System

Abstract

An object of the system according to the embodiment is to provide highly reliable information and solve user's questions and troubles.SOLUTION: A system includes a question analysis part, an information provision part, and a reliability evaluation part. The question analysis unit analyzes a question of a user. The information providing unit provides information based on the question analyzed by the question analysis unit. The reliability evaluation unit evaluates reliability of the information provided by the information providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have the drawback of being difficult to judge the reliability of information searches in specialized fields, making them impractical.

[0005] The system according to the embodiment aims to provide highly reliable information and resolve the user's questions and concerns. [Means for solving the problem]

[0006] A system according to an embodiment includes a question analysis unit, an information providing unit, and a reliability evaluation unit. The question analysis unit analyzes a user's question. The information providing unit provides information based on the question analyzed by the question analysis unit. The reliability evaluation unit evaluates the reliability of the information provided by the information providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide highly reliable information and solve the user's questions and problems. [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) An information provision system according to an embodiment of the present invention is a system that improves the reliability and practicality of information searches in specialized fields. This system uses AI that supports various specialized fields to provide knowledge and reference information for resolving minor problems and questions that arise in daily life and for decision-making. This allows the information provision system to aim to improve the quality of life (QOL) of all people, regardless of their academic background or educational level.

[0029] An information provision system according to an embodiment includes a question analysis unit, an information provision unit, and a reliability evaluation unit. The question analysis unit analyzes a user's question. For example, the question analysis unit analyzes a user's text-based question using natural language processing technology. The question analysis unit can also analyze a user's voice-based question using speech recognition technology. The question analysis unit can also identify important parts of the question using keyword extraction technology. For example, the question analysis unit analyzes the user's question and extracts related keywords. The information provision unit provides information based on the question analyzed by the question analysis unit. For example, the information provision unit provides text information. The information provision unit can also provide multimedia information. The information provision unit can also provide videos or illustrations related to the user's question. For example, the information provision unit provides videos related to the user's question. The reliability evaluation unit evaluates the reliability of the information provided by the information provision unit. For example, the reliability evaluation unit evaluates the reliability of an information source. The reliability evaluation unit can also evaluate the reliability of the information using an evaluation algorithm. The reliability evaluation unit can also evaluate the reliability of information based on user feedback. For example, the reliability evaluation unit analyzes user feedback and evaluates the reliability of information. As a result, the information providing system according to the embodiment can improve the reliability and practicality of information search in specialized fields. For example, the information providing system enables users to quickly obtain reliable information. Furthermore, the information providing system enables users to easily search for required information. Furthermore, the information providing system enables users to evaluate the reliability of provided information.

[0030] The reliability evaluation unit can automatically incorporate expert reviews. For example, the reliability evaluation unit builds a system that automatically incorporates expert reviews into information provided by the generation AI. For example, information in the medical field is reviewed by doctors, and information in the legal field is reviewed by lawyers. The reliability evaluation unit also evaluates the reliability of the information provided by the generation AI based on the expert reviews, and provides only reliable information to users. For example, it assigns a reliability mark to information that has been reviewed. In addition, to automate expert reviews, the reliability evaluation unit stores information reviewed by experts in a database, and the generation AI references that database to determine the reliability of the information. This can improve the reliability of the information.

[0031] The reliability evaluation unit can perform reliability evaluation based on the user's past search history or behavioral patterns. For example, the reliability evaluation unit analyzes the user's past search history and evaluates the reliability of the information provided by the generation AI based on that history. For example, a user who has used a lot of reliable information in the past is provided with similarly reliable information. The reliability evaluation unit also analyzes the user's behavioral patterns and evaluates the reliability of the information provided by the generation AI based on those patterns. For example, a user who is knowledgeable in a particular field is provided with specialized information in that field. The reliability evaluation unit also builds a system that evaluates the reliability of the information provided by the generation AI based on the user's past feedback. For example, information that the user has given a high rating is provided preferentially. This makes it possible to provide the user with a personalized reliability evaluation.

