System

Abstract

A system is provided.SOLUTION: The system includes a means for allowing a user to input information such as a text, an image and a voice, a means for transmitting the inputted information to a server, a means for determining whether the inputted information is correct or not by using a generation AI model in the server, and a means for presenting the determination result and the basis to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Fake news and fake images have become a major social problem in recent years. This misinformation is particularly prevalent through social media and comment functions, often causing social confusion and misunderstanding. Furthermore, verifying the accuracy of information often requires specialized knowledge, and there is a lack of reliable methods that are easily accessible to the general public. Against this background, there is a demand for technology that can quickly and easily determine the accuracy of information. [Means for solving the problem]

[0005] The present invention is a system that includes a means for users to input information such as text, images, and audio; a terminal means for transmitting the input information to a server; a means on the server to determine whether the input information is correct using a generative AI model; and a means for presenting the determination results and their rationale to the user. This system's generative AI model supports a variety of input formats (text, images, audio), and is also equipped with a function for compiling and publishing misinformation, thereby reducing the risk of users unintentionally spreading misinformation. Furthermore, by compiling and publishing misinformation periodically, it is possible to raise widespread awareness and improve information literacy throughout society.

[0006] A "user" is a person or entity who uses the system to input information and obtain a correct / incorrect result.

[0007] "Text" is an information format consisting of a string of characters, and is a type of information entered by a user.

[0008] An "image" is visual content and a form of information that a user inputs into a system.

[0009] "Voice" is data that a user can input into a system in the form of sound information.

[0010] "Input means" refers to the method or device by which a user provides information to a system, such as text, images, or voice.

[0011] A "server" is a computer system that receives information sent by a user and uses a generative AI model to determine whether the information is correct.

[0012] "Terminal means" refers to a device or application through which a user inputs information and transmits that information to a server.

[0013] A "generative AI model" is an AI system that uses machine learning algorithms to analyze and determine the accuracy of input information.

[0014] "Means for determining accuracy" refers to the process of using a generative AI model to evaluate the reliability of input information and generate the results.

[0015] A "verdict result" is the conclusion reached after the generative AI model analyzes the accuracy of the input information.

[0016] "Evidence" refers to the data and theoretical background used by the generative AI model in the process of determining whether it is correct or incorrect.

[0017] A "database" is an information system for systematically storing and managing specific data, such as misinformation.

[0018] "Aggregation" refers to the process of compiling and statistically analyzing data accumulated over a period of time.

[0019] "Means of publication" refers to the methods and procedures for making information such as aggregated results widely available to the public. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user. This system supports various information formats, such as text, images, and audio, and uses a generative AI model to determine the accuracy of the information. Specific embodiments are described below.

[0042] Overall system configuration

[0043] This system mainly consists of the following elements:

[0044] 1. User Input Method

[0045] 2. Means of transmitting information from the terminal to the server

[0046] 3. A method for determining the accuracy of information using a server-generated AI model

[0047] 4. Method of presenting the judgment results

[0048] 5. Methods for compiling and publishing misinformation

[0049] System Operation

[0050] 1. User Input Method

[0051] Users input information such as text, images, and voice using the comment function of LINE messages or Yahoo! News. For example, a user inputs text information such as "A new virus has been discovered."

[0052] 2. Means of transmitting information from the terminal to the server

[0053] The entered information is sent from the device (e.g., a smartphone or computer) to the server. The device converts it into an appropriate data format and sends the information to the server. For example, text information is converted into JSON format and sent to the server.

[0054] 3. A method for determining the accuracy of information using a server-generated AI model

[0055] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. As a specific example, the generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is incorrect."

[0056] 4. Method of presenting the judgment results

[0057] The server returns the judgment result from the generative AI model and the reasoning behind it to the user. For example, the user may be presented with a result such as, "This information is incorrect, and there are no new viruses currently being confirmed."

[0058] 5. Methods for compiling and publishing misinformation

[0059] Misinformation is stored in a server database and periodically compiled. The server periodically analyzes and publishes the misinformation. For example, if a large number of people receive false information claiming that "certain foods can ward off coronavirus," the server will publish that information as a warning on social media and in the comments section.

[0060] Specific examples

[0061] Example 1: Judging the accuracy of text information

[0062] The user enters the information "A new virus has been discovered" in a LINE message.

[0063] The terminal sends this text information to the server.

[0064] The server uses a generative AI model to determine the accuracy of this information.

[0065] The generative AI model determines that the information is incorrect and that no new virus has been identified to date.

[0066] The server presents the judgment result and the reasoning to the user.

[0067] Example 2: Image information accuracy

[0068] A user uploads an image using Yahoo! News' comment function, writing, "This image is from the disaster site."

[0069] The terminal transmits the image data to the server.

[0070] The server uses a generative AI model to determine the accuracy of the image.

[0071] The generative AI model determines that this image was actually taken somewhere else.

[0072] The server presents the judgment result to the user.

[0073] Example 3: Aggregating and publishing misinformation

[0074] Multiple users provide information claiming that certain foods can ward off coronavirus.

[0075] The server stores the information in the database as false information.

[0076] The server will compile data over a certain period of time and issue warnings about misinformation that certain foods can ward off coronavirus.

[0077] As described above, the system of the present invention can handle a variety of information formats and quickly and easily verify the accuracy of information, thereby preventing the spread of false information and improving social credibility.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The user inputs information such as text, images, and voice via the device. For example, the user inputs the text information "A new virus has been discovered" via LINE message.

[0081] Step 2:

[0082] The device converts the input information into an appropriate data format. For example, it converts text information into JSON format.

[0083] Step 3:

[0084] The device sends the converted data to the server using an internet connection.

[0085] Step 4:

[0086] The server verifies the received data and passes it to the generative AI model, which then begins analyzing the information.

[0087] Step 5:

[0088] The generative AI model analyzes the information provided and determines whether it is true or false. For example, it determines whether the text "A new virus has been discovered" is medically incorrect.

[0089] Step 6:

[0090] The generative AI model generates a judgment result and its rationale, which are then sent back to the server. The rationale includes reliable databases and expert opinions.

[0091] Step 7:

[0092] The server receives the judgment results and rationale from the generative AI model and converts them into a data format that can be presented to the user.

[0093] Step 8:

[0094] The server then sends the converted data back to the device via the Internet, for example, returning a result that "this information is incorrect and there are no currently confirmed new viruses."

[0095] Step 9:

[0096] The terminal displays the result of the judgment received from the server to the user, allowing the user to confirm the accuracy of the input information.

[0097] Step 10:

[0098] If a user provides false information, that information is stored in the server's database. For example, the false information "a new virus has been discovered" is stored.

[0099] Step 11:

[0100] The server periodically aggregates the misinformation in the database, thereby identifying misinformation that has a significant social impact.

[0101] Step 12:

[0102] The server publishes the results of the survey on social media and in applications with comment functions, preventing the spread of misinformation among users.

[0103] In this way, the system provides the ability to quickly and reliably determine the accuracy of information and prevent the spread of misinformation.

[0104] Example 1

[0105] 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."

[0106] In modern society, inaccurate and false information is easily spread, causing misunderstandings and anxiety, and undermining trust in society as a whole. Furthermore, it is difficult to assess information in a variety of formats, making it difficult to quickly and accurately determine the authenticity of information using conventional methods. Furthermore, because misinformation is not regularly compiled and published, there is a lack of ongoing warnings and countermeasures. To address these issues, a new system needs to be built.

[0107] 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.

[0108] In this invention, the server includes a means for a user to input information, a terminal means for transmitting the input information, a means for determining whether the input information is correct using a generative AI model, a means for presenting the determination result and its basis to the user, and a means for storing false information in a database and periodically compiling and publishing the information. This automates the entire process from inputting information to determining whether it is correct, presenting the result, and compiling and publishing the false information, making it possible to prevent the spread of inaccurate information and improve trust in society as a whole.

[0109] A "user" is an entity that inputs information and makes various requests to the system.

[0110] "Information" is data that is input in the form of text, images, audio, etc., and that is processed and analyzed.

[0111] A "terminal" is a device that receives information entered by a user, converts it into an appropriate data format, and sends it to a server.

[0112] A "server" is a remote device or cloud-based system used to analyze input information and generate results.

[0113] A "generative AI model" is an artificial intelligence model based on machine learning algorithms and used to determine whether information is correct or incorrect using extensive data sets.

[0114] "Correctness determination" is the process of analyzing and determining the accuracy and authenticity of input information.

[0115] The "judgment result" is the analysis result regarding the accuracy of the information obtained by the generative AI model.

[0116] The "data format" refers to the structure or format of data used when a terminal transmits information from a user to a server.

[0117] A "database" is an organized collection of data that stores misinformation and allows it to be accessed at a later time.

[0118] "Aggregation" is a method of organizing stored misinformation over a period of time and analyzing statistics and trends.

[0119] "Publication" is the process of issuing warnings and providing information based on the collected misinformation.

[0120] This invention is a system that automatically determines the accuracy of information entered by users, and is composed of three main elements: a server, a terminal, and a user. This system utilizes a generative AI model to analyze the accuracy of information, and compiles and publishes false information.

[0121] 1. Enter your information

[0122] Users can input information such as text, images, and audio via LINE messages or the comment function on news sites using their smartphones or computers.

[0123] Examples:

[0124] The user enters the information "A new virus has been discovered" in a LINE message.

[0125] 2. Transmission of information

[0126] The device receives the information entered by the user, converts it into an appropriate data format, such as JSON, and sends it to the server. The device then sends the information via an internet connection.

[0127] Examples:

[0128] The device converts the text information "A new virus has been discovered" into JSON format and sends it to the server.

[0129] 3. Performing a correct / incorrect judgment

[0130] The server inputs the received information into a generative AI model, which uses extensive datasets and machine learning algorithms to determine whether the input information is correct or incorrect.

[0131] The generative AI models used here are models specialized for natural language processing (e.g., GPT-3) and are trained on large amounts of data.

[0132] Examples:

[0133] The server inputs the text "A new virus has been discovered" into the generative AI model and has it analyze its accuracy. The model then refers to databases and official information and determines that the information is false.

[0134] Example prompt sentence:

[0135] "Please determine the accuracy of the information that a new virus has been discovered."

[0136] "Please check whether this image is really from the disaster site."

[0137] 4. Return of the judgment results

[0138] The server analyzes and organizes the judgment results obtained from the generative AI model and their rationale, and returns them to the user. The device then displays the received judgment results to the user.

[0139] Examples:

[0140] The server sends the determination result that "this information is incorrect and there are no currently confirmed new viruses" to the terminal, and the terminal notifies the user of the result.

[0141] 5. Aggregation and publication of misinformation

[0142] The server stores the misinformation in a database and periodically compiles the data. The results are analyzed and the most frequently occurring misinformation is published, allowing for ongoing awareness raising.

[0143] Examples:

[0144] If there is a large amount of misinformation, such as "certain foods can ward off coronavirus," the information will be published as a warning on social media and in the comments section.

[0145] As a result, the system of the present invention can accommodate a variety of information formats and not only can it quickly and accurately verify the accuracy of information, but it can also prevent the spread of false information and improve trust in society as a whole.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] A user opens a LINE message or the comment function of a news site and enters information. This information can be entered in the form of text, image, audio, etc. For example, a user enters the text information "A new virus has been discovered" in a LINE message.

[0149] Input: Information entered by the user (text, images, audio)

[0150] Output: Input text, image, and audio data

[0151] Specific behavior:

[0152] A user enters text using a smartphone or computer keyboard.

[0153] In the case of images or audio, the user selects and uploads the file.

[0154] Step 2:

[0155] The device converts the input information into an appropriate data format and sends it to the server. For example, text information is converted into JSON format.

[0156] Input: User-entered text, images, and audio data

[0157] Output: Information converted into an appropriate data format, such as JSON.

[0158] Specific behavior:

[0159] The terminal converts the text data into JSON format through a program.

[0160] Image and audio data is converted into binary format.

[0161] The device sends the data to the server via an internet connection.

[0162] Step 3:

[0163] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. For example, the server inputs the text "A new virus has been discovered" into the generative AI model, and the model verifies this information and makes a judgment.

[0164] Input: JSON formatted text, images, and audio data

[0165] Output: True / false judgment result (e.g., "That information is false")

[0166] Specific behavior:

[0167] The server parses the received JSON data and inputs it into the API for the generative AI model.

[0168] The generative AI model references the dataset and determines whether the information is correct.

[0169] The server obtains the judgment result.

[0170] Step 4:

[0171] The server analyzes the judgment results and their rationale from the generative AI model and returns them to the user. The results are formatted in a way that is easy for the user to understand. For example, the judgment result may be presented to the user as "This information is incorrect, and there are no new viruses currently being confirmed."

[0172] Input: Correct / incorrect result

[0173] Output: Formatted results for presentation to the user

[0174] Specific behavior:

[0175] The server reads the results from the JSON format and formats them in user-friendly language.

[0176] Send the formatted result to the terminal.

[0177] The terminal notifies the user of the received result.

[0178] Step 5:

[0179] The server stores the results of the assessment in a database and periodically compiles the misinformation. The results are analyzed, and frequently occurring misinformation is made public. For example, if a large number of people report misinformation such as "certain foods can ward off coronavirus," the information will be published as a warning on social media and through the comments function.

[0180] Input: Judgment result and its basis

[0181] Output: Aggregated misinformation analysis results

[0182] Specific behavior:

[0183] The server stores the judgment results in a database.

[0184] We use tools to regularly aggregate the contents of the database and analyze trends in misinformation.

[0185] The results will be compiled and published, and warnings will be posted on social media and websites.

[0186] (Application example 1)

[0187] 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."

[0188] The proliferation of false and unreliable information on the Internet, especially in content distribution services, has become a problem. Because users have no way of determining the accuracy of this information, they run the risk of acting on it. For this reason, there is a demand for a system that allows users to easily check the accuracy of the content they watch or view in real time.

[0189] 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.

[0190] In this invention, the server includes a means for a user to input information such as text, images, and audio, a terminal means for transmitting the input information to the server, a means for determining the accuracy of the input information using a generative AI model in the server, a means for presenting the determination result and its basis to the user, and a means for checking the accuracy of videos or articles that the user intends to view or read on the content distribution service in advance. This allows the user to quickly evaluate the reliability of content and prevent the spread of misinformation.

[0191] A "user" is an individual or corporation who uses a content distribution service to view or browse information such as text, images, and audio.

[0192] "Information" refers to all data provided in the form of text, images, audio, etc.

[0193] A "terminal" is a device used by a user to input information and send it to a server, and includes a smartphone, computer, tablet, etc.

[0194] The "server" is a central device that receives information sent by users and uses a generative AI model to determine whether the information is correct.

[0195] A "generative AI model" is an artificial intelligence system that is trained on a large dataset and is used to analyze the accuracy of input information.

[0196] "Accuracy check" is the process of determining whether the entered information is accurate.

[0197] The "judgment result" is the conclusion reached by the generative AI model after analyzing the accuracy of the information.

[0198] "Basis" refers to the evidence and reasons that support the judgment result.

[0199] A "content distribution service" is an online platform that provides users with digital content such as videos, articles, and audio.

[0200] "Video" is a medium that consists of a series of images or videos.

[0201] An "article" refers to text data written to provide news or information.

[0202] The system for carrying out the present invention is mainly composed of user input means, a terminal, and a server. The specific configuration and operation of the system will be described below.

[0203] User input method

[0204] Users input information such as text, images, and audio. This input is performed using devices such as smartphones, computers, and tablets. For example, a user may input the title and content of a news article they wish to view in text format on a content distribution service.

[0205] Terminal means

[0206] The information entered by the user is converted into an appropriate data format and sent from the device to the server. For example, text information is converted into JSON format and sent to the server. The device can be a smartphone, computer, tablet, etc.

[0207] Server Means

[0208] The server uses a generative AI model to determine whether the input information is correct. A generative AI model is an artificial intelligence trained on a large dataset and can handle a variety of input formats, including text, images, and audio. For example, a generative AI model analyzes the accuracy of text information in a news article and determines that the information is incorrect.

[0209] Method of presenting the judgment results

[0210] The server presents the judgment results obtained from the generative AI model and the reasons for them to the user. For example, the user may be provided with a judgment result such as "This information is incorrect, and there are no new viruses currently being confirmed."

[0211] How misinformation is collected and published

[0212] The server has the function of storing misinformation in a database and periodically compiling and publishing it. For example, if a large amount of misinformation is received, the information will be analyzed and a warning will be issued via social media and the comment function.

[0213] Specific examples

[0214] Verification of text information

[0215] A user enters text information such as "A new virus has been discovered" using a smartphone app.

[0216] The device converts this text information into JSON format and sends it to the server.

[0217] The server uses a generative AI model to determine the accuracy of the text information.

[0218] The generative AI model determines that the information is incorrect.

[0219] The server presents the judgment result and its basis to the user.

[0220] Prompt Sentence Examples

[0221] "We check news articles about new virus discoveries. If the facts aren't verified, we flag them as misinformation."

[0222] This allows users to quickly obtain accurate information and prevents the spread of false information.

[0223] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0224] Step 1:

[0225] The user inputs information such as text, images, or audio.

[0226] Input: A user enters information using an input device (e.g., smartphone, tablet, computer).

[0227] Output: Information entered by the user (text, images, audio).

[0228] Step 2:

[0229] The terminal converts the input information into the appropriate data format.

[0230] Input: Raw data entered by the user (text, images, audio).

[0231] Data processing: Encode text information into JSON format, and images and audio into Base64 format.

[0232] Output: Information in converted data format (e.g. text information in JSON format).

[0233] Step 3:

[0234] The terminal transmits the converted information to the server.

[0235] Input: Information in converted data format.

[0236] Sending data: Sending data to the server using an HTTP request.

[0237] Output: Information sent to the server.

[0238] Step 4:

[0239] The information received by the server is input into the generative AI model to determine whether it is correct or incorrect.

[0240] Input: Information received by the server.

[0241] Data Computation: Input information into a generative AI model and use extensive data sets and machine learning algorithms to determine the accuracy of the information.

[0242] Output: The result of the judgment (e.g., "correct", "incorrect", etc.) and its rationale.

[0243] Step 5:

[0244] The server presents the judgment results from the generated AI model and their rationale to the user.

[0245] Input: The judgment result output by the generative AI model and its rationale.

[0246] Send data: Send data to the device using an HTTP response.

[0247] Output: The judgment result and its rationale presented to the user.

[0248] Step 6:

[0249] The server stores the misinformation in a database, which is then periodically compiled and published.

[0250] Input: Information that the generative AI model determines to be incorrect.

[0251] Data Retention: Storing false information in the database.

[0252] Data calculation: Collecting and analyzing stored misinformation over a period of time.

[0253] Output: A warning published via social media and the comments function.

[0254] 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.

[0255] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user. This system supports various information formats, such as text, images, and audio, and uses a generative AI model and emotion engine to determine the accuracy of the information and recognize the user's emotions. Specific embodiments are described below.

[0256] Overall system configuration

[0257] This system mainly consists of the following elements:

[0258] 1. User Input Method

[0259] 2. Means of transmitting information from the terminal to the server

[0260] 3. A method for determining the accuracy of information using a server-generated AI model

[0261] 4. User Emotion Recognition Method Using Emotion Engine

[0262] 5. Means of presenting the judgment results and their rationale to the user

[0263] 6. Methods for compiling and publishing misinformation

[0264] System Operation

[0265] 1. User Input Method

[0266] Users input information such as text, images, and voice using the comment function of LINE messages or Yahoo! News. For example, a user inputs text information such as "A new virus has been discovered."

