system

Abstract

To provide a system according to an embodiment, capable of effectively performing a fact check.SOLUTION: A system according to an embodiment, comprises a specification part, a generation part, and a confirmation part. The specification part specifies an objective portion of a fact check. The generation part generates an inquiry against a place specified by the specification part. The confirmation part confirms fact relevance on the basis of the inquiry generated by the generation part.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 technology has had the problem that fact-checking requires a large amount of manpower and is difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently perform fact-checking. [Means for solving the problem]

[0006] The system according to the embodiment includes an identification unit, a generation unit, and a verification unit. The identification unit identifies a target passage for fact checking. The generation unit generates a question for the passage identified by the identification unit. The verification unit verifies the facts based on the question generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform fact-checking. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A fact-checking system according to an embodiment of the present invention utilizes a large-scale language model (LLM) to improve the efficiency of fact-checking. In this fact-checking system, the LLM first analyzes newly written text and identifies sections that require fact-checking. The LLM then generates questions for those sections. The generated questions are then used to search reliable sources on the Internet using a browsing function to confirm the facts. Finally, based on the confirmed facts, the system determines whether the text needs to be revised. This mechanism significantly improves the efficiency of fact-checking and reduces the burden of manual work. For example, the LLM automatically generates questions and uses a browsing function to confirm facts, enabling faster and more accurate fact-checking than traditional manual methods. Furthermore, because the questions generated by the LLM are aimed at confirming specific facts, unnecessary searches are reduced, resulting in more efficient information collection. Furthermore, when the LLM uses its browsing function to confirm facts, it is designed to prioritize reliable sources. This prevents fact-checking based on incorrect information and provides accurate information. For example, websites of government agencies and academic institutions, as well as reliable news media, are used as sources of information. In this way, a fact-checking system using LLM can efficiently and accurately verify facts, and is expected to improve the reliability of articles in the media. As a result, the fact-checking system can significantly improve the efficiency of fact-checking and reduce the burden of manual work.

[0029] A fact-checking system according to an embodiment includes an identification unit, a generation unit, and a verification unit. The identification unit identifies target passages for fact-checking. For example, the identification unit analyzes text using a natural language processing algorithm to identify passages that require fact-checking. The identification unit can also use a machine learning model to analyze past fact-checking history and identify target passages based on specific patterns. For example, the identification unit prioritizes identifying passages that have previously contained many errors. The identification unit can also analyze the context of the text and identify target passages based on specific keywords or phrases. For example, the identification unit identifies target passages based on specific keywords (e.g., statistical data, quotations) in the text. The generation unit generates questions for the passages identified by the identification unit. For example, the generation unit generates questions for specific data or events using a generation AI. The generation AI generates questions based on specific factual information using a text generation AI (e.g., LLM). The generation unit can also generate optimal questions by referring to past question data. For example, the generation unit generates optimal questions based on question data used in the past. The verification unit verifies the factual information based on the questions generated by the generation unit. The verification unit, for example, uses a browsing function to search for reliable information sources on the Internet and verify facts. The verification unit is designed to prioritize searching for reliable information sources. For example, the verification unit uses websites of government agencies and academic institutions, reliable news media, etc. as information sources. This allows the fact-checking system according to the embodiment to significantly improve the efficiency of fact-checking and reduce the burden of manual work. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit may input questions generated by the generation unit and verify facts using an AI model that verifies facts.

[0030] The identification unit identifies target passages for fact-checking. For example, the identification unit uses a natural language processing algorithm to analyze text and identify passages that require fact-checking. Specifically, the natural language processing algorithm analyzes the grammatical structure and semantics of the text and extracts important and questionable information based on the context. For example, the identification unit detects statistical data, quotations, claims, etc. in the text and determines that they are likely to be subject to fact-checking. The identification unit can also use a machine learning model to analyze past fact-check history and identify target passages based on specific patterns. For example, the identification unit prioritizes identifying passages that have frequently contained errors in the past or information related to a specific theme. This allows the identification unit to efficiently find passages that require fact-checking. Furthermore, the identification unit can analyze the context of the text and identify target passages based on specific keywords or phrases. For example, the identification unit detects keywords such as "statistical data" or "quote" in the text and identifies information related to these keywords as subject to fact-checking. This allows the identification unit to efficiently extract important information from a wide range of text and improve the accuracy of fact-checking.

[0031] The generation unit generates questions for the passages identified by the identification unit. For example, the generation unit uses a generation AI to generate questions for specific data or events. The generation AI uses a text generation AI (e.g., LLM) to generate questions based on specific facts. Specifically, the generation AI generates appropriate questions based on information related to the passages identified by the identification unit. For example, the generation AI generates questions such as "What is the source of this data?" or "How was this statistical data collected?" for statistical data. The generation unit can also generate optimal questions by referring to past question data. For example, the generation unit generates optimal questions for similar situations based on previously used question data. This allows the generation unit to generate questions efficiently and effectively and improve the accuracy of fact-checking. Furthermore, the generation unit can evaluate the quality of the generated questions and make corrections or improvements as necessary. For example, if a question generated by the generation AI is inappropriate, the generation unit can modify the content of the question and generate a more appropriate question. This allows the generation unit to consistently provide high-quality questions and improve the accuracy and efficiency of fact-checking.

[0032] The verification unit verifies the facts based on the questions generated by the generation unit. For example, the verification unit uses a browsing function to search for reliable sources on the Internet and verify the facts. Specifically, the verification unit uses a search engine to search for related information using the generated questions as input. The verification unit is designed to prioritize searching reliable sources. For example, the verification unit may use websites of government agencies and academic institutions, reliable news media, etc. as sources. This allows the verification unit to collect accurate and reliable information and verify the facts. Furthermore, the verification unit analyzes the collected information and provides answers to the questions generated by the generation unit. For example, the verification unit may verify the sources and collection methods of statistical data based on the collected information and provide accurate answers. The verification unit may also verify the facts using AI. For example, the verification unit may use an AI model that verifies the facts using the questions generated by the generation unit as input. This allows the verification unit to verify the facts efficiently and accurately, improving the accuracy and efficiency of fact-checking. Furthermore, the verification unit may record the verification results and use them as a reference for future fact-checking. This allows the verification department to continuously improve the accuracy and efficiency of fact-checking.

