system

Abstract

The system according to this embodiment aims to efficiently determine the truthfulness of online posts and provide this information to users. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects online posts. The analysis unit analyzes the posts collected by the collection unit. The determination unit determines the truthfulness of the posts based on the results analyzed by the analysis unit. The provision unit provides the user with the results determined by the determination unit.

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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 prior art, there was a problem that the authenticity of posted content on the Internet was not sufficiently determined efficiently and provided to users.

[0005] The system according to the embodiment aims to efficiently determine the authenticity of posted content on the Internet and provide it to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects online posts. The analysis unit analyzes the posts collected by the collection unit. The determination unit determines the truthfulness of the posts based on the results analyzed by the analysis unit. The provision unit provides the results determined by the determination unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently determine the truthfulness of online 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 numbered 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 receiving 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 receiving 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 determination system according to an embodiment of the present invention is a system that automatically determines the truthfulness of online news and posts. This truthfulness determination system collects online posts, analyzes them using AI, and determines their truthfulness. The determination results are provided to the user, realizing a world where people are not misled by fake news. First, online posts are collected. In this case, the posts include news articles and social media posts. This allows for the collection of posts from a wide range of information sources. Next, the collected posts are analyzed by AI. In order to determine the truthfulness of the posts, the AI ​​checks the data source and the dissemination information. For example, it checks for information distributed by public institutions related to the posts and whether the same content has been distributed multiple times. This allows for the determination of the truthfulness of the posts with high accuracy. The determination results are provided to the user. For example, the truthfulness of the posts is evaluated in three stages: A, B, and C, and displayed to the user. This allows the user to check the truthfulness of the posts at a glance. For example, truthfulness A is evaluated as "source available," truthfulness B as "distributed by public institutions related to the keywords," and truthfulness C as "source unknown." This system makes it possible to create a world free from fake news. Users can easily verify the truthfulness of posted content, allowing them to obtain accurate information without being misled by fake news. Furthermore, because AI automatically filters the information, users can obtain highly truthful information without any effort. For example, the AI ​​can filter real-time search results to display only highly truthful information. This allows users to efficiently obtain accurate information. Thus, the present invention is a system that realizes a world free from fake news by automatically determining the truthfulness of online news and posts and providing this information to users. In this way, the truthfulness determination system can realize a world free from fake news by automatically determining the truthfulness of online news and posts and providing this information to users.

[0029] The truthfulness determination system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects online postings. The collection unit can collect, for example, news articles and social media posts. The collection unit can collect postings using, for example, web scraping technology. The collection unit can collect postings using, for example, an API. The analysis unit analyzes the postings collected by the collection unit. The analysis unit can analyze postings using, for example, natural language processing technology. The analysis unit can analyze postings using, for example, machine learning algorithms. The analysis unit can, for example, verify data sources and check disseminated information. The determination unit determines truthfulness based on the results analyzed by the analysis unit. The determination unit can determine truthfulness using, for example, a reliability score. The determination unit can determine truthfulness using, for example, a cross-checking method. The determination unit can determine truthfulness by referring to multiple data sources. The provision unit provides the results determined by the determination unit to the user. The provision unit can display the results through, for example, a web application. The service provider can, for example, display the results through a mobile application. The service provider can, for example, send the results via email. In this way, the truthfulness determination system according to the embodiment can automatically determine the truthfulness of online news and posts and provide this information to the user, thereby realizing a world where people are not misled by fake news.

[0030] The data collection unit collects online content. For example, it can collect news articles and social media posts. Specifically, it uses web scraping technology to automatically collect text data from news sites, blogs, forums, and other sources. Web scraping technology analyzes the HTML structure of a specific webpage and extracts the necessary information. For example, it can use libraries such as Python's BeautifulSoup or Scrapy to collect articles related to specific keywords or topics. The data collection unit can also collect content using APIs. Using APIs allows the data collection unit to acquire data efficiently and accurately. Furthermore, the data collection unit has a database for centralized management of the collected data. The collected data is updated in real time and made accessible to the analysis and judgment units. This allows the data collection unit to gather a wide range of information from diverse data sources, improving the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes the content of posts collected by the collection unit. The analysis unit can analyze the content of posts using, for example, natural language processing (NLP) techniques. Specifically, it uses NLP techniques to tokenize the collected text data, tag parts of speech, perform grammatical analysis, and perform semantic analysis. For example, it can use libraries such as Python's NLTK and spaCy to analyze text data and extract important keywords and phrases. The analysis unit can also analyze the content of posts using machine learning algorithms. For example, it can use algorithms such as Support Vector Machines (SVM) and Random Forests to classify and cluster the content of posts. Furthermore, the analysis unit can verify data sources and check the information being sent. Specifically, it evaluates the origin and reliability of the senders of the collected data and provides basic information for determining the truthfulness of the data. For example, it verifies whether the source of a news article is a reliable media outlet and checks whether a social media post is from an official account. In this way, the analysis unit can analyze the collected data from multiple angles and provide basic information for determining truthfulness.

[0032] The judgment unit determines truthfulness based on the results analyzed by the analysis unit. For example, the judgment unit can determine truthfulness using a reliability score. Specifically, it quantifies the reliability of the data provided by the analysis unit and evaluates its truthfulness based on certain criteria. For example, if the reliability score is high, it is judged to be highly truthful, and if the score is low, it is judged to be less truthful. The judgment unit can also determine truthfulness using a cross-checking method. Specifically, it refers to multiple data sources and checks whether the same information is provided by multiple reliable sources. For example, if the same news is reported by multiple reliable media outlets, it is judged to be highly truthful. Furthermore, the judgment unit can determine truthfulness by referring to multiple data sources based on the data provided by the analysis unit. Specifically, it compares the collected data with other reliable databases and information sources, and judges that the truthfulness is high if there is a lot of matching information. In this way, the judgment unit can evaluate the data provided by the analysis unit from multiple angles and determine truthfulness with high accuracy.

[0033] The service provider delivers the results determined by the judgment unit to the user. The service provider can display results, for example, through a web application. Specifically, it can provide a website or dashboard accessible to the user, visually displaying the judgment results. For example, reliability scores and truthfulness evaluation results can be displayed in graphs and charts to allow users to understand them intuitively. The service provider can also display results through a mobile application. Specifically, it can provide apps for smartphones and tablets, allowing users to check the judgment results anytime, anywhere. For example, important judgment results can be notified to the user in real time using push notifications. Furthermore, the service provider can send results via email. Specifically, it can periodically send judgment results to the email address registered by the user, ensuring that the user is always up-to-date. This allows the service provider to deliver the results determined by the judgment unit to users in a variety of ways, helping users avoid being misled by fake news. Additionally, the service provider can collect user feedback to continuously improve the system's accuracy and usability. For example, user evaluations and comments on the provided information can identify areas for system improvement and reflect them in future updates. This allows the service provider to deliver high-quality information to users, improving the overall reliability of the system and user satisfaction.

[0034] The analysis unit includes a verification unit that checks data sources and transmitted information. The verification unit can, for example, evaluate the reliability of data sources. The verification unit can, for example, verify the type of data source. The verification unit can, for example, check the accuracy of transmitted information. The verification unit can, for example, verify the source of information. By verifying data sources and checking transmitted information, the accuracy of the analysis is improved. Some or all of the above-described processes in the verification unit may be performed using AI or not. For example, the verification unit can use an AI model to score the reliability of data sources in order to evaluate their reliability.

