System

Abstract

An object of the system according to the embodiment is to efficiently collect papers published on the Web and provide easy-to-understand summaries and term explanations.SOLUTION: A system includes a paper collection part, an analysis part, a summary generation part, a term explanation part, a search word explanation part, and a user interface part. A paper collection part collects papers disclosed on the Web by using the generation AI. The analysis unit analyzes the paper collected by the paper collection unit. A summary generation part summarizes the contents of the paper analyzed by the analysis part. The term explanation unit adds a term explanation to the summary generated by the summary generation unit. A retrieval word explanation part picks up a word whose retrieval number is large on the Web, and explains it. The user interface portion provides a summary and a glossary to the user.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 had the problem of not being able to efficiently collect papers published on the web and provide easy-to-understand summaries and glossaries.

[0005] The system according to the embodiment aims to efficiently collect papers published on the Web and provide easy-to-understand summaries and glossaries. [Means for solving the problem]

[0006] The system according to the embodiment comprises a paper collection unit, an analysis unit, a summary generation unit, a term explanation unit, a search word explanation unit, and a user interface unit. The paper collection unit uses a generation AI to collect papers published on the web. The analysis unit analyzes the papers collected by the paper collection unit. The summary generation unit summarizes the contents of the papers analyzed by the analysis unit. The term explanation unit adds term explanations to the summaries generated by the summary generation unit. The search word explanation unit picks out words that are frequently searched for on the web and provides explanations for them. The user interface unit provides the summaries and term explanations to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect papers published on the Web and provide easy-to-understand summaries and glossaries. [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 summarization service according to an embodiment of the present invention is a system that summarizes online papers with easy-to-understand terminology explanations for patients with cancer or incurable diseases and their families. This system has a function to select and explain frequently searched words. This allows the paper summarization service to help patients with cancer or incurable diseases and their families understand the latest research information in an easy-to-understand manner.

[0029] An article summarization system according to an embodiment includes an article collection unit, an analysis unit, a summary generation unit, a term explanation unit, a search word explanation unit, and a user interface unit. The article collection unit uses a generation AI to collect articles published on the web. For example, the generation AI searches an online article database based on specific keywords and automatically collects related articles. The generation AI can also collect articles based on a theme specified by a user. The analysis unit analyzes the articles collected by the article collection unit. For example, the generation AI analyzes the structure of the article and extracts important information. The generation AI also analyzes the content of the article using natural language processing technology to provide data for summary generation. The summary generation unit summarizes the content of the article analyzed by the analysis unit. For example, the generation AI uses a text generation AI (e.g., LLM) to concisely summarize the main points of the article. The generation AI can also summarize the content of the article using a multimodal generation AI. The term explanation unit adds term explanations to the summaries generated by the summary generation unit. For example, the generation AI adds easy-to-understand explanations for technical terms and difficult terms in the summary. The search word explanation unit selects frequently searched words on the web and provides explanations for them. For example, the generation AI analyzes search engine trend data, selects frequently searched words, and provides detailed explanations for them. The user interface unit provides the summary and terminology explanations to the user. For example, the user can view the papers summarized by the generation AI and the terminology explanations through a dedicated website or app. This allows the paper summarization system according to the embodiment to help patients suffering from cancer or incurable diseases and their families easily understand the latest research information. For example, users can quickly view papers summarized by the generation AI and increase their treatment options. Furthermore, the explanations of technical terms facilitate communication with doctors.

[0030] The paper collection section can limit its collection to the latest publications from specific research institutions or universities. For example, the generative AI automatically collects the latest research papers from the official websites of specific research institutions or universities. For example, it targets the latest publications from the medical schools of Harvard University and Stanford University. The generative AI also regularly checks the latest issues of specific academic journals to collect reliable papers. For example, it targets prestigious journals such as The New England Journal of Medicine and The Lancet. The generative AI also crawls the personal pages of researchers at specific research institutions or universities to collect the latest research results. For example, it retrieves the latest papers from the pages of specific professors or researchers. This allows it to collect only reliable information.

[0031] The analysis unit can evaluate the importance of a paper by taking into account the paper's author's past research results or number of citations. For example, the analysis unit uses the generation AI to retrieve the paper's author's past research results from a database and evaluate the paper's importance based on those results. For example, it prioritizes analysis of papers written by researchers who have received high ratings in the past. The generation AI also analyzes the number of citations of a paper and determines its importance by ranking papers with a high number of citations highly. For example, it prioritizes collection of papers with 100 or more citations. The generation AI also takes into account the h-index of the paper's author and evaluates papers written by authors with a high h-index as being highly important. For example, it prioritizes analysis of papers written by authors with an h-index of 50 or more. This allows for the analysis of papers with a high degree of importance to be prioritized.

[0032] The paper collection unit can collect not only papers, but also medical-related news articles or blog posts. In this paper collection unit, for example, the generation AI collects the latest articles from medical news sites and analyzes them together with papers. For example, it obtains information from medical news sites such as Medical News Today and WebMD. The generation AI also collects blog posts from medical experts and analyzes them together with papers. For example, it obtains posts about the latest treatments from blogs written by prominent doctors and researchers. The generation AI also collects user posts from medical forums and community sites and analyzes them together with papers. For example, it obtains information from medical forums on Reddit and the medical category on Quora. This makes it possible to provide multifaceted information.

[0033] The paper collection unit can automatically translate papers published in different languages ​​and provide information from an international perspective. For example, the generation AI automatically translates papers published in different languages ​​and collects papers in languages ​​other than English. For example, medical papers in Japanese or German are translated into English and analyzed. The generation AI also uses a multilingual translation engine to provide information from an international perspective. For example, papers in French or Spanish are automatically translated and analyzed. The generation AI also analyzes the translated papers and provides information including international research results. For example, the latest research papers in Chinese or Korean are translated into English and analyzed. This makes it possible to provide information from an international perspective.