[0032] The information providing unit can provide information in real time through a voice assistant or chatbot. For example, the information providing unit integrates information provided by the generation AI into a voice assistant to build a system in which a user can input questions by voice and receive answers in real time. For example, the information providing unit provides information using a smart speaker. The information providing unit also develops a system using a chatbot to provide information provided by the generation AI to a user in real time. For example, the information providing unit provides information using a chatbot within a website or app. The information providing unit also optimizes the interface to instantly provide information provided by the generation AI to a user through a voice assistant or chatbot. For example, the information providing unit quickly generates answers to user questions, allowing the user to instantly use the information.

[0033] The information provision unit integrates information from different fields of expertise and can respond to questions that span multiple fields. For example, the information provision unit builds a database that integrates information from different fields of expertise, allowing the generation AI to respond to questions that span multiple fields. For example, it provides integrated medical and legal information. The information provision unit also analyzes information from multiple fields of expertise and develops a system that allows the generation AI to respond to complex questions based on that information. For example, it provides combined information on technology and economics. The information provision unit also integrates information provided by experts in different fields of expertise, allowing the generation AI to respond to complex questions based on that information. For example, it provides integrated information on education and psychology. This makes it possible to respond to complex questions.

[0034] The information providing unit generates optimal answers based on past learning data and can adjust the level of detail of the answer according to the user's level of understanding. For example, the information providing unit builds a system in which a generation AI generates optimal answers to user questions based on past learning data. For example, for questions in the field of education, answers are generated by referring to past learning data. The information providing unit also develops a system in which the level of detail of the answer provided by the generation AI is adjusted according to the user's level of understanding. For example, it provides simple explanations for beginners and detailed explanations for experts. The information providing unit also generates optimal answers to user questions based on past learning data and adjusts the level of detail of the answer based on user feedback. For example, it simplifies the answer to make it easier for the user to understand. This makes it possible to provide optimal answers according to the user's level of understanding.

[0035] The information provision unit can provide related videos and illustrations to provide information that is visually easy to understand. For example, the information provision unit builds a system in which a generation AI provides related videos and illustrations in response to a user's question. For example, it provides explanatory videos and illustrations for math problems. The information provision unit also develops a system that automatically generates videos and illustrations so that the generation AI can provide visually easy-to-understand information in response to a user's question. For example, it shows an illustration of how to solve a program error. The information provision unit also provides related videos and illustrations in response to a user's question, and complements the answer based on that information to make it easier for the user to understand. For example, it shows a cooking recipe in video. This makes it possible to provide information that is visually easy to understand.

[0036] The information providing unit can refer to similar questions and their answers from other users and provide the most appropriate answer. For example, the information providing unit builds a system in which the generation AI refers to similar questions and their answers from other users in response to a user's question. For example, it generates an answer by referring to a database of past questions. The information providing unit also develops a system in which the generation AI provides the most appropriate answer based on similar questions and their answers from other users. For example, it provides an integrated version of answers to similar questions. The information providing unit also generates the optimal answer based on that information in which the generation AI refers to similar questions and their answers from other users in response to a user's question. For example, it provides an answer based on past success stories. This makes it possible to provide the optimal answer by referring to similar questions and their answers from the past.

[0037] The information provision unit can provide in-depth knowledge by providing summaries of related academic papers and specialized books. For example, the information provision unit will build a system in which a generation AI provides summaries of related academic papers and specialized books in response to a user's question. For example, it will provide summaries of medical papers. The information provision unit will also develop a system in which a generation AI automatically generates summaries of related academic papers and specialized books in response to a user's question. For example, it will provide summaries of technical books. The information provision unit will also provide in-depth knowledge based on that information by providing summaries of related academic papers and specialized books in response to a user's question. For example, it will provide summaries of legal books. This will allow for in-depth knowledge to be provided.

[0038] The information provision unit can propose optimal solutions based on past user data and provide information tailored to the user's lifestyle. For example, the information provision unit builds a system in which a generation AI analyzes a user's past question history and behavioral data and proposes optimal solutions based on that data. For example, a user who has asked many cooking questions in the past is given priority in being provided with cooking-related solutions. Furthermore, in order to provide information tailored to the user's lifestyle, the generation AI proposes personalized solutions based on the user's past data. For example, a user who has asked many questions about health care is given health care solutions. Furthermore, the information provision unit develops a system in which a generation AI proposes optimal solutions based on the user's past data and evaluates whether the solutions match the user's lifestyle. For example, a solution tailored to the user's lifestyle rhythm is provided. This makes it possible to provide optimal solutions tailored to the user's lifestyle.