[0035] The service provider evaluates the truthfulness of the posted content on a three-level scale (A, B, and C) and displays the result to the user. For example, the service provider can evaluate truthfulness A as "Source available." For example, the service provider can evaluate truthfulness B as "Distributed by a public institution related to the relevant keywords." For example, the service provider can evaluate truthfulness C as "Source unknown." The service provider can, for example, display the evaluation results to the user. For example, the service provider can display the evaluation results as a graph or chart. This allows users to quickly verify the truthfulness of the posted content by evaluating and displaying the truthfulness of the posted content on a three-level scale. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can use an AI model to evaluate the truthfulness of the posted content and display the result to the user.

[0036] The analysis unit includes a processing unit that performs natural language processing. The processing unit can, for example, perform morphological analysis. The processing unit can, for example, perform grammatical analysis. The processing unit can, for example, perform semantic analysis. The processing unit can, for example, perform text mining. By performing natural language processing, the accuracy of analyzing the posted content is improved. Some or all of the above-described processing in the processing unit may be performed using AI or not. For example, the processing unit can perform natural language processing using an AI model to analyze the posted content.

[0037] The analysis unit includes a learning unit that performs machine learning. The learning unit can, for example, perform supervised learning. The learning unit can, for example, perform unsupervised learning. The learning unit can, for example, perform reinforcement learning. The learning unit can, for example, train a model using a dataset. As a result, the accuracy of the analysis is continuously improved by performing machine learning. Some or all of the above-described processes in the learning unit may be performed using AI or not. For example, the learning unit can perform machine learning using an AI model to improve the accuracy of the analysis model.

[0038] The data collection unit evaluates the reliability of submitted content before collection and prioritizes collection from reliable sources. For example, the data collection unit can prioritize collecting content from public institutions or reliable news sites. For example, the data collection unit can prioritize collecting content from sources whose reliability has been confirmed in the past. For example, the data collection unit can use AI to evaluate the reliability of submitted content and prioritize collecting content from reliable sources. This improves the reliability of the information collected by prioritizing collection from reliable sources. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model to evaluate the reliability of submitted content and determine the collection priority based on the results.

[0039] The data collection unit applies different collection algorithms depending on the category of the posted content during collection. For example, the data collection unit can apply a collection algorithm that prioritizes reliable sources to posts in the news category. For example, the data collection unit can apply a collection algorithm that prioritizes highly topical sources to posts in the entertainment category. For example, the data collection unit can apply an algorithm that prioritizes collecting the latest match results and player information to posts in the sports category. By applying different collection algorithms depending on the category of the posted content, the data collection unit can collect the most appropriate information for each category. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model to classify the categories of the posted content and apply the most appropriate collection algorithm to each category.

[0040] The data collection unit prioritizes collecting relevant posts by considering the user's geographical location. For example, the data collection unit can prioritize collecting news and posts related to the user's current location. For example, if the user is traveling, the data collection unit can prioritize collecting information related to their travel destination. For example, based on the user's geographical location, the data collection unit can prioritize collecting local events and news. By prioritizing the collection of relevant posts while considering the user's geographical location, the data collection unit can provide users with highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into an AI model and apply an algorithm that prioritizes the collection of relevant posts.

[0041] The data collection unit analyzes the user's social media activity and collects relevant posts during the collection process. For example, the data collection unit can prioritize collecting posts from accounts the user follows. For example, the data collection unit can collect information related to posts that the user has "liked" or shared. For example, the data collection unit can analyze the user's social media activity and collect posts based on their interests. This allows the collection unit to collect information based on the user's interests by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI model and apply an algorithm to collect relevant posts.

[0042] The analysis unit cross-checks multiple data sources during analysis to evaluate the reliability of the posted content. For example, the analysis unit can cross-check multiple news sites related to the posted content. For example, the analysis unit can cross-check information from public institutions related to the posted content. For example, the analysis unit can cross-check social media posts related to the posted content. By cross-checking multiple data sources in this way, the reliability of the posted content can be increased. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use an AI model to cross-check multiple data sources and evaluate the reliability of the posted content.

[0043] The analysis unit applies different analysis algorithms depending on the category of the posted content during analysis. For example, the analysis unit can apply an analysis algorithm based on reliable sources to posts in the news category. For example, the analysis unit can apply an analysis algorithm that emphasizes topicality to posts in the entertainment category. For example, the analysis unit can apply an analysis algorithm that emphasizes the latest match results and player information to posts in the sports category. By applying different analysis algorithms depending on the category of the posted content, the analysis unit can provide optimal analysis results for each category. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can classify the categories of the posted content using an AI model and apply the most suitable analysis algorithm to each category.

[0044] The analysis unit determines the priority of analysis based on the submission date of the posted content. For example, the analysis unit can prioritize the analysis of the most recent posted content. For example, the analysis unit can prioritize the analysis of content posted during a specific time period. For example, the analysis unit can prioritize the analysis of urgent posted content. This allows for the prioritization of the analysis of the latest information by determining the priority of analysis based on the submission date of the posted content. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use an AI model to evaluate the submission date of the posted content and determine the priority of analysis.

[0045] The analysis unit adjusts the order of analysis based on the relevance of the posts during analysis. For example, the analysis unit can prioritize the analysis of posts with high relevance. For example, the analysis unit can prioritize the analysis of posts related to a specific topic. For example, the analysis unit can prioritize the analysis of posts with high relevance based on the user's interests. In this way, by adjusting the order of analysis based on the relevance of the posts, highly relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use an AI model to evaluate the relevance of the posts and adjust the order of analysis.

[0046] The judgment unit, when making a judgment, refers to past judgment results to evaluate the reliability of the posted content. For example, the judgment unit can make a judgment by referring to posted content whose reliability has been confirmed in the past. For example, the judgment unit can prioritize reliable information sources based on past judgment results. For example, the judgment unit can analyze past judgment results and optimize the judgment criteria. This makes it possible to improve the reliability of posted content by referring to past judgment results. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit may use an AI model to refer to past judgment results and evaluate the reliability of posted content.

[0047] The judgment unit applies different judgment algorithms depending on the category of the posted content during the judgment process. For example, the judgment unit may apply a strict judgment algorithm to posts in the news category. For example, the judgment unit may apply a flexible judgment algorithm to posts in the entertainment category. For example, the judgment unit may apply a judgment algorithm that emphasizes the latest information to posts in the sports category. By applying different judgment algorithms depending on the category of the posted content, the judgment unit can provide the optimal judgment result for each category. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit may use an AI model to classify the categories of the posted content and apply the optimal judgment algorithm to each category.

[0048] The judgment unit determines the priority of the judgment based on the submission date of the posted content. For example, the judgment unit can prioritize the most recent posted content. For example, the judgment unit can prioritize content posted during a specific time period. For example, the judgment unit can prioritize urgent posted content. By determining the priority of the judgment based on the submission date of the posted content, the latest information can be prioritized. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can use an AI model to evaluate the submission date of the posted content and determine the priority of the judgment.

