System

Abstract

An object of a system according to an embodiment is to efficiently search for information on academic papers and help a user to understand the information.SOLUTION: A system includes a paper retrieval part, an understanding level analysis part, a summary providing part, a technical term explanation part, and a chart simplification part. The article search unit searches for an academic article. An understanding level analysis part analyzes the understanding level of the user on the basis of the academic paper retrieved by the paper retrieval part. The summary provider may provide an appropriate summary based on the understanding level of the user analyzed by the understanding level analyzer. The terminology explanation unit explains the terminology included in the summary provided by the summary providing unit. The diagram simplifying unit simplifies the diagram included in the summary provided by the summary providing unit.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 search and understand the vast amount of information contained in academic papers.

[0005] The system according to the embodiment aims to efficiently search for information in academic papers and to help users understand them. [Means for solving the problem]

[0006] The system according to the embodiment includes a paper search unit, a comprehension analysis unit, a summary providing unit, a terminology explanation unit, and a diagram simplification unit. The paper search unit searches for academic papers. The comprehension analysis unit analyzes the user's level of comprehension based on the academic papers searched by the paper search unit. The summary providing unit provides an appropriate summary based on the user's level of comprehension analyzed by the comprehension analysis unit. The terminology explanation unit explains terminology included in the summary provided by the summary providing unit. The diagram simplification unit simplifies diagrams included in the summary provided by the summary providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently search for information in academic papers and help users understand them. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The paper search system according to an embodiment of the present invention is a system that can access academic papers, search for papers regardless of field of specialization, analyze the user's level of understanding, and provide an appropriate summary. As a result, the paper search system can consistently support everything from searching for academic papers to understanding, summarizing, explaining technical terms, and simplifying figures and tables.

[0029] An article search system according to an embodiment includes an article search unit, a comprehension analysis unit, a summary providing unit, a terminology explanation unit, and a diagram simplification unit. The article search unit searches for academic articles. For example, the generation AI can access over 5 million academic articles published worldwide each year, enabling article searches regardless of field of expertise. Users can quickly find relevant articles by inputting keywords or themes of the articles they want to search for into the generation AI. The comprehension analysis unit analyzes the user's level of comprehension based on the academic articles retrieved by the article search unit. For example, the user's level of comprehension can be evaluated based on the user's past search history and behavioral data. The summary providing unit provides an appropriate summary based on the user's level of comprehension analyzed by the comprehension analysis unit. For example, if the user is not an expert, the generation AI summarizes the contents of the article concisely and clearly, replacing technical terms with simpler language. The terminology explanation unit explains technical terms included in the summary provided by the summary providing unit. For example, by inputting a prompt such as "Please explain the technical terms appearing in this article" to the generation AI, the generation AI can clearly explain the meaning and background of the technical terms. The figure simplification unit simplifies figures and tables included in the summaries provided by the summary provider. For example, it simplifies complex figures and tables, converting them into a form that is visually easy to understand. This allows the paper search system to consistently support academic paper search, from understanding, summarizing, explaining technical terms, and simplifying figures and tables.

[0030] The paper search unit can use generative AI to analyze the citation relationships of papers and automatically recommend related papers. The paper search unit can, for example, use generative AI to analyze the citation relationships of papers and automatically recommend related papers. For example, when searching for a specific paper, other papers that the paper cites and subsequent papers that cite the paper are automatically displayed. This automatically recommends related papers, improving the user's search efficiency.

[0031] The comprehension analysis unit can prioritize the display of papers in fields of interest to the user based on the user's past search history. The comprehension analysis unit, for example, analyzes the user's past search history and prioritizes the display of papers in fields of interest to the user. For example, it recommends related new papers based on keywords or themes searched in the past. This improves search efficiency by prioritized display of related papers based on the user's interests.

[0032] The paper search unit can simultaneously search for related patent documents when searching for papers and present possible technical applications. For example, when searching for a specific paper, the paper search unit can simultaneously search for related patent documents and present possible technical applications. For example, when searching for a specific paper, patent documents related to that paper are automatically displayed. This makes it possible to present possible technical applications by simultaneously searching for related patent documents.

[0033] The summary providing unit provides a summary of the paper by voice, which can accommodate users who are visually impaired or busy. The summary providing unit, for example, provides a summary of the paper by voice, which can accommodate users who are visually impaired or busy. For example, the summary of the paper is automatically converted into voice and provided to the user. In this way, by providing the summary by voice, it can accommodate users who are visually impaired or busy.

[0034] The comprehension analysis unit can monitor the user's level of understanding in real time and adjust the level of detail of the summary according to the level of understanding. The comprehension analysis unit, for example, monitors the user's level of understanding in real time and adjusts the level of detail of the summary according to the level of understanding. For example, the level of detail of the summary can be dynamically adjusted based on the user's reaction and operation history. In this way, by adjusting the level of detail of the summary according to the user's level of understanding, it is possible to provide a summary that is easy to understand.

