System

Abstract

An object of a system according to an embodiment is to quickly acquire highly reliable information from an enormous amount of information on the Internet.SOLUTION: A system according to an embodiment includes an information collection unit, a summary generation unit, a reliability evaluation unit, and a knowledge panel provision unit. The information collection unit collects information on the Internet. The summary generation unit summarizes the information collected by the information collection unit. The reliability evaluation unit evaluates the reliability of the information summarized by the summary generation unit. The knowledge panel providing unit provides the user with information evaluated by the reliability evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly obtain reliable information from the vast amount of information available on the Internet.

[0005] The system according to the embodiment aims to quickly acquire highly reliable information from the vast amount of information on the Internet. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, a summary generation unit, a reliability evaluation unit, and a knowledge panel provision unit. The information collection unit collects information on the Internet. The summary generation unit summarizes the information collected by the information collection unit. The reliability evaluation unit evaluates the reliability of the information summarized by the summary generation unit. The knowledge panel provision unit provides the information evaluated by the reliability evaluation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can quickly acquire highly reliable information from the vast amount of information on the Internet. [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 search information panel according to an embodiment of the present invention is a system that uses generative AI to automatically summarize vast amounts of information on the Internet and provide the most relevant content that users are looking for, allowing users to quickly and easily obtain the reliable information they need.

[0029] A search information panel according to an embodiment includes an information collection unit, a summary generation unit, a reliability evaluation unit, and a knowledge panel provision unit. The information collection unit collects information from the Internet, such as news articles, blog posts, and social media posts. The information collection unit can also automatically collect information using a web crawler. For example, the information collection unit searches for relevant web pages based on specific keywords and collects their content. The summary generation unit summarizes the information collected by the information collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to concisely summarize the information. The generation AI can also summarize the content of the information using a multimodal generation AI. The generation AI can also extract and summarize important parts of a sentence. For example, the text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information and then summarizes it. The reliability evaluation unit evaluates the reliability of the information summarized by the summary generation unit. For example, the reliability evaluation unit evaluates the reliability of information sources. For example, it prioritizes information from reliable sources, such as scientific papers and announcements from public institutions. The reliability evaluation unit can also evaluate the accuracy of information. For example, it checks whether the content of the information is consistent with other reliable information. The knowledge panel providing unit provides the user with the information evaluated by the reliability evaluation unit. For example, the knowledge panel providing unit displays a knowledge panel on the search engine results page. The knowledge panel providing unit can also organize information in a visually easy-to-understand format. For example, the knowledge panel providing unit organizes information by category and visualizes it using graphs and charts. This allows the search information panel according to the embodiment to quickly and easily obtain reliable information needed by the user. For example, if a user searches for "climate change," the generation AI automatically summarizes related information and provides it through the knowledge panel.This allows users to instantly grasp important information such as the causes, effects, and countermeasures of climate change.

[0030] The summary generation unit can provide a personalized summary by referring to the user's past search history and browsing history. For example, the summary generation unit uses a generation AI to analyze the user's past search history and extract related keywords and topics. This allows the summary generation unit to generate a personalized summary based on information that the user has been interested in in the past. The summary generation unit can also refer to the user's browsing history and prioritize summarizing related information. For example, it summarizes related information based on the content of pages the user has viewed in the past. This allows the user to be provided with a personalized summary.

[0031] The summary generation unit can collect feedback from users in real time regarding the information to be summarized and improve the accuracy of the summary based on that feedback. For example, the summary generation unit builds a system that collects feedback from users in real time after the generation AI provides a summary. For example, it collects ratings and comments on the content of the summary and improves the accuracy of the summary. The summary generation unit can also adjust the content of the summary based on user feedback. For example, if a user provides positive feedback on the content of the summary, it can improve the summary based on that content. This makes it possible to improve the accuracy of the summary based on user feedback.

[0032] The summary generation unit can provide information in a multimedia format, including summaries of audio or video. For example, the summary generation unit builds a system in which a generative AI automatically generates not only text information but also summaries of audio and video. For example, it uses speech recognition technology to convert audio data into text and generate a summary. The summary generation unit can also summarize the content of a video using video analysis technology. For example, it can extract important scenes from a video and generate a summary based on them. This makes it possible to provide information in a multimedia format, including summaries of audio and video.