[0049] The judgment unit adjusts the order of judgments based on the relevance of the posts during the judgment process. For example, the judgment unit can prioritize posts that are highly relevant. For example, the judgment unit can prioritize posts related to a specific topic. For example, the judgment unit can prioritize posts that are highly relevant based on the user's interests. By adjusting the order of judgments based on the relevance of the posts, highly relevant information can be prioritized. Some or all of the above-described processes in the judgment unit may be performed using AI or not. For example, the judgment unit can use an AI model to evaluate the relevance of the posts and adjust the order of judgments.

[0050] The service provider selects the optimal display method by referring to the user's past browsing history at the time of delivery. For example, the service provider can suggest the optimal display method based on the content the user has previously viewed. For example, the service provider can select a display method based on the user's interests from the user's past browsing history. For example, the service provider can provide the optimal display method based on the information sources the user has previously preferred to view. In this way, by referring to the user's past browsing history, the service provider can provide the optimal display method for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can use an AI model to analyze the user's past browsing history and select the optimal display method.

[0051] The information provider adjusts the priority of information based on the user's areas of interest when providing it. For example, the provider can prioritize providing information related to topics the user is interested in. For example, the provider can prioritize providing information based on the user's past browsing history. For example, the provider can analyze the user's social media activity and provide information based on their areas of interest. By adjusting the priority of information based on the user's areas of interest, the provider can provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can use an AI model to analyze the user's areas of interest and adjust the priority of information.

[0052] The information provider prioritizes providing highly relevant information, taking into account the user's geographical location. For example, the provider can prioritize providing news and information related to the user's current location. For example, if the user is traveling, the provider can prioritize providing information related to their travel destination. For example, based on the user's geographical location, the provider can prioritize providing local events and news. By prioritizing the provision of highly relevant information while considering the user's geographical location, the provider can provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's geographical location into an AI model and apply an algorithm that prioritizes the provision of highly relevant information.

[0053] The service provider analyzes the user's social media activity and provides relevant information at the time of delivery. For example, the service provider can provide information related to the content of posts from accounts the user follows. For example, the service provider can provide information related to posts that the user has liked or shared. For example, the service provider can analyze the user's social media activity and provide information based on their interests. This allows the service provider to provide information based on the user's interests by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into an AI model and apply an algorithm that provides relevant information.

[0054] The verification unit cross-checks multiple data sources during verification to evaluate the reliability of the data source. For example, the verification unit can cross-check multiple news sites related to the data source. For example, the verification unit can cross-check information from public institutions related to the data source. For example, the verification unit can cross-check social media posts related to the data source. By cross-checking multiple data sources in this way, the reliability of the data source can be increased. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can use an AI model to cross-check multiple data sources and evaluate the reliability of the data source.

[0055] The verification unit determines the verification priority based on the submission date of the data source during the verification process. For example, the verification unit can prioritize the verification of the most recent data source. For example, the verification unit can prioritize the verification of data sources submitted within a specific time period. For example, the verification unit can prioritize the verification of data sources with high urgency. This ensures that the latest information is prioritized by determining the verification priority based on the submission date of the data source. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit can use an AI model to evaluate the submission date of the data source and determine the verification priority.

[0056] The processing unit applies different natural language processing algorithms depending on the category of the posted content during processing. For example, the processing unit can apply a natural language processing algorithm based on reliable sources to posts in the news category. For example, the processing unit can apply a natural language processing algorithm that prioritizes topicality to posts in the entertainment category. For example, the processing unit can apply a natural language processing algorithm that prioritizes the latest match results and player information to posts in the sports category. By applying different natural language processing algorithms depending on the category of the posted content, the processing unit can provide the most suitable natural language processing results for each category. Some or all of the processing described above in the processing unit may be performed using AI or not. For example, the processing unit can use an AI model to classify the categories of the posted content and apply the most suitable natural language processing algorithm to each category.

[0057] The processing unit determines the priority of natural language processing based on the submission date of the posted content during processing. For example, the processing unit can prioritize natural language processing of the most recent posted content. For example, the processing unit can prioritize natural language processing of content posted within a specific time period. For example, the processing unit can prioritize natural language processing of urgent posted content. This allows for the processing of the latest information by prioritizing natural language processing based on the submission date of the posted content. Some or all of the processing described above in the processing unit may be performed using AI or not. For example, the processing unit can use an AI model to evaluate the submission date of the posted content and determine the priority of natural language processing.

[0058] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. This allows the accuracy of the learning algorithm to be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can analyze past learning data using an AI model and optimize the learning algorithm.

[0059] The learning unit weights the training data based on the submission date of the posted content during training. For example, the learning unit can weight the training data by giving more emphasis to the most recent posted content. For example, the learning unit can weight the training data by giving more emphasis to content posted during a specific time period. For example, the learning unit can weight the training data by giving more emphasis to urgent posted content. This allows for training that prioritizes the latest information by weighting the training data based on the submission date of the posted content. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can use an AI model to evaluate the submission date of the posted content and weight the training data.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit evaluates the reliability of submitted content before collection and prioritizes collection from reliable sources. For example, the data collection unit can prioritize collecting content from public institutions or reliable news sites. For example, the data collection unit can prioritize collecting content from sources whose reliability has been confirmed in the past. For example, the data collection unit can use AI to evaluate the reliability of submitted content and prioritize collecting content from reliable sources. This improves the reliability of the information collected by prioritizing collection from reliable sources. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model to evaluate the reliability of submitted content and determine the collection priority based on the results.

[0062] The data collection unit applies different collection algorithms depending on the category of the posted content during collection. For example, the data collection unit can apply a collection algorithm that prioritizes reliable sources to posts in the news category. For example, the data collection unit can apply a collection algorithm that prioritizes highly topical sources to posts in the entertainment category. For example, the data collection unit can apply an algorithm that prioritizes collecting the latest match results and player information to posts in the sports category. By applying different collection algorithms depending on the category of the posted content, the data collection unit can collect the most appropriate information for each category. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model to classify the categories of the posted content and apply the most appropriate collection algorithm to each category.

[0063] The data collection unit prioritizes collecting relevant posts by considering the user's geographical location. For example, the data collection unit can prioritize collecting news and posts related to the user's current location. For example, if the user is traveling, the data collection unit can prioritize collecting information related to their travel destination. For example, based on the user's geographical location, the data collection unit can prioritize collecting local events and news. By prioritizing the collection of relevant posts while considering the user's geographical location, the data collection unit can provide users with highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into an AI model and apply an algorithm that prioritizes the collection of relevant posts.

[0064] The analysis unit cross-checks multiple data sources during analysis to evaluate the reliability of the posted content. For example, the analysis unit can cross-check multiple news sites related to the posted content. For example, the analysis unit can cross-check information from public institutions related to the posted content. For example, the analysis unit can cross-check social media posts related to the posted content. By cross-checking multiple data sources in this way, the reliability of the posted content can be increased. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use an AI model to cross-check multiple data sources and evaluate the reliability of the posted content.

