system

Abstract

The system according to this embodiment aims to evaluate the reliability of news articles and social media posts and provide this information to users. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects news articles and social media posts. The analysis unit analyzes the information collected by the collection unit and evaluates its reliability by comparing it with past data. The evaluation unit displays the reliability score evaluated by the analysis unit. The provision unit provides the reliability score displayed by the evaluation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the reliability of news articles and SNS posts is not sufficiently evaluated in real time to prevent the spread of false reports and fake news.

[0005] The system according to the embodiment aims to evaluate the reliability of news articles and SNS posts and provide it to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects news articles and social media posts. The analysis unit analyzes the information collected by the collection unit and evaluates its reliability by comparing it with past data. The evaluation unit displays the reliability score evaluated by the analysis unit. The provision unit provides the reliability score displayed by the evaluation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can evaluate the reliability of news articles and social media posts and provide this information to users. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Truthfulness Agent System according to an embodiment of the present invention is a system that analyzes the reliability of news articles and social media posts in real time and provides users with information on its truthfulness. The Truthfulness Agent System collects news articles and social media posts, and the AI ​​works in conjunction with a database to perform comparative analysis with past data and displays a reliability score. This effectively prevents the spread of misinformation and fake news. For example, the Truthfulness Agent System collects news articles and social media posts. Next, the AI ​​analyzes the collected information and evaluates its reliability by comparing it with past data. The evaluation result is provided to the user as a reliability score. This system features deep learning-based text analysis technology, integration with a large-scale database, and a real-time update and automatic learning system. As a result, the average accuracy of the news reliability score is 85% or higher, which is expected to improve users' information literacy and reduce social disruption caused by fake news. It also improves the user experience by making it easier for news consumers and social media users to judge the truthfulness of information. The target audience is all internet users aged 18 and over, and it solves the problem of uncertain information reliability and difficulty in judging the truth. The Truthfulness Agent System utilizes natural language processing AI to automatically evaluate the reliability of text, providing users with only trustworthy information. This aims to prevent misunderstandings and misuse of information, fostering a society where knowledge sharing and opinion formation are conducted correctly. As a result, the Truthfulness Agent System can analyze the reliability of news articles and social media posts in real time, providing users with information that is truthful.

[0029] The truthfulness agent system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects news articles and social media posts. For example, the collection unit automatically collects news articles and social media posts from the internet. The collection unit can also filter information based on specific keywords or topics. For example, the collection unit collects relevant news articles and social media posts based on keywords specified by the user. Furthermore, the collection unit can pre-evaluate the reliability of the information and exclude unreliable information. For example, the collection unit evaluates the reliability of the information source and excludes unreliable information from its collection. The analysis unit analyzes the information collected by the collection unit and evaluates its reliability by comparing it with past data. The analysis unit uses, for example, text analysis techniques using deep learning. Deep learning includes the structure of the neural network, training data, hyperparameters, etc. The analysis unit can also work in conjunction with a large-scale database. The large-scale database includes the database provider, data type, update frequency, etc. The analysis unit includes a real-time update and an automatic learning system. Real-time updates include update frequency, data acquisition method, and delay time. The automated learning system includes learning algorithms, training data, and update methods. The evaluation unit displays the reliability score evaluated by the analysis unit. The evaluation unit displays the reliability score numerically or graphically, for example. The reliability score includes the score range, evaluation items, and weighting. The provision unit provides the reliability score displayed by the evaluation unit to the user. The provision unit provides the reliability score to the user, for example, through a website or mobile application. As a result, the truthfulness agent system according to the embodiment can analyze the reliability of news articles and social media posts in real time and provide users with information on the truthfulness of the information.

[0030] The data collection unit collects news articles and social media posts. For example, it automatically collects news articles and social media posts from the internet. Specifically, it uses web crawlers and APIs to periodically scan publicly available information on the internet and collect information related to specified keywords and topics. Web crawlers visit specified websites to retrieve the latest news articles and social media posts. APIs utilize data access interfaces provided by social media platforms and news sites to retrieve information in real time. By combining these methods, the data collection unit can efficiently collect data from a wide range of sources. The data collection unit can also filter information based on specific keywords and topics. For example, it can collect relevant news articles and social media posts based on keywords specified by the user. This allows for efficient collection of information tailored to the user's interests. Furthermore, the data collection unit can pre-evaluate the reliability of information and exclude unreliable information. For example, it evaluates the reliability of information sources and excludes unreliable information from its collection. The reliability evaluation of information sources considers factors such as the source's past performance, reliability score, and frequency of information dissemination. This allows the data collection unit to efficiently collect highly reliable information and improve the overall reliability of the system.

[0031] The analysis unit analyzes the information collected by the collection unit and evaluates its reliability by comparing it with past data. The analysis unit uses, for example, text analysis techniques using deep learning. Deep learning includes the structure of the neural network, training data, and hyperparameters. Specifically, the analysis unit preprocesses the collected text data and extracts text features using natural language processing techniques. Next, it trains a model to evaluate the reliability of the text using a neural network. This model is trained on past reliability evaluation data and predicts a reliability score for unknown text data. The analysis unit can also work with large-scale databases. Large-scale databases include the database provider, data type, and update frequency. The analysis unit obtains past reliability evaluation data and related information from these databases and uses them for analysis. This allows the analysis unit to perform more accurate reliability evaluations. The analysis unit is equipped with real-time update and automatic learning systems. Real-time updates include update frequency, data acquisition method, and delay time. The analysis unit analyzes the collected data in real time and performs reliability evaluations quickly. The automatic learning system includes learning algorithms, training data, and update methods. The analysis unit continuously trains its model based on newly collected data, improving the accuracy of reliability assessments. This allows the analysis unit to always provide highly accurate reliability assessments based on the latest information.

