.

[1313] Step 3: Gather information

[1314] Based on the keywords extracted by the analysis device, the server uses a collection device to collect related information from external sources. The collection device uses web scraping technology to obtain news articles, research papers, etc., for example, using the Google News API or PubMed API.

[1315] Input: Extracted keywords (e.g., "new virus," "miracle drug," "development").

[1316] Output: Collected relevant information (news articles, research papers, etc.).

[1317] What it does: The server performs web scraping to gather relevant information from external sources.

[1318] Step 4: Evaluate the information

[1319] The server compares the collected information with the information entered by the user using an evaluation device to calculate the degree of match. The evaluation device measures the degree of match using algorithms such as cosine similarity or Jaccard index and calculates a reliability score. For example, if the degree of match is 60%, the reliability score is calculated as 60.

[1320] Input: User input and associated collected information.

[1321] Output: Confidence score (e.g. 60%).

[1322] What happens: The server calculates the match and generates a confidence score.

[1323] Step 5: Notification of results

[1324] The server then communicates the generated reliability score to the user via a notification device, which displays the result visually or audibly, for example, "The reliability of this information is 60%" on a smartphone application.

[1325] Input: Confidence score (e.g. 60%).

[1326] Output: A confidence score that is displayed to the user.

[1327] Specific behavior: The server notifies the user of the reliability score through the application.

[1328] Specific examples of processing steps

[1329] 1. Enter your information

[1330] The user types into the app, "A cure for the new virus has been developed."

[1331] 2. Analysis of Information

[1332] The server extracts "new virus," "miracle drug," and "development" from the input information.

[1333] 3. Collection of information

[1334] The server collects news articles and papers.

[1335] 4. Information Evaluation

[1336] The server calculates the match and generates a confidence score.

[1337] 5. Notification of Results

[1338] The server displays to the app, "This information is 60% reliable."

[1339] (Application example 1)

[1340] 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."

[1341] In recent years, the spread of the Internet and smartphones has led to a proliferation of advertising information. This has made it difficult for consumers to select products and services based on their actual reliability. The increasing number of purchasing decisions based on inaccurate advertising information and the spread of false information are increasing the risk of damaging consumer trust. To solve this problem, a system that can quickly and accurately evaluate the reliability of advertising information is needed.

[1342] 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.

[1343] In this invention, the server includes input means for users to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information and the analyzed information, notification means for notifying users of the evaluation results, and advertising information evaluation means for inputting advertising data and evaluating its content, thereby enabling consumers to quickly evaluate the reliability of advertising information and select products and services based on accurate information.

[1344] "User" means a person or entity that uses the system to input information and receive results.

[1345] "Input means" refers to the interface through which users input information into the system, and includes smartphones and computer applications.

[1346] "Analysis means" refers to a method or device for analyzing input information and extracting important keywords and features, and uses natural language processing or image analysis technology.

[1347] "Collection means" refers to the methods and devices used to collect the necessary data from external sources, such as web scraping technology.

[1348] An "evaluation means" is a method or device that compares the collected information with the analyzed information, evaluates the degree of agreement between them, and generates a reliability score.

[1349] The "notification means" is an interface for notifying the user of the evaluation results, and displays the results visually or audibly.

[1350] The "advertising information evaluation means" refers to a method or device for inputting advertising data and analyzing and evaluating its contents.

[1351] The "reliability score" is an evaluation value generated based on the degree of match between collected information and input information, and indicates the reliability of the information.

[1352] This invention relates to a system for evaluating the reliability of advertising information. This system allows a user to input advertising information, analyzes and evaluates the input information, generates a reliability score, and notifies the user of the result. Specifically, the system is implemented as follows.

[1353] System configuration

[1354] 1. Input Method

[1355] Users use their smartphones or computers to input information such as screenshots and URLs of advertisements into the user interface of an application that accepts information in one or more of the following formats: text, images, and videos.

[1356] 2. Analysis method

[1357] The server receives the entered advertising information and analyzes it using a natural language processing library (e.g., TensorFlow or spaCy). This analysis extracts keywords and features from the advertising content. For example, it extracts important keywords such as "new ingredients," "effectiveness," and "user reviews."

[1358] 3. Collection Method

[1359] Based on the extracted keywords, the server uses a web scraping library (such as BeautifulSoup or Selenium) to collect related information from external databases and websites. For example, it retrieves academic papers and user reviews on "new ingredients" from the Internet.