[0033] The summary generation unit can automatically generate summaries in different languages, making it possible to accommodate international users. For example, the summary generation unit builds a system in which a generation AI automatically generates summaries in different languages. For example, it provides summaries in multiple languages, such as English, Japanese, and French. The summary generation unit can also use translation technology to translate summaries into different languages. For example, after the generation AI generates a summary, it can automatically translate it and provide it in a different language. This allows summaries in different languages ​​to be automatically generated, making it possible to accommodate international users.

[0034] The knowledge panel providing unit dynamically updates the information displayed on the knowledge panel according to the user's level of interest, allowing the most relevant information to always be provided. The knowledge panel providing unit, for example, builds a system that dynamically updates the information displayed on the knowledge panel according to the user's level of interest. For example, it prioritizes displaying highly relevant information based on the user's search history and browsing history. The knowledge panel providing unit can also monitor the user's level of interest in real time and update the information based on the results. For example, if a user shows interest in a particular topic, it prioritizes displaying information related to that topic. This allows the information on the knowledge panel to be dynamically updated according to the user's level of interest.

[0035] The knowledge panel providing unit can provide related links and additional information to the knowledge panel so that the user can dig deeper into the information. The knowledge panel providing unit, for example, builds a system that provides related links and additional information to the knowledge panel. For example, it displays links to articles and papers related to topics that interest the user. The knowledge panel providing unit can also provide additional information to enable the user to dig deeper into the information. For example, it displays detailed explanations and related data. This makes it possible to provide related links and additional information so that the user can dig deeper into the information.

[0036] The knowledge panel providing unit can add infographics and animations to the knowledge panel to make it easier for users to visually understand the information. For example, the knowledge panel providing unit adds infographics to the knowledge panel to make it easier for users to visually understand the information. For example, data and statistical information can be displayed in graphs and charts. The knowledge panel providing unit can also dynamically display information by adding animations. For example, information can be visualized using GIF animations and video animations. This allows infographics and animations to be added to make it easier for users to visually understand the information.

[0037] The knowledge panel providing unit can add a social media integration function to the knowledge panel to make it easier for users to share information. For example, the knowledge panel providing unit can add a social media integration function to the knowledge panel to make it easier for users to share information. For example, it can provide a function that allows information on the knowledge panel to be posted to a social media account with one click. The knowledge panel providing unit can also achieve seamless integration with social media through API integration. For example, when a user shares information on the knowledge panel, it can link with a social media account and automatically post the information. This allows the addition of a social media integration function to make it easier for users to share information.

[0038] The reliability evaluation unit can take into account the information source's past reliability evaluation and user feedback when evaluating the reliability of information. For example, the reliability evaluation unit builds a system in which the generation AI refers to the information source's past reliability evaluation when evaluating the reliability of information. For example, it prioritizes providing information from information sources that have been evaluated as highly reliable in the past. The reliability evaluation unit can also evaluate the reliability of information based on user feedback. For example, it re-evaluates the reliability of information based on evaluations and comments provided by users. This allows the reliability of information to be evaluated taking into account the information source's past reliability evaluation and user feedback.

[0039] When assessing the reliability of information, the reliability evaluation unit can perform scoring based on the expertise and authority of the information source. For example, the reliability evaluation unit constructs a system in which a generative AI evaluates the expertise of an information source and calculates a reliability score based on the results. For example, information from experts or academic institutions is rated high. The reliability evaluation unit can also evaluate the reliability of information based on the authority of the information source. For example, the reliability of information can be determined based on official certification or industry evaluation. This makes it possible to evaluate the reliability of information based on the expertise and authority of the information source.

[0040] The reliability evaluation unit can cross-reference information from different information sources and provide matching information preferentially. For example, the reliability evaluation unit constructs a system in which the generation AI cross-references information from different information sources and provides matching information preferentially. For example, matching data is extracted from multiple reliable information sources. The reliability evaluation unit can also evaluate the reliability of information based on the degree of matching of the information. For example, if information from different information sources matches, that information is evaluated as having a high score. This makes it possible to cross-reference information from different information sources and provide matching information preferentially.