[0032] The evaluation unit displays the reliability score evaluated by the analysis unit. The evaluation unit displays the reliability score numerically or graphically, for example. The reliability score includes the score range, evaluation items, and weighting. Specifically, the evaluation unit receives the reliability score provided by the analysis unit and displays it in a format that users can intuitively understand. In numerical displays, the reliability score is shown on a range from 0 to 100, with higher scores indicating higher reliability. In graphical displays, the reliability score is visually represented using bar graphs or line graphs, allowing users to grasp the fluctuations in the reliability of the information at a glance. The evaluation unit can also display the evaluation items and weightings used to calculate the reliability score. For example, it can show scores for each evaluation item such as the reliability of the information source, the consistency of the information, and the recency of the information, and clearly indicate the weighting of each item. This allows users to understand the basis of the reliability score and judge the reliability of the information more accurately. Furthermore, the evaluation unit can collect user feedback and continuously improve the display method and evaluation criteria of the reliability score. For example, based on feedback provided by users, it can adjust the display format of the reliability score and the weighting of evaluation items to provide reliability evaluations that meet user needs. This allows the evaluation unit to provide users with reliable information and support them in accurately determining the truthfulness of that information.

[0033] The service provider provides users with the reliability scores displayed by the evaluation department. For example, the service provider provides reliability scores to users through websites and mobile applications. Specifically, the service provider displays the reliability scores received from the evaluation department in a user-friendly format. On websites, reliability scores are displayed in real time for news articles and social media posts viewed by users. In mobile applications, reliability scores are displayed when users search for information, allowing them to instantly assess the reliability of the information. The service provider also considers user interface design and usability to ensure users can intuitively understand the reliability scores. For example, reliability scores are displayed using color coding, with highly reliable information shown in green and less reliable information in red. Links and buttons are also provided to display detailed reliability score information, allowing users to check detailed evaluation information as needed. Furthermore, the service provider can collect user feedback and continuously improve the delivery method and display format. For example, based on user feedback, the display location and format of reliability scores are adjusted to provide information that meets user needs. This allows the service provider to provide users with reliable information quickly and accurately, and to support them in judging the truthfulness of information.

[0034] The analysis unit uses text analysis techniques based on deep learning. For example, the analysis unit uses a deep learning model with a neural network structure. For instance, the analysis unit uses a large amount of text data as training data to train the model. The analysis unit can also adjust hyperparameters to build an optimal model. For example, the analysis unit adjusts hyperparameters such as the learning rate and batch size to improve the model's accuracy. This improves the accuracy of text analysis by using deep learning. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input text data into a generative AI, which can then perform text analysis.

[0035] The analysis unit interacts with large-scale databases. For example, the analysis unit obtains data from database providers and uses it for analysis. For example, the analysis unit uses databases of news articles and social media posts to evaluate reliability. The analysis unit can also consider the update frequency of the database and use the latest data. For example, the analysis unit updates the database in real time and performs analysis based on the latest information. This improves the accuracy of reliability evaluation by interacting with large-scale databases. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input data obtained from the database into a generative AI, which can then analyze the data.

[0036] The analysis unit is equipped with a real-time update and an automated learning system. For example, the analysis unit updates data in real time and performs analysis based on the latest information. For example, the analysis unit updates data in real time, taking into account the data acquisition method and latency. The analysis unit can also use an automated learning system to improve the accuracy of the model. For example, the analysis unit uses a learning algorithm to automatically update the model based on training data. This allows for reliability evaluation based on the latest information at all times by incorporating real-time updates and an automated learning system. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data acquired in real time into a generative AI, and the generative AI can perform data analysis.

[0037] The data collection unit can filter information based on specific keywords or topics during collection. For example, the data collection unit can collect relevant news articles and social media posts based on keywords specified by the user. The data collection unit can also, for example, prioritize the collection of information related to a specific topic and filter out other information. The data collection unit can also, for example, collect only highly relevant information based on the user's interests. This allows for the provision of information tailored to the user's interests by filtering information based on specific keywords or topics. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input keywords specified by the user into a generative AI, which can then filter the relevant information.

[0038] The data collection unit can pre-evaluate the reliability of information during collection and exclude unreliable information. For example, the data collection unit can evaluate the reliability of information sources and exclude unreliable information from collection. The data collection unit can also filter out unreliable information by comparing it with past databases. For example, the data collection unit can evaluate the reliability of the source and author of the information and exclude unreliable information. In this way, unreliable information can be excluded by pre-evaluating the reliability of the information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input reliability data of information sources into a generative AI, and the generative AI can perform a reliability evaluation.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of local news articles and social media posts based on the user's current location. The data collection unit can also collect highly relevant information by referring to the user's past location information. For example, the data collection unit can prioritize the collection of local event information based on the user's location information. This allows for the provision of regionally relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then collect highly relevant information.

[0040] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit prioritizes collecting posts from accounts the user follows. The data collection unit can also analyze the user's past posts and collect relevant information. The data collection unit can also collect relevant information based on the user's interests on social media. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then collect relevant information.

[0041] The analysis unit can evaluate the reliability of the information source and author during analysis. For example, the analysis unit evaluates the reliability of the information source and prioritizes the analysis of reliable information. For example, the analysis unit can also analyze the author's past posting history and evaluate their reliability. For example, the analysis unit can evaluate reliability based on the information source. This allows the analysis unit to prioritize the analysis of reliable information by evaluating the reliability of the information source and author. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input reliability data of the information source and author into a generative AI, and the generative AI can perform the reliability evaluation.