[0065] The analysis unit applies different analysis algorithms depending on the category of the posted content during analysis. For example, the analysis unit can apply an analysis algorithm based on reliable sources to posts in the news category. For example, the analysis unit can apply an analysis algorithm that emphasizes topicality to posts in the entertainment category. For example, the analysis unit can apply an analysis algorithm that emphasizes the latest match results and player information to posts in the sports category. By applying different analysis algorithms depending on the category of the posted content, the analysis unit can provide optimal analysis results for each category. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can classify the categories of the posted content using an AI model and apply the most suitable analysis algorithm to each category.

[0066] The judgment unit, when making a judgment, refers to past judgment results to evaluate the reliability of the posted content. For example, the judgment unit can make a judgment by referring to posted content whose reliability has been confirmed in the past. For example, the judgment unit can prioritize reliable information sources based on past judgment results. For example, the judgment unit can analyze past judgment results and optimize the judgment criteria. This makes it possible to improve the reliability of posted content by referring to past judgment results. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit may use an AI model to refer to past judgment results and evaluate the reliability of posted content.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The collection unit collects online content. The collection unit can collect, for example, news articles, social media posts, and articles from news websites. The collection unit can collect content using web scraping technology or APIs. Step 2: The analysis unit analyzes the posted content collected by the collection unit. The analysis unit can analyze the posted content using, for example, natural language processing technology or machine learning algorithms. The analysis unit can also verify the data source and check the information being sent. Step 3: The determination unit determines the truthfulness based on the results analyzed by the analysis unit. The determination unit can determine truthfulness by referring to, for example, a reliability score, a cross-checking method, or multiple data sources. Step 4: The service provider provides the user with the results determined by the judgment unit. The service provider can display the results, for example, through a web application or a mobile application. The service provider can also send the results via email.

[0069] (Example of form 2) The truthfulness determination system according to an embodiment of the present invention is a system that automatically determines the truthfulness of online news and posts. This truthfulness determination system collects online posts, analyzes them using AI, and determines their truthfulness. The determination results are provided to the user, realizing a world where people are not misled by fake news. First, online posts are collected. In this case, the posts include news articles and social media posts. This allows for the collection of posts from a wide range of information sources. Next, the collected posts are analyzed by AI. In order to determine the truthfulness of the posts, the AI ​​checks the data source and the dissemination information. For example, it checks for information distributed by public institutions related to the posts and whether the same content has been distributed multiple times. This allows for the determination of the truthfulness of the posts with high accuracy. The determination results are provided to the user. For example, the truthfulness of the posts is evaluated in three stages: A, B, and C, and displayed to the user. This allows the user to check the truthfulness of the posts at a glance. For example, truthfulness A is evaluated as "source available," truthfulness B as "distributed by public institutions related to the keywords," and truthfulness C as "source unknown." This system makes it possible to create a world free from fake news. Users can easily verify the truthfulness of posted content, allowing them to obtain accurate information without being misled by fake news. Furthermore, because AI automatically filters the information, users can obtain highly truthful information without any effort. For example, the AI ​​can filter real-time search results to display only highly truthful information. This allows users to efficiently obtain accurate information. Thus, the present invention is a system that realizes a world free from fake news by automatically determining the truthfulness of online news and posts and providing this information to users. In this way, the truthfulness determination system can realize a world free from fake news by automatically determining the truthfulness of online news and posts and providing this information to users.

[0070] The truthfulness determination system according to this embodiment comprises a collection unit, an analysis unit, a determination unit, and a provision unit. The collection unit collects online postings. The collection unit can collect, for example, news articles and social media posts. The collection unit can collect postings using, for example, web scraping technology. The collection unit can collect postings using, for example, an API. The analysis unit analyzes the postings collected by the collection unit. The analysis unit can analyze postings using, for example, natural language processing technology. The analysis unit can analyze postings using, for example, machine learning algorithms. The analysis unit can, for example, verify data sources and check disseminated information. The determination unit determines truthfulness based on the results analyzed by the analysis unit. The determination unit can determine truthfulness using, for example, a reliability score. The determination unit can determine truthfulness using, for example, a cross-checking method. The determination unit can determine truthfulness by referring to multiple data sources. The provision unit provides the results determined by the determination unit to the user. The provision unit can display the results through, for example, a web application. The service provider can, for example, display the results through a mobile application. The service provider can, for example, send the results via email. In this way, the truthfulness determination system according to the embodiment can automatically determine the truthfulness of online news and posts and provide this information to the user, thereby realizing a world where people are not misled by fake news.

[0071] The data collection unit collects online content. For example, it can collect news articles and social media posts. Specifically, it uses web scraping technology to automatically collect text data from news sites, blogs, forums, and other sources. Web scraping technology analyzes the HTML structure of a specific webpage and extracts the necessary information. For example, it can use libraries such as Python's BeautifulSoup or Scrapy to collect articles related to specific keywords or topics. The data collection unit can also collect content using APIs. Using APIs allows the data collection unit to acquire data efficiently and accurately. Furthermore, the data collection unit has a database for centralized management of the collected data. The collected data is updated in real time and made accessible to the analysis and judgment units. This allows the data collection unit to gather a wide range of information from diverse data sources, improving the overall accuracy and reliability of the system.

[0072] The analysis unit analyzes the content of posts collected by the collection unit. The analysis unit can analyze the content of posts using, for example, natural language processing (NLP) techniques. Specifically, it uses NLP techniques to tokenize the collected text data, tag parts of speech, perform grammatical analysis, and perform semantic analysis. For example, it can use libraries such as Python's NLTK and spaCy to analyze text data and extract important keywords and phrases. The analysis unit can also analyze the content of posts using machine learning algorithms. For example, it can use algorithms such as Support Vector Machines (SVM) and Random Forests to classify and cluster the content of posts. Furthermore, the analysis unit can verify data sources and check the information being sent. Specifically, it evaluates the origin and reliability of the senders of the collected data and provides basic information for determining the truthfulness of the data. For example, it verifies whether the source of a news article is a reliable media outlet and checks whether a social media post is from an official account. In this way, the analysis unit can analyze the collected data from multiple angles and provide basic information for determining truthfulness.

[0073] The judgment unit determines truthfulness based on the results analyzed by the analysis unit. For example, the judgment unit can determine truthfulness using a reliability score. Specifically, it quantifies the reliability of the data provided by the analysis unit and evaluates its truthfulness based on certain criteria. For example, if the reliability score is high, it is judged to be highly truthful, and if the score is low, it is judged to be less truthful. The judgment unit can also determine truthfulness using a cross-checking method. Specifically, it refers to multiple data sources and checks whether the same information is provided by multiple reliable sources. For example, if the same news is reported by multiple reliable media outlets, it is judged to be highly truthful. Furthermore, the judgment unit can determine truthfulness by referring to multiple data sources based on the data provided by the analysis unit. Specifically, it compares the collected data with other reliable databases and information sources, and judges that the truthfulness is high if there is a lot of matching information. In this way, the judgment unit can evaluate the data provided by the analysis unit from multiple angles and determine truthfulness with high accuracy.

