system

Abstract

The system according to this embodiment aims to learn information collection patterns, automatically classify and tag information, and manage it efficiently. [Solution] The system according to the embodiment comprises a learning unit, a classification unit, a visualization unit, a summarization unit, a fact-checking unit, and a linking unit. The learning unit learns the user's information gathering patterns. The classification unit automatically classifies and tags downloaded materials, web information, news, and seminar content based on the information gathering patterns learned by the learning unit. The visualization unit visualizes important source information for the information classified by the classification unit. The summarization unit summarizes the information obtained through internet searches based on the information visualized by the visualization unit. The fact-checking unit verifies the accuracy of the information summarized by the summarization unit. The linking unit links the information verified by the fact-checking unit with search services.

Description

Technical Field

[0004] ,

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[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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems such as overlooking important data due to information flooding, the trouble of managing duplicate materials, the burden of fact-checking, and the complexity of organizing information after collection.

[0005] The system according to the embodiment aims to learn an information collection pattern, automatically classify and tag information, and manage it efficiently.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a classification unit, a visualization unit, a summarization unit, a fact-checking unit, and a linking unit. The learning unit learns the user's information gathering patterns. The classification unit automatically classifies and tags downloaded materials, web information, news, and seminar content based on the information gathering patterns learned by the learning unit. The visualization unit visualizes important source information for the information classified by the classification unit. The summarization unit summarizes the information obtained through internet searches based on the information visualized by the visualization unit. The fact-checking unit verifies the accuracy of the information summarized by the summarization unit. The linking unit links the information verified by the fact-checking unit with search services. [Effects of the Invention]

[0007] The system according to this embodiment can learn information collection patterns, automatically classify and tag information, and manage it efficiently. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. )acquires 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) An information gathering support system according to an embodiment of the present invention is a system that learns an individual's information gathering patterns and automatically classifies and tags documents, web information, news, and seminar content. This information gathering support system learns the user's information gathering patterns and automatically classifies and tags all downloaded documents, web information, news, and seminar content. It visualizes important source information, allowing the user to instantly identify the information they need. Furthermore, it summarizes information obtained through internet searches, picks out important points, and builds a unique database to store information specified by the user. This function avoids information duplication and enables efficient information utilization. In addition, the generating AI automatically performs fact-checking to clarify sources. Furthermore, by linking with search services, users can manage online and offline information in an integrated manner. For example, the information gathering support system includes a learning unit that learns the user's information gathering patterns. The learning unit learns the user's information gathering patterns. Next, the information gathering support system includes a classification unit. The classification unit automatically classifies and tags all downloaded documents, web information, news, and seminar content. Furthermore, the information gathering support system includes a visualization unit. The visualization unit visualizes important source information, allowing users to instantly identify the information they need. Furthermore, the information gathering support system includes a summarization unit. The summarization unit summarizes information obtained through internet searches and picks out the key points. In addition, the information gathering support system includes a fact-checking unit. The fact-checking unit uses a generating AI to automatically perform fact-checking and clarify sources. Furthermore, the information gathering support system includes an integration unit. The integration unit connects with search services, allowing users to manage online and offline information in an integrated manner. This avoids information duplication and enables efficient information utilization. Moreover, the information gathering support system can be used not only by individuals but also by teams, allowing information to be managed as a team or company rather than by individuals gathering information individually, significantly improving work efficiency.This allows the information gathering support system to learn the user's information gathering patterns and automatically classify, visualize, summarize, fact-check, and link information, enabling efficient information utilization.

[0029] The information gathering support system according to this embodiment comprises a learning unit, a classification unit, a visualization unit, a summarization unit, a fact-checking unit, and a linking unit. The learning unit learns the user's information gathering patterns. For example, the learning unit learns what information sources the user uses and how often they collect information. The learning unit can also analyze the user's information gathering patterns and suggest the optimal information gathering method. For example, the learning unit prioritizes learning information sources that the user frequently accesses. The learning unit can also update the user's information gathering patterns in real time and learn based on the latest patterns. The classification unit automatically classifies and tags downloaded materials, web information, news, and seminar content. For example, the classification unit classifies information based on the algorithm and classification criteria used. The classification unit can also improve the accuracy of classification by considering the interrelationships of information. For example, the classification unit groups related information and classifies it considering its interrelationships. The visualization unit visualizes important source information so that the user can instantly identify the information they need. For example, the visualization unit adjusts the display method of the visualization based on the importance of the information. The visualization unit can also apply different visualization methods depending on the category of information. For example, the visualization unit applies a timeline-style visualization method to news information. The summarization unit summarizes information obtained through internet searches and picks out the important points. The summarization unit adjusts the level of detail in the summary based on the importance of the information, for example. The summarization unit can also apply different summarization algorithms depending on the category of information. For example, the summarization unit applies a summarization algorithm that prioritizes speed to news information. The fact-checking unit uses a generating AI to automatically perform fact-checking and clarify sources. The fact-checking unit improves the accuracy of fact-checking by, for example, analyzing the source of information in detail. The fact-checking unit can also perform fact-checking considering the credibility of the information submitter. For example, the fact-checking unit prioritizes fact-checking information submitted by experts. The integration unit integrates with search services, allowing users to manage online and offline information in an integrated manner. The integration unit applies different integration methods depending on the category of information, for example.The collaboration unit can also adjust the frequency of collaboration based on when the information is submitted. For example, the collaboration unit prioritizes the collaboration of the latest information. As a result, the information gathering support system according to the embodiment learns the user's information gathering patterns and enables efficient information utilization by automatically classifying, visualizing, summarizing, fact-checking, and collaborating information.

[0030] The learning unit learns the user's information gathering patterns. Specifically, it meticulously records and analyzes what information sources the user uses and how often they gather information. For example, if a user frequently visits a particular news site, that site will be prioritized for learning. Also, if a user tends to gather information at a specific time of day, the system will adjust to provide information at that time. The learning unit updates the user's behavior history in real time and learns based on the latest patterns. This allows it to respond quickly to the user's information gathering needs. Furthermore, the learning unit filters information based on the user's interests and suggests the optimal information gathering method. For example, if a user is interested in a particular topic, it will prioritize providing information related to that topic. The learning unit also collects user feedback and continuously improves the accuracy of its learning algorithm. This allows the learning unit to deeply understand the user's information gathering patterns and support efficient and effective information gathering.

[0031] The classification unit automatically categorizes and tags downloaded documents, web information, news, and seminar content. Specifically, it uses natural language processing technology to analyze the content of the information and classify it into appropriate categories. For example, news articles are classified into categories such as politics, economics, and sports, and further detailed tags are added to improve the searchability of the information. The classification unit classifies information based on the algorithms and classification criteria it uses, but these criteria can be customized according to user needs. For example, when collecting information specific to a particular industry, categories and tags related to that industry can be set. The classification unit can also improve the accuracy of classification by considering the interrelationships of information. For example, by grouping related information and classifying it while considering the interrelationships, the relevance of the information is increased. In addition, the classification unit continuously improves the accuracy of its classification algorithm using machine learning. As a result, the classification unit can provide users with the information they need quickly and accurately.

[0032] The visualization unit visualizes important source information, enabling users to instantly identify the information they need. Specifically, it adjusts the display method of visualizations based on the importance of the information. For example, important information is displayed in a large font or a prominent color to attract the user's attention. Different visualization methods can also be applied to different categories of information. For example, news information can be visualized using a timeline format to allow users to grasp the chronological order of the information at a glance. The visualization unit provides information in an intuitively understandable format using visualization tools such as graphs, charts, and maps. For example, economic data can be displayed in graphs to allow users to visually grasp trends and patterns. The visualization unit also collects user feedback and continuously improves its visualization methods. This allows the visualization unit to help users quickly and accurately identify the information they need.

