System

Abstract

An object of a system according to an embodiment is to efficiently collect, organize, and provide information and documents related to research topics and keywords.SOLUTION: A system according to an embodiment includes a reception unit, a collection unit, a sorting unit, and a provision unit. The receiving unit receives an input of a research topic or a keyword. The collection unit analyzes the information received by the reception unit and collects related information or literature. The organizing unit organizes the information collected by the collecting unit. The providing unit provides the information organized by the organizing unit as a report.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of making it difficult to efficiently collect and organize information and literature related to research topics and keywords.

[0005] The system according to the embodiment aims to efficiently collect, organize, and provide information and literature related to research topics and keywords. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, an organization unit, and a provision unit. The reception unit receives input of a research topic or keyword. The collection unit analyzes the information received by the reception unit and collects related information or literature. The organization unit organizes the information collected by the collection unit. The provision unit provides the information organized by the organization unit as a report. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect, organize, and provide information and literature related to research topics and keywords. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A research support system according to an embodiment of the present invention automates the process from inputting a research topic or keyword to collecting, organizing, and providing information. The research support system automatically collects and organizes related information and literature, providing it as a report, simply by inputting a research topic or keyword. For example, a user inputs topics such as "latest trends in AI technology" or "impact of climate change." This information is then entered into the research support system. The research support system then analyzes the input information and automatically collects related information and literature from the Internet and databases, such as academic papers, news articles, and patent documents. The collected information is then organized by the research support system. For example, the information is categorized based on its importance and relevance, and compiled in a format that is easy for users to understand. Finally, the organized information is provided to the user as a report. The report includes a summary and analysis of the collected information, allowing users to conduct their research efficiently. This allows users to quickly obtain the information they need without any effort, significantly improving research efficiency. Furthermore, because the research support system automatically collects and organizes information, users can focus on more important tasks. This allows users to quickly obtain the information they need without any effort, significantly improving research efficiency. In addition, the research support system automatically collects and organizes information, allowing users to focus on more important tasks.

[0029] A research support system according to an embodiment includes a reception unit, a collection unit, an organization unit, and a provision unit. The reception unit allows a user to input a research topic or keyword. For example, a user can input topics such as "latest trends in AI technology" or "impact of climate change." The collection unit analyzes the information received by the reception unit and collects related information and literature. For example, the collection unit collects information such as academic papers, news articles, and patent documents from the Internet or databases. The organization unit organizes the information collected by the collection unit. For example, the organization unit classifies the collected information based on importance and relevance and summarizes it in a format that is easy for a user to understand. The provision unit provides the information organized by the organization unit as a report. For example, the provision unit provides a report including a summary of the organized information and analysis results. This allows the research support system according to an embodiment to automate processes from inputting a research topic or keyword to collecting, organizing, and providing information, thereby improving research efficiency.

[0030] The collection unit can collect information on academic papers, news articles, and patent documents from the Internet or databases. The collection unit, for example, collects information from Internet resources and databases. For example, the collection unit collects academic papers, news articles, and patent documents from Google® Scholar, PubMed, patent databases, etc. The collection unit can also set the type and scope of information to be collected. For example, the collection unit may target peer-reviewed papers, articles from major news sites, published patents, etc. This allows the collection of related information from a variety of sources to broaden the scope of research.

[0031] The organizing unit can classify the collected information based on importance or relevance and organize it into a format that is easy for the user to understand. The organizing unit, for example, classifies the collected information based on importance or relevance. For example, the organizing unit evaluates the importance based on the number of citations of the information, the year of publication, the frequency of appearance of related keywords, etc. The organizing unit can also use co-occurrence networks and relevance scores to evaluate the relevance of the information. Furthermore, the organizing unit organizes the information into a format that is easy for the user to understand. For example, the organizing unit provides the information in the form of summaries, bullet points, graphs, charts, etc. In this way, organizing and classifying the information allows the user to use the information efficiently.

[0032] The providing unit can provide a report including a summary or analysis results of the organized information. The providing unit provides, for example, a report including a summary or analysis results of the organized information. For example, the providing unit can include a summary of main points, statistical analysis results, trend analysis, etc. in the report. The providing unit can also customize the format and content of the report to suit the needs of the user. For example, the providing unit provides the report in a format desired by the user. This improves the efficiency of research by providing the user with organized information in report format.

[0033] The reception unit can guide the user on the input format when entering a research topic or keywords. For example, the reception unit guides the user on the input format when entering a research topic or keywords. For example, the reception unit presents input examples, specifies the format, provides real-time feedback, and the like. The reception unit also supports the user in entering research topics and keywords in an appropriate format. For example, the reception unit can suggest appropriate keywords and topics as the user enters them. This allows the user to enter research topics and keywords in an appropriate format.

[0034] The collection unit can evaluate the reliability of the collected information and prioritize collecting highly reliable information. The collection unit, for example, evaluates the reliability of the collected information. For example, the collection unit evaluates reliability based on the reliability of the information source, the number of citations, the reputation of the publisher, etc. The collection unit also prioritizes collecting highly reliable information. For example, the collection unit collects information from highly reliable information sources such as academic papers and patent documents. Furthermore, the collection unit can evaluate the reliability of news articles and prioritize collecting highly reliable articles. This prioritizes collecting highly reliable information, thereby improving the accuracy of research.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input format. The reception unit, for example, analyzes the user's past input history. For example, the reception unit analyzes past search keywords, input frequency, input patterns, etc. Next, the reception unit suggests the optimal input format. For example, the reception unit automatically displays research topics and keywords that the user has frequently input in the past as candidates. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest research topics and keywords that will be used during a specific time period based on the user's past input history. This makes the user's input work more efficient by suggesting the optimal input format based on the user's past input history.

[0036] The reception unit can present input candidates based on the user's field of expertise when the user inputs a research topic or keyword. The reception unit, for example, identifies the user's field of expertise. For example, the reception unit identifies the field of expertise based on the user's profile information, past research themes, affiliated institution, etc. Next, the reception unit presents input candidates based on the user's field of expertise. For example, the reception unit presents related research topics and keywords as candidates based on the field of expertise previously entered by the user. The reception unit can also automatically suggest the latest research topics and keywords related to the user's field of expertise. Furthermore, the reception unit can preferentially display specific research topics and keywords based on the user's field of expertise. This allows the user to quickly input appropriate topics and keywords by presenting input candidates based on the user's field of expertise.