[0034] The summary generation unit can summarize not only the conclusion of a paper but also its background or methodology. For example, the generative AI generates a summary that includes not only the conclusion of a paper but also the background and methodology of the research. For example, it briefly explains the purpose of the research and the experimental methods used. The generative AI also adds background information about the research to the paper summary to allow users to understand the overall picture of the research. For example, it explains the motivation for the research and its relevance to previous research. The generative AI also includes methodological details in the paper summary to allow users to evaluate the reliability of the research. For example, it briefly explains the dataset and experimental conditions used. This allows users to understand the overall picture of the research.

[0035] The summary generation unit can combine and use multiple summarization algorithms to improve the accuracy of the summary. For example, the generation AI in the summary generation unit combines and uses multiple summarization algorithms to improve the accuracy of the summary. For example, a summary is generated by combining abstractive summarization and extractive summarization. The generation AI also integrates the results of different summarization algorithms to select the most appropriate summary. For example, it compares the summary results of each algorithm and selects the summary with the most information. The generation AI also adjusts the parameters of the summarization algorithm to generate an optimal summary. For example, it adjusts the length of the summary or the weighting of importance to improve accuracy. This can improve the accuracy of the summary.

[0036] The summary generation unit can reconstruct the summary from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, the summary generation unit uses a generating AI to reconstruct the summary from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, it provides a summary from a medical perspective and a sociological perspective. The generating AI can also incorporate the opinions of different experts and reconstruct the summary to promote understanding from multiple perspectives. For example, it can provide a summary that combines the perspectives of a doctor and a psychologist. The generating AI can also incorporate research results from different fields and reconstruct the summary to promote understanding from multiple perspectives. For example, it can provide a summary that combines the perspectives of medicine and economics. This can promote understanding from multiple perspectives.

[0037] When adding a term explanation, the glossary section can promote visual understanding by using related images or diagrams. For example, the generation AI adds images or diagrams related to the term explanation to make it easier to understand visually. For example, an image of immune cells can be added to an explanation of immunotherapy. The generation AI can also add infographics to the term explanation to make it easier to understand visually. For example, a diagram can be used to illustrate the treatment process. The generation AI can also add videos to the term explanation to make it easier to understand visually. For example, an animation can be used to explain the mechanism of a treatment. This makes it possible to provide a term explanation that is easy to understand visually.

[0038] The glossary section can provide glossary explanations in different languages ​​to accommodate international users. For example, the generation AI can automatically translate glossary explanations into different languages ​​to accommodate international users. For example, explanations can be provided in multiple languages, such as English, Japanese, and French. The generation AI can also use a multilingual translation engine to provide glossary explanations in different languages. For example, it can automatically generate explanations in Spanish and German. The generation AI can also analyze the translated glossary explanations to provide information from an international perspective. For example, it can translate explanations in Chinese or Korean into English and provide them. This allows it to accommodate international users.

[0039] The terminology explanation unit can also provide terminology explanations in audio or video format to promote visual or auditory understanding. In the terminology explanation unit, for example, the generation AI provides terminology explanations in audio format to promote auditory understanding. For example, a function is added to play explanations of treatment methods in audio. The generation AI also provides terminology explanations in video format to promote visual understanding. For example, a video is generated that uses animation to explain the mechanism of treatment. The generation AI also provides terminology explanations in slide format to make them easier to understand visually. For example, the treatment process is displayed in a slideshow. This can promote visual and auditory understanding.

[0040] When picking out words with high search volumes, the search word explanation section can track changes in search trends in real time and reflect the latest topics. For example, the generation AI tracks words with high search volumes in real time and reflects the latest topics. For example, it analyzes search engine trend data and picks out the latest search words. The generation AI also analyzes changes in search trends and prioritizes picking out words that are experiencing a sudden increase in search volume. For example, it selects words that have seen a sudden increase in search volume within a specific period of time. The generation AI also monitors changes in search trends in real time and reflects the latest topics. For example, it regularly updates words with high search volumes to provide the latest information. This makes it possible to reflect the latest topics.

[0041] The search word explanation unit adds the latest related research results or news to the explanation for the picked word, thereby keeping the information fresh. For example, the search word explanation unit adds the latest related research results to the explanation for the word picked by the generation AI. For example, it supplements the explanation by citing the latest research papers and academic articles. It also adds the latest related news to the explanation for the word picked by the generation AI. For example, it obtains the latest information from medical news sites and reflects it in the explanation. It also adds the latest related data and statistics to the explanation for the word picked by the generation AI. For example, it includes the success rate of the latest treatments and the results of clinical trials in the explanation. This helps keep the information fresh.

[0042] The search word explanation unit can compare popular search terms across different regions or cultural spheres and provide explanations from a global perspective. For example, the generation AI can compare popular search terms across different regions or cultural spheres and provide explanations from a global perspective. For example, it can compare search trends in the United States and Europe. The generation AI can also collect search data from different regions and pick out popular search terms in each region. For example, it can analyze search trends in Asia and South America. The generation AI can also analyze search data from different cultural spheres and provide explanations that take cultural background into consideration. For example, it can generate explanations based on search trends in a specific cultural sphere. This makes it possible to provide explanations from a global perspective.

[0043] The search word explanation unit can reconstruct explanations for picked-up words from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, the search word explanation unit can reconstruct explanations for words picked up by the generation AI from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, it can provide explanations from both a medical perspective and a sociological perspective. The generation AI can also incorporate the opinions of different experts to reconstruct the explanation, promoting understanding from multiple perspectives. For example, it can provide an explanation that combines the perspectives of a doctor and a psychologist. The generation AI can also incorporate research results from different fields to reconstruct the explanation, promoting understanding from multiple perspectives. For example, it can provide an explanation that combines the perspectives of medicine and economics. This can promote understanding from multiple perspectives.