[0267] 2. Means of transmitting information from the terminal to the server

[0268] The entered information is sent from the device (e.g., a smartphone or computer) to the server. The device converts it into an appropriate data format and sends the information to the server. For example, text information is converted into JSON format and sent to the server.

[0269] 3. A method for determining the accuracy of information using a server-generated AI model

[0270] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. As a specific example, the generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is incorrect."

[0271] 4. User Emotion Recognition Method Using Emotion Engine

[0272] At the same time, the server uses an emotion engine to recognize the user's emotions from the information input by the user. For example, in the case of voice input, the emotion engine analyzes the tone and pitch of the user's voice to determine whether the user is excited or calm.

[0273] 5. Means of presenting the judgment results and their rationale to the user

[0274] The server presents the results of the generative AI model and their rationale to the user. The way the results are presented is adjusted according to the user's emotional state. For example, if the user is excited, the server will present the results in a calm tone to allow the user to receive them calmly.

[0275] 6. Methods for compiling and publishing misinformation

[0276] Misinformation is stored in a server database and periodically compiled. The server periodically analyzes and publishes the misinformation. For example, if a large number of people receive false information claiming that "certain foods can ward off coronavirus," the server will publish that information as a warning on social media and in the comments section.

[0277] Specific examples

[0278] Example 1: Recognizing text information and user emotions

[0279] The user enters the information "A new virus has been discovered" in a LINE message.

[0280] The terminal sends this text information to the server.

[0281] The server uses a generative AI model to determine the accuracy of this information.

[0282] The generative AI model determines that the information is incorrect and that no new virus has been identified to date.

[0283] The emotion engine recognizes the user's anxious emotion from the user's text input.

[0284] The server presents the results and rationale in a tone that reassures anxious users.

[0285] Example 2: Verification of voice information and sentiment analysis

[0286] A user uploads audio using Yahoo! News' comment function, stating, "This image is from the disaster site."

[0287] The terminal transmits the audio data to the server.

[0288] The server uses a generative AI model to determine the accuracy of the speech.

[0289] The generative AI model analyzes the audio information and determines that the image was actually taken somewhere else.

[0290] The emotion engine recognizes the user's emotion (e.g., anger or anxiety) from the tone of their voice.

[0291] The server presents the results of the assessment in a tone adjusted according to the user's emotional state.

[0292] Example 3: Aggregating and publishing misinformation

[0293] Multiple users provide information claiming that certain foods can ward off coronavirus.

[0294] The server stores the information in the database as false information.

[0295] The server will compile data over a certain period of time and issue warnings about misinformation that certain foods can ward off coronavirus.

[0296] By combining a generative AI model and an emotion engine, this system can quickly determine the accuracy of information entered by users and provide appropriate feedback according to the user's emotional state, thereby preventing the spread of misinformation and improving social credibility.

[0297] The processing flow will be explained below.

[0298] Step 1:

[0299] The user inputs information such as text, images, and voice via the device. For example, the user inputs the text information "A new virus has been discovered" via LINE message.

[0300] Step 2:

[0301] The device converts the input information into an appropriate data format. For example, text information is converted into JSON format.

[0302] Step 3:

[0303] The device sends the converted data to the server using an internet connection.

[0304] Step 4:

[0305] The data received by the server is checked to ensure that the data format is consistent and that it is not invalid.

[0306] Step 5:

[0307] The server passes the received data to the generative AI model, which then begins analyzing the information.

[0308] Step 6:

[0309] A generative AI model analyzes data and determines whether it is true or false. For example, it determines whether the information "a new virus has been discovered" is medically incorrect.

[0310] Step 7:

[0311] The generative AI model generates a judgment result and its rationale, which are then sent back to the server. The rationale includes reliable databases and expert opinions.

[0312] Step 8:

[0313] The server receives the judgment results and rationale from the generative AI model and converts them into a data format for presentation to the user.

[0314] Step 9:

[0315] The server uses an emotion engine to recognize the user's emotions based on the information entered by the user, for example, by analyzing the text entered by the user and the tone of the voice.

[0316] Step 10:

[0317] The emotion engine recognizes the user's emotions and sends them back to the server, including emotions such as excitement, worry, and anger.

[0318] Step 11:

[0319] The server adjusts the way the results are presented depending on the user's emotional state. For example, if the user is excited, the server presents the results in a calm tone.

[0320] Step 12:

[0321] The server then sends the converted data back to the device via the Internet, for example, returning a result that "this information is incorrect and there are no currently confirmed new viruses."

[0322] Step 13:

[0323] The terminal displays the result of the judgment received from the server to the user, allowing the user to confirm the accuracy of the input information.

[0324] Step 14:

[0325] If a user provides false information, that information is stored in the server's database. For example, the false information "a new virus has been discovered" is stored.

[0326] Step 15:

[0327] The server periodically aggregates the misinformation in the database, thereby identifying misinformation that has a significant social impact.

[0328] Step 16:

[0329] The server publishes the results of the survey on social media and in applications with comment functions, preventing the spread of misinformation among users.

[0330] In this way, the system can quickly and reliably determine the accuracy of information and provide appropriate feedback according to the user's emotional state. It also provides a function to prevent the spread of misinformation, thereby improving social credibility.

[0331] Example 2

[0332] 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."

[0333] Conventional information verification systems were unable to take the user's emotional state into account when determining the accuracy of the information entered by the user. As a result, the results of the verification were not properly communicated to the user, and the system was not effective in preventing the spread of misinformation. Furthermore, because misinformation was not compiled or published, it was not possible to quickly and effectively warn other users. This could lead to a decline in social credibility.

[0334] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for determining whether input information is correct or incorrect using a generative AI model, a means for recognizing a user's emotion using an emotion engine, a means for presenting a determination result according to the user's emotional state, and a means for aggregating and publishing false information. This makes it possible to quickly and accurately determine the accuracy of information while taking into consideration the user's emotions, prevent the spread of false information, and improve social credibility.

[0335] "User" refers to an individual or corporation that uses the system to input information.

[0336] "Information" refers to data in various formats, such as text, images, and audio.

[0337] "Terminal" refers to a device used by a user to input and transmit information to a server.

[0338] "Server" refers to a computing device that receives information sent by a user and uses a generative AI model and emotion engine to determine the accuracy of the information and the user's emotions.

[0339] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms and extensive data sets to analyze input information for accuracy.

[0340] An "emotion engine" refers to software that analyzes and recognizes the user's emotional state from information input.

[0341] "Accuracy assessment" refers to the process of evaluating the accuracy of input information and determining whether it is correct or incorrect.

[0342] "Misinformation" refers to information entered by a user that is inaccurate.

[0343] A "database" refers to a system that systematically stores data such as input information and its judgment results.

[0344] "Aggregation" refers to the process of statistically organizing misinformation stored in a database over a period of time.

[0345] "Publicity" refers to the activity of disclosing compiled misinformation to the public and raising awareness.

[0346] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user and presents the results according to the user's emotional state. This system supports various information formats, such as text, images, and audio, and uses a generative AI model and an emotion engine to determine the accuracy of the information and recognize the user's emotions. Specific embodiments are described below.

[0347] Overall system configuration

[0348] This system mainly consists of the following elements:

[0349] 1. User Input Method

[0350] 2. Means of transmitting information from the terminal to the server

[0351] 3. A method for determining the accuracy of information using a server-generated AI model

[0352] 4. User Emotion Recognition Method Using Emotion Engine

[0353] 5. Means of presenting the judgment results and their rationale to the user

[0354] 6. Methods for compiling and publishing misinformation

[0355] 1. User Input Method

[0356] Users input information such as text, images, and audio using LINE messages or the comment function of a news site. For example, a user inputs text information such as "A new virus has been discovered."

[0357] 2. Means of transmitting information from the terminal to the server

[0358] The device (smartphone or computer) converts the information entered by the user into an appropriate data format (for example, JSON format) and sends it to the server. For example, text information entered by the user is converted into JSON format and sent to the server.

[0359] 3. A method for determining the accuracy of information using a server-generated AI model

[0360] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. Specifically, it determines that the information "a new virus has been discovered" is false.

[0361] 4. User Emotion Recognition Method Using Emotion Engine

[0362] In parallel, the server uses an emotion engine to recognize the user's emotions from the information input by the user. For example, in the case of voice input, the emotion engine analyzes the tone and pitch of the user's voice to determine whether the user is excited or calm.

[0363] 5. Means of presenting the judgment results and their rationale to the user

[0364] The server presents the results of the generative AI model and the reasons for their findings to the user. The way the results are presented is adjusted according to the user's emotional state. For example, if the user is excited, the server will present the results in a calm tone so that the user can receive them calmly.

[0365] 6. Methods for compiling and publishing misinformation

[0366] The server stores misinformation in a database and periodically compiles and analyzes it. It then issues warnings about frequently occurring misinformation. For example, if a large number of people report the misinformation that "certain foods can ward off coronavirus," the server will publicize the information as a warning via social media and the comment function.

[0367] Specific operation example

[0368] Example 1: Recognizing text information and user emotions

[0369] A user types "A new virus has been discovered" in a LINE message.

[0370] The terminal sends this text information to the server.

[0371] The server uses a generative AI model to determine the accuracy of this information.

[0372] The generative AI model determines that the information is incorrect.

[0373] The emotion engine recognizes the emotions that the user is concerned about.

[0374] The server presents the judgment result and the reasoning to the user in a reassuring tone.

[0375] Example 2: Verification of voice information and sentiment analysis

[0376] A user uses the comment function on a news site to upload an audio message saying, "This image is from the disaster site."

[0377] The terminal transmits the audio data to the server.

[0378] The server uses a generative AI model to determine the accuracy of the audio information.

[0379] The generative AI model analyzes the audio information and determines that the image was actually taken somewhere else.

[0380] The emotion engine recognizes the user's emotion (e.g., anger or anxiety) from the tone of their voice.

[0381] The server presents the judgment result in a tone adjusted according to the emotional state.

[0382] Example 3: Aggregating and publishing misinformation

[0383] Multiple users provide information claiming that certain foods can ward off coronavirus.

[0384] The server stores the information in the database as false information.

[0385] The server aggregates data over a certain period of time.

[0386] The server warns against the misinformation that certain foods can ward off coronavirus.

[0387] By combining a generative AI model and an emotion engine, this system can quickly determine the accuracy of information entered by users and provide appropriate feedback according to the user's emotional state, thereby preventing the spread of misinformation and improving social credibility.

[0388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0389] Step 1:

[0390] The user inputs information such as text, images, or audio.

[0391] Specific operation: The user opens the LINE app on their smartphone, types "A new virus has been discovered," and presses the send button.

[0392] Input: Information entered by the user (text, image, audio).

[0393] Output: Input information that the terminal is instructed to send.

[0394] Step 2:

[0395] The terminal converts the user's input information into an appropriate data format (for example, JSON format).

[0396] Specific operation: The smartphone converts the text information "A new virus has been discovered" into JSON format.

[0397] Input: Information entered by the user.

[0398] Output: The input information converted to JSON format.

[0399] Step 3:

[0400] The terminal transmits the converted information to the server.

[0401] Specific operation: The smartphone generates JSON data and sends it to the server's API endpoint as a POST request.

[0402] Input: Input information converted to JSON format.

[0403] Output: The JSON data sent to the server.

[0404] Step 4:

[0405] The server inputs the received information into a generative AI model to determine the accuracy of the information.

[0406] Specific operation: The server analyzes the received JSON data and inputs it into the generative AI model. The generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is false."

[0407] Input: Input information in JSON format.

[0408] Output: The result of determining whether the information is correct or not.

[0409] Step 5:

[0410] The server inputs the received information into an emotion engine to recognize the user's emotions.

[0411] What it does: The server passes the voice data or text input to the emotion engine, which analyzes the user's emotions (excitement, anxiety, anger, etc.).

[0412] Input: User input (voice, text).

[0413] Output: The perceived emotional state of the user.

[0414] Step 6:

[0415] The server integrates the judgment results from the generative AI model with the emotional state from the emotion engine to create an output to present to the user.

[0416] Specific operation: The server integrates the determination that the information "A new virus has been discovered" is false with the user's feelings of anxiety, and creates a message in a calm tone saying, "That information is false, and here is the correct information."

[0417] Input: Information correctness judgment result, user's emotional state.

[0418] Output: The tailored message to present to the user.

[0419] Step 7:

[0420] The server stores the misinformation in a database, which is then periodically compiled and published.

[0421] Specific operation: The server stores misinformation in a database and periodically compiles the data. For example, if a large number of people receive misinformation claiming that certain foods can ward off coronavirus, the server will publish that information as a warning via social media and the comments function.

[0422] Input: False information collected by the server.

[0423] Output: Aggregate results and published warning information.

[0424] (Application example 2)

[0425] 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."

[0426] In modern society, the accuracy of information is extremely important. However, information provided in a variety of formats often contains false information, which can lead to misunderstandings and confusion. Feedback on the widespread acceptance of false information is also important, and there is a need to provide information in an appropriate manner that takes into account the feelings of the recipient. This requires a system that allows users to respond appropriately to information provided, such as advertisements, and prevents the spread of false information.

[0427] 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 means for determining the accuracy of input information using a generative AI model, means for recognizing the user's emotion, and means for adjusting the feedback content based on the recognized emotion. This makes it possible to automatically determine the accuracy of the information entered by the user and provide feedback that takes the user's emotion into consideration.

[0428] The "means for user input of information" refers to a device or system that allows a user to input information in various formats such as text, images, and voice.

[0429] The "terminal means for transmitting information to a server" refers to a device or system that has the function of transmitting data to a server in order to process input information.

[0430] "Means for determining the correctness of input information using a generative AI model" refers to a system that uses a generative AI model to determine whether the information entered by a user is correct or incorrect.

[0431] The "means for recognizing user emotions" is a system that has the function of recognizing emotions by analyzing the content of information entered by the user and the user's reactions.

[0432] The "means for adjusting the content of feedback based on the recognized emotion" is a system for appropriately changing the content and method of the feedback provided depending on the user's emotional state.

[0433] MODE FOR CARRYING OUT THE INVENTION

[0434] The present invention provides a system for determining the accuracy of advertisements in real time and providing feedback according to the user's emotions. Specific embodiments are described below.

[0435] Overall system configuration

[0436] The system consists of the following hardware and software:

[0437] Hardware: Smart glasses (built-in camera, microphone, display)

[0438] software:

[0439] Generative AI model (commonly known as GPT-4, e.g., made by OpenAI)

[0440] Emotion recognition software (e.g., EmotionAPI, Microsoft Azure)

[0441] Data format: JSON

[0442] Communication means: Wi-Fi, Bluetooth

[0443] System Operation

[0444] The following shows how the system is implemented by the server, terminal, and user.

[0445] User input method

[0446] Users watch advertisements using smart glasses, whose built-in camera and microphone capture the content of the advertisements (text, images, and audio) in real time.

[0447] Means of transmitting information from a terminal to a server

[0448] The smart glasses convert the captured information into JSON format and send it to a server using Wi-Fi or Bluetooth.

[0449] A means of determining the accuracy of information using a server-generated AI model

[0450] The server inputs the received advertising information into a generative AI model (GPT-4) to determine its accuracy. The generative AI model uses a wide range of datasets and machine learning algorithms to analyze the accuracy of the input information.

[0451] A method for recognizing user emotions using an emotion engine

[0452] At the same time, the server uses EmotionAPI to recognize the user's emotions, analyzing the user's emotional state (e.g., excitement, relief, anxiety) based on data obtained from the built-in camera and microphone.

[0453] A means of presenting the judgment results and their rationale to the user

[0454] The server presents the results of the generative AI model and their rationale to the user. The presentation of the results is adjusted according to the user's emotional state. For example, if the user is excited, gentle feedback is provided.

[0455] Feedback is provided in the form of the judgment result and its rationale being displayed on the smart glasses display.

[0456] Specific examples

[0457] Example 1:

[0458] The ad you're seeing: "This product will help you lose weight easily."

[0459] The camera in the smart glasses captures the advertisement and sends it to a server.

[0460] The server uses GPT-4 to determine that "this information is not based on scientific evidence."

[0461] EmotionAPI recognizes when a user is excited.

[0462] The smart glasses display a calming message: "This ad contains misinformation. Please think carefully."

[0463] Example 2:

[0464] The ad you're looking at: "Special discounts on new product launches"

[0465] Smart glasses capture the ad and determine its accuracy, saying, "It's okay to buy here."

[0466] EmotionAPI recognizes that the user is not excited.

[0467] The smart glasses will say "This information is accurate" in a normal tone.

[0468] Prompt Sentence Examples

[0469] "Is the content of this ad accurate? This text: 'This product will help you lose weight easily.'"

[0470] "Determine the accuracy of information regarding new product launches and special discounts."

[0471] Through these processing procedures, the present invention is a system that quickly determines the accuracy of information and provides appropriate feedback according to the user's emotions, thereby achieving high reliability.

[0472] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0473] Program processing steps

[0474] Step 1:

[0475] A user watches an advertisement through smart glasses, and the built-in camera of the smart glasses captures the image and text information of the advertisement, and the microphone records the audio information, which becomes the input data.

[0476] Step 2:

[0477] The captured information is converted into JSON format and sent to the server by the smart glasses. This conversion and transmission is the role of the device. The input is the captured image, text, and audio data, and the output is JSON format data.

[0478] Step 3:

[0479] The server analyzes the received JSON format data and inputs it into a generative AI model (GPT-4).The server then uses the generative AI model to determine the accuracy of the advertising information.The input is the JSON data sent to the server, and the output is the accuracy determination result.

[0480] Step 4:

[0481] At the same time, the server analyzes the user's emotions using the Emotion API. Based on the audio and video data sent from the smart glasses, the server recognizes the user's emotional state (excitement, relief, anxiety, etc.). The input is audio and video data, and the output is the user's emotional state.

[0482] Step 5:

[0483] The server generates feedback content by integrating the judgment results from the generative AI model and the emotion recognition results from the Emotion API. The tone and expression of the feedback are adjusted according to the user's emotional state. The input is the judgment results and emotion recognition results, and the output is the adjusted feedback content.

[0484] Step 6:

[0485] The server sends feedback content to the smart glasses. The feedback content is displayed on the display of the smart glasses. The input is the feedback content sent from the server, and the output is the feedback displayed on the smart glasses.

[0486] Specific operation explanation

[0487] Step 1:

[0488] A user wears the smart glasses and watches any advertisement, and the smart glasses' built-in camera captures screenshots of the advertisement and related text, while the microphone records the corresponding audio.

[0489] Step 2:

[0490] The device (smart glasses) converts captured images, text, and audio data into JSON format and sends it to the server via Wi-Fi or Bluetooth.

[0491] Step 3:

[0492] The server analyzes the received JSON data and inputs it into a generative AI model (GPT-4) along with a prompt. For example, it inputs something like, "Is the content of this advertisement accurate? This text: 'This product will help you lose weight easily.'" The generative AI model determines the accuracy of the information and concludes that "the information is not based on scientific evidence."

[0493] Step 4:

[0494] The server uses EmotionAPI to analyze the audio and video data sent from the smart glasses and recognize whether the user is excited, for example, based on the tone of the voice or facial expression in the video.

