system

Abstract

The system according to this embodiment aims to quickly evaluate and display the reliability of questionable news and information. [Solution] The system according to the embodiment comprises a reception unit, a scraping unit, an analysis unit, an evaluation unit, and a display unit. The reception unit receives input from the user. The scraping unit performs scraping based on the information received by the reception unit. The analysis unit analyzes the information collected by the scraping unit. The evaluation unit evaluates the reliability based on the information analyzed by the analysis unit. The display unit displays the results evaluated by the evaluation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to quickly evaluate the reliability of suspicious news or information.

[0005] The system according to the embodiment aims to quickly evaluate and display the reliability of suspicious news or information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a scraping unit, an analysis unit, an evaluation unit, and a display unit. The reception unit receives input from the user. The scraping unit performs scraping based on the information received by the reception unit. The analysis unit analyzes the information collected by the scraping unit. The evaluation unit evaluates reliability based on the information analyzed by the analysis unit. The display unit displays the results evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly evaluate and display the reliability of questionable news and information. [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, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The generative AI agent according to an embodiment of the present invention is a system that evaluates questionable news and information in real time and displays its reliability. In this system, when a user inputs questionable news or information, the generative AI performs scraping based on that input, collecting information from sources outside of the generative AI's training data. Based on the collected information, the generative AI evaluates the reliability of the news or information and displays the result to the user. This mechanism allows for rapid verification of the truthfulness of information. For example, a user inputs a question such as, "Is this news true?" This information is input to the generative AI. Next, the generative AI analyzes the input information and performs scraping. The generative AI collects relevant information from multiple sources on the internet. For example, it collects information from news sites and social media, obtaining information from sources outside of the generative AI's training data. Based on the collected information, the generative AI evaluates the reliability of the news or information. The generative AI analyzes the collected information and evaluates its degree of agreement with the input information and its reliability. For example, if the same information is obtained from multiple reliable sources, the news or information is evaluated as highly reliable. Finally, the generative AI displays the evaluation result to the user. Users can verify the veracity of news and information based on the evaluation results from the generating AI. For example, an evaluation result such as "This news is highly reliable" will be displayed. This system allows users to quickly verify the veracity of questionable news and information. This helps prevent the spread of fake news and enables users to make decisions based on accurate information. As a result, the generating AI agent can evaluate and display the reliability of questionable news and information in real time.

[0029] The generation AI agent according to this embodiment comprises a reception unit, a scraping unit, an analysis unit, an evaluation unit, and a display unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also be equipped with a microphone and voice recognition technology for receiving voice input. Furthermore, the reception unit may be equipped with a camera and image recognition technology for receiving image input. The scraping unit performs scraping based on the information received by the reception unit. The scraping unit includes, for example, a scraping tool for collecting data from websites. The scraping unit can also obtain data using an API. For example, the scraping unit collects the latest news articles from a specific news site. The scraping unit analyzes the HTML structure of a website and extracts the necessary information. The analysis unit analyzes the information collected by the scraping unit. The analysis unit analyzes the collected information using, for example, a text analysis algorithm. The analysis unit can also analyze image data using image analysis algorithms. For example, the analysis unit can analyze the content of collected news articles and extract important keywords and phrases. The analysis unit can also analyze text data using natural language processing techniques. The evaluation unit evaluates reliability based on the information analyzed by the analysis unit. The evaluation unit includes evaluation criteria for evaluating the reliability of information sources and the consistency of information. For example, the evaluation unit evaluates information as highly reliable if the same information is obtained from multiple reliable sources. The evaluation unit includes algorithms for evaluating the accuracy and reliability of information. The display unit displays the results evaluated by the evaluation unit. For example, the display unit displays the evaluation results in text format. The display unit can also display the evaluation results graphically. For example, the display unit displays reliability scores and evaluation comments. The display unit includes an interface for notifying the evaluation results.This allows the generating AI agent according to the embodiment to evaluate and display the reliability of questionable news and information in real time.

[0030] The reception unit receives input from users. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. Specifically, it provides a form or chat box for users to enter text and processes the entered text in real time. The reception unit may also be equipped with a microphone and speech recognition technology for receiving voice input. In the case of voice input, when a user speaks into the microphone, the voice data is collected and converted into text data by speech recognition technology. Furthermore, the reception unit may be equipped with a camera and image recognition technology for receiving image input. In the case of image input, when a user takes an image using the camera, the image data is collected and analyzed by image recognition technology. This allows the reception unit to handle a variety of input formats from users and flexibly receive information. Furthermore, the reception unit also plays a role in appropriately classifying the user's input and sending it to the subsequent processing department. For example, text input is sent to the text analysis department, voice input to the voice analysis department, and image input to the image analysis department. This allows the reception desk to efficiently process user input and improve the overall system performance.

[0031] The scraping unit performs scraping based on information received by the reception unit. The scraping unit includes, for example, a scraping tool for collecting data from websites. Specifically, it includes a program for analyzing the HTML structure of a website and extracting the necessary information. The scraping tool accesses a specific webpage, analyzes its HTML code, and extracts the specified data. The scraping unit can also obtain data using APIs. Using APIs allows for efficient acquisition of website data and rapid collection of necessary information. For example, the scraping unit collects the latest news articles from a specific news site. It analyzes the HTML structure of the news site and extracts information such as the article title, body text, and publication date. Furthermore, the scraping unit also organizes the collected data and sends it to the analysis unit. This allows the scraping unit to efficiently collect necessary information from websites and APIs, improving the overall information gathering capabilities of the system.

[0032] The analysis unit analyzes the information collected by the scraping unit. For example, the analysis unit analyzes the collected information using text analysis algorithms. Specifically, it analyzes text data using natural language processing techniques to extract important keywords and phrases. For example, it analyzes the content of collected news articles to identify the subject and important information. The analysis unit can also analyze image data using image analysis algorithms. In the case of image analysis, it uses image recognition techniques to identify objects and text within images and understand the image content. For example, it detects specific objects or scenes from collected image data and extracts that information as text data. Furthermore, the analysis unit can also analyze audio data. Audio data is converted into text data using speech recognition techniques and then analyzed by text analysis algorithms. This allows the analysis unit to efficiently analyze diverse collected data and extract important information. Additionally, the analysis unit transmits the analysis results to the evaluation unit, providing basic data for reliability evaluation. This allows the analysis unit to improve the overall information analysis capabilities of the system and provide highly reliable information.

[0033] The evaluation unit assesses reliability based on the information analyzed by the analysis unit. The evaluation unit includes evaluation criteria for assessing the reliability of information sources and the consistency of information. Specifically, it includes algorithms for evaluating the reliability of information sources. For example, it evaluates the reliability of information sources based on their past reliability and the accuracy of the information. The evaluation unit also includes algorithms for evaluating the consistency of information. For example, if the same information is obtained from multiple reliable information sources, it evaluates that the information is highly reliable. Furthermore, the evaluation unit includes algorithms for evaluating the accuracy and reliability of information. For example, it analyzes the content of the information and evaluates its accuracy and reliability. As a result, the evaluation unit can evaluate the reliability of the analyzed information with high accuracy and provide highly reliable information. Furthermore, the evaluation unit also plays a role in providing highly reliable information to users by transmitting the evaluation results to the display unit. As a result, the evaluation unit can improve the reliability of the entire system and provide highly reliable information to users.