[0033] The identification unit can identify target passages for fact-checking using a specific algorithm or method. The identification unit, for example, uses a natural language processing algorithm to analyze text and identify passages that require fact-checking. The natural language processing algorithm analyzes the context of the text and identifies the target passages based on specific keywords or phrases. For example, the identification unit can identify the target passages based on specific keywords (e.g., statistical data, citations) in the text. The identification unit can also use a machine learning model to analyze past fact-checking history and identify the target passages based on specific patterns. For example, the identification unit can prioritize identifying passages that have contained many errors in the past. In this way, by using a specific algorithm or method, it is possible to efficiently identify the target passages for fact-checking. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can identify the target passages using an AI model that analyzes text using a natural language processing algorithm and identifies passages that require fact-checking.

[0034] The generation unit can generate questions for specific data or events. The generation unit, for example, uses a generation AI to generate questions for specific data or events. The generation AI generates questions based on specific facts using a text generation AI (e.g., LLM). For example, the generation unit generates questions based on specific data (e.g., statistical data, historical events). The generation unit can also generate optimal questions by referring to past question data. For example, the generation unit generates optimal questions based on question data used in the past. This enables efficient fact-checking by generating questions for specific data or events. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate questions using an AI model that generates questions based on specific data or events.

[0035] The verification unit can prioritize searching highly reliable sources. For example, the verification unit uses a browsing function to search highly reliable sources on the Internet and verify facts. Examples of highly reliable sources include websites of government agencies and academic institutions, and reliable news media. The verification unit is designed to prioritize searching highly reliable sources. For example, the verification unit evaluates official government agency websites as highly reliable sources and searches them preferentially. The verification unit can also evaluate academic papers as highly reliable sources and search them preferentially. This enables accurate fact-checking by prioritizing searching highly reliable sources. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can verify facts using an AI model that searches highly reliable sources.

[0036] The identification unit can analyze past fact-check history and identify target passages based on specific patterns. For example, the identification unit analyzes past fact-check history and identifies target passages based on specific patterns. The past fact-check history includes, for example, past check results and used data. The identification unit prioritizes identifying passages that have had many errors in the past. The identification unit can also identify passages related to a specific theme or topic from the past fact-check history. For example, the identification unit analyzes the tendencies of a specific author based on the past fact-check history and identifies target passages. In this way, target passages can be efficiently identified by analyzing the past fact-check history. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can identify target passages using an AI model that analyzes past fact-check history and identifies target passages based on specific patterns.

[0037] The identification unit can analyze the context of the text and identify the target portion based on specific keywords or phrases. The identification unit can analyze the context of the text using, for example, a natural language processing algorithm and identify the target portion based on specific keywords or phrases. The natural language processing algorithm can analyze the context of the text and identify the target portion based on specific keywords or phrases. For example, the identification unit can identify the target portion based on specific keywords (e.g., statistical data, quotations) in the text. The identification unit can also identify phrases that are likely to contain factual information based on the context of the text. For example, the identification unit can identify paragraphs that contain important factual information based on the context of the text. In this way, the target portion can be efficiently identified by analyzing the context of the text. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can analyze the context of the text using a natural language processing algorithm and identify the target portion using an AI model that identifies the target portion based on specific keywords or phrases.

[0038] The identification unit can customize a specific algorithm based on the genre or category of the text. The identification unit customizes the algorithm of the identification unit based on, for example, the genre or category of the text. Text genres and categories include, for example, news articles, academic papers, and entertainment articles. For news articles, the identification unit uses an algorithm that emphasizes timeliness. For academic papers, the identification unit can also use an algorithm that performs detailed verification. Furthermore, for entertainment articles, the identification unit can also use an algorithm that prioritizes identifying parts that will attract readers' interest. In this way, by customizing the algorithm based on the genre or category of the text, efficient fact-checking can be performed. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can customize the algorithm of the identification unit using an AI model that customizes the algorithm based on the genre or category of the text.

[0039] The identification unit can double-check the identified passages in cooperation with other AI systems. For example, the identification unit transmits the identified passages to another AI system for double-checking. The identification unit can also evaluate the reliability of the identified passages in cooperation with other AI systems. For example, the identification unit transmits the identified passages to another natural language processing system for double-checking. The identification unit can also determine the optimal identified passages in cooperation with other AI systems. This improves the accuracy of fact-checking by double-checking in cooperation with other AI systems. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI, for example. For example, the identification unit can double-check the identified passages using an AI model that double-checks the identified passages in cooperation with other AI systems.

[0040] When generating a question, the generation unit can generate an optimal question by referring to past question data. The generation unit, for example, generates the optimal question by referring to past question data. The past question data includes, for example, a history of past questions and answer results to questions. The generation unit generates the optimal question based on question data used in the past. The generation unit can also generate a question related to a specific theme from the past question data. For example, the generation unit analyzes past question data to generate an effective question. In this way, the optimal question is generated by referring to the past question data. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a question using an AI model that generates an optimal question by referring to past question data.

[0041] The generation unit can adjust the level of detail of a question based on specific factual relationships when generating a question. The generation unit, for example, adjusts the level of detail of a question based on specific factual relationships. Specific factual relationships include, for example, details of an incident or statistical data. The generation unit generates detailed questions regarding important factual relationships. It can also generate concise questions regarding general factual relationships. Furthermore, it can adjust the level of detail of a question based on specific factual relationships. By adjusting the level of detail of a question based on specific factual relationships, efficient questions are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of a question using an AI model that adjusts the level of detail of a question based on specific factual relationships.