[0044] The user interface unit can add a personalization function based on the user's search history or browsing history to meet individual needs. In the user interface unit, for example, the generation AI analyzes the user's search history or browsing history to provide personalized content. For example, papers and commentaries related to previously searched keywords are preferentially displayed. The generation AI also provides content that meets individual needs based on user behavior data. For example, the latest research papers related to a user who is interested in a specific treatment are displayed. The generation AI also analyzes the user's search history or browsing history in real time to dynamically update the personalized content. For example, information related to newly searched keywords is instantly displayed. This makes it possible to meet individual needs.

[0045] The user interface unit can add a chatbot function in which the generation AI responds in real time when the user inputs a question. The user interface unit, for example, provides a chatbot function in which the generation AI responds to user questions in real time. For example, in response to a question such as "Please tell me about the latest cancer treatments," related papers and explanations are displayed. In addition, a chatbot function is developed in which the generation AI analyzes the user's question and provides the most appropriate answer. For example, it understands the intent of the question and provides relevant information. In addition, the generation AI generates answers to user questions in real time and provides them through the chatbot. For example, it displays related papers and explanations based on keywords entered by the user. This makes it possible to answer user questions in real time.

[0046] The user interface unit can be optimized for different devices, allowing for comfortable use on any device. For example, the user interface unit uses a generation AI to optimize the user interface for different devices, allowing for comfortable use on smartphones, tablets, and PCs. For example, a responsive design can be adopted, adjusting the layout according to the screen size. The generation AI can also optimize the user interface by taking into account the characteristics of different devices. For example, it can provide an interface suitable for touch operation on a smartphone. The generation AI can also analyze the user's device usage data and provide the optimal interface. For example, it can customize the interface to suit the device the user uses most frequently. This allows for comfortable use on any device.

[0047] The user interface unit can add a community function that enables users to share information and exchange opinions with other users. For example, the generation AI adds a community function to the user interface, allowing users to share information with other users. For example, it provides a forum or chat function. The generation AI also develops a community function to promote the exchange of opinions among users. For example, it provides a bulletin board where users can post questions and comments. The generation AI also adds a function to support users' community activities. For example, it provides a function that enables users to create groups and hold discussions on specific topics. This allows users to share information and exchange opinions with other users.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The paper collection unit can limit collection of papers to the latest publications from specific research institutions or universities. For example, the generative AI can automatically collect the latest research papers from the official websites of specific research institutions or universities. For example, it can target the latest publications from the medical schools of Harvard University and Stanford University. The generative AI can also regularly check the latest issues of specific academic journals to collect reliable papers. For example, it can target prestigious journals such as The New England Journal of Medicine and The Lancet. The generative AI can also crawl the personal pages of researchers at specific research institutions or universities to collect the latest research results. For example, it can retrieve the latest papers from the pages of specific professors or researchers. This allows it to collect only reliable information.

[0050] The analysis unit can evaluate the importance of a paper by taking into account the paper's author's past research results or number of citations. For example, the generation AI retrieves the paper's author's past research results from a database and evaluates the paper's importance based on those results. For example, it prioritizes analysis of papers by researchers who have received high ratings in the past. The generation AI also analyzes the number of citations of a paper and determines its importance by ranking papers with a high number of citations as highly ranked. For example, it prioritizes collection of papers with 100 or more citations. The generation AI also takes into account the h-index of the paper's author and evaluates papers by authors with a high h-index as being highly important. For example, it prioritizes analysis of papers by authors with an h-index of 50 or more. This allows for the analysis of highly important papers to be prioritized.

[0051] The paper collection unit can collect not only papers, but also medical-related news articles or blog posts. For example, the generation AI collects the latest articles from medical news sites and analyzes them together with papers. For example, it obtains information from medical news sites such as Medical News Today and WebMD. The generation AI also collects blog posts from medical experts and analyzes them together with papers. For example, it obtains posts about the latest treatments from blogs written by prominent doctors and researchers. The generation AI also collects user posts from medical forums and community sites and analyzes them together with papers. For example, it obtains information from medical forums on Reddit and the medical category on Quora. This makes it possible to provide multifaceted information.

[0052] The paper collection unit can automatically translate papers published in different languages ​​and provide information from an international perspective. For example, the generation AI can automatically translate papers published in different languages ​​and collect papers in languages ​​other than English. For example, medical papers in Japanese or German can be translated into English and analyzed. The generation AI can also use a multilingual translation engine to provide information from an international perspective. For example, papers in French or Spanish can be automatically translated and analyzed. The generation AI can also analyze the translated papers and provide information including international research results. For example, the latest research papers in Chinese or Korean can be translated into English and analyzed. This makes it possible to provide information from an international perspective.

[0053] The summary generation unit can summarize not only the conclusion of a paper but also its background or methodology. For example, the generative AI generates a summary that includes not only the conclusion of a paper but also the background and methodology of the research. For example, it briefly explains the purpose of the research and the experimental methods used. The generative AI also adds background information about the research to the paper summary to help users understand the overall picture of the research. For example, it explains the motivation for the research and its relevance to previous research. The generative AI also includes methodological details in the paper summary to help users evaluate the reliability of the research. For example, it briefly explains the dataset and experimental conditions used. This allows users to understand the overall picture of the research.

[0054] The summary generation unit can combine and use multiple summarization algorithms to improve the accuracy of the summary. For example, the generation AI can combine and use multiple summarization algorithms to improve the accuracy of the summary. For example, it can generate a summary by combining abstractive summarization and extractive summarization. The generation AI can also integrate the results of different summarization algorithms to select the most appropriate summary. For example, it can compare the summary results of each algorithm and select the summary with the most information. The generation AI can also adjust the parameters of the summarization algorithm to generate an optimal summary. For example, it can adjust the length of the summary or the weighting of importance to improve accuracy. This can improve the accuracy of the summary.