[0039] The information provision unit can provide related video tutorials and practical guides, and visually show actual steps. For example, the information provision unit builds a system in which a generation AI provides related video tutorials in response to a user's question. For example, in response to a question about a cooking recipe, it provides a video showing cooking steps. Furthermore, the information provision unit develops a system in which a generation AI automatically generates visually easy-to-understand videos and illustrations in response to a user's question, in order to provide practical guides. For example, it provides a video showing DIY steps. Furthermore, the information provision unit provides related video tutorials and practical guides in response to a user's question, and helps the user actually perform the steps based on that information. For example, it provides a video showing fitness exercises. This makes it easier for the user to understand by visually showing the actual steps.

[0040] The information provision unit can refer to success stories and feedback from other users and provide the most effective solution. For example, the information provision unit builds a system in which the generation AI refers to success stories from other users in response to a user's question. For example, it provides a solution based on success stories from users who have asked a similar question in the past. The information provision unit also develops a system in which the generation AI provides the most effective solution based on feedback from other users. For example, it provides solutions with high feedback ratings preferentially. The information provision unit also allows the generation AI to refer to success stories and feedback from other users in response to a user's question and provide the optimal solution based on that information. For example, it provides a solution that integrates past success stories. This makes it possible to provide the optimal solution based on the success stories and feedback of other users.

[0041] The information provision unit can integrate information from related community forums and Q&A sites and provide solutions from multiple perspectives. For example, the information provision unit builds a system in which the generation AI integrates information from related community forums and Q&A sites in response to a user's question. For example, it collects and provides information from multiple forums. The information provision unit also develops a system in which the generation AI analyzes information from community forums and Q&A sites in response to a user's question, in order to provide solutions from multiple perspectives. For example, it provides solutions from different perspectives. The information provision unit also integrates information from related community forums and Q&A sites in response to a user's question, and provides the optimal solution based on that information. For example, it provides information by integrating multiple information sources. This makes it possible to provide solutions from multiple perspectives.

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

[0043] The information provision system can also obtain the user's geographical location information and provide information specific to the area. For example, if the user is in a specific area, the system can provide weather information, traffic information, and information about local events for that area. The information provision system can also provide information about nearby stores and services based on the geographical location information. For example, if the user is looking for a nearby restaurant, the system can provide reviews and menu information for restaurants in that area. The geographical location information can also be used to provide information about tourist spots and famous places that the user may visit. This allows the user to quickly obtain information specific to the area.

[0044] The information provision system can also monitor the user's health condition and provide health advice. For example, if the user is using a wearable device, advice on exercise and diet can be provided based on data obtained from the device. It can also provide information on appropriate medical institutions and specialists based on the user's health condition. For example, if the user reports a specific symptom, information on medical institutions that can address that symptom can be provided. It can also analyze the user's health data and provide information on preventive medicine. For example, it can suggest the importance of regular health checks and ways to improve lifestyle habits to maintain good health. This allows users to obtain appropriate health information and manage their health.

[0045] The information provision system can also provide personalized study plans based on the user's learning history. For example, it can analyze the content the user has studied in the past and their progress, and use that data to suggest what they should study next. It can also provide study plans tailored to the user's learning style and pace. For example, if the user is the type who likes to study intensively for short periods of time, it can suggest a plan that suits that style. It can also provide related learning resources and teaching materials based on the user's learning history. For example, if the user is interested in a particular field, it can provide information on books and online courses related to that field. This allows the user to study efficiently.

