System

Abstract

An object of a system according to an embodiment is to quickly and accurately evaluate the reliability of acquired information.SOLUTION: A system includes an information analysis part, a reliability evaluation part, a source confirmation part, an update evaluation part, and a user interface part. The information analysis unit analyzes the information using the generated AI. The reliability evaluation unit evaluates reliability of the information analyzed by the information analysis unit. The citation confirmation unit confirms the citation of the information evaluated by the reliability evaluation unit. The update evaluation section re-evaluates the update of the information confirmed by the citation confirmation section. The user interface unit provides the information re-evaluated by the update evaluation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately evaluate the reliability of acquired information.

[0005] The system according to the embodiment aims to quickly and accurately evaluate the reliability of acquired information. [Means for solving the problem]

[0006] The system according to the embodiment includes an information analysis unit, a reliability evaluation unit, a source confirmation unit, an update evaluation unit, and a user interface unit. The information analysis unit analyzes information using a generation AI. The reliability evaluation unit evaluates the reliability of the information analyzed by the information analysis unit. The source confirmation unit confirms the source of the information evaluated by the reliability evaluation unit. The update evaluation unit reevaluates updates to the information confirmed by the source confirmation unit. The user interface unit provides the information reevaluated by the update evaluation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately evaluate the reliability of the acquired information. [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 fact-checking system according to the embodiment of the present invention uses a generative AI to evaluate the reliability of information, verify sources, and reassess updated information, thereby enabling the fact-checking system to provide users with accurate information.

[0029] A fact-checking system according to an embodiment includes an information analysis unit, a reliability evaluation unit, a source verification unit, an update evaluation unit, and a user interface unit. The information analysis unit analyzes information provided by a user using a generation AI. For example, the generation AI analyzes the information using a text generation AI (e.g., GPT-3). The generation AI can also analyze the content of the information using natural language processing technology. The generation AI can also analyze the information using data mining technology. The reliability evaluation unit evaluates the reliability of the information analyzed by the information analysis unit. For example, the reliability evaluation unit evaluates whether the information matches multiple reliable information sources. The reliability evaluation unit can also perform the evaluation based on the reliability score of the information source. The reliability evaluation unit can also perform the evaluation based on past reliability history. The source verification unit verifies the source of the information evaluated by the reliability evaluation unit. For example, the source verification unit identifies the source from which the information comes. The source verification unit can also evaluate the reliability of the source. The source verification unit can also verify the history of the source. The update evaluation unit reevaluates updates to the information confirmed by the source confirmation unit. For example, if the information is newly updated, the update evaluation unit reanalyzes the information and reevaluates its reliability. The update evaluation unit can also evaluate the reliability of the update content. The update evaluation unit can also perform evaluation based on the update frequency. The user interface unit provides the information reevaluated by the update evaluation unit to the user. For example, the user interface unit provides an intuitive user interface, simplifying the process from the user entering information to the generation AI analyzing the information and displaying the results. The user interface unit can also analyze the user's operation history and provide an interface optimized for each individual user. The user interface unit can also use an emotion estimation function to analyze the user's emotional response and provide an emotionally friendly interface. This allows the fact-checking system according to the embodiment to provide accurate information to the user. For example, the fact-checking system can quickly and accurately evaluate information from various sources, such as news articles, social media posts, and corporate press releases.This makes it easier to select and reject information and helps prevent the spread of misinformation and fake news.

[0030] The information analysis unit can refer to news articles and academic papers to understand the context of information provided by users and make judgments based on the context. For example, when the generation AI performs fact-checking, the information analysis unit automatically collects and analyzes past news articles and academic papers to understand the context of the information provided by users. For example, when checking information about a specific incident, the information analysis unit refers to past news articles related to that incident to understand the context. The information analysis unit can also allow the generation AI to analyze the context of the information using a context analysis algorithm. For example, the generation AI analyzes the context based on selection criteria for related news articles and academic papers. This allows for more accurate fact-checking by understanding the context of the information.

[0031] The reliability evaluation unit can refer to the information source's past reliability history and take into account fluctuations in reliability. For example, when the generation AI performs fact-checking, the reliability evaluation unit refers to the information source's past reliability history and takes into account fluctuations in reliability in order to evaluate the reliability of information. For example, the reliability evaluation unit refers to the past reliability history of a specific news site and determines whether the site is highly reliable. The reliability evaluation unit can also allow the generation AI to calculate the reliability score of the information source based on the reliability history data. For example, the reliability of the information source is evaluated based on the past reliability score. This allows for more accurate reliability evaluation by taking into account the information source's reliability history.