[0041] The reliability evaluation unit can evaluate reliability by taking into account the update frequency and recency of an information source. For example, the reliability evaluation unit constructs a system in which a generation AI evaluates the update frequency of an information source and calculates a reliability score based on the results. For example, information sources that are updated frequently are given a high score. The reliability evaluation unit can also evaluate the reliability of information based on the recency of the information source. For example, the reliability of information can be determined based on the latest research results or publication date. This makes it possible to evaluate reliability by taking into account the update frequency and recency of an information source.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The search information panel can also monitor the user's health status and provide health-related information. For example, it can analyze the user's heart rate and sleep patterns based on data obtained from the wearable device and provide health advice. It can also suggest diet and exercise based on the user's health status. This allows users to understand their own health status and take appropriate measures.

[0044] The search information panel can also refer to the user's learning history and provide information useful for learning. For example, it can suggest related study materials and reference books based on the user's past learning content and progress. It can also provide learning methods that suit the user's learning style. This allows users to study efficiently.

[0045] The search information panel can also analyze a user's hobbies and interests to suggest related events and activities. For example, it can suggest related events and workshops based on events the user has previously attended or topics of interest. It can also introduce communities and groups based on the user's interests. This allows users to discover new hobbies and interests.

[0046] The search information panel can also refer to the user's purchase history to suggest related products and services. For example, it can suggest related products and services based on products and services the user has purchased in the past. It can also provide special offers and discount information based on the user's purchase history. This allows the user to obtain valuable information.

[0047] The search information panel can also analyze the user's location information and provide information about the surrounding area. For example, it can suggest nearby restaurants and tourist spots based on the user's current location. It can also provide traffic information and weather forecasts based on the user's location information. This allows users to get the information they need even while on the move.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The information collection unit collects information from the internet. For example, it collects news articles, blog posts, social media posts, etc. The information collection unit can also collect information automatically using a web crawler. For example, the information collection unit searches for relevant web pages based on specific keywords and collects their content. Step 2: The summary generation unit summarizes the information collected by the information collection unit. For example, the generation AI uses text generation AI (e.g., LLM) to concisely summarize the information. The generation AI can also use multimodal generation AI to summarize the content of the information. The generation AI can also extract and summarize important parts of a sentence. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the information and use that to create a summary. Step 3: The reliability evaluation unit evaluates the reliability of the information summarized by the summary generation unit. For example, the reliability evaluation unit evaluates the reliability of the information source. For example, the reliability evaluation unit prioritizes information from reliable sources, such as scientific papers or announcements from official institutions. The reliability evaluation unit can also evaluate the accuracy of the information. For example, the reliability evaluation unit checks whether the content of the information is consistent with other reliable information. Step 4: The knowledge panel provider provides the user with the information evaluated by the reliability evaluation unit. For example, the knowledge panel provider displays a knowledge panel on the search engine results page. The knowledge panel provider can also organize information in a visually easy-to-understand format. For example, the knowledge panel provider organizes information by category and visualizes it using graphs and charts. This allows the search information panel according to the embodiment to quickly and easily obtain the reliable information the user needs. For example, if a user searches for "climate change," the generation AI automatically summarizes the relevant information and provides it through the knowledge panel. This allows the user to instantly grasp important information such as the causes, effects, and countermeasures of climate change.

[0050] (Example 2) The search information panel according to an embodiment of the present invention is a system that uses generative AI to automatically summarize vast amounts of information on the Internet and provide the most relevant content that users are looking for, allowing users to quickly and easily obtain the reliable information they need.