[0495] Step 5:

[0496] The server combines the results of the generative AI model with the emotion recognition results of the Emotion API and adjusts the feedback content according to the user's emotional state. For example, if the user is excited, it generates calm feedback such as, "This ad contains misinformation. Please think about it calmly."

[0497] Step 6:

[0498] The server sends the generated feedback to the smart glasses, which then display the adjusted feedback on their display and inform the user whether the information is accurate.

[0499] 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.

[0500] 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.

[0501] 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.

[0502] [Second embodiment]

[0503] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0504] 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.

[0505] 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).

[0506] 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.

[0507] 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.

[0508] 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).

[0509] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0510] 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.

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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."

[0515] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user. This system supports various information formats, such as text, images, and audio, and uses a generative AI model to determine the accuracy of the information. Specific embodiments are described below.

[0516] Overall system configuration

[0517] This system mainly consists of the following elements:

[0518] 1. User Input Method

[0519] 2. Means of transmitting information from the terminal to the server

[0520] 3. A method for determining the accuracy of information using a server-generated AI model

[0521] 4. Method of presenting the judgment results

[0522] 5. Methods for compiling and publishing misinformation

[0523] System Operation

[0524] 1. User Input Method

[0525] Users input information such as text, images, and voice using the comment function of LINE messages or Yahoo! News. For example, a user inputs text information such as "A new virus has been discovered."

[0526] 2. Means of transmitting information from the terminal to the server

[0527] The entered information is sent from the device (e.g., a smartphone or computer) to the server. The device converts it into an appropriate data format and sends the information to the server. For example, text information is converted into JSON format and sent to the server.

[0528] 3. A method for determining the accuracy of information using a server-generated AI model

[0529] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. As a specific example, the generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is incorrect."

[0530] 4. Method of presenting the judgment results

[0531] The server returns the judgment result from the generative AI model and the reasoning behind it to the user. For example, the user may be presented with a result such as, "This information is incorrect, and there are no new viruses currently being confirmed."

[0532] 5. Methods for compiling and publishing misinformation

[0533] Misinformation is stored in a server database and periodically compiled. The server periodically analyzes and publishes the misinformation. For example, if a large number of people receive false information claiming that "certain foods can ward off coronavirus," the server will publish that information as a warning on social media and in the comments section.

[0534] Specific examples

[0535] Example 1: Judging the accuracy of text information

[0536] The user enters the information "A new virus has been discovered" in a LINE message.

[0537] The terminal sends this text information to the server.

[0538] The server uses a generative AI model to determine the accuracy of this information.

[0539] The generative AI model determines that the information is incorrect and that no new virus has been identified to date.

[0540] The server presents the judgment result and the reasoning to the user.

[0541] Example 2: Image information accuracy

[0542] A user uploads an image using Yahoo! News' comment function, writing, "This image is from the disaster site."

[0543] The terminal transmits the image data to the server.

[0544] The server uses a generative AI model to determine the accuracy of the image.

[0545] The generative AI model determines that this image was actually taken somewhere else.

[0546] The server presents the judgment result to the user.

[0547] Example 3: Aggregating and publishing misinformation

[0548] Multiple users provide information claiming that certain foods can ward off coronavirus.

[0549] The server stores the information in the database as false information.

[0550] The server will compile data over a certain period of time and issue warnings about misinformation that certain foods can ward off coronavirus.

[0551] As described above, the system of the present invention can handle a variety of information formats and quickly and easily verify the accuracy of information, thereby preventing the spread of false information and improving social credibility.

[0552] The processing flow will be explained below.

[0553] Step 1:

[0554] The user inputs information such as text, images, and voice via the device. For example, the user inputs the text information "A new virus has been discovered" via LINE message.

[0555] Step 2:

[0556] The device converts the input information into an appropriate data format. For example, it converts text information into JSON format.

[0557] Step 3:

[0558] The device sends the converted data to the server using an internet connection.

[0559] Step 4:

[0560] The server verifies the received data and passes it to the generative AI model, which then begins analyzing the information.

[0561] Step 5:

[0562] The generative AI model analyzes the information provided and determines whether it is true or false. For example, it determines whether the text "A new virus has been discovered" is medically incorrect.

[0563] Step 6:

[0564] The generative AI model generates a judgment result and its rationale, which are then sent back to the server. The rationale includes reliable databases and expert opinions.

[0565] Step 7:

[0566] The server receives the judgment results and rationale from the generative AI model and converts them into a data format that can be presented to the user.

[0567] Step 8:

[0568] The server then sends the converted data back to the device via the Internet, for example, returning a result that "this information is incorrect and there are no currently confirmed new viruses."

[0569] Step 9:

[0570] The terminal displays the result of the judgment received from the server to the user, allowing the user to confirm the accuracy of the input information.

[0571] Step 10:

[0572] If a user provides false information, that information is stored in the server's database. For example, the false information "a new virus has been discovered" is stored.

[0573] Step 11:

[0574] The server periodically aggregates the misinformation in the database, thereby identifying misinformation that has a significant social impact.

[0575] Step 12:

[0576] The server publishes the results of the survey on social media and in applications with comment functions, preventing the spread of misinformation among users.

[0577] In this way, the system provides the ability to quickly and reliably determine the accuracy of information and prevent the spread of misinformation.

[0578] Example 1

[0579] 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."

[0580] In modern society, inaccurate and false information is easily spread, causing misunderstandings and anxiety, and undermining trust in society as a whole. Furthermore, it is difficult to assess information in a variety of formats, making it difficult to quickly and accurately determine the authenticity of information using conventional methods. Furthermore, because misinformation is not regularly compiled and published, there is a lack of ongoing warnings and countermeasures. To address these issues, a new system needs to be built.

[0581] 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.

[0582] In this invention, the server includes a means for a user to input information, a terminal means for transmitting the input information, a means for determining whether the input information is correct using a generative AI model, a means for presenting the determination result and its basis to the user, and a means for storing false information in a database and periodically compiling and publishing the information. This automates the entire process from inputting information to determining whether it is correct, presenting the result, and compiling and publishing the false information, making it possible to prevent the spread of inaccurate information and improve trust in society as a whole.

[0583] A "user" is an entity that inputs information and makes various requests to the system.

[0584] "Information" is data that is input in the form of text, images, audio, etc., and that is processed and analyzed.

[0585] A "terminal" is a device that receives information entered by a user, converts it into an appropriate data format, and sends it to a server.

[0586] A "server" is a remote device or cloud-based system used to analyze input information and generate results.

[0587] A "generative AI model" is an artificial intelligence model based on machine learning algorithms and used to determine whether information is correct or incorrect using extensive data sets.

[0588] "Correctness determination" is the process of analyzing and determining the accuracy and authenticity of input information.

[0589] The "judgment result" is the analysis result regarding the accuracy of the information obtained by the generative AI model.

[0590] The "data format" refers to the structure or format of data used when a terminal transmits information from a user to a server.

[0591] A "database" is an organized collection of data that stores misinformation and allows it to be accessed at a later time.

[0592] "Aggregation" is a method of organizing stored misinformation over a period of time and analyzing statistics and trends.

[0593] "Publication" is the process of issuing warnings and providing information based on the collected misinformation.

[0594] This invention is a system that automatically determines the accuracy of information entered by users, and is composed of three main elements: a server, a terminal, and a user. This system utilizes a generative AI model to analyze the accuracy of information, and compiles and publishes false information.

[0595] 1. Enter your information

[0596] Users can input information such as text, images, and audio via LINE messages or the comment function on news sites using their smartphones or computers.

[0597] Examples:

[0598] The user enters the information "A new virus has been discovered" in a LINE message.

[0599] 2. Transmission of information

[0600] The device receives the information entered by the user, converts it into an appropriate data format, such as JSON, and sends it to the server. The device then sends the information via an internet connection.

[0601] Examples:

[0602] The device converts the text information "A new virus has been discovered" into JSON format and sends it to the server.

[0603] 3. Performing a correct / incorrect judgment

[0604] The server inputs the received information into a generative AI model, which uses extensive datasets and machine learning algorithms to determine whether the input information is correct or incorrect.

[0605] The generative AI models used here are models specialized for natural language processing (e.g., GPT-3) and are trained on large amounts of data.

[0606] Examples:

[0607] The server inputs the text "A new virus has been discovered" into the generative AI model and has it analyze its accuracy. The model then refers to databases and official information and determines that the information is false.

[0608] Example prompt sentence:

[0609] "Please determine the accuracy of the information that a new virus has been discovered."

[0610] "Please check whether this image is really from the disaster site."

[0611] 4. Return of the judgment results

[0612] The server analyzes and organizes the judgment results obtained from the generative AI model and their rationale, and returns them to the user. The device then displays the received judgment results to the user.

[0613] Examples:

[0614] The server sends the determination result that "this information is incorrect and there are no currently confirmed new viruses" to the terminal, and the terminal notifies the user of the result.

[0615] 5. Aggregation and publication of misinformation

[0616] The server stores the misinformation in a database and periodically compiles the data. The results are analyzed and the most frequently occurring misinformation is published, allowing for ongoing awareness raising.

[0617] Examples:

[0618] If there is a large amount of misinformation, such as "certain foods can ward off coronavirus," the information will be published as a warning on social media and in the comments section.

[0619] As a result, the system of the present invention can accommodate a variety of information formats and not only can it quickly and accurately verify the accuracy of information, but it can also prevent the spread of false information and improve trust in society as a whole.

[0620] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0621] Step 1:

[0622] A user opens a LINE message or the comment function of a news site and enters information. This information can be entered in the form of text, image, audio, etc. For example, a user enters the text information "A new virus has been discovered" in a LINE message.

[0623] Input: Information entered by the user (text, images, audio)

[0624] Output: Input text, image, and audio data

[0625] Specific behavior:

[0626] A user enters text using a smartphone or computer keyboard.

[0627] In the case of images or audio, the user selects and uploads the file.

[0628] Step 2:

[0629] The device converts the input information into an appropriate data format and sends it to the server. For example, text information is converted into JSON format.

[0630] Input: User-entered text, images, and audio data

[0631] Output: Information converted into an appropriate data format, such as JSON.

[0632] Specific behavior:

[0633] The terminal converts the text data into JSON format through a program.

[0634] Image and audio data is converted into binary format.

[0635] The device sends the data to the server via an internet connection.

[0636] Step 3:

[0637] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. For example, the server inputs the text "A new virus has been discovered" into the generative AI model, and the model verifies this information and makes a judgment.

[0638] Input: JSON formatted text, images, and audio data

[0639] Output: True / false judgment result (e.g., "That information is false")

[0640] Specific behavior:

[0641] The server parses the received JSON data and inputs it into the API for the generative AI model.

[0642] The generative AI model references the dataset and determines whether the information is correct.

[0643] The server obtains the judgment result.

[0644] Step 4:

[0645] The server analyzes the judgment results and their rationale from the generative AI model and returns them to the user. The results are formatted in a way that is easy for the user to understand. For example, the judgment result may be presented to the user as "This information is incorrect, and there are no new viruses currently being confirmed."

[0646] Input: Correct / incorrect result

[0647] Output: Formatted results for presentation to the user

[0648] Specific behavior:

[0649] The server reads the results from the JSON format and formats them in user-friendly language.

[0650] Send the formatted result to the terminal.

[0651] The terminal notifies the user of the received result.

[0652] Step 5:

[0653] The server stores the results of the assessment in a database and periodically compiles the misinformation. The results are analyzed, and frequently occurring misinformation is made public. For example, if a large number of people report misinformation such as "certain foods can ward off coronavirus," the information will be published as a warning on social media and through the comments function.

[0654] Input: Judgment result and its basis

[0655] Output: Aggregated misinformation analysis results

[0656] Specific behavior:

[0657] The server stores the judgment results in a database.

[0658] We use tools to regularly aggregate the contents of the database and analyze trends in misinformation.

[0659] The results will be compiled and published, and warnings will be posted on social media and websites.

[0660] (Application example 1)

[0661] 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."

[0662] The proliferation of false and unreliable information on the Internet, especially in content distribution services, has become a problem. Because users have no way of determining the accuracy of this information, they run the risk of acting on it. For this reason, there is a demand for a system that allows users to easily check the accuracy of the content they watch or view in real time.

[0663] 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.

[0664] In this invention, the server includes a means for a user to input information such as text, images, and audio, a terminal means for transmitting the input information to the server, a means for determining the accuracy of the input information using a generative AI model in the server, a means for presenting the determination result and its basis to the user, and a means for checking the accuracy of videos or articles that the user intends to view or read on the content distribution service in advance. This allows the user to quickly evaluate the reliability of content and prevent the spread of misinformation.

[0665] A "user" is an individual or corporation who uses a content distribution service to view or browse information such as text, images, and audio.

[0666] "Information" refers to all data provided in the form of text, images, audio, etc.

[0667] A "terminal" is a device used by a user to input information and send it to a server, and includes a smartphone, computer, tablet, etc.

[0668] The "server" is a central device that receives information sent by users and uses a generative AI model to determine whether the information is correct.

[0669] A "generative AI model" is an artificial intelligence system that is trained on a large dataset and is used to analyze the accuracy of input information.

[0670] "Accuracy check" is the process of determining whether the entered information is accurate.

[0671] The "judgment result" is the conclusion reached by the generative AI model after analyzing the accuracy of the information.

[0672] "Basis" refers to the evidence and reasons that support the judgment result.

[0673] A "content distribution service" is an online platform that provides users with digital content such as videos, articles, and audio.

[0674] "Video" is a medium that consists of a series of images or videos.

[0675] An "article" refers to text data written to provide news or information.

[0676] The system for carrying out the present invention is mainly composed of user input means, a terminal, and a server. The specific configuration and operation of the system will be described below.

[0677] User input method

[0678] Users input information such as text, images, and audio. This input is performed using devices such as smartphones, computers, and tablets. For example, a user may input the title and content of a news article they wish to view in text format on a content distribution service.

[0679] Terminal means

[0680] The information entered by the user is converted into an appropriate data format and sent from the device to the server. For example, text information is converted into JSON format and sent to the server. The device can be a smartphone, computer, tablet, etc.

[0681] Server Means

[0682] The server uses a generative AI model to determine whether the input information is correct. A generative AI model is an artificial intelligence trained on a large dataset and can handle a variety of input formats, including text, images, and audio. For example, a generative AI model analyzes the accuracy of text information in a news article and determines that the information is incorrect.

[0683] Method of presenting the judgment results

[0684] The server presents the judgment results obtained from the generative AI model and the reasons for them to the user. For example, the user may be provided with a judgment result such as "This information is incorrect, and there are no new viruses currently being confirmed."

[0685] How misinformation is collected and published

[0686] The server has the function of storing misinformation in a database and periodically compiling and publishing it. For example, if a large amount of misinformation is received, the information will be analyzed and a warning will be issued via social media and the comment function.

[0687] Specific examples

[0688] Verification of text information

[0689] A user enters text information such as "A new virus has been discovered" using a smartphone app.

[0690] The device converts this text information into JSON format and sends it to the server.

[0691] The server uses a generative AI model to determine the accuracy of the text information.

[0692] The generative AI model determines that the information is incorrect.

[0693] The server presents the judgment result and its basis to the user.

[0694] Prompt Sentence Examples

[0695] "We check news articles about new virus discoveries. If the facts aren't verified, we flag them as misinformation."

[0696] This allows users to quickly obtain accurate information and prevents the spread of false information.

[0697] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0698] Step 1:

[0699] The user inputs information such as text, images, or audio.

[0700] Input: A user enters information using an input device (e.g., smartphone, tablet, computer).

[0701] Output: Information entered by the user (text, images, audio).

[0702] Step 2:

[0703] The terminal converts the input information into the appropriate data format.

[0704] Input: Raw data entered by the user (text, images, audio).

[0705] Data processing: Encode text information into JSON format, and images and audio into Base64 format.

[0706] Output: Information in converted data format (e.g. text information in JSON format).

[0707] Step 3:

[0708] The terminal transmits the converted information to the server.

[0709] Input: Information in converted data format.

[0710] Sending data: Sending data to the server using an HTTP request.

[0711] Output: Information sent to the server.

[0712] Step 4:

[0713] The information received by the server is input into the generative AI model to determine whether it is correct or incorrect.

[0714] Input: Information received by the server.

[0715] Data Computation: Input information into a generative AI model and use extensive data sets and machine learning algorithms to determine the accuracy of the information.

[0716] Output: The result of the judgment (e.g., "correct", "incorrect", etc.) and its rationale.

[0717] Step 5:

[0718] The server presents the judgment results from the generated AI model and their rationale to the user.

[0719] Input: The judgment result output by the generative AI model and its rationale.

[0720] Send data: Send data to the device using an HTTP response.

[0721] Output: The judgment result and its rationale presented to the user.

[0722] Step 6:

[0723] The server stores the misinformation in a database, which is then periodically compiled and published.

[0724] Input: Information that the generative AI model determines to be incorrect.

[0725] Data Retention: Storing false information in the database.

[0726] Data calculation: Collecting and analyzing stored misinformation over a period of time.

[0727] Output: A warning published via social media and the comments function.

[0728] 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.

[0729] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user. This system supports various information formats, such as text, images, and audio, and uses a generative AI model and emotion engine to determine the accuracy of the information and recognize the user's emotions. Specific embodiments are described below.

[0730] Overall system configuration

[0731] This system mainly consists of the following elements:

[0732] 1. User Input Method

[0733] 2. Means of transmitting information from the terminal to the server

[0734] 3. A method for determining the accuracy of information using a server-generated AI model

[0735] 4. User Emotion Recognition Method Using Emotion Engine

[0736] 5. Means of presenting the judgment results and their rationale to the user

[0737] 6. Methods for compiling and publishing misinformation

[0738] System Operation

[0739] 1. User Input Method

[0740] Users input information such as text, images, and voice using the comment function of LINE messages or Yahoo! News. For example, a user inputs text information such as "A new virus has been discovered."

[0741] 2. Means of transmitting information from the terminal to the server

[0742] The entered information is sent from the device (e.g., a smartphone or computer) to the server. The device converts it into an appropriate data format and sends the information to the server. For example, text information is converted into JSON format and sent to the server.

[0743] 3. A method for determining the accuracy of information using a server-generated AI model

[0744] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. As a specific example, the generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is incorrect."

[0745] 4. User Emotion Recognition Method Using Emotion Engine

[0746] At the same time, the server uses an emotion engine to recognize the user's emotions from the information input by the user. For example, in the case of voice input, the emotion engine analyzes the tone and pitch of the user's voice to determine whether the user is excited or calm.

[0747] 5. Means of presenting the judgment results and their rationale to the user

[0748] The server presents the results of the generative AI model and their rationale to the user. The way the results are presented is adjusted according to the user's emotional state. For example, if the user is excited, the server will present the results in a calm tone to allow the user to receive them calmly.

[0749] 6. Methods for compiling and publishing misinformation

[0750] Misinformation is stored in a server database and periodically compiled. The server periodically analyzes and publishes the misinformation. For example, if a large number of people receive false information claiming that "certain foods can ward off coronavirus," the server will publish that information as a warning on social media and in the comments section.

[0751] Specific examples

[0752] Example 1: Recognizing text information and user emotions

[0753] The user enters the information "A new virus has been discovered" in a LINE message.

[0754] The terminal sends this text information to the server.

[0755] The server uses a generative AI model to determine the accuracy of this information.

[0756] The generative AI model determines that the information is incorrect and that no new virus has been identified to date.