[0046] The information provision system can also make personalized product and service suggestions based on a user's purchasing history. For example, it can analyze data on products and services purchased by the user in the past and suggest related products and services based on that data. It can also provide information on specific brands or categories based on the user's purchasing history. For example, if a user purchases many products from a specific brand, it can provide information on new products and sales from that brand. It can also provide personalized coupons and discount information based on the user's purchasing history. For example, it can provide coupons related to products the user has purchased in the past. This allows users to efficiently find products and services that suit them.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The question analyzer analyzes the user's question. For example, the question analyzer may use natural language processing technology to analyze the user's text-based question. The question analyzer may also use speech recognition technology to analyze the user's voice-based question. Furthermore, the question analyzer may use keyword extraction technology to identify important parts of the question. Step 2: The information providing unit provides information based on the question analyzed by the question analyzing unit. For example, the information providing unit can provide text information or multimedia information. The information providing unit can also provide videos or illustrations related to the user's question. Step 3: The reliability evaluation unit evaluates the reliability of the information provided by the information providing unit. For example, the reliability evaluation unit evaluates the reliability of the information source. The reliability evaluation unit can also evaluate the reliability of the information using an evaluation algorithm. Furthermore, the reliability evaluation unit can also evaluate the reliability of the information based on user feedback.

[0049] (Example 2) An information provision system according to an embodiment of the present invention is a system that improves the reliability and practicality of information searches in specialized fields. This system uses AI that supports various specialized fields to provide knowledge and reference information for resolving minor problems and questions that arise in daily life and for decision-making. This allows the information provision system to aim to improve the quality of life (QOL) of all people, regardless of their academic background or educational level.

[0050] An information provision system according to an embodiment includes a question analysis unit, an information provision unit, and a reliability evaluation unit. The question analysis unit analyzes a user's question. For example, the question analysis unit analyzes a user's text-based question using natural language processing technology. The question analysis unit can also analyze a user's voice-based question using speech recognition technology. The question analysis unit can also identify important parts of the question using keyword extraction technology. For example, the question analysis unit analyzes the user's question and extracts related keywords. The information provision unit provides information based on the question analyzed by the question analysis unit. For example, the information provision unit provides text information. The information provision unit can also provide multimedia information. The information provision unit can also provide videos or illustrations related to the user's question. For example, the information provision unit provides videos related to the user's question. The reliability evaluation unit evaluates the reliability of the information provided by the information provision unit. For example, the reliability evaluation unit evaluates the reliability of an information source. The reliability evaluation unit can also evaluate the reliability of the information using an evaluation algorithm. The reliability evaluation unit can also evaluate the reliability of information based on user feedback. For example, the reliability evaluation unit analyzes user feedback and evaluates the reliability of information. As a result, the information providing system according to the embodiment can improve the reliability and practicality of information search in specialized fields. For example, the information providing system enables users to quickly obtain reliable information. Furthermore, the information providing system enables users to easily search for required information. Furthermore, the information providing system enables users to evaluate the reliability of provided information.

[0051] The reliability evaluation unit can automatically incorporate expert reviews. For example, the reliability evaluation unit builds a system that automatically incorporates expert reviews into information provided by the generation AI. For example, information in the medical field is reviewed by doctors, and information in the legal field is reviewed by lawyers. The reliability evaluation unit also evaluates the reliability of the information provided by the generation AI based on the expert reviews, and provides only reliable information to users. For example, it assigns a reliability mark to information that has been reviewed. In addition, to automate expert reviews, the reliability evaluation unit stores information reviewed by experts in a database, and the generation AI references that database to determine the reliability of the information. This can improve the reliability of the information.

[0052] The reliability evaluation unit can perform reliability evaluation based on the user's past search history or behavioral patterns. For example, the reliability evaluation unit analyzes the user's past search history and evaluates the reliability of the information provided by the generation AI based on that history. For example, a user who has used a lot of reliable information in the past is provided with similarly reliable information. The reliability evaluation unit also analyzes the user's behavioral patterns and evaluates the reliability of the information provided by the generation AI based on those patterns. For example, a user who is knowledgeable in a particular field is provided with specialized information in that field. The reliability evaluation unit also builds a system that evaluates the reliability of the information provided by the generation AI based on the user's past feedback. For example, information that the user has given a high rating is provided preferentially. This makes it possible to provide the user with a personalized reliability evaluation.