[1360] 4. Evaluation Methods

[1361] The collected information is compared with the input advertising information, and a reliability score is generated by evaluating the degree of match. This evaluation is performed using machine learning algorithms (such as scikit-learn). For example, if there is a match with multiple reliable sources, the degree of match will be higher and the reliability score will also be higher.

[1362] 5. Means of notification

[1363] The server notifies the user of the generated reliability score via the smartphone's push notification function. The user can visually check the reliability score (e.g., 85%) on the app.

[1364] Examples and prompts

[1365] Specific examples

[1366] If a user sees an advertisement for a new health supplement and wants to verify its authenticity, they would take the following steps:

[1367] 1. Enter a screenshot or URL of the ad into the app.

[1368] 2. The app analyzes the ad information and extracts key keywords.

[1369] 3. The system collects relevant information based on the keyword and calculates a credibility score.

[1370] 4. The app will notify users by saying, "This ad is 85% trustworthy," allowing them to quickly assess the ad's trustworthiness.

[1371] Prompt Sentence Examples

[1372] When a user uses the app to rate the trustworthiness of a particular ad, they enter:

[1373] "I'd like to learn more about the effects of new ingredient A. Can you tell me how reliable it is?"

[1374] As a result, this invention makes it possible to quickly and accurately evaluate the reliability of advertising information, allowing consumers to select products and services based on reliable information and reducing the influence of inaccurate information.

[1375] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1376] Step 1:

[1377] A user uses a device to input a screenshot or URL of an advertisement. This input can be in the form of text, image, or video. For example, a user enters an advertisement URL for a new health supplement into an input form. The input data is sent to the server.

[1378] Step 2:

[1379] The server analyzes the received advertising data using an analysis means. The analysis means uses natural language processing libraries (such as TensorFlow or spaCy) to extract key keywords and features from the input data. In this case, keywords such as "new ingredients" and "effects" are extracted from the text in the advertisement. A keyword list is generated based on the input.

[1380] Step 3:

[1381] The server's collection method collects related information from the web based on the extracted keywords. A web scraping library (such as BeautifulSoup or Selenium) is used to search for related academic papers, review articles, and news to obtain the necessary data. For example, academic papers and user reviews on a "new ingredient" are collected from the internet. A list of collected data is output based on the input keywords.

[1382] Step 4:

[1383] The server compares the collected data with the input ad data using an evaluation method. It uses a machine learning algorithm (such as scikit-learn) to evaluate the degree of match and generate a reliability score. Specifically, it calculates the similarity between the collected information and the data contained in the ad, and if the degree of match is high, it increases the reliability score. A reliability score is generated based on the input data and collected data.

[1384] Step 5:

[1385] The server notifies the user of the generated reliability score using a notification method. The reliability score is displayed on the app using the smartphone's push notification function. For example, the user can visually confirm on the app that "the reliability of this ad is 85%." The reliability score is received and presented to the user as notification data.

[1386] 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.

[1387] The present invention provides a system that allows a user to quickly evaluate the reliability of information, recognizes emotions based on the evaluation, and adjusts the evaluation result. This system is implemented with the following configuration.

[1388] System Configuration

[1389] 1. Input Devices

[1390] A user can input information using an input device. The input device accepts data in different formats, such as text, images, and videos. For example, a user may input text information such as "A specific drug for the new virus has been developed" into the input screen of a smartphone or computer.

[1391] 2. Analysis device

[1392] The server receives the information entered by the user and analyzes it through an analysis device. The analysis device has functions such as text analysis, image recognition, and video analysis, and understands the content of the entered information and extracts important keywords and features. For example, it extracts keywords such as "new virus," "miracle drug," and "development."

[1393] 3. Collection Device

[1394] Based on the analysis results, the server collects data from related external sources. The collection device uses scraping technology to obtain the necessary information from websites, databases, etc., and can collect related news articles and research papers.

[1395] 4. Evaluation equipment

[1396] The server compares the information obtained by the collection device with the information analyzed by the analysis device and measures the degree of match using the evaluation device. The evaluation device generates a reliability score based on the degree of match. For example, if there is a match with multiple reliable sources on the same topic, the reliability score will be high.

[1397] 5. Emotion Engine

[1398] The emotion engine is a device for recognizing user emotions. It has the ability to analyze the text and voice input by the user into an input device and identify emotions. For example, the emotion engine can determine whether the user is excited or confused based on the context and wording used when inputting text.