[0037] When a research topic or keyword is input, the reception unit can filter the input content based on the user's current project status. The reception unit, for example, identifies the user's current project status. For example, the reception unit identifies the project status based on the project progress, project objectives, related tasks, etc. Next, the reception unit filters the input content based on the user's current project status. For example, the reception unit can preferentially display research topics and keywords related to the user's current project. The reception unit can also suggest appropriate research topics and keywords based on the user's project progress. Furthermore, the reception unit can filter related research topics and keywords in accordance with the user's project goals. In this way, by filtering the input content based on the user's project status, highly relevant information can be preferentially input.

[0038] When a research topic or keyword is input, the reception unit can present highly relevant input candidates taking into account the user's geographical location information. The reception unit, for example, identifies the user's geographical location information. For example, the reception unit identifies the user's geographical location information using GPS data, an IP address, a location information service, or the like. Next, the reception unit presents highly relevant input candidates taking into account the user's geographical location information. For example, the reception unit can suggest research topics and keywords related to a region based on the user's current location. The reception unit can also display research topics from local research institutions and universities as candidates based on the user's geographical location information. Furthermore, the reception unit can suggest research topics and keywords related to specific local issues taking into account the user's geographical location information. This allows the user to quickly input information related to the region by presenting input candidates taking into account the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity when entering a research topic or keyword and present related input candidates. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit analyzes the content of posts, the history of likes and shares, the attributes of followers, etc. Next, the reception unit presents related input candidates based on the user's social media activity. For example, the reception unit displays topics of researchers and organizations the user follows on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related research topics and keywords. Furthermore, the reception unit can suggest related research topics and keywords by referring to the activities of the user's friends on social media. In this way, by presenting input candidates based on the user's social media activity, highly relevant information can be quickly entered.

[0040] The reception unit can customize the input guide by reflecting the user's past feedback when inputting a research topic or keyword. The reception unit, for example, analyzes the user's past feedback. For example, the reception unit analyzes the user's evaluation comments, the frequency of feedback, the content of the feedback, etc. Next, the reception unit customizes the input guide by reflecting the user's past feedback. For example, the reception unit adjusts the content of the input guide based on the feedback provided by the user in the past. The reception unit can also optimize the input procedure by reflecting the user's past feedback. Furthermore, the reception unit can customize the design and layout of the input guide based on the user's past feedback. In this way, customizing the input guide by reflecting the user's past feedback makes it possible to provide input assistance that meets the user's needs.

[0041] The collection unit can evaluate the reliability of information at the time of collection and prioritize collecting highly reliable information. The collection unit, for example, evaluates the reliability of information at the time of collection. For example, the collection unit evaluates reliability based on the reliability of the information source, the number of citations, the reputation of the publisher, etc. Next, the collection unit prioritizes collecting highly reliable information. For example, the collection unit collects information from highly reliable information sources such as academic papers and patent documents. The collection unit can also evaluate the reliability of news articles and prioritize collecting highly reliable articles. This improves the accuracy of research by prioritizing the collection of highly reliable information.

[0042] The collection unit can adjust the level of detail of the collection based on the importance of the information when collecting the information. The collection unit, for example, evaluates the importance of the information. For example, the collection unit evaluates the importance based on the number of citations, the year of publication, the frequency of appearance of related keywords, etc. Next, the collection unit adjusts the level of detail of the collection based on the importance of the information. For example, the collection unit collects detailed information for important information. Furthermore, the collection unit can collect only an outline of information with low importance. Furthermore, the collection unit can adjust the range of information to be collected based on the importance of the information. As a result, by adjusting the level of detail of the collection based on the importance of the information, efficient information collection is possible.

[0043] The collection unit can apply different collection algorithms depending on the category of information when collecting. The collection unit, for example, identifies the category of information. For example, the collection unit identifies categories such as academic papers, news articles, patent documents, etc. Next, the collection unit applies different collection algorithms depending on the category of information. For example, the collection unit applies an algorithm that uses a specific database to collect academic papers. Furthermore, the collection unit can apply an algorithm that crawls news sites to collect news articles. Furthermore, the collection unit can apply an algorithm that searches a patent database to collect patent documents. In this way, efficient information collection is possible by applying collection algorithms depending on the category of information.

[0044] The collection unit can perform collection taking into consideration the geographical distribution of information when collecting the information. The collection unit, for example, identifies the geographical distribution of the information. For example, the collection unit identifies the geographical distribution of the information using regional data, geographical statistical information, etc. Next, the collection unit performs collection taking into consideration the geographical distribution of the information. For example, the collection unit preferentially collects information related to the user's current location. Furthermore, the collection unit can collect information related to a specific region. Furthermore, the collection unit can adjust the collection range of the information based on the geographical distribution. As a result, by performing collection taking into consideration the geographical distribution of the information, it is possible to efficiently collect information related to the region.

[0045] The collection unit can improve the accuracy of the collection by referring to literature related to the information when collecting the information. The collection unit, for example, identifies literature related to the information. For example, the collection unit identifies related literature based on citation relationships, co-author relationships, similarities in theme, etc. Next, the collection unit refers to literature related to the information when collecting the information. For example, the collection unit refers to literature related to the collected information to improve the accuracy of the information. The collection unit can also adjust the range of information to be collected based on the related literature. Furthermore, the collection unit can evaluate the reliability of the collected information by referring to the related literature. As a result, the accuracy of the collected information is improved by referring to the related literature.

[0046] The collection unit can collect information taking into consideration the market value of the information when collecting it. The collection unit, for example, evaluates the market value of the information. For example, the collection unit evaluates the market value based on market research data, sales forecasts, competitive analysis, etc. Next, the collection unit collects information taking into consideration the market value of the information. For example, the collection unit prioritizes collecting information with high market value. The collection unit can also adjust the range of information to be collected based on the market value. Furthermore, the collection unit can evaluate the market value and determine the importance of the collected information. In this way, the value of research is improved by prioritizing the collection of information with high market value.

[0047] The organizing unit can improve the accuracy of organizing by taking into account the interrelationships of information when organizing. The organizing unit, for example, identifies the interrelationships of information. For example, the organizing unit identifies the interrelationships of information using a co-occurrence network, a relevance score, or the like. Next, the organizing unit organizes the information by taking into account the interrelationships of information. For example, the organizing unit analyzes the interrelationships of collected information and organizes related information. The organizing unit can also determine the priority of organizing based on the interrelationships of information. Furthermore, the organizing unit can improve the accuracy of organizing by taking into account the interrelationships of information. As a result, the accuracy of organizing is improved by taking into account the interrelationships of information.