[0042] The analysis unit can improve the reliability of information by cross-checking the content of the information with multiple databases during analysis. For example, the analysis unit evaluates the reliability of the information by comparing it with multiple databases. The analysis unit can also evaluate the reliability of the information by comparing it with past data. The analysis unit can also improve reliability by cross-checking multiple information sources. In this way, the reliability of the information can be improved by cross-checking with multiple databases. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data obtained from multiple databases into a generating AI, and the generating AI can perform the cross-check.

[0043] The analysis unit can apply different analysis algorithms to each category of information during analysis. For example, the analysis unit can apply different analysis algorithms to news articles and social media posts. The analysis unit can also apply the most suitable analysis algorithm to each category of information (politics, economics, entertainment, etc.). The analysis unit can also apply different analysis algorithms to each format of information (text, images, videos, etc.). By applying different analysis algorithms to each category of information, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data for each category of information into a generative AI, and the generative AI can apply different analysis algorithms.

[0044] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant academic papers. The analysis unit can also improve the accuracy of its analysis by referring to relevant news articles. The analysis unit can also improve the accuracy of its analysis by referring to relevant databases. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input relevant literature data into a generating AI, and the generating AI can perform processing to improve the accuracy of the analysis.

[0045] The evaluation unit can improve the accuracy of the reliability score by referring to past evaluation data during the evaluation process. For example, the evaluation unit can improve the accuracy of the reliability score based on past evaluation data. The evaluation unit can also improve the accuracy of the reliability score by comparing it with past evaluation data. The evaluation unit can also improve the accuracy of the reliability score by referring to past evaluation data. In this way, the accuracy of the reliability score can be improved by referring to past evaluation data. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past evaluation data into a generating AI, and the generating AI can perform processing to improve the accuracy of the reliability score.

[0046] The evaluation unit can adjust the level of detail in the reliability score based on the importance of the information during evaluation. For example, the evaluation unit provides a detailed reliability score for information of high importance. The evaluation unit can also provide a concise reliability score for information of low importance. The evaluation unit can also adjust the level of detail in the reliability score based on the importance of the information. By adjusting the level of detail in the reliability score based on the importance of the information, a more appropriate reliability score can be provided. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information importance data into a generative AI, and the generative AI can adjust the level of detail in the reliability score.

[0047] The evaluation unit can determine the priority of reliability scores based on the information submission date during the evaluation process. For example, the evaluation unit can prioritize displaying the reliability score of the most recent information. The evaluation unit can also lower the priority of the reliability score of older information. The evaluation unit can also determine the priority of reliability scores based on the information submission date. This allows the latest information to be displayed preferentially by determining the priority of reliability scores based on the information submission date. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input information submission date data into a generating AI, and the generating AI can determine the priority of reliability scores.

[0048] The evaluation unit can adjust the order of reliability scores based on the relevance of the information during evaluation. For example, the evaluation unit can prioritize displaying reliability scores for highly relevant information. The evaluation unit can also lower the priority of reliability scores for less relevant information. The evaluation unit can also adjust the order of reliability scores based on the relevance of the information. This allows for the prioritization of highly relevant information by adjusting the order of reliability scores based on the relevance of the information. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information relevance data into a generative AI, and the generative AI can adjust the order of reliability scores.

[0049] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the user's past usage history. The service provider can also select the optimal service delivery method by referring to the user's past usage history. The service provider can also select the optimal service delivery method by analyzing the user's past usage history. This allows the service provider to select the optimal service delivery method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the service provider can input the user's past usage history data into a generation AI, and the generation AI can select the optimal service delivery method.

[0050] The service provider can customize the display content of the reliability score based on the user's areas of interest at the time of delivery. For example, the service provider can customize the display content of the reliability score based on the user's areas of interest. The service provider can also, for example, prioritize the display of relevant information based on the user's areas of interest. The service provider can also, for example, adjust the display content of the reliability score based on the user's areas of interest. This allows for the provision of more appropriate information by customizing the display content of the reliability score based on the user's areas of interest. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input user area of ​​interest data into a generative AI, and the generative AI can customize the display content of the reliability score.

[0051] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit can also select a delivery method optimized for a larger screen. For example, if the user is using a smartwatch, the delivery unit can also select a concise and highly visible delivery method. In this way, the optimal delivery method can be selected by taking into account the user's device information. Some or all of the above processing in the delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the delivery unit can input the user's device information into a generative AI, and the generative AI can select the optimal delivery method.

[0052] The service provider can analyze the user's social media activity and adjust the method of providing the reliability score at the time of provision. For example, the service provider can analyze the user's social media activity and adjust the optimal method of provision. The service provider can also adjust the method of providing the reliability score based on the user's interests on social media. The service provider can also adjust the method of providing the reliability score by referring to the user's social media activity. This allows the service provider to adjust the optimal method of provision by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, which can then adjust the optimal method of provision.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The data collection unit can analyze a user's past search history and prioritize the collection of relevant news articles and social media posts. For example, it can collect relevant information based on keywords the user has previously searched for. The data collection unit can also analyze a user's search history and prioritize the collection of information on topics that are likely to interest them. Furthermore, based on the user's search history, the data collection unit can prioritize the collection of information from specific news sources or social media accounts. This allows for the provision of more relevant information by taking into account the user's past search history.

[0055] The evaluation unit can collect user feedback and incorporate it into the evaluation of reliability scores. For example, users can provide feedback on the reliability scores they receive. The evaluation unit can also analyze user feedback and adjust the evaluation criteria for reliability scores. Furthermore, the evaluation unit can improve how reliability scores are displayed based on user feedback. By incorporating user feedback, the accuracy of reliability scores and user satisfaction can be improved.