[1399] 6. Notification device

[1400] The server notifies the user of the reliability score generated by the evaluation device via a notification device. The notification device displays the results to the user visually or audibly. Furthermore, the notification format can be changed according to the user's emotions based on the analysis results of the emotion engine. For example, if the user is excited, the evaluation results will be displayed calmly, while if the user is confused, a notification will be sent that explains the results in a gentle and easy-to-understand manner.

[1401] Processing flow and specific examples

[1402] 1. Enter your information

[1403] The user enters the text information "A specific drug for the new virus has been developed" into the app's input screen.

[1404] 2. Analysis of Information

[1405] The server receives the entered text information and uses an analysis device to extract keywords such as "new virus," "miracle drug," and "development."

[1406] 3. Collection of information

[1407] Based on the extracted keywords, the server collects information by scraping related news articles and research papers from the web via a collection device.

[1408] 4. Information Evaluation

[1409] The server compares the collected information with the input data using an evaluation device, measures the degree of match, and generates a reliability score. If six of the ten pieces of collected information match the input data, the reliability score is 60%.

[1410] 5. Emotion Analysis

[1411] The server's emotion engine analyzes the user's input text and identifies the user's emotion, for example, recognizing that the user is excited.

[1412] 6. Notification of Results

[1413] The server generates a reliability score (60%) and displays it to the user via a notification device. The result is displayed in a format that corresponds to the user's emotions based on the analysis results of the emotion engine. For example, an excited user will be presented with calm details, while a confused user will be notified with a gentle explanation.

[1414] In this way, users can evaluate the reliability of input information in real time and receive appropriate feedback based on their emotions, which will help curb the spread of misinformation and support decisions based on accurate information.

[1415] The processing flow will be explained below.

[1416] Step 1:

[1417] A user uses an input device of a terminal to input information such as text, images, videos, etc. For example, the user inputs the text "A specific drug for the new virus has been developed."

[1418] Step 2:

[1419] The device sends the entered information to the server as an HTTP request, which includes all the data entered by the user.

[1420] Step 3:

[1421] The server receives the HTTP request and passes the input data to the analysis device, which checks the format of the information (text, image, video) and performs the appropriate analysis.

[1422] Step 4:

[1423] The server's analysis device performs text analysis and extracts important keywords and phrases, such as "new virus," "miracle drug," and "development."

[1424] Step 5:

[1425] The server uses the extracted keywords to collect related data from external information sources using a collection device, which retrieves related articles and information from news sites and databases.

[1426] Step 6:

[1427] The server passes the collected information to the evaluation device, which then calculates the degree of match between the input data and the collected data. For example, if 6 out of 10 pieces of collected information match the input data, the degree of match is 60%.

[1428] Step 7:

[1429] The server's evaluator generates a reliability score based on the degree of match, which in this case is 60%.

[1430] Step 8:

[1431] The server passes the user's input data to the emotion engine, which then analyzes the user's input for emotion. For example, it recognizes that the user is excited based on the context and style of the input.

[1432] Step 9:

[1433] The server sends the evaluation result and emotion information to the notification device based on the result of the emotion engine. The notification device adjusts the display format of the reliability score according to the user's emotion.

[1434] Step 10:

[1435] The device displays the reliability score and emotional information received from the notification device to the user. For example, if the user is excited, the evaluation result is displayed in a calm and easy-to-understand format, and if the user is confused, the evaluation result is displayed in a gentle and easy-to-understand format.

[1436] In this way, the present invention can provide appropriate feedback to users by combining the reliability evaluation of information with the user's emotions, allowing users to evaluate the reliability of information in real time and make appropriate decisions based on that evaluation.

[1437] Example 2

[1438] 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."

[1439] In modern society, the overload of information has become a problem, making it difficult for users to quickly identify accurate information. Furthermore, it is necessary to consider the user's feelings toward the information, and appropriate feedback may not be provided. This can lead to the spread of misinformation and inappropriate decision-making. Therefore, there is a need for a system that can evaluate the reliability of input information and provide appropriate feedback based on the user's feelings.

[1440] 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.

[1441] In this invention, the server includes input means for a user to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information with the analyzed information, notification means for notifying the user of the evaluation result, and emotion identification means for identifying the emotion of the user. This enables the reliability of the information input by the user to be quickly evaluated and appropriate feedback according to the emotion based on the evaluation result.