[0048] When organizing, the organizing unit can perform the organizing taking into consideration attribute information of the information submitter. The organizing unit, for example, identifies attribute information of the information submitter. For example, the organizing unit identifies attribute information based on the submitter's occupation, affiliated institution, field of expertise, etc. Next, the organizing unit performs the organizing taking into consideration the attribute information of the information submitter. For example, the organizing unit organizes information based on the field of expertise of the information submitter. The organizing unit can also evaluate the reliability of the information submitter and prioritize organizing highly reliable information. Furthermore, the organizing unit can adjust the organizing criteria taking into consideration the attribute information of the information submitter. As a result, by taking into consideration the attribute information of the information submitter, highly reliable information can be prioritized.

[0049] When organizing, the organizing unit can weight the organizing based on the frequency of information submission. The organizing unit, for example, evaluates the frequency of information submission. For example, the organizing unit evaluates the frequency of submission based on the number of submissions, the submission period, the frequency of activity of the submitter, etc. Next, the organizing unit weights the organizing based on the frequency of information submission. For example, the organizing unit prioritizes organizing information that is submitted more frequently. The organizing unit can also evaluate the importance of information based on the submission frequency. Furthermore, the organizing unit can weight the organizing taking the submission frequency into consideration. In this way, by weighting the organizing based on the submission frequency of information, important information can be prioritized.

[0050] The organizing unit can organize the information taking into consideration the geographical distribution of the information. The organizing unit, for example, identifies the geographical distribution of the information. For example, the organizing unit identifies the geographical distribution of the information using regional data, geographical statistical information, etc. Next, the organizing unit organizes the information taking into consideration the geographical distribution of the information. For example, the organizing unit organizes geographically related information together. Furthermore, the organizing unit can determine the priority of the information based on the geographical distribution. Furthermore, the organizing unit can adjust the organizing criteria for the information taking into consideration the geographical distribution. As a result, by organizing the information taking into consideration the geographical distribution of the information, it is possible to efficiently organize information related to a region.

[0051] The organizing unit can improve the accuracy of the organization by referring to literature related to the information when organizing. The organizing unit, for example, identifies literature related to the information. For example, the organizing unit identifies related literature based on citation relationships, co-author relationships, similarities in theme, etc. Next, the organizing unit refers to literature related to the information when organizing. For example, the organizing unit refers to literature related to the collected information to improve the accuracy of organizing the information. The organizing unit can also adjust the information organizing criteria based on the related literature. Furthermore, the organizing unit can evaluate the reliability of the information by referring to the related literature and improve the accuracy of the organization. In this way, by referring to the related literature, the accuracy of the organized information is improved.

[0052] The organizing unit can organize the information taking into consideration the market value of the information. The organizing unit, for example, evaluates the market value of the information. For example, the organizing unit evaluates the market value based on market research data, sales forecasts, competitive analysis, etc. Next, the organizing unit organizes the information taking into consideration the market value of the information. For example, the organizing unit prioritizes organizing information with high market value. The organizing unit can also adjust the organizing criteria for the information based on the market value. Furthermore, the organizing unit can evaluate the market value and determine the importance of the information before organizing it. In this way, the value of research is improved by organizing information with high market value with priority.

[0053] When providing a report, the providing unit can adjust the level of detail of the report based on the importance of the information. The providing unit, for example, evaluates the importance of the information. For example, the providing unit evaluates the importance based on the number of citations, the year of publication, the frequency of appearance of related keywords, etc. Next, the providing unit adjusts the level of detail of the report based on the importance of the information. For example, the providing unit provides a report that includes a detailed explanation for important information. Furthermore, the providing unit can provide a report that includes only an outline for information of low importance. Furthermore, the providing unit can adjust the level of detail of the report based on the importance of the information. As a result, adjusting the level of detail of the report based on the importance of the information enables efficient information provision.

[0054] When providing a report, the providing unit can apply different report formats depending on the category of information. The providing unit, for example, identifies the category of information. For example, the providing unit identifies categories such as academic papers, news articles, patent documents, etc. Next, the providing unit applies different report formats depending on the category of information. For example, the providing unit can provide a report in paper format for information related to academic papers. Furthermore, the providing unit can provide a report in news format for information related to news articles. Furthermore, the providing unit can provide a report in patent format for information related to patent documents. In this way, by applying a report format according to the category of information, it is possible to provide information with high visibility.

[0055] When providing a report, the providing unit can improve the accuracy of the report by referring to the user's past report results. The providing unit, for example, identifies the user's past report results. For example, the providing unit identifies the past report results based on the content, evaluations, feedback, etc. of the past reports. Next, the providing unit improves the accuracy of the report by referring to the user's past report results. For example, the providing unit improves the accuracy of the report based on feedback on reports the user has received in the past. The providing unit can also analyze the user's past report results and suggest an optimal report format. Furthermore, the providing unit can customize the content of the report by referring to the user's past report results. In this way, the accuracy of the report is improved by referring to the past report results.

[0056] When providing a report, the providing unit can determine the priority of the report based on the time of submission of the information. The providing unit, for example, identifies the time of submission of the information. For example, the providing unit identifies the time of submission of the information based on the submission date, submission period, submission frequency, etc. Next, the providing unit determines the priority of the report based on the time of submission of the information. For example, the providing unit preferentially includes the latest information in the report. Furthermore, the providing unit can evaluate the importance of the information based on the time of submission and determine the priority of the report. Furthermore, the providing unit can adjust the content of the report taking the time of submission into consideration. In this way, by determining the priority of the report based on the time of submission of the information, the latest information can be provided efficiently.

[0057] When providing a report, the providing unit can adjust the order of the reports based on the relevance of the information. The providing unit, for example, evaluates the relevance of the information. For example, the providing unit evaluates the relevance of the information based on a co-occurrence network, a relevance score, etc. Next, the providing unit adjusts the order of the reports based on the relevance of the information. For example, the providing unit preferentially includes highly relevant information in the report. The providing unit can also adjust the order of the reports based on the relevance of the information. Furthermore, the providing unit can include highly relevant information together in the report. In this way, by adjusting the order of the reports based on the relevance of the information, it is possible to provide information with high visibility.