[0757] The emotion engine recognizes the user's anxious emotion from the user's text input.

[0758] The server presents the results and rationale in a tone that reassures anxious users.

[0759] Example 2: Verification of voice information and sentiment analysis

[0760] A user uploads audio using Yahoo! News' comment function, stating, "This image is from the disaster site."

[0761] The terminal transmits the audio data to the server.

[0762] The server uses a generative AI model to determine the accuracy of the speech.

[0763] The generative AI model analyzes the audio information and determines that the image was actually taken somewhere else.

[0764] The emotion engine recognizes the user's emotion (e.g., anger or anxiety) from the tone of their voice.

[0765] The server presents the results of the assessment in a tone adjusted according to the user's emotional state.

[0766] Example 3: Aggregating and publishing misinformation

[0767] Multiple users provide information claiming that certain foods can ward off coronavirus.

[0768] The server stores the information in the database as false information.

[0769] The server will compile data over a certain period of time and issue warnings about misinformation that certain foods can ward off coronavirus.

[0770] By combining a generative AI model and an emotion engine, this system can quickly determine the accuracy of information entered by users and provide appropriate feedback according to the user's emotional state, thereby preventing the spread of misinformation and improving social credibility.

[0771] The processing flow will be explained below.

[0772] Step 1:

[0773] The user inputs information such as text, images, and voice via the device. For example, the user inputs the text information "A new virus has been discovered" via LINE message.

[0774] Step 2:

[0775] The device converts the input information into an appropriate data format. For example, text information is converted into JSON format.

[0776] Step 3:

[0777] The device sends the converted data to the server using an internet connection.

[0778] Step 4:

[0779] The data received by the server is checked to ensure that the data format is consistent and that it is not invalid.

[0780] Step 5:

[0781] The server passes the received data to the generative AI model, which then begins analyzing the information.

[0782] Step 6:

[0783] A generative AI model analyzes data and determines whether it is true or false. For example, it determines whether the information "a new virus has been discovered" is medically incorrect.

[0784] Step 7:

[0785] The generative AI model generates a judgment result and its rationale, which are then sent back to the server. The rationale includes reliable databases and expert opinions.

[0786] Step 8:

[0787] The server receives the judgment results and rationale from the generative AI model and converts them into a data format for presentation to the user.

[0788] Step 9:

[0789] The server uses an emotion engine to recognize the user's emotions based on the information entered by the user, for example, by analyzing the text entered by the user and the tone of the voice.

[0790] Step 10:

[0791] The emotion engine recognizes the user's emotions and sends them back to the server, including emotions such as excitement, worry, and anger.

[0792] Step 11:

[0793] The server adjusts the way the results are presented depending on the user's emotional state. For example, if the user is excited, the server presents the results in a calm tone.

[0794] Step 12:

[0795] The server then sends the converted data back to the device via the Internet, for example, returning a result that "this information is incorrect and there are no currently confirmed new viruses."

[0796] Step 13:

[0797] The terminal displays the result of the judgment received from the server to the user, allowing the user to confirm the accuracy of the input information.

[0798] Step 14:

[0799] If a user provides false information, that information is stored in the server's database. For example, the false information "a new virus has been discovered" is stored.

[0800] Step 15:

[0801] The server periodically aggregates the misinformation in the database, thereby identifying misinformation that has a significant social impact.

[0802] Step 16:

[0803] The server publishes the results of the survey on social media and in applications with comment functions, preventing the spread of misinformation among users.

[0804] In this way, the system can quickly and reliably determine the accuracy of information and provide appropriate feedback according to the user's emotional state. It also provides a function to prevent the spread of misinformation, thereby improving social credibility.

[0805] Example 2

[0806] 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."

[0807] Conventional information verification systems were unable to take the user's emotional state into account when determining the accuracy of the information entered by the user. As a result, the results of the verification were not properly communicated to the user, and the system was not effective in preventing the spread of misinformation. Furthermore, because misinformation was not compiled or published, it was not possible to quickly and effectively warn other users. This could lead to a decline in social credibility.

[0808] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for determining whether input information is correct or incorrect using a generative AI model, a means for recognizing a user's emotion using an emotion engine, a means for presenting a determination result according to the user's emotional state, and a means for aggregating and publishing false information. This makes it possible to quickly and accurately determine the accuracy of information while taking into consideration the user's emotions, prevent the spread of false information, and improve social credibility.

[0809] "User" refers to an individual or corporation that uses the system to input information.

[0810] "Information" refers to data in various formats, such as text, images, and audio.

[0811] "Terminal" refers to a device used by a user to input and transmit information to a server.

[0812] "Server" refers to a computing device that receives information sent by a user and uses a generative AI model and emotion engine to determine the accuracy of the information and the user's emotions.

[0813] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms and extensive data sets to analyze input information for accuracy.

[0814] An "emotion engine" refers to software that analyzes and recognizes the user's emotional state from information input.

[0815] "Accuracy assessment" refers to the process of evaluating the accuracy of input information and determining whether it is correct or incorrect.

[0816] "Misinformation" refers to information entered by a user that is inaccurate.

[0817] A "database" refers to a system that systematically stores data such as input information and its judgment results.

[0818] "Aggregation" refers to the process of statistically organizing misinformation stored in a database over a period of time.

[0819] "Publicity" refers to the activity of disclosing compiled misinformation to the public and raising awareness.

[0820] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user and presents the results according to the user's emotional state. This system supports various information formats, such as text, images, and audio, and uses a generative AI model and an emotion engine to determine the accuracy of the information and recognize the user's emotions. Specific embodiments are described below.

[0821] Overall system configuration

[0822] This system mainly consists of the following elements:

[0823] 1. User Input Method

[0824] 2. Means of transmitting information from the terminal to the server

[0825] 3. A method for determining the accuracy of information using a server-generated AI model

[0826] 4. User Emotion Recognition Method Using Emotion Engine

[0827] 5. Means of presenting the judgment results and their rationale to the user

[0828] 6. Methods for compiling and publishing misinformation

[0829] 1. User Input Method

[0830] Users input information such as text, images, and audio using LINE messages or the comment function of a news site. For example, a user inputs text information such as "A new virus has been discovered."

[0831] 2. Means of transmitting information from the terminal to the server

[0832] The device (smartphone or computer) converts the information entered by the user into an appropriate data format (for example, JSON format) and sends it to the server. For example, text information entered by the user is converted into JSON format and sent to the server.

[0833] 3. A method for determining the accuracy of information using a server-generated AI model

[0834] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. Specifically, it determines that the information "a new virus has been discovered" is false.

[0835] 4. User Emotion Recognition Method Using Emotion Engine

[0836] In parallel, the server uses an emotion engine to recognize the user's emotions from the information input by the user. For example, in the case of voice input, the emotion engine analyzes the tone and pitch of the user's voice to determine whether the user is excited or calm.

[0837] 5. Means of presenting the judgment results and their rationale to the user

[0838] The server presents the results of the generative AI model and the reasons for their findings to the user. The way the results are presented is adjusted according to the user's emotional state. For example, if the user is excited, the server will present the results in a calm tone so that the user can receive them calmly.

[0839] 6. Methods for compiling and publishing misinformation

[0840] The server stores misinformation in a database and periodically compiles and analyzes it. It then issues warnings about frequently occurring misinformation. For example, if a large number of people report the misinformation that "certain foods can ward off coronavirus," the server will publicize the information as a warning via social media and the comment function.

[0841] Specific operation example

[0842] Example 1: Recognizing text information and user emotions

[0843] A user types "A new virus has been discovered" in a LINE message.

[0844] The terminal sends this text information to the server.

[0845] The server uses a generative AI model to determine the accuracy of this information.

[0846] The generative AI model determines that the information is incorrect.

[0847] The emotion engine recognizes the emotions that the user is concerned about.

[0848] The server presents the judgment result and the reasoning to the user in a reassuring tone.

[0849] Example 2: Verification of voice information and sentiment analysis

[0850] A user uses the comment function on a news site to upload an audio message saying, "This image is from the disaster site."

[0851] The terminal transmits the audio data to the server.

[0852] The server uses a generative AI model to determine the accuracy of the audio information.

[0853] The generative AI model analyzes the audio information and determines that the image was actually taken somewhere else.

[0854] The emotion engine recognizes the user's emotion (e.g., anger or anxiety) from the tone of their voice.

[0855] The server presents the judgment result in a tone adjusted according to the emotional state.

[0856] Example 3: Aggregating and publishing misinformation

[0857] Multiple users provide information claiming that certain foods can ward off coronavirus.

[0858] The server stores the information in the database as false information.

[0859] The server aggregates data over a certain period of time.

[0860] The server warns against the misinformation that certain foods can ward off coronavirus.

[0861] By combining a generative AI model and an emotion engine, this system can quickly determine the accuracy of information entered by users and provide appropriate feedback according to the user's emotional state, thereby preventing the spread of misinformation and improving social credibility.

[0862] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0863] Step 1:

[0864] The user inputs information such as text, images, or audio.

[0865] Specific operation: The user opens the LINE app on their smartphone, types "A new virus has been discovered," and presses the send button.

[0866] Input: Information entered by the user (text, image, audio).

[0867] Output: Input information that the terminal is instructed to send.

[0868] Step 2:

[0869] The terminal converts the user's input information into an appropriate data format (for example, JSON format).

[0870] Specific operation: The smartphone converts the text information "A new virus has been discovered" into JSON format.

[0871] Input: Information entered by the user.

[0872] Output: The input information converted to JSON format.

[0873] Step 3:

[0874] The terminal transmits the converted information to the server.

[0875] Specific operation: The smartphone generates JSON data and sends it to the server's API endpoint as a POST request.

[0876] Input: Input information converted to JSON format.

[0877] Output: The JSON data sent to the server.

[0878] Step 4:

[0879] The server inputs the received information into a generative AI model to determine the accuracy of the information.

[0880] Specific operation: The server analyzes the received JSON data and inputs it into the generative AI model. The generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is false."

[0881] Input: Input information in JSON format.

[0882] Output: The result of determining whether the information is correct or not.

[0883] Step 5:

[0884] The server inputs the received information into an emotion engine to recognize the user's emotions.

[0885] What it does: The server passes the voice data or text input to the emotion engine, which analyzes the user's emotions (excitement, anxiety, anger, etc.).

[0886] Input: User input (voice, text).

[0887] Output: The perceived emotional state of the user.

[0888] Step 6:

[0889] The server integrates the judgment results from the generative AI model with the emotional state from the emotion engine to create an output to present to the user.

[0890] Specific operation: The server integrates the determination that the information "A new virus has been discovered" is false with the user's feelings of anxiety, and creates a message in a calm tone saying, "That information is false, and here is the correct information."

[0891] Input: Information correctness judgment result, user's emotional state.

[0892] Output: The tailored message to present to the user.

[0893] Step 7:

[0894] The server stores the misinformation in a database, which is then periodically compiled and published.

[0895] Specific operation: The server stores misinformation in a database and periodically compiles the data. For example, if a large number of people receive misinformation claiming that certain foods can ward off coronavirus, the server will publish that information as a warning via social media and the comments function.

[0896] Input: False information collected by the server.

[0897] Output: Aggregate results and published warning information.

[0898] (Application example 2)

[0899] 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."

[0900] In modern society, the accuracy of information is extremely important. However, information provided in a variety of formats often contains false information, which can lead to misunderstandings and confusion. Feedback on the widespread acceptance of false information is also important, and there is a need to provide information in an appropriate manner that takes into account the feelings of the recipient. This requires a system that allows users to respond appropriately to information provided, such as advertisements, and prevents the spread of false information.

[0901] 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 means for determining the accuracy of input information using a generative AI model, means for recognizing the user's emotion, and means for adjusting the feedback content based on the recognized emotion. This makes it possible to automatically determine the accuracy of the information entered by the user and provide feedback that takes the user's emotion into consideration.

[0902] The "means for user input of information" refers to a device or system that allows a user to input information in various formats such as text, images, and voice.

[0903] The "terminal means for transmitting information to a server" refers to a device or system that has the function of transmitting data to a server in order to process input information.

[0904] "Means for determining the correctness of input information using a generative AI model" refers to a system that uses a generative AI model to determine whether the information entered by a user is correct or incorrect.

[0905] The "means for recognizing user emotions" is a system that has the function of recognizing emotions by analyzing the content of information entered by the user and the user's reactions.

[0906] The "means for adjusting the content of feedback based on the recognized emotion" is a system for appropriately changing the content and method of the feedback provided depending on the user's emotional state.

[0907] MODE FOR CARRYING OUT THE INVENTION

[0908] The present invention provides a system for determining the accuracy of advertisements in real time and providing feedback according to the user's emotions. Specific embodiments are described below.

[0909] Overall system configuration

[0910] The system consists of the following hardware and software:

[0911] Hardware: Smart glasses (built-in camera, microphone, display)

[0912] software:

[0913] Generative AI model (commonly known as GPT-4, e.g., made by OpenAI)

[0914] Emotion recognition software (e.g., EmotionAPI, Microsoft Azure)

[0915] Data format: JSON

[0916] Communication means: Wi-Fi, Bluetooth

[0917] System Operation

[0918] The following shows how the system is implemented by the server, terminal, and user.

[0919] User input method

[0920] Users watch advertisements using smart glasses, whose built-in camera and microphone capture the content of the advertisements (text, images, and audio) in real time.

[0921] Means of transmitting information from a terminal to a server

[0922] The smart glasses convert the captured information into JSON format and send it to a server using Wi-Fi or Bluetooth.

[0923] A means of determining the accuracy of information using a server-generated AI model

[0924] The server inputs the received advertising information into a generative AI model (GPT-4) to determine its accuracy. The generative AI model uses a wide range of datasets and machine learning algorithms to analyze the accuracy of the input information.

[0925] A method for recognizing user emotions using an emotion engine

[0926] At the same time, the server uses EmotionAPI to recognize the user's emotions, analyzing the user's emotional state (e.g., excitement, relief, anxiety) based on data obtained from the built-in camera and microphone.

[0927] A means of presenting the judgment results and their rationale to the user

[0928] The server presents the results of the generative AI model and their rationale to the user. The presentation of the results is adjusted according to the user's emotional state. For example, if the user is excited, gentle feedback is provided.

[0929] Feedback is provided in the form of the judgment result and its rationale being displayed on the smart glasses display.

[0930] Specific examples

[0931] Example 1:

[0932] The ad you're seeing: "This product will help you lose weight easily."

[0933] The camera in the smart glasses captures the advertisement and sends it to a server.

[0934] The server uses GPT-4 to determine that "this information is not based on scientific evidence."

[0935] EmotionAPI recognizes when a user is excited.

[0936] The smart glasses display a calming message: "This ad contains misinformation. Please think carefully."

[0937] Example 2:

[0938] The ad you're looking at: "Special discounts on new product launches"

[0939] Smart glasses capture the ad and determine its accuracy, saying, "It's okay to buy here."

[0940] EmotionAPI recognizes that the user is not excited.

[0941] The smart glasses will say "This information is accurate" in a normal tone.

[0942] Prompt Sentence Examples

[0943] "Is the content of this ad accurate? This text: 'This product will help you lose weight easily.'"

[0944] "Determine the accuracy of information regarding new product launches and special discounts."

[0945] Through these processing procedures, the present invention is a system that quickly determines the accuracy of information and provides appropriate feedback according to the user's emotions, thereby achieving high reliability.

[0946] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0947] Program processing steps

[0948] Step 1:

[0949] A user watches an advertisement through smart glasses, and the built-in camera of the smart glasses captures the image and text information of the advertisement, and the microphone records the audio information, which becomes the input data.

[0950] Step 2:

[0951] The captured information is converted into JSON format and sent to the server by the smart glasses. This conversion and transmission is the role of the device. The input is the captured image, text, and audio data, and the output is JSON format data.

[0952] Step 3:

[0953] The server analyzes the received JSON format data and inputs it into a generative AI model (GPT-4).The server then uses the generative AI model to determine the accuracy of the advertising information.The input is the JSON data sent to the server, and the output is the accuracy determination result.

[0954] Step 4:

[0955] At the same time, the server analyzes the user's emotions using the Emotion API. Based on the audio and video data sent from the smart glasses, the server recognizes the user's emotional state (excitement, relief, anxiety, etc.). The input is audio and video data, and the output is the user's emotional state.

[0956] Step 5:

[0957] The server generates feedback content by integrating the judgment results from the generative AI model and the emotion recognition results from the Emotion API. The tone and expression of the feedback are adjusted according to the user's emotional state. The input is the judgment results and emotion recognition results, and the output is the adjusted feedback content.

[0958] Step 6:

[0959] The server sends feedback content to the smart glasses. The feedback content is displayed on the display of the smart glasses. The input is the feedback content sent from the server, and the output is the feedback displayed on the smart glasses.

[0960] Specific operation explanation

[0961] Step 1:

[0962] A user wears the smart glasses and watches any advertisement, and the smart glasses' built-in camera captures screenshots of the advertisement and related text, while the microphone records the corresponding audio.

[0963] Step 2:

[0964] The device (smart glasses) converts captured images, text, and audio data into JSON format and sends it to the server via Wi-Fi or Bluetooth.

[0965] Step 3:

[0966] The server analyzes the received JSON data and inputs it into a generative AI model (GPT-4) along with a prompt. For example, it inputs something like, "Is the content of this advertisement accurate? This text: 'This product will help you lose weight easily.'" The generative AI model determines the accuracy of the information and concludes that "the information is not based on scientific evidence."

[0967] Step 4:

[0968] The server uses EmotionAPI to analyze the audio and video data sent from the smart glasses and recognize whether the user is excited, for example, based on the tone of the voice or facial expression in the video.

[0969] Step 5:

[0970] The server combines the results of the generative AI model with the emotion recognition results of the Emotion API and adjusts the feedback content according to the user's emotional state. For example, if the user is excited, it generates calm feedback such as, "This ad contains misinformation. Please think about it calmly."

[0971] Step 6:

[0972] The server sends the generated feedback to the smart glasses, which then display the adjusted feedback on their display and inform the user whether the information is accurate.

[0973] 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.

[0974] 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.

[0975] 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.

[0976] [Third embodiment]

[0977] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0978] 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.

[0979] 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).

[0980] 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.

[0981] 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.

[0982] 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).

[0983] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0984] 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.

[0985] 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.

[0986] 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.

[0987] 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.

[0988] 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."

[0989] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user. This system supports various information formats, such as text, images, and audio, and uses a generative AI model to determine the accuracy of the information. Specific embodiments are described below.

[0990] Overall system configuration

[0991] This system mainly consists of the following elements:

[0992] 1. User Input Method

[0993] 2. Means of transmitting information from the terminal to the server

[0994] 3. A method for determining the accuracy of information using a server-generated AI model

[0995] 4. Method of presenting the judgment results

[0996] 5. Methods for compiling and publishing misinformation

[0997] System Operation

[0998] 1. User Input Method

[0999] Users input information such as text, images, and voice using the comment function of LINE messages or Yahoo! News. For example, a user inputs text information such as "A new virus has been discovered."

[1000] 2. Means of transmitting information from the terminal to the server

[1001] The entered information is sent from the device (e.g., a smartphone or computer) to the server. The device converts it into an appropriate data format and sends the information to the server. For example, text information is converted into JSON format and sent to the server.

[1002] 3. A method for determining the accuracy of information using a server-generated AI model

[1003] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. As a specific example, the generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is incorrect."