[0034] The display unit displays the results evaluated by the evaluation unit. For example, the display unit displays the evaluation results in text format. Specifically, it includes an interface to present the evaluation results in an easy-to-understand manner for the user. For example, it displays the evaluation results in text format, along with confidence scores and evaluation comments. The display unit can also display the evaluation results graphically. For example, it can display confidence scores in graphs or charts, providing users with visually easy-to-understand information. Furthermore, the display unit includes an interface for notifying users of the evaluation results. For example, it can notify users of the evaluation results in real time, providing them with information quickly. This allows the display unit to provide users with reliable information quickly and clearly. Additionally, the display unit plays a role in collecting user feedback and continuously improving the accuracy and display method of the evaluation results. This allows the display unit to improve the overall usability of the system and provide users with reliable information.

[0035] The scraping unit includes a selection unit for selecting reliable information sources. The selection unit selects reliable information sources such as official websites, certified news sites, and academic papers. The selection unit includes criteria for evaluating the reliability of information sources. The selection unit evaluates reliability based on the information source's past performance and third-party evaluations. The selection unit calculates a reliability score for the information source and selects reliable information sources. As a result, the scraping unit improves the reliability of the information it collects by selecting reliable information sources. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input the reliability evaluation of information sources into a generating AI and have the generating AI perform the selection of reliable information sources.

[0036] The analysis unit includes algorithms for analyzing collected information. The analysis unit includes algorithms for analyzing collected information. For example, the analysis unit analyzes text data using a natural language processing algorithm. For example, the analysis unit analyzes collected information using a machine learning algorithm. For example, the analysis unit analyzes image data using an image analysis algorithm. For example, the analysis unit analyzes the content of collected news articles and extracts important keywords and phrases. For example, the analysis unit performs sentiment analysis on text data and evaluates the emotional tone of the information. For example, the analysis unit performs feature extraction on image data and analyzes the content of images. As a result, the accuracy of the analysis is improved by the analysis unit including algorithms for analyzing collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected information into a generating AI and have the generating AI perform the analysis of the information.

[0037] The evaluation unit includes evaluation criteria for evaluating reliability. The evaluation unit includes evaluation criteria for evaluating reliability. The evaluation unit includes, for example, criteria for evaluating the accuracy and reliability of information. The evaluation unit includes, for example, criteria for evaluating the reliability of information sources and the consistency of information. The evaluation unit includes, for example, an algorithm for evaluating the accuracy of information. The evaluation unit includes, for example, a scoring system for evaluating the reliability of information sources. The evaluation unit includes, for example, criteria for evaluating the consistency of information. By including evaluation criteria for evaluating reliability, the evaluation unit improves the accuracy of its evaluations. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or without AI. For example, the evaluation unit can input the reliability evaluation of information into a generating AI and have the generating AI perform the reliability evaluation based on the evaluation criteria.

[0038] The display unit displays the evaluation results to the user. The display unit displays the evaluation results to the user. For example, the display unit displays the evaluation results in text format. For example, the display unit displays the evaluation results graphically. For example, the display unit displays a confidence score and evaluation comments. For example, the display unit includes an interface for notifying the user of the evaluation results. For example, the display unit displays the evaluation results in real time. This allows the user to verify the reliability of the information by displaying the evaluation results to the user. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the display of the evaluation results to a generating AI and have the generating AI execute the method for displaying the evaluation results.

[0039] The reception desk analyzes the user's past input history and proposes the optimal input method. The reception desk analyzes the user's past input history and proposes the optimal input method. For example, the reception desk automatically displays suspicious news or information that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk predicts and suggests suspicious news or information that the user might use at a specific time based on their past input history. In this way, the reception desk can propose the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform the task of suggesting the optimal input method.

[0040] The reception unit filters the input information based on the user's current areas of interest. For example, the reception unit filters out suspicious information based on the news categories the user is currently interested in. For example, if the user is interested in a specific topic, the reception unit prioritizes retrieving suspicious information related to that topic. For example, the reception unit filters out highly relevant suspicious information based on the user's areas of interest. This allows the reception unit to retrieve highly relevant information by filtering based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0041] The reception unit prioritizes retrieving highly relevant information by considering the user's geographical location when acquiring input information. For example, if the user is in a specific region, the reception unit prioritizes retrieving suspicious news and information related to that region. For example, the reception unit filters out highly relevant suspicious information based on the user's geographical location. For example, if the user is on the move, the reception unit retrieves highly relevant suspicious information based on their current location. This allows the reception unit to prioritize retrieving highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI retrieve highly relevant information.

[0042] The reception unit analyzes the user's social media activity when acquiring input information and retrieves relevant information. The reception unit prioritizes acquiring suspicious news and information shared by the user on social media, for example. The reception unit filters highly relevant suspicious information based on the user's social media activity. The reception unit prioritizes acquiring suspicious information from accounts followed by the user, for example. This allows the reception unit to acquire relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI acquire relevant information.

[0043] The scraping unit determines the priority of information to collect based on the reliability of the information source during scraping. The scraping unit prioritizes collecting information from reliable news sites, for example. The scraping unit postpones collecting information from unreliable sources, for example. The scraping unit dynamically adjusts the priority of information to collect based on the reliability of the information source. This allows the scraping unit to prioritize the collection of reliable information by determining the priority of information to collect based on the reliability of the information source. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input information source reliability data into a generating AI and have the generating AI perform the determination of the priority of information to collect.

[0044] The scraping unit applies different scraping algorithms depending on the category of information during scraping. For example, for political news, the scraping unit applies an algorithm that prioritizes reliable sources. For entertainment news, the scraping unit applies an algorithm that collects information from a wide range of sources. For science news, the scraping unit applies an algorithm that prioritizes specialized sources. In this way, the scraping unit can collect appropriate information by applying different scraping algorithms depending on the category of information. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input information category data into a generating AI and have the generating AI execute the application of scraping algorithms.

[0045] The scraping unit selects information to collect while considering the geographical distribution of information sources. The scraping unit selects information to collect while considering the geographical distribution of information sources. For example, the scraping unit prioritizes collecting news and information related to a specific region. For example, the scraping unit collects information from geographically widespread information sources. For example, the scraping unit selects reliable information sources for each region and performs scraping. In this way, the scraping unit can collect highly relevant information by considering the geographical distribution of information sources. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input geographical distribution data of information sources into a generating AI and have the generating AI perform the selection of information to collect.

[0046] The scraping unit improves the accuracy of the information it collects by referring to related literature of the information source during scraping. The scraping unit improves the accuracy of the information it collects by referring to related literature of the information source during scraping. The scraping unit, for example, refers to related literature of the information source to collect reliable information. The scraping unit, for example, evaluates the accuracy of the information based on related literature. The scraping unit, for example, improves the accuracy of the information it collects by referring to related literature. In this way, the scraping unit improves the accuracy of the information it collects by referring to related literature of the information source. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input related literature data of the information source into a generating AI and have the generating AI perform the improvement of the accuracy of the information it collects.