[0042] When generating questions, the generation unit can customize the format of the questions based on the genre or category of the text. The generation unit customizes the format of the questions based on, for example, the genre or category of the text. Text genres and categories include, for example, news articles, academic papers, and entertainment articles. In the case of news articles, the generation unit generates questions that emphasize timeliness. In the case of academic papers, the generation unit can also generate questions that require detailed verification. Furthermore, in the case of entertainment articles, the generation unit can also generate questions that attract readers' interest. In this way, by customizing the format of the questions based on the genre or category of the text, efficient questions are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can customize the format of the questions using an AI model that customizes the format of the questions based on the genre or category of the text.

[0043] When generating a question, the generation unit can improve the accuracy of the question in cooperation with other AI systems. For example, the generation unit can improve the accuracy of the question in cooperation with other AI systems. The generation unit can evaluate the accuracy of the generated question in cooperation with other AI systems. For example, the generation unit can evaluate the accuracy of the generated question in cooperation with other natural language processing systems. The generation unit can also improve the accuracy of the question based on feedback from the other AI systems. Furthermore, the generation unit can generate optimal questions in cooperation with other AI systems. In this way, the accuracy of the question is improved by cooperation with other AI systems. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can improve the accuracy of the question using an AI model that improves the accuracy of the question in cooperation with other AI systems.

[0044] The verification unit can optimize the verification algorithm by referring to past verification data during verification. The verification unit, for example, optimizes the verification algorithm by referring to past verification data. The past verification data includes, for example, past verification results and used data. The verification unit selects an optimal verification algorithm based on the past verification data. The verification unit can also optimize a verification algorithm related to a specific theme from the past verification data. Furthermore, the verification unit can analyze the past verification data and optimize an effective verification algorithm. In this way, the verification algorithm is optimized by referring to the past verification data. Some or all of the above-described processing in the verification unit may be performed, for example, using AI or may be performed without using AI. For example, the verification unit can optimize the verification algorithm using an AI model that optimizes the verification algorithm by referring to the past verification data.

[0045] The verification unit can evaluate the reliability of a specific information source during verification and reflect this in the verification result. For example, the verification unit evaluates the reliability of a specific information source and reflects this in the verification result. Examples of the reliability of specific information sources include official government agency websites, academic papers, and reliable news media. The verification unit evaluates official government agency websites as reliable information sources. The verification unit can also evaluate academic papers as reliable information sources. Furthermore, the verification unit can evaluate reliable news media as information sources. This allows accurate verification results to be obtained by evaluating the reliability of specific information sources. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can evaluate the reliability of a specific information source and reflect this in the verification result using an AI model that evaluates the reliability of a specific information source and reflects this in the verification result.

[0046] The verification unit can customize the verification algorithm based on the genre or category of the text during verification. The verification unit customizes the verification algorithm based on, for example, the genre or category of the text. Text genres and categories include, for example, news articles, academic papers, and entertainment articles. For news articles, the verification unit uses a verification algorithm that emphasizes timeliness. For academic papers, the verification unit can also use a verification algorithm that performs detailed verification. Furthermore, for entertainment articles, the verification unit can also use a verification algorithm that attracts readers' interest. In this way, by customizing the verification algorithm based on the genre or category of the text, efficient verification can be performed. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can customize the verification algorithm using an AI model that customizes the verification algorithm based on the genre or category of the text.

[0047] During verification, the verification unit can improve the accuracy of the verification result in cooperation with other AI systems. For example, the verification unit can improve the accuracy of the verification result in cooperation with other AI systems. The verification unit can evaluate the accuracy of the verification result in cooperation with other AI systems. For example, the verification unit can evaluate the accuracy of the verification result in cooperation with other natural language processing systems. The verification unit can also improve the accuracy of the verification result based on feedback from the other AI systems. Furthermore, the verification unit can provide optimal verification results in cooperation with other AI systems. This improves the accuracy of the verification result by collaborating with other AI systems. Some or all of the above-described processing in the verification unit may be performed, for example, using AI or may be performed without using AI. For example, the verification unit can improve the accuracy of the verification result using an AI model that improves the accuracy of the verification result in cooperation with other AI systems.

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

[0049] The identification unit can also analyze the user's past search history and identify target passages for fact checking based on specific patterns. For example, it can prioritize identifying keywords or topics that the user has frequently searched for in the past. The identification unit can also identify passages related to specific themes or topics from the user's past search history. Furthermore, the identification unit can analyze the tendencies of specific authors based on the user's past search history and identify target passages. This allows for efficient identification of target passages by analyzing the user's past search history.

[0050] The generation unit can also analyze the user's past question history and generate optimal questions based on specific patterns. For example, it can prioritize keywords or topics that the user has frequently asked questions about in the past. The generation unit can also generate questions related to specific themes or topics from the user's past question history. Furthermore, the generation unit can analyze specific question formats based on the user's past question history and generate optimal questions. In this way, optimal questions can be generated efficiently by analyzing the user's past question history.

[0051] The verification unit can also analyze the user's past verification history and optimize the verification algorithm based on a specific pattern. For example, it can prioritize verification of keywords or topics that the user has frequently verified in the past. The verification unit can also optimize the verification algorithm related to a specific theme or topic based on the user's past verification history. Furthermore, the verification unit can analyze a specific verification format based on the user's past verification history and select an optimal verification algorithm. In this way, the verification algorithm can be efficiently optimized by analyzing the user's past verification history.

[0052] The identification unit can also analyze the user's past search history and identify target passages for fact checking based on specific patterns. For example, it can prioritize identifying keywords or topics that the user has frequently searched for in the past. The identification unit can also identify passages related to specific themes or topics from the user's past search history. Furthermore, the identification unit can analyze the tendencies of specific authors based on the user's past search history and identify target passages. This allows for efficient identification of target passages by analyzing the user's past search history.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The Identification Unit identifies target passages for fact-checking. The Identification Unit uses natural language processing algorithms to analyze the text and identify passages that need fact-checking. It can also use machine learning models to analyze past fact-check history and identify target passages based on specific patterns. It also analyzes the context of the text and identifies target passages based on specific keywords or phrases. Step 2: The generator generates questions for the parts identified by the identifyr. The generator uses a generation AI to generate questions for specific data or events. The generator uses a text generation AI (e.g., LLM) to generate questions based on specific facts. It can also generate optimal questions by referencing past question data. Step 3: The verification unit verifies the facts based on the questions generated by the generation unit. The verification unit uses the browsing function to search for reliable information sources on the Internet and verify the facts. The verification unit uses websites of government agencies and academic institutions, reliable news media, etc. as information sources. The processing in the verification unit may be performed using AI or without AI.