[1004] 4. Method of presenting the judgment results

[1005] The server returns the judgment result from the generative AI model and the reasoning behind it to the user. For example, the user may be presented with a result such as, "This information is incorrect, and there are no new viruses currently being confirmed."

[1006] 5. Methods for compiling and publishing misinformation

[1007] Misinformation is stored in a server database and periodically compiled. The server periodically analyzes and publishes the misinformation. For example, if a large number of people receive false information claiming that "certain foods can ward off coronavirus," the server will publish that information as a warning on social media and in the comments section.

[1008] Specific examples

[1009] Example 1: Judging the accuracy of text information

[1010] The user enters the information "A new virus has been discovered" in a LINE message.

[1011] The terminal sends this text information to the server.

[1012] The server uses a generative AI model to determine the accuracy of this information.

[1013] The generative AI model determines that the information is incorrect and that no new virus has been identified to date.

[1014] The server presents the judgment result and the reasoning to the user.

[1015] Example 2: Image information accuracy

[1016] A user uploads an image using Yahoo! News' comment function, writing, "This image is from the disaster site."

[1017] The terminal transmits the image data to the server.

[1018] The server uses a generative AI model to determine the accuracy of the image.

[1019] The generative AI model determines that this image was actually taken somewhere else.

[1020] The server presents the judgment result to the user.

[1021] Example 3: Aggregating and publishing misinformation

[1022] Multiple users provide information claiming that certain foods can ward off coronavirus.

[1023] The server stores the information in the database as false information.

[1024] The server will compile data over a certain period of time and issue warnings about misinformation that certain foods can ward off coronavirus.

[1025] As described above, the system of the present invention can handle a variety of information formats and quickly and easily verify the accuracy of information, thereby preventing the spread of false information and improving social credibility.

[1026] The processing flow will be explained below.

[1027] Step 1:

[1028] The user inputs information such as text, images, and voice via the device. For example, the user inputs the text information "A new virus has been discovered" via LINE message.

[1029] Step 2:

[1030] The device converts the input information into an appropriate data format. For example, it converts text information into JSON format.

[1031] Step 3:

[1032] The device sends the converted data to the server using an internet connection.

[1033] Step 4:

[1034] The server verifies the received data and passes it to the generative AI model, which then begins analyzing the information.

[1035] Step 5:

[1036] The generative AI model analyzes the information provided and determines whether it is true or false. For example, it determines whether the text "A new virus has been discovered" is medically incorrect.

[1037] Step 6:

[1038] The generative AI model generates a judgment result and its rationale, which are then sent back to the server. The rationale includes reliable databases and expert opinions.

[1039] Step 7:

[1040] The server receives the judgment results and rationale from the generative AI model and converts them into a data format that can be presented to the user.

[1041] Step 8:

[1042] The server then sends the converted data back to the device via the Internet, for example, returning a result that "this information is incorrect and there are no currently confirmed new viruses."

[1043] Step 9:

[1044] The terminal displays the result of the judgment received from the server to the user, allowing the user to confirm the accuracy of the input information.

[1045] Step 10:

[1046] If a user provides false information, that information is stored in the server's database. For example, the false information "a new virus has been discovered" is stored.

[1047] Step 11:

[1048] The server periodically aggregates the misinformation in the database, thereby identifying misinformation that has a significant social impact.

[1049] Step 12:

[1050] The server publishes the results of the survey on social media and in applications with comment functions, preventing the spread of misinformation among users.

[1051] In this way, the system provides the ability to quickly and reliably determine the accuracy of information and prevent the spread of misinformation.

[1052] Example 1

[1053] 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."

[1054] In modern society, inaccurate and false information is easily spread, causing misunderstandings and anxiety, and undermining trust in society as a whole. Furthermore, it is difficult to assess information in a variety of formats, making it difficult to quickly and accurately determine the authenticity of information using conventional methods. Furthermore, because misinformation is not regularly compiled and published, there is a lack of ongoing warnings and countermeasures. To address these issues, a new system needs to be built.

[1055] 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.

[1056] In this invention, the server includes a means for a user to input information, a terminal means for transmitting the input information, a means for determining whether the input information is correct using a generative AI model, a means for presenting the determination result and its basis to the user, and a means for storing false information in a database and periodically compiling and publishing the information. This automates the entire process from inputting information to determining whether it is correct, presenting the result, and compiling and publishing the false information, making it possible to prevent the spread of inaccurate information and improve trust in society as a whole.

[1057] A "user" is an entity that inputs information and makes various requests to the system.

[1058] "Information" is data that is input in the form of text, images, audio, etc., and that is processed and analyzed.

[1059] A "terminal" is a device that receives information entered by a user, converts it into an appropriate data format, and sends it to a server.

[1060] A "server" is a remote device or cloud-based system used to analyze input information and generate results.

[1061] A "generative AI model" is an artificial intelligence model based on machine learning algorithms and used to determine whether information is correct or incorrect using extensive data sets.

[1062] "Correctness determination" is the process of analyzing and determining the accuracy and authenticity of input information.

[1063] The "judgment result" is the analysis result regarding the accuracy of the information obtained by the generative AI model.

[1064] The "data format" refers to the structure or format of data used when a terminal transmits information from a user to a server.

[1065] A "database" is an organized collection of data that stores misinformation and allows it to be accessed at a later time.

[1066] "Aggregation" is a method of organizing stored misinformation over a period of time and analyzing statistics and trends.

[1067] "Publication" is the process of issuing warnings and providing information based on the collected misinformation.

[1068] This invention is a system that automatically determines the accuracy of information entered by users, and is composed of three main elements: a server, a terminal, and a user. This system utilizes a generative AI model to analyze the accuracy of information, and compiles and publishes false information.

[1069] 1. Enter your information

[1070] Users can input information such as text, images, and audio via LINE messages or the comment function on news sites using their smartphones or computers.

[1071] Examples:

[1072] The user enters the information "A new virus has been discovered" in a LINE message.

[1073] 2. Transmission of information

[1074] The device receives the information entered by the user, converts it into an appropriate data format, such as JSON, and sends it to the server. The device then sends the information via an internet connection.

[1075] Examples:

[1076] The device converts the text information "A new virus has been discovered" into JSON format and sends it to the server.

[1077] 3. Performing a correct / incorrect judgment

[1078] The server inputs the received information into a generative AI model, which uses extensive datasets and machine learning algorithms to determine whether the input information is correct or incorrect.

[1079] The generative AI models used here are models specialized for natural language processing (e.g., GPT-3) and are trained on large amounts of data.

[1080] Examples:

[1081] The server inputs the text "A new virus has been discovered" into the generative AI model and has it analyze its accuracy. The model then refers to databases and official information and determines that the information is false.

[1082] Example prompt sentence:

[1083] "Please determine the accuracy of the information that a new virus has been discovered."

[1084] "Please check whether this image is really from the disaster site."

[1085] 4. Return of the judgment results

[1086] The server analyzes and organizes the judgment results obtained from the generative AI model and their rationale, and returns them to the user. The device then displays the received judgment results to the user.

[1087] Examples:

[1088] The server sends the determination result that "this information is incorrect and there are no currently confirmed new viruses" to the terminal, and the terminal notifies the user of the result.

[1089] 5. Aggregation and publication of misinformation

[1090] The server stores the misinformation in a database and periodically compiles the data. The results are analyzed and the most frequently occurring misinformation is published, allowing for ongoing awareness raising.

[1091] Examples:

[1092] If there is a large amount of misinformation, such as "certain foods can ward off coronavirus," the information will be published as a warning on social media and in the comments section.

[1093] As a result, the system of the present invention can accommodate a variety of information formats and not only can it quickly and accurately verify the accuracy of information, but it can also prevent the spread of false information and improve trust in society as a whole.

[1094] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1095] Step 1:

[1096] A user opens a LINE message or the comment function of a news site and enters information. This information can be entered in the form of text, image, audio, etc. For example, a user enters the text information "A new virus has been discovered" in a LINE message.

[1097] Input: Information entered by the user (text, images, audio)

[1098] Output: Input text, image, and audio data

[1099] Specific behavior:

[1100] A user enters text using a smartphone or computer keyboard.

[1101] In the case of images or audio, the user selects and uploads the file.

[1102] Step 2:

[1103] The device converts the input information into an appropriate data format and sends it to the server. For example, text information is converted into JSON format.

[1104] Input: User-entered text, images, and audio data

[1105] Output: Information converted into an appropriate data format, such as JSON.

[1106] Specific behavior:

[1107] The terminal converts the text data into JSON format through a program.

[1108] Image and audio data is converted into binary format.

[1109] The device sends the data to the server via an internet connection.

[1110] Step 3:

[1111] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. For example, the server inputs the text "A new virus has been discovered" into the generative AI model, and the model verifies this information and makes a judgment.

[1112] Input: JSON formatted text, images, and audio data

[1113] Output: True / false judgment result (e.g., "That information is false")

[1114] Specific behavior:

[1115] The server parses the received JSON data and inputs it into the API for the generative AI model.

[1116] The generative AI model references the dataset and determines whether the information is correct.

[1117] The server obtains the judgment result.

[1118] Step 4:

[1119] The server analyzes the judgment results and their rationale from the generative AI model and returns them to the user. The results are formatted in a way that is easy for the user to understand. For example, the judgment result may be presented to the user as "This information is incorrect, and there are no new viruses currently being confirmed."

[1120] Input: Correct / incorrect result

[1121] Output: Formatted results for presentation to the user

[1122] Specific behavior:

[1123] The server reads the results from the JSON format and formats them in user-friendly language.

[1124] Send the formatted result to the terminal.

[1125] The terminal notifies the user of the received result.

[1126] Step 5:

[1127] The server stores the results of the assessment in a database and periodically compiles the misinformation. The results are analyzed, and frequently occurring misinformation is made public. For example, if a large number of people report misinformation such as "certain foods can ward off coronavirus," the information will be published as a warning on social media and through the comments function.

[1128] Input: Judgment result and its basis

[1129] Output: Aggregated misinformation analysis results

[1130] Specific behavior:

[1131] The server stores the judgment results in a database.

[1132] We use tools to regularly aggregate the contents of the database and analyze trends in misinformation.

[1133] The results will be compiled and published, and warnings will be posted on social media and websites.

[1134] (Application example 1)

[1135] 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."

[1136] The proliferation of false and unreliable information on the Internet, especially in content distribution services, has become a problem. Because users have no way of determining the accuracy of this information, they run the risk of acting on it. For this reason, there is a demand for a system that allows users to easily check the accuracy of the content they watch or view in real time.

[1137] 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.

[1138] In this invention, the server includes a means for a user to input information such as text, images, and audio, a terminal means for transmitting the input information to the server, a means for determining the accuracy of the input information using a generative AI model in the server, a means for presenting the determination result and its basis to the user, and a means for checking the accuracy of videos or articles that the user intends to view or read on the content distribution service in advance. This allows the user to quickly evaluate the reliability of content and prevent the spread of misinformation.

[1139] A "user" is an individual or corporation who uses a content distribution service to view or browse information such as text, images, and audio.

[1140] "Information" refers to all data provided in the form of text, images, audio, etc.

[1141] A "terminal" is a device used by a user to input information and send it to a server, and includes a smartphone, computer, tablet, etc.

[1142] The "server" is a central device that receives information sent by users and uses a generative AI model to determine whether the information is correct.

[1143] A "generative AI model" is an artificial intelligence system that is trained on a large dataset and is used to analyze the accuracy of input information.

[1144] "Accuracy check" is the process of determining whether the entered information is accurate.

[1145] The "judgment result" is the conclusion reached by the generative AI model after analyzing the accuracy of the information.

[1146] "Basis" refers to the evidence and reasons that support the judgment result.

[1147] A "content distribution service" is an online platform that provides users with digital content such as videos, articles, and audio.

[1148] "Video" is a medium that consists of a series of images or videos.

[1149] An "article" refers to text data written to provide news or information.

[1150] The system for carrying out the present invention is mainly composed of user input means, a terminal, and a server. The specific configuration and operation of the system will be described below.

[1151] User input method

[1152] Users input information such as text, images, and audio. This input is performed using devices such as smartphones, computers, and tablets. For example, a user may input the title and content of a news article they wish to view in text format on a content distribution service.

[1153] Terminal means

[1154] The information entered by the user is converted into an appropriate data format and sent from the device to the server. For example, text information is converted into JSON format and sent to the server. The device can be a smartphone, computer, tablet, etc.

[1155] Server Means

[1156] The server uses a generative AI model to determine whether the input information is correct. A generative AI model is an artificial intelligence trained on a large dataset and can handle a variety of input formats, including text, images, and audio. For example, a generative AI model analyzes the accuracy of text information in a news article and determines that the information is incorrect.

[1157] Method of presenting the judgment results

[1158] The server presents the judgment results obtained from the generative AI model and the reasons for them to the user. For example, the user may be provided with a judgment result such as "This information is incorrect, and there are no new viruses currently being confirmed."

[1159] How misinformation is collected and published

[1160] The server has the function of storing misinformation in a database and periodically compiling and publishing it. For example, if a large amount of misinformation is received, the information will be analyzed and a warning will be issued via social media and the comment function.

[1161] Specific examples

[1162] Verification of text information

[1163] A user enters text information such as "A new virus has been discovered" using a smartphone app.

[1164] The device converts this text information into JSON format and sends it to the server.

[1165] The server uses a generative AI model to determine the accuracy of the text information.

[1166] The generative AI model determines that the information is incorrect.

[1167] The server presents the judgment result and its basis to the user.

[1168] Prompt Sentence Examples

[1169] "We check news articles about new virus discoveries. If the facts aren't verified, we flag them as misinformation."

[1170] This allows users to quickly obtain accurate information and prevents the spread of false information.

[1171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1172] Step 1:

[1173] The user inputs information such as text, images, or audio.

[1174] Input: A user enters information using an input device (e.g., smartphone, tablet, computer).

[1175] Output: Information entered by the user (text, images, audio).

[1176] Step 2:

[1177] The terminal converts the input information into the appropriate data format.

[1178] Input: Raw data entered by the user (text, images, audio).

[1179] Data processing: Encode text information into JSON format, and images and audio into Base64 format.

[1180] Output: Information in converted data format (e.g. text information in JSON format).

[1181] Step 3:

[1182] The terminal transmits the converted information to the server.

[1183] Input: Information in converted data format.

[1184] Sending data: Sending data to the server using an HTTP request.

[1185] Output: Information sent to the server.

[1186] Step 4:

[1187] The information received by the server is input into the generative AI model to determine whether it is correct or incorrect.

[1188] Input: Information received by the server.

[1189] Data Computation: Input information into a generative AI model and use extensive data sets and machine learning algorithms to determine the accuracy of the information.

[1190] Output: The result of the judgment (e.g., "correct", "incorrect", etc.) and its rationale.

[1191] Step 5:

[1192] The server presents the judgment results from the generated AI model and their rationale to the user.

[1193] Input: The judgment result output by the generative AI model and its rationale.

[1194] Send data: Send data to the device using an HTTP response.

[1195] Output: The judgment result and its rationale presented to the user.

[1196] Step 6:

[1197] The server stores the misinformation in a database, which is then periodically compiled and published.

[1198] Input: Information that the generative AI model determines to be incorrect.

[1199] Data Retention: Storing false information in the database.

[1200] Data calculation: Collecting and analyzing stored misinformation over a period of time.

[1201] Output: A warning published via social media and the comments function.

[1202] 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.

[1203] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user. This system supports various information formats, such as text, images, and audio, and uses a generative AI model and emotion engine to determine the accuracy of the information and recognize the user's emotions. Specific embodiments are described below.

[1204] Overall system configuration

[1205] This system mainly consists of the following elements:

[1206] 1. User Input Method

[1207] 2. Means of transmitting information from the terminal to the server

[1208] 3. A method for determining the accuracy of information using a server-generated AI model

[1209] 4. User Emotion Recognition Method Using Emotion Engine

[1210] 5. Means of presenting the judgment results and their rationale to the user

[1211] 6. Methods for compiling and publishing misinformation

[1212] System Operation

[1213] 1. User Input Method

[1214] Users input information such as text, images, and voice using the comment function of LINE messages or Yahoo! News. For example, a user inputs text information such as "A new virus has been discovered."

[1215] 2. Means of transmitting information from the terminal to the server

[1216] The entered information is sent from the device (e.g., a smartphone or computer) to the server. The device converts it into an appropriate data format and sends the information to the server. For example, text information is converted into JSON format and sent to the server.

[1217] 3. A method for determining the accuracy of information using a server-generated AI model

[1218] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. As a specific example, the generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is incorrect."

[1219] 4. User Emotion Recognition Method Using Emotion Engine

[1220] At the same time, the server uses an emotion engine to recognize the user's emotions from the information input by the user. For example, in the case of voice input, the emotion engine analyzes the tone and pitch of the user's voice to determine whether the user is excited or calm.

[1221] 5. Means of presenting the judgment results and their rationale to the user

[1222] The server presents the results of the generative AI model and their rationale to the user. The way the results are presented is adjusted according to the user's emotional state. For example, if the user is excited, the server will present the results in a calm tone to allow the user to receive them calmly.

[1223] 6. Methods for compiling and publishing misinformation

[1224] Misinformation is stored in a server database and periodically compiled. The server periodically analyzes and publishes the misinformation. For example, if a large number of people receive false information claiming that "certain foods can ward off coronavirus," the server will publish that information as a warning on social media and in the comments section.

[1225] Specific examples

[1226] Example 1: Recognizing text information and user emotions

[1227] The user enters the information "A new virus has been discovered" in a LINE message.

[1228] The terminal sends this text information to the server.

[1229] The server uses a generative AI model to determine the accuracy of this information.

[1230] The generative AI model determines that the information is incorrect and that no new virus has been identified to date.

[1231] The emotion engine recognizes the user's anxious emotion from the user's text input.

[1232] The server presents the results and rationale in a tone that reassures anxious users.

[1233] Example 2: Verification of voice information and sentiment analysis

[1234] A user uploads audio using Yahoo! News' comment function, stating, "This image is from the disaster site."

[1235] The terminal transmits the audio data to the server.

[1236] The server uses a generative AI model to determine the accuracy of the speech.

[1237] The generative AI model analyzes the audio information and determines that the image was actually taken somewhere else.

[1238] The emotion engine recognizes the user's emotion (e.g., anger or anxiety) from the tone of their voice.

[1239] The server presents the results of the assessment in a tone adjusted according to the user's emotional state.

[1240] Example 3: Aggregating and publishing misinformation

[1241] Multiple users provide information claiming that certain foods can ward off coronavirus.

[1242] The server stores the information in the database as false information.

[1243] The server will compile data over a certain period of time and issue warnings about misinformation that certain foods can ward off coronavirus.

[1244] By combining a generative AI model and an emotion engine, this system can quickly determine the accuracy of information entered by users and provide appropriate feedback according to the user's emotional state, thereby preventing the spread of misinformation and improving social credibility.

[1245] The processing flow will be explained below.

[1246] Step 1:

[1247] The user inputs information such as text, images, and voice via the device. For example, the user inputs the text information "A new virus has been discovered" via LINE message.

[1248] Step 2:

[1249] The device converts the input information into an appropriate data format. For example, text information is converted into JSON format.

[1250] Step 3:

[1251] The device sends the converted data to the server using an internet connection.

[1252] Step 4:

[1253] The data received by the server is checked to ensure that the data format is consistent and that it is not invalid.