[1442] "Input means" refers to a device or interface that allows a user to input information.

[1443] "Analysis means" refers to a device or program that analyzes input information and extracts key keywords and features.

[1444] "Collection tools" are devices or programs used to collect relevant data from external sources.

[1445] The "evaluation means" is a device or program that compares the collected information with the analyzed information to evaluate its reliability.

[1446] "Notification means" refers to a device or interface for notifying the user of the evaluation results.

[1447] The "emotion identification means" is a device or program for identifying an emotion from information input by a user.

[1448] A "reliability score" is a number generated by measuring the degree of match between collected information and input information.

[1449] "Text" is data in a format that includes character information.

[1450] An "image" is a form of data that contains visual information.

[1451] "Video" is a type of data that contains dynamic visual information.

[1452] The "notification format" refers to the display format or method used when the notification means notifies the user.

[1453] The present invention provides a system that quickly evaluates the reliability of information entered by a user and provides emotional feedback based on the evaluation results. Specific embodiments and the hardware and software used are described below.

[1454] System Configuration

[1455] 1. Input Method

[1456] Users input information using input means (devices such as smartphones or computers). The input format can be text, images, videos, or other formats.

[1457] As a specific example, a user enters text information such as "A specific drug for the new virus has been developed" into the input screen of an app.

[1458] 2. Analysis method

[1459] The server receives the information sent by the user and analyzes it using analytical means such as a natural language processing engine or image analysis software.

[1460] As a specific example, keywords such as "new virus," "miracle drug," and "development" are extracted from the input text.

[1461] 3. Collection Method

[1462] Based on the analysis results, the server uses collection methods to collect data from relevant external sources, such as web scraping technology and APIs.

[1463] As a specific example, news articles and research papers related to "new viruses," "miracle drugs," and "developments" are scraped from the web.

[1464] 4. Evaluation Methods

[1465] The server-collected information and the analyzed user-entered information are compared by an evaluation means to measure the degree of match, the evaluation means including an algorithm for generating a reliability score.

[1466] For example, if 6 out of 10 pieces of information collected match the input information, the reliability score is 60%.

[1467] 5. Emotion Identification Measures

[1468] The server uses emotion recognition means to identify emotions from the information and attitudes of the user, often using natural language processing or voice analysis technology.

[1469] As a specific example, it is determined from the input text that the user is excited.

[1470] 6. Means of notification

[1471] The server notifies the user of the result using the notification means based on the reliability score generated by the evaluation means and the emotion identified by the emotion identification means. The notification is performed using a mobile app or a web interface.

[1472] For example, provide results with a 60% confidence score in a calm manner, while providing a gentle explanation for confused users.

[1473] Specific examples

[1474] For example, if a user enters "A miracle cure for the new virus has been developed" into the app's input screen, the server receives this information and uses the analysis means to extract keywords. It then uses the collection means to collect related news articles and research papers from the web. It compares the collected information with the user's input information to generate a reliability score. It then uses the emotion identification means to analyze the user's emotions and notifies the user of the reliability score based on the results.

[1475] This allows users to evaluate the reliability of the information they input in real time and receive appropriate feedback based on their emotions, which can help prevent the spread of misinformation and support decision-making based on accurate information.

[1476] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1477] Step 1:

[1478] The user inputs information using the terminal.

[1479] Input: The user enters the text information "A specific drug for the new virus has been developed" into the app's input screen.

[1480] Output: The terminal sends the entered text information to the server.

[1481] Step 2:

[1482] The server receives the text information sent from the terminal.

[1483] Input: Text information sent from the device.

[1484] Output: Passes the input information to a natural language processing engine.

[1485] Step 3:

[1486] The server analyzes the text information using a natural language processing engine.

[1487] Input: Received text information.

[1488] Data processing: Extract keywords such as "new virus," "miracle drug," and "development" from text information.

[1489] Output: Parsed keyword list.

[1490] Step 4:

[1491] The server uses the collection means to collect data from external information sources based on the extracted keyword list.

[1492] Input: Parsed keyword list.

[1493] Data Computing: Scrape relevant information from the web based on keywords.

[1494] Output: A list of collected news articles and research papers.

[1495] Step 5:

[1496] The server compares the collected information with the analyzed user input information using an evaluation means.

[1497] Input: A list of collected information and user-entered information.