[0035] The comprehension analysis unit can provide an appropriate summary by taking into account the user's level of expertise during comprehension analysis. For example, the comprehension analysis unit can provide an appropriate summary by taking into account the user's level of expertise during comprehension analysis. For example, the user's level of expertise is registered in advance, and the level of detail of the summary is adjusted based on that. This makes it possible to provide an appropriate summary according to the user's level of expertise, thereby providing an easy-to-understand summary.

[0036] The summary providing unit can provide summaries in multiple formats, allowing a user to select one based on their preferences. For example, the summary providing unit can provide summaries in multiple formats (text, visual, audio) allowing a user to select one based on their preferences. For example, text summaries, visual summaries, and audio summaries can be prepared and the user can select one. By providing summaries in multiple formats, it is possible to provide summaries that are easy to understand and that match the user's preferences.

[0037] The summary providing unit can link the content of the summary to related videos and presentation materials to deepen understanding. The summary providing unit can, for example, link the content of the summary to related videos and presentation materials to deepen understanding. For example, when a keyword in the summary is clicked, related videos and materials are displayed. In this way, by linking the content of the summary to related videos and presentation materials, understanding can be deepened.

[0038] The terminology explanation unit can add examples and real-life examples related to the explanation of the terminology to aid in understanding. The terminology explanation unit can, for example, add examples and real-life examples related to the explanation of the terminology to aid in understanding. For example, it can insert specific examples and real-life examples into the explanation of the terminology. In this way, adding examples and real-life examples related to the explanation of the terminology can aid in understanding.

[0039] The diagram simplification unit can convert a diagram into an optimal format by taking into consideration the user's visual comprehension when simplifying the diagram. For example, the diagram simplification unit can convert a diagram into an optimal format by taking into consideration the user's visual comprehension when simplifying the diagram. For example, the user's visual comprehension is registered in advance and the format of the diagram is adjusted based on that. In this way, by converting the diagram into an optimal format by taking into consideration the user's visual comprehension, it is possible to provide a diagram that is easy to understand.

[0040] The terminology explanation unit provides explanations of technical terms in the form of an interactive quiz, thereby enhancing learning effectiveness. The terminology explanation unit, for example, provides explanations of technical terms in the form of an interactive quiz, thereby enhancing learning effectiveness. For example, a quiz may be inserted into the explanation of a technical term, and the user may answer the quiz to deepen their understanding. In this way, by providing explanations of technical terms in the form of an interactive quiz, learning effectiveness can be enhanced.

[0041] The diagram simplification unit can animate the diagram to make dynamically changing data visually easier to understand. The diagram simplification unit, for example, animates the diagram to make dynamically changing data visually easier to understand. For example, the diagram simplification unit displays changes in data using animation to make it easier for the user to visually understand. In this way, animating the diagram can make dynamically changing data visually easier to understand.

[0042] The summary providing unit can use the generation AI to make specific suggestions on how the contents of a paper can be applied to a specific project of the company. For example, the summary providing unit uses the generation AI to make specific suggestions on how the contents of a paper can be applied to a specific project of the company. For example, the summary providing unit searches for papers related to a specific project and suggests specific application methods based on the contents of the papers. This improves practicality by making specific suggestions on how to apply the contents of the paper to a specific project of the company.

[0043] The summary providing unit can analyze the user's business content in detail and customize and provide the optimal diversion method. For example, the summary providing unit can analyze the user's business content in detail and customize and provide the optimal diversion method. For example, the user's business content can be registered in advance and the optimal diversion method can be proposed based on that. This allows the user's business content to be analyzed in detail and the optimal diversion method to be customized and provided, thereby improving practicality.

[0044] The summary providing unit can generate ideas for adapting the content of a paper to different industries or uses. The summary providing unit, for example, generates ideas for adapting the content of a paper to different industries or uses. For example, the summary providing unit proposes specific ideas for applying a specific technology to a different industry. This generates ideas for adapting the content of a paper to different industries or uses, thereby improving its practicality.

[0045] The summary providing unit can provide the diversion method as a step-by-step guide to make it easier to execute. For example, the summary providing unit provides the diversion method as a step-by-step guide to make it easier to execute. For example, a guide showing specific steps is provided to make it easier for the user to execute. In this way, by providing the diversion method as a step-by-step guide, it becomes easier to execute.

[0046] The summary provider can use generative AI to match corporate needs with university research themes and propose optimal collaborative research partners. For example, the summary provider can use generative AI to match corporate needs with university research themes and propose optimal collaborative research partners. For example, when a company's needs are input, universities and researchers with related research themes are automatically proposed. This improves the success rate of collaborative research by matching corporate needs with university research themes and proposing optimal collaborative research partners.

