System

Abstract

An object of a system according to an embodiment is to generate contents of a discussion based on a highly reliable information source.SOLUTION: A system includes an information source selection part and a discussion generation part. The information source selection unit selects an information source with high reliability. The discussion generation unit generates the content of the discussion based on the highly reliable information source selected by the information source selection 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] With conventional technology, there was a problem in that it was difficult to obtain discussion content based on reliable information due to the influence of fake news and filter bubbles.

[0005] The system according to the embodiment aims to generate discussion content based on reliable information sources. [Means for solving the problem]

[0006] The system according to the embodiment includes an information source selection unit and a discussion generation unit. The information source selection unit selects highly reliable information sources. The discussion generation unit generates the content of a discussion based on the highly reliable information sources selected by the information source selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate discussion content based on reliable information sources. [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 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 information provision system according to the embodiment of the present invention is a system in which a generation AI outputs the content of discussions on each issue based on a highly reliable information source, thereby enabling users to gain a broader and deeper understanding of each issue.

[0029] An information provision system according to an embodiment includes an information source selection unit and a discussion generation unit. The information source selection unit selects reliable information sources. For example, the information source selection unit selects reliable information sources such as press conferences, parliamentary debates, statements by university professors, and official corporate documents. Furthermore, the information source selection unit selects reliable information sources using a model fine-tuned in advance by a generation AI. For example, the generation AI receives a prompt including the instruction "select a reliable information source" as input information and selects appropriate information sources based on the prompt. The discussion generation unit generates discussion content based on the reliable information sources selected by the information source selection unit. For example, the generation AI receives a prompt including the instruction "generate discussion content on climate change" as input information and generates discussion content on climate change based on the prompt. Furthermore, the generation AI provides the generated discussion content to a user. For example, the generation AI provides the generated discussion content to a user via a web application or a mobile application. As a result, the information provision system according to an embodiment generates discussion content based on reliable information sources, allowing users to gain a broader and deeper understanding of each issue. For example, users will be able to obtain truthful and fair information based on reliable sources, and will be able to gain a multifaceted perspective without being influenced by fake news or filter bubbles. This is expected to lead to a healthier information environment for society as a whole.

[0030] The information source selection unit can compare the reliability of the information sources selected by the generation AI with a third-party organization's evaluation database and assign a reliability score. The information source selection unit, for example, compares the information sources selected by the generation AI with a third-party organization's evaluation database and assigns a reliability score. For example, it compares the information sources with a list of reliable information sources registered in the evaluation database and assigns a high score to matching information sources. The information source selection unit also selects information sources based on the reliability score. For example, it preferentially selects information sources with high reliability scores. This improves the accuracy of selecting reliable information sources.

[0031] The information source selection unit can track the accuracy of past statements or documents of the selected information source and dynamically adjust the selection criteria based on the reliability history. The information source selection unit, for example, tracks the accuracy of past statements or documents of the selected information source and dynamically adjusts the selection criteria based on the reliability history. For example, it checks whether past statements are consistent with the facts and preferentially selects information sources with high accuracy. The information source selection unit also dynamically adjusts the selection criteria based on the reliability history. For example, it preferentially selects information sources with a good reliability history. This dynamically adjusts the criteria for selecting highly reliable information sources, improving accuracy.

[0032] The information source selection unit can simultaneously select reliable information sources from different languages ​​or cultural spheres, thereby providing information from a global perspective. The information source selection unit can simultaneously select reliable information sources from different languages ​​or cultural spheres, thereby providing information from a global perspective. For example, the information source selection unit selects information sources in English, Chinese, French, etc. The information source selection unit can also select information sources from different cultural spheres. For example, the information source selection unit can select information sources from Asia, Europe, America, etc. This makes it possible to provide information from a global perspective.

[0033] The information source selection unit can provide the content of the selected information source in different media formats, such as text, audio, or video, to deepen the user's understanding. The information source selection unit, for example, provides the content of the selected information source in different media formats, such as text, audio, or video, to deepen the user's understanding. For example, it provides video or audio of a press conference. The information source selection unit also provides information in text format. For example, it converts the content of a press conference into text and provides it. In this way, providing information in different media formats deepens the user's understanding.

[0034] The discussion generation unit can introduce a process to improve the accuracy of the discussion content generated by the generation AI through expert review. The discussion generation unit, for example, introduces a process to improve the accuracy of the discussion content generated by the generation AI through expert review. For example, an expert checks the discussion content and points out areas for correction. The discussion generation unit also improves the accuracy of the discussion content through expert review. For example, an expert evaluates the discussion content and provides feedback. In this way, the accuracy of the discussion content is improved through expert review.