[1254] Step 5:

[1255] The server passes the received data to the generative AI model, which then begins analyzing the information.

[1256] Step 6:

[1257] A generative AI model analyzes data and determines whether it is true or false. For example, it determines whether the information "a new virus has been discovered" is medically incorrect.

[1258] Step 7:

[1259] The generative AI model generates a judgment result and its rationale, which are then sent back to the server. The rationale includes reliable databases and expert opinions.

[1260] Step 8:

[1261] The server receives the judgment results and rationale from the generative AI model and converts them into a data format for presentation to the user.

[1262] Step 9:

[1263] The server uses an emotion engine to recognize the user's emotions based on the information entered by the user, for example, by analyzing the text entered by the user and the tone of the voice.

[1264] Step 10:

[1265] The emotion engine recognizes the user's emotions and sends them back to the server, including emotions such as excitement, worry, and anger.

[1266] Step 11:

[1267] The server adjusts the way the results are presented depending on the user's emotional state. For example, if the user is excited, the server presents the results in a calm tone.

[1268] Step 12:

[1269] The server then sends the converted data back to the device via the Internet, for example, returning a result that "this information is incorrect and there are no currently confirmed new viruses."

[1270] Step 13:

[1271] The terminal displays the result of the judgment received from the server to the user, allowing the user to confirm the accuracy of the input information.

[1272] Step 14:

[1273] If a user provides false information, that information is stored in the server's database. For example, the false information "a new virus has been discovered" is stored.

[1274] Step 15:

[1275] The server periodically aggregates the misinformation in the database, thereby identifying misinformation that has a significant social impact.

[1276] Step 16:

[1277] The server publishes the results of the survey on social media and in applications with comment functions, preventing the spread of misinformation among users.

[1278] In this way, the system can quickly and reliably determine the accuracy of information and provide appropriate feedback according to the user's emotional state. It also provides a function to prevent the spread of misinformation, thereby improving social credibility.

[1279] Example 2

[1280] 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."

[1281] Conventional information verification systems were unable to take the user's emotional state into account when determining the accuracy of the information entered by the user. As a result, the results of the verification were not properly communicated to the user, and the system was not effective in preventing the spread of misinformation. Furthermore, because misinformation was not compiled or published, it was not possible to quickly and effectively warn other users. This could lead to a decline in social credibility.

[1282] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for determining whether input information is correct or incorrect using a generative AI model, a means for recognizing a user's emotion using an emotion engine, a means for presenting a determination result according to the user's emotional state, and a means for aggregating and publishing false information. This makes it possible to quickly and accurately determine the accuracy of information while taking into consideration the user's emotions, prevent the spread of false information, and improve social credibility.

[1283] "User" refers to an individual or corporation that uses the system to input information.

[1284] "Information" refers to data in various formats, such as text, images, and audio.

[1285] "Terminal" refers to a device used by a user to input and transmit information to a server.

[1286] "Server" refers to a computing device that receives information sent by a user and uses a generative AI model and emotion engine to determine the accuracy of the information and the user's emotions.

[1287] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms and extensive data sets to analyze input information for accuracy.

[1288] An "emotion engine" refers to software that analyzes and recognizes the user's emotional state from information input.

[1289] "Accuracy assessment" refers to the process of evaluating the accuracy of input information and determining whether it is correct or incorrect.

[1290] "Misinformation" refers to information entered by a user that is inaccurate.

[1291] A "database" refers to a system that systematically stores data such as input information and its judgment results.

[1292] "Aggregation" refers to the process of statistically organizing misinformation stored in a database over a period of time.

[1293] "Publicity" refers to the activity of disclosing compiled misinformation to the public and raising awareness.

[1294] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user and presents the results according to the user's emotional state. This system supports various information formats, such as text, images, and audio, and uses a generative AI model and an emotion engine to determine the accuracy of the information and recognize the user's emotions. Specific embodiments are described below.

[1295] Overall system configuration

[1296] This system mainly consists of the following elements:

[1297] 1. User Input Method

[1298] 2. Means of transmitting information from the terminal to the server

[1299] 3. A method for determining the accuracy of information using a server-generated AI model

[1300] 4. User Emotion Recognition Method Using Emotion Engine

[1301] 5. Means of presenting the judgment results and their rationale to the user

[1302] 6. Methods for compiling and publishing misinformation

[1303] 1. User Input Method

[1304] Users input information such as text, images, and audio using LINE messages or the comment function of a news site. For example, a user inputs text information such as "A new virus has been discovered."

[1305] 2. Means of transmitting information from the terminal to the server

[1306] The device (smartphone or computer) converts the information entered by the user into an appropriate data format (for example, JSON format) and sends it to the server. For example, text information entered by the user is converted into JSON format and sent to the server.

[1307] 3. A method for determining the accuracy of information using a server-generated AI model

[1308] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. Specifically, it determines that the information "a new virus has been discovered" is false.

[1309] 4. User Emotion Recognition Method Using Emotion Engine

[1310] In parallel, the server uses an emotion engine to recognize the user's emotions from the information input by the user. For example, in the case of voice input, the emotion engine analyzes the tone and pitch of the user's voice to determine whether the user is excited or calm.

[1311] 5. Means of presenting the judgment results and their rationale to the user

[1312] The server presents the results of the generative AI model and the reasons for their findings to the user. The way the results are presented is adjusted according to the user's emotional state. For example, if the user is excited, the server will present the results in a calm tone so that the user can receive them calmly.

[1313] 6. Methods for compiling and publishing misinformation

[1314] The server stores misinformation in a database and periodically compiles and analyzes it. It then issues warnings about frequently occurring misinformation. For example, if a large number of people report the misinformation that "certain foods can ward off coronavirus," the server will publicize the information as a warning via social media and the comment function.

[1315] Specific operation example

[1316] Example 1: Recognizing text information and user emotions

[1317] A user types "A new virus has been discovered" in a LINE message.

[1318] The terminal sends this text information to the server.

[1319] The server uses a generative AI model to determine the accuracy of this information.

[1320] The generative AI model determines that the information is incorrect.

[1321] The emotion engine recognizes the emotions that the user is concerned about.

[1322] The server presents the judgment result and the reasoning to the user in a reassuring tone.

[1323] Example 2: Verification of voice information and sentiment analysis

[1324] A user uses the comment function on a news site to upload an audio message saying, "This image is from the disaster site."

[1325] The terminal transmits the audio data to the server.

[1326] The server uses a generative AI model to determine the accuracy of the audio information.

[1327] The generative AI model analyzes the audio information and determines that the image was actually taken somewhere else.

[1328] The emotion engine recognizes the user's emotion (e.g., anger or anxiety) from the tone of their voice.

[1329] The server presents the judgment result in a tone adjusted according to the emotional state.

[1330] Example 3: Aggregating and publishing misinformation

[1331] Multiple users provide information claiming that certain foods can ward off coronavirus.

[1332] The server stores the information in the database as false information.

[1333] The server aggregates data over a certain period of time.

[1334] The server warns against the misinformation that certain foods can ward off coronavirus.

[1335] By combining a generative AI model and an emotion engine, this system can quickly determine the accuracy of information entered by users and provide appropriate feedback according to the user's emotional state, thereby preventing the spread of misinformation and improving social credibility.

[1336] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1337] Step 1:

[1338] The user inputs information such as text, images, or audio.

[1339] Specific operation: The user opens the LINE app on their smartphone, types "A new virus has been discovered," and presses the send button.

[1340] Input: Information entered by the user (text, image, audio).

[1341] Output: Input information that the terminal is instructed to send.

[1342] Step 2:

[1343] The terminal converts the user's input information into an appropriate data format (for example, JSON format).

[1344] Specific operation: The smartphone converts the text information "A new virus has been discovered" into JSON format.

[1345] Input: Information entered by the user.

[1346] Output: The input information converted to JSON format.

[1347] Step 3:

[1348] The terminal transmits the converted information to the server.

[1349] Specific operation: The smartphone generates JSON data and sends it to the server's API endpoint as a POST request.

[1350] Input: Input information converted to JSON format.

[1351] Output: The JSON data sent to the server.

[1352] Step 4:

[1353] The server inputs the received information into a generative AI model to determine the accuracy of the information.

[1354] Specific operation: The server analyzes the received JSON data and inputs it into the generative AI model. The generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is false."

[1355] Input: Input information in JSON format.

[1356] Output: The result of determining whether the information is correct or not.

[1357] Step 5:

[1358] The server inputs the received information into an emotion engine to recognize the user's emotions.

[1359] What it does: The server passes the voice data or text input to the emotion engine, which analyzes the user's emotions (excitement, anxiety, anger, etc.).

[1360] Input: User input (voice, text).

[1361] Output: The perceived emotional state of the user.

[1362] Step 6:

[1363] The server integrates the judgment results from the generative AI model with the emotional state from the emotion engine to create an output to present to the user.

[1364] Specific operation: The server integrates the determination that the information "A new virus has been discovered" is false with the user's feelings of anxiety, and creates a message in a calm tone saying, "That information is false, and here is the correct information."

[1365] Input: Information correctness judgment result, user's emotional state.

[1366] Output: The tailored message to present to the user.

[1367] Step 7:

[1368] The server stores the misinformation in a database, which is then periodically compiled and published.

[1369] Specific operation: The server stores misinformation in a database and periodically compiles the data. For example, if a large number of people receive misinformation claiming that certain foods can ward off coronavirus, the server will publish that information as a warning via social media and the comments function.

[1370] Input: False information collected by the server.

[1371] Output: Aggregate results and published warning information.

[1372] (Application example 2)

[1373] 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."

[1374] In modern society, the accuracy of information is extremely important. However, information provided in a variety of formats often contains false information, which can lead to misunderstandings and confusion. Feedback on the widespread acceptance of false information is also important, and there is a need to provide information in an appropriate manner that takes into account the feelings of the recipient. This requires a system that allows users to respond appropriately to information provided, such as advertisements, and prevents the spread of false information.

[1375] 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 means for determining the accuracy of input information using a generative AI model, means for recognizing the user's emotion, and means for adjusting the feedback content based on the recognized emotion. This makes it possible to automatically determine the accuracy of the information entered by the user and provide feedback that takes the user's emotion into consideration.

[1376] The "means for user input of information" refers to a device or system that allows a user to input information in various formats such as text, images, and voice.

[1377] The "terminal means for transmitting information to a server" refers to a device or system that has the function of transmitting data to a server in order to process input information.

[1378] "Means for determining the correctness of input information using a generative AI model" refers to a system that uses a generative AI model to determine whether the information entered by a user is correct or incorrect.

[1379] The "means for recognizing user emotions" is a system that has the function of recognizing emotions by analyzing the content of information entered by the user and the user's reactions.

[1380] The "means for adjusting the content of feedback based on the recognized emotion" is a system for appropriately changing the content and method of the feedback provided depending on the user's emotional state.

[1381] MODE FOR CARRYING OUT THE INVENTION

[1382] The present invention provides a system for determining the accuracy of advertisements in real time and providing feedback according to the user's emotions. Specific embodiments are described below.

[1383] Overall system configuration

[1384] The system consists of the following hardware and software:

[1385] Hardware: Smart glasses (built-in camera, microphone, display)

[1386] software:

[1387] Generative AI model (commonly known as GPT-4, e.g., made by OpenAI)

[1388] Emotion recognition software (e.g., EmotionAPI, Microsoft Azure)

[1389] Data format: JSON

[1390] Communication means: Wi-Fi, Bluetooth

[1391] System Operation

[1392] The following shows how the system is implemented by the server, terminal, and user.

[1393] User input method

[1394] Users watch advertisements using smart glasses, whose built-in camera and microphone capture the content of the advertisements (text, images, and audio) in real time.

[1395] Means of transmitting information from a terminal to a server

[1396] The smart glasses convert the captured information into JSON format and send it to a server using Wi-Fi or Bluetooth.

[1397] A means of determining the accuracy of information using a server-generated AI model

[1398] The server inputs the received advertising information into a generative AI model (GPT-4) to determine its accuracy. The generative AI model uses a wide range of datasets and machine learning algorithms to analyze the accuracy of the input information.

[1399] A method for recognizing user emotions using an emotion engine

[1400] At the same time, the server uses EmotionAPI to recognize the user's emotions, analyzing the user's emotional state (e.g., excitement, relief, anxiety) based on data obtained from the built-in camera and microphone.

[1401] A means of presenting the judgment results and their rationale to the user

[1402] The server presents the results of the generative AI model and their rationale to the user. The presentation of the results is adjusted according to the user's emotional state. For example, if the user is excited, gentle feedback is provided.

[1403] Feedback is provided in the form of the judgment result and its rationale being displayed on the smart glasses display.

[1404] Specific examples

[1405] Example 1:

[1406] The ad you're seeing: "This product will help you lose weight easily."

[1407] The camera in the smart glasses captures the advertisement and sends it to a server.

[1408] The server uses GPT-4 to determine that "this information is not based on scientific evidence."

[1409] EmotionAPI recognizes when a user is excited.

[1410] The smart glasses display a calming message: "This ad contains misinformation. Please think carefully."

[1411] Example 2:

[1412] The ad you're looking at: "Special discounts on new product launches"

[1413] Smart glasses capture the ad and determine its accuracy, saying, "It's okay to buy here."

[1414] EmotionAPI recognizes that the user is not excited.

[1415] The smart glasses will say "This information is accurate" in a normal tone.

[1416] Prompt Sentence Examples

[1417] "Is the content of this ad accurate? This text: 'This product will help you lose weight easily.'"

[1418] "Determine the accuracy of information regarding new product launches and special discounts."

[1419] Through these processing procedures, the present invention is a system that quickly determines the accuracy of information and provides appropriate feedback according to the user's emotions, thereby achieving high reliability.

[1420] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1421] Program processing steps

[1422] Step 1:

[1423] A user watches an advertisement through smart glasses, and the built-in camera of the smart glasses captures the image and text information of the advertisement, and the microphone records the audio information, which becomes the input data.

[1424] Step 2:

[1425] The captured information is converted into JSON format and sent to the server by the smart glasses. This conversion and transmission is the role of the device. The input is the captured image, text, and audio data, and the output is JSON format data.

[1426] Step 3:

[1427] The server analyzes the received JSON format data and inputs it into a generative AI model (GPT-4).The server then uses the generative AI model to determine the accuracy of the advertising information.The input is the JSON data sent to the server, and the output is the accuracy determination result.

[1428] Step 4:

[1429] At the same time, the server analyzes the user's emotions using the Emotion API. Based on the audio and video data sent from the smart glasses, the server recognizes the user's emotional state (excitement, relief, anxiety, etc.). The input is audio and video data, and the output is the user's emotional state.

[1430] Step 5:

[1431] The server generates feedback content by integrating the judgment results from the generative AI model and the emotion recognition results from the Emotion API. The tone and expression of the feedback are adjusted according to the user's emotional state. The input is the judgment results and emotion recognition results, and the output is the adjusted feedback content.

[1432] Step 6:

[1433] The server sends feedback content to the smart glasses. The feedback content is displayed on the display of the smart glasses. The input is the feedback content sent from the server, and the output is the feedback displayed on the smart glasses.

[1434] Specific operation explanation

[1435] Step 1:

[1436] A user wears the smart glasses and watches any advertisement, and the smart glasses' built-in camera captures screenshots of the advertisement and related text, while the microphone records the corresponding audio.

[1437] Step 2:

[1438] The device (smart glasses) converts captured images, text, and audio data into JSON format and sends it to the server via Wi-Fi or Bluetooth.

[1439] Step 3:

[1440] The server analyzes the received JSON data and inputs it into a generative AI model (GPT-4) along with a prompt. For example, it inputs something like, "Is the content of this advertisement accurate? This text: 'This product will help you lose weight easily.'" The generative AI model determines the accuracy of the information and concludes that "the information is not based on scientific evidence."

[1441] Step 4:

[1442] The server uses EmotionAPI to analyze the audio and video data sent from the smart glasses and recognize whether the user is excited, for example, based on the tone of the voice or facial expression in the video.

[1443] Step 5:

[1444] The server combines the results of the generative AI model with the emotion recognition results of the Emotion API and adjusts the feedback content according to the user's emotional state. For example, if the user is excited, it generates calm feedback such as, "This ad contains misinformation. Please think about it calmly."

[1445] Step 6:

[1446] The server sends the generated feedback to the smart glasses, which then display the adjusted feedback on their display and inform the user whether the information is accurate.

[1447] 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.

[1448] 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.

[1449] 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.

[1450] [Fourth embodiment]

[1451] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1452] 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.

[1453] 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).

[1454] 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.

[1455] 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.

[1456] 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).

[1457] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1458] 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.

[1459] 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.

[1460] 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.

[1461] 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.

[1462] 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.

[1463] 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."

[1464] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user. This system supports various information formats, such as text, images, and audio, and uses a generative AI model to determine the accuracy of the information. Specific embodiments are described below.

[1465] Overall system configuration

[1466] This system mainly consists of the following elements:

[1467] 1. User Input Method

[1468] 2. Means of transmitting information from the terminal to the server

[1469] 3. A method for determining the accuracy of information using a server-generated AI model

[1470] 4. Method of presenting the judgment results

[1471] 5. Methods for compiling and publishing misinformation

[1472] System Operation

[1473] 1. User Input Method

[1474] Users input information such as text, images, and voice using the comment function of LINE messages or Yahoo! News. For example, a user inputs text information such as "A new virus has been discovered."

[1475] 2. Means of transmitting information from the terminal to the server

[1476] The entered information is sent from the device (e.g., a smartphone or computer) to the server. The device converts it into an appropriate data format and sends the information to the server. For example, text information is converted into JSON format and sent to the server.

[1477] 3. A method for determining the accuracy of information using a server-generated AI model

[1478] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. As a specific example, the generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is incorrect."

[1479] 4. Method of presenting the judgment results

[1480] The server returns the judgment result from the generative AI model and the reasoning behind it to the user. For example, the user may be presented with a result such as, "This information is incorrect, and there are no new viruses currently being confirmed."

[1481] 5. Methods for compiling and publishing misinformation

[1482] Misinformation is stored in a server database and periodically compiled. The server periodically analyzes and publishes the misinformation. For example, if a large number of people receive false information claiming that "certain foods can ward off coronavirus," the server will publish that information as a warning on social media and in the comments section.

[1483] Specific examples

[1484] Example 1: Judging the accuracy of text information

[1485] The user enters the information "A new virus has been discovered" in a LINE message.

[1486] The terminal sends this text information to the server.

[1487] The server uses a generative AI model to determine the accuracy of this information.

[1488] The generative AI model determines that the information is incorrect and that no new virus has been identified to date.

[1489] The server presents the judgment result and the reasoning to the user.

[1490] Example 2: Image information accuracy

[1491] A user uploads an image using Yahoo! News' comment function, writing, "This image is from the disaster site."

[1492] The terminal transmits the image data to the server.

[1493] The server uses a generative AI model to determine the accuracy of the image.

[1494] The generative AI model determines that this image was actually taken somewhere else.

[1495] The server presents the judgment result to the user.

[1496] Example 3: Aggregating and publishing misinformation

[1497] Multiple users provide information claiming that certain foods can ward off coronavirus.

[1498] The server stores the information in the database as false information.

[1499] The server will compile data over a certain period of time and issue warnings about misinformation that certain foods can ward off coronavirus.

[1500] As described above, the system of the present invention can handle a variety of information formats and quickly and easily verify the accuracy of information, thereby preventing the spread of false information and improving social credibility.