[1498] Data calculation: Applying an algorithm to calculate the degree of match.

[1499] Output: Match score.

[1500] Step 6:

[1501] The server generates a reliability score based on the match score obtained by the evaluation means.

[1502] Input: Match score.

[1503] Data calculation: Calculate the reliability score.

[1504] Specific behavior: If 6 out of 10 pieces of information collected match, set the confidence score to 60%.

[1505] Output: A confidence score.

[1506] Step 7:

[1507] The server uses an emotion identification means to identify emotions from the user's input text.

[1508] Input: The text entered by the user.

[1509] Data processing: Analyze sentiment using natural language processing techniques.

[1510] Output: The user's emotional state (e.g., excited, confused).

[1511] Step 8:

[1512] The server notifies the result based on the confidence score generated by the evaluation means and the emotion determined by the emotion identification means.

[1513] Input: Confidence score and user's emotional state.

[1514] Data calculation: Select the notification format according to the emotional state.

[1515] Specific behavior: Calmly provide details to excited users and gently explain things to confused users.

[1516] Output: The notification format that is displayed to the user.

[1517] (Application example 2)

[1518] 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."

[1519] In today's information society, a huge amount of information is distributed instantly, but much of it is false information, requiring users to quickly select reliable information. To prevent the spread of false information, it is important not only to evaluate the reliability of information but also to provide appropriate feedback based on the user's feelings. However, conventional technology has been unable to provide a system that can adequately solve these issues.

[1520] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes input means for the user to input information, analysis means for analyzing the input information, collection means for collecting information from external information sources, evaluation means for comparing and evaluating the collected information and the analyzed information, and notification means including emotion recognition means for notifying the user of the evaluation result and analyzing the user's emotion. This allows the user to evaluate the reliability of the input information in real time and receive appropriate feedback according to their emotion based on the evaluation result.

[1521] "User" means an individual or group of people who use the Content Delivery Service to input and evaluate information.

[1522] "Input means" refers to a device that allows a user to input information in the form of text, images, video, etc.

[1523] "Analysis means" refers to a mechanism within the system that analyzes information input through the input means and extracts important keywords and features.

[1524] "Collection means" refers to the mechanism for collecting relevant information from external sources, for example, using web scraping techniques.

[1525] "Evaluation means" refers to the mechanism within the system that compares the collected information with the analyzed information, measures the degree of agreement, and generates a reliability score.

[1526] "Notification means" refers to a system for communicating to a user the confidence score generated by the evaluation means and the emotion analysis results from the emotion recognition means.

[1527] "Emotion recognition means" refers to a mechanism for analyzing user input information and identifying the user's emotional state.

[1528] The present invention provides a system for evaluating the reliability of information and providing feedback according to the user's feelings. Specific embodiments will be described below.

[1529] System Configuration

[1530] 1. Input Method

[1531] The server is equipped with an input means for users to input information. This input means includes smartphones and computer input devices. Users can input text, images, videos, etc. For example, a user may input text information such as "A specific drug for the new virus has been developed."

[1532] 2. Analysis method

[1533] The server has an analysis means for analyzing the information input through the input means. The analysis means uses a natural language processing library (such as spaCy or NLTK). Important keywords and features are extracted from the text information. For example, keywords such as "new virus," "miracle drug," and "development" are extracted.

[1534] 3. Collection Method

[1535] The server includes a collection unit for collecting related information from external sources based on the keywords extracted by the analysis unit. This collection unit uses a web scraping tool (e.g., BeautifulSoup or Scrapy). Related news articles and research papers can be collected.

[1536] 4. Evaluation Methods

[1537] The server has an evaluation means for comparing the collected information with the analyzed information, measuring the degree of match, and generating a reliability score. If, using the evaluation means, six of the ten pieces of collected information match the input data, the reliability score is calculated as 60%.

[1538] 5. Emotion recognition means

[1539] The server is equipped with an emotion recognition unit to analyze emotions from user input. Here, emotions are identified using a generative AI model (e.g., a model using BERT or GPT-3). From the text entered by the user, it can determine whether the user is excited or confused.

[1540] 6. Means of notification

[1541] The server includes a notification means for notifying the user of the reliability score generated by the evaluation means and the analysis result of the emotion recognition means. The notification means includes technology for displaying the results to the user visually or audibly. To provide feedback according to the user's emotional state, a notification system such as Firebase Cloud Messaging is used. For example, an excited user may be notified in a calm manner with detailed information, while a confused user may be notified in a gentle manner with an explanation of the results.