[0035] When generating discussion content, the discussion generation unit can refer to data on similar past discussions to generate more consistent content. When generating discussion content, the discussion generation unit, for example, refers to data on similar past discussions to generate more consistent content. For example, it refers to a database of past discussions to extract related discussion content. The discussion generation unit also generates discussion content based on data on similar past discussions. For example, it analyzes past discussion content to generate consistent discussion content. In this way, by referring to data on similar past discussions, consistent discussion content can be generated.

[0036] The discussion generation unit can simultaneously generate discussion content from different perspectives or positions, thereby providing users with a multifaceted view. The discussion generation unit, for example, simultaneously generates discussion content from different perspectives or positions, thereby providing users with a multifaceted view. For example, it simultaneously generates pro and con opinions. The discussion generation unit also provides discussion content from different perspectives or positions. For example, it generates discussion content that includes political perspectives, economic perspectives, social perspectives, etc. This makes it possible to provide users with a multifaceted view by providing discussion content from different perspectives or positions.

[0037] The discussion generation unit can provide the generated discussion content in an interactive format and add a function that allows users to participate in the discussion themselves. The discussion generation unit, for example, provides the generated discussion content in an interactive format and adds a function that allows users to participate in the discussion themselves. For example, it provides a function that allows users to post comments and questions. The discussion generation unit also provides the discussion content in an interactive format. For example, it provides the discussion content in a chat format or a forum format. This provides an interactive format that allows users to participate in the discussion themselves, and is expected to deepen the discussion.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The information provision system may further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit may analyze, for example, articles and videos viewed by the user in the past, search history, and the like, to identify the user's interests. The behavior analysis unit may also determine the optimal timing for providing information based on the user's behavioral patterns. For example, if a user tends to view information during a specific time period, information may be provided according to that time period. This makes it possible to provide information based on the user's interests, which is expected to improve user satisfaction.

[0040] The information provision system may further include a feedback collection unit that collects user feedback. The feedback collection unit provides, for example, a function that allows users to leave ratings and comments on the provided information. The feedback collection unit may also analyze the collected feedback and identify areas for improvement to improve the quality of information provision. For example, measures may be taken to improve information sources or discussion contents that have received low user ratings. This makes it possible to provide information that reflects user opinions, which is expected to improve the reliability of the system.

[0041] The information provision system can further include a health monitor unit that monitors the user's health condition and provides information according to the health condition. The health monitor unit monitors, for example, the user's heart rate and sleep state and provides information according to the health condition. The health monitor unit can also provide information to help the user relax if the user is tired. This makes it possible to provide information according to the user's health condition, which is expected to contribute to the user's health maintenance.

[0042] The information provision system can further include a learning style adaptation unit that provides information according to the user's learning style. For example, the learning style adaptation unit can provide information that makes extensive use of diagrams and graphs to a user who prefers visual learning. It can also provide information using audio and video to a user who prefers auditory learning. This makes it possible to provide information according to the user's learning style, which is expected to deepen the user's understanding.

[0043] The information provision system may further include a customization unit that customizes information based on the user's interests. For example, if the user is interested in a particular theme, the customization unit may provide information related to that theme preferentially. The customization unit may also suggest information that the user may be interested in based on the user's past browsing history and search history. This makes it possible to provide information that matches the user's interests, which is expected to improve user satisfaction.

[0044] The information provision system may further include a network analysis unit that analyzes the user's social network and provides information based on information shared within the network. The network analysis unit may, for example, analyze information shared by the user's friends and followers and provide related information. The network analysis unit may also prioritize providing information on topics that are trending within the user's network. This makes it possible to provide information based on the user's social network, which is expected to more easily attract the user's attention.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The source selection unit selects reliable sources. Examples include press conferences, parliamentary debates, statements by university professors, and official corporate documents. The generation AI then selects reliable sources using a pre-fine-tuned model. The generation AI receives a prompt containing the instruction "select a reliable source" as input, and selects appropriate sources based on that prompt. Step 2: The discussion generation unit generates discussion content based on the reliable information sources selected by the information source selection unit. For example, the generation AI receives a prompt containing the instruction "Generate discussion content on climate change" as input information, and generates discussion content on climate change based on the prompt. The generated discussion content is then provided to users via a web application or mobile application.

