system

Abstract

An object of a system according to an embodiment is to make it possible to intuitively grasp the reliability of the answer content of the generated AI.SOLUTION: A system according to an embodiment includes a generation AI, a reliability evaluation unit, and a visualization unit. The generation AI generates an answer to a question from a user. The reliability evaluation unit evaluates the reliability of the generated answer content. The visualization unit displays the reliability evaluated by the reliability evaluation unit on a UI of a meter or a gauge.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 the problem that it is difficult for users to intuitively grasp the reliability of the answers provided by the generating AI.

[0005] The system according to the embodiment aims to enable users to intuitively grasp the reliability of the answers given by the generation AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a reliability evaluation unit, and a visualization unit. The generation AI generates an answer to a question from a user. The reliability evaluation unit evaluates the reliability of the generated answer. The visualization unit displays the reliability evaluated by the reliability evaluation unit in a meter or gauge UI. [Effects of the Invention]

[0007] The system according to the embodiment allows users to intuitively grasp the reliability of the answers given by the generation AI. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A medical information reliability assessment system according to an embodiment of the present invention evaluates the reliability of search results provided by a generative AI and answers provided by an assistant, and displays the results in an easy-to-understand manner for users. Based on big data such as papers and citation counts, the system graphically visualizes the reliability of answers provided by the generative AI using a UI such as a meter or gauge, allowing users to make better decisions. This allows the medical information reliability assessment system to minimize the impact of unintentional biases introduced by the generative AI, enabling users to make decisions based on highly reliable information.

[0029] A medical information reliability evaluation system according to an embodiment includes a reliability evaluation unit and a visualization unit. The reliability evaluation unit evaluates the reliability of an answer generated by a generation AI in response to a user's question. For example, the reliability evaluation unit calculates reliability based on the number of related papers and the number of citations. The reliability evaluation unit also checks the consistency of the answer provided by the generation AI with the related papers. The reliability evaluation unit also analyzes the number of citations of papers related to the answer provided by the generation AI and uses the result as part of the reliability evaluation. For example, the reliability evaluation unit calculates a reliability score based on the number of related papers and the number of citations, and evaluates the reliability of the answer based on the score. The visualization unit displays the reliability evaluated by the reliability evaluation unit using a UI such as a meter or gauge. For example, a green meter is displayed when reliability is high, and a red meter is displayed when reliability is low. The visualization unit also displays detailed analysis results of the reliability evaluation in text and graphs, allowing the user to understand the basis for the reliability. The visualization unit also personalizes the reliability evaluation results based on the user's past search history and behavioral patterns, providing an individually optimized display. For example, the results of the reliability evaluation are personalized based on the user's past search history and behavioral patterns, and an individually optimized display is provided. This allows the medical information reliability evaluation system according to the embodiment to minimize the influence of unintentional bias caused by the generating AI, allowing users to make decisions based on highly reliable information. For example, when a doctor recommends a treatment for a patient, the doctor can make a decision based on more reliable information by referring to the reliability evaluation results of the generating AI. Furthermore, because the answers of the generating AI are evaluated based on papers and the number of citations, users can use the information with peace of mind.

[0030] The reliability evaluation unit can calculate the reliability scores of related papers in real time when generating an answer and dynamically adjust the reliability of the answer based on those scores. For example, when the generation AI generates an answer, the reliability evaluation unit calculates the reliability scores of related papers in real time. For example, it calculates a reliability score based on the publication year of the paper, the reliability of the author, the number of citations, etc., and dynamically adjusts the reliability of the answer based on that score. This makes it possible to calculate the reliability scores of related papers in real time and dynamically adjust the reliability of the answer.

[0031] The reliability evaluation unit can integrate and analyze multiple different data sources to evaluate reliability when generating an answer. For example, when the generation AI generates an answer, the reliability evaluation unit integrates and analyzes multiple different data sources. For example, it evaluates reliability by combining medical databases, expert opinions, patient case data, etc. This makes it possible to integrate and analyze multiple different data sources to evaluate reliability.