[0053] The credibility evaluation unit can use the emotion estimation function to analyze the user's emotional response and optimize the information provision method to elicit a positive response. For example, the credibility evaluation unit uses the emotion estimation function to build a system that analyzes the user's emotional response in real time when receiving information. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The credibility evaluation unit also optimizes the content and format of the information provided by the generation AI based on the user's emotional response data. For example, it adjusts the way the information is presented to elicit a positive emotional response. The credibility evaluation unit also develops a system that prioritizes providing information to which the user has a positive emotion based on the emotion estimation data. For example, it re-provides information to which the user has previously responded positively. This makes it possible to provide optimal information according to the user's emotions.

[0054] The information providing unit can provide information in real time through a voice assistant or chatbot. For example, the information providing unit integrates information provided by the generation AI into a voice assistant to build a system in which a user can input questions by voice and receive answers in real time. For example, the information providing unit provides information using a smart speaker. The information providing unit also develops a system using a chatbot to provide information provided by the generation AI to a user in real time. For example, the information providing unit provides information using a chatbot within a website or app. The information providing unit also optimizes the interface to instantly provide information provided by the generation AI to a user through a voice assistant or chatbot. For example, the information providing unit quickly generates answers to user questions, allowing the user to instantly use the information.

[0055] The information provision unit integrates information from different fields of expertise and can respond to questions that span multiple fields. For example, the information provision unit builds a database that integrates information from different fields of expertise, allowing the generation AI to respond to questions that span multiple fields. For example, it provides integrated medical and legal information. The information provision unit also analyzes information from multiple fields of expertise and develops a system that allows the generation AI to respond to complex questions based on that information. For example, it provides combined information on technology and economics. The information provision unit also integrates information provided by experts in different fields of expertise, allowing the generation AI to respond to complex questions based on that information. For example, it provides integrated information on education and psychology. This makes it possible to respond to complex questions.

[0056] The information providing unit can use the emotion estimation function to monitor the user's emotional state in real time and provide an interface for reducing stress. The information providing unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional state in real time when searching for information. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information providing unit also provides an interface for reducing stress based on the user's emotional state. For example, it uses designs and colors that help the user relax. The information providing unit also develops a system that optimizes the information search process that causes the user stress, based on the emotion estimation data. For example, it simplifies the search process when the user feels stressed, thereby reducing the user's stress.

[0057] The information providing unit generates optimal answers based on past learning data and can adjust the level of detail of the answer according to the user's level of understanding. For example, the information providing unit builds a system in which a generation AI generates optimal answers to user questions based on past learning data. For example, for questions in the field of education, answers are generated by referring to past learning data. The information providing unit also develops a system in which the level of detail of the answer provided by the generation AI is adjusted according to the user's level of understanding. For example, it provides simple explanations for beginners and detailed explanations for experts. The information providing unit also generates optimal answers to user questions based on past learning data and adjusts the level of detail of the answer based on user feedback. For example, it simplifies the answer to make it easier for the user to understand. This makes it possible to provide optimal answers according to the user's level of understanding.

[0058] The information provision unit can provide related videos and illustrations to provide information that is visually easy to understand. For example, the information provision unit builds a system in which a generation AI provides related videos and illustrations in response to a user's question. For example, it provides explanatory videos and illustrations for math problems. The information provision unit also develops a system that automatically generates videos and illustrations so that the generation AI can provide visually easy-to-understand information in response to a user's question. For example, it shows an illustration of how to solve a program error. The information provision unit also provides related videos and illustrations in response to a user's question, and complements the answer based on that information to make it easier for the user to understand. For example, it shows a cooking recipe in video. This makes it possible to provide information that is visually easy to understand.

[0059] The information provision unit can use the emotion estimation function to analyze the emotional response to the user's question and carefully repeat the answer until the user is satisfied. For example, the information provision unit uses the emotion estimation function to build a system that analyzes the emotional response to the user's question in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information provision unit also develops a system in which the generation AI carefully repeats the answer until the user is satisfied based on the user's emotional response data. For example, it tries different explanation methods until the user understands. The information provision unit also provides an interface for carefully repeating the answer until the user is satisfied based on the emotion estimation data. For example, it provides additional information until the user is satisfied. This makes it possible to provide careful answers until the user is satisfied.