[0047] The analysis unit improves the accuracy of the analysis by considering the interrelationships of the collected information during the analysis. The analysis unit improves the accuracy of the analysis by considering the interrelationships of the collected information during the analysis. For example, the analysis unit analyzes the interrelationships of the collected information and identifies reliable information. The analysis unit improves the accuracy of the analysis by considering the interrelationships of the information. For example, the analysis unit evaluates the accuracy of the information based on the interrelationships. In this way, the analysis unit improves the accuracy of the analysis by considering the interrelationships of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationship data of the collected information into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0048] The analysis unit performs the analysis while considering the attribute information of the information submitter. The analysis unit evaluates the reliability of the information submitter and reflects this in the analysis results. The analysis unit evaluates the accuracy of the information based on the submitter's attribute information. The analysis unit improves the accuracy of the analysis by considering the submitter's past information provision history. In this way, the analysis unit improves the accuracy of the analysis by considering the attribute information of the information submitter. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the attribute information data of the information submitter into a generating AI and have the generating AI perform the analysis.

[0049] The analysis unit performs analysis while considering the geographical distribution of the information. The analysis unit performs analysis while considering the geographical distribution of the information. For example, the analysis unit analyzes geographically widespread information and identifies highly reliable information. For example, the analysis unit analyzes information for each region and evaluates its accuracy. For example, the analysis unit improves the accuracy of the analysis by considering the geographical distribution. In this way, the analysis unit improves the accuracy of the analysis by considering the geographical distribution of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of the information into a generating AI and have the generating AI perform the analysis.

[0050] The analysis unit improves the accuracy of the analysis by referring to relevant literature during the analysis. The analysis unit improves the accuracy of the analysis by referring to relevant literature during the analysis. For example, the analysis unit refers to relevant literature and evaluates the accuracy of the information. For example, the analysis unit improves the accuracy of the analysis based on relevant literature. For example, the analysis unit refers to relevant literature and identifies reliable information. In this way, the analysis unit improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data of the information into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0051] The evaluation unit adjusts the level of detail of the evaluation based on the reliability of the collected information during the evaluation. The evaluation unit performs a detailed evaluation for information from reliable sources, for example. The evaluation unit performs a simplified evaluation for information from unreliable sources, for example. The evaluation unit dynamically adjusts the level of detail of the evaluation based on the reliability of the information, for example. This allows the evaluation unit to perform an appropriate evaluation by adjusting the level of detail of the evaluation based on the reliability of the collected information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input information reliability data into a generating AI and have the generating AI perform the adjustment of the level of detail of the evaluation.

[0052] The evaluation unit applies different evaluation algorithms depending on the category of information during the evaluation process. For example, for political news, the evaluation unit applies an evaluation algorithm that prioritizes reliable sources. For example, for entertainment news, the evaluation unit applies an evaluation algorithm that collects information from a wide range of sources. For example, for science news, the evaluation unit applies an evaluation algorithm that prioritizes specialized sources. This allows the evaluation unit to perform appropriate evaluations by applying different evaluation algorithms depending on the category of information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input information category data into a generating AI and have the generating AI perform the application of the evaluation algorithm.

[0053] The evaluation unit determines the priority of evaluation based on the timing of information submission during the evaluation process. The evaluation unit prioritizes the evaluation of the most recent information, for example. The evaluation unit postpones the evaluation of older information, for example. The evaluation unit dynamically adjusts the evaluation priority based on the timing of information submission. This allows the evaluation unit to perform appropriate evaluations by determining the evaluation priority based on the timing of information submission. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input information submission timing data into a generating AI and have the generating AI perform the determination of evaluation priority.

[0054] The evaluation unit adjusts the order of evaluation based on the relevance of the information during the evaluation process. The evaluation unit prioritizes evaluating highly relevant information, for example. The evaluation unit postpones evaluating less relevant information, for example. The evaluation unit dynamically adjusts the order of evaluation based on the relevance of the information. This allows the evaluation unit to perform an appropriate evaluation by adjusting the order of evaluation based on the relevance of the information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the evaluation order.

[0055] The display unit selects the optimal display method by referring to the user's past operation history when displaying information. The display unit selects the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit prioritizes providing display methods that the user has preferred to use in the past. For example, the display unit proposes the optimal display method based on the user's past operation history. For example, the display unit analyzes the user's operation history and provides a display method with high visibility. In this way, the display unit can select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0056] The display unit selects the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method adapted to the screen size. For example, if the user is using a tablet, the display unit provides a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit provides a concise and highly visible display method. This allows the display unit to select the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception system can automatically search for and present relevant past news and information based on user input. For example, if a user enters news about a specific event, the reception system will search for and present past news articles and information related to that event. This allows users to refer to relevant past information as well as current news, leading to a more comprehensive understanding. The reception system can also automatically extract and present relevant topics and keywords based on user input. For example, if a user enters news about "coronavirus," the reception system will extract and present relevant keywords such as "vaccine," "number of infected people," and "preventive measures." This allows users to obtain information on related topics as well. Furthermore, the reception system can automatically search for and present relevant images and videos based on user input. For example, if a user enters news about the "Olympics," the reception system will search for and present images and videos related to the Olympics. This allows users to obtain visual information as well, enjoying a richer information experience.

[0059] The scraping unit can refer to the past reliability history of information sources to evaluate the reliability of the collected information. For example, if a particular news site has provided reliable information in the past, it can prioritize collecting information from that site. The scraping unit can also refer to evaluation data from third-party organizations to evaluate the reliability of information sources. For example, if a particular news site has received a high rating from a third-party organization, it can prioritize collecting information from that site. Furthermore, the scraping unit can refer to the past misinformation history of information sources to evaluate their reliability. For example, if a particular news site has provided misinformation in the past, caution can be exercised when collecting information from that site. This allows the scraping unit to take a multifaceted approach to evaluating the reliability of information sources and improve the reliability of the information it collects.

[0060] The analysis unit can consider the reliability of the source and author of the collected information in order to assess its reliability. For example, if a particular author has provided reliable information in the past, the analysis unit can prioritize the analysis of that author's information. The analysis unit can also verify whether the source of the information is an official institution or a certified news site in order to assess its reliability. For example, information from government agencies and major news sites is considered highly reliable. Furthermore, the analysis unit can verify whether the content of the information is consistent with other reliable sources in order to assess its reliability. For example, if the same information is obtained from multiple reliable sources, the information is considered highly reliable. This allows the analysis unit to take a multifaceted approach to assessing the reliability of the information and improve the accuracy of the analysis.

[0061] The evaluation unit can assess the reliability of information by checking whether the content of the information is consistent with other reliable sources. For example, if the same information is obtained from multiple reliable sources, the information will be rated as highly reliable. The evaluation unit can also assess the reliability of information by checking whether the content of the information is consistent with past data and statistics. For example, if a particular news story is consistent with past data and statistics, that news story will be rated as highly reliable. Furthermore, the evaluation unit can also assess the reliability of information by checking whether the content of the information is consistent with expert opinions and analyses. For example, if a particular news story is consistent with expert opinions and analyses, that news story will be rated as highly reliable. This allows the evaluation unit to take a multifaceted approach to assessing the reliability of information and improve the accuracy of the evaluation.

[0062] The display unit can customize the display of evaluation results to the user according to the user's interests. For example, if a user is interested in a particular topic, the display unit can prioritize displaying evaluation results related to that topic. The display unit can also customize the display content by considering the user's past browsing history when displaying evaluation results to the user. For example, it can prioritize displaying evaluation results related to topics that the user has frequently viewed in the past. Furthermore, the display unit can provide a display method optimized for the user's device when displaying evaluation results to the user. For example, it can provide a display method that matches the screen size for users using smartphones, and a display method optimized for larger screens for users using tablets. In this way, the display unit can provide the best possible information to the user by customizing the display according to the user's interests and device.