[0032] When verifying the source of information, the source verification unit can refer to the source's past reliability history to evaluate the reliability of the source. For example, when the generation AI verifies the source of information, the source verification unit refers to the source's past reliability history to evaluate the source's reliability. For example, the source verification unit refers to the past reliability history of a specific news site to determine whether the site is highly reliable. The source verification unit can also allow the generation AI to evaluate the source's reliability based on the source's reliability score. For example, the source's reliability is evaluated based on its past reliability score. This allows more accurate source verification by referring to the source's reliability history.

[0033] When updating and re-evaluating information, the update evaluation unit can refer to past reliability history to evaluate the reliability of the updated information. For example, when the generation AI updates and re-evaluates information, the update evaluation unit refers to past reliability history to evaluate the reliability of the updated information. For example, the update evaluation unit refers to the past reliability history of a specific news site to determine whether the site is highly reliable. The update evaluation unit can also evaluate the reliability of the updated content based on the reliability score of the updated information by the generation AI. For example, the reliability of the information is re-evaluated based on the reliability of the updated content. This allows the reliability of the updated information to be evaluated, thereby always providing the latest and accurate information.

[0034] The user interface unit can analyze the user's operation history and provide an interface optimized for each individual user. For example, in a user interface provided by a generation AI, the user interface unit can analyze the user's operation history and provide an interface optimized for each individual user. For example, it can prioritize displaying functions that the user uses frequently. The user interface unit can also customize the interface based on the user's operation history by analyzing the operation pattern using the generation AI. For example, it can optimize the interface layout based on the user's click data. In this way, an interface optimized for each individual user can be provided by analyzing the user's operation history.

[0035] The user interface unit can provide an interface compatible with different devices. For example, the user interface unit provides an interface compatible with different devices (smartphone, tablet, PC) in the user interface provided by the generation AI. For example, an interface for smartphones and an interface for PCs are provided. The user interface unit can also provide a UI optimized for each device by using responsive design by the generation AI. For example, the layout of the interface can be adjusted depending on the screen size of the device. This improves user convenience by providing an interface compatible with different devices.

[0036] The user interface unit can introduce new input methods such as voice input and gesture operation. For example, the user interface unit introduces new input methods such as voice input and gesture operation in a user interface provided by the generation AI. For example, the user inputs information using voice input. The user interface unit also allows the user to operate the interface using gesture operation, with the generation AI using gesture recognition technology. For example, the user inputs information using gesture operation. This introduces new input methods, improving user operability.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The fact-checking system can also include a geographic information unit that acquires the user's geographic information and evaluates the reliability of information by region. For example, when evaluating the reliability of news in a specific region, the system can refer to the region's past reliability history. The geographic information unit can also perform evaluations based on the reliability scores of information sources by region. This allows for more accurate evaluation of the reliability of region-specific information.

[0039] The fact-checking system may further include a search history analysis unit that analyzes a user's search history and evaluates the reliability of information based on past search patterns. For example, the reliability of current information is evaluated based on the reliability of information sources the user has previously searched. The search history analysis unit may also analyze the user's search patterns and prioritize the display of highly reliable information sources. This makes it possible to provide more reliable information based on the user's past behavior.

[0040] The fact-checking system may also include a social media analysis unit that analyzes a user's social media activity and evaluates the reliability of information on social media. For example, the reliability of information may be evaluated based on the reliability of the accounts the user follows. The social media analysis unit may also analyze the spread of information on social media and identify unreliable information. This makes it possible to prevent the spread of misinformation on social media.

[0041] The fact-checking system may further include an operation history analysis unit that analyzes a user's operation history and evaluates the reliability of information based on past operation patterns. For example, the reliability of current information is evaluated based on the reliability of information sources that the user has used in the past. The operation history analysis unit may also analyze the user's operation patterns and prioritize the display of highly reliable information sources. This makes it possible to provide more reliable information based on the user's past behavior.