[0056] The data collection unit can adjust its information gathering methods based on the user's device usage. For example, if the user is using a smartphone, it will prioritize collecting short articles and social media posts. If the user is using a tablet, the unit can also collect detailed news articles and longer social media posts. Furthermore, if the user is using a desktop computer, it can integrate and provide information from multiple sources. This allows for the provision of more relevant information by adjusting the information collection method according to the user's device usage.

[0057] The evaluation unit can customize how reliability scores are displayed by taking into account the user's profile information. For example, it can adjust how reliability scores are displayed based on the user's age and occupation. The evaluation unit can also customize the content of the reliability score display based on the user's interests and concerns. Furthermore, it can adjust how reliability scores are displayed based on the user's region and cultural background. This allows for the display of more appropriate reliability scores by taking user profile information into consideration.

[0058] The data collection unit can adjust its information collection method based on the user's internet connection status. For example, if the user is using a slow internet connection, it will prioritize collecting lightweight information. If the user is using a high-speed internet connection, the data collection unit can also collect detailed information and large amounts of data. Furthermore, if the user is offline, it can provide pre-cached information. This allows for the provision of more relevant information by adjusting the information collection method according to the user's internet connection status.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects news articles and social media posts. The data collection unit can automatically collect news articles and social media posts from the internet and filter the information based on specific keywords and topics. The data collection unit can also pre-evaluate the reliability of the information sources and exclude unreliable information. Step 2: The analysis unit analyzes the information collected by the collection unit and evaluates its reliability by comparing it with past data. The analysis unit uses deep learning-based text analysis technology and is equipped with a real-time update and automatic learning system in conjunction with a large-scale database. Step 3: The evaluation unit displays the reliability score evaluated by the analysis unit. The evaluation unit displays the reliability score numerically or graphically. Step 4: The provider provides the user with the reliability score displayed by the evaluation unit. The provider provides the user with the reliability score via a website or mobile application.

[0061] (Example of form 2) The Truthfulness Agent System according to an embodiment of the present invention is a system that analyzes the reliability of news articles and social media posts in real time and provides users with information on its truthfulness. The Truthfulness Agent System collects news articles and social media posts, and the AI ​​works in conjunction with a database to perform comparative analysis with past data and displays a reliability score. This effectively prevents the spread of misinformation and fake news. For example, the Truthfulness Agent System collects news articles and social media posts. Next, the AI ​​analyzes the collected information and evaluates its reliability by comparing it with past data. The evaluation result is provided to the user as a reliability score. This system features deep learning-based text analysis technology, integration with a large-scale database, and a real-time update and automatic learning system. As a result, the average accuracy of the news reliability score is 85% or higher, which is expected to improve users' information literacy and reduce social disruption caused by fake news. It also improves the user experience by making it easier for news consumers and social media users to judge the truthfulness of information. The target audience is all internet users aged 18 and over, and it solves the problem of uncertain information reliability and difficulty in judging the truth. The Truthfulness Agent System utilizes natural language processing AI to automatically evaluate the reliability of text, providing users with only trustworthy information. This aims to prevent misunderstandings and misuse of information, fostering a society where knowledge sharing and opinion formation are conducted correctly. As a result, the Truthfulness Agent System can analyze the reliability of news articles and social media posts in real time, providing users with information that is truthful.

[0062] The truthfulness agent system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a provision unit. The collection unit collects news articles and social media posts. For example, the collection unit automatically collects news articles and social media posts from the internet. The collection unit can also filter information based on specific keywords or topics. For example, the collection unit collects relevant news articles and social media posts based on keywords specified by the user. Furthermore, the collection unit can pre-evaluate the reliability of the information and exclude unreliable information. For example, the collection unit evaluates the reliability of the information source and excludes unreliable information from its collection. The analysis unit analyzes the information collected by the collection unit and evaluates its reliability by comparing it with past data. The analysis unit uses, for example, text analysis techniques using deep learning. Deep learning includes the structure of the neural network, training data, hyperparameters, etc. The analysis unit can also work in conjunction with a large-scale database. The large-scale database includes the database provider, data type, update frequency, etc. The analysis unit includes a real-time update and an automatic learning system. Real-time updates include update frequency, data acquisition method, and delay time. The automated learning system includes learning algorithms, training data, and update methods. The evaluation unit displays the reliability score evaluated by the analysis unit. The evaluation unit displays the reliability score numerically or graphically, for example. The reliability score includes the score range, evaluation items, and weighting. The provision unit provides the reliability score displayed by the evaluation unit to the user. The provision unit provides the reliability score to the user, for example, through a website or mobile application. As a result, the truthfulness agent system according to the embodiment can analyze the reliability of news articles and social media posts in real time and provide users with information on the truthfulness of the information.

[0063] The data collection unit collects news articles and social media posts. For example, it automatically collects news articles and social media posts from the internet. Specifically, it uses web crawlers and APIs to periodically scan publicly available information on the internet and collect information related to specified keywords and topics. Web crawlers visit specified websites to retrieve the latest news articles and social media posts. APIs utilize data access interfaces provided by social media platforms and news sites to retrieve information in real time. By combining these methods, the data collection unit can efficiently collect data from a wide range of sources. The data collection unit can also filter information based on specific keywords and topics. For example, it can collect relevant news articles and social media posts based on keywords specified by the user. This allows for efficient collection of information tailored to the user's interests. Furthermore, the data collection unit can pre-evaluate the reliability of information and exclude unreliable information. For example, it evaluates the reliability of information sources and excludes unreliable information from its collection. The reliability evaluation of information sources considers factors such as the source's past performance, reliability score, and frequency of information dissemination. This allows the data collection unit to efficiently collect highly reliable information and improve the overall reliability of the system.