[0055] The summary generation unit can reconstruct the summary from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, the generation AI can reconstruct the summary from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, it can provide a summary from both a medical perspective and a sociological perspective. The generation AI can also incorporate the opinions of different experts and reconstruct the summary to promote understanding from multiple perspectives. For example, it can provide a summary that combines the perspectives of a doctor and a psychologist. The generation AI can also incorporate research results from different fields and reconstruct the summary to promote understanding from multiple perspectives. For example, it can provide a summary that combines the perspectives of medicine and economics. This can promote understanding from multiple perspectives.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The paper collection unit uses the generation AI to collect papers published on the web. For example, the generation AI can search a database of papers on the web based on specific keywords and automatically collect related papers. The generation AI can also collect papers based on a theme specified by the user. Step 2: The analysis unit analyzes the papers collected by the paper collection unit. For example, the generation AI analyzes the structure of the paper and extracts important information. The generation AI also analyzes the content of the paper using natural language processing technology and provides data for generating summaries. Step 3: The summary generation unit summarizes the content of the paper analyzed by the analysis unit. For example, the generation AI may use a text generation AI (e.g., LLM) to concisely summarize the main points of the paper. The generation AI may also use a multimodal generation AI to summarize the content of the paper. Step 4: The glossary section adds glossary information to the summary generated by the summary generator. For example, the AI ​​adds easy-to-understand explanations for technical terms or difficult terms in the summary. Step 5: The search word explanation section picks out words that are frequently searched for on the web and provides explanations for them. For example, the generation AI analyzes search engine trend data, selects words that are frequently searched for, and provides detailed explanations for them. Step 6: The user interface unit provides the summary and glossary to the user. For example, the user can view the paper and glossary summarized by the generation AI through a dedicated website or app.

[0058] (Example 2) The paper summarization service according to an embodiment of the present invention is a system that summarizes online papers with easy-to-understand terminology explanations for patients with cancer or incurable diseases and their families. This system has a function to select and explain frequently searched words. This allows the paper summarization service to help patients with cancer or incurable diseases and their families understand the latest research information in an easy-to-understand manner.

[0059] An article summarization system according to an embodiment includes an article collection unit, an analysis unit, a summary generation unit, a term explanation unit, a search word explanation unit, and a user interface unit. The article collection unit uses a generation AI to collect articles published on the web. For example, the generation AI searches an online article database based on specific keywords and automatically collects related articles. The generation AI can also collect articles based on a theme specified by a user. The analysis unit analyzes the articles collected by the article collection unit. For example, the generation AI analyzes the structure of the article and extracts important information. The generation AI also analyzes the content of the article using natural language processing technology to provide data for summary generation. The summary generation unit summarizes the content of the article analyzed by the analysis unit. For example, the generation AI uses a text generation AI (e.g., LLM) to concisely summarize the main points of the article. The generation AI can also summarize the content of the article using a multimodal generation AI. The term explanation unit adds term explanations to the summaries generated by the summary generation unit. For example, the generation AI adds easy-to-understand explanations for technical terms and difficult terms in the summary. The search word explanation unit selects frequently searched words on the web and provides explanations for them. For example, the generation AI analyzes search engine trend data, selects frequently searched words, and provides detailed explanations for them. The user interface unit provides the summary and terminology explanations to the user. For example, the user can view the papers summarized by the generation AI and the terminology explanations through a dedicated website or app. This allows the paper summarization system according to the embodiment to help patients suffering from cancer or incurable diseases and their families easily understand the latest research information. For example, users can quickly view papers summarized by the generation AI and increase their treatment options. Furthermore, the explanations of technical terms facilitate communication with doctors.

[0060] The paper collection section can limit its collection to the latest publications from specific research institutions or universities. For example, the generative AI automatically collects the latest research papers from the official websites of specific research institutions or universities. For example, it targets the latest publications from the medical schools of Harvard University and Stanford University. The generative AI also regularly checks the latest issues of specific academic journals to collect reliable papers. For example, it targets prestigious journals such as The New England Journal of Medicine and The Lancet. The generative AI also crawls the personal pages of researchers at specific research institutions or universities to collect the latest research results. For example, it retrieves the latest papers from the pages of specific professors or researchers. This allows it to collect only reliable information.

[0061] The analysis unit can evaluate the importance of a paper by taking into account the paper's author's past research results or number of citations. For example, the analysis unit uses the generation AI to retrieve the paper's author's past research results from a database and evaluate the paper's importance based on those results. For example, it prioritizes analysis of papers written by researchers who have received high ratings in the past. The generation AI also analyzes the number of citations of a paper and determines its importance by ranking papers with a high number of citations highly. For example, it prioritizes collection of papers with 100 or more citations. The generation AI also takes into account the h-index of the paper's author and evaluates papers written by authors with a high h-index as being highly important. For example, it prioritizes analysis of papers written by authors with an h-index of 50 or more. This allows for the analysis of papers with a high degree of importance to be prioritized.

[0062] The analysis unit uses the emotion estimation function to evaluate the emotional impact of the content of an article on patients or their families, and can prioritize the collection of articles that have a positive impact. For example, the analysis unit uses the generation AI to perform emotion analysis on the content of an article and prioritize the collection of articles that evoke positive emotions. For example, it selects articles that include successful treatment cases or research results that inspire hope. The generation AI also performs emotion analysis on the abstracts of articles and prioritizes the collection of articles with high positive emotion scores. For example, it selects articles about treatments that improve the quality of life of patients. The generation AI also performs emotion analysis on the conclusions of articles and prioritizes the collection of articles with positive conclusions. For example, it selects articles that confirm the effectiveness of new treatments. This makes it possible to prioritize the collection of articles that have a positive impact.