[0033] The summarization unit summarizes information obtained through internet searches and picks out the key points. Specifically, it uses natural language processing technology to analyze the content of the information and extract the important points. For example, for news articles, it creates a summary based on the article's headline and lead paragraph, conveying the important information concisely. The summarization unit adjusts the level of detail of the summary based on the importance of the information. For example, it provides a concise summary for information requiring speed and a more detailed summary for information requiring detailed analysis. The summarization unit can also apply different summarization algorithms depending on the category of information. For example, it can apply a summarization algorithm that prioritizes speed to news information and an algorithm that provides a detailed summary to academic papers. In this way, the summarization unit can help users quickly and accurately grasp the information they need. Furthermore, the summarization unit collects user feedback and continuously improves the accuracy of its summarization algorithm. In this way, the summarization unit can provide the optimal summary that meets the user's needs.

[0034] The fact-checking department uses a generating AI to automatically perform fact-checks and clarify sources. Specifically, the generating AI analyzes information sources in detail and evaluates their reliability. For example, it verifies whether the source of a news article is a reliable media outlet and whether the information provider is an expert. The generating AI cross-checks the content of the information and compares it with other reliable sources to evaluate its accuracy. The fact-checking department can also perform fact-checks considering the reliability of the information provider. For example, if the information provider is an expert, that information will be given priority for fact-checking. Furthermore, the fact-checking department provides users with the evaluation results regarding the reliability of the information, which they can use as a reference to judge the reliability of the information. This allows the fact-checking department to help users quickly identify reliable information. In addition, the fact-checking department collects user feedback and continuously improves the accuracy of its fact-checking algorithms. This allows the fact-checking department to always provide highly accurate fact-checks based on the latest information.

[0035] The Integration Unit integrates with search services, allowing users to manage online and offline information comprehensively. Specifically, the Integration Unit integrates and centrally manages information from the internet and information stored locally by users. For example, it can manage information collected online and documents stored offline in a related manner. The Integration Unit applies different integration methods depending on the category of information. For example, news information is updated in real time, so it is integrated frequently to provide the latest information. On the other hand, information such as academic papers is updated regularly, so the frequency of integration can be adjusted. The Integration Unit can also adjust the frequency of integration based on when the information was submitted. For example, it prioritizes integrating the latest information and integrates older information as needed. This allows the Integration Unit to efficiently manage the information users need and help them access it quickly. Furthermore, the Integration Unit collects user feedback and continuously improves its integration methods. This allows the Integration Unit to provide optimal information management tailored to user needs.

[0036] The learning unit can analyze the user's past information gathering history and select the optimal learning algorithm. For example, the learning unit can select an algorithm that prioritizes learning information sources that the user has frequently accessed in the past. The learning unit can also analyze the categories of information the user has collected in the past and select a learning algorithm specialized for a specific category. The learning unit can also analyze the user's past information gathering patterns and select an algorithm for efficient information gathering. In this way, the optimal learning algorithm can be selected by analyzing the user's past information gathering history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past information gathering history data into a generating AI and have the generating AI perform the selection of the optimal learning algorithm.

[0037] The learning unit can update the user's information gathering patterns in real time and perform learning based on the latest patterns. For example, if the user adds a new information source, the learning unit can update the learning patterns in real time and reflect it immediately. The learning unit can also update the learning patterns based on information if the user starts to collect certain information frequently. If the user's information gathering patterns change, the learning unit can also update the learning patterns in real time to perform optimal information gathering. This enables learning based on the latest information gathering patterns by updating the user's information gathering patterns in real time. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's information gathering pattern data into a generating AI and have the generating AI perform real-time updates.

[0038] The learning unit can learn information gathering patterns while taking the user's geographical location into consideration. For example, if the user is in a specific region, the learning unit can prioritize learning information related to that region. If the user is traveling, the learning unit can also prioritize learning information related to the travel destination. If the user is at home, the learning unit can also prioritize learning information around the user's home. This allows the learning unit to learn more appropriate information gathering patterns by taking the user's geographical location into consideration. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location data into a generating AI and have the generating AI perform the learning of information gathering patterns.

[0039] The learning unit can analyze a user's social media activity and reflect it in their information gathering patterns. For example, the learning unit can learn the information a user frequently shares on social media and prioritize collecting similar information. The learning unit can also learn information about accounts a user follows on social media and collect relevant information. The learning unit can also learn topics a user has shown interest in on social media and collect information related to those topics. This allows the information gathering patterns to be influenced by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of reflecting it in the information gathering patterns.

[0040] The classification unit can improve the accuracy of classification by considering the interrelationships of information. For example, the classification unit can group related information and classify it while considering its interrelationships. The classification unit can also analyze the sources and content relationships of information to perform highly accurate classifications. The classification unit can also tag information and classify it while considering its interrelationships. This improves the accuracy of classification by considering the interrelationships of information. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input interrelationship data of information into a generating AI and have the generating AI perform the task of improving the accuracy of classification.

[0041] The classification unit can perform classification while considering the attribute information of the information submitter. For example, if the information submitter is an expert, the classification unit will prioritize classifying that information. The classification unit can also prioritize classifying information if the information submitter is a highly reliable institution. The classification unit can also determine the priority of classification by considering the past performance of the information submitter. This makes it possible to classify information with greater reliability by considering the attribute information of the information submitter. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input the attribute information data of the information submitter into a generating AI and have the generating AI perform the classification.

[0042] The classification unit can perform classification while considering the geographical distribution of information. For example, the classification unit can group and classify information that is geographically close. The classification unit can also classify information while considering its geographical relevance. The classification unit can also classify information based on geographical data. This makes it possible to classify information more appropriately by considering its geographical distribution. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input geographical distribution data of information into a generating AI and have the generating AI perform the classification.

[0043] The classification unit can improve the accuracy of its classification by referring to related literature for the information. For example, the classification unit can refer to related literature and adjust the classification criteria for the information. The classification unit can also analyze the content of related literature to improve the accuracy of its classification. The classification unit can also tag related literature and reflect this in the classification of the information. In this way, the accuracy of classification is improved by referring to related literature for the information. Some or all of the above processes in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input related literature data into a generating AI and have the generating AI perform the classification accuracy improvement.

[0044] The visualization unit can optimize the current visualization method by referring to past visualization data. For example, the visualization unit can analyze past visualization data and select the optimal display method. The visualization unit can also provide a display method tailored to the user's preferences based on past visualization data. The visualization unit can also improve the accuracy of the visualization based on past visualization data. This allows the current visualization method to be optimized by referring to past visualization data. Some or all of the above processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past visualization data into a generating AI and have the generating AI perform the optimization of the visualization method.

[0045] The visualization unit can apply different visualization methods to each category of information. For example, the visualization unit can apply a timeline-style visualization method to news information. It can also apply a slide-style visualization method to seminar content. It can also apply a document-style visualization method to materials. By applying different visualization methods to each category of information, it becomes possible to visualize information more appropriately. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input information category data into a generating AI and have the generating AI execute the application of visualization methods.

[0046] The visualization unit can analyze changes in visualization based on the timing of information submission. For example, the visualization unit can highlight the most recent information. It can also display older information fainter and make the latest information stand out. The visualization unit can also adjust the color tones and fonts of the visualization based on the timing of information submission. This allows for more appropriate information visualization by analyzing changes in visualization based on the timing of information submission. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input information submission timing data into a generating AI and have the generating AI perform an analysis of changes in visualization.

[0047] The visualization unit can perform visualizations by referring to relevant market data for the information. For example, the visualization unit visualizes the information based on market data. The visualization unit can also provide visualizations that reflect trends in market data. The visualization unit can also reflect the results of market data analysis in the visualizations. This makes it possible to visualize the information more appropriately by referring to relevant market data for the information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input relevant market data into a generating AI and have the generating AI perform the visualization.

