System

Abstract

An object of a system according to an embodiment is to provide a quick and appropriate solution when a user faces a technical problem.SOLUTION: A system includes an input unit, an analysis unit, and a provision unit. The input unit receives a question from a user. The analysis unit analyzes the question received by the input unit. The providing unit provides a solution based on the result analyzed by the analyzing 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 technologies have had the problem that it is difficult for users to obtain a quick and appropriate solution when they encounter a technical problem.

[0005] The system according to the embodiment aims to provide a quick and appropriate solution when a user encounters a technical problem. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a provision unit. The input unit receives a question from a user. The analysis unit analyzes the question received by the input unit. The provision unit provides a solution based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a quick and appropriate solution when a user encounters a technical problem. [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) The TechHelp ChatBot system according to an embodiment of the present invention is a system that provides real-time assistance for technical problems faced by users. With this system, users simply input their questions or issues via chat, and the TechHelp ChatBot provides appropriate solutions and procedures to support problem resolution. This allows users to quickly solve problems and improve productivity.

[0029] The TechHelp ChatBot system according to the embodiment includes an input unit, an analysis unit, and a provision unit. The input unit accepts questions from users. For example, a user can enter a question such as, "My computer won't start. What should I do?" The input unit can also accept voice input and convert it into text using speech recognition technology. The analysis unit analyzes the questions accepted by the input unit. For example, the generation AI can analyze the user's question using natural language processing technology and understand its content. The generation AI can also suggest optimal solutions by referencing past solutions to similar problems. The generation AI can also automatically determine the urgency of the problem and respond accordingly. The provision unit provides solutions based on the results of the analysis by the analysis unit. For example, the generation AI can suggest procedures such as "Make sure the power cable is connected properly" or "Try booting in safe mode." The generation AI can also analyze the user's emotions using an emotion estimation function and provide support messages to reduce stress and anxiety. This allows the TechHelp ChatBot system to quickly provide solutions to user questions.

[0030] The analysis unit can suggest optimal solutions by referring to the solution history of similar problems in the past. For example, the analysis unit analyzes a question entered by a user and searches a database for solutions to similar problems that have occurred in the past. For example, in response to a question such as "My computer won't start," the analysis unit suggests past solutions such as "check the power cable" and "start in safe mode." The analysis unit also analyzes the user's input using natural language processing technology and suggests optimal solutions by referring to the past solution history. For example, in response to a problem such as "My printer won't work," the analysis unit suggests past solutions such as "reinstalling the driver" and "checking the connection cable." The analysis unit also analyzes the user's input and builds a system that suggests optimal solutions based on the past solution history of similar problems. For example, in response to a problem such as "A software error message is displayed," the analysis unit suggests past solutions such as "reinstalling the software" and "checking the error message." This makes it possible to provide optimal solutions by utilizing the past solution history.

[0031] The analysis unit can automatically determine the urgency of the problem and take action according to the urgency. The analysis unit, for example, analyzes questions entered by a user and builds a system that automatically determines the urgency of the problem. For example, the problem "My computer won't start" is determined to be high urgency, and the problem "I don't know how to operate the software" is determined to be low urgency. The analysis unit also develops a system that analyzes the user's input and takes action according to the urgency. For example, high-urgency problems are dealt with immediately, and low-urgency problems are put off until later. The analysis unit also builds a system that automatically determines the urgency of the problem based on the user's input and takes action according to the urgency. For example, high-urgency problems are dealt with first, and low-urgency problems are put off until later. This makes it possible to take appropriate action according to the urgency of the problem.

[0032] The input unit can accept voice input and convert it into text using voice recognition technology. The input unit, for example, allows a user to use voice input and builds a system that converts it into text using voice recognition technology. For example, if a user speaks, "My computer won't start," the system converts it into text and analyzes it. The input unit also develops a system that allows a user to use voice input using voice recognition technology. For example, if a user speaks, "My printer won't work," the system converts it into text and analyzes it. The input unit also develops a system that allows a user to use voice input and builds a system that converts it into text using voice recognition technology. For example, if a user speaks, "A software error message is displayed," the system converts it into text and analyzes it. In this way, voice input can be converted into text and analyzed.

[0033] The input unit can accept images and videos and analyze problems based on visual information. For example, the input unit can be added with a function that allows users to upload images and videos, and a system can be built that analyzes problems based on visual information. For example, a user may say, "My computer won't start," and upload an image showing the status of their computer. The input unit can also be added with a function that allows users to upload images and videos, and a system can be developed that analyzes problems based on visual information. For example, a user may say, "My printer isn't working," and upload a video showing the status of the printer. The input unit can also be added with a function that allows users to upload images and videos, and a system can be built that analyzes problems based on visual information. For example, a user may say, "A software error message is displayed," and upload a screenshot of the error message. This allows problems to be analyzed based on visual information.