[0063] The reception desk can analyze a user's past input history and learn their input patterns. For example, if a user frequently inputs information on a specific topic during a particular time period, the reception desk can prioritize displaying information related to that time period. The reception desk can also analyze a user's input history to evaluate the reliability of information the user has previously entered. For example, if information a user has previously entered is reliable, the reception desk can prioritize processing that user's input. Furthermore, the reception desk can analyze a user's input history to understand trends in the information they have previously entered. For example, if a user frequently inputs information on a specific topic, the reception desk can prioritize displaying information related to that topic. In this way, by analyzing a user's past input history, the reception desk can learn their input patterns and provide optimal information.

[0064] The reception system can prioritize information based on the user's current areas of interest when acquiring input information. For example, it can prioritize retrieving suspicious information based on the news categories the user is currently interested in. The reception system can also automatically filter relevant information based on the user's areas of interest. For example, if a user is interested in a specific topic, it can prioritize retrieving suspicious information related to that topic. Furthermore, the reception system can prioritize displaying highly relevant information based on the user's areas of interest. For example, if a user is interested in a specific news category, it can prioritize displaying information related to that category. In this way, the reception system can provide users with highly relevant information by prioritizing information based on their areas of interest.

[0065] The reception system can prioritize retrieving highly relevant information by considering the user's geographical location when acquiring input information. For example, if a user is in a specific region, it can prioritize retrieving suspicious news and information related to that region. The reception system can also filter highly relevant information based on the user's geographical location. For example, if a user is in a specific region, it can prioritize displaying information related to that region. Furthermore, if a user is on the move, the reception system can retrieve highly relevant information based on their current location. For example, if a user is on the move, it can prioritize retrieving information related to their destination. In this way, the reception system can prioritize retrieving highly relevant information by considering the user's geographical location.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception unit receives input from the user. User input includes text input, voice input, and image input. The reception unit may be equipped with an interface for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The scraping unit performs scraping based on the information received by the reception unit. The scraping unit can obtain data using scraping tools or APIs to collect data from websites. For example, it can collect the latest news articles from a specific news site and extract the necessary information by analyzing the HTML structure of the website. Step 3: The analysis unit analyzes the information collected by the scraping unit. The analysis unit analyzes the collected information using text analysis algorithms and image analysis algorithms. For example, it analyzes the content of collected news articles and extracts important keywords and phrases. The analysis unit can also analyze text data using natural language processing techniques. Step 4: The evaluation unit assesses reliability based on the information analyzed by the analysis unit. The evaluation unit has evaluation criteria for evaluating the reliability of information sources and the consistency of information. For example, if the same information is obtained from multiple reliable sources, the evaluation unit assesses the reliability of that information. The evaluation unit has algorithms for evaluating the accuracy and reliability of information. Step 5: The display unit displays the results evaluated by the evaluation unit. The display unit can display the evaluation results in text format or graphically. For example, it can display confidence scores and evaluation comments, and has an interface for notifying users of the evaluation results.

[0068] (Example of form 2) The generative AI agent according to an embodiment of the present invention is a system that evaluates questionable news and information in real time and displays its reliability. In this system, when a user inputs questionable news or information, the generative AI performs scraping based on that input, collecting information from sources outside of the generative AI's training data. Based on the collected information, the generative AI evaluates the reliability of the news or information and displays the result to the user. This mechanism allows for rapid verification of the truthfulness of information. For example, a user inputs a question such as, "Is this news true?" This information is input to the generative AI. Next, the generative AI analyzes the input information and performs scraping. The generative AI collects relevant information from multiple sources on the internet. For example, it collects information from news sites and social media, obtaining information from sources outside of the generative AI's training data. Based on the collected information, the generative AI evaluates the reliability of the news or information. The generative AI analyzes the collected information and evaluates its degree of agreement with the input information and its reliability. For example, if the same information is obtained from multiple reliable sources, the news or information is evaluated as highly reliable. Finally, the generative AI displays the evaluation result to the user. Users can verify the veracity of news and information based on the evaluation results from the generating AI. For example, an evaluation result such as "This news is highly reliable" will be displayed. This system allows users to quickly verify the veracity of questionable news and information. This helps prevent the spread of fake news and enables users to make decisions based on accurate information. As a result, the generating AI agent can evaluate and display the reliability of questionable news and information in real time.

[0069] The generation AI agent according to this embodiment comprises a reception unit, a scraping unit, an analysis unit, an evaluation unit, and a display unit. The reception unit receives input from the user. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also be equipped with a microphone and voice recognition technology for receiving voice input. Furthermore, the reception unit may be equipped with a camera and image recognition technology for receiving image input. The scraping unit performs scraping based on the information received by the reception unit. The scraping unit includes, for example, a scraping tool for collecting data from websites. The scraping unit can also obtain data using an API. For example, the scraping unit collects the latest news articles from a specific news site. The scraping unit analyzes the HTML structure of a website and extracts the necessary information. The analysis unit analyzes the information collected by the scraping unit. The analysis unit analyzes the collected information using, for example, a text analysis algorithm. The analysis unit can also analyze image data using image analysis algorithms. For example, the analysis unit can analyze the content of collected news articles and extract important keywords and phrases. The analysis unit can also analyze text data using natural language processing techniques. The evaluation unit evaluates reliability based on the information analyzed by the analysis unit. The evaluation unit includes evaluation criteria for evaluating the reliability of information sources and the consistency of information. For example, the evaluation unit evaluates information as highly reliable if the same information is obtained from multiple reliable sources. The evaluation unit includes algorithms for evaluating the accuracy and reliability of information. The display unit displays the results evaluated by the evaluation unit. For example, the display unit displays the evaluation results in text format. The display unit can also display the evaluation results graphically. For example, the display unit displays reliability scores and evaluation comments. The display unit includes an interface for notifying the evaluation results.This allows the generating AI agent according to the embodiment to evaluate and display the reliability of questionable news and information in real time.

[0070] The reception unit receives input from users. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. Specifically, it provides a form or chat box for users to enter text and processes the entered text in real time. The reception unit may also be equipped with a microphone and speech recognition technology for receiving voice input. In the case of voice input, when a user speaks into the microphone, the voice data is collected and converted into text data by speech recognition technology. Furthermore, the reception unit may be equipped with a camera and image recognition technology for receiving image input. In the case of image input, when a user takes an image using the camera, the image data is collected and analyzed by image recognition technology. This allows the reception unit to handle a variety of input formats from users and flexibly receive information. Furthermore, the reception unit also plays a role in appropriately classifying the user's input and sending it to the subsequent processing department. For example, text input is sent to the text analysis department, voice input to the voice analysis department, and image input to the image analysis department. This allows the reception desk to efficiently process user input and improve the overall system performance.

[0071] The scraping unit performs scraping based on information received by the reception unit. The scraping unit includes, for example, a scraping tool for collecting data from websites. Specifically, it includes a program for analyzing the HTML structure of a website and extracting the necessary information. The scraping tool accesses a specific webpage, analyzes its HTML code, and extracts the specified data. The scraping unit can also obtain data using APIs. Using APIs allows for efficient acquisition of website data and rapid collection of necessary information. For example, the scraping unit collects the latest news articles from a specific news site. It analyzes the HTML structure of the news site and extracts information such as the article title, body text, and publication date. Furthermore, the scraping unit also organizes the collected data and sends it to the analysis unit. This allows the scraping unit to efficiently collect necessary information from websites and APIs, improving the overall information gathering capabilities of the system.