[0063] The paper collection unit can collect not only papers, but also medical-related news articles or blog posts. In this paper collection unit, for example, the generation AI collects the latest articles from medical news sites and analyzes them together with papers. For example, it obtains information from medical news sites such as Medical News Today and WebMD. The generation AI also collects blog posts from medical experts and analyzes them together with papers. For example, it obtains posts about the latest treatments from blogs written by prominent doctors and researchers. The generation AI also collects user posts from medical forums and community sites and analyzes them together with papers. For example, it obtains information from medical forums on Reddit and the medical category on Quora. This makes it possible to provide multifaceted information.

[0064] The paper collection unit can automatically translate papers published in different languages ​​and provide information from an international perspective. For example, the generation AI automatically translates papers published in different languages ​​and collects papers in languages ​​other than English. For example, medical papers in Japanese or German are translated into English and analyzed. The generation AI also uses a multilingual translation engine to provide information from an international perspective. For example, papers in French or Spanish are automatically translated and analyzed. The generation AI also analyzes the translated papers and provides information including international research results. For example, the latest research papers in Chinese or Korean are translated into English and analyzed. This makes it possible to provide information from an international perspective.

[0065] The analysis unit can use the emotion estimation function to analyze the user's emotional responses to the collected papers and identify themes that interest the user most. For example, the analysis unit collects the user's emotional responses to the papers collected by the generation AI in real time and identifies themes of high interest based on the emotion score. For example, it prioritizes displaying themes with a high number of positive emotional responses. The generation AI also analyzes the user's emotional response data and identifies themes of high interest. For example, it extracts themes of papers with high emotion scores. The generation AI also uses the emotion estimation function to analyze the user's emotional responses and identify themes of high interest. For example, it ranks themes based on the user's emotion score. This makes it possible to identify the themes of the user's greatest interest.

[0066] The summary generation unit can summarize not only the conclusion of a paper but also its background or methodology. For example, the generative AI generates a summary that includes not only the conclusion of a paper but also the background and methodology of the research. For example, it briefly explains the purpose of the research and the experimental methods used. The generative AI also adds background information about the research to the paper summary to allow users to understand the overall picture of the research. For example, it explains the motivation for the research and its relevance to previous research. The generative AI also includes methodological details in the paper summary to allow users to evaluate the reliability of the research. For example, it briefly explains the dataset and experimental conditions used. This allows users to understand the overall picture of the research.

[0067] The summary generation unit can combine and use multiple summarization algorithms to improve the accuracy of the summary. For example, the generation AI in the summary generation unit combines and uses multiple summarization algorithms to improve the accuracy of the summary. For example, a summary is generated by combining abstractive summarization and extractive summarization. The generation AI also integrates the results of different summarization algorithms to select the most appropriate summary. For example, it compares the summary results of each algorithm and selects the summary with the most information. The generation AI also adjusts the parameters of the summarization algorithm to generate an optimal summary. For example, it adjusts the length of the summary or the weighting of importance to improve accuracy. This can improve the accuracy of the summary.

[0068] The summary generation unit uses an emotion estimation function to evaluate the emotional impact that the summary has on the user and prioritizes the use of positive expressions. For example, the generation AI performs emotion analysis on the summary and prioritizes the use of positive expressions. For example, it selects expressions that inspire hope and positive words. The generation AI also evaluates the emotion score of the summary and generates a summary that evokes positive emotions. For example, it highlights successful cases of treatment and signs of improvement. The generation AI also uses the emotion estimation function to eliminate negative elements in the summary and replace them with positive expressions. For example, it modifies negative expressions to positive expressions. This prioritizes the use of positive expressions, thereby improving the emotional impact on the user.

[0069] The summary generation unit can reconstruct the summary from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, the summary generation unit uses a generating AI to reconstruct the summary from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, it provides a summary from a medical perspective and a sociological perspective. The generating AI can also incorporate the opinions of different experts and reconstruct the summary to promote understanding from multiple perspectives. For example, it can provide a summary that combines the perspectives of a doctor and a psychologist. The generating AI can also incorporate research results from different fields and reconstruct the summary to promote understanding from multiple perspectives. For example, it can provide a summary that combines the perspectives of medicine and economics. This can promote understanding from multiple perspectives.

[0070] The summary generation unit uses an emotion estimation function to collect users' emotional reactions to the summary in real time, and can continuously improve the content of the summary. For example, the summary generation unit uses a generation AI to collect users' emotional reactions to the summary in real time and continuously improve the content of the summary based on that data. For example, summaries with a high number of positive reactions are displayed preferentially. The generation AI also uses the emotion estimation function to analyze users' emotional scores for the summary and regenerates the summary if there are a high number of negative reactions. The generation AI also analyzes users' emotional reaction data and identifies areas for improvement in the summary based on the results. For example, it makes suggestions to correct parts with low emotional scores. This allows the content of the summary to be continuously improved.

[0071] When adding a term explanation, the glossary section can promote visual understanding by using related images or diagrams. For example, the generation AI adds images or diagrams related to the term explanation to make it easier to understand visually. For example, an image of immune cells can be added to an explanation of immunotherapy. The generation AI can also add infographics to the term explanation to make it easier to understand visually. For example, a diagram can be used to illustrate the treatment process. The generation AI can also add videos to the term explanation to make it easier to understand visually. For example, an animation can be used to explain the mechanism of a treatment. This makes it possible to provide a term explanation that is easy to understand visually.

[0072] The terminology explanation unit uses an emotion estimation function to evaluate the emotional impact that a term explanation has on the user and prioritizes the use of positive expressions. For example, the generation AI performs emotion analysis on the term explanation and prioritizes the use of positive expressions. For example, it selects expressions that emphasize the effectiveness of a treatment. The generation AI also evaluates the emotion score of the term explanation and generates an explanation that evokes positive emotions. For example, it emphasizes successful cases of treatment and signs of improvement. The generation AI also uses the emotion estimation function to eliminate negative elements in the term explanation and replace them with positive expressions. For example, it modifies negative expressions to positive expressions. This prioritizes the use of positive expressions, thereby improving the emotional impact on the user.