[0051] A search information panel according to an embodiment includes an information collection unit, a summary generation unit, a reliability evaluation unit, and a knowledge panel provision unit. The information collection unit collects information from the Internet, such as news articles, blog posts, and social media posts. The information collection unit can also automatically collect information using a web crawler. For example, the information collection unit searches for relevant web pages based on specific keywords and collects their content. The summary generation unit summarizes the information collected by the information collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to concisely summarize the information. The generation AI can also summarize the content of the information using a multimodal generation AI. The generation AI can also extract and summarize important parts of a sentence. For example, the text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information and then summarizes it. The reliability evaluation unit evaluates the reliability of the information summarized by the summary generation unit. For example, the reliability evaluation unit evaluates the reliability of information sources. For example, it prioritizes information from reliable sources, such as scientific papers and announcements from public institutions. The reliability evaluation unit can also evaluate the accuracy of information. For example, it checks whether the content of the information is consistent with other reliable information. The knowledge panel providing unit provides the user with the information evaluated by the reliability evaluation unit. For example, the knowledge panel providing unit displays a knowledge panel on the search engine results page. The knowledge panel providing unit can also organize information in a visually easy-to-understand format. For example, the knowledge panel providing unit organizes information by category and visualizes it using graphs and charts. This allows the search information panel according to the embodiment to quickly and easily obtain reliable information needed by the user. For example, if a user searches for "climate change," the generation AI automatically summarizes related information and provides it through the knowledge panel.This allows users to instantly grasp important information such as the causes, effects, and countermeasures of climate change.

[0052] The summary generation unit can provide a personalized summary by referring to the user's past search history and browsing history. For example, the summary generation unit uses a generation AI to analyze the user's past search history and extract related keywords and topics. This allows the summary generation unit to generate a personalized summary based on information that the user has been interested in in the past. The summary generation unit can also refer to the user's browsing history and prioritize summarizing related information. For example, it summarizes related information based on the content of pages the user has viewed in the past. This allows the user to be provided with a personalized summary.

[0053] The summary generation unit can collect feedback from users in real time regarding the information to be summarized and improve the accuracy of the summary based on that feedback. For example, the summary generation unit builds a system that collects feedback from users in real time after the generation AI provides a summary. For example, it collects ratings and comments on the content of the summary and improves the accuracy of the summary. The summary generation unit can also adjust the content of the summary based on user feedback. For example, if a user provides positive feedback on the content of the summary, it can improve the summary based on that content. This makes it possible to improve the accuracy of the summary based on user feedback.

[0054] The summary generation unit uses the emotion estimation function to analyze the emotional response of the user when reading the summary, and can generate a summary that elicits positive emotions. For example, when the generation AI provides a summary, the summary generation unit analyzes the user's facial expressions and voice and estimates the emotional response in real time. For example, it analyzes the user's emotions using a camera or microphone and generates a summary that elicits positive emotions. The summary generation unit can also use the emotion estimation function to adjust the content of the summary based on the user's emotional response. For example, if the user expresses positive emotions, the summary can be improved based on that content. This makes it possible to generate a summary that elicits positive emotions from the user.

[0055] The summary generation unit can provide information in a multimedia format, including summaries of audio or video. For example, the summary generation unit builds a system in which a generative AI automatically generates not only text information but also summaries of audio and video. For example, it uses speech recognition technology to convert audio data into text and generate a summary. The summary generation unit can also summarize the content of a video using video analysis technology. For example, it can extract important scenes from a video and generate a summary based on them. This makes it possible to provide information in a multimedia format, including summaries of audio and video.

[0056] The summary generation unit can automatically generate summaries in different languages, making it possible to accommodate international users. For example, the summary generation unit builds a system in which a generation AI automatically generates summaries in different languages. For example, it provides summaries in multiple languages, such as English, Japanese, and French. The summary generation unit can also use translation technology to translate summaries into different languages. For example, after the generation AI generates a summary, it can automatically translate it and provide it in a different language. This allows summaries in different languages ​​to be automatically generated, making it possible to accommodate international users.

[0057] The summary generation unit can use the emotion estimation function to monitor the user's emotional response in real time when reading the summary and dynamically adjust the content of the summary. For example, the summary generation unit can build a system that monitors the user's emotional response in real time when the generation AI provides a summary. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. The summary generation unit can also dynamically adjust the content of the summary based on the user's emotional response using the emotion estimation function. For example, if the user expresses negative emotions, the summary can be improved based on that content. This makes it possible to monitor the user's emotional response in real time and dynamically adjust the content of the summary.