[0047] The summary providing unit can monitor the progress of the collaborative research in real time and make adjustments as necessary. The summary providing unit, for example, monitors the progress of the collaborative research in real time and makes adjustments as necessary. For example, it automatically updates the progress of the research and issues an alert if a problem occurs. In this way, by monitoring the progress of the collaborative research in real time and making adjustments as necessary, the success rate of the research is improved.

[0048] The summary providing unit can share the results of the joint research with other companies and research institutions, thereby promoting broader collaboration. The summary providing unit can, for example, share the results of the joint research with other companies and research institutions, thereby promoting broader collaboration. For example, the summary providing unit can register the results of the joint research in a database so that other companies and research institutions can access it. In this way, the results of the joint research can be shared with other companies and research institutions, thereby promoting broader collaboration.

[0049] The summary providing unit can visualize the collaborative research proposal and provide it as presentation materials. The summary providing unit, for example, visualizes the collaborative research proposal and provides it as presentation materials. For example, the summary providing unit visually displays the contents of the collaborative research proposal in graphs and charts. In this way, by visualizing the collaborative research proposal and providing it as presentation materials, the proposal can be made easier to understand.

[0050] The summary providing unit can use the generation AI to propose solutions to specific regional issues and provide specific action plans. The summary providing unit, for example, uses the generation AI to propose solutions to specific regional issues and provide specific action plans. For example, it proposes specific procedures for promoting DX for regional small and medium-sized enterprises. In this way, by proposing solutions to specific regional issues and providing specific action plans, regional revitalization is promoted.

[0051] The summary provider can analyze the needs of local companies and local governments in detail and customize and provide the optimal DX strategy. For example, the summary provider can analyze the needs of local companies and local governments in detail and customize and provide the optimal DX strategy. For example, it can register the needs of local small and medium-sized enterprises in advance and propose the optimal DX strategy based on that. This will promote regional revitalization by analyzing the needs of local companies and local governments in detail and customizing and providing the optimal DX strategy.

[0052] The summary provision unit can spread successful cases of regional revitalization to other regions, promoting DX in a wide range of regions. The summary provision unit can, for example, spread successful cases of regional revitalization to other regions, promoting DX in a wide range of regions. For example, it can register successful cases in a database and make them accessible to other regions. In this way, spreading successful cases of regional revitalization to other regions will promote DX in a wide range of regions.

[0053] The summary providing unit can provide the local DX proposal in an interactive workshop format to deepen the understanding of participants. The summary providing unit, for example, provides the local DX proposal in an interactive workshop format to deepen the understanding of participants. For example, it provides a demo that participants can actually operate during the workshop. In this way, by providing the local DX proposal in an interactive workshop format, the understanding of participants is deepened.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The paper search unit allows users to quickly find related papers by entering the keywords and themes of the paper they want to search for. Furthermore, the paper search unit can estimate the user's interests based on data on papers the user has previously searched for and recommend new related papers. For example, it can analyze the keywords and themes of papers the user has previously searched for and automatically display new related papers. Furthermore, if the user is interested in a particular field, the paper search unit can prioritize displaying the latest papers related to that field. This improves search efficiency by prioritizing the display of related papers based on the user's interests.

[0056] The comprehension analysis unit can prioritize the display of papers in fields of interest to the user based on the user's past search history. For example, it can recommend new related papers based on keywords or themes previously searched. Furthermore, the comprehension analysis unit can make more accurate recommendations by taking into account not only the user's search history but also the content and time of the papers viewed by the user. This improves search efficiency by prioritizing the display of related papers based on the user's interests.

[0057] The summary provider provides audio summaries of papers, making it possible to accommodate visually impaired users and busy users. For example, the summary provider automatically converts the paper summary into audio and provides it to the user. Furthermore, the summary provider also has a function that allows the user to adjust the playback speed of the audio summary. This allows for audio summaries to accommodate visually impaired users and busy users.

[0058] The summary provider can provide summaries in multiple formats (text, visual, and audio) and allow users to select from them according to their preferences. For example, text summaries, visual summaries, and audio summaries can be prepared and the user can select from them. Furthermore, the summary provider can record a summary format previously selected by the user and automatically prioritize that format the next time a summary is provided. In this way, by providing summaries in multiple formats, it is possible to provide summaries that are easy to understand and that suit the user's preferences.

[0059] The terminology explanation section can add examples and examples related to the explanation of the term to aid in understanding. For example, it can insert specific examples and examples into the explanation of the term. Furthermore, the terminology explanation section can automatically display examples and examples related to the terminology previously searched by the user. This can aid in understanding by adding examples and examples related to the explanation of the terminology.