[0042] The fact-checking system may further include an interest analysis unit that analyzes a user's interests and evaluates the reliability of information based on those interests. For example, the reliability of current information may be evaluated based on the reliability of information sources in which the user has shown interest in the past. The interest analysis unit may also analyze a user's interests and prioritize the display of highly reliable information sources. This makes it possible to provide more reliable information based on the user's interests.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The information analysis unit uses the generation AI to analyze the information provided by the user. For example, the generation AI may analyze the information using a text generation AI (e.g., GPT-3). The generation AI may also analyze the content of the information using natural language processing technology or data mining technology. Step 2: The reliability evaluation unit evaluates the reliability of the information analyzed by the information analysis unit. For example, the reliability evaluation unit evaluates whether the information matches multiple reliable information sources, based on the reliability scores of the information sources, and past reliability history. Step 3: The source verification unit verifies the source of the information evaluated by the reliability evaluation unit. For example, the source verification unit identifies the source from which the information comes and evaluates the reliability and history of the source. Step 4: The update evaluation unit reevaluates the updates to the information confirmed by the source confirmation unit. For example, if the information has been updated, the update evaluation unit reanalyzes the information and reevaluates its reliability. The evaluation is also based on the reliability of the update content and the frequency of updates. Step 5: The user interface unit provides the user with the information reevaluated by the update evaluation unit. For example, the user interface unit provides an intuitive user interface, simplifying the process from when the user inputs information to when the generation AI analyzes that information and displays the results. It can also analyze the user's operation history and provide an interface optimized for each individual user or an interface with an emotion estimation function.

[0045] (Example 2) The fact-checking system according to the embodiment of the present invention uses a generative AI to evaluate the reliability of information, verify sources, and reassess updated information, thereby enabling the fact-checking system to provide users with accurate information.

[0046] A fact-checking system according to an embodiment includes an information analysis unit, a reliability evaluation unit, a source verification unit, an update evaluation unit, and a user interface unit. The information analysis unit analyzes information provided by a user using a generation AI. For example, the generation AI analyzes the information using a text generation AI (e.g., GPT-3). The generation AI can also analyze the content of the information using natural language processing technology. The generation AI can also analyze the information using data mining technology. The reliability evaluation unit evaluates the reliability of the information analyzed by the information analysis unit. For example, the reliability evaluation unit evaluates whether the information matches multiple reliable information sources. The reliability evaluation unit can also perform the evaluation based on the reliability score of the information source. The reliability evaluation unit can also perform the evaluation based on past reliability history. The source verification unit verifies the source of the information evaluated by the reliability evaluation unit. For example, the source verification unit identifies the source from which the information comes. The source verification unit can also evaluate the reliability of the source. The source verification unit can also verify the history of the source. The update evaluation unit reevaluates updates to the information confirmed by the source confirmation unit. For example, if the information is newly updated, the update evaluation unit reanalyzes the information and reevaluates its reliability. The update evaluation unit can also evaluate the reliability of the update content. The update evaluation unit can also perform evaluation based on the update frequency. The user interface unit provides the information reevaluated by the update evaluation unit to the user. For example, the user interface unit provides an intuitive user interface, simplifying the process from the user entering information to the generation AI analyzing the information and displaying the results. The user interface unit can also analyze the user's operation history and provide an interface optimized for each individual user. The user interface unit can also use an emotion estimation function to analyze the user's emotional response and provide an emotionally friendly interface. This allows the fact-checking system according to the embodiment to provide accurate information to the user. For example, the fact-checking system can quickly and accurately evaluate information from various sources, such as news articles, social media posts, and corporate press releases.This makes it easier to select and reject information and helps prevent the spread of misinformation and fake news.

[0047] The information analysis unit can refer to news articles and academic papers to understand the context of information provided by users and make judgments based on the context. For example, when the generation AI performs fact-checking, the information analysis unit automatically collects and analyzes past news articles and academic papers to understand the context of the information provided by users. For example, when checking information about a specific incident, the information analysis unit refers to past news articles related to that incident to understand the context. The information analysis unit can also allow the generation AI to analyze the context of the information using a context analysis algorithm. For example, the generation AI analyzes the context based on selection criteria for related news articles and academic papers. This allows for more accurate fact-checking by understanding the context of the information.

[0048] The reliability evaluation unit can refer to the information source's past reliability history and take into account fluctuations in reliability. For example, when the generation AI performs fact-checking, the reliability evaluation unit refers to the information source's past reliability history and takes into account fluctuations in reliability in order to evaluate the reliability of information. For example, the reliability evaluation unit refers to the past reliability history of a specific news site and determines whether the site is highly reliable. The reliability evaluation unit can also allow the generation AI to calculate the reliability score of the information source based on the reliability history data. For example, the reliability of the information source is evaluated based on the past reliability score. This allows for more accurate reliability evaluation by taking into account the information source's reliability history.

[0049] The credibility evaluation unit can use the emotion estimation function to analyze the emotional response to information provided by the user and identify emotionally biased information. For example, when the generation AI performs a fact check, the credibility evaluation unit uses the emotion estimation function to analyze the emotional response to information provided by the user. For example, it determines whether the information provided by the user is emotionally biased. The credibility evaluation unit can also use the emotion analysis algorithm of the generation AI to calculate an emotional score for the information. For example, it evaluates whether the information is emotionally biased based on the emotion scoring. This enables more objective fact checking by identifying emotionally biased information.