[1501] The processing flow will be explained below.

[1502] Step 1:

[1503] The user inputs information such as text, images, and voice via the device. For example, the user inputs the text information "A new virus has been discovered" via LINE message.

[1504] Step 2:

[1505] The device converts the input information into an appropriate data format. For example, it converts text information into JSON format.

[1506] Step 3:

[1507] The device sends the converted data to the server using an internet connection.

[1508] Step 4:

[1509] The server verifies the received data and passes it to the generative AI model, which then begins analyzing the information.

[1510] Step 5:

[1511] The generative AI model analyzes the information provided and determines whether it is true or false. For example, it determines whether the text "A new virus has been discovered" is medically incorrect.

[1512] Step 6:

[1513] The generative AI model generates a judgment result and its rationale, which are then sent back to the server. The rationale includes reliable databases and expert opinions.

[1514] Step 7:

[1515] The server receives the judgment results and rationale from the generative AI model and converts them into a data format that can be presented to the user.

[1516] Step 8:

[1517] The server then sends the converted data back to the device via the Internet, for example, returning a result that "this information is incorrect and there are no currently confirmed new viruses."

[1518] Step 9:

[1519] The terminal displays the result of the judgment received from the server to the user, allowing the user to confirm the accuracy of the input information.

[1520] Step 10:

[1521] If a user provides false information, that information is stored in the server's database. For example, the false information "a new virus has been discovered" is stored.

[1522] Step 11:

[1523] The server periodically aggregates the misinformation in the database, thereby identifying misinformation that has a significant social impact.

[1524] Step 12:

[1525] The server publishes the results of the survey on social media and in applications with comment functions, preventing the spread of misinformation among users.

[1526] In this way, the system provides the ability to quickly and reliably determine the accuracy of information and prevent the spread of misinformation.

[1527] Example 1

[1528] 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."

[1529] In modern society, inaccurate and false information is easily spread, causing misunderstandings and anxiety, and undermining trust in society as a whole. Furthermore, it is difficult to assess information in a variety of formats, making it difficult to quickly and accurately determine the authenticity of information using conventional methods. Furthermore, because misinformation is not regularly compiled and published, there is a lack of ongoing warnings and countermeasures. To address these issues, a new system needs to be built.

[1530] 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.

[1531] In this invention, the server includes a means for a user to input information, a terminal means for transmitting the input information, a means for determining whether the input information is correct using a generative AI model, a means for presenting the determination result and its basis to the user, and a means for storing false information in a database and periodically compiling and publishing the information. This automates the entire process from inputting information to determining whether it is correct, presenting the result, and compiling and publishing the false information, making it possible to prevent the spread of inaccurate information and improve trust in society as a whole.

[1532] A "user" is an entity that inputs information and makes various requests to the system.

[1533] "Information" is data that is input in the form of text, images, audio, etc., and that is processed and analyzed.

[1534] A "terminal" is a device that receives information entered by a user, converts it into an appropriate data format, and sends it to a server.

[1535] A "server" is a remote device or cloud-based system used to analyze input information and generate results.

[1536] A "generative AI model" is an artificial intelligence model based on machine learning algorithms and used to determine whether information is correct or incorrect using extensive data sets.

[1537] "Correctness determination" is the process of analyzing and determining the accuracy and authenticity of input information.

[1538] The "judgment result" is the analysis result regarding the accuracy of the information obtained by the generative AI model.

[1539] The "data format" refers to the structure or format of data used when a terminal transmits information from a user to a server.

[1540] A "database" is an organized collection of data that stores misinformation and allows it to be accessed at a later time.

[1541] "Aggregation" is a method of organizing stored misinformation over a period of time and analyzing statistics and trends.

[1542] "Publication" is the process of issuing warnings and providing information based on the collected misinformation.

[1543] This invention is a system that automatically determines the accuracy of information entered by users, and is composed of three main elements: a server, a terminal, and a user. This system utilizes a generative AI model to analyze the accuracy of information, and compiles and publishes false information.

[1544] 1. Enter your information

[1545] Users can input information such as text, images, and audio via LINE messages or the comment function on news sites using their smartphones or computers.

[1546] Examples:

[1547] The user enters the information "A new virus has been discovered" in a LINE message.

[1548] 2. Transmission of information

[1549] The device receives the information entered by the user, converts it into an appropriate data format, such as JSON, and sends it to the server. The device then sends the information via an internet connection.

[1550] Examples:

[1551] The device converts the text information "A new virus has been discovered" into JSON format and sends it to the server.

[1552] 3. Performing a correct / incorrect judgment

[1553] The server inputs the received information into a generative AI model, which uses extensive datasets and machine learning algorithms to determine whether the input information is correct or incorrect.

[1554] The generative AI models used here are models specialized for natural language processing (e.g., GPT-3) and are trained on large amounts of data.

[1555] Examples:

[1556] The server inputs the text "A new virus has been discovered" into the generative AI model and has it analyze its accuracy. The model then refers to databases and official information and determines that the information is false.

[1557] Example prompt sentence:

[1558] "Please determine the accuracy of the information that a new virus has been discovered."

[1559] "Please check whether this image is really from the disaster site."

[1560] 4. Return of the judgment results

[1561] The server analyzes and organizes the judgment results obtained from the generative AI model and their rationale, and returns them to the user. The device then displays the received judgment results to the user.

[1562] Examples:

[1563] The server sends the determination result that "this information is incorrect and there are no currently confirmed new viruses" to the terminal, and the terminal notifies the user of the result.

[1564] 5. Aggregation and publication of misinformation

[1565] The server stores the misinformation in a database and periodically compiles the data. The results are analyzed and the most frequently occurring misinformation is published, allowing for ongoing awareness raising.

[1566] Examples:

[1567] If there is a large amount of misinformation, such as "certain foods can ward off coronavirus," the information will be published as a warning on social media and in the comments section.

[1568] As a result, the system of the present invention can accommodate a variety of information formats and not only can it quickly and accurately verify the accuracy of information, but it can also prevent the spread of false information and improve trust in society as a whole.

[1569] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1570] Step 1:

[1571] A user opens a LINE message or the comment function of a news site and enters information. This information can be entered in the form of text, image, audio, etc. For example, a user enters the text information "A new virus has been discovered" in a LINE message.

[1572] Input: Information entered by the user (text, images, audio)

[1573] Output: Input text, image, and audio data

[1574] Specific behavior:

[1575] A user enters text using a smartphone or computer keyboard.

[1576] In the case of images or audio, the user selects and uploads the file.

[1577] Step 2:

[1578] The device converts the input information into an appropriate data format and sends it to the server. For example, text information is converted into JSON format.

[1579] Input: User-entered text, images, and audio data

[1580] Output: Information converted into an appropriate data format, such as JSON.

[1581] Specific behavior:

[1582] The terminal converts the text data into JSON format through a program.

[1583] Image and audio data is converted into binary format.

[1584] The device sends the data to the server via an internet connection.

[1585] Step 3:

[1586] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. For example, the server inputs the text "A new virus has been discovered" into the generative AI model, and the model verifies this information and makes a judgment.

[1587] Input: JSON formatted text, images, and audio data

[1588] Output: True / false judgment result (e.g., "That information is false")

[1589] Specific behavior:

[1590] The server parses the received JSON data and inputs it into the API for the generative AI model.

[1591] The generative AI model references the dataset and determines whether the information is correct.

[1592] The server obtains the judgment result.

[1593] Step 4:

[1594] The server analyzes the judgment results and their rationale from the generative AI model and returns them to the user. The results are formatted in a way that is easy for the user to understand. For example, the judgment result may be presented to the user as "This information is incorrect, and there are no new viruses currently being confirmed."

[1595] Input: Correct / incorrect result

[1596] Output: Formatted results for presentation to the user

[1597] Specific behavior:

[1598] The server reads the results from the JSON format and formats them in user-friendly language.

[1599] Send the formatted result to the terminal.

[1600] The terminal notifies the user of the received result.

[1601] Step 5:

[1602] The server stores the results of the assessment in a database and periodically compiles the misinformation. The results are analyzed, and frequently occurring misinformation is made public. For example, if a large number of people report misinformation such as "certain foods can ward off coronavirus," the information will be published as a warning on social media and through the comments function.

[1603] Input: Judgment result and its basis

[1604] Output: Aggregated misinformation analysis results

[1605] Specific behavior:

[1606] The server stores the judgment results in a database.

[1607] We use tools to regularly aggregate the contents of the database and analyze trends in misinformation.

[1608] The results will be compiled and published, and warnings will be posted on social media and websites.

[1609] (Application example 1)

[1610] 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."

[1611] The proliferation of false and unreliable information on the Internet, especially in content distribution services, has become a problem. Because users have no way of determining the accuracy of this information, they run the risk of acting on it. For this reason, there is a demand for a system that allows users to easily check the accuracy of the content they watch or view in real time.

[1612] 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.

[1613] In this invention, the server includes a means for a user to input information such as text, images, and audio, a terminal means for transmitting the input information to the server, a means for determining the accuracy of the input information using a generative AI model in the server, a means for presenting the determination result and its basis to the user, and a means for checking the accuracy of videos or articles that the user intends to view or read on the content distribution service in advance. This allows the user to quickly evaluate the reliability of content and prevent the spread of misinformation.

[1614] A "user" is an individual or corporation who uses a content distribution service to view or browse information such as text, images, and audio.

[1615] "Information" refers to all data provided in the form of text, images, audio, etc.

[1616] A "terminal" is a device used by a user to input information and send it to a server, and includes a smartphone, computer, tablet, etc.

[1617] The "server" is a central device that receives information sent by users and uses a generative AI model to determine whether the information is correct.

[1618] A "generative AI model" is an artificial intelligence system that is trained on a large dataset and is used to analyze the accuracy of input information.

[1619] "Accuracy check" is the process of determining whether the entered information is accurate.

[1620] The "judgment result" is the conclusion reached by the generative AI model after analyzing the accuracy of the information.

[1621] "Basis" refers to the evidence and reasons that support the judgment result.

[1622] A "content distribution service" is an online platform that provides users with digital content such as videos, articles, and audio.

[1623] "Video" is a medium that consists of a series of images or videos.

[1624] An "article" refers to text data written to provide news or information.

[1625] The system for carrying out the present invention is mainly composed of user input means, a terminal, and a server. The specific configuration and operation of the system will be described below.

[1626] User input method

[1627] Users input information such as text, images, and audio. This input is performed using devices such as smartphones, computers, and tablets. For example, a user may input the title and content of a news article they wish to view in text format on a content distribution service.

[1628] Terminal means

[1629] The information entered by the user is converted into an appropriate data format and sent from the device to the server. For example, text information is converted into JSON format and sent to the server. The device can be a smartphone, computer, tablet, etc.

[1630] Server Means

[1631] The server uses a generative AI model to determine whether the input information is correct. A generative AI model is an artificial intelligence trained on a large dataset and can handle a variety of input formats, including text, images, and audio. For example, a generative AI model analyzes the accuracy of text information in a news article and determines that the information is incorrect.

[1632] Method of presenting the judgment results

[1633] The server presents the judgment results obtained from the generative AI model and the reasons for them to the user. For example, the user may be provided with a judgment result such as "This information is incorrect, and there are no new viruses currently being confirmed."

[1634] How misinformation is collected and published

[1635] The server has the function of storing misinformation in a database and periodically compiling and publishing it. For example, if a large amount of misinformation is received, the information will be analyzed and a warning will be issued via social media and the comment function.

[1636] Specific examples

[1637] Verification of text information

[1638] A user enters text information such as "A new virus has been discovered" using a smartphone app.

[1639] The device converts this text information into JSON format and sends it to the server.

[1640] The server uses a generative AI model to determine the accuracy of the text information.

[1641] The generative AI model determines that the information is incorrect.

[1642] The server presents the judgment result and its basis to the user.

[1643] Prompt Sentence Examples

[1644] "We check news articles about new virus discoveries. If the facts aren't verified, we flag them as misinformation."

[1645] This allows users to quickly obtain accurate information and prevents the spread of false information.

[1646] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1647] Step 1:

[1648] The user inputs information such as text, images, or audio.

[1649] Input: A user enters information using an input device (e.g., smartphone, tablet, computer).

[1650] Output: Information entered by the user (text, images, audio).

[1651] Step 2:

[1652] The terminal converts the input information into the appropriate data format.

[1653] Input: Raw data entered by the user (text, images, audio).

[1654] Data processing: Encode text information into JSON format, and images and audio into Base64 format.

[1655] Output: Information in converted data format (e.g. text information in JSON format).

[1656] Step 3:

[1657] The terminal transmits the converted information to the server.

[1658] Input: Information in converted data format.

[1659] Sending data: Sending data to the server using an HTTP request.

[1660] Output: Information sent to the server.

[1661] Step 4:

[1662] The information received by the server is input into the generative AI model to determine whether it is correct or incorrect.

[1663] Input: Information received by the server.

[1664] Data Computation: Input information into a generative AI model and use extensive data sets and machine learning algorithms to determine the accuracy of the information.

[1665] Output: The result of the judgment (e.g., "correct", "incorrect", etc.) and its rationale.

[1666] Step 5:

[1667] The server presents the judgment results from the generated AI model and their rationale to the user.

[1668] Input: The judgment result output by the generative AI model and its rationale.

[1669] Send data: Send data to the device using an HTTP response.

[1670] Output: The judgment result and its rationale presented to the user.

[1671] Step 6:

[1672] The server stores the misinformation in a database, which is then periodically compiled and published.

[1673] Input: Information that the generative AI model determines to be incorrect.

[1674] Data Retention: Storing false information in the database.

[1675] Data calculation: Collecting and analyzing stored misinformation over a period of time.

[1676] Output: A warning published via social media and the comments function.

[1677] 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.

[1678] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user. This system supports various information formats, such as text, images, and audio, and uses a generative AI model and emotion engine to determine the accuracy of the information and recognize the user's emotions. Specific embodiments are described below.

[1679] Overall system configuration

[1680] This system mainly consists of the following elements:

[1681] 1. User Input Method

[1682] 2. Means of transmitting information from the terminal to the server

[1683] 3. A method for determining the accuracy of information using a server-generated AI model

[1684] 4. User Emotion Recognition Method Using Emotion Engine

[1685] 5. Means of presenting the judgment results and their rationale to the user

[1686] 6. Methods for compiling and publishing misinformation

[1687] System Operation

[1688] 1. User Input Method

[1689] Users input information such as text, images, and voice using the comment function of LINE messages or Yahoo! News. For example, a user inputs text information such as "A new virus has been discovered."

[1690] 2. Means of transmitting information from the terminal to the server

[1691] The entered information is sent from the device (e.g., a smartphone or computer) to the server. The device converts it into an appropriate data format and sends the information to the server. For example, text information is converted into JSON format and sent to the server.

[1692] 3. A method for determining the accuracy of information using a server-generated AI model

[1693] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. As a specific example, the generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is incorrect."

[1694] 4. User Emotion Recognition Method Using Emotion Engine

[1695] At the same time, the server uses an emotion engine to recognize the user's emotions from the information input by the user. For example, in the case of voice input, the emotion engine analyzes the tone and pitch of the user's voice to determine whether the user is excited or calm.

[1696] 5. Means of presenting the judgment results and their rationale to the user

[1697] The server presents the results of the generative AI model and their rationale to the user. The way the results are presented is adjusted according to the user's emotional state. For example, if the user is excited, the server will present the results in a calm tone to allow the user to receive them calmly.

[1698] 6. Methods for compiling and publishing misinformation

[1699] Misinformation is stored in a server database and periodically compiled. The server periodically analyzes and publishes the misinformation. For example, if a large number of people receive false information claiming that "certain foods can ward off coronavirus," the server will publish that information as a warning on social media and in the comments section.

[1700] Specific examples

[1701] Example 1: Recognizing text information and user emotions

[1702] The user enters the information "A new virus has been discovered" in a LINE message.

[1703] The terminal sends this text information to the server.

[1704] The server uses a generative AI model to determine the accuracy of this information.

[1705] The generative AI model determines that the information is incorrect and that no new virus has been identified to date.

[1706] The emotion engine recognizes the user's anxious emotion from the user's text input.

[1707] The server presents the results and rationale in a tone that reassures anxious users.

[1708] Example 2: Verification of voice information and sentiment analysis

[1709] A user uploads audio using Yahoo! News' comment function, stating, "This image is from the disaster site."

[1710] The terminal transmits the audio data to the server.

[1711] The server uses a generative AI model to determine the accuracy of the speech.

[1712] The generative AI model analyzes the audio information and determines that the image was actually taken somewhere else.

[1713] The emotion engine recognizes the user's emotion (e.g., anger or anxiety) from the tone of their voice.

[1714] The server presents the results of the assessment in a tone adjusted according to the user's emotional state.

[1715] Example 3: Aggregating and publishing misinformation

[1716] Multiple users provide information claiming that certain foods can ward off coronavirus.

[1717] The server stores the information in the database as false information.

[1718] The server will compile data over a certain period of time and issue warnings about misinformation that certain foods can ward off coronavirus.

[1719] By combining a generative AI model and an emotion engine, this system can quickly determine the accuracy of information entered by users and provide appropriate feedback according to the user's emotional state, thereby preventing the spread of misinformation and improving social credibility.

[1720] The processing flow will be explained below.

[1721] Step 1:

[1722] The user inputs information such as text, images, and voice via the device. For example, the user inputs the text information "A new virus has been discovered" via LINE message.

[1723] Step 2:

[1724] The device converts the input information into an appropriate data format. For example, text information is converted into JSON format.

[1725] Step 3:

[1726] The device sends the converted data to the server using an internet connection.

[1727] Step 4:

[1728] The data received by the server is checked to ensure that the data format is consistent and that it is not invalid.

[1729] Step 5:

[1730] The server passes the received data to the generative AI model, which then begins analyzing the information.

[1731] Step 6:

[1732] A generative AI model analyzes data and determines whether it is true or false. For example, it determines whether the information "a new virus has been discovered" is medically incorrect.

[1733] Step 7:

[1734] The generative AI model generates a judgment result and its rationale, which are then sent back to the server. The rationale includes reliable databases and expert opinions.

[1735] Step 8:

[1736] The server receives the judgment results and rationale from the generative AI model and converts them into a data format for presentation to the user.

[1737] Step 9:

[1738] The server uses an emotion engine to recognize the user's emotions based on the information entered by the user, for example, by analyzing the text entered by the user and the tone of the voice.

[1739] Step 10:

[1740] The emotion engine recognizes the user's emotions and sends them back to the server, including emotions such as excitement, worry, and anger.

[1741] Step 11:

[1742] The server adjusts the way the results are presented depending on the user's emotional state. For example, if the user is excited, the server presents the results in a calm tone.

[1743] Step 12:

[1744] The server then sends the converted data back to the device via the Internet, for example, returning a result that "this information is incorrect and there are no currently confirmed new viruses."

[1745] Step 13:

[1746] The terminal displays the result of the judgment received from the server to the user, allowing the user to confirm the accuracy of the input information.

[1747] Step 14:

[1748] If a user provides false information, that information is stored in the server's database. For example, the false information "a new virus has been discovered" is stored.

[1749] Step 15:

[1750] The server periodically aggregates the misinformation in the database, thereby identifying misinformation that has a significant social impact.

[1751] Step 16:

[1752] The server publishes the results of the survey on social media and in applications with comment functions, preventing the spread of misinformation among users.