[0032] The reliability evaluation unit can analyze multimodal data including voice input or image input when generating an answer and reflect the results in the reliability evaluation. For example, when the generation AI generates an answer, the reliability evaluation unit analyzes multimodal data including voice input and image input and reflects the results in the reliability evaluation. For example, the reliability is evaluated by analyzing a doctor's voice memo or a patient's image data. This allows the multimodal data including voice input and image input to be analyzed and reflected in the reliability evaluation.

[0033] The reliability evaluation unit can combine and analyze AI models from different specialized fields when generating an answer, and perform a multifaceted reliability evaluation. For example, when the generation AI generates an answer, the reliability evaluation unit can combine and analyze AI models from different specialized fields, and perform a multifaceted reliability evaluation. For example, AI models from internal medicine, surgery, and pharmacy can be integrated to evaluate reliability. This allows AI models from different specialized fields to be combined and analyzed, and perform a multifaceted reliability evaluation.

[0034] The visualization unit can display the detailed analysis results of the reliability evaluation in text or graphs, allowing the user to understand the basis of the reliability. For example, the visualization unit displays the detailed analysis results of the reliability evaluation in text or graphs in addition to displaying meters or gauges. For example, it displays the calculation process of the reliability score and a list of referenced papers. In this way, the detailed analysis results of the reliability evaluation can be displayed in text or graphs, allowing the user to understand the basis of the reliability.

[0035] The visualization unit can personalize the results of the reliability evaluation based on the user's past search history or behavioral patterns, and provide an individually optimized display. For example, the visualization unit can personalize the results of the reliability evaluation based on the user's past search history or behavioral patterns, and provide an individually optimized display. For example, information related to keywords searched in the past is preferentially displayed. This allows the results of the reliability evaluation to be personalized based on the user's past search history or behavioral patterns, and provide an individually optimized display.

[0036] The visualization unit provides audio feedback of the reliability evaluation results, making it possible to accommodate visually impaired people. The visualization unit, for example, constructs a system that provides audio feedback of the reliability evaluation results in addition to displaying meters and gauges. For example, the reliability score and analysis results are read aloud. This allows the reliability evaluation results to be fed back audio, making it possible to accommodate visually impaired people.

[0037] The visualization unit can display the reliability evaluation results on a dashboard that can be customized by the user, allowing the necessary information to be confirmed at a glance. The visualization unit, for example, builds a system that displays the reliability evaluation results on a dashboard that can be customized by the user. For example, the user can freely set the display items and layout. This allows the reliability evaluation results to be displayed on a dashboard that can be customized by the user, allowing the necessary information to be confirmed at a glance.

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

[0039] The reliability evaluation unit analyzes a user's past search history and behavioral patterns to perform an individually optimized reliability evaluation. For example, for a user who has previously shown interest in a specific medical field, papers and data related to that field can be prioritized in the evaluation. Also, by placing emphasis on information sources that the user has previously rated as highly reliable, an individual reliability evaluation can be performed. Furthermore, it is also possible to dynamically adjust the reliability evaluation criteria based on the user's behavioral patterns.

[0040] The reliability evaluation unit can combine and analyze AI models from different fields of expertise to perform multifaceted reliability evaluations. For example, it can integrate AI models from internal medicine, surgery, and pharmacy to evaluate reliability. It can also evaluate the reliability of medical information by region by combining medical data from different regions. Furthermore, it can evaluate the reliability of medical information by era by combining data from different eras.

[0041] When generating a response, the reliability evaluation unit can analyze multimodal data, including voice input or image input, and reflect the results in the reliability evaluation. For example, it can analyze a doctor's voice memo or a patient's image data to evaluate reliability. Furthermore, by analyzing voice input, it is possible to understand the user's intentions and emotions and evaluate reliability based on that. Furthermore, by analyzing image input, it is possible to evaluate reliability based on visual information.