[0050] When verifying the source of information, the source verification unit can refer to the source's past reliability history to evaluate the reliability of the source. For example, when the generation AI verifies the source of information, the source verification unit refers to the source's past reliability history to evaluate the source's reliability. For example, the source verification unit refers to the past reliability history of a specific news site to determine whether the site is highly reliable. The source verification unit can also allow the generation AI to evaluate the source's reliability based on the source's reliability score. For example, the source's reliability is evaluated based on its past reliability score. This allows more accurate source verification by referring to the source's reliability history.

[0051] When updating and re-evaluating information, the update evaluation unit can refer to past reliability history to evaluate the reliability of the updated information. For example, when the generation AI updates and re-evaluates information, the update evaluation unit refers to past reliability history to evaluate the reliability of the updated information. For example, the update evaluation unit refers to the past reliability history of a specific news site to determine whether the site is highly reliable. The update evaluation unit can also evaluate the reliability of the updated content based on the reliability score of the updated information by the generation AI. For example, the reliability of the information is re-evaluated based on the reliability of the updated content. This allows the reliability of the updated information to be evaluated, thereby always providing the latest and accurate information.

[0052] The user interface unit can analyze the user's operation history and provide an interface optimized for each individual user. For example, in a user interface provided by a generation AI, the user interface unit can analyze the user's operation history and provide an interface optimized for each individual user. For example, it can prioritize displaying functions that the user uses frequently. The user interface unit can also customize the interface based on the user's operation history by analyzing the operation pattern using the generation AI. For example, it can optimize the interface layout based on the user's click data. In this way, an interface optimized for each individual user can be provided by analyzing the user's operation history.

[0053] The user interface unit can use the emotion estimation function to analyze the user's emotional response and provide an emotionally easy-to-use interface. For example, in a user interface provided by a generation AI, the user interface unit can use the emotion estimation function to analyze the user's emotional response and provide an emotionally easy-to-use interface. For example, it can provide an interface that is emotionally easy for the user. The user interface unit can also collect emotional feedback from the generation AI and provide a UI / UX design that corresponds to the user's emotions. For example, the interface design can be adjusted based on the user's emotion score. In this way, an emotionally easy-to-use interface can be provided by analyzing the user's emotional response.

[0054] The user interface unit can provide an interface compatible with different devices. For example, the user interface unit provides an interface compatible with different devices (smartphone, tablet, PC) in the user interface provided by the generation AI. For example, an interface for smartphones and an interface for PCs are provided. The user interface unit can also provide a UI optimized for each device by using responsive design by the generation AI. For example, the layout of the interface can be adjusted depending on the screen size of the device. This improves user convenience by providing an interface compatible with different devices.

[0055] The user interface unit can introduce new input methods such as voice input and gesture operation. For example, the user interface unit introduces new input methods such as voice input and gesture operation in a user interface provided by the generation AI. For example, the user inputs information using voice input. The user interface unit also allows the user to operate the interface using gesture operation, with the generation AI using gesture recognition technology. For example, the user inputs information using gesture operation. This introduces new input methods, improving user operability.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The fact-checking system can also include a geographic information unit that acquires the user's geographic information and evaluates the reliability of information by region. For example, when evaluating the reliability of news in a specific region, the system can refer to the region's past reliability history. The geographic information unit can also perform evaluations based on the reliability scores of information sources by region. This allows for more accurate evaluation of the reliability of region-specific information.

[0058] The fact-checking system may further include a search history analysis unit that analyzes a user's search history and evaluates the reliability of information based on past search patterns. For example, the reliability of current information is evaluated based on the reliability of information sources the user has previously searched. The search history analysis unit may also analyze the user's search patterns and prioritize the display of highly reliable information sources. This makes it possible to provide more reliable information based on the user's past behavior.

[0059] The fact-checking system may also include a social media analysis unit that analyzes a user's social media activity and evaluates the reliability of information on social media. For example, the reliability of information may be evaluated based on the reliability of the accounts the user follows. The social media analysis unit may also analyze the spread of information on social media and identify unreliable information. This makes it possible to prevent the spread of misinformation on social media.

[0060] The fact-checking system may further include an emotion evaluation unit that estimates a user's emotions and evaluates the reliability of information based on the estimated emotions. For example, the emotion evaluation unit may analyze the user's emotional response when entering information and identify emotionally biased information. The emotion evaluation unit may also evaluate the reliability of information based on the user's emotion score. This allows for the elimination of emotionally biased information and enables more objective fact-checking.