[0034] The analysis unit learns the success rates of past solutions and can propose the most effective solution. For example, the analysis unit builds a system in which the generation AI learns the success rates of past solutions and proposes the most effective solution. For example, for the problem "The computer won't start," the analysis unit suggests "check the power cable" and "start in safe mode," which have a high past success rate. The analysis unit also develops a system in which the generation AI learns the success rates of past solutions when analyzing a problem and proposes the most effective solution. For example, for the problem "The printer won't work," the analysis unit suggests "reinstall the driver" and "check the connection cable," which have a high past success rate. The analysis unit also builds a system in which the generation AI learns the success rates of past solutions and proposes the most effective solution. For example, for the problem "A software error message is displayed," the analysis unit suggests "reinstall the software" and "check the content of the error message," which have a high past success rate. This makes it possible to provide the most effective solution based on past success rates.

[0035] The analysis unit can customize the problem analysis results according to the user's technical level and provide them in an easy-to-understand format. The analysis unit, for example, builds a system that customizes the problem analysis results according to the user's technical level and provides them in an easy-to-understand format. For example, it provides simple procedures for beginners and detailed procedures for advanced users. The analysis unit also develops a system that customizes the problem analysis results according to the user's technical level and provides them in an easy-to-understand format. For example, it provides procedures using diagrams or videos for beginners and command line instructions for advanced users. The analysis unit also builds a system that customizes the problem analysis results according to the user's technical level and provides them in an easy-to-understand format. For example, it provides a step-by-step guide for beginners and technical details for advanced users. In this way, it is possible to provide analysis results according to the user's technical level.

[0036] The analysis unit can integrate knowledge from different fields and propose comprehensive solutions. For example, the analysis unit builds a system in which the generative AI integrates knowledge from different fields to propose more comprehensive solutions. For example, for the problem of "my computer won't start," it proposes solutions from both the hardware and software perspectives. The analysis unit also develops a system in which the generative AI integrates knowledge from different fields to propose comprehensive solutions when analyzing a problem. For example, for the problem of "my printer won't work," it proposes solutions from both the network settings and driver installation perspectives. The analysis unit also builds a system in which the generative AI integrates knowledge from different fields to propose more comprehensive solutions. For example, for the problem of "my software error message is displayed," it proposes solutions from both the program code debugging and settings checking perspectives. This makes it possible to integrate knowledge from different fields to provide comprehensive solutions.

[0037] The analysis unit can analyze error message logs, identify the frequency and patterns of errors, and propose solutions. For example, the analysis unit builds a system in which a generation AI analyzes error message logs and identifies the frequency and patterns of errors. For example, if "Error 404" occurs frequently, it identifies the cause and proposes a solution. The analysis unit also develops a system in which a generation AI analyzes error message logs, identifies the frequency and patterns of errors, and proposes a solution. For example, if "Error 500" occurs frequently, it identifies the cause and proposes a solution. The analysis unit also builds a system in which a generation AI analyzes error message logs, identifies the frequency and patterns of errors, and proposes a solution. For example, if "Error 403" occurs frequently, it identifies the cause and proposes a solution. This makes it possible to analyze error message logs and provide solutions.

[0038] The analysis unit can analyze the contents of an error message and automatically search for and provide bug reports and patch information for related software. The analysis unit, for example, analyzes the contents of an error message and builds a system that automatically searches for and provides bug reports and patch information for related software. For example, when "Error 404" is displayed, the bug reports and patch information are automatically searched for and provided. The analysis unit also develops a system that analyzes the contents of an error message and automatically searches for and provides bug reports and patch information for related software. For example, when "Error 500" is displayed, the bug reports and patch information are automatically searched for and provided. The analysis unit also builds a system that analyzes the contents of an error message and automatically searches for and provides bug reports and patch information for related software. For example, when "Error 403" is displayed, the bug reports and patch information are automatically searched for and provided. This makes it possible to analyze the contents of an error message and provide related information.

[0039] The analysis unit can customize the analysis results of error messages according to the user's technical level and provide them in an easy-to-understand format. The analysis unit, for example, builds a system that customizes the analysis results of error messages according to the user's technical level and provides them in an easy-to-understand format. For example, it provides simple procedures for beginners and detailed procedures for advanced users. The analysis unit also develops a system that customizes the analysis results of error messages according to the user's technical level and provides them in an easy-to-understand format. For example, it provides procedures using diagrams or videos for beginners and command line instructions for advanced users. The analysis unit also builds a system that customizes the analysis results of error messages according to the user's technical level and provides them in an easy-to-understand format. For example, it provides a step-by-step guide for beginners and technical details for advanced users. In this way, it is possible to provide analysis results of error messages according to the user's technical level.

[0040] The analysis unit can automatically translate the contents of error messages into different languages ​​to accommodate international users. The analysis unit, for example, automatically translates the contents of error messages into different languages ​​to build a system that can accommodate international users. For example, when "Error 404" is displayed, the contents are automatically translated into English, French, Chinese, etc. and provided. The analysis unit also develops a system that automatically translates the contents of error messages into different languages ​​to accommodate international users. For example, when "Error 500" is displayed, the contents are automatically translated into English, French, Chinese, etc. and provided. The analysis unit also automatically translates the contents of error messages into different languages ​​to build a system that can accommodate international users. For example, when "Error 403" is displayed, the contents are automatically translated into English, French, Chinese, etc. and provided. This allows error messages to be automatically translated into different languages ​​to accommodate international users.