[0072] The analysis unit analyzes the information collected by the scraping unit. For example, the analysis unit analyzes the collected information using text analysis algorithms. Specifically, it analyzes text data using natural language processing techniques to extract important keywords and phrases. For example, it analyzes the content of collected news articles to identify the subject and important information. The analysis unit can also analyze image data using image analysis algorithms. In the case of image analysis, it uses image recognition techniques to identify objects and text within images and understand the image content. For example, it detects specific objects or scenes from collected image data and extracts that information as text data. Furthermore, the analysis unit can also analyze audio data. Audio data is converted into text data using speech recognition techniques and then analyzed by text analysis algorithms. This allows the analysis unit to efficiently analyze diverse collected data and extract important information. Additionally, the analysis unit transmits the analysis results to the evaluation unit, providing basic data for reliability evaluation. This allows the analysis unit to improve the overall information analysis capabilities of the system and provide highly reliable information.

[0073] The evaluation unit assesses reliability based on the information analyzed by the analysis unit. The evaluation unit includes evaluation criteria for assessing the reliability of information sources and the consistency of information. Specifically, it includes algorithms for evaluating the reliability of information sources. For example, it evaluates the reliability of information sources based on their past reliability and the accuracy of the information. The evaluation unit also includes algorithms for evaluating the consistency of information. For example, if the same information is obtained from multiple reliable information sources, it evaluates that the information is highly reliable. Furthermore, the evaluation unit includes algorithms for evaluating the accuracy and reliability of information. For example, it analyzes the content of the information and evaluates its accuracy and reliability. As a result, the evaluation unit can evaluate the reliability of the analyzed information with high accuracy and provide highly reliable information. Furthermore, the evaluation unit also plays a role in providing highly reliable information to users by transmitting the evaluation results to the display unit. As a result, the evaluation unit can improve the reliability of the entire system and provide highly reliable information to users.

[0074] The display unit displays the results evaluated by the evaluation unit. For example, the display unit displays the evaluation results in text format. Specifically, it includes an interface to present the evaluation results in an easy-to-understand manner for the user. For example, it displays the evaluation results in text format, along with confidence scores and evaluation comments. The display unit can also display the evaluation results graphically. For example, it can display confidence scores in graphs or charts, providing users with visually easy-to-understand information. Furthermore, the display unit includes an interface for notifying users of the evaluation results. For example, it can notify users of the evaluation results in real time, providing them with information quickly. This allows the display unit to provide users with reliable information quickly and clearly. Additionally, the display unit plays a role in collecting user feedback and continuously improving the accuracy and display method of the evaluation results. This allows the display unit to improve the overall usability of the system and provide users with reliable information.

[0075] The scraping unit includes a selection unit for selecting reliable information sources. The selection unit selects reliable information sources such as official websites, certified news sites, and academic papers. The selection unit includes criteria for evaluating the reliability of information sources. The selection unit evaluates reliability based on the information source's past performance and third-party evaluations. The selection unit calculates a reliability score for the information source and selects reliable information sources. As a result, the scraping unit improves the reliability of the information it collects by selecting reliable information sources. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input the reliability evaluation of information sources into a generating AI and have the generating AI perform the selection of reliable information sources.

[0076] The analysis unit includes algorithms for analyzing collected information. The analysis unit includes algorithms for analyzing collected information. For example, the analysis unit analyzes text data using a natural language processing algorithm. For example, the analysis unit analyzes collected information using a machine learning algorithm. For example, the analysis unit analyzes image data using an image analysis algorithm. For example, the analysis unit analyzes the content of collected news articles and extracts important keywords and phrases. For example, the analysis unit performs sentiment analysis on text data and evaluates the emotional tone of the information. For example, the analysis unit performs feature extraction on image data and analyzes the content of images. As a result, the accuracy of the analysis is improved by the analysis unit including algorithms for analyzing collected information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected information into a generating AI and have the generating AI perform the analysis of the information.

[0077] The evaluation unit includes evaluation criteria for evaluating reliability. The evaluation unit includes evaluation criteria for evaluating reliability. The evaluation unit includes, for example, criteria for evaluating the accuracy and reliability of information. The evaluation unit includes, for example, criteria for evaluating the reliability of information sources and the consistency of information. The evaluation unit includes, for example, an algorithm for evaluating the accuracy of information. The evaluation unit includes, for example, a scoring system for evaluating the reliability of information sources. The evaluation unit includes, for example, criteria for evaluating the consistency of information. By including evaluation criteria for evaluating reliability, the evaluation unit improves the accuracy of its evaluations. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or without AI. For example, the evaluation unit can input the reliability evaluation of information into a generating AI and have the generating AI perform the reliability evaluation based on the evaluation criteria.

[0078] The display unit displays the evaluation results to the user. The display unit displays the evaluation results to the user. For example, the display unit displays the evaluation results in text format. For example, the display unit displays the evaluation results graphically. For example, the display unit displays a confidence score and evaluation comments. For example, the display unit includes an interface for notifying the user of the evaluation results. For example, the display unit displays the evaluation results in real time. This allows the user to verify the reliability of the information by displaying the evaluation results to the user. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the display of the evaluation results to a generating AI and have the generating AI execute the method for displaying the evaluation results.

[0079] The reception unit estimates the user's emotions and prioritizes input information based on the estimated emotions. For example, if the user is feeling anxious, the reception unit prioritizes receiving suspicious news or information. For example, if the user is excited, the reception unit prioritizes receiving high-priority news or information. For example, if the user is relaxed, the reception unit prioritizes receiving normal news or information. This allows the reception unit to prioritize important information by prioritizing input information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception desk analyzes the user's past input history and proposes the optimal input method. The reception desk analyzes the user's past input history and proposes the optimal input method. For example, the reception desk automatically displays suspicious news or information that the user has frequently entered in the past as suggestions. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk predicts and suggests suspicious news or information that the user might use at a specific time based on their past input history. In this way, the reception desk can propose the optimal input method by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI perform the task of suggesting the optimal input method.

[0081] The reception unit filters the input information based on the user's current areas of interest. For example, the reception unit filters out suspicious information based on the news categories the user is currently interested in. For example, if the user is interested in a specific topic, the reception unit prioritizes retrieving suspicious information related to that topic. For example, the reception unit filters out highly relevant suspicious information based on the user's areas of interest. This allows the reception unit to retrieve highly relevant information by filtering based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0082] The reception unit estimates the user's emotions and adjusts the timing of acquiring input information based on the estimated emotions. The reception unit estimates the user's emotions and adjusts the timing of acquiring input information based on the estimated emotions. For example, if the user is feeling anxious, the reception unit prompts the user to quickly input suspicious news or information. For example, if the user is relaxed, the reception unit acquires input information at the normal time. For example, if the user is excited, the reception unit prioritizes prompting the user to input high-priority news or information. In this way, the reception unit can acquire information at the appropriate time by adjusting the timing of acquiring input information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reception unit prioritizes retrieving highly relevant information by considering the user's geographical location when acquiring input information. For example, if the user is in a specific region, the reception unit prioritizes retrieving suspicious news and information related to that region. For example, the reception unit filters out highly relevant suspicious information based on the user's geographical location. For example, if the user is on the move, the reception unit retrieves highly relevant suspicious information based on their current location. This allows the reception unit to prioritize retrieving highly relevant information by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI retrieve highly relevant information.