[0060] The information providing unit can refer to similar questions and their answers from other users and provide the most appropriate answer. For example, the information providing unit builds a system in which the generation AI refers to similar questions and their answers from other users in response to a user's question. For example, it generates an answer by referring to a database of past questions. The information providing unit also develops a system in which the generation AI provides the most appropriate answer based on similar questions and their answers from other users. For example, it provides an integrated version of answers to similar questions. The information providing unit also generates the optimal answer based on that information in which the generation AI refers to similar questions and their answers from other users in response to a user's question. For example, it provides an answer based on past success stories. This makes it possible to provide the optimal answer by referring to similar questions and their answers from the past.

[0061] The information provision unit can provide in-depth knowledge by providing summaries of related academic papers and specialized books. For example, the information provision unit will build a system in which a generation AI provides summaries of related academic papers and specialized books in response to a user's question. For example, it will provide summaries of medical papers. The information provision unit will also develop a system in which a generation AI automatically generates summaries of related academic papers and specialized books in response to a user's question. For example, it will provide summaries of technical books. The information provision unit will also provide in-depth knowledge based on that information by providing summaries of related academic papers and specialized books in response to a user's question. For example, it will provide summaries of legal books. This will allow for in-depth knowledge to be provided.

[0062] The information providing unit can use the emotion estimation function to monitor the emotional state of the user when asking a question in real time and adjust the interface so that the user can ask the question in a relaxed state. The information providing unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the user when asking a question in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information providing unit also develops a system that adjusts the interface based on the user's emotional state so that the user can ask the question in a relaxed state. For example, it uses designs and colors that help the user relax. The information providing unit also optimizes the interface based on the emotion estimation data so that the user can ask the question in a relaxed state. For example, it simplifies the interface if the user feels stressed. This allows the user to ask the question in a relaxed state.

[0063] The information provision unit can propose optimal solutions based on past user data and provide information tailored to the user's lifestyle. For example, the information provision unit builds a system in which a generation AI analyzes a user's past question history and behavioral data and proposes optimal solutions based on that data. For example, a user who has asked many cooking questions in the past is given priority in being provided with cooking-related solutions. Furthermore, in order to provide information tailored to the user's lifestyle, the generation AI proposes personalized solutions based on the user's past data. For example, a user who has asked many questions about health care is given health care solutions. Furthermore, the information provision unit develops a system in which a generation AI proposes optimal solutions based on the user's past data and evaluates whether the solutions match the user's lifestyle. For example, a solution tailored to the user's lifestyle rhythm is provided. This makes it possible to provide optimal solutions tailored to the user's lifestyle.

[0064] The information provision unit can provide related video tutorials and practical guides, and visually show actual steps. For example, the information provision unit builds a system in which a generation AI provides related video tutorials in response to a user's question. For example, in response to a question about a cooking recipe, it provides a video showing cooking steps. Furthermore, the information provision unit develops a system in which a generation AI automatically generates visually easy-to-understand videos and illustrations in response to a user's question, in order to provide practical guides. For example, it provides a video showing DIY steps. Furthermore, the information provision unit provides related video tutorials and practical guides in response to a user's question, and helps the user actually perform the steps based on that information. For example, it provides a video showing fitness exercises. This makes it easier for the user to understand by visually showing the actual steps.

[0065] The information provision unit can use the emotion estimation function to analyze the emotional reactions of users when they solve a problem and provide solutions that elicit positive emotions. For example, the information provision unit uses the emotion estimation function to build a system that analyzes the emotional reactions of users when they solve a problem in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information provision unit also develops a system that provides solutions that allow the generation AI to elicit positive emotions based on the user's emotional reaction data. For example, it provides solutions that satisfy the user preferentially. The information provision unit also builds a system that provides solutions that elicit positive emotions when the user solves a problem based on the emotion estimation data. For example, it provides solutions that make the user feel happy. This makes it possible to provide solutions that elicit positive emotions.

[0066] The information provision unit can refer to success stories and feedback from other users and provide the most effective solution. For example, the information provision unit builds a system in which the generation AI refers to success stories from other users in response to a user's question. For example, it provides a solution based on success stories from users who have asked a similar question in the past. The information provision unit also develops a system in which the generation AI provides the most effective solution based on feedback from other users. For example, it provides solutions with high feedback ratings preferentially. The information provision unit also allows the generation AI to refer to success stories and feedback from other users in response to a user's question and provide the optimal solution based on that information. For example, it provides a solution that integrates past success stories. This makes it possible to provide the optimal solution based on the success stories and feedback of other users.