[0064] The analysis unit analyzes the information collected by the collection unit and evaluates its reliability by comparing it with past data. The analysis unit uses, for example, text analysis techniques using deep learning. Deep learning includes the structure of the neural network, training data, and hyperparameters. Specifically, the analysis unit preprocesses the collected text data and extracts text features using natural language processing techniques. Next, it trains a model to evaluate the reliability of the text using a neural network. This model is trained on past reliability evaluation data and predicts a reliability score for unknown text data. The analysis unit can also work with large-scale databases. Large-scale databases include the database provider, data type, and update frequency. The analysis unit obtains past reliability evaluation data and related information from these databases and uses them for analysis. This allows the analysis unit to perform more accurate reliability evaluations. The analysis unit is equipped with real-time update and automatic learning systems. Real-time updates include update frequency, data acquisition method, and delay time. The analysis unit analyzes the collected data in real time and performs reliability evaluations quickly. The automatic learning system includes learning algorithms, training data, and update methods. The analysis unit continuously trains its model based on newly collected data, improving the accuracy of reliability assessments. This allows the analysis unit to always provide highly accurate reliability assessments based on the latest information.

[0065] The evaluation unit displays the reliability score evaluated by the analysis unit. The evaluation unit displays the reliability score numerically or graphically, for example. The reliability score includes the score range, evaluation items, and weighting. Specifically, the evaluation unit receives the reliability score provided by the analysis unit and displays it in a format that users can intuitively understand. In numerical displays, the reliability score is shown on a range from 0 to 100, with higher scores indicating higher reliability. In graphical displays, the reliability score is visually represented using bar graphs or line graphs, allowing users to grasp the fluctuations in the reliability of the information at a glance. The evaluation unit can also display the evaluation items and weightings used to calculate the reliability score. For example, it can show scores for each evaluation item such as the reliability of the information source, the consistency of the information, and the recency of the information, and clearly indicate the weighting of each item. This allows users to understand the basis of the reliability score and judge the reliability of the information more accurately. Furthermore, the evaluation unit can collect user feedback and continuously improve the display method and evaluation criteria of the reliability score. For example, based on feedback provided by users, it can adjust the display format of the reliability score and the weighting of evaluation items to provide reliability evaluations that meet user needs. This allows the evaluation unit to provide users with reliable information and support them in accurately determining the truthfulness of that information.

[0066] The service provider provides users with the reliability scores displayed by the evaluation department. For example, the service provider provides reliability scores to users through websites and mobile applications. Specifically, the service provider displays the reliability scores received from the evaluation department in a user-friendly format. On websites, reliability scores are displayed in real time for news articles and social media posts viewed by users. In mobile applications, reliability scores are displayed when users search for information, allowing them to instantly assess the reliability of the information. The service provider also considers user interface design and usability to ensure users can intuitively understand the reliability scores. For example, reliability scores are displayed using color coding, with highly reliable information shown in green and less reliable information in red. Links and buttons are also provided to display detailed reliability score information, allowing users to check detailed evaluation information as needed. Furthermore, the service provider can collect user feedback and continuously improve the delivery method and display format. For example, based on user feedback, the display location and format of reliability scores are adjusted to provide information that meets user needs. This allows the service provider to provide users with reliable information quickly and accurately, and to support them in judging the truthfulness of information.

[0067] The analysis unit uses text analysis techniques based on deep learning. For example, the analysis unit uses a deep learning model with a neural network structure. For instance, the analysis unit uses a large amount of text data as training data to train the model. The analysis unit can also adjust hyperparameters to build an optimal model. For example, the analysis unit adjusts hyperparameters such as the learning rate and batch size to improve the model's accuracy. This improves the accuracy of text analysis by using deep learning. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input text data into a generative AI, which can then perform text analysis.

[0068] The analysis unit interacts with large-scale databases. For example, the analysis unit obtains data from database providers and uses it for analysis. For example, the analysis unit uses databases of news articles and social media posts to evaluate reliability. The analysis unit can also consider the update frequency of the database and use the latest data. For example, the analysis unit updates the database in real time and performs analysis based on the latest information. This improves the accuracy of reliability evaluation by interacting with large-scale databases. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input data obtained from the database into a generative AI, which can then analyze the data.

[0069] The analysis unit is equipped with a real-time update and an automated learning system. For example, the analysis unit updates data in real time and performs analysis based on the latest information. For example, the analysis unit updates data in real time, taking into account the data acquisition method and latency. The analysis unit can also use an automated learning system to improve the accuracy of the model. For example, the analysis unit uses a learning algorithm to automatically update the model based on training data. This allows for reliability evaluation based on the latest information at all times by incorporating real-time updates and an automated learning system. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data acquired in real time into a generative AI, and the generative AI can perform data analysis.

[0070] The data collection unit can estimate the user's emotions and adjust the types of news articles and social media posts it collects based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting news articles and social media posts that provide a sense of security. For example, if the user is excited, the data collection unit may also collect entertainment-related news articles and social media posts that are of interest to the user. For example, if the user is depressed, the data collection unit may also collect news articles and social media posts that are encouraging or positive. This allows for the provision of more appropriate information by adjusting the types of information collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0071] The data collection unit can filter information based on specific keywords or topics during collection. For example, the data collection unit can collect relevant news articles and social media posts based on keywords specified by the user. The data collection unit can also, for example, prioritize the collection of information related to a specific topic and filter out other information. The data collection unit can also, for example, collect only highly relevant information based on the user's interests. This allows for the provision of information tailored to the user's interests by filtering information based on specific keywords or topics. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input keywords specified by the user into a generative AI, which can then filter the relevant information.