[0084] The reception unit analyzes the user's social media activity when acquiring input information and retrieves relevant information. The reception unit prioritizes acquiring suspicious news and information shared by the user on social media, for example. The reception unit filters highly relevant suspicious information based on the user's social media activity. The reception unit prioritizes acquiring suspicious information from accounts followed by the user, for example. This allows the reception unit to acquire relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI acquire relevant information.

[0085] The scraping unit estimates the user's emotions and selects target sites for scraping based on the estimated emotions. For example, if the user is feeling anxious, the scraping unit prioritizes scraping reliable news sites. For example, if the user is excited, the scraping unit prioritizes scraping high-priority news sites. For example, if the user is relaxed, the scraping unit scrapes regular news sites. This allows the scraping unit to collect reliable information by selecting target sites based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scraping unit may be performed using AI, or not. For example, the scraping unit can input user sentiment data into a generating AI and have the generating AI select the target sites for scraping.

[0086] The scraping unit determines the priority of information to collect based on the reliability of the information source during scraping. The scraping unit prioritizes collecting information from reliable news sites, for example. The scraping unit postpones collecting information from unreliable sources, for example. The scraping unit dynamically adjusts the priority of information to collect based on the reliability of the information source. This allows the scraping unit to prioritize the collection of reliable information by determining the priority of information to collect based on the reliability of the information source. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input information source reliability data into a generating AI and have the generating AI perform the determination of the priority of information to collect.

[0087] The scraping unit applies different scraping algorithms depending on the category of information during scraping. For example, for political news, the scraping unit applies an algorithm that prioritizes reliable sources. For entertainment news, the scraping unit applies an algorithm that collects information from a wide range of sources. For science news, the scraping unit applies an algorithm that prioritizes specialized sources. In this way, the scraping unit can collect appropriate information by applying different scraping algorithms depending on the category of information. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input information category data into a generating AI and have the generating AI execute the application of scraping algorithms.

[0088] The scraping unit estimates the user's emotions and adjusts the scraping frequency based on the estimated emotions. The scraping unit estimates the user's emotions and adjusts the scraping frequency based on the estimated emotions. For example, if the user is feeling anxious, the scraping unit increases the scraping frequency. For example, if the user is relaxed, the scraping unit performs scraping at a normal frequency. For example, if the user is excited, the scraping unit prioritizes scraping high-priority information sources. In this way, the scraping unit can collect information at an appropriate frequency by adjusting the scraping frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scraping unit may be performed using AI or not using AI. For example, the scraping unit can input user emotion data into a generating AI and have the generating AI adjust the scraping frequency.

[0089] The scraping unit selects information to collect while considering the geographical distribution of information sources. The scraping unit selects information to collect while considering the geographical distribution of information sources. For example, the scraping unit prioritizes collecting news and information related to a specific region. For example, the scraping unit collects information from geographically widespread information sources. For example, the scraping unit selects reliable information sources for each region and performs scraping. In this way, the scraping unit can collect highly relevant information by considering the geographical distribution of information sources. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input geographical distribution data of information sources into a generating AI and have the generating AI perform the selection of information to collect.

[0090] The scraping unit improves the accuracy of the information it collects by referring to related literature of the information source during scraping. The scraping unit improves the accuracy of the information it collects by referring to related literature of the information source during scraping. The scraping unit, for example, refers to related literature of the information source to collect reliable information. The scraping unit, for example, evaluates the accuracy of the information based on related literature. The scraping unit, for example, improves the accuracy of the information it collects by referring to related literature. In this way, the scraping unit improves the accuracy of the information it collects by referring to related literature of the information source. Some or all of the above processing in the scraping unit may be performed using AI, for example, or without AI. For example, the scraping unit can input related literature data of the information source into a generating AI and have the generating AI perform the improvement of the accuracy of the information it collects.

[0091] The analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit applies an algorithm that prioritizes analyzing reliable information. For example, if the user is relaxed, the analysis unit applies a normal analysis algorithm. For example, if the user is excited, the analysis unit applies an algorithm that prioritizes analyzing information of high importance. This allows the analysis unit to perform appropriate analysis by adjusting the analysis algorithm based on the user's emotions. 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-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0092] The analysis unit improves the accuracy of the analysis by considering the interrelationships of the collected information during the analysis. The analysis unit improves the accuracy of the analysis by considering the interrelationships of the collected information during the analysis. For example, the analysis unit analyzes the interrelationships of the collected information and identifies reliable information. The analysis unit improves the accuracy of the analysis by considering the interrelationships of the information. For example, the analysis unit evaluates the accuracy of the information based on the interrelationships. In this way, the analysis unit improves the accuracy of the analysis by considering the interrelationships of the collected information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationship data of the collected information into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0093] The analysis unit performs the analysis while considering the attribute information of the information submitter. The analysis unit evaluates the reliability of the information submitter and reflects this in the analysis results. The analysis unit evaluates the accuracy of the information based on the submitter's attribute information. The analysis unit improves the accuracy of the analysis by considering the submitter's past information provision history. In this way, the analysis unit improves the accuracy of the analysis by considering the attribute information of the information submitter. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the attribute information data of the information submitter into a generating AI and have the generating AI perform the analysis.

[0094] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. 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 provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is excited, the analysis unit provides a display method that highlights highly important information. In this way, the analysis unit can provide an appropriate display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0095] The analysis unit performs analysis while considering the geographical distribution of the information. The analysis unit performs analysis while considering the geographical distribution of the information. For example, the analysis unit analyzes geographically widespread information and identifies highly reliable information. For example, the analysis unit analyzes information for each region and evaluates its accuracy. For example, the analysis unit improves the accuracy of the analysis by considering the geographical distribution. In this way, the analysis unit improves the accuracy of the analysis by considering the geographical distribution of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical distribution data of the information into a generating AI and have the generating AI perform the analysis.

[0096] The analysis unit improves the accuracy of the analysis by referring to relevant literature during the analysis. The analysis unit improves the accuracy of the analysis by referring to relevant literature during the analysis. For example, the analysis unit refers to relevant literature and evaluates the accuracy of the information. For example, the analysis unit improves the accuracy of the analysis based on relevant literature. For example, the analysis unit refers to relevant literature and identifies reliable information. In this way, the analysis unit improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data of the information into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0097] The evaluation unit estimates the user's emotions and adjusts the evaluation criteria based on the estimated emotions. The evaluation unit estimates the user's emotions and adjusts the evaluation criteria based on the estimated emotions. For example, if the user is feeling anxious, the evaluation unit applies criteria that prioritize evaluating reliable information. For example, if the user is relaxed, the evaluation unit applies normal evaluation criteria. For example, if the user is excited, the evaluation unit applies criteria that prioritize evaluating information of high importance. In this way, the evaluation unit can make an appropriate evaluation by adjusting the evaluation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0098] The evaluation unit adjusts the level of detail of the evaluation based on the reliability of the collected information during the evaluation. The evaluation unit performs a detailed evaluation for information from reliable sources, for example. The evaluation unit performs a simplified evaluation for information from unreliable sources, for example. The evaluation unit dynamically adjusts the level of detail of the evaluation based on the reliability of the information, for example. This allows the evaluation unit to perform an appropriate evaluation by adjusting the level of detail of the evaluation based on the reliability of the collected information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input information reliability data into a generating AI and have the generating AI perform the adjustment of the level of detail of the evaluation.