[0048] The summarization unit can adjust the level of detail in the summary based on the importance of the information. For example, the summarization unit provides a detailed summary for important information. The summarization unit can also provide a concise summary for general information. The summarization unit can also adjust the level of detail in the summary based on the importance specified by the user. This allows for the provision of more appropriate summaries by adjusting the level of detail in the summary based on the importance of the information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the summary.

[0049] The summarization unit can apply different summarization algorithms depending on the category of information. For example, the summarization unit can apply a summarization algorithm that prioritizes speed to news information. The summarization unit can also apply a summarization algorithm that includes detailed explanations to seminar content. The summarization unit can also apply a summarization algorithm that focuses on key points to documents. By applying different summarization algorithms depending on the category of information, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input information category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0050] The summarization unit can determine the priority of summaries based on when the information was submitted. For example, the summarization unit may prioritize summarizing the most recent information. The summarization unit may also prioritize summarizing the most recent information, delaying older information. The summarization unit may also adjust the order of summaries based on when the information was submitted. This ensures that more appropriate summaries are provided by prioritizing summaries based on when the information was submitted. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input information submission time data into a generating AI and have the generating AI perform the determination of the summaries' priority.

[0051] The summarization unit can adjust the order of summaries based on the relevance of the information. For example, the summarization unit may prioritize summarizing highly relevant information. The summarization unit may also prioritize summarizing highly relevant information and postpone less relevant information. The summarization unit can also adjust the order of summaries based on the relevance of the information. This provides a more appropriate summary by adjusting the order of summaries based on the relevance of the information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the order of summaries.

[0052] The fact-checking unit can improve the accuracy of fact-checking by analyzing the sources of information in detail. For example, the fact-checking unit can analyze the sources of information in detail and prioritize fact-checking highly reliable information. The fact-checking unit can also evaluate the reliability of the sources of information and improve the accuracy of fact-checking. The fact-checking unit can also improve the accuracy of fact-checking by considering the past performance of the sources of information. As a result, the accuracy of fact-checking is improved by analyzing the sources of information in detail. Some or all of the above processes in the fact-checking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the fact-checking unit can input source data of information into a generative AI and have the generative AI perform the task of improving the accuracy of fact-checking.

[0053] The fact-checking unit can perform fact-checking while considering the credibility of the information submitter. For example, if the information submitter is an expert, the fact-checking unit will prioritize fact-checking that information. The fact-checking unit may also prioritize fact-checking if the information submitter is a highly reliable organization. The fact-checking unit can also determine the priority of fact-checking by considering the information submitter's past performance. This ensures that more reliable fact-checking is provided by considering the credibility of the information submitter. Some or all of the above-described processes in the fact-checking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the fact-checking unit can input credibility data of the information submitter into a generative AI and have the generative AI perform the fact-checking.

[0054] The fact-checking unit can perform fact-checking while considering the geographical distribution of information. For example, the fact-checking unit prioritizes fact-checking information that is geographically close. The fact-checking unit can also fact-check information while considering its geographical relevance. The fact-checking unit can also determine the priority of fact-checking based on geographical data. This allows for more appropriate fact-checking by considering the geographical distribution of information. Some or all of the above-described processes in the fact-checking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the fact-checking unit can input geographical distribution data of information into a generative AI and have the generative AI perform the fact-checking.

[0055] The fact-checking unit can improve the accuracy of fact-checking by referring to relevant literature. For example, the fact-checking unit can refer to relevant literature and adjust the fact-checking criteria for the information. The fact-checking unit can also analyze the content of relevant literature to improve the accuracy of fact-checking. The fact-checking unit can also tag relevant literature and reflect this in the fact-checking of the information. In this way, the accuracy of fact-checking is improved by referring to relevant literature. Some or all of the above processes in the fact-checking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the fact-checking unit can input relevant literature data into a generative AI and have the generative AI perform the fact-checking accuracy improvement.

[0056] The integration unit can optimize the integration algorithm by referring to past integration data. For example, the integration unit can analyze past integration data and select the optimal integration method. The integration unit can also provide integration methods tailored to user preferences based on past integration data. The integration unit can also improve the accuracy of integration based on past integration data. This allows the integration algorithm to be optimized by referring to past integration data. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input past integration data into a generative AI and have the generative AI perform the optimization of the integration algorithm.

[0057] The collaboration unit can apply different collaboration methods to each category of information. For example, the collaboration unit may apply a collaboration method that prioritizes speed to news information. The collaboration unit may also apply a collaboration method that includes detailed explanations to seminar content. The collaboration unit may also apply a collaboration method that focuses on key points to documents. By applying different collaboration methods to each category of information, more appropriate collaboration is provided. Some or all of the above processing in the collaboration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collaboration unit can input information category data into a generative AI and have the generative AI execute the application of the collaboration method.

[0058] The collaboration unit can adjust the frequency of collaboration based on the timing of information submission. For example, the collaboration unit may prioritize the collaboration of the latest information. The collaboration unit may also prioritize the collaboration of the latest information, delaying the collaboration of older information. The collaboration unit can also adjust the frequency of collaboration based on the timing of information submission. This allows for more appropriate collaboration by adjusting the frequency of collaboration based on the timing of information submission. Some or all of the above-described processes in the collaboration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the collaboration unit can input information submission timing data into a generating AI and have the generating AI perform the adjustment of the collaboration frequency.

[0059] The integration unit can perform integration by referring to relevant market data for the information. For example, the integration unit integrates information based on market data. The integration unit can also provide integration that reflects trends in market data. The integration unit can also reflect the results of market data analysis in the integration. This allows for more appropriate integration by referring to relevant market data for the information. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit can input relevant market data into a generating AI and have the generating AI perform the integration.

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

[0061] The information gathering support system can also include a health management unit that monitors the user's health status. This unit collects health data such as the user's heart rate and sleep patterns, and incorporates this data into the information gathering pattern. For example, if the user is fatigued, the frequency of information gathering can be reduced, and relaxing content can be prioritized. Conversely, if the user is in good health, the frequency of information gathering can be increased, providing the latest news and trend information. This enables information gathering tailored to the user's health status, leading to more efficient information utilization.

[0062] The information gathering support system can also include a hobby learning section that learns the user's hobbies and interests. This section analyzes data such as content the user has previously viewed and events they have attended, and provides information based on the user's hobbies and interests. For example, if the user is interested in music, it can prioritize providing the latest music news and concert information. Similarly, if the user is interested in sports, it can provide sports-related news and event information. This enables the provision of information tailored to the user's hobbies and interests, resulting in more personalized information gathering.

[0063] The information gathering support system can also be equipped with a behavioral learning unit that learns the user's behavioral patterns. This unit analyzes the user's past behavioral data and proposes the optimal information gathering method. For example, if a user tends to gather information during a specific time period, the system can provide information tailored to that time. Similarly, if a user tends to gather information in a specific location, the system can provide information related to that location. This enables information gathering that aligns with the user's behavioral patterns, leading to more efficient information utilization.

[0064] The information gathering support system can also include a learning style analysis unit that analyzes the user's learning style. This unit analyzes how users learn information and proposes the optimal information delivery method. For example, if a user prefers visual information, it can provide information using graphics and videos extensively. If a user prefers text-based information, it can provide detailed text information. This enables information delivery tailored to the user's learning style, resulting in more effective information gathering.

[0065] The information gathering support system can also be equipped with a communication learning unit that learns the user's communication style. This unit learns how the user communicates and suggests the optimal method of information delivery. For example, if the user prefers email, information can be provided via email. Similarly, if the user prefers chat, information can be provided in chat format. This enables information delivery tailored to the user's communication style, resulting in more effective information gathering.