[1542] Specific examples of processing

[1543] Prompt Sentence Examples

[1544] For example, if a user enters "A specific drug for the new virus has been developed" into the app's input screen, the system will process it as follows:

[1545] 1. Enter your information:

[1546] The user inputs the text information "A specific drug for the new virus has been developed" into the input means.

[1547] 2. Analysis of Information:

[1548] The server extracts keywords such as "new virus," "miracle drug," and "development" from the text information.

[1549] 3. Collection of Information:

[1550] Based on the extracted keywords, the server uses collection means to collect related news articles and research papers from the Web.

[1551] 4. Information Evaluation:

[1552] The server compares the collected information with the input data using an evaluation tool and generates a reliability score (e.g., 60%).

[1553] 5. Emotion Analysis:

[1554] The server's emotion recognition means analyzes the user's input text and identifies the user's emotion, for example, recognizing that the user is excited from the input content.

[1555] 6. Notification of Results:

[1556] The reliability score (60%) generated by the server is displayed to the user via a notification method. At this time, the result is displayed in a format that corresponds to the user's emotion based on the analysis results of the emotion recognition method. For example, an excited user will be shown detailed information in a calm manner, while a confused user will be notified in a gentle manner explaining the result.

[1557] In this way, users can evaluate the reliability of input information in real time and make decisions based on reliable information.

[1558] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1559] Step 1:

[1560] The user enters information.

[1561] Input: A user uses a smartphone or computer to input the text information, "A cure for the new virus has been developed."

[1562] Specific behavior: The user enters text into the application's input screen and presses the "Send" button.

[1563] Output: The input text information is sent to the server and passed to the analysis means.

[1564] Step 2:

[1565] The server analyzes the entered information.

[1566] Input: Text information sent from your smartphone or computer.

[1567] Data processing: The server uses a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and features from the text information.

[1568] Specific operation: The analysis means on the server extracts keywords such as "new virus," "miracle drug," and "development."

[1569] Output: Extracted keywords.

[1570] Step 3:

[1571] The server collects information from external sources.

[1572] Input: Extracted keywords.

[1573] Data processing: The server uses web scraping tools (e.g., BeautifulSoup or Scrapy) to collect relevant news articles and research papers from the web.

[1574] Specific operations: The server's collection method uses APIs such as Google Search to obtain the content of websites related to keywords such as "new virus," "miracle drug," and "development."

[1575] Output: Collected relevant information (news articles, research papers, etc.).

[1576] Step 4:

[1577] The server evaluates the collected information and input data.

[1578] Input: Collected relevant information and user-entered text information.

[1579] Data Calculation: The server's evaluation means compares the collected information with the analyzed information, measures the degree of agreement, and generates a reliability score.

[1580] Specific behavior: If the server finds that 6 out of 10 pieces of collected information match the user's input text information, it calculates a reliability score of 60%.

[1581] Output: A confidence score (e.g., 60%).

[1582] Step 5:

[1583] The server analyzes the sentiment of the input text information.

[1584] Input: User-entered text information.

[1585] Data computation: The server uses a generative AI model (e.g., BERT or GPT-3) to identify emotions from text information.

[1586] What it does: The sentiment analysis model determines the user's emotions from the text content, recognizing, for example, whether the user is excited or confused.

[1587] Output: The user's emotional state (e.g. excited).

[1588] Step 6:

[1589] The server notifies the user of the results.

[1590] Input: Confidence score and sentiment analysis results.

[1591] Data calculation: The server's notification mechanism generates feedback in the most appropriate format depending on the reliability score and emotional state.

[1592] What it does: The server uses a notification system such as Firebase Cloud Messaging to calmly provide details if the user is excited, or gently explain the outcome to confused users.

[1593] Output: Notification of results (e.g., in a calm display format or a friendly explanation format).

[1594] 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.

[1595] 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.

[1596] 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.

[1597] 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.

[1598] FIG. 9 illustrates 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 behaviors 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.

[1599] 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.

[1600] 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).

[1601] 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.

[1602] 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."

[1603] 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.

[1604] 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).

[1605] 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.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] 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.

[1612] 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.

[1613] 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.

[1614] 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.

[1615] The following is further disclosed regarding the above embodiment.