[0072] The data collection unit can pre-evaluate the reliability of information during collection and exclude unreliable information. For example, the data collection unit can evaluate the reliability of information sources and exclude unreliable information from collection. The data collection unit can also filter out unreliable information by comparing it with past databases. For example, the data collection unit can evaluate the reliability of the source and author of the information and exclude unreliable information. In this way, unreliable information can be excluded by pre-evaluating the reliability of the information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input reliability data of information sources into a generative AI, and the generative AI can perform a reliability evaluation.

[0073] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting information that promotes relaxation. For example, if the user is excited, the data collection unit may prioritize collecting information that is highly entertaining. For example, if the user is depressed, the data collection unit may prioritize collecting positive information. This allows for the provision of more appropriate information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0074] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during the collection process. For example, the data collection unit can prioritize the collection of local news articles and social media posts based on the user's current location. The data collection unit can also collect highly relevant information by referring to the user's past location information. For example, the data collection unit can prioritize the collection of local event information based on the user's location information. This allows for the provision of regionally relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then collect highly relevant information.

[0075] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit prioritizes collecting posts from accounts the user follows. The data collection unit can also analyze the user's past posts and collect relevant information. The data collection unit can also collect relevant information based on the user's interests on social media. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, which can then collect relevant information.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide reassuring analysis results. For example, if the user is excited, the analysis unit can also provide interesting analysis results. For example, if the user is depressed, the analysis unit can also provide encouraging or positive analysis results. By adjusting the analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0077] The analysis unit can evaluate the reliability of the information source and author during analysis. For example, the analysis unit evaluates the reliability of the information source and prioritizes the analysis of reliable information. For example, the analysis unit can also analyze the author's past posting history and evaluate their reliability. For example, the analysis unit can evaluate reliability based on the information source. This allows the analysis unit to prioritize the analysis of reliable information by evaluating the reliability of the information source and author. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input reliability data of the information source and author into a generative AI, and the generative AI can perform the reliability evaluation.

[0078] The analysis unit can improve the reliability of information by cross-checking the content of the information with multiple databases during analysis. For example, the analysis unit evaluates the reliability of the information by comparing it with multiple databases. The analysis unit can also evaluate the reliability of the information by comparing it with past data. The analysis unit can also improve reliability by cross-checking multiple information sources. In this way, the reliability of the information can be improved by cross-checking with multiple databases. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data obtained from multiple databases into a generating AI, and the generating AI can perform the cross-check.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a display method that provides reassurance. For example, if the user is excited, the analysis unit can also provide an engaging display method. For example, if the user is depressed, the analysis unit can also provide an encouraging or positive display method. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0080] The analysis unit can apply different analysis algorithms to each category of information during analysis. For example, the analysis unit can apply different analysis algorithms to news articles and social media posts. The analysis unit can also apply the most suitable analysis algorithm to each category of information (politics, economics, entertainment, etc.). The analysis unit can also apply different analysis algorithms to each format of information (text, images, videos, etc.). By applying different analysis algorithms to each category of information, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data for each category of information into a generative AI, and the generative AI can apply different analysis algorithms.

[0081] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant academic papers. The analysis unit can also improve the accuracy of its analysis by referring to relevant news articles. The analysis unit can also improve the accuracy of its analysis by referring to relevant databases. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input relevant literature data into a generating AI, and the generating AI can perform processing to improve the accuracy of the analysis.

[0082] The evaluation unit can estimate the user's emotions and adjust the display method of the reliability score based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit can provide a reliable score display method that provides reassurance. For example, if the user is excited, the evaluation unit can also provide an interesting reliable score display method. For example, if the user is depressed, the evaluation unit can also provide an encouraging or positive reliable score display method. By adjusting the display method of the reliability score according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or not using a generative AI. For example, the evaluation unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0083] The evaluation unit can improve the accuracy of the reliability score by referring to past evaluation data during the evaluation process. For example, the evaluation unit can improve the accuracy of the reliability score based on past evaluation data. The evaluation unit can also improve the accuracy of the reliability score by comparing it with past evaluation data. The evaluation unit can also improve the accuracy of the reliability score by referring to past evaluation data. In this way, the accuracy of the reliability score can be improved by referring to past evaluation data. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input past evaluation data into a generating AI, and the generating AI can perform processing to improve the accuracy of the reliability score.

[0084] The evaluation unit can adjust the level of detail in the reliability score based on the importance of the information during evaluation. For example, the evaluation unit provides a detailed reliability score for information of high importance. The evaluation unit can also provide a concise reliability score for information of low importance. The evaluation unit can also adjust the level of detail in the reliability score based on the importance of the information. By adjusting the level of detail in the reliability score based on the importance of the information, a more appropriate reliability score can be provided. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information importance data into a generative AI, and the generative AI can adjust the level of detail in the reliability score.

[0085] The evaluation unit can estimate the user's emotions and adjust the display order of reliability scores based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit may prioritize displaying reliability scores that provide a sense of security. For example, if the user is excited, the evaluation unit may prioritize displaying reliability scores that are interesting. For example, if the user is depressed, the evaluation unit may prioritize displaying encouraging or positive reliability scores. By adjusting the display order of reliability scores according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or not using a generative AI. For example, the evaluation unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0086] The evaluation unit can determine the priority of reliability scores based on the information submission date during the evaluation process. For example, the evaluation unit can prioritize displaying the reliability score of the most recent information. The evaluation unit can also lower the priority of the reliability score of older information. The evaluation unit can also determine the priority of reliability scores based on the information submission date. This allows the latest information to be displayed preferentially by determining the priority of reliability scores based on the information submission date. Some or all of the above processing in the evaluation unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the evaluation unit can input information submission date data into a generating AI, and the generating AI can determine the priority of reliability scores.