[0066] The information gathering support system can also include a time management unit to assist users with time management. The time management unit analyzes the user's schedule and proposes the optimal timing for information delivery. For example, it might avoid providing important information during busy periods and instead deliver it during less busy times. Furthermore, if a user tends to gather information at specific times, the system can tailor information delivery to those times. This enables information delivery that aligns with the user's time management, resulting in more efficient information gathering.

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

[0068] Step 1: The learning unit learns the user's information gathering patterns. For example, it can learn what information sources the user uses and how often they gather information, and then suggest the optimal information gathering method. The learning unit can also update the user's information gathering patterns in real time and learn based on the latest patterns. Step 2: The classification unit automatically categorizes and tags downloaded materials, web information, news, and seminar content. For example, it can classify information based on the algorithms and classification criteria used, and improve the accuracy of the classification by considering the interrelationships between the information. Step 3: The visualization unit visualizes important source information, allowing users to instantly identify the information they need. For example, it can adjust how the visualization is displayed based on the importance of the information, and apply different visualization techniques to different categories of information. Step 4: The summarization section summarizes the information obtained through internet searches and picks out the key points. For example, the level of detail in the summary can be adjusted based on the importance of the information, and different summarization algorithms can be applied depending on the category of the information. Step 5: The fact-checking section uses a generating AI to automatically perform fact-checks and clarify sources. For example, it can improve the accuracy of fact-checking by analyzing the source of information in detail, and it can also perform fact-checking while considering the reliability of the information submitter. Step 6: The integration unit integrates with the search service, allowing users to manage online and offline information in an integrated manner. For example, different integration methods can be applied to each category of information, and the frequency of integration can be adjusted based on when the information was submitted.

[0069] (Example of form 2) An information gathering support system according to an embodiment of the present invention is a system that learns an individual's information gathering patterns and automatically classifies and tags documents, web information, news, and seminar content. This information gathering support system learns the user's information gathering patterns and automatically classifies and tags all downloaded documents, web information, news, and seminar content. It visualizes important source information, allowing the user to instantly identify the information they need. Furthermore, it summarizes information obtained through internet searches, picks out important points, and builds a unique database to store information specified by the user. This function avoids information duplication and enables efficient information utilization. In addition, the generating AI automatically performs fact-checking to clarify sources. Furthermore, by linking with search services, users can manage online and offline information in an integrated manner. For example, the information gathering support system includes a learning unit that learns the user's information gathering patterns. The learning unit learns the user's information gathering patterns. Next, the information gathering support system includes a classification unit. The classification unit automatically classifies and tags all downloaded documents, web information, news, and seminar content. Furthermore, the information gathering support system includes a visualization unit. The visualization unit visualizes important source information, allowing users to instantly identify the information they need. Furthermore, the information gathering support system includes a summarization unit. The summarization unit summarizes information obtained through internet searches and picks out the key points. In addition, the information gathering support system includes a fact-checking unit. The fact-checking unit uses a generating AI to automatically perform fact-checking and clarify sources. Furthermore, the information gathering support system includes an integration unit. The integration unit connects with search services, allowing users to manage online and offline information in an integrated manner. This avoids information duplication and enables efficient information utilization. Moreover, the information gathering support system can be used not only by individuals but also by teams, allowing information to be managed as a team or company rather than by individuals gathering information individually, significantly improving work efficiency.This allows the information gathering support system to learn the user's information gathering patterns and automatically classify, visualize, summarize, fact-check, and link information, enabling efficient information utilization.

[0070] The information gathering support system according to this embodiment comprises a learning unit, a classification unit, a visualization unit, a summarization unit, a fact-checking unit, and a linking unit. The learning unit learns the user's information gathering patterns. For example, the learning unit learns what information sources the user uses and how often they collect information. The learning unit can also analyze the user's information gathering patterns and suggest the optimal information gathering method. For example, the learning unit prioritizes learning information sources that the user frequently accesses. The learning unit can also update the user's information gathering patterns in real time and learn based on the latest patterns. The classification unit automatically classifies and tags downloaded materials, web information, news, and seminar content. For example, the classification unit classifies information based on the algorithm and classification criteria used. The classification unit can also improve the accuracy of classification by considering the interrelationships of information. For example, the classification unit groups related information and classifies it considering its interrelationships. The visualization unit visualizes important source information so that the user can instantly identify the information they need. For example, the visualization unit adjusts the display method of the visualization based on the importance of the information. The visualization unit can also apply different visualization methods depending on the category of information. For example, the visualization unit applies a timeline-style visualization method to news information. The summarization unit summarizes information obtained through internet searches and picks out the important points. The summarization unit adjusts the level of detail in the summary based on the importance of the information, for example. The summarization unit can also apply different summarization algorithms depending on the category of information. For example, the summarization unit applies a summarization algorithm that prioritizes speed to news information. The fact-checking unit uses a generating AI to automatically perform fact-checking and clarify sources. The fact-checking unit improves the accuracy of fact-checking by, for example, analyzing the source of information in detail. The fact-checking unit can also perform fact-checking considering the credibility of the information submitter. For example, the fact-checking unit prioritizes fact-checking information submitted by experts. The integration unit integrates with search services, allowing users to manage online and offline information in an integrated manner. The integration unit applies different integration methods depending on the category of information, for example.The collaboration unit can also adjust the frequency of collaboration based on when the information is submitted. For example, the collaboration unit prioritizes the collaboration of the latest information. As a result, the information gathering support system according to the embodiment learns the user's information gathering patterns and enables efficient information utilization by automatically classifying, visualizing, summarizing, fact-checking, and collaborating information.

[0071] The learning unit learns the user's information gathering patterns. Specifically, it meticulously records and analyzes what information sources the user uses and how often they gather information. For example, if a user frequently visits a particular news site, that site will be prioritized for learning. Also, if a user tends to gather information at a specific time of day, the system will adjust to provide information at that time. The learning unit updates the user's behavior history in real time and learns based on the latest patterns. This allows it to respond quickly to the user's information gathering needs. Furthermore, the learning unit filters information based on the user's interests and suggests the optimal information gathering method. For example, if a user is interested in a particular topic, it will prioritize providing information related to that topic. The learning unit also collects user feedback and continuously improves the accuracy of its learning algorithm. This allows the learning unit to deeply understand the user's information gathering patterns and support efficient and effective information gathering.

[0072] The classification unit automatically categorizes and tags downloaded documents, web information, news, and seminar content. Specifically, it uses natural language processing technology to analyze the content of the information and classify it into appropriate categories. For example, news articles are classified into categories such as politics, economics, and sports, and further detailed tags are added to improve the searchability of the information. The classification unit classifies information based on the algorithms and classification criteria it uses, but these criteria can be customized according to user needs. For example, when collecting information specific to a particular industry, categories and tags related to that industry can be set. The classification unit can also improve the accuracy of classification by considering the interrelationships of information. For example, by grouping related information and classifying it while considering the interrelationships, the relevance of the information is increased. In addition, the classification unit continuously improves the accuracy of its classification algorithm using machine learning. As a result, the classification unit can provide users with the information they need quickly and accurately.

[0073] The visualization unit visualizes important source information, enabling users to instantly identify the information they need. Specifically, it adjusts the display method of visualizations based on the importance of the information. For example, important information is displayed in a large font or a prominent color to attract the user's attention. Different visualization methods can also be applied to different categories of information. For example, news information can be visualized using a timeline format to allow users to grasp the chronological order of the information at a glance. The visualization unit provides information in an intuitively understandable format using visualization tools such as graphs, charts, and maps. For example, economic data can be displayed in graphs to allow users to visually grasp trends and patterns. The visualization unit also collects user feedback and continuously improves its visualization methods. This allows the visualization unit to help users quickly and accurately identify the information they need.