[0058] When providing a report, the providing unit can adjust the use of technical terms in the report according to the user's level of expertise. The providing unit, for example, identifies the user's level of expertise. For example, the providing unit identifies the level of expertise based on the user's occupation, educational background, past research topics, etc. Next, the providing unit adjusts the use of technical terms in the report according to the user's level of expertise. For example, the providing unit adjusts the use of technical terms according to the user's level of expertise. Furthermore, the providing unit can provide a report that explains things in simple terms to a user with little expertise. Furthermore, the providing unit can provide a report that includes detailed technical terms to a user with high expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a report with high visibility.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection department can evaluate the reliability of the collected information and prioritize collecting highly reliable information. For example, the collection department evaluates reliability based on the reliability of the information source, the number of citations, the reputation of the publisher, etc. The collection department also prioritizes collecting highly reliable information. For example, the collection department collects information from highly reliable sources such as academic papers and patent documents. Furthermore, the collection department can evaluate the reliability of news articles and prioritize collecting highly reliable articles. This prioritizes collecting highly reliable information, thereby improving the accuracy of research.

[0061] The organizer classifies the collected information based on importance or relevance, and organizes it in a format that is easy for users to understand. For example, the organizer evaluates the importance based on the number of citations of the information, the year of publication, the frequency of appearance of related keywords, etc. The organizer can also use co-occurrence networks and relevance scores to evaluate the relevance of the information. Furthermore, the organizer organizes the information in a format that is easy for users to understand. For example, the organizer provides the information in the form of summaries, bullet points, graphs, charts, etc. In this way, by organizing and classifying the information, the user can use the information efficiently.

[0062] The providing unit can provide a report including a summary or analysis results of the organized information. For example, the providing unit can include a summary of key points, statistical analysis results, trend analysis, etc. in the report. The providing unit can also customize the format and content of the report to meet the needs of the user. For example, the providing unit can provide the report in a format desired by the user. This improves the efficiency of research by providing the user with organized information in report format.

[0063] The reception unit can guide the user on the input format when entering a research topic or keyword. For example, the reception unit can provide input examples, specify the format, and provide real-time feedback. The reception unit also assists the user in entering the research topic or keyword in an appropriate format. For example, the reception unit can suggest appropriate keywords or topics as the user enters the research topic or keyword. This allows the user to enter the research topic or keyword in an appropriate format.

[0064] The collection department can evaluate the reliability of information when collecting it and prioritize collecting highly reliable information. For example, the collection department evaluates reliability based on the reliability of the information source, the number of citations, the reputation of the publisher, etc. Next, the collection department prioritizes collecting highly reliable information. For example, the collection department collects information from highly reliable sources such as academic papers and patent documents. The collection department can also evaluate the reliability of news articles and prioritize collecting highly reliable articles. This prioritizes collecting highly reliable information, thereby improving the accuracy of research.

[0065] The organizing unit can improve the accuracy of organizing by taking into account the interrelationships of information when organizing. For example, the organizing unit identifies the interrelationships of information using a co-occurrence network, a relevance score, or the like. Next, the organizing unit performs organizing by taking into account the interrelationships of information. For example, the organizing unit analyzes the interrelationships of collected information and compiles related information. The organizing unit can also determine the priority of organizing based on the interrelationships of information. Furthermore, the organizing unit can improve the accuracy of organizing by taking into account the interrelationships of information. In this way, the accuracy of organizing is improved by taking into account the interrelationships of information.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The user inputs a research topic or keyword into the reception unit. For example, a user can input topics such as "latest trends in AI technology" or "impact of climate change." Step 2: The collection unit analyzes the information received by the reception unit and collects related information and literature. For example, the collection unit collects information such as academic papers, news articles, and patent documents from the Internet and databases. Step 3: The organizer organizes the information collected by the collector. For example, the organizer classifies the collected information based on importance and relevance, and organizes it in a format that is easy for the user to understand. Step 4: The providing unit provides the information organized by the organizing unit as a report. For example, the providing unit provides a report including a summary of the organized information and analysis results.

[0068] (Example 2) A research support system according to an embodiment of the present invention automates the process from inputting a research topic or keyword to collecting, organizing, and providing information. The research support system automatically collects and organizes related information and literature, providing it as a report, simply by inputting a research topic or keyword. For example, a user inputs topics such as "latest trends in AI technology" or "impact of climate change." This information is then entered into the research support system. The research support system then analyzes the input information and automatically collects related information and literature from the Internet and databases, such as academic papers, news articles, and patent documents. The collected information is then organized by the research support system. For example, the information is categorized based on its importance and relevance, and compiled in a format that is easy for users to understand. Finally, the organized information is provided to the user as a report. The report includes a summary and analysis of the collected information, allowing users to conduct their research efficiently. This allows users to quickly obtain the information they need without any effort, significantly improving research efficiency. Furthermore, because the research support system automatically collects and organizes information, users can focus on more important tasks. This allows users to quickly obtain the information they need without any effort, significantly improving research efficiency. In addition, the research support system automatically collects and organizes information, allowing users to focus on more important tasks.

[0069] A research support system according to an embodiment includes a reception unit, a collection unit, an organization unit, and a provision unit. The reception unit allows a user to input a research topic or keyword. For example, a user can input topics such as "latest trends in AI technology" or "impact of climate change." The collection unit analyzes the information received by the reception unit and collects related information and literature. For example, the collection unit collects information such as academic papers, news articles, and patent documents from the Internet or databases. The organization unit organizes the information collected by the collection unit. For example, the organization unit classifies the collected information based on importance and relevance and summarizes it in a format that is easy for a user to understand. The provision unit provides the information organized by the organization unit as a report. For example, the provision unit provides a report including a summary of the organized information and analysis results. This allows the research support system according to an embodiment to automate processes from inputting a research topic or keyword to collecting, organizing, and providing information, thereby improving research efficiency.

[0070] The collection unit can collect information on academic papers, news articles, and patent documents from the Internet or databases. The collection unit, for example, collects information from Internet resources and databases. For example, the collection unit collects academic papers, news articles, and patent documents from Google Scholar, PubMed, patent databases, etc. The collection unit can also set the type and scope of information to be collected. For example, the collection unit may target peer-reviewed papers, articles from major news sites, published patents, etc. This allows the collection of related information from a variety of sources to broaden the scope of research.

[0071] The organizing unit can classify the collected information based on importance or relevance and organize it into a format that is easy for the user to understand. The organizing unit, for example, classifies the collected information based on importance or relevance. For example, the organizing unit evaluates the importance based on the number of citations of the information, the year of publication, the frequency of appearance of related keywords, etc. The organizing unit can also use co-occurrence networks and relevance scores to evaluate the relevance of the information. Furthermore, the organizing unit organizes the information into a format that is easy for the user to understand. For example, the organizing unit provides the information in the form of summaries, bullet points, graphs, charts, etc. In this way, organizing and classifying the information allows the user to use the information efficiently.