[0074] The service provider delivers the results determined by the judgment unit to the user. The service provider can display results, for example, through a web application. Specifically, it can provide a website or dashboard accessible to the user, visually displaying the judgment results. For example, reliability scores and truthfulness evaluation results can be displayed in graphs and charts to allow users to understand them intuitively. The service provider can also display results through a mobile application. Specifically, it can provide apps for smartphones and tablets, allowing users to check the judgment results anytime, anywhere. For example, important judgment results can be notified to the user in real time using push notifications. Furthermore, the service provider can send results via email. Specifically, it can periodically send judgment results to the email address registered by the user, ensuring that the user is always up-to-date. This allows the service provider to deliver the results determined by the judgment unit to users in a variety of ways, helping users avoid being misled by fake news. Additionally, the service provider can collect user feedback to continuously improve the system's accuracy and usability. For example, user evaluations and comments on the provided information can identify areas for system improvement and reflect them in future updates. This allows the service provider to deliver high-quality information to users, improving the overall reliability of the system and user satisfaction.

[0075] The analysis unit includes a verification unit that checks data sources and transmitted information. The verification unit can, for example, evaluate the reliability of data sources. The verification unit can, for example, verify the type of data source. The verification unit can, for example, check the accuracy of transmitted information. The verification unit can, for example, verify the source of information. By verifying data sources and checking transmitted information, the accuracy of the analysis is improved. Some or all of the above-described processes in the verification unit may be performed using AI or not. For example, the verification unit can use an AI model to score the reliability of data sources in order to evaluate their reliability.

[0076] The service provider evaluates the truthfulness of the posted content on a three-level scale (A, B, and C) and displays the result to the user. For example, the service provider can evaluate truthfulness A as "Source available." For example, the service provider can evaluate truthfulness B as "Distributed by a public institution related to the relevant keywords." For example, the service provider can evaluate truthfulness C as "Source unknown." The service provider can, for example, display the evaluation results to the user. For example, the service provider can display the evaluation results as a graph or chart. This allows users to quickly verify the truthfulness of the posted content by evaluating and displaying the truthfulness of the posted content on a three-level scale. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can use an AI model to evaluate the truthfulness of the posted content and display the result to the user.

[0077] The analysis unit includes a processing unit that performs natural language processing. The processing unit can, for example, perform morphological analysis. The processing unit can, for example, perform grammatical analysis. The processing unit can, for example, perform semantic analysis. The processing unit can, for example, perform text mining. By performing natural language processing, the accuracy of analyzing the posted content is improved. Some or all of the above-described processing in the processing unit may be performed using AI or not. For example, the processing unit can perform natural language processing using an AI model to analyze the posted content.

[0078] The analysis unit includes a learning unit that performs machine learning. The learning unit can, for example, perform supervised learning. The learning unit can, for example, perform unsupervised learning. The learning unit can, for example, perform reinforcement learning. The learning unit can, for example, train a model using a dataset. As a result, the accuracy of the analysis is continuously improved by performing machine learning. Some or all of the above-described processes in the learning unit may be performed using AI or not. For example, the learning unit can perform machine learning using an AI model to improve the accuracy of the analysis model.

[0079] The data collection unit estimates the user's emotions and determines the priority of posts to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting posts from reliable sources. For example, if the user is excited, the data collection unit can prioritize collecting entertaining posts. For example, if the user is relaxed, the data collection unit can collect posts from balanced sources. This allows for the collection of information that is optimal for the user by prioritizing posts based on their 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The data collection unit evaluates the reliability of submitted content before collection and prioritizes collection from reliable sources. For example, the data collection unit can prioritize collecting content from public institutions or reliable news sites. For example, the data collection unit can prioritize collecting content from sources whose reliability has been confirmed in the past. For example, the data collection unit can use AI to evaluate the reliability of submitted content and prioritize collecting content from reliable sources. This improves the reliability of the information collected by prioritizing collection from reliable sources. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model to evaluate the reliability of submitted content and determine the collection priority based on the results.

[0081] The data collection unit applies different collection algorithms depending on the category of the posted content during collection. For example, the data collection unit can apply a collection algorithm that prioritizes reliable sources to posts in the news category. For example, the data collection unit can apply a collection algorithm that prioritizes highly topical sources to posts in the entertainment category. For example, the data collection unit can apply an algorithm that prioritizes collecting the latest match results and player information to posts in the sports category. By applying different collection algorithms depending on the category of the posted content, the data collection unit can collect the most appropriate information for each category. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model to classify the categories of the posted content and apply the most appropriate collection algorithm to each category.

[0082] The data collection unit estimates the user's emotions and filters the collected posts based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can filter out negative posts. For example, if the user is excited, the data collection unit can filter out positive posts. For example, if the user is relaxed, the data collection unit can filter out balanced posts. By filtering posts based on the user's emotions, the system can provide users with information that is appropriate for them. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The data collection unit prioritizes collecting relevant posts by considering the user's geographical location. For example, the data collection unit can prioritize collecting news and posts related to the user's current location. For example, if the user is traveling, the data collection unit can prioritize collecting information related to their travel destination. For example, based on the user's geographical location, the data collection unit can prioritize collecting local events and news. By prioritizing the collection of relevant posts while considering the user's geographical location, the data collection unit can provide users with highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into an AI model and apply an algorithm that prioritizes the collection of relevant posts.

[0084] The data collection unit analyzes the user's social media activity and collects relevant posts during the collection process. For example, the data collection unit can prioritize collecting posts from accounts the user follows. For example, the data collection unit can collect information related to posts that the user has "liked" or shared. For example, the data collection unit can analyze the user's social media activity and collect posts based on their interests. This allows the collection unit to collect information based on the user's interests by analyzing their social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity data into an AI model and apply an algorithm to collect relevant posts.

[0085] The analysis unit estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can improve the accuracy of the analysis to provide more reliable results. For example, if the user is excited, the analysis unit can adjust the accuracy of the analysis to provide more entertaining results. For example, if the user is relaxed, the analysis unit can provide balanced analysis results. In this way, by adjusting the accuracy of the analysis based on the user's emotions, reliable analysis results can be provided to the user. 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 AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The analysis unit cross-checks multiple data sources during analysis to evaluate the reliability of the posted content. For example, the analysis unit can cross-check multiple news sites related to the posted content. For example, the analysis unit can cross-check information from public institutions related to the posted content. For example, the analysis unit can cross-check social media posts related to the posted content. By cross-checking multiple data sources in this way, the reliability of the posted content can be increased. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use an AI model to cross-check multiple data sources and evaluate the reliability of the posted content.

[0087] The analysis unit applies different analysis algorithms depending on the category of the posted content during analysis. For example, the analysis unit can apply an analysis algorithm based on reliable sources to posts in the news category. For example, the analysis unit can apply an analysis algorithm that emphasizes topicality to posts in the entertainment category. For example, the analysis unit can apply an analysis algorithm that emphasizes the latest match results and player information to posts in the sports category. By applying different analysis algorithms depending on the category of the posted content, the analysis unit can provide optimal analysis results for each category. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can classify the categories of the posted content using an AI model and apply the most suitable analysis algorithm to each category.