[1616] (Claim 1)

[1617] an input device for a user to input information;

[1618] an analysis device that analyzes input information;

[1619] a collection device for collecting information from external sources;

[1620] An evaluation device that compares and evaluates the collected information and the analyzed information;

[1621] a notification device that notifies a user of the evaluation result;

[1622] A system including:

[1623] (Claim 2)

[1624] 10. The system of claim 1, wherein the evaluation device measures the degree of agreement between the collected information and the input information to generate a reliability score.

[1625] (Claim 3)

[1626] 10. The system of claim 1, wherein the analysis device processes information input in one or more of the following formats: text, image, and video.

[1627] "Example 1"

[1628] (Claim 1)

[1629] an input means for a user to input information;

[1630] an analysis means for analyzing input information;

[1631] collection means for collecting information from external sources;

[1632] An evaluation method for comparing and evaluating the collected information and analyzed information;

[1633] a notification means for notifying a user of the evaluation result;

[1634] a collection means for scraping related information from external information sources based on the keywords extracted by the analysis means;

[1635] a means for the evaluation means to measure the degree of agreement and generate a reliability score;

[1636] A system including:

[1637] (Claim 2)

[1638] 2. The system of claim 1, wherein the evaluation means measures the degree of agreement between the collected information and the input information to generate a reliability score.

[1639] (Claim 3)

[1640] 2. The system of claim 1, wherein the analysis means processes information input in one or more of the following formats: text, image, and video.

[1641] "Application Example 1"

[1642] (Claim 1)

[1643] an input means for a user to input information;

[1644] an analysis means for analyzing input information;

[1645] collection means for collecting information from external sources;

[1646] An evaluation method for comparing and evaluating the collected information and analyzed information;

[1647] a notification means for notifying a user of the evaluation result;

[1648] advertising information evaluation means for inputting advertising data and evaluating the content thereof;

[1649] A system including:

[1650] (Claim 2)

[1651] 2. The system of claim 1, wherein the evaluation means measures the degree of agreement between the collected information and the input information to generate a reliability score.

[1652] (Claim 3)

[1653] 2. The system according to claim 1, wherein the analysis means processes information input in one or more of the following formats: text, image, and video.

[1654] "Example 2: Combining Emotion Engines"

[1655] (Claim 1)

[1656] an input means for a user to input information;

[1657] an analysis means for analyzing input information;

[1658] collection means for collecting information from external sources;

[1659] An evaluation method for comparing and evaluating the collected information and analyzed information;

[1660] a notification means for notifying a user of the evaluation result;

[1661] Emotion identification means for identifying the emotion of a user;

[1662] A system including:

[1663] (Claim 2)

[1664] 2. The system of claim 1, wherein the evaluation means measures the degree of agreement between the collected information and the input information to generate a reliability score.

[1665] (Claim 3)

[1666] 2. The system according to claim 1, wherein the analysis means processes information input in one or more of the following formats: text, image, and video.

[1667] (Claim 4)

[1668] 2. The system according to claim 1, wherein the notification means changes the notification format based on the result of the emotion identification means.

[1669] "Application example 2 when combining emotion engines"

[1670] (Claim 1)

[1671] an input means for a user to input information;

[1672] an analysis means for analyzing input information;

[1673] collection means for collecting information from external sources;

[1674] An evaluation method for comparing and evaluating the collected information and analyzed information;

[1675] The system notifies the user of the evaluation results and includes emotion recognition means for analyzing the user's emotions.

[1676] A notification means;

[1677] A system including:

[1678] (Claim 2)

[1679] The evaluation means includes an evaluation means for measuring the degree of agreement between the collected information and the input information and generating a reliability score, and a notification means for dynamically changing the notification format according to the emotional state of the user.

[1680] 10. The system of claim 1.

[1681] (Claim 3)

[1682] The analysis means processes the input information in one or more of the forms of text, image, and video, and the emotion recognition means identifies the emotion based on the input information of the user.

[1683] 10. The system of claim 1. [Explanation of symbols]

[1684] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input device for a user to input information; an analysis device that analyzes input information; a collection device for collecting information from external sources; an evaluation device that compares and evaluates the collected information and the analyzed information; a notification device that notifies a user of the evaluation result; A system including:

2. 2. The system of claim 1, wherein the evaluator measures the degree of agreement between the collected information and the input information to generate a reliability score.

3. 10. The system of claim 1, wherein the analysis device processes information input in one or more of the following formats: text, images, and video.

Images