[0074] The summarization unit summarizes information obtained through internet searches and picks out the key points. Specifically, it uses natural language processing technology to analyze the content of the information and extract the important points. For example, for news articles, it creates a summary based on the article's headline and lead paragraph, conveying the important information concisely. The summarization unit adjusts the level of detail of the summary based on the importance of the information. For example, it provides a concise summary for information requiring speed and a more detailed summary for information requiring detailed analysis. The summarization unit can also apply different summarization algorithms depending on the category of information. For example, it can apply a summarization algorithm that prioritizes speed to news information and an algorithm that provides a detailed summary to academic papers. In this way, the summarization unit can help users quickly and accurately grasp the information they need. Furthermore, the summarization unit collects user feedback and continuously improves the accuracy of its summarization algorithm. In this way, the summarization unit can provide the optimal summary that meets the user's needs.

[0075] The fact-checking department uses a generating AI to automatically perform fact-checks and clarify sources. Specifically, the generating AI analyzes information sources in detail and evaluates their reliability. For example, it verifies whether the source of a news article is a reliable media outlet and whether the information provider is an expert. The generating AI cross-checks the content of the information and compares it with other reliable sources to evaluate its accuracy. The fact-checking department can also perform fact-checks considering the reliability of the information provider. For example, if the information provider is an expert, that information will be given priority for fact-checking. Furthermore, the fact-checking department provides users with the evaluation results regarding the reliability of the information, which they can use as a reference to judge the reliability of the information. This allows the fact-checking department to help users quickly identify reliable information. In addition, the fact-checking department collects user feedback and continuously improves the accuracy of its fact-checking algorithms. This allows the fact-checking department to always provide highly accurate fact-checks based on the latest information.

[0076] The Integration Unit integrates with search services, allowing users to manage online and offline information comprehensively. Specifically, the Integration Unit integrates and centrally manages information from the internet and information stored locally by users. For example, it can manage information collected online and documents stored offline in a related manner. The Integration Unit applies different integration methods depending on the category of information. For example, news information is updated in real time, so it is integrated frequently to provide the latest information. On the other hand, information such as academic papers is updated regularly, so the frequency of integration can be adjusted. The Integration Unit can also adjust the frequency of integration based on when the information was submitted. For example, it prioritizes integrating the latest information and integrates older information as needed. This allows the Integration Unit to efficiently manage the information users need and help them access it quickly. Furthermore, the Integration Unit collects user feedback and continuously improves its integration methods. This allows the Integration Unit to provide optimal information management tailored to user needs.

[0077] The learning unit can estimate the user's emotions and adjust the learning method of information gathering patterns based on the estimated user emotions. For example, if the user is stressed, the learning unit can reduce the frequency of information gathering and prioritize learning relaxing content. If the user is excited, the learning unit can also increase the frequency of information gathering and prioritize learning the latest news and trending information. If the user is tired, the learning unit can adjust the timing of information gathering to ensure rest time. This allows for more appropriate information gathering by adjusting the learning method of information gathering patterns according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning method of information gathering patterns.

[0078] The learning unit can analyze the user's past information gathering history and select the optimal learning algorithm. For example, the learning unit can select an algorithm that prioritizes learning information sources that the user has frequently accessed in the past. The learning unit can also analyze the categories of information the user has collected in the past and select a learning algorithm specialized for a specific category. The learning unit can also analyze the user's past information gathering patterns and select an algorithm for efficient information gathering. In this way, the optimal learning algorithm can be selected by analyzing the user's past information gathering history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past information gathering history data into a generating AI and have the generating AI perform the selection of the optimal learning algorithm.

[0079] The learning unit can update the user's information gathering patterns in real time and perform learning based on the latest patterns. For example, if the user adds a new information source, the learning unit can update the learning patterns in real time and reflect it immediately. The learning unit can also update the learning patterns based on information if the user starts to collect certain information frequently. If the user's information gathering patterns change, the learning unit can also update the learning patterns in real time to perform optimal information gathering. This enables learning based on the latest information gathering patterns by updating the user's information gathering patterns in real time. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's information gathering pattern data into a generating AI and have the generating AI perform real-time updates.

[0080] The learning unit can estimate the user's emotions and determine learning priorities based on the estimated emotions. For example, if the user is stressed, the learning unit will prioritize learning information that promotes relaxation. If the user is excited, the learning unit can also prioritize learning the latest news and trending information. If the user is tired, the learning unit can also prioritize learning information related to rest. This allows for more appropriate information gathering by determining learning priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI determine the learning priorities.

[0081] The learning unit can learn information gathering patterns while taking the user's geographical location into consideration. For example, if the user is in a specific region, the learning unit can prioritize learning information related to that region. If the user is traveling, the learning unit can also prioritize learning information related to the travel destination. If the user is at home, the learning unit can also prioritize learning information around the user's home. This allows the learning unit to learn more appropriate information gathering patterns by taking the user's geographical location into consideration. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location data into a generating AI and have the generating AI perform the learning of information gathering patterns.

[0082] The learning unit can analyze a user's social media activity and reflect it in their information gathering patterns. For example, the learning unit can learn the information a user frequently shares on social media and prioritize collecting similar information. The learning unit can also learn information about accounts a user follows on social media and collect relevant information. The learning unit can also learn topics a user has shown interest in on social media and collect information related to those topics. This allows the information gathering patterns to be influenced by analyzing the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of reflecting it in the information gathering patterns.

[0083] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated emotions. For example, if the user is stressed, the classification unit can apply simple classification criteria to facilitate information organization. If the user is relaxed, the classification unit can also apply detailed classification criteria to improve the accuracy of the information. If the user is in a hurry, the classification unit can also apply criteria that allow for quick classification of information. This allows for more appropriate information classification by adjusting the classification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the classification criteria.

[0084] The classification unit can improve the accuracy of classification by considering the interrelationships of information. For example, the classification unit can group related information and classify it while considering its interrelationships. The classification unit can also analyze the sources and content relationships of information to perform highly accurate classifications. The classification unit can also tag information and classify it while considering its interrelationships. This improves the accuracy of classification by considering the interrelationships of information. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input interrelationship data of information into a generating AI and have the generating AI perform the task of improving the accuracy of classification.

[0085] The classification unit can perform classification while considering the attribute information of the information submitter. For example, if the information submitter is an expert, the classification unit will prioritize classifying that information. The classification unit can also prioritize classifying information if the information submitter is a highly reliable institution. The classification unit can also determine the priority of classification by considering the past performance of the information submitter. This makes it possible to classify information with greater reliability by considering the attribute information of the information submitter. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input the attribute information data of the information submitter into a generating AI and have the generating AI perform the classification.

[0086] The classification unit can estimate the user's emotions and adjust the order in which the classification results are displayed based on the estimated emotions. For example, if the user is stressed, the classification unit will prioritize displaying important information. If the user is relaxed, the classification unit may also prioritize displaying detailed information. If the user is in a hurry, the classification unit may also prioritize displaying concise information. By adjusting the order in which the classification results are displayed according to the user's emotions, it becomes possible to display more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, or not using AI. For example, the classification unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the classification results.

[0087] The classification unit can perform classification while considering the geographical distribution of information. For example, the classification unit can group and classify information that is geographically close. The classification unit can also classify information while considering its geographical relevance. The classification unit can also classify information based on geographical data. This makes it possible to classify information more appropriately by considering its geographical distribution. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input geographical distribution data of information into a generating AI and have the generating AI perform the classification.