[0067] The information provision unit can integrate information from related community forums and Q&A sites and provide solutions from multiple perspectives. For example, the information provision unit builds a system in which the generation AI integrates information from related community forums and Q&A sites in response to a user's question. For example, it collects and provides information from multiple forums. The information provision unit also develops a system in which the generation AI analyzes information from community forums and Q&A sites in response to a user's question, in order to provide solutions from multiple perspectives. For example, it provides solutions from different perspectives. The information provision unit also integrates information from related community forums and Q&A sites in response to a user's question, and provides the optimal solution based on that information. For example, it provides information by integrating multiple information sources. This makes it possible to provide solutions from multiple perspectives.

[0068] The information providing unit can use the emotion estimation function to monitor the emotional state of the user when solving a problem in real time and provide support to reduce stress. The information providing unit, for example, uses the emotion estimation function to build a system that monitors the emotional state of the user when solving a problem in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The information providing unit also develops a system that provides support to reduce stress based on the user's emotional state. For example, it provides advice and suggestions to help the user relax. The information providing unit also builds a system that provides support to reduce stress when solving a problem based on the emotion estimation data. For example, it suggests ways to relax if the user feels stressed. This makes it possible to provide support to reduce the user's stress.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The information provision system can also obtain the user's geographical location information and provide information specific to the area. For example, if the user is in a specific area, the system can provide weather information, traffic information, and information about local events for that area. The information provision system can also provide information about nearby stores and services based on the geographical location information. For example, if the user is looking for a nearby restaurant, the system can provide reviews and menu information for restaurants in that area. The geographical location information can also be used to provide information about tourist spots and famous places that the user may visit. This allows the user to quickly obtain information specific to the area.

[0071] The information provision system can also monitor the user's health condition and provide health advice. For example, if the user is using a wearable device, advice on exercise and diet can be provided based on data obtained from the device. It can also provide information on appropriate medical institutions and specialists based on the user's health condition. For example, if the user reports a specific symptom, information on medical institutions that can address that symptom can be provided. It can also analyze the user's health data and provide information on preventive medicine. For example, it can suggest the importance of regular health checks and ways to improve lifestyle habits to maintain good health. This allows users to obtain appropriate health information and manage their health.

[0072] The information provision system can also provide personalized study plans based on the user's learning history. For example, it can analyze the content the user has studied in the past and their progress, and use that data to suggest what they should study next. It can also provide study plans tailored to the user's learning style and pace. For example, if the user is the type who likes to study intensively for short periods of time, it can suggest a plan that suits that style. It can also provide related learning resources and teaching materials based on the user's learning history. For example, if the user is interested in a particular field, it can provide information on books and online courses related to that field. This allows the user to study efficiently.

[0073] The information provision system can also make personalized product and service suggestions based on a user's purchasing history. For example, it can analyze data on products and services purchased by the user in the past and suggest related products and services based on that data. It can also provide information on specific brands or categories based on the user's purchasing history. For example, if a user purchases many products from a specific brand, it can provide information on new products and sales from that brand. It can also provide personalized coupons and discount information based on the user's purchasing history. For example, it can provide coupons related to products the user has purchased in the past. This allows users to efficiently find products and services that suit them.

[0074] The information provision system can also use the user's emotion estimation function to analyze the user's emotional state when receiving information and provide information at the optimal timing. For example, if the user is feeling stressed, the system can detect that state and provide relaxing information or entertainment. If the user is in a positive emotional state, the system can provide information to help the user maintain that state. For example, the system can provide the latest news and articles on topics that interest the user. The system can also adjust the way information is presented based on the user's emotional state. For example, if the user is tired, the system can provide information in a concise and easy-to-understand format. This allows the user to receive information that is tailored to their emotional state.