[0060] The diagram simplification unit can animate diagrams to make dynamically changing data easier to understand visually. For example, the diagram simplification unit can display changes in data using animation to make it easier for users to understand visually. Furthermore, the diagram simplification unit also has a function that allows users to customize the speed and style of the animation. This makes it easier for users to understand dynamically changing data visually by animating the diagram.

[0061] The summary provider uses generation AI to match corporate needs with university research themes and propose optimal collaborative research partners. For example, when a company's needs are entered, universities and researchers with related research themes are automatically proposed. Furthermore, the summary provider can generate specific collaborative research proposals based on the results of matching corporate needs with university research themes. This improves the success rate of collaborative research by matching corporate needs with university research themes and proposing optimal collaborative research partners.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The paper search unit searches for academic papers. For example, using generative AI, it can access over 5 million academic papers published worldwide each year, making it possible to search for papers regardless of field of expertise. Users can quickly find relevant papers by inputting keywords or themes of the paper they want to search for into the generative AI. Step 2: The understanding analysis unit analyzes the user's level of understanding based on the academic papers retrieved by the paper search unit. For example, the unit evaluates the user's level of understanding based on the user's past search history and behavioral data. Step 3: The summary provider provides an appropriate summary based on the user's level of understanding analyzed by the comprehension analyzer. For example, if the user is not an expert, the generator AI will summarize the content of the paper concisely and clearly, replacing technical terms with simple language. Step 4: The terminology explanation section explains the technical terms contained in the summary provided by the summary provider. For example, by inputting a prompt such as "Please explain the technical terms that appear in this paper" into the generation AI, the generation AI will provide an easy-to-understand explanation of the meaning and background of the technical terms. Step 5: The diagram simplification unit simplifies the diagrams included in the summary provided by the summary providing unit, for example, by simplifying complex diagrams and converting them into a form that is visually easy to understand.

[0064] (Example 2) The paper search system according to an embodiment of the present invention is a system that can access academic papers, search for papers regardless of field of specialization, analyze the user's level of understanding, and provide an appropriate summary. As a result, the paper search system can consistently support everything from searching for academic papers to understanding, summarizing, explaining technical terms, and simplifying figures and tables.

[0065] An article search system according to an embodiment includes an article search unit, a comprehension analysis unit, a summary providing unit, a terminology explanation unit, and a diagram simplification unit. The article search unit searches for academic articles. For example, the generation AI can access over 5 million academic articles published worldwide each year, enabling article searches regardless of field of expertise. Users can quickly find relevant articles by inputting keywords or themes of the articles they want to search for into the generation AI. The comprehension analysis unit analyzes the user's level of comprehension based on the academic articles retrieved by the article search unit. For example, the user's level of comprehension can be evaluated based on the user's past search history and behavioral data. The summary providing unit provides an appropriate summary based on the user's level of comprehension analyzed by the comprehension analysis unit. For example, if the user is not an expert, the generation AI summarizes the contents of the article concisely and clearly, replacing technical terms with simpler language. The terminology explanation unit explains technical terms included in the summary provided by the summary providing unit. For example, by inputting a prompt such as "Please explain the technical terms appearing in this article" to the generation AI, the generation AI can clearly explain the meaning and background of the technical terms. The figure simplification unit simplifies figures and tables included in the summaries provided by the summary provider. For example, it simplifies complex figures and tables, converting them into a form that is visually easy to understand. This allows the paper search system to consistently support academic paper search, from understanding, summarizing, explaining technical terms, and simplifying figures and tables.

[0066] The paper search unit can use generative AI to analyze the citation relationships of papers and automatically recommend related papers. The paper search unit can, for example, use generative AI to analyze the citation relationships of papers and automatically recommend related papers. For example, when searching for a specific paper, other papers that the paper cites and subsequent papers that cite the paper are automatically displayed. This automatically recommends related papers, improving the user's search efficiency.

[0067] The comprehension analysis unit can prioritize the display of papers in fields of interest to the user based on the user's past search history. The comprehension analysis unit, for example, analyzes the user's past search history and prioritizes the display of papers in fields of interest to the user. For example, it recommends related new papers based on keywords or themes searched in the past. This improves search efficiency by prioritized display of related papers based on the user's interests.

[0068] The comprehension analysis unit can use the emotion estimation function to estimate and recommend papers that the user is likely to be interested in. For example, the comprehension analysis unit uses the emotion estimation function to estimate and recommend papers that the user is likely to be interested in. For example, it analyzes the emotional state of the user at the time of searching and preferentially displays papers on topics that evoke strong positive emotions. This improves search efficiency by recommending related papers based on the user's emotions.

[0069] The paper search unit can simultaneously search for related patent documents when searching for papers and present possible technical applications. For example, when searching for a specific paper, the paper search unit can simultaneously search for related patent documents and present possible technical applications. For example, when searching for a specific paper, patent documents related to that paper are automatically displayed. This makes it possible to present possible technical applications by simultaneously searching for related patent documents.