[0088] The classification unit can improve the accuracy of its classification by referring to related literature for the information. For example, the classification unit can refer to related literature and adjust the classification criteria for the information. The classification unit can also analyze the content of related literature to improve the accuracy of its classification. The classification unit can also tag related literature and reflect this in the classification of the information. In this way, the accuracy of classification is improved by referring to related literature for the information. Some or all of the above processes in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input related literature data into a generating AI and have the generating AI perform the classification accuracy improvement.

[0089] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is nervous, the visualization unit can provide a simple and highly visible display method. If the user is relaxed, the visualization unit can also provide a display method that includes detailed information. If the user is in a hurry, the visualization unit can also provide a display method that gets straight to the point. By adjusting the display method of the visualization according to the user's emotions, it becomes possible to visualize more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the visualization.

[0090] The visualization unit can optimize the current visualization method by referring to past visualization data. For example, the visualization unit can analyze past visualization data and select the optimal display method. The visualization unit can also provide a display method tailored to the user's preferences based on past visualization data. The visualization unit can also improve the accuracy of the visualization based on past visualization data. This allows the current visualization method to be optimized by referring to past visualization data. Some or all of the above processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past visualization data into a generating AI and have the generating AI perform the optimization of the visualization method.

[0091] The visualization unit can apply different visualization methods to each category of information. For example, the visualization unit can apply a timeline-style visualization method to news information. It can also apply a slide-style visualization method to seminar content. It can also apply a document-style visualization method to materials. By applying different visualization methods to each category of information, it becomes possible to visualize information more appropriately. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input information category data into a generating AI and have the generating AI execute the application of visualization methods.

[0092] The visualization unit can estimate the user's emotions and adjust the importance of the visualizations based on the estimated emotions. For example, if the user is stressed, the visualization unit can highlight important information. If the user is relaxed, the visualization unit can also display detailed information. If the user is in a hurry, the visualization unit can also display concise information. This allows for more appropriate information to be visualized by adjusting the importance of the visualizations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input user emotion data into the generative AI and have the generative AI adjust the importance of the visualizations.

[0093] The visualization unit can analyze changes in visualization based on the timing of information submission. For example, the visualization unit can highlight the most recent information. It can also display older information fainter and make the latest information stand out. The visualization unit can also adjust the color tones and fonts of the visualization based on the timing of information submission. This allows for more appropriate information visualization by analyzing changes in visualization based on the timing of information submission. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input information submission timing data into a generating AI and have the generating AI perform an analysis of changes in visualization.

[0094] The visualization unit can perform visualizations by referring to relevant market data for the information. For example, the visualization unit visualizes the information based on market data. The visualization unit can also provide visualizations that reflect trends in market data. The visualization unit can also reflect the results of market data analysis in the visualizations. This makes it possible to visualize the information more appropriately by referring to relevant market data for the information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input relevant market data into a generating AI and have the generating AI perform the visualization.

[0095] The summarization unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is stressed, the summarization unit can provide a simple and easy-to-read summary. If the user is relaxed, the summarization unit can also provide a summary that includes detailed information. If the user is in a hurry, the summarization unit can also provide a summary that gets straight to the point. This allows for a more appropriate summary to be provided by adjusting the way the summary is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input user emotion data into the generative AI and have the generative AI adjust the way the summary is presented.

[0096] The summarization unit can adjust the level of detail in the summary based on the importance of the information. For example, the summarization unit provides a detailed summary for important information. The summarization unit can also provide a concise summary for general information. The summarization unit can also adjust the level of detail in the summary based on the importance specified by the user. This allows for the provision of more appropriate summaries by adjusting the level of detail in the summary based on the importance of the information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the summary.

[0097] The summarization unit can apply different summarization algorithms depending on the category of information. For example, the summarization unit can apply a summarization algorithm that prioritizes speed to news information. The summarization unit can also apply a summarization algorithm that includes detailed explanations to seminar content. The summarization unit can also apply a summarization algorithm that focuses on key points to documents. By applying different summarization algorithms depending on the category of information, a more appropriate summary can be provided. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input information category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0098] The summarization unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the summarization unit can provide a short, concise summary. If the user is relaxed, the summarization unit can also provide a longer summary with more detailed explanations. If the user is excited, the summarization unit can also provide a summary with visually stimulating effects. By adjusting the length of the summary according to the user's emotions, a more appropriate summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI or not. For example, the summarization unit can input user emotion data into a generative AI and have the generative AI adjust the length of the summary.

[0099] The summarization unit can determine the priority of summaries based on when the information was submitted. For example, the summarization unit may prioritize summarizing the most recent information. The summarization unit may also prioritize summarizing the most recent information, delaying older information. The summarization unit may also adjust the order of summaries based on when the information was submitted. This ensures that more appropriate summaries are provided by prioritizing summaries based on when the information was submitted. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input information submission time data into a generating AI and have the generating AI perform the determination of the summaries' priority.

[0100] The summarization unit can adjust the order of summaries based on the relevance of the information. For example, the summarization unit may prioritize summarizing highly relevant information. The summarization unit may also prioritize summarizing highly relevant information and postpone less relevant information. The summarization unit can also adjust the order of summaries based on the relevance of the information. This provides a more appropriate summary by adjusting the order of summaries based on the relevance of the information. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the order of summaries.

[0101] The fact-checking unit can estimate the user's emotions and adjust the fact-checking method based on the estimated emotions. For example, if the user is nervous, the fact-checking unit can provide simple and easy-to-understand fact-checking results. If the user is relaxed, the fact-checking unit can also provide detailed fact-checking results. If the user is in a hurry, the fact-checking unit can also provide concise fact-checking results. This allows for more appropriate fact-checking by adjusting the fact-checking method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the fact-checking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the fact-checking unit can input user emotion data into a generative AI and have the generative AI adjust the fact-checking method.

[0102] The fact-checking unit can improve the accuracy of fact-checking by analyzing the sources of information in detail. For example, the fact-checking unit can analyze the sources of information in detail and prioritize fact-checking highly reliable information. The fact-checking unit can also evaluate the reliability of the sources of information and improve the accuracy of fact-checking. The fact-checking unit can also improve the accuracy of fact-checking by considering the past performance of the sources of information. As a result, the accuracy of fact-checking is improved by analyzing the sources of information in detail. Some or all of the above processes in the fact-checking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the fact-checking unit can input source data of information into a generative AI and have the generative AI perform the task of improving the accuracy of fact-checking.

[0103] The fact-checking unit can perform fact-checking while considering the credibility of the information submitter. For example, if the information submitter is an expert, the fact-checking unit will prioritize fact-checking that information. The fact-checking unit may also prioritize fact-checking if the information submitter is a highly reliable organization. The fact-checking unit can also determine the priority of fact-checking by considering the information submitter's past performance. This ensures that more reliable fact-checking is provided by considering the credibility of the information submitter. Some or all of the above-described processes in the fact-checking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the fact-checking unit can input credibility data of the information submitter into a generative AI and have the generative AI perform the fact-checking.

[0104] The fact-checking unit can estimate the user's emotions and adjust the order in which fact-checking results are displayed based on the estimated emotions. For example, if the user is stressed, the fact-checking unit will prioritize displaying important fact-checking results. If the user is relaxed, the fact-checking unit may also display detailed fact-checking results. If the user is in a hurry, the fact-checking unit may also display concise fact-checking results. This allows for more appropriate fact-checking by adjusting the order in which fact-checking results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the fact-checking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the fact-checking unit can input user emotion data into a generative AI and have the generative AI adjust the display order of fact-checking results.