[0088] The analysis unit estimates the user's emotions and adjusts 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 simple and highly visible display method. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. For example, if the user is relaxed, the analysis unit can provide a balanced display method. In this way, by adjusting the display method of the analysis results based on the user's emotions, a highly visible display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The analysis unit determines the priority of analysis based on the submission date of the posted content. For example, the analysis unit can prioritize the analysis of the most recent posted content. For example, the analysis unit can prioritize the analysis of content posted during a specific time period. For example, the analysis unit can prioritize the analysis of urgent posted content. This allows for the prioritization of the analysis of the latest information by determining the priority of analysis based on the submission date of the posted content. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use an AI model to evaluate the submission date of the posted content and determine the priority of analysis.

[0090] The analysis unit adjusts the order of analysis based on the relevance of the posts during analysis. For example, the analysis unit can prioritize the analysis of posts with high relevance. For example, the analysis unit can prioritize the analysis of posts related to a specific topic. For example, the analysis unit can prioritize the analysis of posts with high relevance based on the user's interests. In this way, by adjusting the order of analysis based on the relevance of the posts, highly relevant information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use an AI model to evaluate the relevance of the posts and adjust the order of analysis.

[0091] The judgment unit estimates the user's emotions and adjusts the truthfulness criteria based on the estimated user emotions. For example, if the user is feeling anxious, the judgment unit may apply strict criteria. For example, if the user is excited, the judgment unit may apply flexible criteria. For example, if the user is relaxed, the judgment unit may apply balanced criteria. By adjusting the truthfulness criteria based on the user's emotions, the judgment unit can provide the user with a more reliable judgment result. Emotion estimation is achieved using an emotion estimation function, for example, using 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 processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The judgment unit, when making a judgment, refers to past judgment results to evaluate the reliability of the posted content. For example, the judgment unit can make a judgment by referring to posted content whose reliability has been confirmed in the past. For example, the judgment unit can prioritize reliable information sources based on past judgment results. For example, the judgment unit can analyze past judgment results and optimize the judgment criteria. This makes it possible to improve the reliability of posted content by referring to past judgment results. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit may use an AI model to refer to past judgment results and evaluate the reliability of posted content.

[0093] The judgment unit applies different judgment algorithms depending on the category of the posted content during the judgment process. For example, the judgment unit may apply a strict judgment algorithm to posts in the news category. For example, the judgment unit may apply a flexible judgment algorithm to posts in the entertainment category. For example, the judgment unit may apply a judgment algorithm that emphasizes the latest information to posts in the sports category. By applying different judgment algorithms depending on the category of the posted content, the judgment unit can provide the optimal judgment result for each category. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit may use an AI model to classify the categories of the posted content and apply the optimal judgment algorithm to each category.

[0094] The judgment unit estimates the user's emotions and adjusts the display method of the judgment result based on the estimated user emotions. For example, if the user is feeling anxious, the judgment unit can provide a simple and highly visible display method. For example, if the user is excited, the judgment unit can provide a visually stimulating display method. For example, if the user is relaxed, the judgment unit can provide a balanced display method. In this way, by adjusting the display method of the judgment result based on the user's emotions, a highly visible display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The judgment unit determines the priority of the judgment based on the submission date of the posted content. For example, the judgment unit can prioritize the most recent posted content. For example, the judgment unit can prioritize content posted during a specific time period. For example, the judgment unit can prioritize urgent posted content. By determining the priority of the judgment based on the submission date of the posted content, the latest information can be prioritized. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit can use an AI model to evaluate the submission date of the posted content and determine the priority of the judgment.

[0096] The judgment unit adjusts the order of judgments based on the relevance of the posts during the judgment process. For example, the judgment unit can prioritize posts that are highly relevant. For example, the judgment unit can prioritize posts related to a specific topic. For example, the judgment unit can prioritize posts that are highly relevant based on the user's interests. By adjusting the order of judgments based on the relevance of the posts, highly relevant information can be prioritized. Some or all of the above-described processes in the judgment unit may be performed using AI or not. For example, the judgment unit can use an AI model to evaluate the relevance of the posts and adjust the order of judgments.

[0097] The service provider estimates the user's emotions and adjusts the display method of the information provided based on the estimated user emotions. For example, if the user is feeling anxious, the service provider can provide a simple and highly visible display method. For example, if the user is excited, the service provider can provide a visually stimulating display method. For example, if the user is relaxed, the service provider can provide a balanced display method. By adjusting the display method of the information provided based on the user's emotions, a highly visible display method can be provided to the user. 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 AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The service provider selects the optimal display method by referring to the user's past browsing history at the time of delivery. For example, the service provider can suggest the optimal display method based on the content the user has previously viewed. For example, the service provider can select a display method based on the user's interests from the user's past browsing history. For example, the service provider can provide the optimal display method based on the information sources the user has previously preferred to view. In this way, by referring to the user's past browsing history, the service provider can provide the optimal display method for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can use an AI model to analyze the user's past browsing history and select the optimal display method.

[0099] The information provider adjusts the priority of information based on the user's areas of interest when providing it. For example, the provider can prioritize providing information related to topics the user is interested in. For example, the provider can prioritize providing information based on the user's past browsing history. For example, the provider can analyze the user's social media activity and provide information based on their areas of interest. By adjusting the priority of information based on the user's areas of interest, the provider can provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the provider can use an AI model to analyze the user's areas of interest and adjust the priority of information.

[0100] The service provider estimates the user's emotions and prioritizes the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the service provider can prioritize providing reliable information. For example, if the user is excited, the service provider can prioritize providing highly entertaining information. For example, if the user is relaxed, the service provider can provide balanced information. In this way, by prioritizing the information to be provided based on the user's emotions, the service provider can provide the user with the most optimal information. 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 AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The information provider prioritizes providing highly relevant information, taking into account the user's geographical location. For example, the provider can prioritize providing news and information related to the user's current location. For example, if the user is traveling, the provider can prioritize providing information related to their travel destination. For example, based on the user's geographical location, the provider can prioritize providing local events and news. By prioritizing the provision of highly relevant information while considering the user's geographical location, the provider can provide information that is highly relevant to the user. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the user's geographical location into an AI model and apply an algorithm that prioritizes the provision of highly relevant information.

[0102] The service provider analyzes the user's social media activity and provides relevant information at the time of delivery. For example, the service provider can provide information related to the content of posts from accounts the user follows. For example, the service provider can provide information related to posts that the user has liked or shared. For example, the service provider can analyze the user's social media activity and provide information based on their interests. This allows the service provider to provide information based on the user's interests by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the user's social media activity data into an AI model and apply an algorithm that provides relevant information.

[0103] The verification unit estimates the user's emotions and adjusts the accuracy of the verification based on the estimated emotions. For example, if the user is feeling anxious, the verification unit can increase the accuracy of the verification to provide a more reliable result. For example, if the user is excited, the verification unit can adjust the accuracy of the verification to provide a more entertaining result. For example, if the user is relaxed, the verification unit can provide a balanced verification result. In this way, by adjusting the accuracy of the verification based on the user's emotions, a more reliable verification result can be provided to the user. 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 verification unit may be performed using AI or not using AI. For example, the verification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The verification unit cross-checks multiple data sources during verification to evaluate the reliability of the data source. For example, the verification unit can cross-check multiple news sites related to the data source. For example, the verification unit can cross-check information from public institutions related to the data source. For example, the verification unit can cross-check social media posts related to the data source. By cross-checking multiple data sources in this way, the reliability of the data source can be increased. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit can use an AI model to cross-check multiple data sources and evaluate the reliability of the data source.