[0073] The glossary section can provide glossary explanations in different languages ​​to accommodate international users. For example, the generation AI can automatically translate glossary explanations into different languages ​​to accommodate international users. For example, explanations can be provided in multiple languages, such as English, Japanese, and French. The generation AI can also use a multilingual translation engine to provide glossary explanations in different languages. For example, it can automatically generate explanations in Spanish and German. The generation AI can also analyze the translated glossary explanations to provide information from an international perspective. For example, it can translate explanations in Chinese or Korean into English and provide them. This allows it to accommodate international users.

[0074] The terminology explanation unit can also provide terminology explanations in audio or video format to promote visual or auditory understanding. In the terminology explanation unit, for example, the generation AI provides terminology explanations in audio format to promote auditory understanding. For example, a function is added to play explanations of treatment methods in audio. The generation AI also provides terminology explanations in video format to promote visual understanding. For example, a video is generated that uses animation to explain the mechanism of treatment. The generation AI also provides terminology explanations in slide format to make them easier to understand visually. For example, the treatment process is displayed in a slideshow. This can promote visual and auditory understanding.

[0075] The term explanation unit uses the emotion estimation function to collect users' emotional reactions to term explanations in real time, allowing for continuous improvement of the content of the explanations. For example, the generation AI in the term explanation unit collects users' emotional reactions to term explanations in real time and continuously improves the content of the explanations based on that data. For example, it prioritizes displaying explanations with a high number of positive reactions. The generation AI also uses the emotion estimation function to analyze users' emotional scores for the term explanations and regenerates the explanations if there are a high number of negative reactions. The generation AI also analyzes users' emotional reaction data and identifies areas for improvement in the explanations based on the results. For example, it makes suggestions to correct parts with low emotional scores. This allows for continuous improvement of the content of the explanations.

[0076] When picking out words with high search volumes, the search word explanation section can track changes in search trends in real time and reflect the latest topics. For example, the generation AI tracks words with high search volumes in real time and reflects the latest topics. For example, it analyzes search engine trend data and picks out the latest search words. The generation AI also analyzes changes in search trends and prioritizes picking out words that are experiencing a sudden increase in search volume. For example, it selects words that have seen a sudden increase in search volume within a specific period of time. The generation AI also monitors changes in search trends in real time and reflects the latest topics. For example, it regularly updates words with high search volumes to provide the latest information. This makes it possible to reflect the latest topics.

[0077] The search word explanation unit adds the latest related research results or news to the explanation for the picked word, thereby keeping the information fresh. For example, the search word explanation unit adds the latest related research results to the explanation for the word picked by the generation AI. For example, it supplements the explanation by citing the latest research papers and academic articles. It also adds the latest related news to the explanation for the word picked by the generation AI. For example, it obtains the latest information from medical news sites and reflects it in the explanation. It also adds the latest related data and statistics to the explanation for the word picked by the generation AI. For example, it includes the success rate of the latest treatments and the results of clinical trials in the explanation. This helps keep the information fresh.

[0078] The search word explanation unit uses an emotion estimation function to analyze users' emotional reactions to frequently searched words and can prioritize providing explanations that have a positive impact. For example, the search word explanation unit uses a generation AI to collect users' emotional reactions to frequently searched words in real time and prioritize providing explanations that have a positive impact. For example, explanations with high emotion scores are displayed preferentially. The generation AI also uses the emotion estimation function to analyze users' emotion scores for frequently searched words and generate explanations that evoke positive emotions. The generation AI also analyzes users' emotional reaction data and prioritizes providing explanations that have a positive impact. For example, explanations are ranked based on the user's emotion score. This makes it possible to prioritize providing explanations that have a positive impact.

[0079] The search word explanation unit can compare popular search terms across different regions or cultural spheres and provide explanations from a global perspective. For example, the generation AI can compare popular search terms across different regions or cultural spheres and provide explanations from a global perspective. For example, it can compare search trends in the United States and Europe. The generation AI can also collect search data from different regions and pick out popular search terms in each region. For example, it can analyze search trends in Asia and South America. The generation AI can also analyze search data from different cultural spheres and provide explanations that take cultural background into consideration. For example, it can generate explanations based on search trends in a specific cultural sphere. This makes it possible to provide explanations from a global perspective.

[0080] The search word explanation unit can reconstruct explanations for picked-up words from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, the search word explanation unit can reconstruct explanations for words picked up by the generation AI from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, it can provide explanations from both a medical perspective and a sociological perspective. The generation AI can also incorporate the opinions of different experts to reconstruct the explanation, promoting understanding from multiple perspectives. For example, it can provide an explanation that combines the perspectives of a doctor and a psychologist. The generation AI can also incorporate research results from different fields to reconstruct the explanation, promoting understanding from multiple perspectives. For example, it can provide an explanation that combines the perspectives of medicine and economics. This can promote understanding from multiple perspectives.

[0081] The user interface unit can add a personalization function based on the user's search history or browsing history to meet individual needs. In the user interface unit, for example, the generation AI analyzes the user's search history or browsing history to provide personalized content. For example, papers and commentaries related to previously searched keywords are preferentially displayed. The generation AI also provides content that meets individual needs based on user behavior data. For example, the latest research papers related to a user who is interested in a specific treatment are displayed. The generation AI also analyzes the user's search history or browsing history in real time to dynamically update the personalized content. For example, information related to newly searched keywords is instantly displayed. This makes it possible to meet individual needs.