[0041] The analysis unit can analyze hardware diagnostic logs, identify the cause of the problem, and propose solutions. The analysis unit, for example, builds a system in which a generating AI analyzes hardware diagnostic logs, identifies the cause of the problem, and proposes solutions. For example, for the problem "the printer won't work," the analysis unit analyzes the diagnostic log and suggests "reinstalling the driver" or "checking the connection cable." The analysis unit also develops a system in which a generating AI analyzes hardware diagnostic logs, identifies the cause of the problem, and proposes solutions. For example, for the problem "the computer won't start," the analysis unit analyzes the diagnostic log and suggests "checking the power cable" or "starting in safe mode." The analysis unit also builds a system in which a generating AI analyzes hardware diagnostic logs, identifies the cause of the problem, and proposes solutions. For example, for the problem "the monitor is not displaying," the analysis unit analyzes the diagnostic log and suggests "checking the connection cable" or "checking the monitor's power supply." In this way, the hardware diagnostic log can be analyzed to identify the cause of the problem and provide a solution.

[0042] The analysis unit can refer to past solution history and propose the most effective solution to a hardware problem. The analysis unit, for example, builds a system in which a generation AI refers to past solution history and proposes the most effective solution to a hardware problem. For example, for a problem such as "the printer won't work," past solutions include "reinstalling the driver" and "checking the connection cable." The analysis unit also develops a system in which a generation AI refers to past solution history and proposes the most effective solution to a hardware problem. For example, for a problem such as "the computer won't start," past solutions include "check the power cable" and "start in safe mode." The analysis unit also builds a system in which a generation AI refers to past solution history and proposes the most effective solution to a hardware problem. For example, for a problem such as "the monitor won't display," past solutions include "check the connection cable" and "check the monitor's power supply." This makes it possible to provide the most effective solution by referring to past solution history.

[0043] The analysis unit can customize solutions to hardware problems according to the user's technical level and provide them in an easy-to-understand format. The analysis unit, for example, builds a system that customizes solutions to hardware problems according to the user's technical level and provides them in an easy-to-understand format. For example, it provides simple procedures for beginners and detailed procedures for advanced users. The analysis unit also develops a system that customizes solutions to hardware problems according to the user's technical level and provides them in an easy-to-understand format. For example, it provides procedures using diagrams or videos for beginners and command line instructions for advanced users. The analysis unit also builds a system that customizes solutions to hardware problems according to the user's technical level and provides them in an easy-to-understand format. For example, it provides a step-by-step guide for beginners and technical details for advanced users. This makes it possible to provide solutions to hardware problems according to the user's technical level.

[0044] The analysis unit integrates knowledge from different fields and can propose comprehensive solutions to hardware problems. For example, the analysis unit builds a system in which the generative AI integrates knowledge from different fields to propose more comprehensive solutions. For example, for the problem of "my printer isn't working," it proposes solutions from both the perspectives of network settings and driver installation. The analysis unit also develops a system in which the generative AI integrates knowledge from different fields to propose comprehensive solutions to hardware problems. For example, for the problem of "my computer won't start," it proposes solutions from both the perspectives of hardware and software. The analysis unit also builds a system in which the generative AI integrates knowledge from different fields to propose more comprehensive solutions. For example, for the problem of "my monitor isn't displaying," it proposes solutions from both the perspectives of checking the connection cable and setting up the monitor. This makes it possible to integrate knowledge from different fields to provide comprehensive solutions to hardware problems.

[0045] The analysis unit can analyze application operation logs, identify user operation errors, and propose improvement measures. The analysis unit, for example, builds a system in which a generating AI analyzes application operation logs, identifies user operation errors, and proposes improvement measures. For example, for the problem "I don't know how to create a new project," the operation log is analyzed and specific steps are suggested, such as "Select 'File' from the menu, then click 'New Project'." The analysis unit also develops a system in which a generating AI analyzes application operation logs, identifies user operation errors, and proposes improvement measures. For example, for the problem "I don't know how to save a file," the operation log is analyzed and specific steps are suggested, such as "Select 'File' from the menu, then click 'Save'." The analysis unit also builds a system in which a generating AI analyzes application operation logs, identifies user operation errors, and proposes improvement measures. For example, for the problem "I don't know how to print," the operation log is analyzed and specific steps are suggested, such as "Select 'File' from the menu, then click 'Print'." This makes it possible to analyze application operation logs, identify user operation errors, and propose improvement measures.

[0046] The analysis unit can provide visual instructions using videos and images in response to questions about how to operate an application. For example, the analysis unit builds a system in which a generative AI provides visual instructions using videos and images in response to questions about how to operate an application. For example, in response to a question such as, "I don't know how to create a new project," a video showing the operating procedures is provided. The analysis unit also develops a system in which a generative AI provides visual instructions using videos and images in response to questions about how to operate an application. For example, in response to a question such as, "I don't know how to save a file," an image showing the operating procedures is provided. The analysis unit also builds a system in which a generative AI provides visual instructions using videos and images in response to questions about how to operate an application. For example, in response to a question such as, "I don't know how to print," a video showing the operating procedures is provided. In this way, visual instructions can be provided in response to questions about how to operate an application.