[0042] When generating answers, the reliability evaluation unit combines and analyzes AI models from different fields, enabling multifaceted reliability evaluation. For example, it can integrate AI models from internal medicine, surgery, and pharmacy to evaluate reliability. It is also possible to evaluate the reliability of medical information by region by combining medical data from different regions. Furthermore, it is possible to evaluate the reliability of medical information by era by combining data from different eras.

[0043] The visualization unit can display the detailed analysis results of the reliability assessment in text or graphs, allowing users to understand the basis for the reliability. For example, it can display the calculation process of the reliability score or a list of referenced papers. It can also use interactive graphs and charts to visually display the results of the reliability assessment in an easy-to-understand manner. It can also provide summary information to allow users to easily understand the detailed analysis results.

[0044] The visualization unit can provide audio feedback of the reliability evaluation results, making it possible to accommodate visually impaired users. For example, a system can be built that reads out the reliability scores and analysis results aloud. It is also possible to accommodate individual needs by allowing users to customize the content of the audio feedback. Furthermore, by providing audio feedback in real time, users can instantly check the reliability evaluation results.

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

[0046] Step 1: The reliability evaluation unit evaluates the reliability of the answers generated by the generation AI in response to user questions. For example, the reliability evaluation unit calculates reliability based on the number of related papers and the number of citations. It also checks how consistent the answers provided by the generation AI are with related papers and calculates a reliability score by analyzing the number of citations of the related papers. Step 2: The visualization unit displays the trustworthiness assessed by the trustworthiness assessment unit in a UI such as a meter or gauge. For example, if trustworthiness is high, a green meter is displayed, and if trustworthiness is low, a red meter is displayed. The visualization unit also displays detailed analysis results of the trustworthiness assessment in text and graphs, allowing users to understand the basis for trustworthiness. Furthermore, the visualization unit personalizes the trustworthiness assessment results based on the user's past search history and behavioral patterns, providing an individually optimized display.

[0047] (Example 2) A medical information reliability assessment system according to an embodiment of the present invention evaluates the reliability of search results provided by a generative AI and answers provided by an assistant, and displays the results in an easy-to-understand manner for users. Based on big data such as papers and citation counts, the system graphically visualizes the reliability of answers provided by the generative AI using a UI such as a meter or gauge, allowing users to make better decisions. This allows the medical information reliability assessment system to minimize the impact of unintentional biases introduced by the generative AI, enabling users to make decisions based on highly reliable information.

[0048] A medical information reliability evaluation system according to an embodiment includes a reliability evaluation unit and a visualization unit. The reliability evaluation unit evaluates the reliability of an answer generated by a generation AI in response to a user's question. For example, the reliability evaluation unit calculates reliability based on the number of related papers and the number of citations. The reliability evaluation unit also checks the consistency of the answer provided by the generation AI with the related papers. The reliability evaluation unit also analyzes the number of citations of papers related to the answer provided by the generation AI and uses the result as part of the reliability evaluation. For example, the reliability evaluation unit calculates a reliability score based on the number of related papers and the number of citations, and evaluates the reliability of the answer based on the score. The visualization unit displays the reliability evaluated by the reliability evaluation unit using a UI such as a meter or gauge. For example, a green meter is displayed when reliability is high, and a red meter is displayed when reliability is low. The visualization unit also displays detailed analysis results of the reliability evaluation in text and graphs, allowing the user to understand the basis for the reliability. The visualization unit also personalizes the reliability evaluation results based on the user's past search history and behavioral patterns, providing an individually optimized display. For example, the results of the reliability evaluation are personalized based on the user's past search history and behavioral patterns, and an individually optimized display is provided. This allows the medical information reliability evaluation system according to the embodiment to minimize the influence of unintentional bias caused by the generating AI, allowing users to make decisions based on highly reliable information. For example, when a doctor recommends a treatment for a patient, the doctor can make a decision based on more reliable information by referring to the reliability evaluation results of the generating AI. Furthermore, because the answers of the generating AI are evaluated based on papers and the number of citations, users can use the information with peace of mind.