[0070] The summary providing unit provides a summary of the paper by voice, which can accommodate users who are visually impaired or busy. The summary providing unit, for example, provides a summary of the paper by voice, which can accommodate users who are visually impaired or busy. For example, the summary of the paper is automatically converted into voice and provided to the user. In this way, by providing the summary by voice, it can accommodate users who are visually impaired or busy.

[0071] The comprehension analysis unit can use the emotion estimation function to analyze the user's emotion regarding the topic of the paper they are searching for and optimize the search results. For example, the comprehension analysis unit uses the emotion estimation function to analyze the user's emotion regarding the topic of the paper they want to search for and optimize the search results. For example, related papers are filtered and displayed based on the user's emotion score. This improves search efficiency by optimizing the search results based on the user's emotion.

[0072] The comprehension analysis unit can monitor the user's level of understanding in real time and adjust the level of detail of the summary according to the level of understanding. The comprehension analysis unit, for example, monitors the user's level of understanding in real time and adjusts the level of detail of the summary according to the level of understanding. For example, the level of detail of the summary can be dynamically adjusted based on the user's reaction and operation history. In this way, by adjusting the level of detail of the summary according to the user's level of understanding, it is possible to provide a summary that is easy to understand.

[0073] The comprehension analysis unit can provide an appropriate summary by taking into account the user's level of expertise during comprehension analysis. For example, the comprehension analysis unit can provide an appropriate summary by taking into account the user's level of expertise during comprehension analysis. For example, the user's level of expertise is registered in advance, and the level of detail of the summary is adjusted based on that. This makes it possible to provide an appropriate summary according to the user's level of expertise, thereby providing an easy-to-understand summary.

[0074] The comprehension analysis unit can use the emotion estimation function to analyze the user's emotion regarding the level of understanding, and provide supplemental explanation if the level of understanding is low. The comprehension analysis unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the level of understanding, and provide supplemental explanation if the level of understanding is low. For example, supplemental explanation is automatically displayed based on the user's emotion score. This makes it possible to deepen understanding by analyzing the user's emotion regarding the level of understanding and providing supplemental explanation if the level of understanding is low.

[0075] The summary providing unit can provide summaries in multiple formats, allowing a user to select one based on their preferences. For example, the summary providing unit can provide summaries in multiple formats (text, visual, audio) allowing a user to select one based on their preferences. For example, text summaries, visual summaries, and audio summaries can be prepared and the user can select one. By providing summaries in multiple formats, it is possible to provide summaries that are easy to understand and that match the user's preferences.

[0076] The summary providing unit can link the content of the summary to related videos and presentation materials to deepen understanding. The summary providing unit can, for example, link the content of the summary to related videos and presentation materials to deepen understanding. For example, when a keyword in the summary is clicked, related videos and materials are displayed. In this way, by linking the content of the summary to related videos and presentation materials, understanding can be deepened.

[0077] The summary providing unit can use the emotion estimation function to collect the user's emotional reactions to the summary and improve the quality of the summary. The summary providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the summary and improve the quality of the summary. For example, the summary providing unit dynamically adjusts the content of the summary based on the user's emotion score. In this way, by collecting the user's emotional reactions and improving the quality of the summary, it is possible to provide a summary that is easier to understand.

[0078] The terminology explanation unit can add examples and real-life examples related to the explanation of the terminology to aid in understanding. The terminology explanation unit can, for example, add examples and real-life examples related to the explanation of the terminology to aid in understanding. For example, it can insert specific examples and real-life examples into the explanation of the terminology. In this way, adding examples and real-life examples related to the explanation of the terminology can aid in understanding.

[0079] The diagram simplification unit can convert a diagram into an optimal format by taking into consideration the user's visual comprehension when simplifying the diagram. For example, the diagram simplification unit can convert a diagram into an optimal format by taking into consideration the user's visual comprehension when simplifying the diagram. For example, the user's visual comprehension is registered in advance and the format of the diagram is adjusted based on that. In this way, by converting the diagram into an optimal format by taking into consideration the user's visual comprehension, it is possible to provide a diagram that is easy to understand.

[0080] The diagram simplification unit can use the emotion estimation function to analyze the user's emotion toward technical terms and diagrams and adjust them into an easy-to-understand format. The diagram simplification unit, for example, uses the emotion estimation function to analyze the user's emotion toward technical terms and diagrams and adjust them into an easy-to-understand format. For example, the format of technical terms and diagrams is dynamically adjusted based on the user's emotion score. In this way, the format of technical terms and diagrams can be adjusted based on the user's emotion, thereby converting them into an easy-to-understand format.