[0072] The providing unit can provide a report including a summary or analysis results of the organized information. The providing unit provides, for example, a report including a summary or analysis results of the organized information. For example, the providing unit can include a summary of main points, statistical analysis results, trend analysis, etc. in the report. The providing unit can also customize the format and content of the report to suit the needs of the user. For example, the providing unit provides the report in a format desired by the user. This improves the efficiency of research by providing the user with organized information in report format.

[0073] The reception unit can guide the user on the input format when entering a research topic or keywords. For example, the reception unit guides the user on the input format when entering a research topic or keywords. For example, the reception unit presents input examples, specifies the format, provides real-time feedback, and the like. The reception unit also supports the user in entering research topics and keywords in an appropriate format. For example, the reception unit can suggest appropriate keywords and topics as the user enters them. This allows the user to enter research topics and keywords in an appropriate format.

[0074] The collection unit can evaluate the reliability of the collected information and prioritize collecting highly reliable information. The collection unit, for example, evaluates the reliability of the collected information. For example, the collection unit evaluates reliability based on the reliability of the information source, the number of citations, the reputation of the publisher, etc. The collection unit also prioritizes collecting highly reliable information. For example, the collection unit collects information from highly reliable information sources such as academic papers and patent documents. Furthermore, the collection unit can evaluate the reliability of news articles and prioritize collecting highly reliable articles. This prioritizes collecting highly reliable information, thereby improving the accuracy of research.

[0075] The reception unit can estimate the user's emotions and adjust the content of the input guide based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit estimates the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. Then, the reception unit adjusts the content of the input guide based on the estimated user emotions. For example, if the user is stressed, the reception unit provides a simple interface and minimizes input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input, allowing the user to quickly enter research topics and keywords. This reduces the user's stress by adjusting the input guide based on the user's emotions and supports efficient input. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reception unit can analyze the user's past input history and suggest the optimal input format. The reception unit, for example, analyzes the user's past input history. For example, the reception unit analyzes past search keywords, input frequency, input patterns, etc. Next, the reception unit suggests the optimal input format. For example, the reception unit automatically displays research topics and keywords that the user has frequently input in the past as candidates. The reception unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest research topics and keywords that will be used during a specific time period based on the user's past input history. This makes the user's input work more efficient by suggesting the optimal input format based on the user's past input history.

[0077] The reception unit can present input candidates based on the user's field of expertise when the user inputs a research topic or keyword. The reception unit, for example, identifies the user's field of expertise. For example, the reception unit identifies the field of expertise based on the user's profile information, past research themes, affiliated institution, etc. Next, the reception unit presents input candidates based on the user's field of expertise. For example, the reception unit presents related research topics and keywords as candidates based on the field of expertise previously entered by the user. The reception unit can also automatically suggest the latest research topics and keywords related to the user's field of expertise. Furthermore, the reception unit can preferentially display specific research topics and keywords based on the user's field of expertise. This allows the user to quickly input appropriate topics and keywords by presenting input candidates based on the user's field of expertise.

[0078] When a research topic or keyword is input, the reception unit can filter the input content based on the user's current project status. The reception unit, for example, identifies the user's current project status. For example, the reception unit identifies the project status based on the project progress, project objectives, related tasks, etc. Next, the reception unit filters the input content based on the user's current project status. For example, the reception unit can preferentially display research topics and keywords related to the user's current project. The reception unit can also suggest appropriate research topics and keywords based on the user's project progress. Furthermore, the reception unit can filter related research topics and keywords in accordance with the user's project goals. In this way, by filtering the input content based on the user's project status, highly relevant information can be preferentially input.

[0079] The reception unit can estimate the user's emotions and prioritize inputs based on the estimated user emotions. The reception unit, for example, estimates the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. The reception unit then prioritizes inputs based on the estimated user emotions. For example, if the user is stressed, the reception unit can prioritize input of important research topics and keywords. If the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing for quick input of research topics and keywords. This supports efficient input by prioritizing inputs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] When a research topic or keyword is input, the reception unit can present highly relevant input candidates taking into account the user's geographical location information. The reception unit, for example, identifies the user's geographical location information. For example, the reception unit identifies the user's geographical location information using GPS data, an IP address, a location information service, or the like. Next, the reception unit presents highly relevant input candidates taking into account the user's geographical location information. For example, the reception unit can suggest research topics and keywords related to a region based on the user's current location. The reception unit can also display research topics from local research institutions and universities as candidates based on the user's geographical location information. Furthermore, the reception unit can suggest research topics and keywords related to specific local issues taking into account the user's geographical location information. This allows the user to quickly input information related to the region by presenting input candidates taking into account the user's geographical location information.

[0081] The reception unit can analyze the user's social media activity when entering a research topic or keyword and present related input candidates. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit analyzes the content of posts, the history of likes and shares, the attributes of followers, etc. Next, the reception unit presents related input candidates based on the user's social media activity. For example, the reception unit displays topics of researchers and organizations the user follows on social media as candidates. The reception unit can also analyze the content of the user's social media posts and suggest related research topics and keywords. Furthermore, the reception unit can suggest related research topics and keywords by referring to the activities of the user's friends on social media. In this way, by presenting input candidates based on the user's social media activity, highly relevant information can be quickly entered.

[0082] The reception unit can customize the input guide by reflecting the user's past feedback when inputting a research topic or keyword. The reception unit, for example, analyzes the user's past feedback. For example, the reception unit analyzes the user's evaluation comments, the frequency of feedback, the content of the feedback, etc. Next, the reception unit customizes the input guide by reflecting the user's past feedback. For example, the reception unit adjusts the content of the input guide based on the feedback provided by the user in the past. The reception unit can also optimize the input procedure by reflecting the user's past feedback. Furthermore, the reception unit can customize the design and layout of the input guide based on the user's past feedback. In this way, customizing the input guide by reflecting the user's past feedback makes it possible to provide input assistance that meets the user's needs.