[0105] The verification unit estimates the user's emotions and adjusts the display method of the verification results based on the estimated user emotions. For example, if the user is feeling anxious, the verification unit can provide a simple and highly visible display method. For example, if the user is excited, the verification unit can provide a visually stimulating display method. For example, if the user is relaxed, the verification unit can provide a balanced display method. In this way, by adjusting the display method of the verification results based on the user's emotions, a highly visible display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the verification unit may be performed using AI or not using AI. For example, the verification unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The verification unit determines the verification priority based on the submission date of the data source during the verification process. For example, the verification unit can prioritize the verification of the most recent data source. For example, the verification unit can prioritize the verification of data sources submitted within a specific time period. For example, the verification unit can prioritize the verification of data sources with high urgency. This ensures that the latest information is prioritized by determining the verification priority based on the submission date of the data source. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit can use an AI model to evaluate the submission date of the data source and determine the verification priority.

[0107] The processing unit estimates the user's emotions and adjusts the accuracy of natural language processing based on the estimated emotions. For example, if the user is feeling anxious, the processing unit can improve the accuracy of natural language processing to provide reliable results. For example, if the user is excited, the processing unit can adjust the accuracy of natural language processing to provide highly entertaining results. For example, if the user is relaxed, the processing unit can provide balanced natural language processing results. By adjusting the accuracy of natural language processing based on the user's emotions, reliable natural language processing results can be provided to the user. 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 processing unit may be performed using AI or not using AI. For example, the processing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The processing unit applies different natural language processing algorithms depending on the category of the posted content during processing. For example, the processing unit can apply a natural language processing algorithm based on reliable sources to posts in the news category. For example, the processing unit can apply a natural language processing algorithm that prioritizes topicality to posts in the entertainment category. For example, the processing unit can apply a natural language processing algorithm that prioritizes the latest match results and player information to posts in the sports category. By applying different natural language processing algorithms depending on the category of the posted content, the processing unit can provide the most suitable natural language processing results for each category. Some or all of the processing described above in the processing unit may be performed using AI or not. For example, the processing unit can use an AI model to classify the categories of the posted content and apply the most suitable natural language processing algorithm to each category.

[0109] The processing unit estimates the user's emotions and adjusts the display method of the natural language processing results based on the estimated user emotions. For example, if the user is feeling anxious, the processing unit can provide a simple and highly visible display method. For example, if the user is excited, the processing unit can provide a visually stimulating display method. For example, if the user is relaxed, the processing unit can provide a balanced display method. In this way, by adjusting the display method of the natural language processing results based on the user's emotions, a highly visible display method can be provided to the user. 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 processing unit may be performed using AI or not using AI. For example, the processing unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The processing unit determines the priority of natural language processing based on the submission date of the posted content during processing. For example, the processing unit can prioritize natural language processing of the most recent posted content. For example, the processing unit can prioritize natural language processing of content posted within a specific time period. For example, the processing unit can prioritize natural language processing of urgent posted content. This allows for the processing of the latest information by prioritizing natural language processing based on the submission date of the posted content. Some or all of the processing described above in the processing unit may be performed using AI or not. For example, the processing unit can use an AI model to evaluate the submission date of the posted content and determine the priority of natural language processing.

[0111] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is feeling anxious, the learning unit can select highly reliable data as training data. For example, if the user is excited, the learning unit can select highly entertaining data as training data. For example, if the user is relaxed, the learning unit can select balanced data as training data. By selecting training data based on the user's emotions, the learning unit can provide the user with highly reliable learning results. 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 learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data to improve the accuracy of the learning algorithm. For example, the learning unit can adjust the parameters of the learning algorithm by referring to past learning data. This allows the accuracy of the learning algorithm to be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can analyze past learning data using an AI model and optimize the learning algorithm.

[0113] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is feeling anxious, the learning unit can increase the learning frequency to provide more reliable results. For example, if the user is excited, the learning unit can adjust the learning frequency to provide more entertaining results. For example, if the user is relaxed, the learning unit can provide balanced learning results. In this way, by adjusting the learning frequency based on the user's emotions, reliable learning results can be provided to the user. 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 learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The learning unit weights the training data based on the submission date of the posted content during training. For example, the learning unit can weight the training data by giving more emphasis to the most recent posted content. For example, the learning unit can weight the training data by giving more emphasis to content posted during a specific time period. For example, the learning unit can weight the training data by giving more emphasis to urgent posted content. This allows for training that prioritizes the latest information by weighting the training data based on the submission date of the posted content. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can use an AI model to evaluate the submission date of the posted content and weight the training data.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The data collection unit estimates the user's emotions and determines the priority of posts to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting posts from reliable sources. For example, if the user is excited, the data collection unit can prioritize collecting entertaining posts. For example, if the user is relaxed, the data collection unit can collect posts from balanced sources. This allows for the collection of information that is optimal for the user by prioritizing posts based on their 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The data collection unit evaluates the reliability of submitted content before collection and prioritizes collection from reliable sources. For example, the data collection unit can prioritize collecting content from public institutions or reliable news sites. For example, the data collection unit can prioritize collecting content from sources whose reliability has been confirmed in the past. For example, the data collection unit can use AI to evaluate the reliability of submitted content and prioritize collecting content from reliable sources. This improves the reliability of the information collected by prioritizing collection from reliable sources. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model to evaluate the reliability of submitted content and determine the collection priority based on the results.

[0118] The data collection unit applies different collection algorithms depending on the category of the posted content during collection. For example, the data collection unit can apply a collection algorithm that prioritizes reliable sources to posts in the news category. For example, the data collection unit can apply a collection algorithm that prioritizes highly topical sources to posts in the entertainment category. For example, the data collection unit can apply an algorithm that prioritizes collecting the latest match results and player information to posts in the sports category. By applying different collection algorithms depending on the category of the posted content, the data collection unit can collect the most appropriate information for each category. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use an AI model to classify the categories of the posted content and apply the most appropriate collection algorithm to each category.

[0119] The data collection unit estimates the user's emotions and filters the collected posts based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can filter out negative posts. For example, if the user is excited, the data collection unit can filter out positive posts. For example, if the user is relaxed, the data collection unit can filter out balanced posts. By filtering posts based on the user's emotions, the system can provide users with information that is appropriate for them. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The data collection unit prioritizes collecting relevant posts by considering the user's geographical location. For example, the data collection unit can prioritize collecting news and posts related to the user's current location. For example, if the user is traveling, the data collection unit can prioritize collecting information related to their travel destination. For example, based on the user's geographical location, the data collection unit can prioritize collecting local events and news. By prioritizing the collection of relevant posts while considering the user's geographical location, the data collection unit can provide users with highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location into an AI model and apply an algorithm that prioritizes the collection of relevant posts.