[0047] The analysis unit can customize solutions regarding application operation methods according to the user's technical level and provide them in an easy-to-understand format. The analysis unit, for example, builds a system that customizes solutions regarding application operation methods according to the user's technical level and provides them in an easy-to-understand format. For example, it provides simple procedures for beginners and detailed procedures for advanced users. The analysis unit also develops a system that customizes solutions regarding application operation methods according to the user's technical level and provides them in an easy-to-understand format. For example, it provides procedures using diagrams or videos for beginners and command line instructions for advanced users. The analysis unit also builds a system that customizes solutions regarding application operation methods according to the user's technical level and provides them in an easy-to-understand format. For example, it provides a step-by-step guide for beginners and technical details for advanced users. In this way, it is possible to provide solutions regarding application operation methods according to the user's technical level.

[0048] The analysis unit can automatically translate questions about how to operate an application into different languages, making it possible to accommodate international users. The analysis unit, for example, uses a generation AI to automatically translate questions about how to operate an application into different languages, building a system that can accommodate international users. For example, for a question such as "I don't know how to create a new project," the system automatically translates the procedure into English, French, Chinese, etc. and provides it. The analysis unit also develops a system in which a generation AI automatically translates questions about how to operate an application into different languages, making it possible to accommodate international users. For example, for a question such as "I don't know how to save a file," the system automatically translates the procedure into English, French, Chinese, etc. The analysis unit also develops a system in which a generation AI automatically translates questions about how to operate an application into different languages, making it possible to accommodate international users. For example, for a question such as "I don't know how to print," the system automatically translates the procedure into English, French, Chinese, etc. and provides it. This allows questions about how to operate an application to be automatically translated into different languages, making it possible to accommodate international users.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The TechHelp ChatBot system can also be customized to suit the user's skill level. For example, it can provide simple instructions to novice users and detailed technical explanations to advanced users. The analysis unit can also automatically determine the user's skill level and provide solutions accordingly. For example, it can provide illustrated or video instructions to beginners and command line instructions to advanced users. This allows it to provide optimal support according to the user's skill level.

[0051] The TechHelp ChatBot system can also integrate knowledge from different fields to propose comprehensive solutions. For example, it can analyze problems from both hardware and software perspectives and propose solutions. It can also integrate knowledge from other related fields, such as network settings and security measures, to provide more comprehensive solutions. For example, if a printer is not working, it will propose solutions from multiple perspectives, such as checking the network settings and reinstalling the driver. This allows users to solve multiple problems at once.

[0052] The TechHelp ChatBot system can also analyze user operation logs to identify operational errors and suggest solutions. For example, if a user repeatedly makes a mistake in a particular operation, it can provide specific instructions for that operation. It can also predict common user mistakes based on the operation logs and provide advance warnings. For example, if a user does not know how to save a file, it can display a message explaining the saving procedure. This reduces user operational errors and supports efficient operation.

[0053] The TechHelp ChatBot system can also automatically translate error messages into different languages, making it suitable for international users. For example, when an error message is displayed, the message is automatically translated into English, French, Chinese, etc. The analysis unit can also analyze the error message and automatically search for and provide related software bug reports and patch information. This allows for fast and appropriate support for international users.

[0054] The TechHelp ChatBot system can also analyze hardware diagnostic logs to identify the cause of the problem and suggest solutions. For example, if a printer isn't working, it will analyze the diagnostic logs and suggest reinstalling the driver or checking the connection cable. If a computer won't start, it will suggest checking the power cable or starting in safe mode. This allows the system to quickly identify hardware problems and provide appropriate solutions.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The input unit accepts questions from the user. For example, a user can enter a question such as, "My computer won't start. What should I do?" The input unit can also accept voice input and convert it into text using voice recognition technology. Step 2: The analysis unit analyzes the question received by the input unit. For example, the generation AI uses natural language processing technology to analyze the user's question and understand its content. The generation AI can also suggest the optimal solution by referring to the history of solving similar problems in the past. Furthermore, the generation AI can automatically determine the urgency of the problem and take action according to the urgency. Step 3: The provision unit provides solutions based on the results of the analysis by the analysis unit. For example, the generation AI suggests steps such as "Make sure the power cable is connected properly" or "Try booting in safe mode." The generation AI can also analyze the user's emotions using its emotion estimation function and provide support messages to reduce stress and anxiety.

[0057] (Example 2) The TechHelp ChatBot system according to an embodiment of the present invention is a system that provides real-time assistance for technical problems faced by users. With this system, users simply input their questions or issues via chat, and the TechHelp ChatBot provides appropriate solutions and procedures to support problem resolution. This allows users to quickly solve problems and improve productivity.