[0047] (Example 2) The information provision system according to the embodiment of the present invention is a system in which a generation AI outputs the content of discussions on each issue based on a highly reliable information source, thereby enabling users to gain a broader and deeper understanding of each issue.

[0048] An information provision system according to an embodiment includes an information source selection unit and a discussion generation unit. The information source selection unit selects reliable information sources. For example, the information source selection unit selects reliable information sources such as press conferences, parliamentary debates, statements by university professors, and official corporate documents. Furthermore, the information source selection unit selects reliable information sources using a model fine-tuned in advance by a generation AI. For example, the generation AI receives a prompt including the instruction "select a reliable information source" as input information and selects appropriate information sources based on the prompt. The discussion generation unit generates discussion content based on the reliable information sources selected by the information source selection unit. For example, the generation AI receives a prompt including the instruction "generate discussion content on climate change" as input information and generates discussion content on climate change based on the prompt. Furthermore, the generation AI provides the generated discussion content to a user. For example, the generation AI provides the generated discussion content to a user via a web application or a mobile application. As a result, the information provision system according to an embodiment generates discussion content based on reliable information sources, allowing users to gain a broader and deeper understanding of each issue. For example, users will be able to obtain truthful and fair information based on reliable sources, and will be able to gain a multifaceted perspective without being influenced by fake news or filter bubbles. This is expected to lead to a healthier information environment for society as a whole.

[0049] The information source selection unit can compare the reliability of the information sources selected by the generation AI with a third-party organization's evaluation database and assign a reliability score. The information source selection unit, for example, compares the information sources selected by the generation AI with a third-party organization's evaluation database and assigns a reliability score. For example, it compares the information sources with a list of reliable information sources registered in the evaluation database and assigns a high score to matching information sources. The information source selection unit also selects information sources based on the reliability score. For example, it preferentially selects information sources with high reliability scores. This improves the accuracy of selecting reliable information sources.

[0050] The information source selection unit can track the accuracy of past statements or documents of the selected information source and dynamically adjust the selection criteria based on the reliability history. The information source selection unit, for example, tracks the accuracy of past statements or documents of the selected information source and dynamically adjusts the selection criteria based on the reliability history. For example, it checks whether past statements are consistent with the facts and preferentially selects information sources with high accuracy. The information source selection unit also dynamically adjusts the selection criteria based on the reliability history. For example, it preferentially selects information sources with a good reliability history. This dynamically adjusts the criteria for selecting highly reliable information sources, improving accuracy.

[0051] The information source selection unit can use the emotion estimation function to evaluate the emotional bias of the statements and documents of information sources and preferentially select information sources with less bias. The information source selection unit, for example, uses the emotion estimation function to evaluate the emotional bias of the statements and documents of information sources and preferentially select information sources with less bias. For example, it selects information sources with emotion scores that are close to neutral. The information source selection unit also preferentially selects information sources with less emotional bias. For example, it selects information sources with less emotional bias. In this way, by selecting information sources with less emotional bias, it becomes possible to provide more neutral information.

[0052] The information source selection unit can simultaneously select reliable information sources from different languages ​​or cultural spheres, thereby providing information from a global perspective. The information source selection unit can simultaneously select reliable information sources from different languages ​​or cultural spheres, thereby providing information from a global perspective. For example, the information source selection unit selects information sources in English, Chinese, French, etc. The information source selection unit can also select information sources from different cultural spheres. For example, the information source selection unit can select information sources from Asia, Europe, America, etc. This makes it possible to provide information from a global perspective.

[0053] The information source selection unit can provide the content of the selected information source in different media formats, such as text, audio, or video, to deepen the user's understanding. The information source selection unit, for example, provides the content of the selected information source in different media formats, such as text, audio, or video, to deepen the user's understanding. For example, it provides video or audio of a press conference. The information source selection unit also provides information in text format. For example, it converts the content of a press conference into text and provides it. In this way, providing information in different media formats deepens the user's understanding.

[0054] The information source selection unit can use the emotion estimation function to identify the information source that the user trusts most and provide that information source preferentially. The information source selection unit, for example, uses the emotion estimation function to identify the information source that the user trusts most and provide that information source preferentially. For example, the information source selection unit selects an information source with a high emotion score for the user. The information source selection unit also provides the information source that the user trusts most. For example, the information source selection unit selects an information source based on user feedback data. In this way, by providing the information source that the user trusts most, the user's reliability is improved.