[0055] (Example 2) A fact-checking system according to an embodiment of the present invention utilizes a large-scale language model (LLM) to improve the efficiency of fact-checking. In this fact-checking system, the LLM first analyzes newly written text and identifies sections that require fact-checking. The LLM then generates questions for those sections. The generated questions are then used to search reliable sources on the Internet using a browsing function to confirm the facts. Finally, based on the confirmed facts, the system determines whether the text needs to be revised. This mechanism significantly improves the efficiency of fact-checking and reduces the burden of manual work. For example, the LLM automatically generates questions and uses a browsing function to confirm facts, enabling faster and more accurate fact-checking than traditional manual methods. Furthermore, because the questions generated by the LLM are aimed at confirming specific facts, unnecessary searches are reduced, resulting in more efficient information collection. Furthermore, when the LLM uses its browsing function to confirm facts, it is designed to prioritize reliable sources. This prevents fact-checking based on incorrect information and provides accurate information. For example, websites of government agencies and academic institutions, as well as reliable news media, are used as sources of information. In this way, a fact-checking system using LLM can efficiently and accurately verify facts, and is expected to improve the reliability of articles in the media. As a result, the fact-checking system can significantly improve the efficiency of fact-checking and reduce the burden of manual work.

[0056] A fact-checking system according to an embodiment includes an identification unit, a generation unit, and a verification unit. The identification unit identifies target passages for fact-checking. For example, the identification unit analyzes text using a natural language processing algorithm to identify passages that require fact-checking. The identification unit can also use a machine learning model to analyze past fact-checking history and identify target passages based on specific patterns. For example, the identification unit prioritizes identifying passages that have previously contained many errors. The identification unit can also analyze the context of the text and identify target passages based on specific keywords or phrases. For example, the identification unit identifies target passages based on specific keywords (e.g., statistical data, quotations) in the text. The generation unit generates questions for the passages identified by the identification unit. For example, the generation unit generates questions for specific data or events using a generation AI. The generation AI generates questions based on specific factual information using a text generation AI (e.g., LLM). The generation unit can also generate optimal questions by referring to past question data. For example, the generation unit generates optimal questions based on question data used in the past. The verification unit verifies the factual information based on the questions generated by the generation unit. The verification unit, for example, uses a browsing function to search for reliable information sources on the Internet and verify facts. The verification unit is designed to prioritize searching for reliable information sources. For example, the verification unit uses websites of government agencies and academic institutions, reliable news media, etc. as information sources. This allows the fact-checking system according to the embodiment to significantly improve the efficiency of fact-checking and reduce the burden of manual work. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit may input questions generated by the generation unit and verify facts using an AI model that verifies facts.

[0057] The identification unit identifies target passages for fact-checking. For example, the identification unit uses a natural language processing algorithm to analyze text and identify passages that require fact-checking. Specifically, the natural language processing algorithm analyzes the grammatical structure and semantics of the text and extracts important and questionable information based on the context. For example, the identification unit detects statistical data, quotations, claims, etc. in the text and determines that they are likely to be subject to fact-checking. The identification unit can also use a machine learning model to analyze past fact-check history and identify target passages based on specific patterns. For example, the identification unit prioritizes identifying passages that have frequently contained errors in the past or information related to a specific theme. This allows the identification unit to efficiently find passages that require fact-checking. Furthermore, the identification unit can analyze the context of the text and identify target passages based on specific keywords or phrases. For example, the identification unit detects keywords such as "statistical data" or "quote" in the text and identifies information related to these keywords as subject to fact-checking. This allows the identification unit to efficiently extract important information from a wide range of text and improve the accuracy of fact-checking.

[0058] The generation unit generates questions for the passages identified by the identification unit. For example, the generation unit uses a generation AI to generate questions for specific data or events. The generation AI uses a text generation AI (e.g., LLM) to generate questions based on specific facts. Specifically, the generation AI generates appropriate questions based on information related to the passages identified by the identification unit. For example, the generation AI generates questions such as "What is the source of this data?" or "How was this statistical data collected?" for statistical data. The generation unit can also generate optimal questions by referring to past question data. For example, the generation unit generates optimal questions for similar situations based on previously used question data. This allows the generation unit to generate questions efficiently and effectively and improve the accuracy of fact-checking. Furthermore, the generation unit can evaluate the quality of the generated questions and make corrections or improvements as necessary. For example, if a question generated by the generation AI is inappropriate, the generation unit can modify the content of the question and generate a more appropriate question. This allows the generation unit to consistently provide high-quality questions and improve the accuracy and efficiency of fact-checking.

[0059] The verification unit verifies the facts based on the questions generated by the generation unit. For example, the verification unit uses a browsing function to search for reliable sources on the Internet and verify the facts. Specifically, the verification unit uses a search engine to search for related information using the generated questions as input. The verification unit is designed to prioritize searching reliable sources. For example, the verification unit may use websites of government agencies and academic institutions, reliable news media, etc. as sources. This allows the verification unit to collect accurate and reliable information and verify the facts. Furthermore, the verification unit analyzes the collected information and provides answers to the questions generated by the generation unit. For example, the verification unit may verify the sources and collection methods of statistical data based on the collected information and provide accurate answers. The verification unit may also verify the facts using AI. For example, the verification unit may use an AI model that verifies the facts using the questions generated by the generation unit as input. This allows the verification unit to verify the facts efficiently and accurately, improving the accuracy and efficiency of fact-checking. Furthermore, the verification unit may record the verification results and use them as a reference for future fact-checking. This allows the verification department to continuously improve the accuracy and efficiency of fact-checking.