[0049] The reliability evaluation unit can calculate the reliability scores of related papers in real time when generating an answer and dynamically adjust the reliability of the answer based on those scores. For example, when the generation AI generates an answer, the reliability evaluation unit calculates the reliability scores of related papers in real time. For example, it calculates a reliability score based on the publication year of the paper, the reliability of the author, the number of citations, etc., and dynamically adjusts the reliability of the answer based on that score. This makes it possible to calculate the reliability scores of related papers in real time and dynamically adjust the reliability of the answer.

[0050] The reliability evaluation unit can integrate and analyze multiple different data sources to evaluate reliability when generating an answer. For example, when the generation AI generates an answer, the reliability evaluation unit integrates and analyzes multiple different data sources. For example, it evaluates reliability by combining medical databases, expert opinions, patient case data, etc. This makes it possible to integrate and analyze multiple different data sources to evaluate reliability.

[0051] The trustworthiness evaluation unit can use the emotion estimation function to analyze the user's emotion regarding the question and dynamically adjust the trustworthiness evaluation criteria based on the emotion. For example, the trustworthiness evaluation unit can use the emotion estimation function to analyze the user's emotion regarding the question and dynamically adjust the trustworthiness evaluation criteria based on the emotion. For example, if the user is feeling anxious, more detailed information can be provided. This allows the user's emotion regarding the question to be analyzed and the trustworthiness evaluation criteria to be dynamically adjusted based on the emotion.

[0052] The reliability evaluation unit can analyze multimodal data including voice input or image input when generating an answer and reflect the results in the reliability evaluation. For example, when the generation AI generates an answer, the reliability evaluation unit analyzes multimodal data including voice input and image input and reflects the results in the reliability evaluation. For example, the reliability is evaluated by analyzing a doctor's voice memo or a patient's image data. This allows the multimodal data including voice input and image input to be analyzed and reflected in the reliability evaluation.

[0053] The reliability evaluation unit can combine and analyze AI models from different specialized fields when generating an answer, and perform a multifaceted reliability evaluation. For example, when the generation AI generates an answer, the reliability evaluation unit can combine and analyze AI models from different specialized fields, and perform a multifaceted reliability evaluation. For example, AI models from internal medicine, surgery, and pharmacy can be integrated to evaluate reliability. This allows AI models from different specialized fields to be combined and analyzed, and perform a multifaceted reliability evaluation.

[0054] The reliability evaluation unit can use the emotion estimation function to monitor in real time the emotions that the user feels toward the answer of the generating AI, and adjust the reliability evaluation based on those emotions. For example, the reliability evaluation unit can use the emotion estimation function to monitor in real time the emotions that the user feels toward the answer of the generating AI, and adjust the reliability evaluation based on those emotions. For example, answers that give the user a sense of security can be provided preferentially. This makes it possible to monitor in real time the emotions that the user feels toward the answer of the generating AI, and adjust the reliability evaluation based on those emotions.

[0055] The visualization unit can display the detailed analysis results of the reliability evaluation in text or graphs, allowing the user to understand the basis of the reliability. For example, the visualization unit displays the detailed analysis results of the reliability evaluation in text or graphs in addition to displaying meters or gauges. For example, it displays the calculation process of the reliability score and a list of referenced papers. In this way, the detailed analysis results of the reliability evaluation can be displayed in text or graphs, allowing the user to understand the basis of the reliability.

[0056] The visualization unit can personalize the results of the reliability evaluation based on the user's past search history or behavioral patterns, and provide an individually optimized display. For example, the visualization unit can personalize the results of the reliability evaluation based on the user's past search history or behavioral patterns, and provide an individually optimized display. For example, information related to keywords searched in the past is preferentially displayed. This allows the results of the reliability evaluation to be personalized based on the user's past search history or behavioral patterns, and provide an individually optimized display.