[0083] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. Next, the collection unit determines the priority of information to be collected based on the estimated user emotions. For example, if the user is stressed, the collection unit prioritizes collecting important information. Also, if the user is relaxed, the collection unit can collect detailed information. Furthermore, if the user is in a hurry, the collection unit prioritizes collecting information that can be collected quickly. This supports efficient information collection by determining the priority of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The collection unit can evaluate the reliability of information at the time of collection and prioritize collecting highly reliable information. The collection unit, for example, evaluates the reliability of information at the time of collection. For example, the collection unit evaluates reliability based on the reliability of the information source, the number of citations, the reputation of the publisher, etc. Next, the collection unit prioritizes collecting highly reliable information. For example, the collection unit collects information from highly reliable information sources such as academic papers and patent documents. The collection unit can also evaluate the reliability of news articles and prioritize collecting highly reliable articles. This improves the accuracy of research by prioritizing the collection of highly reliable information.

[0085] The collection unit can adjust the level of detail of the collection based on the importance of the information when collecting the information. The collection unit, for example, evaluates the importance of the information. For example, the collection unit evaluates the importance based on the number of citations, the year of publication, the frequency of appearance of related keywords, etc. Next, the collection unit adjusts the level of detail of the collection based on the importance of the information. For example, the collection unit collects detailed information for important information. Furthermore, the collection unit can collect only an outline of information with low importance. Furthermore, the collection unit can adjust the range of information to be collected based on the importance of the information. As a result, by adjusting the level of detail of the collection based on the importance of the information, efficient information collection is possible.

[0086] The collection unit can apply different collection algorithms depending on the category of information when collecting. The collection unit, for example, identifies the category of information. For example, the collection unit identifies categories such as academic papers, news articles, patent documents, etc. Next, the collection unit applies different collection algorithms depending on the category of information. For example, the collection unit applies an algorithm that uses a specific database to collect academic papers. Furthermore, the collection unit can apply an algorithm that crawls news sites to collect news articles. Furthermore, the collection unit can apply an algorithm that searches a patent database to collect patent documents. In this way, efficient information collection is possible by applying collection algorithms depending on the category of information.

[0087] The collection unit can estimate the user's emotions and adjust the display method of the collected information based on the estimated user emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit estimates the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. Next, the collection unit adjusts the display method of the collected information based on the estimated user emotions. For example, if the user is stressed, the collection unit provides a simple, highly visible display method. Alternatively, if the user is relaxed, the collection unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the collection unit provides a display method that focuses on the main points. This allows the provision of highly visible information by adjusting the display method of information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The collection unit can perform collection taking into consideration the geographical distribution of information when collecting the information. The collection unit, for example, identifies the geographical distribution of the information. For example, the collection unit identifies the geographical distribution of the information using regional data, geographical statistical information, etc. Next, the collection unit performs collection taking into consideration the geographical distribution of the information. For example, the collection unit preferentially collects information related to the user's current location. Furthermore, the collection unit can collect information related to a specific region. Furthermore, the collection unit can adjust the collection range of the information based on the geographical distribution. As a result, by performing collection taking into consideration the geographical distribution of the information, it is possible to efficiently collect information related to the region.

[0089] The collection unit can improve the accuracy of the collection by referring to literature related to the information when collecting the information. The collection unit, for example, identifies literature related to the information. For example, the collection unit identifies related literature based on citation relationships, co-author relationships, similarities in theme, etc. Next, the collection unit refers to literature related to the information when collecting the information. For example, the collection unit refers to literature related to the collected information to improve the accuracy of the information. The collection unit can also adjust the range of information to be collected based on the related literature. Furthermore, the collection unit can evaluate the reliability of the collected information by referring to the related literature. As a result, the accuracy of the collected information is improved by referring to the related literature.

[0090] The collection unit can collect information taking into consideration the market value of the information when collecting it. The collection unit, for example, evaluates the market value of the information. For example, the collection unit evaluates the market value based on market research data, sales forecasts, competitive analysis, etc. Next, the collection unit collects information taking into consideration the market value of the information. For example, the collection unit prioritizes collecting information with high market value. The collection unit can also adjust the range of information to be collected based on the market value. Furthermore, the collection unit can evaluate the market value and determine the importance of the collected information. In this way, the value of research is improved by prioritizing the collection of information with high market value.

[0091] The organizing unit can estimate the user's emotions and adjust the organizing criteria based on the estimated user emotions. The organizing unit, for example, estimates the user's emotions. For example, the organizing unit estimates the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. Next, the organizing unit adjusts the organizing criteria based on the estimated user emotions. For example, if the user is stressed, the organizing unit applies simple organizing criteria. On the other hand, if the user is relaxed, the organizing unit can apply detailed organizing criteria. Furthermore, if the user is in a hurry, the organizing unit applies criteria that allow for quick organization. This supports efficient information organization by adjusting the organizing criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The organizing unit can improve the accuracy of organizing by taking into account the interrelationships of information when organizing. The organizing unit, for example, identifies the interrelationships of information. For example, the organizing unit identifies the interrelationships of information using a co-occurrence network, a relevance score, or the like. Next, the organizing unit organizes the information by taking into account the interrelationships of information. For example, the organizing unit analyzes the interrelationships of collected information and organizes related information. The organizing unit can also determine the priority of organizing based on the interrelationships of information. Furthermore, the organizing unit can improve the accuracy of organizing by taking into account the interrelationships of information. As a result, the accuracy of organizing is improved by taking into account the interrelationships of information.

[0093] When organizing, the organizing unit can perform the organizing taking into consideration attribute information of the information submitter. The organizing unit, for example, identifies attribute information of the information submitter. For example, the organizing unit identifies attribute information based on the submitter's occupation, affiliated institution, field of expertise, etc. Next, the organizing unit performs the organizing taking into consideration the attribute information of the information submitter. For example, the organizing unit organizes information based on the field of expertise of the information submitter. The organizing unit can also evaluate the reliability of the information submitter and prioritize organizing highly reliable information. Furthermore, the organizing unit can adjust the organizing criteria taking into consideration the attribute information of the information submitter. As a result, by taking into consideration the attribute information of the information submitter, highly reliable information can be prioritized.

[0094] When organizing, the organizing unit can weight the organizing based on the frequency of information submission. The organizing unit, for example, evaluates the frequency of information submission. For example, the organizing unit evaluates the frequency of submission based on the number of submissions, the submission period, the frequency of activity of the submitter, etc. Next, the organizing unit weights the organizing based on the frequency of information submission. For example, the organizing unit prioritizes organizing information that is submitted more frequently. The organizing unit can also evaluate the importance of information based on the submission frequency. Furthermore, the organizing unit can weight the organizing taking the submission frequency into consideration. In this way, by weighting the organizing based on the submission frequency of information, important information can be prioritized.