[0099] The evaluation unit applies different evaluation algorithms depending on the category of information during the evaluation process. For example, for political news, the evaluation unit applies an evaluation algorithm that prioritizes reliable sources. For example, for entertainment news, the evaluation unit applies an evaluation algorithm that collects information from a wide range of sources. For example, for science news, the evaluation unit applies an evaluation algorithm that prioritizes specialized sources. This allows the evaluation unit to perform appropriate evaluations by applying different evaluation algorithms depending on the category of information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input information category data into a generating AI and have the generating AI perform the application of the evaluation algorithm.

[0100] The evaluation unit estimates the user's emotions and adjusts the display method of the evaluation results based on the estimated user emotions. The evaluation unit estimates the user's emotions and adjusts the display method of the evaluation results based on the estimated user emotions. For example, if the user is feeling anxious, the evaluation unit provides a simple and highly visible display method. For example, if the user is relaxed, the evaluation unit provides a display method that includes detailed information. For example, if the user is excited, the evaluation unit provides a display method that highlights highly important information. In this way, the evaluation unit can provide an appropriate display by adjusting the display method of the evaluation results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the evaluation results.

[0101] The evaluation unit determines the priority of evaluation based on the timing of information submission during the evaluation process. The evaluation unit prioritizes the evaluation of the most recent information, for example. The evaluation unit postpones the evaluation of older information, for example. The evaluation unit dynamically adjusts the evaluation priority based on the timing of information submission. This allows the evaluation unit to perform appropriate evaluations by determining the evaluation priority based on the timing of information submission. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input information submission timing data into a generating AI and have the generating AI perform the determination of evaluation priority.

[0102] The evaluation unit adjusts the order of evaluation based on the relevance of the information during the evaluation process. The evaluation unit prioritizes evaluating highly relevant information, for example. The evaluation unit postpones evaluating less relevant information, for example. The evaluation unit dynamically adjusts the order of evaluation based on the relevance of the information. This allows the evaluation unit to perform an appropriate evaluation by adjusting the order of evaluation based on the relevance of the information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the evaluation order.

[0103] The display unit estimates the user's emotions and adjusts the display method based on the estimated emotions. The display unit estimates the user's emotions and adjusts the display method based on the estimated emotions. For example, if the user is feeling anxious, the display unit provides a simple and highly visible display method. For example, if the user is relaxed, the display unit provides a display method that includes detailed information. For example, if the user is excited, the display unit provides a display method that highlights highly important information. In this way, the display unit can provide an appropriate display by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0104] The display unit selects the optimal display method by referring to the user's past operation history when displaying information. The display unit selects the optimal display method by referring to the user's past operation history when displaying information. For example, the display unit prioritizes providing display methods that the user has preferred to use in the past. For example, the display unit proposes the optimal display method based on the user's past operation history. For example, the display unit analyzes the user's operation history and provides a display method with high visibility. In this way, the display unit can select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's past operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0105] The display unit estimates the user's emotions and determines the priority of the displayed content based on the estimated emotions. For example, if the user is feeling anxious, the display unit prioritizes displaying reliable information. For example, if the user is relaxed, the display unit provides normal content. For example, if the user is excited, the display unit prioritizes displaying information of high importance. This allows the display unit to prioritize displaying appropriate information by determining the priority of the displayed content based on the user's emotions. 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 display unit may be performed using AI, or not. For example, the display unit can input user emotion data into a generative AI and have the generative AI determine the priority of the displayed content.

[0106] The display unit selects the optimal display method when displaying information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method adapted to the screen size. For example, if the user is using a tablet, the display unit provides a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit provides a concise and highly visible display method. This allows the display unit to select the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input user device information data into a generating AI and have the generating AI select the optimal display method.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The reception system can automatically search for and present relevant past news and information based on user input. For example, if a user enters news about a specific event, the reception system will search for and present past news articles and information related to that event. This allows users to refer to relevant past information as well as current news, leading to a more comprehensive understanding. The reception system can also automatically extract and present relevant topics and keywords based on user input. For example, if a user enters news about "coronavirus," the reception system will extract and present relevant keywords such as "vaccine," "number of infected people," and "preventive measures." This allows users to obtain information on related topics as well. Furthermore, the reception system can automatically search for and present relevant images and videos based on user input. For example, if a user enters news about the "Olympics," the reception system will search for and present images and videos related to the Olympics. This allows users to obtain visual information as well, enjoying a richer information experience.

[0109] The scraping unit can refer to the past reliability history of information sources to evaluate the reliability of the collected information. For example, if a particular news site has provided reliable information in the past, it can prioritize collecting information from that site. The scraping unit can also refer to evaluation data from third-party organizations to evaluate the reliability of information sources. For example, if a particular news site has received a high rating from a third-party organization, it can prioritize collecting information from that site. Furthermore, the scraping unit can refer to the past misinformation history of information sources to evaluate their reliability. For example, if a particular news site has provided misinformation in the past, caution can be exercised when collecting information from that site. This allows the scraping unit to take a multifaceted approach to evaluating the reliability of information sources and improve the reliability of the information it collects.

[0110] The analysis unit can consider the reliability of the source and author of the collected information in order to assess its reliability. For example, if a particular author has provided reliable information in the past, the analysis unit can prioritize the analysis of that author's information. The analysis unit can also verify whether the source of the information is an official institution or a certified news site in order to assess its reliability. For example, information from government agencies and major news sites is considered highly reliable. Furthermore, the analysis unit can verify whether the content of the information is consistent with other reliable sources in order to assess its reliability. For example, if the same information is obtained from multiple reliable sources, the information is considered highly reliable. This allows the analysis unit to take a multifaceted approach to assessing the reliability of the information and improve the accuracy of the analysis.

[0111] The evaluation unit can assess the reliability of information by checking whether the content of the information is consistent with other reliable sources. For example, if the same information is obtained from multiple reliable sources, the information will be rated as highly reliable. The evaluation unit can also assess the reliability of information by checking whether the content of the information is consistent with past data and statistics. For example, if a particular news story is consistent with past data and statistics, that news story will be rated as highly reliable. Furthermore, the evaluation unit can also assess the reliability of information by checking whether the content of the information is consistent with expert opinions and analyses. For example, if a particular news story is consistent with expert opinions and analyses, that news story will be rated as highly reliable. This allows the evaluation unit to take a multifaceted approach to assessing the reliability of information and improve the accuracy of the evaluation.

[0112] The display unit can customize the display of evaluation results to the user according to the user's interests. For example, if a user is interested in a particular topic, the display unit can prioritize displaying evaluation results related to that topic. The display unit can also customize the display content by considering the user's past browsing history when displaying evaluation results to the user. For example, it can prioritize displaying evaluation results related to topics that the user has frequently viewed in the past. Furthermore, the display unit can provide a display method optimized for the user's device when displaying evaluation results to the user. For example, it can provide a display method that matches the screen size for users using smartphones, and a display method optimized for larger screens for users using tablets. In this way, the display unit can provide the best possible information to the user by customizing the display according to the user's interests and device.