[0058] The TechHelp ChatBot system according to the embodiment includes an input unit, an analysis unit, and a provision unit. The input unit accepts questions from users. For example, a user can enter a question such as, "My computer won't start. What should I do?" The input unit can also accept voice input and convert it into text using speech recognition technology. The analysis unit analyzes the questions accepted by the input unit. For example, the generation AI can analyze the user's question using natural language processing technology and understand its content. The generation AI can also suggest optimal solutions by referencing past solutions to similar problems. The generation AI can also automatically determine the urgency of the problem and respond accordingly. The provision unit provides solutions based on the results of the analysis by the analysis unit. For example, the generation AI can suggest procedures such as "Make sure the power cable is connected properly" or "Try booting in safe mode." The generation AI can also analyze the user's emotions using an emotion estimation function and provide support messages to reduce stress and anxiety. This allows the TechHelp ChatBot system to quickly provide solutions to user questions.

[0059] The analysis unit can suggest optimal solutions by referring to the solution history of similar problems in the past. For example, the analysis unit analyzes a question entered by a user and searches a database for solutions to similar problems that have occurred in the past. For example, in response to a question such as "My computer won't start," the analysis unit suggests past solutions such as "check the power cable" and "start in safe mode." The analysis unit also analyzes the user's input using natural language processing technology and suggests optimal solutions by referring to the past solution history. For example, in response to a problem such as "My printer won't work," the analysis unit suggests past solutions such as "reinstalling the driver" and "checking the connection cable." The analysis unit also analyzes the user's input and builds a system that suggests optimal solutions based on the past solution history of similar problems. For example, in response to a problem such as "A software error message is displayed," the analysis unit suggests past solutions such as "reinstalling the software" and "checking the error message." This makes it possible to provide optimal solutions by utilizing the past solution history.

[0060] The analysis unit can automatically determine the urgency of the problem and take action according to the urgency. The analysis unit, for example, analyzes questions entered by a user and builds a system that automatically determines the urgency of the problem. For example, the problem "My computer won't start" is determined to be high urgency, and the problem "I don't know how to operate the software" is determined to be low urgency. The analysis unit also develops a system that analyzes the user's input and takes action according to the urgency. For example, high-urgency problems are dealt with immediately, and low-urgency problems are put off until later. The analysis unit also builds a system that automatically determines the urgency of the problem based on the user's input and takes action according to the urgency. For example, high-urgency problems are dealt with first, and low-urgency problems are put off until later. This makes it possible to take appropriate action according to the urgency of the problem.

[0061] The analysis unit can use the emotion estimation function to analyze a user's emotions and provide a support message to reduce stress and anxiety. For example, the analysis unit uses the emotion estimation function to analyze emotions in response to a question entered by a user and provide a support message to reduce stress and anxiety. For example, in response to a question such as "My computer won't start," a message such as "Don't worry, we'll provide a solution right away" is displayed. The analysis unit also analyzes the user's input content, analyzes emotions using the emotion estimation function, and provides a support message to reduce stress and anxiety. For example, in response to a question such as "My printer isn't working," a message such as "I see you're having trouble. We'll find a solution right away" is displayed. The analysis unit also uses the emotion estimation function to analyze the emotions of a user when entering input, thereby building a system that provides a support message to reduce stress and anxiety. For example, in response to a question such as "A software error message is displayed," a message such as "Don't worry, we'll provide a solution right away" is displayed. This makes it possible to provide a support message that corresponds to the user's emotions.

[0062] The input unit can accept voice input and convert it into text using voice recognition technology. The input unit, for example, allows a user to use voice input and builds a system that converts it into text using voice recognition technology. For example, if a user speaks, "My computer won't start," the system converts it into text and analyzes it. The input unit also develops a system that allows a user to use voice input using voice recognition technology. For example, if a user speaks, "My printer won't work," the system converts it into text and analyzes it. The input unit also develops a system that allows a user to use voice input and builds a system that converts it into text using voice recognition technology. For example, if a user speaks, "A software error message is displayed," the system converts it into text and analyzes it. In this way, voice input can be converted into text and analyzed.

[0063] The input unit can accept images and videos and analyze problems based on visual information. For example, the input unit can be added with a function that allows users to upload images and videos, and a system can be built that analyzes problems based on visual information. For example, a user may say, "My computer won't start," and upload an image showing the status of their computer. The input unit can also be added with a function that allows users to upload images and videos, and a system can be developed that analyzes problems based on visual information. For example, a user may say, "My printer isn't working," and upload a video showing the status of the printer. The input unit can also be added with a function that allows users to upload images and videos, and a system can be built that analyzes problems based on visual information. For example, a user may say, "A software error message is displayed," and upload a screenshot of the error message. This allows problems to be analyzed based on visual information.

[0064] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time and provide positive feedback. For example, the analysis unit uses the emotion estimation function to analyze the user's emotions when inputting information in real time and build a system that provides positive feedback. For example, when a user inputs "My computer won't start," a message such as "Don't worry, we'll find a solution right away" is displayed. The analysis unit also develops a system that analyzes the user's input content, analyzes the emotions in real time using the emotion estimation function, and provides positive feedback. For example, when a user inputs "My printer isn't working," a message such as "We hear you're having trouble. We'll find a solution right away" is displayed. The analysis unit also uses the emotion estimation function to analyze the user's emotions when inputting information in real time and build a system that provides positive feedback. For example, when a user inputs "A software error message is displayed," a message such as "Don't worry, we'll find a solution right away" is displayed. This allows for providing positive feedback according to the user's emotions.