[0060] The identification unit can identify target passages for fact-checking using a specific algorithm or method. The identification unit, for example, uses a natural language processing algorithm to analyze text and identify passages that require fact-checking. The natural language processing algorithm analyzes the context of the text and identifies the target passages based on specific keywords or phrases. For example, the identification unit can identify the target passages based on specific keywords (e.g., statistical data, citations) in the text. The identification unit can also use a machine learning model to analyze past fact-checking history and identify the target passages based on specific patterns. For example, the identification unit can prioritize identifying passages that have contained many errors in the past. In this way, by using a specific algorithm or method, it is possible to efficiently identify the target passages for fact-checking. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without AI. For example, the identification unit can identify the target passages using an AI model that analyzes text using a natural language processing algorithm and identifies passages that require fact-checking.

[0061] The generation unit can generate questions for specific data or events. The generation unit, for example, uses a generation AI to generate questions for specific data or events. The generation AI generates questions based on specific facts using a text generation AI (e.g., LLM). For example, the generation unit generates questions based on specific data (e.g., statistical data, historical events). The generation unit can also generate optimal questions by referring to past question data. For example, the generation unit generates optimal questions based on question data used in the past. This enables efficient fact-checking by generating questions for specific data or events. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate questions using an AI model that generates questions based on specific data or events.

[0062] The verification unit can prioritize searching highly reliable sources. For example, the verification unit uses a browsing function to search highly reliable sources on the Internet and verify facts. Examples of highly reliable sources include websites of government agencies and academic institutions, and reliable news media. The verification unit is designed to prioritize searching highly reliable sources. For example, the verification unit evaluates official government agency websites as highly reliable sources and searches them preferentially. The verification unit can also evaluate academic papers as highly reliable sources and search them preferentially. This enables accurate fact-checking by prioritizing searching highly reliable sources. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can verify facts using an AI model that searches highly reliable sources.

[0063] The identification unit can estimate the user's emotions and prioritize target passages for fact-checking based on the estimated user emotions. The identification unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the identification unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The identification unit can also estimate the user's emotions using text analysis technology. For example, the identification unit can analyze text written by the user and estimate the emotions. The identification unit can prioritize target passages for fact-checking based on the estimated user emotions. For example, if the user is feeling anxious, the identification unit can prioritize passages related to important facts. Furthermore, if the user is relaxed, the identification unit can prioritize target passages based on the estimated user emotions. Furthermore, if the user is in a hurry, the identification unit can prioritize passages that can be checked in a short time. This enables more effective fact-checking by determining the priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing by the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may identify target passages using an AI model that estimates a user's emotions and prioritizes target passages for fact-checking based on the estimated emotions.

[0064] The identification unit can analyze past fact-check history and identify target passages based on specific patterns. For example, the identification unit analyzes past fact-check history and identifies target passages based on specific patterns. The past fact-check history includes, for example, past check results and used data. The identification unit prioritizes identifying passages that have had many errors in the past. The identification unit can also identify passages related to a specific theme or topic from the past fact-check history. For example, the identification unit analyzes the tendencies of a specific author based on the past fact-check history and identifies target passages. In this way, target passages can be efficiently identified by analyzing the past fact-check history. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can identify target passages using an AI model that analyzes past fact-check history and identifies target passages based on specific patterns.

[0065] The identification unit can analyze the context of the text and identify the target portion based on specific keywords or phrases. The identification unit can analyze the context of the text using, for example, a natural language processing algorithm and identify the target portion based on specific keywords or phrases. The natural language processing algorithm can analyze the context of the text and identify the target portion based on specific keywords or phrases. For example, the identification unit can identify the target portion based on specific keywords (e.g., statistical data, quotations) in the text. The identification unit can also identify phrases that are likely to contain factual information based on the context of the text. For example, the identification unit can identify paragraphs that contain important factual information based on the context of the text. In this way, the target portion can be efficiently identified by analyzing the context of the text. Some or all of the above-described processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can analyze the context of the text using a natural language processing algorithm and identify the target portion using an AI model that identifies the target portion based on specific keywords or phrases.

[0066] The identification unit can estimate the user's emotion and adjust its operation based on the estimated user's emotion. The identification unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the identification unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The identification unit can also estimate the user's emotion using text analysis technology. For example, the identification unit analyzes text written by the user to estimate the emotion. The identification unit adjusts its operation based on the estimated user's emotion. For example, if the user is stressed, the identification unit can perform its operation quickly. Also, if the user is relaxed, the identification unit can perform its operation in detail. Furthermore, if the user is in a hurry, the identification unit can simplify its operation. This allows for more effective fact-checking by adjusting the operation of the identification unit based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit may estimate the user's emotion and adjust the operation of the identification unit using an AI model that adjusts the operation of the identification unit based on the estimated emotion.

[0067] The identification unit can customize a specific algorithm based on the genre or category of the text. The identification unit customizes the algorithm of the identification unit based on, for example, the genre or category of the text. Text genres and categories include, for example, news articles, academic papers, and entertainment articles. For news articles, the identification unit uses an algorithm that emphasizes timeliness. For academic papers, the identification unit can also use an algorithm that performs detailed verification. Furthermore, for entertainment articles, the identification unit can also use an algorithm that prioritizes identifying parts that will attract readers' interest. In this way, by customizing the algorithm based on the genre or category of the text, efficient fact-checking can be performed. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can customize the algorithm of the identification unit using an AI model that customizes the algorithm based on the genre or category of the text.

[0068] The identification unit can double-check the identified passages in cooperation with other AI systems. For example, the identification unit transmits the identified passages to another AI system for double-checking. The identification unit can also evaluate the reliability of the identified passages in cooperation with other AI systems. For example, the identification unit transmits the identified passages to another natural language processing system for double-checking. The identification unit can also determine the optimal identified passages in cooperation with other AI systems. This improves the accuracy of fact-checking by double-checking in cooperation with other AI systems. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI, for example. For example, the identification unit can double-check the identified passages using an AI model that double-checks the identified passages in cooperation with other AI systems.