[0081] The terminology explanation unit provides explanations of technical terms in the form of an interactive quiz, thereby enhancing learning effectiveness. The terminology explanation unit, for example, provides explanations of technical terms in the form of an interactive quiz, thereby enhancing learning effectiveness. For example, a quiz may be inserted into the explanation of a technical term, and the user may answer the quiz to deepen their understanding. In this way, by providing explanations of technical terms in the form of an interactive quiz, learning effectiveness can be enhanced.

[0082] The diagram simplification unit can animate the diagram to make dynamically changing data visually easier to understand. The diagram simplification unit, for example, animates the diagram to make dynamically changing data visually easier to understand. For example, the diagram simplification unit displays changes in data using animation to make it easier for the user to visually understand. In this way, animating the diagram can make dynamically changing data visually easier to understand.

[0083] The diagram simplification unit can use the emotion estimation function to collect the user's emotional reactions to the explanations and diagrams and provide an optimal explanation method. The diagram simplification unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the explanations and diagrams and provide an optimal explanation method. For example, the format of the explanations and diagrams is dynamically adjusted based on the user's emotion score. In this way, by collecting the user's emotional reactions and providing an optimal explanation method, an easy-to-understand explanation can be provided.

[0084] The summary providing unit can use the generation AI to make specific suggestions on how the contents of a paper can be applied to a specific project of the company. For example, the summary providing unit uses the generation AI to make specific suggestions on how the contents of a paper can be applied to a specific project of the company. For example, the summary providing unit searches for papers related to a specific project and suggests specific application methods based on the contents of the papers. This improves practicality by making specific suggestions on how to apply the contents of the paper to a specific project of the company.

[0085] The summary providing unit can analyze the user's business content in detail and customize and provide the optimal diversion method. For example, the summary providing unit can analyze the user's business content in detail and customize and provide the optimal diversion method. For example, the user's business content can be registered in advance and the optimal diversion method can be proposed based on that. This allows the user's business content to be analyzed in detail and the optimal diversion method to be customized and provided, thereby improving practicality.

[0086] The summary providing unit can use the emotion estimation function to estimate and propose the diversion method that the user is most interested in. For example, the summary providing unit uses the emotion estimation function to estimate and propose the diversion method that the user is most likely to be interested in. For example, the summary providing unit proposes the optimal diversion method based on the user's emotion score. This improves practicality by suggesting the diversion method that the user is most interested in based on the user's emotion.

[0087] The summary providing unit can generate ideas for adapting the content of a paper to different industries or uses. The summary providing unit, for example, generates ideas for adapting the content of a paper to different industries or uses. For example, the summary providing unit proposes specific ideas for applying a specific technology to a different industry. This generates ideas for adapting the content of a paper to different industries or uses, thereby improving its practicality.

[0088] The summary providing unit can provide the diversion method as a step-by-step guide to make it easier to execute. For example, the summary providing unit provides the diversion method as a step-by-step guide to make it easier to execute. For example, a guide showing specific steps is provided to make it easier for the user to execute. In this way, by providing the diversion method as a step-by-step guide, it becomes easier to execute.

[0089] The summary providing unit can use the emotion estimation function to collect the user's emotional reactions to the diversion methods and make optimal suggestions. The summary providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the diversion methods and make optimal suggestions. For example, the summary providing unit suggests an optimal diversion method based on the user's emotion score. In this way, by collecting the user's emotional reactions and making optimal suggestions, practicality is improved.

[0090] The summary provider can use generative AI to match corporate needs with university research themes and propose optimal collaborative research partners. For example, the summary provider can use generative AI to match corporate needs with university research themes and propose optimal collaborative research partners. For example, when a company's needs are input, universities and researchers with related research themes are automatically proposed. This improves the success rate of collaborative research by matching corporate needs with university research themes and proposing optimal collaborative research partners.

[0091] The summary providing unit can monitor the progress of the collaborative research in real time and make adjustments as necessary. The summary providing unit, for example, monitors the progress of the collaborative research in real time and makes adjustments as necessary. For example, it automatically updates the progress of the research and issues an alert if a problem occurs. In this way, by monitoring the progress of the collaborative research in real time and making adjustments as necessary, the success rate of the research is improved.

[0092] The summary providing unit can use the emotion estimation function to make suggestions to facilitate communication between companies and universities. The summary providing unit, for example, uses the emotion estimation function to make suggestions to facilitate communication between companies and universities. For example, it makes appropriate suggestions based on emotion scores during communication. In this way, by making suggestions to facilitate communication between companies and universities, the success rate of joint research improves.

[0093] The summary providing unit can share the results of the joint research with other companies and research institutions, thereby promoting broader collaboration. The summary providing unit can, for example, share the results of the joint research with other companies and research institutions, thereby promoting broader collaboration. For example, the summary providing unit can register the results of the joint research in a database so that other companies and research institutions can access it. In this way, the results of the joint research can be shared with other companies and research institutions, thereby promoting broader collaboration.