[0105] The fact-checking unit can perform fact-checking while considering the geographical distribution of information. For example, the fact-checking unit prioritizes fact-checking information that is geographically close. The fact-checking unit can also fact-check information while considering its geographical relevance. The fact-checking unit can also determine the priority of fact-checking based on geographical data. This allows for more appropriate fact-checking by considering the geographical distribution of information. Some or all of the above-described processes in the fact-checking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the fact-checking unit can input geographical distribution data of information into a generative AI and have the generative AI perform the fact-checking.

[0106] The fact-checking unit can improve the accuracy of fact-checking by referring to relevant literature. For example, the fact-checking unit can refer to relevant literature and adjust the fact-checking criteria for the information. The fact-checking unit can also analyze the content of relevant literature to improve the accuracy of fact-checking. The fact-checking unit can also tag relevant literature and reflect this in the fact-checking of the information. In this way, the accuracy of fact-checking is improved by referring to relevant literature. Some or all of the above processes in the fact-checking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the fact-checking unit can input relevant literature data into a generative AI and have the generative AI perform the fact-checking accuracy improvement.

[0107] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated emotions. For example, if the user is nervous, the interaction unit can provide a simple and highly visible interaction method. If the user is relaxed, the interaction unit can also provide a detailed interaction method. If the user is in a hurry, the interaction unit can also provide a concise interaction method. By adjusting the interaction method according to the user's emotions, more appropriate interaction is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using a generative AI, or not using a generative AI. For example, the interaction unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the interaction method.

[0108] The integration unit can optimize the integration algorithm by referring to past integration data. For example, the integration unit can analyze past integration data and select the optimal integration method. The integration unit can also provide integration methods tailored to user preferences based on past integration data. The integration unit can also improve the accuracy of integration based on past integration data. This allows the integration algorithm to be optimized by referring to past integration data. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit can input past integration data into a generative AI and have the generative AI perform the optimization of the integration algorithm.

[0109] The collaboration unit can apply different collaboration methods to each category of information. For example, the collaboration unit may apply a collaboration method that prioritizes speed to news information. The collaboration unit may also apply a collaboration method that includes detailed explanations to seminar content. The collaboration unit may also apply a collaboration method that focuses on key points to documents. By applying different collaboration methods to each category of information, more appropriate collaboration is provided. Some or all of the above processing in the collaboration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collaboration unit can input information category data into a generative AI and have the generative AI execute the application of the collaboration method.

[0110] The collaboration unit can estimate the user's emotions and determine the priority of collaborations based on the estimated emotions. For example, if the user is stressed, the collaboration unit will prioritize important collaborations. If the user is relaxed, the collaboration unit can also perform detailed collaborations. If the user is in a hurry, the collaboration unit can perform concise collaborations. This ensures that more appropriate collaborations are provided by determining the priority of collaborations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collaboration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collaboration unit can input user emotion data into a generative AI and have the generative AI determine the priority of collaborations.

[0111] The collaboration unit can adjust the frequency of collaboration based on the timing of information submission. For example, the collaboration unit may prioritize the collaboration of the latest information. The collaboration unit may also prioritize the collaboration of the latest information, delaying the collaboration of older information. The collaboration unit can also adjust the frequency of collaboration based on the timing of information submission. This allows for more appropriate collaboration by adjusting the frequency of collaboration based on the timing of information submission. Some or all of the above-described processes in the collaboration unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the collaboration unit can input information submission timing data into a generating AI and have the generating AI perform the adjustment of the collaboration frequency.

[0112] The integration unit can perform integration by referring to relevant market data for the information. For example, the integration unit integrates information based on market data. The integration unit can also provide integration that reflects trends in market data. The integration unit can also reflect the results of market data analysis in the integration. This allows for more appropriate integration by referring to relevant market data for the information. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit can input relevant market data into a generating AI and have the generating AI perform the integration.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The information gathering support system can also include a health management unit that monitors the user's health status. This unit collects health data such as the user's heart rate and sleep patterns, and incorporates this data into the information gathering pattern. For example, if the user is fatigued, the frequency of information gathering can be reduced, and relaxing content can be prioritized. Conversely, if the user is in good health, the frequency of information gathering can be increased, providing the latest news and trend information. This enables information gathering tailored to the user's health status, leading to more efficient information utilization.

[0115] The information gathering support system can also include a hobby learning section that learns the user's hobbies and interests. This section analyzes data such as content the user has previously viewed and events they have attended, and provides information based on the user's hobbies and interests. For example, if the user is interested in music, it can prioritize providing the latest music news and concert information. Similarly, if the user is interested in sports, it can provide sports-related news and event information. This enables the provision of information tailored to the user's hobbies and interests, resulting in more personalized information gathering.

[0116] The information gathering support system may also include an emotion display unit that estimates the user's emotions and adjusts the information display method based on the estimated emotions. When the user is stressed, the emotion display unit provides a simple and highly visible display method. For example, it might highlight important information to allow the user to quickly obtain the necessary information. Conversely, when the user is relaxed, it can provide a display method that includes detailed information. This enables the display of information in accordance with the user's emotions, resulting in more appropriate information delivery.

[0117] The information gathering support system can also be equipped with a behavioral learning unit that learns the user's behavioral patterns. This unit analyzes the user's past behavioral data and proposes the optimal information gathering method. For example, if a user tends to gather information during a specific time period, the system can provide information tailored to that time. Similarly, if a user tends to gather information in a specific location, the system can provide information related to that location. This enables information gathering that aligns with the user's behavioral patterns, leading to more efficient information utilization.

[0118] The information gathering support system may also include an emotion-prioritizing unit that estimates the user's emotions and adjusts the priority of information based on those emotions. If the user is feeling stressed, the emotion-prioritizing unit will prioritize providing information that promotes relaxation. For example, it might provide relaxing music or videos to reduce stress. Conversely, if the user is excited, it could prioritize providing the latest news and trending information. This adjusts the priority of information according to the user's emotions, resulting in more appropriate information delivery.

[0119] The information gathering support system can also include a learning style analysis unit that analyzes the user's learning style. This unit analyzes how users learn information and proposes the optimal information delivery method. For example, if a user prefers visual information, it can provide information using graphics and videos extensively. If a user prefers text-based information, it can provide detailed text information. This enables information delivery tailored to the user's learning style, resulting in more effective information gathering.

[0120] The information gathering support system may further include an emotion summarization unit that estimates the user's emotions and adjusts the information summarization method based on the estimated emotions. When the user is stressed, the emotion summarization unit provides a simple and highly visual summary. For example, it might provide a concise summary of key points, allowing the user to quickly understand the information. When the user is relaxed, it can also provide a summary containing detailed information. This enables information summarization tailored to the user's emotions, resulting in more appropriate information delivery.

[0121] The information gathering support system can also be equipped with a communication learning unit that learns the user's communication style. This unit learns how the user communicates and suggests the optimal method of information delivery. For example, if the user prefers email, information can be provided via email. Similarly, if the user prefers chat, information can be provided in chat format. This enables information delivery tailored to the user's communication style, resulting in more effective information gathering.

[0122] The information gathering support system may further include an emotion fact-checking unit that estimates the user's emotions and adjusts the fact-checking method based on the estimated emotions. When the user is stressed, the emotion fact-checking unit provides simple and highly visual fact-checking results. For example, it might highlight important points to allow the user to understand them quickly. Conversely, when the user is relaxed, it can provide detailed fact-checking results. This enables fact-checking tailored to the user's emotions, resulting in more appropriate information provision.