[0121] The analysis unit estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can improve the accuracy of the analysis to provide more reliable results. For example, if the user is excited, the analysis unit can adjust the accuracy of the analysis to provide more entertaining results. For example, if the user is relaxed, the analysis unit can provide balanced analysis results. In this way, by adjusting the accuracy of the analysis based on the user's emotions, reliable analysis results can be provided to the user. 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 AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0122] The analysis unit cross-checks multiple data sources during analysis to evaluate the reliability of the posted content. For example, the analysis unit can cross-check multiple news sites related to the posted content. For example, the analysis unit can cross-check information from public institutions related to the posted content. For example, the analysis unit can cross-check social media posts related to the posted content. By cross-checking multiple data sources in this way, the reliability of the posted content can be increased. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use an AI model to cross-check multiple data sources and evaluate the reliability of the posted content.

[0123] The analysis unit applies different analysis algorithms depending on the category of the posted content during analysis. For example, the analysis unit can apply an analysis algorithm based on reliable sources to posts in the news category. For example, the analysis unit can apply an analysis algorithm that emphasizes topicality to posts in the entertainment category. For example, the analysis unit can apply an analysis algorithm that emphasizes the latest match results and player information to posts in the sports category. By applying different analysis algorithms depending on the category of the posted content, the analysis unit can provide optimal analysis results for each category. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can classify the categories of the posted content using an AI model and apply the most suitable analysis algorithm to each category.

[0124] The judgment unit estimates the user's emotions and adjusts the truthfulness criteria based on the estimated user emotions. For example, if the user is feeling anxious, the judgment unit may apply strict criteria. For example, if the user is excited, the judgment unit may apply flexible criteria. For example, if the user is relaxed, the judgment unit may apply balanced criteria. By adjusting the truthfulness criteria based on the user's emotions, the judgment unit can provide the user with a more reliable judgment result. Emotion estimation is achieved using an emotion estimation function, for example, using 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 processing in the judgment unit may be performed using AI or not using AI. For example, the judgment unit may input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0125] The judgment unit, when making a judgment, refers to past judgment results to evaluate the reliability of the posted content. For example, the judgment unit can make a judgment by referring to posted content whose reliability has been confirmed in the past. For example, the judgment unit can prioritize reliable information sources based on past judgment results. For example, the judgment unit can analyze past judgment results and optimize the judgment criteria. This makes it possible to improve the reliability of posted content by referring to past judgment results. Some or all of the above processing in the judgment unit may be performed using AI or not. For example, the judgment unit may use an AI model to refer to past judgment results and evaluate the reliability of posted content.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The collection unit collects online content. The collection unit can collect, for example, news articles, social media posts, and articles from news websites. The collection unit can collect content using web scraping technology or APIs. Step 2: The analysis unit analyzes the posted content collected by the collection unit. The analysis unit can analyze the posted content using, for example, natural language processing technology or machine learning algorithms. The analysis unit can also verify the data source and check the information being sent. Step 3: The determination unit determines the truthfulness based on the results analyzed by the analysis unit. The determination unit can determine truthfulness by referring to, for example, a reliability score, a cross-checking method, or multiple data sources. Step 4: The service provider provides the user with the results determined by the judgment unit. The service provider can display the results, for example, through a web application or a mobile application. The service provider can also send the results via email.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, determination 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 can collect online postings using the computer 36 and camera 42 of the smart device 14. The analysis unit can analyze the collected postings using the identification processing unit 290 of the data processing unit 12. The determination unit can determine the truthfulness of the postings based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit can provide the determination results to the user using the display 40A and speaker 40B 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 various modifications are possible.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and provision unit, can be implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect online postings using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit can analyze the collected postings using the identification processing unit 290 of the data processing unit 12. The determination unit can determine the truthfulness of the postings based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit can provide the determination results to the user using the speaker 240 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 various modifications are possible.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, determination 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 can collect online postings using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit can analyze the collected postings using the identification processing unit 290 of the data processing unit 12. The determination unit can determine the truthfulness of the postings based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit can provide the determination results to the user using the display 343 and speaker 240 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 various modifications are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Each of the multiple elements described above, including the collection unit, analysis unit, determination unit, and provision unit, can be implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect online postings using the camera 42 and microphone 238 of the robot 414. The analysis unit can analyze the collected postings using the identification processing unit 290 of the data processing unit 12. The determination unit can determine the truthfulness of the postings based on the analysis results using the identification processing unit 290 of the data processing unit 12. The provision unit can provide the determination results to the user using the speaker 240 and display device of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] (Note 1) The collection department collects online posts, An analysis unit analyzes the posted content collected by the aforementioned collection unit, A determination unit that determines truthfulness based on the results of analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the user with the result determined by the determination unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a verification unit that checks the data source and the transmitted information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, The truthfulness of the posted content is evaluated on a three-point scale (A, B, C) and displayed to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, It includes a processing unit for natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, It has a learning unit that performs machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates user sentiment and determines the priority of posts to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During the collection process, the reliability of submitted content is evaluated in advance, and priority is given to collecting from reliable sources. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, different collection algorithms are applied depending on the category of the submitted content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's sentiment and filters the collected posts based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant posts by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant posts. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, multiple data sources are cross-checked to evaluate the reliability of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the submission date of the submitted content. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, We estimate the user's emotions and adjust the truthfulness criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, When making a judgment, past judgment results are referenced to evaluate the reliability of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, When making a judgment, a different judgment algorithm is applied depending on the category of the post content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, When evaluating submissions, priority will be determined based on the submission date of the submitted content. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, During the evaluation process, the order of evaluation will be adjusted based on the relevance of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing content, the system selects the optimal display method by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, we adjust the priority of information based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, we prioritize providing highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned verification unit is The system estimates the user's emotions and adjusts the accuracy of the confirmation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned verification unit is During verification, cross-check multiple data sources to evaluate the reliability of the data source. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned verification unit is The system estimates the user's emotions and adjusts how the confirmation results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned verification unit is During the verification process, we will prioritize the verification based on when the data source was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned processing unit, It estimates the user's emotions and adjusts the accuracy of natural language processing based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned processing unit, During processing, different natural language processing algorithms are applied depending on the category of the posted content. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned processing unit, It estimates the user's emotions and adjusts how natural language processing results are displayed based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned processing unit, During processing, the priority of natural language processing is determined based on when the submitted content was received. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned learning unit, During training, the training data is weighted based on the submission date of the submitted content. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0200] 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 online posts, An analysis unit analyzes the posted content collected by the aforementioned collection unit, A determination unit that determines truthfulness based on the results of analysis performed by the aforementioned analysis unit, The system includes a providing unit that provides the user with the result determined by the determination unit. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a verification unit that checks the data source and the transmitted information. The system according to feature 1.

3. The aforementioned supply unit is, The truthfulness of the posted content is evaluated on a three-point scale (A, B, C) and displayed to the user. The system according to feature 1.

4. The aforementioned analysis unit, It includes a processing unit for natural language processing. The system according to feature 1.

5. The aforementioned analysis unit, It has a learning unit that performs machine learning. The system according to feature 1.

6. The aforementioned collection unit is It estimates user sentiment and determines the priority of posts to collect based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned collection unit is During the collection process, the reliability of submitted content is evaluated in advance, and priority is given to collecting from reliable sources. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, different collection algorithms are applied depending on the category of the submitted content. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's sentiment and filters the collected posts based on that estimated sentiment. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant posts by considering the user's geographical location. The system according to feature 1.

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