[0069] The generation unit can estimate the user's emotion and adjust the way the question is phrased based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit analyzes text written by the user to estimate the emotion. The generation unit adjusts the way the question is phrased based on the estimated user's emotion. For example, if the user is nervous, the generation unit generates a concise and clear question. If the user is relaxed, the generation unit can generate a detailed question. Furthermore, if the user is in a hurry, the generation unit can generate a question that can be answered quickly. In this way, by adjusting the way the question is phrased based on the user's emotion, more effective questions can be generated. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may adjust the way a question is phrased using an AI model that estimates a user's emotions and adjusts the way a question is phrased based on the estimated emotions.

[0070] When generating a question, the generation unit can generate an optimal question by referring to past question data. The generation unit, for example, generates the optimal question by referring to past question data. The past question data includes, for example, a history of past questions and answer results to questions. The generation unit generates the optimal question based on question data used in the past. The generation unit can also generate a question related to a specific theme from the past question data. For example, the generation unit analyzes past question data to generate an effective question. In this way, the optimal question is generated by referring to the past question data. Some or all of the above-described processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can generate a question using an AI model that generates an optimal question by referring to past question data.

[0071] The generation unit can adjust the level of detail of a question based on specific factual relationships when generating a question. The generation unit, for example, adjusts the level of detail of a question based on specific factual relationships. Specific factual relationships include, for example, details of an incident or statistical data. The generation unit generates detailed questions regarding important factual relationships. It can also generate concise questions regarding general factual relationships. Furthermore, it can adjust the level of detail of a question based on specific factual relationships. By adjusting the level of detail of a question based on specific factual relationships, efficient questions are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of a question using an AI model that adjusts the level of detail of a question based on specific factual relationships.

[0072] The generation unit can estimate the user's emotions and prioritize questions based on the estimated user emotions. The generation unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The generation unit can also estimate the user's emotions using text analysis technology. For example, the generation unit analyzes text written by the user to estimate the emotions. The generation unit prioritizes questions based on the estimated user emotions. For example, if the user is feeling anxious, the generation unit prioritizes important questions. Also, if the user is relaxed, the generation unit can generate questions taking into account overall balance. Furthermore, if the user is in a hurry, the generation unit can prioritize questions that can be answered quickly. In this way, by prioritizing questions based on the user's emotions, more effective questions are generated. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may determine the priority of questions using an AI model that estimates a user's emotions and determines the priority of questions based on the estimated emotions.

[0073] When generating questions, the generation unit can customize the format of the questions based on the genre or category of the text. The generation unit customizes the format of the questions based on, for example, the genre or category of the text. Text genres and categories include, for example, news articles, academic papers, and entertainment articles. In the case of news articles, the generation unit generates questions that emphasize timeliness. In the case of academic papers, the generation unit can also generate questions that require detailed verification. Furthermore, in the case of entertainment articles, the generation unit can also generate questions that attract readers' interest. In this way, by customizing the format of the questions based on the genre or category of the text, efficient questions are generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can customize the format of the questions using an AI model that customizes the format of the questions based on the genre or category of the text.

[0074] When generating a question, the generation unit can improve the accuracy of the question in cooperation with other AI systems. For example, the generation unit can improve the accuracy of the question in cooperation with other AI systems. The generation unit can evaluate the accuracy of the generated question in cooperation with other AI systems. For example, the generation unit can evaluate the accuracy of the generated question in cooperation with other natural language processing systems. The generation unit can also improve the accuracy of the question based on feedback from the other AI systems. Furthermore, the generation unit can generate optimal questions in cooperation with other AI systems. In this way, the accuracy of the question is improved by cooperation with other AI systems. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can improve the accuracy of the question using an AI model that improves the accuracy of the question in cooperation with other AI systems.

[0075] The confirmation unit can estimate the user's emotions and adjust the display method of the confirmation result based on the estimated user's emotions. The confirmation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the confirmation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The confirmation unit can also estimate the user's emotions using text analysis technology. For example, the confirmation unit can analyze the text written by the user to estimate the emotion. The confirmation unit can adjust the display method of the confirmation result based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more effective display by adjusting the display method of the confirmation result based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may estimate the user's emotion and adjust the display method of the confirmation result based on the estimated emotion using an AI model.

[0076] The verification unit can optimize the verification algorithm by referring to past verification data during verification. The verification unit, for example, optimizes the verification algorithm by referring to past verification data. The past verification data includes, for example, past verification results and used data. The verification unit selects an optimal verification algorithm based on the past verification data. The verification unit can also optimize a verification algorithm related to a specific theme from the past verification data. Furthermore, the verification unit can analyze the past verification data and optimize an effective verification algorithm. In this way, the verification algorithm is optimized by referring to the past verification data. Some or all of the above-described processing in the verification unit may be performed, for example, using AI or may be performed without using AI. For example, the verification unit can optimize the verification algorithm using an AI model that optimizes the verification algorithm by referring to the past verification data.

[0077] The verification unit can evaluate the reliability of a specific information source during verification and reflect this in the verification result. For example, the verification unit evaluates the reliability of a specific information source and reflects this in the verification result. Examples of the reliability of specific information sources include official government agency websites, academic papers, and reliable news media. The verification unit evaluates official government agency websites as reliable information sources. The verification unit can also evaluate academic papers as reliable information sources. Furthermore, the verification unit can evaluate reliable news media as information sources. This allows accurate verification results to be obtained by evaluating the reliability of specific information sources. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can evaluate the reliability of a specific information source and reflect this in the verification result using an AI model that evaluates the reliability of a specific information source and reflects this in the verification result.

[0078] The confirmation unit can estimate the user's emotions and prioritize the confirmation results based on the estimated user emotions. The confirmation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the confirmation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The confirmation unit can also estimate the user's emotions using text analysis technology. For example, the confirmation unit can analyze text written by the user to estimate the emotions. The confirmation unit can prioritize the confirmation results based on the estimated user emotions. For example, if the user is feeling anxious, important confirmation results can be displayed with priority. Also, if the user is relaxed, confirmation results can be displayed with consideration for overall balance. Furthermore, if the user is in a hurry, results that can be confirmed quickly can be displayed with priority. This allows for more effective confirmation by prioritizing the confirmation results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit may determine the priority of the confirmation results using an AI model that estimates the user's emotions and determines the priority of the confirmation results based on the estimated emotions.