[0058] The knowledge panel providing unit dynamically updates the information displayed on the knowledge panel according to the user's level of interest, allowing the most relevant information to always be provided. The knowledge panel providing unit, for example, builds a system that dynamically updates the information displayed on the knowledge panel according to the user's level of interest. For example, it prioritizes displaying highly relevant information based on the user's search history and browsing history. The knowledge panel providing unit can also monitor the user's level of interest in real time and update the information based on the results. For example, if a user shows interest in a particular topic, it prioritizes displaying information related to that topic. This allows the information on the knowledge panel to be dynamically updated according to the user's level of interest.

[0059] The knowledge panel providing unit can provide related links and additional information to the knowledge panel so that the user can dig deeper into the information. The knowledge panel providing unit, for example, builds a system that provides related links and additional information to the knowledge panel. For example, it displays links to articles and papers related to topics that interest the user. The knowledge panel providing unit can also provide additional information to enable the user to dig deeper into the information. For example, it displays detailed explanations and related data. This makes it possible to provide related links and additional information so that the user can dig deeper into the information.

[0060] The knowledge panel providing unit can use the emotion estimation function to analyze the emotional response of a user when viewing a knowledge panel and prioritize displaying information that elicits positive emotions. The knowledge panel providing unit, for example, uses the emotion estimation function to build a system that analyzes the emotional response of a user when viewing a knowledge panel in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The knowledge panel providing unit can also use the emotion estimation function to adjust the display content based on the user's emotional response. For example, if a user expresses positive emotions, it prioritizes displaying information based on that content. This makes it possible to prioritize displaying information that elicits positive emotions from the user.

[0061] The knowledge panel providing unit can add infographics and animations to the knowledge panel to make it easier for users to visually understand the information. For example, the knowledge panel providing unit adds infographics to the knowledge panel to make it easier for users to visually understand the information. For example, data and statistical information can be displayed in graphs and charts. The knowledge panel providing unit can also dynamically display information by adding animations. For example, information can be visualized using GIF animations and video animations. This allows infographics and animations to be added to make it easier for users to visually understand the information.

[0062] The knowledge panel providing unit can add a social media integration function to the knowledge panel to make it easier for users to share information. For example, the knowledge panel providing unit can add a social media integration function to the knowledge panel to make it easier for users to share information. For example, it can provide a function that allows information on the knowledge panel to be posted to a social media account with one click. The knowledge panel providing unit can also achieve seamless integration with social media through API integration. For example, when a user shares information on the knowledge panel, it can link with a social media account and automatically post the information. This allows the addition of a social media integration function to make it easier for users to share information.

[0063] The knowledge panel providing unit can use the emotion estimation function to monitor the emotional response of a user when viewing a knowledge panel in real time and dynamically adjust the display content. The knowledge panel providing unit, for example, uses the emotion estimation function to build a system that monitors the emotional response of a user when viewing a knowledge panel in real time. For example, the knowledge panel providing unit analyzes the user's facial expressions and voice and calculates an emotion score. The knowledge panel providing unit can also use the emotion estimation function to dynamically adjust the display content based on the user's emotional response. For example, if the user expresses negative emotions, the information can be improved based on that content. This makes it possible to monitor the user's emotional response in real time and dynamically adjust the display content.

[0064] The reliability evaluation unit can take into account the information source's past reliability evaluation and user feedback when evaluating the reliability of information. For example, the reliability evaluation unit builds a system in which the generation AI refers to the information source's past reliability evaluation when evaluating the reliability of information. For example, it prioritizes providing information from information sources that have been evaluated as highly reliable in the past. The reliability evaluation unit can also evaluate the reliability of information based on user feedback. For example, it re-evaluates the reliability of information based on evaluations and comments provided by users. This allows the reliability of information to be evaluated taking into account the information source's past reliability evaluation and user feedback.