[0057] The visualization unit can use the emotion estimation function to analyze how the user will receive the trustworthiness evaluation result and dynamically adjust the display method based on the result. The visualization unit can, for example, use the emotion estimation function to analyze how the user will receive the trustworthiness evaluation result and dynamically adjust the display method based on the result. For example, if the user feels anxious, more detailed information is displayed. This allows the visualization unit to analyze how the user will receive the trustworthiness evaluation result and dynamically adjust the display method based on the result.

[0058] The visualization unit provides audio feedback of the reliability evaluation results, making it possible to accommodate visually impaired people. The visualization unit, for example, constructs a system that provides audio feedback of the reliability evaluation results in addition to displaying meters and gauges. For example, the reliability score and analysis results are read aloud. This allows the reliability evaluation results to be fed back audio, making it possible to accommodate visually impaired people.

[0059] The visualization unit can display the reliability evaluation results on a dashboard that can be customized by the user, allowing the necessary information to be confirmed at a glance. The visualization unit, for example, builds a system that displays the reliability evaluation results on a dashboard that can be customized by the user. For example, the user can freely set the display items and layout. This allows the reliability evaluation results to be displayed on a dashboard that can be customized by the user, allowing the necessary information to be confirmed at a glance.

[0060] The visualization unit can use the emotion estimation function to provide real-time feedback on the emotions the user feels about the reliability evaluation result and adjust the display content based on the emotions. For example, the visualization unit can use the emotion estimation function to provide real-time feedback on the emotions the user feels about the reliability evaluation result and adjust the display content based on the emotions. For example, if the user feels anxious, detailed information can be additionally displayed. This allows the visualization unit to provide real-time feedback on the emotions the user feels about the reliability evaluation result and adjust the display content based on the emotions.

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

[0062] The reliability evaluation unit analyzes a user's past search history and behavioral patterns to perform an individually optimized reliability evaluation. For example, for a user who has previously shown interest in a specific medical field, papers and data related to that field can be prioritized in the evaluation. Also, by placing emphasis on information sources that the user has previously rated as highly reliable, an individual reliability evaluation can be performed. Furthermore, it is also possible to dynamically adjust the reliability evaluation criteria based on the user's behavioral patterns.

[0063] The reliability assessment unit can estimate the user's emotions and dynamically adjust the reliability assessment criteria based on those emotions. For example, if the user feels anxious, it can provide more detailed information to reassure them. Also, if the user shows interest, it can provide additional information according to that interest. Furthermore, if the user has doubts, it can increase reliability by preferentially displaying reliable information sources.

[0064] The reliability evaluation unit can combine and analyze AI models from different fields of expertise to perform multifaceted reliability evaluations. For example, it can integrate AI models from internal medicine, surgery, and pharmacy to evaluate reliability. It can also evaluate the reliability of medical information by region by combining medical data from different regions. Furthermore, it can evaluate the reliability of medical information by era by combining data from different eras.

[0065] The trust assessment unit uses an emotion estimation function to monitor in real time the emotions users have toward the AI's answers, and can adjust the trust assessment based on those emotions. For example, it can prioritize answers that reassure the user. If the user feels anxious, it can reassure them by providing more detailed information. Furthermore, if the user has doubts, it can increase trust by prioritizing the display of highly reliable sources of information.

[0066] When generating a response, the reliability evaluation unit can analyze multimodal data, including voice input or image input, and reflect the results in the reliability evaluation. For example, it can analyze a doctor's voice memo or a patient's image data to evaluate reliability. Furthermore, by analyzing voice input, it is possible to understand the user's intentions and emotions and evaluate reliability based on that. Furthermore, by analyzing image input, it is possible to evaluate reliability based on visual information.

[0067] The reliability assessment unit can estimate the user's emotions and dynamically adjust the reliability assessment criteria based on those emotions. For example, if the user feels anxious, it can provide more detailed information to reassure them. Also, if the user shows interest, it can provide additional information according to that interest. Furthermore, if the user has doubts, it can increase reliability by preferentially displaying reliable information sources.