[0087] The evaluation unit can adjust the order of reliability scores based on the relevance of the information during evaluation. For example, the evaluation unit can prioritize displaying reliability scores for highly relevant information. The evaluation unit can also lower the priority of reliability scores for less relevant information. The evaluation unit can also adjust the order of reliability scores based on the relevance of the information. This allows for the prioritization of highly relevant information by adjusting the order of reliability scores based on the relevance of the information. Some or all of the above processing in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input information relevance data into a generative AI, and the generative AI can adjust the order of reliability scores.

[0088] The service provider can estimate the user's emotions and adjust the method of providing the reliability score based on the estimated user emotions. For example, if the user is feeling anxious, the service provider can provide a reliability score that provides reassurance. For example, if the user is excited, the service provider can also provide a reliability score that is engaging. For example, if the user is depressed, the service provider can also provide an encouraging or positive reliability score. By adjusting the method of providing the reliability score according to the user's emotions, a more appropriate service can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0089] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can select the optimal service delivery method based on the user's past usage history. The service provider can also select the optimal service delivery method by referring to the user's past usage history. The service provider can also select the optimal service delivery method by analyzing the user's past usage history. This allows the service provider to select the optimal service delivery method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without using a generation AI. For example, the service provider can input the user's past usage history data into a generation AI, and the generation AI can select the optimal service delivery method.

[0090] The service provider can customize the display content of the reliability score based on the user's areas of interest at the time of delivery. For example, the service provider can customize the display content of the reliability score based on the user's areas of interest. The service provider can also, for example, prioritize the display of relevant information based on the user's areas of interest. The service provider can also, for example, adjust the display content of the reliability score based on the user's areas of interest. This allows for the provision of more appropriate information by customizing the display content of the reliability score based on the user's areas of interest. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input user area of ​​interest data into a generative AI, and the generative AI can customize the display content of the reliability score.

[0091] The service provider can estimate the user's emotions and adjust the frequency of providing confidence scores based on the estimated emotions. For example, if the user is feeling anxious, the service provider will provide confidence scores more frequently. For example, if the user is excited, the service provider may provide confidence scores at a moderate frequency. For example, if the user is depressed, the service provider may provide encouragement or positive confidence scores. By adjusting the frequency of providing confidence scores according to the user's emotions, more appropriate provision becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then perform emotion estimation.

[0092] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. For example, if the user is using a tablet, the delivery unit can also select a delivery method optimized for a larger screen. For example, if the user is using a smartwatch, the delivery unit can also select a concise and highly visible delivery method. In this way, the optimal delivery method can be selected by taking into account the user's device information. Some or all of the above processing in the delivery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the delivery unit can input the user's device information into a generative AI, and the generative AI can select the optimal delivery method.

[0093] The service provider can analyze the user's social media activity and adjust the method of providing the reliability score at the time of provision. For example, the service provider can analyze the user's social media activity and adjust the optimal method of provision. The service provider can also adjust the method of providing the reliability score based on the user's interests on social media. The service provider can also adjust the method of providing the reliability score by referring to the user's social media activity. This allows the service provider to adjust the optimal method of provision by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without using a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, which can then adjust the optimal method of provision.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The data collection unit can analyze a user's past search history and prioritize the collection of relevant news articles and social media posts. For example, it can collect relevant information based on keywords the user has previously searched for. The data collection unit can also analyze a user's search history and prioritize the collection of information on topics that are likely to interest them. Furthermore, based on the user's search history, the data collection unit can prioritize the collection of information from specific news sources or social media accounts. This allows for the provision of more relevant information by taking into account the user's past search history.

[0096] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on those emotions. For example, if the user is feeling anxious, it can provide detailed analysis results to reassure them. If the user is excited, the analysis unit can also provide concise and engaging analysis results. Furthermore, if the user is feeling down, it can provide positive analysis results to encourage them. In this way, by adjusting the level of detail in the analysis results according to the user's emotions, more appropriate information can be provided.

[0097] The evaluation unit can collect user feedback and incorporate it into the evaluation of reliability scores. For example, users can provide feedback on the reliability scores they receive. The evaluation unit can also analyze user feedback and adjust the evaluation criteria for reliability scores. Furthermore, the evaluation unit can improve how reliability scores are displayed based on user feedback. By incorporating user feedback, the accuracy of reliability scores and user satisfaction can be improved.

[0098] The information delivery unit can estimate the user's emotions and adjust the timing of information delivery based on those estimates. For example, if the user is feeling stressed, information can be delivered during times when they are likely to relax. If the user is excited, information can be delivered during times when their concentration is at its highest. Furthermore, if the user is feeling down, the timing of positive information delivery can be adjusted. By adjusting the timing of information delivery according to the user's emotions, more effective information delivery becomes possible.

[0099] The data collection unit can adjust its information gathering methods based on the user's device usage. For example, if the user is using a smartphone, it will prioritize collecting short articles and social media posts. If the user is using a tablet, the unit can also collect detailed news articles and longer social media posts. Furthermore, if the user is using a desktop computer, it can integrate and provide information from multiple sources. This allows for the provision of more relevant information by adjusting the information collection method according to the user's device usage.

[0100] The analysis unit can estimate the user's emotions and select an analysis algorithm based on those emotions. For example, if the user is feeling anxious, it can use an analysis algorithm that provides a sense of security. If the user is excited, the analysis unit can also use an analysis algorithm that attracts interest. Furthermore, if the user is depressed, it can use a positive analysis algorithm. By selecting an analysis algorithm according to the user's emotions, it is possible to provide more appropriate analysis results.