[0065] The analysis unit learns the success rates of past solutions and can propose the most effective solution. For example, the analysis unit builds a system in which the generation AI learns the success rates of past solutions and proposes the most effective solution. For example, for the problem "The computer won't start," the analysis unit suggests "check the power cable" and "start in safe mode," which have a high past success rate. The analysis unit also develops a system in which the generation AI learns the success rates of past solutions when analyzing a problem and proposes the most effective solution. For example, for the problem "The printer won't work," the analysis unit suggests "reinstall the driver" and "check the connection cable," which have a high past success rate. The analysis unit also builds a system in which the generation AI learns the success rates of past solutions and proposes the most effective solution. For example, for the problem "A software error message is displayed," the analysis unit suggests "reinstall the software" and "check the content of the error message," which have a high past success rate. This makes it possible to provide the most effective solution based on past success rates.

[0066] The analysis unit can use the emotion estimation function to provide a solution that corresponds to the user's emotional state. The analysis unit, for example, uses the emotion estimation function to build a system that provides a solution that corresponds to the user's emotional state. For example, when a user inputs "My computer won't start," if the user is emotionally unstable, a message such as "Don't worry, we'll provide a solution right away" is displayed. The analysis unit also develops a system that uses the emotion estimation function to provide a solution that corresponds to the user's emotional state. For example, when a user inputs "My printer won't work," if the user is emotionally unstable, a message such as "We hear you're having trouble. We'll find a solution right away" is displayed. The analysis unit also uses the emotion estimation function to build a system that provides a solution that corresponds to the user's emotional state. For example, when a user inputs "A software error message is displayed," if the user is emotionally unstable, a message such as "Don't worry, we'll provide a solution right away" is displayed. This makes it possible to provide a solution that corresponds to the user's emotional state.

[0067] The analysis unit can customize the problem analysis results according to the user's technical level and provide them in an easy-to-understand format. The analysis unit, for example, builds a system that customizes the problem analysis results according to the user's technical level and provides them in an easy-to-understand format. For example, it provides simple procedures for beginners and detailed procedures for advanced users. The analysis unit also develops a system that customizes the problem analysis results according to the user's technical level and provides them in an easy-to-understand format. For example, it provides procedures using diagrams or videos for beginners and command line instructions for advanced users. The analysis unit also builds a system that customizes the problem analysis results according to the user's technical level and provides them in an easy-to-understand format. For example, it provides a step-by-step guide for beginners and technical details for advanced users. In this way, it is possible to provide analysis results according to the user's technical level.

[0068] The analysis unit can integrate knowledge from different fields and propose comprehensive solutions. For example, the analysis unit builds a system in which the generative AI integrates knowledge from different fields to propose more comprehensive solutions. For example, for the problem of "my computer won't start," it proposes solutions from both the hardware and software perspectives. The analysis unit also develops a system in which the generative AI integrates knowledge from different fields to propose comprehensive solutions when analyzing a problem. For example, for the problem of "my printer won't work," it proposes solutions from both the network settings and driver installation perspectives. The analysis unit also builds a system in which the generative AI integrates knowledge from different fields to propose more comprehensive solutions. For example, for the problem of "my software error message is displayed," it proposes solutions from both the program code debugging and settings checking perspectives. This makes it possible to integrate knowledge from different fields to provide comprehensive solutions.

[0069] The analysis unit can use the emotion estimation function to prioritize solutions based on the user's emotions and provide the most appropriate solution. The analysis unit, for example, uses the emotion estimation function to build a system that prioritizes solutions based on the user's emotions. For example, when a user inputs "My computer won't start," if the user is emotionally unstable, the simplest and quickest solution is provided preferentially. The analysis unit also develops a system that uses the emotion estimation function to prioritize solutions based on the user's emotions and provide the most appropriate solution. For example, when a user inputs "My printer won't work," if the user is emotionally unstable, the simplest and quickest solution is provided preferentially. The analysis unit also uses the emotion estimation function to prioritize solutions based on the user's emotions and build a system that provides the most appropriate solution. For example, when a user inputs "A software error message is displayed," if the user is emotionally unstable, the simplest and quickest solution is provided preferentially. This makes it possible to set the priority of solutions based on the user's emotions.

[0070] The analysis unit can analyze error message logs, identify the frequency and patterns of errors, and propose solutions. For example, the analysis unit builds a system in which a generation AI analyzes error message logs and identifies the frequency and patterns of errors. For example, if "Error 404" occurs frequently, it identifies the cause and proposes a solution. The analysis unit also develops a system in which a generation AI analyzes error message logs, identifies the frequency and patterns of errors, and proposes a solution. For example, if "Error 500" occurs frequently, it identifies the cause and proposes a solution. The analysis unit also builds a system in which a generation AI analyzes error message logs, identifies the frequency and patterns of errors, and proposes a solution. For example, if "Error 403" occurs frequently, it identifies the cause and proposes a solution. This makes it possible to analyze error message logs and provide solutions.