[0082] The user interface unit can add a chatbot function in which the generation AI responds in real time when the user inputs a question. The user interface unit, for example, provides a chatbot function in which the generation AI responds to user questions in real time. For example, in response to a question such as "Please tell me about the latest cancer treatments," related papers and explanations are displayed. In addition, a chatbot function is developed in which the generation AI analyzes the user's question and provides the most appropriate answer. For example, it understands the intent of the question and provides relevant information. In addition, the generation AI generates answers to user questions in real time and provides them through the chatbot. For example, it displays related papers and explanations based on keywords entered by the user. This makes it possible to answer user questions in real time.

[0083] The user interface unit can be optimized for different devices, allowing for comfortable use on any device. For example, the user interface unit uses a generation AI to optimize the user interface for different devices, allowing for comfortable use on smartphones, tablets, and PCs. For example, a responsive design can be adopted, adjusting the layout according to the screen size. The generation AI can also optimize the user interface by taking into account the characteristics of different devices. For example, it can provide an interface suitable for touch operation on a smartphone. The generation AI can also analyze the user's device usage data and provide the optimal interface. For example, it can customize the interface to suit the device the user uses most frequently. This allows for comfortable use on any device.

[0084] The user interface unit can add a community function that enables users to share information and exchange opinions with other users. For example, the generation AI adds a community function to the user interface, allowing users to share information with other users. For example, it provides a forum or chat function. The generation AI also develops a community function to promote the exchange of opinions among users. For example, it provides a bulletin board where users can post questions and comments. The generation AI also adds a function to support users' community activities. For example, it provides a function that enables users to create groups and hold discussions on specific topics. This allows users to share information and exchange opinions with other users.

[0085] The user interface unit uses the emotion estimation function to collect the user's emotional reactions to the user interface in real time, and can continuously improve the design or functions of the interface. In the user interface unit, for example, the generation AI uses the emotion estimation function to collect the user's emotional reactions to the user interface in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation AI also continuously improves the interface design and functions based on the user's emotional reaction data. For example, it prioritizes the adoption of designs that have a high number of positive emotional reactions. The generation AI also collects emotion estimation data in real time and dynamically adjusts the interface design and functions. For example, it customizes the interface according to changes in the user's emotions. This allows the interface design and functions to be continuously improved.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The paper collection unit can limit collection of papers to the latest publications from specific research institutions or universities. For example, the generative AI can automatically collect the latest research papers from the official websites of specific research institutions or universities. For example, it can target the latest publications from the medical schools of Harvard University and Stanford University. The generative AI can also regularly check the latest issues of specific academic journals to collect reliable papers. For example, it can target prestigious journals such as The New England Journal of Medicine and The Lancet. The generative AI can also crawl the personal pages of researchers at specific research institutions or universities to collect the latest research results. For example, it can retrieve the latest papers from the pages of specific professors or researchers. This allows it to collect only reliable information.

[0088] The analysis unit can evaluate the importance of a paper by taking into account the paper's author's past research results or number of citations. For example, the generation AI retrieves the paper's author's past research results from a database and evaluates the paper's importance based on those results. For example, it prioritizes analysis of papers by researchers who have received high ratings in the past. The generation AI also analyzes the number of citations of a paper and determines its importance by ranking papers with a high number of citations as highly ranked. For example, it prioritizes collection of papers with 100 or more citations. The generation AI also takes into account the h-index of the paper's author and evaluates papers by authors with a high h-index as being highly important. For example, it prioritizes analysis of papers by authors with an h-index of 50 or more. This allows for the analysis of highly important papers to be prioritized.

[0089] The analysis unit uses the emotion estimation function to evaluate the emotional impact of the contents of an article on patients or their families, and can prioritize the collection of articles that have a positive impact. For example, the generation AI performs emotion analysis on the contents of an article and prioritizes the collection of articles that evoke positive emotions. For example, it selects articles that include successful treatment cases or research results that inspire hope. The generation AI also performs emotion analysis on the abstracts of articles and prioritizes the collection of articles with high positive emotion scores. For example, it selects articles about treatments that improve the quality of life of patients. The generation AI also performs emotion analysis on the conclusions of articles and prioritizes the collection of articles with positive conclusions. For example, it selects articles that confirm the effectiveness of new treatments. This makes it possible to prioritize the collection of articles that have a positive impact.

[0090] The paper collection unit can collect not only papers, but also medical-related news articles or blog posts. For example, the generation AI collects the latest articles from medical news sites and analyzes them together with papers. For example, it obtains information from medical news sites such as Medical News Today and WebMD. The generation AI also collects blog posts from medical experts and analyzes them together with papers. For example, it obtains posts about the latest treatments from blogs written by prominent doctors and researchers. The generation AI also collects user posts from medical forums and community sites and analyzes them together with papers. For example, it obtains information from medical forums on Reddit and the medical category on Quora. This makes it possible to provide multifaceted information.

[0091] The paper collection unit can automatically translate papers published in different languages ​​and provide information from an international perspective. For example, the generation AI can automatically translate papers published in different languages ​​and collect papers in languages ​​other than English. For example, medical papers in Japanese or German can be translated into English and analyzed. The generation AI can also use a multilingual translation engine to provide information from an international perspective. For example, papers in French or Spanish can be automatically translated and analyzed. The generation AI can also analyze the translated papers and provide information including international research results. For example, the latest research papers in Chinese or Korean can be translated into English and analyzed. This makes it possible to provide information from an international perspective.

[0092] The analysis unit uses the emotion estimation function to analyze the user's emotional responses to collected papers and can identify themes that interest the user most. For example, the generation AI collects the user's emotional responses to collected papers in real time and identifies themes of high interest based on the emotional score. For example, it prioritizes displaying themes with a high number of positive emotional responses. The generation AI also analyzes the user's emotional response data and identifies themes of highest interest. For example, it extracts themes of papers with high emotional scores. The generation AI also uses the emotion estimation function to analyze the user's emotional responses and identify themes of highest interest. For example, it ranks themes based on the user's emotional score. This makes it possible to identify the themes of highest interest to the user.