[0079] The verification unit can customize the verification algorithm based on the genre or category of the text during verification. The verification unit customizes the verification algorithm based on, for example, the genre or category of the text. Text genres and categories include, for example, news articles, academic papers, and entertainment articles. For news articles, the verification unit uses a verification algorithm that emphasizes timeliness. For academic papers, the verification unit can also use a verification algorithm that performs detailed verification. Furthermore, for entertainment articles, the verification unit can also use a verification algorithm that attracts readers' interest. In this way, by customizing the verification algorithm based on the genre or category of the text, efficient verification can be performed. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can customize the verification algorithm using an AI model that customizes the verification algorithm based on the genre or category of the text.

[0080] During verification, the verification unit can improve the accuracy of the verification result in cooperation with other AI systems. For example, the verification unit can improve the accuracy of the verification result in cooperation with other AI systems. The verification unit can evaluate the accuracy of the verification result in cooperation with other AI systems. For example, the verification unit can evaluate the accuracy of the verification result in cooperation with other natural language processing systems. The verification unit can also improve the accuracy of the verification result based on feedback from the other AI systems. Furthermore, the verification unit can provide optimal verification results in cooperation with other AI systems. This improves the accuracy of the verification result by collaborating with other AI systems. Some or all of the above-described processing in the verification unit may be performed, for example, using AI or may be performed without using AI. For example, the verification unit can improve the accuracy of the verification result using an AI model that improves the accuracy of the verification result in cooperation with other AI systems.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The identification unit can also analyze the user's past search history and identify target passages for fact checking based on specific patterns. For example, it can prioritize identifying keywords or topics that the user has frequently searched for in the past. The identification unit can also identify passages related to specific themes or topics from the user's past search history. Furthermore, the identification unit can analyze the tendencies of specific authors based on the user's past search history and identify target passages. This allows for efficient identification of target passages by analyzing the user's past search history.

[0083] The generation unit can also analyze the user's past question history and generate optimal questions based on specific patterns. For example, it can prioritize keywords or topics that the user has frequently asked questions about in the past. The generation unit can also generate questions related to specific themes or topics from the user's past question history. Furthermore, the generation unit can analyze specific question formats based on the user's past question history and generate optimal questions. In this way, optimal questions can be generated efficiently by analyzing the user's past question history.

[0084] The verification unit can also analyze the user's past verification history and optimize the verification algorithm based on a specific pattern. For example, it can prioritize verification of keywords or topics that the user has frequently verified in the past. The verification unit can also optimize the verification algorithm related to a specific theme or topic based on the user's past verification history. Furthermore, the verification unit can analyze a specific verification format based on the user's past verification history and select an optimal verification algorithm. In this way, the verification algorithm can be efficiently optimized by analyzing the user's past verification history.

[0085] The identification unit can estimate the user's emotions and adjust the operation of the identification unit based on the estimated user's emotions. For example, if the user is feeling stressed, the identification unit can operate quickly. If the user is relaxed, the identification unit can operate in detail. Furthermore, if the user is in a hurry, the identification unit can simplify its operation. In this way, more effective fact checking can be performed by adjusting the operation of the identification unit based on the user's emotions.

[0086] The generation unit can estimate the user's emotions and adjust the way questions are phrased based on the estimated user emotions. For example, if the user is nervous, a concise and clear question can be generated. If the user is relaxed, a detailed question can be generated. Furthermore, if the user is in a hurry, a question that can be answered quickly can be generated. In this way, by adjusting the way questions are phrased based on the user's emotions, more effective questions can be generated.

[0087] The confirmation unit can estimate the user's emotions and adjust the display method of the confirmation results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the confirmation results based on the user's emotions, a more effective display can be achieved.

[0088] The identification unit can estimate the user's emotions and prioritize the target passages for fact-checking based on the estimated user's emotions. For example, if the user is feeling anxious, it can prioritize identifying passages related to important facts. Also, if the user is relaxed, it can identify target passages taking into consideration the overall balance. Furthermore, if the user is in a hurry, it can prioritize identifying passages that can be checked in a short amount of time. In this way, more effective fact-checking can be performed by determining the priority based on the user's emotions.

[0089] The generation unit can estimate the user's emotions and determine the priority of questions based on the estimated user's emotions. For example, if the user is feeling anxious, important questions can be generated with priority. Also, if the user is relaxed, questions can be generated with consideration given to overall balance. Furthermore, if the user is in a hurry, questions that can be answered quickly can be generated with priority. In this way, by determining the priority of questions based on the user's emotions, more effective questions can be generated.

[0090] The confirmation unit can estimate the user's emotions and determine the priority of confirmation results based on the estimated user's emotions. For example, if the user is feeling anxious, important confirmation results can be displayed with priority. Also, if the user is relaxed, confirmation results can be displayed with consideration given to the overall balance. Furthermore, if the user is in a hurry, results that can be confirmed quickly can be displayed with priority. Thus, by determining the priority of confirmation results based on the user's emotions, more effective confirmation can be performed.