[0055] The discussion generation unit can introduce a process to improve the accuracy of the discussion content generated by the generation AI through expert review. The discussion generation unit, for example, introduces a process to improve the accuracy of the discussion content generated by the generation AI through expert review. For example, an expert checks the discussion content and points out areas for correction. The discussion generation unit also improves the accuracy of the discussion content through expert review. For example, an expert evaluates the discussion content and provides feedback. In this way, the accuracy of the discussion content is improved through expert review.

[0056] When generating discussion content, the discussion generation unit can refer to data on similar past discussions to generate more consistent content. When generating discussion content, the discussion generation unit, for example, refers to data on similar past discussions to generate more consistent content. For example, it refers to a database of past discussions to extract related discussion content. The discussion generation unit also generates discussion content based on data on similar past discussions. For example, it analyzes past discussion content to generate consistent discussion content. In this way, by referring to data on similar past discussions, consistent discussion content can be generated.

[0057] The discussion generation unit can use the emotion estimation function to monitor users' emotional reactions in real time and generate discussion content that elicits positive reactions. The discussion generation unit, for example, uses the emotion estimation function to monitor users' emotional reactions in real time and generate discussion content that elicits positive reactions. For example, the discussion content is adjusted based on the user's emotion score. The discussion generation unit also generates discussion content based on the user's emotional reactions. For example, the discussion generation unit analyzes the user's emotion data and generates discussion content that elicits positive reactions. This makes it possible to monitor users' emotional reactions and generate discussion content that elicits positive reactions.

[0058] The discussion generation unit can simultaneously generate discussion content from different perspectives or positions, thereby providing users with a multifaceted view. The discussion generation unit, for example, simultaneously generates discussion content from different perspectives or positions, thereby providing users with a multifaceted view. For example, it simultaneously generates pro and con opinions. The discussion generation unit also provides discussion content from different perspectives or positions. For example, it generates discussion content that includes political perspectives, economic perspectives, social perspectives, etc. This makes it possible to provide users with a multifaceted view by providing discussion content from different perspectives or positions.

[0059] The discussion generation unit can provide the generated discussion content in an interactive format and add a function that allows users to participate in the discussion themselves. The discussion generation unit, for example, provides the generated discussion content in an interactive format and adds a function that allows users to participate in the discussion themselves. For example, it provides a function that allows users to post comments and questions. The discussion generation unit also provides the discussion content in an interactive format. For example, it provides the discussion content in a chat format or a forum format. This provides an interactive format that allows users to participate in the discussion themselves, and is expected to deepen the discussion.

[0060] The discussion generation unit can use the emotion estimation function to identify the discussion topic that the user is most interested in and prioritize generating discussion content related to that topic. The discussion generation unit, for example, uses the emotion estimation function to identify the discussion topic that the user is most interested in and prioritize generating discussion content related to that topic. For example, it selects a topic with a high emotion score. The discussion generation unit also identifies a discussion topic based on the user's interests. For example, it selects a discussion topic based on the user's past preferences and feedback data. This makes it easier to attract the user's attention by identifying the discussion topic that the user is most interested in and priority generation of discussion content related to that topic.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The information provision system may further include a behavior analysis unit that analyzes the user's behavioral history. The behavior analysis unit may analyze, for example, articles and videos viewed by the user in the past, search history, and the like, to identify the user's interests. The behavior analysis unit may also determine the optimal timing for providing information based on the user's behavioral patterns. For example, if a user tends to view information during a specific time period, information may be provided according to that time period. This makes it possible to provide information based on the user's interests, which is expected to improve user satisfaction.

[0063] The information provision system may further include a feedback collection unit that collects user feedback. The feedback collection unit provides, for example, a function that allows users to leave ratings and comments on the provided information. The feedback collection unit may also analyze the collected feedback and identify areas for improvement to improve the quality of information provision. For example, measures may be taken to improve information sources or discussion contents that have received low user ratings. This makes it possible to provide information that reflects user opinions, which is expected to improve the reliability of the system.

[0064] The information provision system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the method of providing information based on the estimated emotion. For example, if the user is feeling stressed, the emotion adjustment unit may provide information that helps the user to relax. Furthermore, if the user is excited, the emotion adjustment unit may provide information that helps the user to think calmly. This makes it possible to provide information that corresponds to the user's emotional state, and is expected to improve user satisfaction.