[0095] The organizing unit can estimate the user's emotions and adjust the order in which the organized results are displayed based on the estimated user emotions. The organizing unit, for example, estimates the user's emotions. For example, the organizing unit estimates the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. Next, the organizing unit adjusts the order in which the organized results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the organizing unit prioritizes displaying important information. Also, if the user is relaxed, the organizing unit can display information in an order that includes detailed information. Furthermore, if the user is in a hurry, the organizing unit displays information in an order that highlights the main points. This allows for the provision of highly visible information by adjusting the display order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] The organizing unit can organize the information taking into consideration the geographical distribution of the information. The organizing unit, for example, identifies the geographical distribution of the information. For example, the organizing unit identifies the geographical distribution of the information using regional data, geographical statistical information, etc. Next, the organizing unit organizes the information taking into consideration the geographical distribution of the information. For example, the organizing unit organizes geographically related information together. Furthermore, the organizing unit can determine the priority of the information based on the geographical distribution. Furthermore, the organizing unit can adjust the organizing criteria for the information taking into consideration the geographical distribution. As a result, by organizing the information taking into consideration the geographical distribution of the information, it is possible to efficiently organize information related to a region.

[0097] The organizing unit can improve the accuracy of the organization by referring to literature related to the information when organizing. The organizing unit, for example, identifies literature related to the information. For example, the organizing unit identifies related literature based on citation relationships, co-author relationships, similarities in theme, etc. Next, the organizing unit refers to literature related to the information when organizing. For example, the organizing unit refers to literature related to the collected information to improve the accuracy of organizing the information. The organizing unit can also adjust the information organizing criteria based on the related literature. Furthermore, the organizing unit can evaluate the reliability of the information by referring to the related literature and improve the accuracy of the organization. In this way, by referring to the related literature, the accuracy of the organized information is improved.

[0098] The organizing unit can organize the information taking into consideration the market value of the information. The organizing unit, for example, evaluates the market value of the information. For example, the organizing unit evaluates the market value based on market research data, sales forecasts, competitive analysis, etc. Next, the organizing unit organizes the information taking into consideration the market value of the information. For example, the organizing unit prioritizes organizing information with high market value. The organizing unit can also adjust the organizing criteria for the information based on the market value. Furthermore, the organizing unit can evaluate the market value and determine the importance of the information before organizing it. In this way, the value of research is improved by organizing information with high market value with priority.

[0099] The providing unit can estimate the user's emotions and adjust the presentation style of the report based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit estimates the user's emotions using technologies such as facial expression recognition, text analysis, and voice analysis. Next, the providing unit adjusts the presentation style of the report based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible report. On the other hand, if the user is relaxed, the providing unit can provide a report with detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a report that focuses on the main points. This allows the presentation of a highly visible report to be provided by adjusting the presentation style of the report according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] When providing a report, the providing unit can adjust the level of detail of the report based on the importance of the information. The providing unit, for example, evaluates the importance of the information. For example, the providing unit evaluates the importance based on the number of citations, the year of publication, the frequency of appearance of related keywords, etc. Next, the providing unit adjusts the level of detail of the report based on the importance of the information. For example, the providing unit provides a report that includes a detailed explanation for important information. Furthermore, the providing unit can provide a report that includes only an outline for information of low importance. Furthermore, the providing unit can adjust the level of detail of the report based on the importance of the information. As a result, adjusting the level of detail of the report based on the importance of the information enables efficient information provision.

[0101] When providing a report, the providing unit can apply different report formats depending on the category of information. The providing unit, for example, identifies the category of information. For example, the providing unit identifies categories such as academic papers, news articles, patent documents, etc. Next, the providing unit applies different report formats depending on the category of information. For example, the providing unit can provide a report in paper format for information related to academic papers. Furthermore, the providing unit can provide a report in news format for information related to news articles. Furthermore, the providing unit can provide a report in patent format for information related to patent documents. In this way, by applying a report format according to the category of information, it is possible to provide information with high visibility.

[0102] When providing a report, the providing unit can improve the accuracy of the report by referring to the user's past report results. The providing unit, for example, identifies the user's past report results. For example, the providing unit identifies the past report results based on the content, evaluations, feedback, etc. of the past reports. Next, the providing unit improves the accuracy of the report by referring to the user's past report results. For example, the providing unit improves the accuracy of the report based on feedback on reports the user has received in the past. The providing unit can also analyze the user's past report results and suggest an optimal report format. Furthermore, the providing unit can customize the content of the report by referring to the user's past report results. In this way, the accuracy of the report is improved by referring to the past report results.

[0103] The providing unit can estimate the user's emotion and adjust the length of the report based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion. For example, the providing unit estimates the user's emotion using technologies such as facial expression recognition, text analysis, and voice analysis. Next, the providing unit adjusts the length of the report based on the estimated user's emotion. For example, if the user is stressed, the providing unit can provide a short, concise report. On the other hand, if the user is relaxed, the providing unit can provide a longer report with detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide a short report that can be read quickly. This allows the report length to be adjusted according to the user's emotion, making it possible to provide a highly readable report. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] When providing a report, the providing unit can determine the priority of the report based on the time of submission of the information. The providing unit, for example, identifies the time of submission of the information. For example, the providing unit identifies the time of submission of the information based on the submission date, submission period, submission frequency, etc. Next, the providing unit determines the priority of the report based on the time of submission of the information. For example, the providing unit preferentially includes the latest information in the report. Furthermore, the providing unit can evaluate the importance of the information based on the time of submission and determine the priority of the report. Furthermore, the providing unit can adjust the content of the report taking the time of submission into consideration. In this way, by determining the priority of the report based on the time of submission of the information, the latest information can be provided efficiently.

[0105] When providing a report, the providing unit can adjust the order of the reports based on the relevance of the information. The providing unit, for example, evaluates the relevance of the information. For example, the providing unit evaluates the relevance of the information based on a co-occurrence network, a relevance score, etc. Next, the providing unit adjusts the order of the reports based on the relevance of the information. For example, the providing unit preferentially includes highly relevant information in the report. The providing unit can also adjust the order of the reports based on the relevance of the information. Furthermore, the providing unit can include highly relevant information together in the report. In this way, by adjusting the order of the reports based on the relevance of the information, it is possible to provide information with high visibility.