[0113] The reception desk can estimate the user's emotions and provide feedback on the input information based on those estimated emotions. For example, if the user is feeling anxious, the reception desk can provide reassuring feedback. Specifically, it can display a reassuring message such as, "This information is highly reliable." If the user is excited, the reception desk can also provide feedback that encourages calmness. Specifically, it can display a message that encourages calmness, such as, "This information needs to be carefully reviewed." Furthermore, if the user is relaxed, the reception desk can provide normal feedback. Specifically, it can provide normal feedback such as, "This information is highly reliable." In this way, the reception desk can support the user's understanding of the information by providing appropriate feedback based on the user's emotions.

[0114] The reception desk can analyze a user's past input history and learn their input patterns. For example, if a user frequently inputs information on a specific topic during a particular time period, the reception desk can prioritize displaying information related to that time period. The reception desk can also analyze a user's input history to evaluate the reliability of information the user has previously entered. For example, if information a user has previously entered is reliable, the reception desk can prioritize processing that user's input. Furthermore, the reception desk can analyze a user's input history to understand trends in the information they have previously entered. For example, if a user frequently inputs information on a specific topic, the reception desk can prioritize displaying information related to that topic. In this way, by analyzing a user's past input history, the reception desk can learn their input patterns and provide optimal information.

[0115] The reception system can prioritize information based on the user's current areas of interest when acquiring input information. For example, it can prioritize retrieving suspicious information based on the news categories the user is currently interested in. The reception system can also automatically filter relevant information based on the user's areas of interest. For example, if a user is interested in a specific topic, it can prioritize retrieving suspicious information related to that topic. Furthermore, the reception system can prioritize displaying highly relevant information based on the user's areas of interest. For example, if a user is interested in a specific news category, it can prioritize displaying information related to that category. In this way, the reception system can provide users with highly relevant information by prioritizing information based on their areas of interest.

[0116] The reception desk can estimate the user's emotions and adjust how it retrieves input information based on those emotions. For example, if the user is feeling anxious, the reception desk can prompt them to quickly input suspicious news or information. Specifically, it can display a message such as, "This information is important, please check it immediately." If the user is relaxed, the reception desk can retrieve input information at a normal time. Specifically, it can display a message such as, "Please check this information at a normal time." Furthermore, if the user is agitated, the reception desk can prioritize prompting them to input high-priority news or information. Specifically, it can display a message such as, "This information is very important, please check it as soon as possible." In this way, the reception desk can retrieve information at the appropriate time by adjusting how it retrieves input information based on the user's emotions.

[0117] The reception system can prioritize retrieving highly relevant information by considering the user's geographical location when acquiring input information. For example, if a user is in a specific region, it can prioritize retrieving suspicious news and information related to that region. The reception system can also filter highly relevant information based on the user's geographical location. For example, if a user is in a specific region, it can prioritize displaying information related to that region. Furthermore, if a user is on the move, the reception system can retrieve highly relevant information based on their current location. For example, if a user is on the move, it can prioritize retrieving information related to their destination. In this way, the reception system can prioritize retrieving highly relevant information by considering the user's geographical location.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception unit receives input from the user. User input includes text input, voice input, and image input. The reception unit may be equipped with an interface for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The scraping unit performs scraping based on the information received by the reception unit. The scraping unit can obtain data using scraping tools or APIs to collect data from websites. For example, it can collect the latest news articles from a specific news site and extract the necessary information by analyzing the HTML structure of the website. Step 3: The analysis unit analyzes the information collected by the scraping unit. The analysis unit analyzes the collected information using text analysis algorithms and image analysis algorithms. For example, it analyzes the content of collected news articles and extracts important keywords and phrases. The analysis unit can also analyze text data using natural language processing techniques. Step 4: The evaluation unit assesses reliability based on the information analyzed by the analysis unit. The evaluation unit has evaluation criteria for evaluating the reliability of information sources and the consistency of information. For example, if the same information is obtained from multiple reliable sources, the evaluation unit assesses the reliability of that information. The evaluation unit has algorithms for evaluating the accuracy and reliability of information. Step 5: The display unit displays the results evaluated by the evaluation unit. The display unit can display the evaluation results in text format or graphically. For example, it can display confidence scores and evaluation comments, and has an interface for notifying users of the evaluation results.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the reception unit, scraping unit, analysis unit, evaluation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user. The scraping unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects data from websites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the reliability of the information. The display unit is implemented by the control unit 46A of the smart device 14 and displays the evaluation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, scraping unit, analysis unit, evaluation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from the user. The scraping unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects data from websites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the reliability of the information. The display unit is implemented by the control unit 46A of the smart glasses 214 and displays the evaluation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, scraping unit, analysis unit, evaluation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from the user. The scraping unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects data from websites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the reliability of the information. The display unit is implemented by the control unit 46A of the headset terminal 314 and displays the evaluation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, scraping unit, analysis unit, evaluation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user. The scraping unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects data from websites. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the reliability of the information. The display unit is implemented by the control unit 46A of the robot 414 and displays the evaluation results to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A reception area that receives input from users, A scraping unit that performs scraping based on the information received by the aforementioned reception unit, An analysis unit that analyzes the information collected by the scraping unit, An evaluation unit that evaluates reliability based on the information analyzed by the analysis unit, The system includes a display unit that displays the results evaluated by the evaluation unit. A system characterized by the following features. (Note 2) The scraping unit is, It includes a selection department for choosing reliable information sources. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It includes algorithms for analyzing the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Evaluate criteria for assessing reliability The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is Display the evaluation results to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and prioritizes input information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When acquiring input information, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of acquiring input information based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When retrieving input information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When acquiring input information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The scraping unit is, The system estimates user sentiment and selects target websites for scraping based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The scraping unit is, When scraping, prioritize the information to collect based on the reliability of the information source. The system described in Appendix 1, characterized by the features described herein. (Note 14) The scraping unit is, When scraping, apply different scraping algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The scraping unit is, It estimates the user's sentiment and adjusts the scraping frequency based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The scraping unit is, When scraping, select the information to collect by considering the geographical distribution of the information source. The system described in Appendix 1, characterized by the features described herein. (Note 17) The scraping unit is, When scraping, refer to related literature of the source to improve the accuracy of the information collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the interrelationships of the collected information are taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, the attribute information of the information submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned analysis unit, During the analysis, the geographical distribution of the information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During the evaluation, adjust the level of detail based on the reliability of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit, During evaluation, different evaluation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit, During the evaluation process, the priority of evaluations will be determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit, During the evaluation process, the order of evaluation will be adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is When displaying information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is It estimates the user's emotions and determines the priority of displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0192] 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. A reception area that receives input from users, A scraping unit that performs scraping based on the information received by the aforementioned reception unit, An analysis unit that analyzes the information collected by the scraping unit, An evaluation unit that evaluates reliability based on the information analyzed by the analysis unit, The system includes a display unit that displays the results evaluated by the evaluation unit. A system characterized by the following features.

2. The scraping unit is, It includes a selection department for choosing reliable information sources. The system according to feature 1.

3. The aforementioned analysis unit, It includes algorithms for analyzing the collected information. The system according to feature 1.

4. The evaluation unit described above, Evaluate criteria for assessing reliability The system according to feature 1.

5. The aforementioned display unit is Display the evaluation results to the user. The system according to feature 1.

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

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When acquiring input information, filtering is performed based on the user's current areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of acquiring input information based on the estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When retrieving input information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system according to feature 1.

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