[0065] When assessing the reliability of information, the reliability evaluation unit can perform scoring based on the expertise and authority of the information source. For example, the reliability evaluation unit constructs a system in which a generative AI evaluates the expertise of an information source and calculates a reliability score based on the results. For example, information from experts or academic institutions is rated high. The reliability evaluation unit can also evaluate the reliability of information based on the authority of the information source. For example, the reliability of information can be determined based on official certification or industry evaluation. This makes it possible to evaluate the reliability of information based on the expertise and authority of the information source.

[0066] The reliability evaluation unit can use the emotion estimation function to analyze the emotional reaction of the user to the provided information and provide highly reliable information preferentially. The reliability evaluation unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reaction of the user to the provided information in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The reliability evaluation unit can also use the emotion estimation function to evaluate the reliability of information based on the user's emotional reaction. For example, it can provide information in which the user has expressed positive emotions preferentially. This makes it possible to analyze the user's emotional reaction and provide highly reliable information preferentially.

[0067] The reliability evaluation unit can cross-reference information from different information sources and provide matching information preferentially. For example, the reliability evaluation unit constructs a system in which the generation AI cross-references information from different information sources and provides matching information preferentially. For example, matching data is extracted from multiple reliable information sources. The reliability evaluation unit can also evaluate the reliability of information based on the degree of matching of the information. For example, if information from different information sources matches, that information is evaluated as having a high score. This makes it possible to cross-reference information from different information sources and provide matching information preferentially.

[0068] The reliability evaluation unit can evaluate reliability by taking into account the update frequency and recency of an information source. For example, the reliability evaluation unit constructs a system in which a generation AI evaluates the update frequency of an information source and calculates a reliability score based on the results. For example, information sources that are updated frequently are given a high score. The reliability evaluation unit can also evaluate the reliability of information based on the recency of the information source. For example, the reliability of information can be determined based on the latest research results or publication date. This makes it possible to evaluate reliability by taking into account the update frequency and recency of an information source.

[0069] The trustworthiness evaluation unit can use the emotion estimation function to monitor the emotional reaction of the user to the provided information in real time and dynamically adjust the trustworthiness evaluation criteria. The trustworthiness evaluation unit, for example, uses the emotion estimation function to build a system that monitors the emotional reaction of the user to the provided information in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The trustworthiness evaluation unit can also use the emotion estimation function to dynamically adjust the trustworthiness evaluation criteria based on the user's emotional reaction. For example, if the user expresses negative emotions, the trustworthiness evaluation criteria can be changed based on the content of those emotions. This makes it possible to monitor the user's emotional reactions in real time and dynamically adjust the trustworthiness evaluation criteria.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The search information panel can also monitor the user's health status and provide health-related information. For example, it can analyze the user's heart rate and sleep patterns based on data obtained from the wearable device and provide health advice. It can also suggest diet and exercise based on the user's health status. This allows users to understand their own health status and take appropriate measures.

[0072] The search information panel can also refer to the user's learning history and provide information useful for learning. For example, it can suggest related study materials and reference books based on the user's past learning content and progress. It can also provide learning methods that suit the user's learning style. This allows users to study efficiently.

[0073] The search information panel can also analyze a user's hobbies and interests to suggest related events and activities. For example, it can suggest related events and workshops based on events the user has previously attended or topics of interest. It can also introduce communities and groups based on the user's interests. This allows users to discover new hobbies and interests.

[0074] The search information panel can also refer to the user's purchase history to suggest related products and services. For example, it can suggest related products and services based on products and services the user has purchased in the past. It can also provide special offers and discount information based on the user's purchase history. This allows the user to obtain valuable information.

[0075] The search information panel can also analyze the user's location information and provide information about the surrounding area. For example, it can suggest nearby restaurants and tourist spots based on the user's current location. It can also provide traffic information and weather forecasts based on the user's location information. This allows users to get the information they need even while on the move.

[0076] The search information panel can use its emotion estimation function to analyze a user's stress level and suggest relaxation methods. For example, it can analyze the user's facial expressions and voice to estimate their stress level in real time. It can also provide relaxation methods and meditation guides according to the user's stress level. This allows the user to reduce stress and relax.