[0094] The summary providing unit can visualize the collaborative research proposal and provide it as presentation materials. The summary providing unit, for example, visualizes the collaborative research proposal and provides it as presentation materials. For example, the summary providing unit visually displays the contents of the collaborative research proposal in graphs and charts. In this way, by visualizing the collaborative research proposal and providing it as presentation materials, the proposal can be made easier to understand.

[0095] The summary providing unit can use the emotion estimation function to collect the emotional reactions of the participants to the collaborative research and increase the success rate of the project. The summary providing unit, for example, uses the emotion estimation function to collect the emotional reactions of the participants to the collaborative research and increase the success rate of the project. For example, the progress of the project is adjusted based on the emotion scores of the participants. In this way, the results of the collaborative research are maximized by collecting the emotional reactions of the participants and increasing the success rate of the project.

[0096] The summary providing unit can use the generation AI to propose solutions to specific regional issues and provide specific action plans. The summary providing unit, for example, uses the generation AI to propose solutions to specific regional issues and provide specific action plans. For example, it proposes specific procedures for promoting DX for regional small and medium-sized enterprises. In this way, by proposing solutions to specific regional issues and providing specific action plans, regional revitalization is promoted.

[0097] The summary provider can analyze the needs of local companies and local governments in detail and customize and provide the optimal DX strategy. For example, the summary provider can analyze the needs of local companies and local governments in detail and customize and provide the optimal DX strategy. For example, it can register the needs of local small and medium-sized enterprises in advance and propose the optimal DX strategy based on that. This will promote regional revitalization by analyzing the needs of local companies and local governments in detail and customizing and providing the optimal DX strategy.

[0098] The summary providing unit can use the emotion estimation function to analyze the emotions of local residents and businesses and make proposals that are easy to accept. The summary providing unit, for example, uses the emotion estimation function to analyze the emotions of local residents and businesses and make proposals that are easy to accept. For example, it makes optimal proposals based on the emotion scores of residents. In this way, regional revitalization is promoted by analyzing the emotions of local residents and businesses and making proposals that are easy to accept.

[0099] The summary provision unit can spread successful cases of regional revitalization to other regions, promoting DX in a wide range of regions. The summary provision unit can, for example, spread successful cases of regional revitalization to other regions, promoting DX in a wide range of regions. For example, it can register successful cases in a database and make them accessible to other regions. In this way, spreading successful cases of regional revitalization to other regions will promote DX in a wide range of regions.

[0100] The summary providing unit can provide the local DX proposal in an interactive workshop format to deepen the understanding of participants. The summary providing unit, for example, provides the local DX proposal in an interactive workshop format to deepen the understanding of participants. For example, it provides a demo that participants can actually operate during the workshop. In this way, by providing the local DX proposal in an interactive workshop format, the understanding of participants is deepened.

[0101] The summary providing unit can use the emotion estimation function to collect the emotional reactions of residents and businesses regarding regional DX and make optimal proposals. The summary providing unit, for example, uses the emotion estimation function to collect the emotional reactions of residents and businesses regarding regional DX and make optimal proposals. For example, it makes optimal proposals based on the emotional scores of residents and businesses. In this way, regional revitalization is promoted by collecting the emotional reactions of residents and businesses regarding regional DX and making optimal proposals.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The paper search unit allows users to quickly find related papers by entering the keywords and themes of the paper they want to search for. Furthermore, the paper search unit can estimate the user's interests based on data on papers the user has previously searched for and recommend new related papers. For example, it can analyze the keywords and themes of papers the user has previously searched for and automatically display new related papers. Furthermore, if the user is interested in a particular field, the paper search unit can prioritize displaying the latest papers related to that field. This improves search efficiency by prioritizing the display of related papers based on the user's interests.

[0104] The comprehension analysis unit can prioritize the display of papers in fields of interest to the user based on the user's past search history. For example, it can recommend new related papers based on keywords or themes previously searched. Furthermore, the comprehension analysis unit can make more accurate recommendations by taking into account not only the user's search history but also the content and time of the papers viewed by the user. This improves search efficiency by prioritizing the display of related papers based on the user's interests.

[0105] The summary provider provides audio summaries of papers, making it possible to accommodate visually impaired users and busy users. For example, the summary provider automatically converts the paper summary into audio and provides it to the user. Furthermore, the summary provider also has a function that allows the user to adjust the playback speed of the audio summary. This allows for audio summaries to accommodate visually impaired users and busy users.

[0106] The comprehension analysis unit can use the emotion estimation function to estimate and recommend papers that the user is likely to be interested in. For example, it can analyze the user's emotional state at the time of searching and prioritize papers on topics that evoke strong positive emotions. Furthermore, the comprehension analysis unit can also recommend new related papers based on the themes of papers that the user has previously expressed positive emotions about. This improves search efficiency by recommending related papers based on the user's emotions.