[0071] The analysis unit can analyze the contents of an error message and automatically search for and provide bug reports and patch information for related software. The analysis unit, for example, analyzes the contents of an error message and builds a system that automatically searches for and provides bug reports and patch information for related software. For example, when "Error 404" is displayed, the bug reports and patch information are automatically searched for and provided. The analysis unit also develops a system that analyzes the contents of an error message and automatically searches for and provides bug reports and patch information for related software. For example, when "Error 500" is displayed, the bug reports and patch information are automatically searched for and provided. The analysis unit also builds a system that analyzes the contents of an error message and automatically searches for and provides bug reports and patch information for related software. For example, when "Error 403" is displayed, the bug reports and patch information are automatically searched for and provided. This makes it possible to analyze the contents of an error message and provide related information.

[0072] The analysis unit can use the emotion estimation function to analyze a user's emotional response to an error message and provide a support message to reduce stress. The analysis unit, for example, uses the emotion estimation function to analyze a user's emotional response to an error message and build a system that provides a support message to reduce stress. For example, when "Error 404" is displayed, a message such as "Don't worry, we will provide a solution immediately" is displayed. The analysis unit also develops a system that analyzes a user's emotional response to an error message and uses the emotion estimation function to provide a support message to reduce stress. For example, when "Error 500" is displayed, a message such as "We see you're having trouble. We will find a solution immediately" is displayed. The analysis unit also uses the emotion estimation function to analyze a user's emotional response to an error message and build a system that provides a support message to reduce stress. For example, when "Error 403" is displayed, a message such as "Don't worry, we will provide a solution immediately" is displayed. In this way, a support message can be provided by analyzing a user's emotional response to an error message.

[0073] The analysis unit can customize the analysis results of error messages according to the user's technical level and provide them in an easy-to-understand format. The analysis unit, for example, builds a system that customizes the analysis results of error messages according to the user's technical level and provides them in an easy-to-understand format. For example, it provides simple procedures for beginners and detailed procedures for advanced users. The analysis unit also develops a system that customizes the analysis results of error messages according to the user's technical level and provides them in an easy-to-understand format. For example, it provides procedures using diagrams or videos for beginners and command line instructions for advanced users. The analysis unit also builds a system that customizes the analysis results of error messages according to the user's technical level and provides them in an easy-to-understand format. For example, it provides a step-by-step guide for beginners and technical details for advanced users. In this way, it is possible to provide analysis results of error messages according to the user's technical level.

[0074] The analysis unit can automatically translate the contents of error messages into different languages ​​to accommodate international users. The analysis unit, for example, automatically translates the contents of error messages into different languages ​​to build a system that can accommodate international users. For example, when "Error 404" is displayed, the contents are automatically translated into English, French, Chinese, etc. and provided. The analysis unit also develops a system that automatically translates the contents of error messages into different languages ​​to accommodate international users. For example, when "Error 500" is displayed, the contents are automatically translated into English, French, Chinese, etc. and provided. The analysis unit also automatically translates the contents of error messages into different languages ​​to build a system that can accommodate international users. For example, when "Error 403" is displayed, the contents are automatically translated into English, French, Chinese, etc. and provided. This allows error messages to be automatically translated into different languages ​​to accommodate international users.

[0075] The analysis unit can analyze hardware diagnostic logs, identify the cause of the problem, and propose solutions. The analysis unit, for example, builds a system in which a generating AI analyzes hardware diagnostic logs, identifies the cause of the problem, and proposes solutions. For example, for the problem "the printer won't work," the analysis unit analyzes the diagnostic log and suggests "reinstalling the driver" or "checking the connection cable." The analysis unit also develops a system in which a generating AI analyzes hardware diagnostic logs, identifies the cause of the problem, and proposes solutions. For example, for the problem "the computer won't start," the analysis unit analyzes the diagnostic log and suggests "checking the power cable" or "starting in safe mode." The analysis unit also builds a system in which a generating AI analyzes hardware diagnostic logs, identifies the cause of the problem, and proposes solutions. For example, for the problem "the monitor is not displaying," the analysis unit analyzes the diagnostic log and suggests "checking the connection cable" or "checking the monitor's power supply." In this way, the hardware diagnostic log can be analyzed to identify the cause of the problem and provide a solution.

[0076] The analysis unit can refer to past solution history and propose the most effective solution to a hardware problem. The analysis unit, for example, builds a system in which a generation AI refers to past solution history and proposes the most effective solution to a hardware problem. For example, for a problem such as "the printer won't work," past solutions include "reinstalling the driver" and "checking the connection cable." The analysis unit also develops a system in which a generation AI refers to past solution history and proposes the most effective solution to a hardware problem. For example, for a problem such as "the computer won't start," past solutions include "check the power cable" and "start in safe mode." The analysis unit also builds a system in which a generation AI refers to past solution history and proposes the most effective solution to a hardware problem. For example, for a problem such as "the monitor won't display," past solutions include "check the connection cable" and "check the monitor's power supply." This makes it possible to provide the most effective solution by referring to past solution history.