[0106] When providing a report, the providing unit can adjust the use of technical terms in the report according to the user's level of expertise. The providing unit, for example, identifies the user's level of expertise. For example, the providing unit identifies the level of expertise based on the user's occupation, educational background, past research topics, etc. Next, the providing unit adjusts the use of technical terms in the report according to the user's level of expertise. For example, the providing unit adjusts the use of technical terms according to the user's level of expertise. Furthermore, the providing unit can provide a report that explains things in simple terms to a user with little expertise. Furthermore, the providing unit can provide a report that includes detailed technical terms to a user with high expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a report with high visibility. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, collection unit, organization unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and a user inputs a research topic or keywords. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects related information from the Internet or a database. The organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and organizes the collected information. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the organized information as a report. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, collection unit, organization unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and a user inputs a research topic or keywords. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects related information from the Internet or a database. The organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and organizes the collected information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the organized information as a report. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, organization unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user inputs a research topic or keywords. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects related information from the Internet or a database. The organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and organizes the collected information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the organized information as a report. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, organization unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and a user inputs a research topic or keywords. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects related information from the Internet or a database. The organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and organizes the collected information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the organized information as a report.

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

[0108] The reception unit can estimate the user's emotions and adjust the content of the input guide based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input, allowing the user to quickly enter research topics and keywords. In this way, adjusting the input guide according to the user's emotions reduces the user's stress and supports efficient input.

[0109] The collection department can evaluate the reliability of the collected information and prioritize collecting highly reliable information. For example, the collection department evaluates reliability based on the reliability of the information source, the number of citations, the reputation of the publisher, etc. The collection department also prioritizes collecting highly reliable information. For example, the collection department collects information from highly reliable sources such as academic papers and patent documents. Furthermore, the collection department can evaluate the reliability of news articles and prioritize collecting highly reliable articles. This prioritizes collecting highly reliable information, thereby improving the accuracy of research.

[0110] The organizer classifies the collected information based on importance or relevance, and organizes it in a format that is easy for users to understand. For example, the organizer evaluates the importance based on the number of citations of the information, the year of publication, the frequency of appearance of related keywords, etc. The organizer can also use co-occurrence networks and relevance scores to evaluate the relevance of the information. Furthermore, the organizer organizes the information in a format that is easy for users to understand. For example, the organizer provides the information in the form of summaries, bullet points, graphs, charts, etc. In this way, by organizing and classifying the information, the user can use the information efficiently.

[0111] The providing unit can provide a report including a summary or analysis results of the organized information. For example, the providing unit can include a summary of key points, statistical analysis results, trend analysis, etc. in the report. The providing unit can also customize the format and content of the report to meet the needs of the user. For example, the providing unit can provide the report in a format desired by the user. This improves the efficiency of research by providing the user with organized information in report format.

[0112] The reception unit can guide the user on the input format when entering a research topic or keyword. For example, the reception unit can provide input examples, specify the format, and provide real-time feedback. The reception unit also assists the user in entering the research topic or keyword in an appropriate format. For example, the reception unit can suggest appropriate keywords or topics as the user enters the research topic or keyword. This allows the user to enter the research topic or keyword in an appropriate format.

[0113] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit will prioritize collecting important information. If the user is relaxed, the collection unit can collect detailed information. Furthermore, if the user is in a hurry, the collection unit will prioritize collecting information that can be collected quickly. In this way, efficient information collection is supported by determining the priority of information according to the user's emotions.

[0114] The collection department can evaluate the reliability of information when collecting it and prioritize collecting highly reliable information. For example, the collection department evaluates reliability based on the reliability of the information source, the number of citations, the reputation of the publisher, etc. Next, the collection department prioritizes collecting highly reliable information. For example, the collection department collects information from highly reliable sources such as academic papers and patent documents. The collection department can also evaluate the reliability of news articles and prioritize collecting highly reliable articles. This prioritizes collecting highly reliable information, thereby improving the accuracy of research.

[0115] The organizing unit can estimate the user's emotions and adjust the organizing criteria based on the estimated user's emotions. For example, if the user is stressed, the organizing unit can apply simple organizing criteria. If the user is relaxed, the organizing unit can apply detailed organizing criteria. Furthermore, if the user is in a hurry, the organizing unit can apply criteria that allow for quick organizing. In this way, adjusting the organizing criteria according to the user's emotions supports efficient information organization.

[0116] The organizing unit can improve the accuracy of organizing by taking into account the interrelationships of information when organizing. For example, the organizing unit identifies the interrelationships of information using a co-occurrence network, a relevance score, or the like. Next, the organizing unit performs organizing by taking into account the interrelationships of information. For example, the organizing unit analyzes the interrelationships of collected information and compiles related information. The organizing unit can also determine the priority of organizing based on the interrelationships of information. Furthermore, the organizing unit can improve the accuracy of organizing by taking into account the interrelationships of information. In this way, the accuracy of organizing is improved by taking into account the interrelationships of information.

[0117] The providing unit can estimate the user's emotions and adjust the way the report is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible report. If the user is relaxed, the providing unit can provide a report including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a report that focuses on the main points. In this way, by adjusting the way the report is presented according to the user's emotions, it is possible to provide a highly visible report.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The user inputs a research topic or keyword into the reception unit. For example, a user can input topics such as "latest trends in AI technology" or "impact of climate change." Step 2: The collection unit analyzes the information received by the reception unit and collects related information and literature. For example, the collection unit collects information such as academic papers, news articles, and patent documents from the Internet and databases. Step 3: The organizer organizes the information collected by the collector. For example, the organizer classifies the collected information based on importance and relevance, and organizes it in a format that is easy for the user to understand. Step 4: The providing unit provides the information organized by the organizing unit as a report. For example, the providing unit provides a report including a summary of the organized information and analysis results.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving input of a research topic or keyword; a collection unit that analyzes the information received by the reception unit and collects related information or literature; an organizing unit that organizes the information collected by the collecting unit; a providing unit that provides the information organized by the organizing unit as a report. A system characterized by:

2. The collecting unit Gather information on academic papers, news articles, and patent documents from the internet or databases 2. The system of claim 1.

3. The organizing unit Categorize collected information based on importance or relevance and organize it into a format that is easy for users to understand 2. The system of claim 1.

4. The providing unit Providing reports containing organized summaries or analytical results 2. The system of claim 1.

5. The reception unit Guide users through the input format when entering research topics or keywords 2. The system of claim 1.

6. The collecting unit Evaluate the reliability of collected information and prioritize collection of reliable information 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the input guide content based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input format 2. The system of claim 1.

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