[0107] The summary provider can provide summaries in multiple formats (text, visual, and audio) and allow users to select from them according to their preferences. For example, text summaries, visual summaries, and audio summaries can be prepared and the user can select from them. Furthermore, the summary provider can record a summary format previously selected by the user and automatically prioritize that format the next time a summary is provided. In this way, by providing summaries in multiple formats, it is possible to provide summaries that are easy to understand and that suit the user's preferences.

[0108] The comprehension analysis unit can use the emotion estimation function to analyze the user's emotions regarding the topic of the paper they are searching for and optimize the search results. For example, it can filter and display related papers based on the user's emotion score. Furthermore, the comprehension analysis unit can also prioritize the display of new papers related to topics for which the user has previously expressed positive emotions. This improves search efficiency by optimizing search results based on the user's emotions.

[0109] The terminology explanation section can add examples and examples related to the explanation of the term to aid in understanding. For example, it can insert specific examples and examples into the explanation of the term. Furthermore, the terminology explanation section can automatically display examples and examples related to the terminology previously searched by the user. This can aid in understanding by adding examples and examples related to the explanation of the terminology.

[0110] The diagram simplification unit can animate diagrams to make dynamically changing data easier to understand visually. For example, the diagram simplification unit can display changes in data using animation to make it easier for users to understand visually. Furthermore, the diagram simplification unit also has a function that allows users to customize the speed and style of the animation. This makes it easier for users to understand dynamically changing data visually by animating the diagram.

[0111] The summary provider can collect the user's emotional responses to the summaries using the emotion estimation function and improve the quality of the summaries. For example, the summary provider can dynamically adjust the content of the summaries based on the user's emotion score. Furthermore, the summary provider can provide new summaries based on summary formats and content for which the user has previously expressed positive emotions. In this way, by collecting the user's emotional responses and improving the quality of the summaries, it is possible to provide summaries that are easier to understand.

[0112] The summary provider uses generation AI to match corporate needs with university research themes and propose optimal collaborative research partners. For example, when a company's needs are entered, universities and researchers with related research themes are automatically proposed. Furthermore, the summary provider can generate specific collaborative research proposals based on the results of matching corporate needs with university research themes. This improves the success rate of collaborative research by matching corporate needs with university research themes and proposing optimal collaborative research partners.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The paper search unit searches for academic papers. For example, using generative AI, it can access over 5 million academic papers published worldwide each year, making it possible to search for papers regardless of field of expertise. Users can quickly find relevant papers by inputting keywords or themes of the paper they want to search for into the generative AI. Step 2: The understanding analysis unit analyzes the user's level of understanding based on the academic papers retrieved by the paper search unit. For example, the unit evaluates the user's level of understanding based on the user's past search history and behavioral data. Step 3: The summary provider provides an appropriate summary based on the user's level of understanding analyzed by the comprehension analyzer. For example, if the user is not an expert, the generator AI will summarize the content of the paper concisely and clearly, replacing technical terms with simple language. Step 4: The terminology explanation section explains the technical terms contained in the summary provided by the summary provider. For example, by inputting a prompt such as "Please explain the technical terms that appear in this paper" into the generation AI, the generation AI will provide an easy-to-understand explanation of the meaning and background of the technical terms. Step 5: The diagram simplification unit simplifies the diagrams included in the summary provided by the summary providing unit, for example, by simplifying complex diagrams and converting them into a form that is visually easy to understand.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0117] 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.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0159] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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. [Explanation of symbols]

[0182] 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 paper search section for searching academic papers; a comprehension level analysis unit that analyzes the user's comprehension level based on the academic papers searched by the paper search unit; a summary providing unit that provides an appropriate summary based on the user's level of understanding analyzed by the comprehension level analyzing unit; a terminology explanation unit that explains terminology included in the summary provided by the summary providing unit; a diagram simplification unit that simplifies diagrams included in the summary provided by the summary providing unit. A system characterized by:

2. The paper search unit Analyzing citation relationships between papers using generative AI and automatically recommending related papers 2. The system of claim 1.

3. The understanding level analysis unit Based on the user's past search history, papers in the field of interest are displayed preferentially.

2. The system of claim 1.

4. The summary providing unit The abstract of the paper is provided in audio format to accommodate visually impaired or busy users.

2. The system of claim 1.

5. The technical term explanation section Add examples and examples related to the explanation of the term to aid understanding 2. The system of claim 1.

6. The diagram simplification unit Analyzing the user's feelings about the technical terms or the diagrams and adjusting them to an easy-to-understand format 2. The system of claim 1.

7. The summary providing unit Estimate and suggest the diversion method that the user is most interested in.

2. The system of claim 1.

8. The summary providing unit Analyze the sentiment of local residents and businesses and make acceptable proposals 2. The system of claim 1.

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