[0093] The summary generation unit can summarize not only the conclusion of a paper but also its background or methodology. For example, the generative AI generates a summary that includes not only the conclusion of a paper but also the background and methodology of the research. For example, it briefly explains the purpose of the research and the experimental methods used. The generative AI also adds background information about the research to the paper summary to help users understand the overall picture of the research. For example, it explains the motivation for the research and its relevance to previous research. The generative AI also includes methodological details in the paper summary to help users evaluate the reliability of the research. For example, it briefly explains the dataset and experimental conditions used. This allows users to understand the overall picture of the research.

[0094] The summary generation unit can combine and use multiple summarization algorithms to improve the accuracy of the summary. For example, the generation AI can combine and use multiple summarization algorithms to improve the accuracy of the summary. For example, it can generate a summary by combining abstractive summarization and extractive summarization. The generation AI can also integrate the results of different summarization algorithms to select the most appropriate summary. For example, it can compare the summary results of each algorithm and select the summary with the most information. The generation AI can also adjust the parameters of the summarization algorithm to generate an optimal summary. For example, it can adjust the length of the summary or the weighting of importance to improve accuracy. This can improve the accuracy of the summary.

[0095] The summary generation unit uses the emotion estimation function to evaluate the emotional impact that the summary has on the user and prioritizes the use of positive expressions. For example, the generation AI performs emotion analysis on the summary and prioritizes the use of positive expressions. For example, it selects expressions that inspire hope and positive words. The generation AI also evaluates the emotion score of the summary and generates a summary that evokes positive emotions. For example, it highlights successful cases of treatment and signs of improvement. The generation AI also uses the emotion estimation function to eliminate negative elements in the summary and replace them with positive expressions. For example, it modifies negative expressions to positive ones. This prioritizes the use of positive expressions, thereby improving the emotional impact on the user.

[0096] The summary generation unit can reconstruct the summary from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, the generation AI can reconstruct the summary from the perspectives of different fields of expertise, promoting understanding from multiple perspectives. For example, it can provide a summary from both a medical perspective and a sociological perspective. The generation AI can also incorporate the opinions of different experts and reconstruct the summary to promote understanding from multiple perspectives. For example, it can provide a summary that combines the perspectives of a doctor and a psychologist. The generation AI can also incorporate research results from different fields and reconstruct the summary to promote understanding from multiple perspectives. For example, it can provide a summary that combines the perspectives of medicine and economics. This can promote understanding from multiple perspectives.

[0097] The summary generation unit uses the emotion estimation function to collect users' emotional reactions to the summary in real time, allowing it to continuously improve the content of the summary. For example, the generation AI collects users' emotional reactions to the summary in real time and continuously improves the content of the summary based on that data. For example, summaries with a high number of positive reactions can be displayed preferentially. The generation AI also uses the emotion estimation function to analyze users' emotional scores for the summary and regenerates the summary if there are a high number of negative reactions. The generation AI also analyzes users' emotional reaction data and identifies areas for improvement in the summary based on the results. For example, it makes suggestions to correct parts with low emotional scores. This allows the content of the summary to be continuously improved.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The paper collection unit uses the generation AI to collect papers published on the web. For example, the generation AI can search a database of papers on the web based on specific keywords and automatically collect related papers. The generation AI can also collect papers based on a theme specified by the user. Step 2: The analysis unit analyzes the papers collected by the paper collection unit. For example, the generation AI analyzes the structure of the paper and extracts important information. The generation AI also analyzes the content of the paper using natural language processing technology and provides data for generating summaries. Step 3: The summary generation unit summarizes the content of the paper analyzed by the analysis unit. For example, the generation AI may use a text generation AI (e.g., LLM) to concisely summarize the main points of the paper. The generation AI may also use a multimodal generation AI to summarize the content of the paper. Step 4: The glossary section adds glossary information to the summary generated by the summary generator. For example, the AI ​​adds easy-to-understand explanations for technical terms or difficult terms in the summary. Step 5: The search word explanation section picks out words that are frequently searched for on the web and provides explanations for them. For example, the generation AI analyzes search engine trend data, selects words that are frequently searched for, and provides detailed explanations for them. Step 6: The user interface unit provides the summary and glossary to the user. For example, the user can view the paper and glossary summarized by the generation AI through a dedicated website or app.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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, in order to avoid confusion and to 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.

[0166] 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]

[0167] 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 collection department that uses generative AI to collect papers published on the web; an analysis unit that analyzes the papers collected by the paper collection unit; a summary generation unit that summarizes the content of the paper analyzed by the analysis unit; a term explanation unit that adds a term explanation to the summary generated by the summary generation unit; A search word explanation section that picks out words that are frequently searched on the web and provides explanations for them; a user interface unit that provides the summary and the glossary to a user; A system characterized by:

2. The paper collection section Collect the latest papers from specific research institutes or universities 2. The system of claim 1.

3. The analysis unit Evaluate the importance of the paper by taking into account the author's past research achievements or number of citations.

2. The system of claim 1.

4. The summary generation unit Summarize the paper's background or methodology as well as its conclusions 2. The system of claim 1.

5. The term explanation section When adding the glossary, use relevant images or diagrams to facilitate visual understanding.

2. The system of claim 1.

6. The search word explanation unit When selecting the most popular search terms, we track changes in search trends in real time and reflect the latest topics.

2. The system of claim 1.

7. The user interface unit Add personalization features based on the user's search history or browsing history to meet individual needs.

2. The system of claim 1.

8. The analysis unit Evaluate the emotional impact of the content of the article on patients or their families, and prioritize collection of articles that have a positive impact.

2. The system of claim 1.

Images