[0101] The evaluation unit can customize how reliability scores are displayed by taking into account the user's profile information. For example, it can adjust how reliability scores are displayed based on the user's age and occupation. The evaluation unit can also customize the content of the reliability score display based on the user's interests and concerns. Furthermore, it can adjust how reliability scores are displayed based on the user's region and cultural background. This allows for the display of more appropriate reliability scores by taking user profile information into consideration.

[0102] The information delivery system can estimate the user's emotions and adjust the format of information delivery based on those estimates. For example, if the user is feeling anxious, the information can be delivered in a reassuring format. If the user is excited, the information can be delivered in a visually appealing format. Furthermore, if the user is depressed, the information can be delivered in a positive format. By adjusting the format of information delivery according to the user's emotions, more effective information delivery becomes possible.

[0103] The data collection unit can adjust its information collection method based on the user's internet connection status. For example, if the user is using a slow internet connection, it will prioritize collecting lightweight information. If the user is using a high-speed internet connection, the data collection unit can also collect detailed information and large amounts of data. Furthermore, if the user is offline, it can provide pre-cached information. This allows for the provision of more relevant information by adjusting the information collection method according to the user's internet connection status.

[0104] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, it will use a notification method that provides reassurance. If the user is excited, the analysis unit can also use a notification method that attracts attention. Furthermore, if the user is depressed, it can use a positive notification method. By adjusting the notification method of the analysis results according to the user's emotions, more effective notifications become possible.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The data collection unit collects news articles and social media posts. The data collection unit can automatically collect news articles and social media posts from the internet and filter the information based on specific keywords and topics. The data collection unit can also pre-evaluate the reliability of the information sources and exclude unreliable information. Step 2: The analysis unit analyzes the information collected by the collection unit and evaluates its reliability by comparing it with past data. The analysis unit uses deep learning-based text analysis technology and is equipped with a real-time update and automatic learning system in conjunction with a large-scale database. Step 3: The evaluation unit displays the reliability score evaluated by the analysis unit. The evaluation unit displays the reliability score numerically or graphically. Step 4: The provider provides the user with the reliability score displayed by the evaluation unit. The provider provides the user with the reliability score via a website or mobile application.

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit automatically collects news articles and social media posts from the internet using the control unit 46A of the smart device 14. The analysis unit analyzes the collected information using deep learning-based text analysis technology, using the specific processing unit 290 of the data processing unit 12. The evaluation unit displays the reliability score numerically or graphically using the specific processing unit 290 of the data processing unit 12. The provision unit provides the reliability score to the user via a website or mobile application, using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit automatically collects news articles and social media posts from the internet using the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected information using deep learning-based text analysis technology, for example, using the specific processing unit 290 of the data processing unit 12. The evaluation unit displays the reliability score numerically or graphically using the specific processing unit 290 of the data processing unit 12. The provision unit provides the reliability score to the user via a website or mobile application, for example, using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit automatically collects news articles and social media posts from the internet using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected information using deep learning-based text analysis technology, for example, using the specific processing unit 290 of the data processing unit 12. The evaluation unit displays the reliability score numerically or graphically using the specific processing unit 290 of the data processing unit 12. The provision unit provides the reliability score to the user via a website or mobile application, for example, using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the 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.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit automatically collects news articles and social media posts from the internet using the control unit 46A of the robot 414. The analysis unit analyzes the collected information using deep learning-based text analysis technology, for example, using the specific processing unit 290 of the data processing unit 12. The evaluation unit displays the reliability score numerically or graphically using the specific processing unit 290 of the data processing unit 12. The provision unit provides the reliability score to the user via a website or mobile application, for example, using the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) The collection department collects news articles and social media posts, An analysis unit analyzes the information collected by the aforementioned collection unit and evaluates its reliability by comparing it with past data, An evaluation unit that displays the reliability score evaluated by the analysis unit, The system includes a providing unit that provides the user with the reliability score displayed by the evaluation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, This uses text analysis techniques based on deep learning. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Integrating with large-scale databases The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Features real-time updates and an automated learning system. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It estimates user sentiment and adjusts the types of news articles and social media posts collected based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting information, filter it based on specific keywords or topics. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, the reliability of the information is evaluated in advance, and unreliable information is excluded. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the source of the information and the reliability of the authors are evaluated. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the reliability of the information is improved by cross-checking the content against multiple databases. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied to each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, It estimates the user's sentiment and adjusts how the confidence score is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, During evaluation, past evaluation data is referenced to improve the accuracy of the reliability score. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During evaluation, adjust the level of detail in the reliability score based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, It estimates the user's sentiment and adjusts the display order of the reliability score based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation, reliability scores are prioritized based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During evaluation, the order of reliability scores is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide confidence scores based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the displayed reliability score will be customized based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the user's sentiment and adjusts the frequency of providing confidence scores based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and adjust how the reliability score is provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects news articles and social media posts, An analysis unit analyzes the information collected by the aforementioned collection unit and evaluates its reliability by comparing it with past data, An evaluation unit that displays the reliability score evaluated by the analysis unit, The system includes a providing unit that provides the user with the reliability score displayed by the evaluation unit. A system characterized by the following features.

2. The aforementioned analysis unit, This uses text analysis techniques based on deep learning. The system according to feature 1.

3. The aforementioned analysis unit, Integrating with large-scale databases The system according to feature 1.

4. The aforementioned analysis unit, Features real-time updates and an automated learning system. The system according to feature 1.

5. The aforementioned collection unit is It estimates user sentiment and adjusts the types of news articles and social media posts collected based on the estimated user sentiment. The system according to feature 1.

6. The aforementioned collection unit is When collecting information, filter it based on specific keywords or topics. The system according to feature 1.

7. The aforementioned collection unit is During data collection, the reliability of the information is evaluated in advance, and unreliable information is excluded. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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