[1753] In this way, the system can quickly and reliably determine the accuracy of information and provide appropriate feedback according to the user's emotional state. It also provides a function to prevent the spread of misinformation, thereby improving social credibility.

[1754] Example 2

[1755] 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."

[1756] Conventional information verification systems were unable to take the user's emotional state into account when determining the accuracy of the information entered by the user. As a result, the results of the verification were not properly communicated to the user, and the system was not effective in preventing the spread of misinformation. Furthermore, because misinformation was not compiled or published, it was not possible to quickly and effectively warn other users. This could lead to a decline in social credibility.

[1757] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for determining whether input information is correct or incorrect using a generative AI model, a means for recognizing a user's emotion using an emotion engine, a means for presenting a determination result according to the user's emotional state, and a means for aggregating and publishing false information. This makes it possible to quickly and accurately determine the accuracy of information while taking into consideration the user's emotions, prevent the spread of false information, and improve social credibility.

[1758] "User" refers to an individual or corporation that uses the system to input information.

[1759] "Information" refers to data in various formats, such as text, images, and audio.

[1760] "Terminal" refers to a device used by a user to input and transmit information to a server.

[1761] "Server" refers to a computing device that receives information sent by a user and uses a generative AI model and emotion engine to determine the accuracy of the information and the user's emotions.

[1762] A "generative AI model" is an artificial intelligence model that uses machine learning algorithms and extensive data sets to analyze input information for accuracy.

[1763] An "emotion engine" refers to software that analyzes and recognizes the user's emotional state from information input.

[1764] "Accuracy assessment" refers to the process of evaluating the accuracy of input information and determining whether it is correct or incorrect.

[1765] "Misinformation" refers to information entered by a user that is inaccurate.

[1766] A "database" refers to a system that systematically stores data such as input information and its judgment results.

[1767] "Aggregation" refers to the process of statistically organizing misinformation stored in a database over a period of time.

[1768] "Publicity" refers to the activity of disclosing compiled misinformation to the public and raising awareness.

[1769] This invention provides a system called "FactChecker" that automatically determines the accuracy of information entered by a user and presents the results according to the user's emotional state. This system supports various information formats, such as text, images, and audio, and uses a generative AI model and an emotion engine to determine the accuracy of the information and recognize the user's emotions. Specific embodiments are described below.

[1770] Overall system configuration

[1771] This system mainly consists of the following elements:

[1772] 1. User Input Method

[1773] 2. Means of transmitting information from the terminal to the server

[1774] 3. A method for determining the accuracy of information using a server-generated AI model

[1775] 4. User Emotion Recognition Method Using Emotion Engine

[1776] 5. Means of presenting the judgment results and their rationale to the user

[1777] 6. Methods for compiling and publishing misinformation

[1778] 1. User Input Method

[1779] Users input information such as text, images, and audio using LINE messages or the comment function of a news site. For example, a user inputs text information such as "A new virus has been discovered."

[1780] 2. Means of transmitting information from the terminal to the server

[1781] The device (smartphone or computer) converts the information entered by the user into an appropriate data format (for example, JSON format) and sends it to the server. For example, text information entered by the user is converted into JSON format and sent to the server.

[1782] 3. A method for determining the accuracy of information using a server-generated AI model

[1783] The server inputs the received information into a generative AI model to determine its accuracy. The generative AI model uses a wide range of data sets and machine learning algorithms to analyze the accuracy of the input information. Specifically, it determines that the information "a new virus has been discovered" is false.

[1784] 4. User Emotion Recognition Method Using Emotion Engine

[1785] In parallel, the server uses an emotion engine to recognize the user's emotions from the information input by the user. For example, in the case of voice input, the emotion engine analyzes the tone and pitch of the user's voice to determine whether the user is excited or calm.

[1786] 5. Means of presenting the judgment results and their rationale to the user

[1787] The server presents the results of the generative AI model and the reasons for their findings to the user. The way the results are presented is adjusted according to the user's emotional state. For example, if the user is excited, the server will present the results in a calm tone so that the user can receive them calmly.

[1788] 6. Methods for compiling and publishing misinformation

[1789] The server stores misinformation in a database and periodically compiles and analyzes it. It then issues warnings about frequently occurring misinformation. For example, if a large number of people report the misinformation that "certain foods can ward off coronavirus," the server will publicize the information as a warning via social media and the comment function.

[1790] Specific operation example

[1791] Example 1: Recognizing text information and user emotions

[1792] A user types "A new virus has been discovered" in a LINE message.

[1793] The terminal sends this text information to the server.

[1794] The server uses a generative AI model to determine the accuracy of this information.

[1795] The generative AI model determines that the information is incorrect.

[1796] The emotion engine recognizes the emotions that the user is concerned about.

[1797] The server presents the judgment result and the reasoning to the user in a reassuring tone.

[1798] Example 2: Verification of voice information and sentiment analysis

[1799] A user uses the comment function on a news site to upload an audio message saying, "This image is from the disaster site."

[1800] The terminal transmits the audio data to the server.

[1801] The server uses a generative AI model to determine the accuracy of the audio information.

[1802] The generative AI model analyzes the audio information and determines that the image was actually taken somewhere else.

[1803] The emotion engine recognizes the user's emotion (e.g., anger or anxiety) from the tone of their voice.

[1804] The server presents the judgment result in a tone adjusted according to the emotional state.

[1805] Example 3: Aggregating and publishing misinformation

[1806] Multiple users provide information claiming that certain foods can ward off coronavirus.

[1807] The server stores the information in the database as false information.

[1808] The server aggregates data over a certain period of time.

[1809] The server warns against the misinformation that certain foods can ward off coronavirus.

[1810] By combining a generative AI model and an emotion engine, this system can quickly determine the accuracy of information entered by users and provide appropriate feedback according to the user's emotional state, thereby preventing the spread of misinformation and improving social credibility.

[1811] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1812] Step 1:

[1813] The user inputs information such as text, images, or audio.

[1814] Specific operation: The user opens the LINE app on their smartphone, types "A new virus has been discovered," and presses the send button.

[1815] Input: Information entered by the user (text, image, audio).

[1816] Output: Input information that the terminal is instructed to send.

[1817] Step 2:

[1818] The terminal converts the user's input information into an appropriate data format (for example, JSON format).

[1819] Specific operation: The smartphone converts the text information "A new virus has been discovered" into JSON format.

[1820] Input: Information entered by the user.

[1821] Output: The input information converted to JSON format.

[1822] Step 3:

[1823] The terminal transmits the converted information to the server.

[1824] Specific operation: The smartphone generates JSON data and sends it to the server's API endpoint as a POST request.

[1825] Input: Input information converted to JSON format.

[1826] Output: The JSON data sent to the server.

[1827] Step 4:

[1828] The server inputs the received information into a generative AI model to determine the accuracy of the information.

[1829] Specific operation: The server analyzes the received JSON data and inputs it into the generative AI model. The generative AI model verifies the information that "a new virus has been discovered" and determines that "the information is false."

[1830] Input: Input information in JSON format.

[1831] Output: The result of determining whether the information is correct or not.

[1832] Step 5:

[1833] The server inputs the received information into an emotion engine to recognize the user's emotions.

[1834] What it does: The server passes the voice data or text input to the emotion engine, which analyzes the user's emotions (excitement, anxiety, anger, etc.).

[1835] Input: User input (voice, text).

[1836] Output: The perceived emotional state of the user.

[1837] Step 6:

[1838] The server integrates the judgment results from the generative AI model with the emotional state from the emotion engine to create an output to present to the user.

[1839] Specific operation: The server integrates the determination that the information "A new virus has been discovered" is false with the user's feelings of anxiety, and creates a message in a calm tone saying, "That information is false, and here is the correct information."

[1840] Input: Information correctness judgment result, user's emotional state.

[1841] Output: The tailored message to present to the user.

[1842] Step 7:

[1843] The server stores the misinformation in a database, which is then periodically compiled and published.

[1844] Specific operation: The server stores misinformation in a database and periodically compiles the data. For example, if a large number of people receive misinformation claiming that certain foods can ward off coronavirus, the server will publish that information as a warning via social media and the comments function.

[1845] Input: False information collected by the server.

[1846] Output: Aggregate results and published warning information.

[1847] (Application example 2)

[1848] 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."

[1849] In modern society, the accuracy of information is extremely important. However, information provided in a variety of formats often contains false information, which can lead to misunderstandings and confusion. Feedback on the widespread acceptance of false information is also important, and there is a need to provide information in an appropriate manner that takes into account the feelings of the recipient. This requires a system that allows users to respond appropriately to information provided, such as advertisements, and prevents the spread of false information.

[1850] 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 means for determining the accuracy of input information using a generative AI model, means for recognizing the user's emotion, and means for adjusting the feedback content based on the recognized emotion. This makes it possible to automatically determine the accuracy of the information entered by the user and provide feedback that takes the user's emotion into consideration.

[1851] The "means for user input of information" refers to a device or system that allows a user to input information in various formats such as text, images, and voice.

[1852] The "terminal means for transmitting information to a server" refers to a device or system that has the function of transmitting data to a server in order to process input information.

[1853] "Means for determining the correctness of input information using a generative AI model" refers to a system that uses a generative AI model to determine whether the information entered by a user is correct or incorrect.

[1854] The "means for recognizing user emotions" is a system that has the function of recognizing emotions by analyzing the content of information entered by the user and the user's reactions.

[1855] The "means for adjusting the content of feedback based on the recognized emotion" is a system for appropriately changing the content and method of the feedback provided depending on the user's emotional state.

[1856] MODE FOR CARRYING OUT THE INVENTION

[1857] The present invention provides a system for determining the accuracy of advertisements in real time and providing feedback according to the user's emotions. Specific embodiments are described below.

[1858] Overall system configuration

[1859] The system consists of the following hardware and software:

[1860] Hardware: Smart glasses (built-in camera, microphone, display)

[1861] software:

[1862] Generative AI model (commonly known as GPT-4, e.g., made by OpenAI)

[1863] Emotion recognition software (e.g., EmotionAPI, Microsoft Azure)

[1864] Data format: JSON

[1865] Communication means: Wi-Fi, Bluetooth

[1866] System Operation

[1867] The following shows how the system is implemented by the server, terminal, and user.

[1868] User input method

[1869] Users watch advertisements using smart glasses, whose built-in camera and microphone capture the content of the advertisements (text, images, and audio) in real time.

[1870] Means of transmitting information from a terminal to a server

[1871] The smart glasses convert the captured information into JSON format and send it to a server using Wi-Fi or Bluetooth.

[1872] A means of determining the accuracy of information using a server-generated AI model

[1873] The server inputs the received advertising information into a generative AI model (GPT-4) to determine its accuracy. The generative AI model uses a wide range of datasets and machine learning algorithms to analyze the accuracy of the input information.

[1874] A method for recognizing user emotions using an emotion engine

[1875] At the same time, the server uses EmotionAPI to recognize the user's emotions, analyzing the user's emotional state (e.g., excitement, relief, anxiety) based on data obtained from the built-in camera and microphone.

[1876] A means of presenting the judgment results and their rationale to the user

[1877] The server presents the results of the generative AI model and their rationale to the user. The presentation of the results is adjusted according to the user's emotional state. For example, if the user is excited, gentle feedback is provided.

[1878] Feedback is provided in the form of the judgment result and its rationale being displayed on the smart glasses display.

[1879] Specific examples

[1880] Example 1:

[1881] The ad you're seeing: "This product will help you lose weight easily."

[1882] The camera in the smart glasses captures the advertisement and sends it to a server.

[1883] The server uses GPT-4 to determine that "this information is not based on scientific evidence."

[1884] EmotionAPI recognizes when a user is excited.

[1885] The smart glasses display a calming message: "This ad contains misinformation. Please think carefully."

[1886] Example 2:

[1887] The ad you're looking at: "Special discounts on new product launches"

[1888] Smart glasses capture the ad and determine its accuracy, saying, "It's okay to buy here."

[1889] EmotionAPI recognizes that the user is not excited.

[1890] The smart glasses will say "This information is accurate" in a normal tone.

[1891] Prompt Sentence Examples

[1892] "Is the content of this ad accurate? This text: 'This product will help you lose weight easily.'"

[1893] "Determine the accuracy of information regarding new product launches and special discounts."

[1894] Through these processing procedures, the present invention is a system that quickly determines the accuracy of information and provides appropriate feedback according to the user's emotions, thereby achieving high reliability.

[1895] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1896] Program processing steps

[1897] Step 1:

[1898] A user watches an advertisement through smart glasses, and the built-in camera of the smart glasses captures the image and text information of the advertisement, and the microphone records the audio information, which becomes the input data.

[1899] Step 2:

[1900] The captured information is converted into JSON format and sent to the server by the smart glasses. This conversion and transmission is the role of the device. The input is the captured image, text, and audio data, and the output is JSON format data.

[1901] Step 3:

[1902] The server analyzes the received JSON format data and inputs it into a generative AI model (GPT-4).The server then uses the generative AI model to determine the accuracy of the advertising information.The input is the JSON data sent to the server, and the output is the accuracy determination result.

[1903] Step 4:

[1904] At the same time, the server analyzes the user's emotions using the Emotion API. Based on the audio and video data sent from the smart glasses, the server recognizes the user's emotional state (excitement, relief, anxiety, etc.). The input is audio and video data, and the output is the user's emotional state.

[1905] Step 5:

[1906] The server generates feedback content by integrating the judgment results from the generative AI model and the emotion recognition results from the Emotion API. The tone and expression of the feedback are adjusted according to the user's emotional state. The input is the judgment results and emotion recognition results, and the output is the adjusted feedback content.

[1907] Step 6:

[1908] The server sends feedback content to the smart glasses. The feedback content is displayed on the display of the smart glasses. The input is the feedback content sent from the server, and the output is the feedback displayed on the smart glasses.

[1909] Specific operation explanation

[1910] Step 1:

[1911] A user wears the smart glasses and watches any advertisement, and the smart glasses' built-in camera captures screenshots of the advertisement and related text, while the microphone records the corresponding audio.

[1912] Step 2:

[1913] The device (smart glasses) converts captured images, text, and audio data into JSON format and sends it to the server via Wi-Fi or Bluetooth.

[1914] Step 3:

[1915] The server analyzes the received JSON data and inputs it into a generative AI model (GPT-4) along with a prompt. For example, it inputs something like, "Is the content of this advertisement accurate? This text: 'This product will help you lose weight easily.'" The generative AI model determines the accuracy of the information and concludes that "the information is not based on scientific evidence."

[1916] Step 4:

[1917] The server uses EmotionAPI to analyze the audio and video data sent from the smart glasses and recognize whether the user is excited, for example, based on the tone of the voice or facial expression in the video.

[1918] Step 5:

[1919] The server combines the results of the generative AI model with the emotion recognition results of the Emotion API and adjusts the feedback content according to the user's emotional state. For example, if the user is excited, it generates calm feedback such as, "This ad contains misinformation. Please think about it calmly."

[1920] Step 6:

[1921] The server sends the generated feedback to the smart glasses, which then display the adjusted feedback on their display and inform the user whether the information is accurate.

[1922] 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.

[1923] 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.

[1924] 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.

[1925] 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.

[1926] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1927] 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.

[1928] 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).

[1929] 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.

[1930] 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."

[1931] 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.

[1932] 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).

[1933] 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.

[1934] 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.

[1935] 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.

[1936] 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.

[1937] 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.

[1938] 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.

[1939] 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.

[1940] 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.

[1941] 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.

[1942] 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.

[1943] The following is further disclosed regarding the above embodiment.

[1944] (Claim 1)

[1945] a means for a user to input information, such as text, images, or audio;

[1946] a terminal means for transmitting input information to a server;

[1947] A means for determining the accuracy of input information using a generative AI model in the server;

[1948] means for presenting the determination result and its basis to the user;

[1949] A system including:

[1950] (Claim 2)

[1951] 10. The system of claim 1, wherein the generative AI model accommodates a variety of input formats, including text, images, and audio.

[1952] (Claim 3)

[1953] The system according to claim 1, further comprising means for storing the misinformation in a database, and for compiling and publishing the information at regular intervals.

[1954] "Example 1"

[1955] (Claim 1)

[1956] a means for a user to input information;

[1957] a terminal means for transmitting input information to a server;

[1958] A means for determining the accuracy of input information using a generative AI model in the server;

[1959] means for presenting the determination result and its basis to the user;

[1960] A method for storing false information in a database and periodically compiling and publishing it;

[1961] A system including:

[1962] (Claim 2)

[1963] 10. The system of claim 1, wherein the generative AI model accommodates a variety of input formats, including text, images, and audio.

[1964] (Claim 3)

[1965] 2. The system according to claim 1, wherein the terminal comprises means for converting input information into an appropriate data format and transmitting the converted information to the server.

[1966] "Application Example 1"

[1967] (Claim 1)

[1968] a means for a user to input information, such as text, images, or audio;

[1969] a terminal means for transmitting input information to a server;

[1970] A means for determining the accuracy of input information using a generative AI model in the server;

[1971] means for presenting the determination result and its basis to the user;

[1972] A means for users to check the accuracy of videos or articles they intend to watch or read on content distribution services in advance;

[1973] A system including:

[1974] (Claim 2)

[1975] The system of claim 1, wherein the generative AI model supports a variety of input formats, including text, images, and audio, and has a data checking function for content distribution services.

[1976] (Claim 3)

[1977] The system according to claim 1, further comprising means for storing false information in a database, aggregating and publishing the information at regular intervals, and presenting the information assessment results in real time.

[1978] "Example 2: Combining Emotion Engines"

[1979] (Claim 1)

[1980] a means for a user to input information, such as text, images, or audio;

[1981] a terminal means for transmitting input information to a server;

[1982] A means for determining the accuracy of input information using a generative AI model in the server;

[1983] a means for recognizing a user's emotion using an emotion engine;

[1984] means for presenting a determination result according to the emotional state of the user;

[1985] means of aggregating and publishing misinformation;

[1986] A system including:

[1987] (Claim 2)

[1988] 10. The system of claim 1, wherein the generative AI model accommodates a variety of input formats, including text, images, and audio.

[1989] (Claim 3)

[1990] The system according to claim 1, further comprising means for storing the misinformation in a database, and for compiling and publishing the information at regular intervals.

[1991] "Application example 2 when combining emotion engines"

[1992] (Claim 1)

[1993] a means for a user to input information, such as text, images, or audio;

[1994] a terminal means for transmitting input information to a server;

[1995] A means for determining the accuracy of input information using a generative AI model in the server;

[1996] means for presenting the determination result and its basis to the user;

[1997] means for recognizing a user's emotion;

[1998] a means for adjusting feedback content based on the perceived emotion; and

[1999] A system including:

[2000] (Claim 2)

[2001] 10. The system of claim 1, wherein the generative AI model accommodates a variety of input formats, including text, images, and audio.

[2002] (Claim 3)

[2003] The system according to claim 1, further comprising means for storing the misinformation in a database, and for compiling and publishing the information at regular intervals. [Explanation of symbols]

[2004] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input information, such as text, images, or audio; a terminal means for transmitting input information to a server; A means for determining the accuracy of input information using a generative AI model in the server; means for presenting the determination result and its basis to the user; A system including:

2. The system of claim 1, wherein the generative AI model accommodates a variety of input formats, including text, images, and audio.

3. 2. The system according to claim 1, further comprising means for storing the false information in a database, and for periodically compiling and publishing the information.

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