[0077] The search information panel can use its emotion estimation function to suggest music and videos that match the user's emotions. For example, by analyzing the user's emotions, it can suggest relaxing music when they want to relax, or uplifting videos when they want to cheer up. It can also automatically generate playlists based on the user's emotions, allowing users to enjoy content that matches their emotions.

[0078] The search information panel can use the emotion estimation function to provide news that corresponds to the user's emotions. For example, it can analyze the user's emotions and display positive news preferentially. Also, if the user expresses negative emotions, it can provide news that alleviates those emotions. This allows the user to receive news that corresponds to their emotions.

[0079] The search information panel can use its emotion estimation function to suggest exercises that match the user's emotions. For example, by analyzing the user's emotions, it can suggest yoga or stretching when they want to relax, or running or dancing when they want to release energy. It can also automatically generate an exercise plan based on the user's emotions, allowing the user to do exercises that match their emotions.

[0080] The search information panel can use its emotion estimation function to suggest reading lists that correspond to the user's emotions. For example, by analyzing the user's emotions, it can suggest relaxing books when they want to relax, or uplifting books when they want to cheer up. It can also automatically generate reading lists that correspond to the user's emotions. This allows users to enjoy reading that matches their emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The information collection unit collects information from the internet. For example, it collects news articles, blog posts, social media posts, etc. The information collection unit can also collect information automatically using a web crawler. For example, the information collection unit searches for relevant web pages based on specific keywords and collects their content. Step 2: The summary generation unit summarizes the information collected by the information collection unit. For example, the generation AI uses text generation AI (e.g., LLM) to concisely summarize the information. The generation AI can also use multimodal generation AI to summarize the content of the information. The generation AI can also extract and summarize important parts of a sentence. For example, the text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the information and use that to create a summary. Step 3: The reliability evaluation unit evaluates the reliability of the information summarized by the summary generation unit. For example, the reliability evaluation unit evaluates the reliability of the information source. For example, the reliability evaluation unit prioritizes information from reliable sources, such as scientific papers or announcements from official institutions. The reliability evaluation unit can also evaluate the accuracy of the information. For example, the reliability evaluation unit checks whether the content of the information is consistent with other reliable information. Step 4: The knowledge panel provider provides the user with the information evaluated by the reliability evaluation unit. For example, the knowledge panel provider displays a knowledge panel on the search engine results page. The knowledge panel provider can also organize information in a visually easy-to-understand format. For example, the knowledge panel provider organizes information by category and visualizes it using graphs and charts. This allows the search information panel according to the embodiment to quickly and easily obtain the reliable information the user needs. For example, if a user searches for "climate change," the generation AI automatically summarizes the relevant information and provides it through the knowledge panel. This allows the user to instantly grasp important information such as the causes, effects, and countermeasures of climate change.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 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. an information collection unit that collects information on the Internet; a summary generation unit that summarizes the information collected by the information collection unit; a reliability evaluation unit that evaluates the reliability of the information summarized by the summary generation unit; a knowledge panel providing unit that provides the information evaluated by the reliability evaluation unit to a user. A system characterized by:

2. The summary generation unit Referencing the user's past search and browsing history to provide a personalized summary 2. The system of claim 1.

3. The summary generation unit Provide information in multimedia format, including audio or video summaries of the information 2. The system of claim 1.

4. The knowledge panel providing unit The information displayed in the knowledge panel is dynamically updated based on the user's interest, ensuring the most relevant information is always provided.

2. The system of claim 1.

5. The reliability evaluation unit When assessing the reliability of the information, the source's past reliability ratings and feedback from the user are taken into consideration.

2. The system of claim 1.

6. The summary generation unit Analyze the emotional response of the user when reading the summary and generate a summary that elicits positive emotions 2. The system of claim 1.

7. The knowledge panel providing unit Analyze the emotional response of the user when viewing the knowledge panel and prioritize displaying information that elicits positive emotions.

2. The system of claim 1.

8. The reliability evaluation unit Analyze the emotional response of the user to the provided information and provide highly reliable information preferentially.

2. The system of claim 1.

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