[0123] The information gathering support system can also include a time management unit to assist users with time management. The time management unit analyzes the user's schedule and proposes the optimal timing for information delivery. For example, it might avoid providing important information during busy periods and instead deliver it during less busy times. Furthermore, if a user tends to gather information at specific times, the system can tailor information delivery to those times. This enables information delivery that aligns with the user's time management, resulting in more efficient information gathering.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The learning unit learns the user's information gathering patterns. For example, it can learn what information sources the user uses and how often they gather information, and then suggest the optimal information gathering method. The learning unit can also update the user's information gathering patterns in real time and learn based on the latest patterns. Step 2: The classification unit automatically categorizes and tags downloaded materials, web information, news, and seminar content. For example, it can classify information based on the algorithms and classification criteria used, and improve the accuracy of the classification by considering the interrelationships between the information. Step 3: The visualization unit visualizes important source information, allowing users to instantly identify the information they need. For example, it can adjust how the visualization is displayed based on the importance of the information, and apply different visualization techniques to different categories of information. Step 4: The summarization section summarizes the information obtained through internet searches and picks out the key points. For example, the level of detail in the summary can be adjusted based on the importance of the information, and different summarization algorithms can be applied depending on the category of the information. Step 5: The fact-checking section uses a generating AI to automatically perform fact-checks and clarify sources. For example, it can improve the accuracy of fact-checking by analyzing the source of information in detail, and it can also perform fact-checking while considering the reliability of the information submitter. Step 6: The integration unit integrates with the search service, allowing users to manage online and offline information in an integrated manner. For example, different integration methods can be applied to each category of information, and the frequency of integration can be adjusted based on when the information was submitted.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the learning unit, classification unit, visualization unit, summarization unit, fact-checking unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 and learns the user's information gathering patterns. The classification unit is implemented by the identification processing unit 290 of the data processing device 12 and automatically classifies and tags downloaded materials, web information, news, and seminar content. The visualization unit is implemented by the control unit 46A of the smart device 14 and visualizes important source information. The summarization unit is implemented by the identification processing unit 290 of the data processing device 12 and summarizes information obtained through internet searches. The fact-checking unit is implemented by the identification processing unit 290 of the data processing device 12 and the generating AI automatically performs fact-checking. The collaboration unit is implemented by the control unit 46A of the smart device 14 and collaborates with search services. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the learning unit, classification unit, visualization unit, summarization unit, fact-checking unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 and learns the user's information gathering patterns. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically classifies and tags downloaded materials, web information, news, and seminar content. The visualization unit is implemented by the control unit 46A of the smart glasses 214 and visualizes important source information. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes information obtained through internet searches. The fact-checking unit is implemented by the identification processing unit 290 of the data processing unit 12 and the generating AI automatically performs fact-checking. The collaboration unit is implemented by the control unit 46A of the smart glasses 214 and collaborates with search services. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the learning unit, classification unit, visualization unit, summarization unit, fact-checking unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 and learns the user's information gathering patterns. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically classifies and tags downloaded materials, web information, news, and seminar content. The visualization unit is implemented by the control unit 46A of the headset terminal 314 and visualizes important source information. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes information obtained through internet searches. The fact-checking unit is implemented by the identification processing unit 290 of the data processing unit 12 and the generating AI automatically performs fact-checking. The collaboration unit is implemented by the control unit 46A of the headset terminal 314 and collaborates with search services. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the learning unit, classification unit, visualization unit, summarization unit, fact-checking unit, and collaboration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 and learns the user's information gathering patterns. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically classifies and tags downloaded materials, web information, news, and seminar content. The visualization unit is implemented by the control unit 46A of the robot 414 and visualizes important source information. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and summarizes information obtained through internet searches. The fact-checking unit is implemented by the identification processing unit 290 of the data processing unit 12 and the generating AI automatically performs fact-checking. The collaboration unit is implemented by the control unit 46A of the robot 414 and collaborates with search services. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) A learning unit that learns the user's information gathering patterns, Based on the information gathering patterns learned by the aforementioned learning unit, the classification unit automatically classifies and tags downloaded materials, web information, news, and seminar content. A visualization unit that visualizes important source information of the information classified by the classification unit, A summarization unit summarizes information obtained through internet search based on the information visualized by the visualization unit, A fact-checking unit that verifies the accuracy of the information summarized by the summarization unit, The system includes a linking unit that connects the information confirmed by the fact-checking unit with a search service. A system characterized by the following features. (Note 2) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning method for information gathering patterns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, The system analyzes the user's past information gathering history and selects the optimal learning algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, The system updates user information gathering patterns in real time and learns based on the latest patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, It estimates the user's emotions and determines learning priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, Learn information collection patterns by taking the user's geographical location into account. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, Analyze users' social media activity and reflect it in their information gathering patterns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned classification unit is It estimates the user's emotions and adjusts the classification criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned classification unit is Improve classification accuracy by considering the interrelationships of information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned classification unit is Classification is performed taking into account the attribute information of the information submitter. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned classification unit is It estimates the user's emotions and adjusts the order in which the classification results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned classification unit is Classification is performed considering the geographical distribution of information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned classification unit is Improve the accuracy of classification by referring to related literature for information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The visualization unit is, It estimates the user's emotions and adjusts how the visualization is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The visualization unit is, Optimize the current visualization method by referring to past visualization data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The visualization unit is, Apply different visualization methods to each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The visualization unit is, It estimates the user's emotions and adjusts the importance of visualizations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The visualization unit is, Analyze changes in visualization based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The visualization unit is, Visualize the information by referencing relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The summary section above is, It estimates the user's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The summary section above is, Adjust the level of detail in the summary based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The summary section above is, Apply different summarization algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The summary section above is, It estimates the user's sentiment and adjusts the length of the summary based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The summary section above is, Prioritize summaries based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The summary section above is, Adjust the order of the summaries based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The fact-checking unit, We estimate user sentiment and adjust our fact-checking methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The fact-checking unit, Detailed analysis of information sources improves the accuracy of fact-checking. The system described in Appendix 1, characterized by the features described herein. (Note 28) The fact-checking unit, Fact-checking should take into account the credibility of the information submitter. The system described in Appendix 1, characterized by the features described herein. (Note 29) The fact-checking unit, It estimates the user's sentiment and adjusts the order in which fact-checking results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The fact-checking unit, Fact-checking should take into account the geographical distribution of information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The fact-checking unit, Improve the accuracy of fact-checking by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, Optimize the integration algorithm by referring to past integration data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, Apply different linking methods for each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, Adjust the frequency of collaboration based on when the information is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned linkage unit is, The information is linked to relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0198] 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 learning unit that learns the user's information gathering patterns, Based on the information gathering patterns learned by the aforementioned learning unit, the classification unit automatically classifies and tags downloaded materials, web information, news, and seminar content. A visualization unit that visualizes important source information of the information classified by the classification unit, A summarization unit summarizes information obtained through internet search based on the information visualized by the visualization unit, A fact-checking unit that verifies the accuracy of the information summarized by the summarization unit, The system includes a linking unit that connects the information confirmed by the fact-checking unit with a search service. A system characterized by the following features.

2. The aforementioned learning unit, It estimates the user's emotions and adjusts the learning method for information gathering patterns based on the estimated user emotions. The system according to feature 1.

3. The aforementioned learning unit, The system analyzes the user's past information gathering history and selects the optimal learning algorithm. The system according to feature 1.

4. The aforementioned learning unit, The system updates user information gathering patterns in real time and learns based on the latest patterns. The system according to feature 1.

5. The aforementioned learning unit, It estimates the user's emotions and determines learning priorities based on the estimated user emotions. The system according to feature 1.

6. The aforementioned learning unit, Learn information collection patterns by taking the user's geographical location into account. The system according to feature 1.

7. The aforementioned learning unit, Analyze users' social media activity and reflect it in their information gathering patterns. The system according to feature 1.

8. The aforementioned classification unit is It estimates the user's emotions and adjusts the classification criteria based on the estimated user emotions. The system according to feature 1.

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