[0075] The information provision system can further use a user's emotion estimation function to monitor the user's emotional state in real time when searching for information and optimize search results. For example, if the user is feeling frustrated, the system can detect that state and provide narrower search results. Also, if the user is in a positive emotional state, the system can prioritize providing relevant information to maintain that state. For example, the system can provide detailed information on topics that interest the user. The system can also adjust the way search results are displayed based on the user's emotional state. For example, if the user is relaxed, the system can provide search results in a visually appealing format. This allows the user to receive search results that are tailored to their emotional state.

[0076] The information provision system can further use a user emotion estimation function to analyze the emotional response of the user when receiving information and evaluate the reliability of the information. For example, if the user shows a positive emotional response when receiving information, the reliability of the information can be determined to be high. On the other hand, if the user shows a negative emotional response, the reliability of the information can be determined to be low. For example, if the user has doubts about the information, the reliability of the information can be reevaluated. Furthermore, an algorithm can be developed to evaluate the reliability of information based on the user's emotional response data. For example, the emotional response data of multiple users can be analyzed and the reliability of the information can be evaluated comprehensively. This allows the user to receive highly reliable information.

[0077] The information provision system can further use the user's emotion estimation function to analyze the user's emotional state when receiving information and optimize the way information is presented. For example, if the user is feeling stressed when receiving information, the system can detect that state and provide information in a concise format. Alternatively, if the user is relaxed, the system can provide detailed information to help the user maintain that state. For example, it can provide in-depth knowledge about topics that interest the user. The system can also adjust the way information is presented based on the user's emotional state. For example, if the user is tired, the system can provide information in a visually easy-to-understand format. This allows the user to receive information that is tailored to their emotional state.

[0078] The information provision system can further use a user emotion estimation function to analyze the emotional response of the user when receiving information and evaluate the reliability of the information. For example, if the user shows a positive emotional response when receiving information, the reliability of the information can be determined to be high. On the other hand, if the user shows a negative emotional response, the reliability of the information can be determined to be low. For example, if the user has doubts about the information, the reliability of the information can be reevaluated. Furthermore, an algorithm can be developed to evaluate the reliability of information based on the user's emotional response data. For example, the emotional response data of multiple users can be analyzed and the reliability of the information can be evaluated comprehensively. This allows the user to receive highly reliable information.

[0079] The information provision system can further use the user's emotion estimation function to analyze the user's emotional state when receiving information and optimize the way information is presented. For example, if the user is feeling stressed when receiving information, the system can detect that state and provide information in a concise format. Alternatively, if the user is relaxed, the system can provide detailed information to help the user maintain that state. For example, it can provide in-depth knowledge about topics that interest the user. The system can also adjust the way information is presented based on the user's emotional state. For example, if the user is tired, the system can provide information in a visually easy-to-understand format. This allows the user to receive information that is tailored to their emotional state.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The question analyzer analyzes the user's question. For example, the question analyzer may use natural language processing technology to analyze the user's text-based question. The question analyzer may also use speech recognition technology to analyze the user's voice-based question. Furthermore, the question analyzer may use keyword extraction technology to identify important parts of the question. Step 2: The information providing unit provides information based on the question analyzed by the question analyzing unit. For example, the information providing unit can provide text information or multimedia information. The information providing unit can also provide videos or illustrations related to the user's question. Step 3: The reliability evaluation unit evaluates the reliability of the information provided by the information providing unit. For example, the reliability evaluation unit evaluates the reliability of the information source. The reliability evaluation unit can also evaluate the reliability of the information using an evaluation algorithm. Furthermore, the reliability evaluation unit can also evaluate the reliability of the information based on user feedback.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] 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]

[0149] 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 question analysis unit that analyzes a user's question; an information providing unit that provides information based on the question analyzed by the question analyzing unit; a reliability evaluation unit that evaluates the reliability of the information provided by the information providing unit. A system characterized by:

2. The reliability evaluation unit Automatically incorporate expert reviews 2. The system of claim 1.

3. The reliability evaluation unit Evaluate the reliability of the user based on their past search history or behavioral patterns 2. The system of claim 1.

4. The reliability evaluation unit Analyze the user's emotional response and optimize the information delivery method to elicit a positive response.

2. The system of claim 1.

5. The information providing unit Providing real-time information through a voice assistant or chatbot 2. The system of claim 1.

Images