[0065] The information provision system can further include a health monitor unit that monitors the user's health condition and provides information according to the health condition. The health monitor unit monitors, for example, the user's heart rate and sleep state and provides information according to the health condition. The health monitor unit can also provide information to help the user relax if the user is tired. This makes it possible to provide information according to the user's health condition, which is expected to contribute to the user's health maintenance.

[0066] The information provision system may further include a priority determination unit that estimates the user's emotions and determines the priority of information based on the estimated emotions. For example, if the user is feeling anxious, the priority determination unit may preferentially provide information that gives the user a sense of security. The priority determination unit may also preferentially provide information on topics that interest the user. This makes it possible to provide information according to the user's emotions, and is expected to improve user satisfaction.

[0067] The information provision system can further include a learning style adaptation unit that provides information according to the user's learning style. For example, the learning style adaptation unit can provide information that makes extensive use of diagrams and graphs to a user who prefers visual learning. It can also provide information using audio and video to a user who prefers auditory learning. This makes it possible to provide information according to the user's learning style, which is expected to deepen the user's understanding.

[0068] The information provision system may further include a format adjustment unit that estimates the user's emotions and adjusts the format of the information based on the estimated emotions. For example, if the user is tired, the format adjustment unit may provide information in a concise and easy-to-understand format. Alternatively, if the user is excited, the format adjustment unit may provide information with detailed and in-depth content. This makes it possible to adjust the information format according to the user's emotional state, which is expected to deepen the user's understanding.

[0069] The information provision system may further include a customization unit that customizes information based on the user's interests. For example, if the user is interested in a particular theme, the customization unit may provide information related to that theme preferentially. The customization unit may also suggest information that the user may be interested in based on the user's past browsing history and search history. This makes it possible to provide information that matches the user's interests, which is expected to improve user satisfaction.

[0070] The information provision system may further include a difficulty level adjustment unit that estimates the user's emotions and adjusts the difficulty level of the information based on the estimated emotions. For example, when the user is feeling stressed, the difficulty level adjustment unit may provide simple, easy-to-understand information. In addition, when the user is relaxed, the difficulty level adjustment unit may provide detailed, specialized information. This makes it possible to adjust the difficulty level of the information according to the user's emotional state, which is expected to deepen the user's understanding.

[0071] The information provision system may further include a network analysis unit that analyzes the user's social network and provides information based on information shared within the network. The network analysis unit may, for example, analyze information shared by the user's friends and followers and provide related information. The network analysis unit may also prioritize providing information on topics that are trending within the user's network. This makes it possible to provide information based on the user's social network, which is expected to more easily attract the user's attention.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The source selection unit selects reliable sources. Examples include press conferences, parliamentary debates, statements by university professors, and official corporate documents. The generation AI then selects reliable sources using a pre-fine-tuned model. The generation AI receives a prompt containing the instruction "select a reliable source" as input, and selects appropriate sources based on that prompt. Step 2: The discussion generation unit generates discussion content based on the reliable information sources selected by the information source selection unit. For example, the generation AI receives a prompt containing the instruction "Generate discussion content on climate change" as input information, and generates discussion content on climate change based on the prompt. The generated discussion content is then provided to users via a web application or mobile application.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 source selection unit that selects a highly reliable information source; a discussion generation unit that generates the content of a discussion based on the highly reliable information source selected by the information source selection unit. A system characterized by:

2. The information source selection unit Tracking the accuracy of past statements or documents of the selected sources and dynamically adjusting selection criteria based on the reliability history.

2. The system of claim 1.

3. The information source selection unit Simultaneously select reliable information sources from different languages ​​or cultural spheres to provide information from a global perspective.

2. The system of claim 1.

4. The discussion generation unit A process will be introduced to improve the accuracy of the discussion content generated by the AI ​​through expert review.

2. The system of claim 1.

5. The information source selection unit Using an emotion estimation function, the emotional bias of the statements and documents of the information sources is evaluated, and the information sources with less bias are preferentially selected.

2. The system of claim 1.

6. The discussion generation unit Using emotion estimation functionality, we monitor users' emotional reactions in real time and generate discussion content that elicits positive responses.

2. The system of claim 1.

7. The discussion generation unit Simultaneously generate discussion content from different perspectives or positions, providing users with a multifaceted view 2. The system of claim 1.

8. The discussion generation unit The generated discussion content will be provided in an interactive format, and a function will be added that allows users to participate in the discussion themselves.

2. The system of claim 1.

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