[0061] The fact-checking system may further include an emotion display adjustment unit that estimates a user's emotion and adjusts the way information is displayed based on the estimated emotion. For example, the emotion display adjustment unit may analyze the user's emotional response when viewing information and display the information in a format that is emotionally acceptable. The emotion display adjustment unit may also adjust the display order and format of the information based on the user's emotion score. This makes it easier for the user to understand the information.

[0062] The fact-checking system may further include an emotion importance evaluation unit that estimates a user's emotion and evaluates the importance of information based on the estimated emotion. For example, the system may analyze the user's emotional response when inputting information and prioritize information that the user finds emotionally important. The emotion importance evaluation unit may also evaluate the importance of information based on the user's emotion score. This allows the system to prioritize the provision of information that is important to the user.

[0063] The fact-checking system may further include an emotion reassessment unit that estimates a user's emotions and reassess the reliability of information based on the estimated emotions. For example, the system may analyze the user's emotional response when viewing information and reassess information that the user feels is emotionally unreliable. The emotion reassessment unit may also reassess the reliability of information based on the user's emotion score. This makes it possible to eliminate information that the user feels is emotionally unreliable and provide more accurate information.

[0064] The fact-checking system may further include an emotion credibility evaluation unit that estimates a user's emotion and evaluates the reliability of information based on the estimated emotion. For example, the system may analyze the user's emotional response when entering information and prioritize information that the user feels is emotionally reliable. The emotion credibility evaluation unit may also evaluate the reliability of information based on the user's emotion score. This makes it possible to prioritize the provision of information that the user feels is emotionally reliable.

[0065] The fact-checking system may further include an operation history analysis unit that analyzes a user's operation history and evaluates the reliability of information based on past operation patterns. For example, the reliability of current information is evaluated based on the reliability of information sources that the user has used in the past. The operation history analysis unit may also analyze the user's operation patterns and prioritize the display of highly reliable information sources. This makes it possible to provide more reliable information based on the user's past behavior.

[0066] The fact-checking system may further include an interest analysis unit that analyzes a user's interests and evaluates the reliability of information based on those interests. For example, the reliability of current information may be evaluated based on the reliability of information sources in which the user has shown interest in the past. The interest analysis unit may also analyze a user's interests and prioritize the display of highly reliable information sources. This makes it possible to provide more reliable information based on the user's interests.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The information analysis unit uses the generation AI to analyze the information provided by the user. For example, the generation AI may analyze the information using a text generation AI (e.g., GPT-3). The generation AI may also analyze the content of the information using natural language processing technology or data mining technology. Step 2: The reliability evaluation unit evaluates the reliability of the information analyzed by the information analysis unit. For example, the reliability evaluation unit evaluates whether the information matches multiple reliable information sources, based on the reliability scores of the information sources, and past reliability history. Step 3: The source verification unit verifies the source of the information evaluated by the reliability evaluation unit. For example, the source verification unit identifies the source from which the information comes and evaluates the reliability and history of the source. Step 4: The update evaluation unit reevaluates the updates to the information confirmed by the source confirmation unit. For example, if the information has been updated, the update evaluation unit reanalyzes the information and reevaluates its reliability. The evaluation is also based on the reliability of the update content and the frequency of updates. Step 5: The user interface unit provides the user with the information reevaluated by the update evaluation unit. For example, the user interface unit provides an intuitive user interface, simplifying the process from when the user inputs information to when the generation AI analyzes that information and displays the results. It can also analyze the user's operation history and provide an interface optimized for each individual user or an interface with an emotion estimation function.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the 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.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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]

[0136] 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 analysis section using generative AI, a reliability evaluation unit that evaluates the reliability of the information analyzed by the information analysis unit; a source confirmation unit that confirms the source of the information evaluated by the reliability evaluation unit; an update evaluation unit that reevaluates updates to the information confirmed by the source confirmation unit; a user interface unit that provides the information re-evaluated by the update evaluation unit to a user; A system characterized by:

2. The information analysis unit Refer to news articles or academic papers to understand the context of the information provided by the user and make decisions based on that context 2. The system of claim 1.

3. The reliability evaluation unit Refer to the source's past reliability history and take into account fluctuations in that reliability 2. The system of claim 1.

4. The source confirmation unit When verifying the source of the information, refer to the source's past reliability history to assess the source's reliability.

2. The system of claim 1.

5. The user interface unit Analyzing the operation history of the user and providing an interface optimized for each individual user 2. The system of claim 1.

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