[0068] When generating answers, the reliability evaluation unit combines and analyzes AI models from different fields, enabling multifaceted reliability evaluation. For example, it can integrate AI models from internal medicine, surgery, and pharmacy to evaluate reliability. It is also possible to evaluate the reliability of medical information by region by combining medical data from different regions. Furthermore, it is possible to evaluate the reliability of medical information by era by combining data from different eras.

[0069] The visualization unit can display the detailed analysis results of the reliability assessment in text or graphs, allowing users to understand the basis for the reliability. For example, it can display the calculation process of the reliability score or a list of referenced papers. It can also use interactive graphs and charts to visually display the results of the reliability assessment in an easy-to-understand manner. It can also provide summary information to allow users to easily understand the detailed analysis results.

[0070] The visualization unit uses the emotion estimation function to analyze how the user perceives the reliability evaluation results and can dynamically adjust the display method based on the results. For example, if the user feels anxious, more detailed information can be displayed. On the other hand, if the user feels relieved, simple information can be displayed, reducing the burden on the user. Furthermore, it is also possible to adjust the priority of the information to be displayed depending on the user's emotions.

[0071] The visualization unit can provide audio feedback of the reliability evaluation results, making it possible to accommodate visually impaired users. For example, a system can be built that reads out the reliability scores and analysis results aloud. It is also possible to accommodate individual needs by allowing users to customize the content of the audio feedback. Furthermore, by providing audio feedback in real time, users can instantly check the reliability evaluation results.

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

[0073] Step 1: The reliability evaluation unit evaluates the reliability of the answers generated by the generation AI in response to user questions. For example, the reliability evaluation unit calculates reliability based on the number of related papers and the number of citations. It also checks how consistent the answers provided by the generation AI are with related papers and calculates a reliability score by analyzing the number of citations of the related papers. Step 2: The visualization unit displays the trustworthiness assessed by the trustworthiness assessment unit in a UI such as a meter or gauge. For example, if trustworthiness is high, a green meter is displayed, and if trustworthiness is low, a red meter is displayed. The visualization unit also displays detailed analysis results of the trustworthiness assessment in text and graphs, allowing users to understand the basis for trustworthiness. Furthermore, the visualization unit personalizes the trustworthiness assessment results based on the user's past search history and behavioral patterns, providing an individually optimized display.

[0074] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0079] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0080] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0081] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0084] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0085] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0088] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0108] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0115] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0124] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the 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.

[0128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0130] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0131] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0132] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0133] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0134] The hardware resource for executing a specific process can be any of the following 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.

[0135] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0136] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0138] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with generative AI, The generated AI is Generate answers to user questions, a reliability evaluation unit that evaluates the reliability of the generated answer content; a visualization unit that displays the reliability evaluated by the reliability evaluation unit in a UI of a meter or gauge. A system characterized by:

2. The reliability evaluation unit When generating the answer, calculate the credibility scores of related papers in real time and dynamically adjust the credibility of the answer based on the scores. The system of claim 1 .

3. The reliability evaluation unit When generating the answer, multimodal data including voice input or image input is analyzed and reflected in the reliability evaluation. The system of claim 1 .

4. The visualization unit Displaying detailed analysis results of the reliability assessment in text or graphs, allowing the user to understand the basis of the reliability. The system of claim 1 .

5. The reliability evaluation unit Analyzing the user's sentiment toward the question and dynamically adjusting the credibility assessment criteria based on the sentiment. The system of claim 1 .

6. The reliability evaluation unit When generating the answer, the AI ​​models from different fields of expertise are combined and analyzed, and reliability is evaluated from multiple angles. The system of claim 1 .

7. The visualization unit Analyzing how the user receives the trust assessment results and dynamically adjusting the display method based on the results. The system of claim 1 .

8. The visualization unit The reliability evaluation results are provided as audio feedback, making it suitable for visually impaired people. The system of claim 1 .

Images