[0091] The identification unit can also analyze the user's past search history and identify target passages for fact checking based on specific patterns. For example, it can prioritize identifying keywords or topics that the user has frequently searched for in the past. The identification unit can also identify passages related to specific themes or topics from the user's past search history. Furthermore, the identification unit can analyze the tendencies of specific authors based on the user's past search history and identify target passages. This allows for efficient identification of target passages by analyzing the user's past search history.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The Identification Unit identifies target passages for fact-checking. The Identification Unit uses natural language processing algorithms to analyze the text and identify passages that need fact-checking. It can also use machine learning models to analyze past fact-check history and identify target passages based on specific patterns. It also analyzes the context of the text and identifies target passages based on specific keywords or phrases. Step 2: The generator generates questions for the parts identified by the identifyr. The generator uses a generation AI to generate questions for specific data or events. The generator uses a text generation AI (e.g., LLM) to generate questions based on specific facts. It can also generate optimal questions by referencing past question data. Step 3: The verification unit verifies the facts based on the questions generated by the generation unit. The verification unit uses the browsing function to search for reliable information sources on the Internet and verify the facts. The verification unit uses websites of government agencies and academic institutions, reliable news media, etc. as information sources. The processing in the verification unit may be performed using AI or without AI.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. The AIs other than the generation AI are, 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 are 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 in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0096] 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, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

[0097] Each of the multiple elements including the above-mentioned identification unit, generation unit, and verification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the identification unit is realized by the processor 46 of the smart device 14 and analyzes text to identify sections that require fact-checking. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates questions for the identified sections. The verification unit is realized, for example, by the control unit 46A of the smart device 14 and searches reliable information sources on the Internet based on the generated questions to verify the facts. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] Each of the multiple elements, including the above-described identification unit, generation unit, and verification unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the identification unit is realized by the processor 46 of the smart glasses 214 and analyzes text to identify sections that require fact-checking. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates questions for the identified sections. The verification unit is realized, for example, by the control unit 46A of the smart glasses 214 and searches reliable information sources on the Internet based on the generated questions to verify the facts. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] Each of the multiple elements including the above-mentioned identification unit, generation unit, and verification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the identification unit is realized by the processor 46 of the headset type terminal 314 and analyzes text to identify sections that require fact-checking. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates questions for the identified sections. The verification unit is realized, for example, by the control unit 46A of the headset type terminal 314 and searches reliable information sources on the Internet based on the generated questions to verify the facts. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] Each of the multiple elements including the above-mentioned identification unit, generation unit, and verification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the identification unit is realized by the processor 46 of the robot 414 and analyzes text to identify sections that require fact-checking. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates questions for the identified sections. The verification unit is realized, for example, by the control unit 46A of the robot 414 and searches reliable information sources on the Internet based on the generated questions to verify the facts. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] 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.

[0165] (Appendix 1) an identification unit that identifies the target part of the fact check; a generation unit that generates a question for the part identified by the identification unit; a confirmation unit that confirms factual relationships based on the questions generated by the generation unit; Equipped with A system characterized by: (Appendix 2) The identification unit Use specific algorithms or methods to identify fact-checked passages 2. The system of claim 1. (Appendix 3) The generation unit Generate questions for specific data or events 2. The system of claim 1. (Appendix 4) The confirmation unit Prioritize searching reliable sources 2. The system of claim 1. (Appendix 5) The identification unit Estimate user sentiment and prioritize fact-checking targets based on the estimated sentiment 2. The system of claim 1. (Appendix 6) The identification unit Analyzes past fact-check history and identifies target passages based on specific patterns 2. The system of claim 1. (Appendix 7) The identification unit Analyzes the context of text and identifies target passages based on specific keywords or phrases 2. The system of claim 1. (Appendix 8) The identification unit The user's emotion is estimated, and the operation of a specific part is adjusted based on the estimated user's emotion. 2. The system of claim 1. (Appendix 9) The identification unit Customize specific algorithms based on the genre or category of the text 2. The system of claim 1. (Appendix 10) The identification unit Double-check identified areas in collaboration with other AI systems 2. The system of claim 1. (Appendix 11) The generation unit Inferring user sentiment and adjusting question wording based on the inferred sentiment 2. The system of claim 1. (Appendix 12) The generation unit When generating questions, the most appropriate questions are generated by referencing past question data. 2. The system of claim 1. (Appendix 13) The generation unit Adjust the level of detail of questions based on specific facts when generating questions 2. The system of claim 1. (Appendix 14) The generation unit Estimate user sentiment and prioritize questions based on the estimated sentiment 2. The system of claim 1. (Appendix 15) The generation unit Customize question formatting based on text genre or category during question generation 2. The system of claim 1. (Appendix 16) The generation unit When generating questions, work with other AI systems to improve the accuracy of the questions. 2. The system of claim 1. (Appendix 17) The confirmation unit Inferring user emotions and adjusting the display method of confirmation results based on the estimated user emotions 2. The system of claim 1. (Appendix 18) The confirmation unit During verification, the verification algorithm is optimized by referencing past verification data. 2. The system of claim 1. (Appendix 19) The confirmation unit During verification, the reliability of specific sources is assessed and reflected in the verification results. 2. The system of claim 1. (Appendix 20) The confirmation unit Estimate the user's feelings and prioritize the confirmation results based on the estimated user's feelings. 2. The system of claim 1. (Appendix 21) The confirmation unit During verification, customize the verification algorithm based on the genre or category of the text 2. The system of claim 1. (Appendix 22) The confirmation unit During verification, the system works with other AI systems to improve the accuracy of verification results. 2. The system of claim 1. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A computer comprising: an identification unit that estimates the user's emotions by analyzing an image of the user's facial expression taken by a camera or text entered by the user, and when the estimated user's emotions indicate anxiety, prioritizes identifying portions related to important information in the text that is the subject of fact-checking as the subject of fact-checking; a generation unit that generates a question inquiring about at least one of the source and collection method of the target passage identified by the identification unit; a verification unit that, based on the question generated by the generation unit, searches preferentially at least one website of a government agency, an academic institution, or a news media that is considered to be a reliable source of information, and verifies the accuracy of the facts of the target passage; Equipped with A system characterized by:

2. The identification unit If the estimated user's emotions are relaxed, the target section is identified taking into consideration the balance of the entire text to be fact-checked, and if the user is in a hurry, the target section is identified by priority as a section where the factual accuracy can be confirmed in a short time.

2. The system of claim 1.

3. The identification unit Analyzing past fact-checking history and identifying the target passage based on at least one of passages with a high number of past errors and information related to a specific topic 2. The system of claim 1.

4. The identification unit Analyzing the context of the text being fact-checked and identifying the target passage based on specific keywords or phrases based on the analysis 2. The system of claim 1.

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