[0077] The analysis unit can use the emotion estimation function to analyze a user's emotional response to a hardware problem and provide a support message to reduce stress. For example, the analysis unit uses the emotion estimation function to analyze a user's emotional response to a hardware problem and build a system that provides a support message to reduce stress. For example, for a problem such as "My printer isn't working," a message such as "Don't worry, we'll provide a solution right away" is displayed. The analysis unit can also develop a system that analyzes a user's emotional response to a hardware problem and provides a support message to reduce stress using the emotion estimation function. For example, for a problem such as "My computer won't start," a message such as "We're sorry you're having trouble. We'll find a solution right away" is displayed. The analysis unit can also use the emotion estimation function to analyze a user's emotional response to a hardware problem and build a system that provides a support message to reduce stress. For example, for a problem such as "My monitor isn't displaying," a message such as "Don't worry, we'll provide a solution right away" is displayed. In this way, a support message can be provided by analyzing a user's emotional response to a hardware problem.

[0078] The analysis unit can customize solutions to hardware problems according to the user's technical level and provide them in an easy-to-understand format. The analysis unit, for example, builds a system that customizes solutions to hardware problems according to the user's technical level and provides them in an easy-to-understand format. For example, it provides simple procedures for beginners and detailed procedures for advanced users. The analysis unit also develops a system that customizes solutions to hardware problems according to the user's technical level and provides them in an easy-to-understand format. For example, it provides procedures using diagrams or videos for beginners and command line instructions for advanced users. The analysis unit also builds a system that customizes solutions to hardware problems according to the user's technical level and provides them in an easy-to-understand format. For example, it provides a step-by-step guide for beginners and technical details for advanced users. This makes it possible to provide solutions to hardware problems according to the user's technical level.

[0079] The analysis unit integrates knowledge from different fields and can propose comprehensive solutions to hardware problems. For example, the analysis unit builds a system in which the generative AI integrates knowledge from different fields to propose more comprehensive solutions. For example, for the problem of "my printer isn't working," it proposes solutions from both the perspectives of network settings and driver installation. The analysis unit also develops a system in which the generative AI integrates knowledge from different fields to propose comprehensive solutions to hardware problems. For example, for the problem of "my computer won't start," it proposes solutions from both the perspectives of hardware and software. The analysis unit also builds a system in which the generative AI integrates knowledge from different fields to propose more comprehensive solutions. For example, for the problem of "my monitor isn't displaying," it proposes solutions from both the perspectives of checking the connection cable and setting up the monitor. This makes it possible to integrate knowledge from different fields to provide comprehensive solutions to hardware problems.

[0080] The analysis unit can use the emotion estimation function to analyze a user's emotional response to a hardware problem in real time and provide positive feedback. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes a user's emotional response to a hardware problem in real time and provides positive feedback. For example, for a problem such as "my printer won't work," a message such as "don't worry, we'll provide a solution right away" is displayed. The analysis unit also develops a system that analyzes a user's emotional response to a hardware problem in real time and provides positive feedback using the emotion estimation function. For example, for a problem such as "my computer won't start," a message such as "we're sorry to hear you're having trouble, we'll find a solution right away" is displayed. The analysis unit also uses the emotion estimation function to build a system that analyzes a user's emotional response to a hardware problem in real time and provides positive feedback. For example, for a problem such as "my monitor won't display," a message such as "don't worry, we'll provide a solution right away" is displayed. In this way, a user's emotional response to a hardware problem can be analyzed in real time and provided positive feedback.

[0081] The analysis unit can analyze application operation logs, identify user operation errors, and propose improvement measures. The analysis unit, for example, builds a system in which a generating AI analyzes application operation logs, identifies user operation errors, and proposes improvement measures. For example, for the problem "I don't know how to create a new project," the operation log is analyzed and specific steps are suggested, such as "Select 'File' from the menu, then click 'New Project'." The analysis unit also develops a system in which a generating AI analyzes application operation logs, identifies user operation errors, and proposes improvement measures. For example, for the problem "I don't know how to save a file," the operation log is analyzed and specific steps are suggested, such as "Select 'File' from the menu, then click 'Save'." The analysis unit also builds a system in which a generating AI analyzes application operation logs, identifies user operation errors, and proposes improvement measures. For example, for the problem "I don't know how to print," the operation log is analyzed and specific steps are suggested, such as "Select 'File' from the menu, then click 'Print'." This makes it possible to analyze application operation logs, identify user operation errors, and propose improvement measures.

[0082] The analysis unit can provide visual instructions using videos and images in response to questions about how to operate an application. For example, the analysis unit builds a system in which a generative AI provides visual instructions using videos and images in response to questions about how to operate an application. For example, in response to a question such as, "I don't know how to create a new project," a video showing the operating procedures is provided. The analysis unit also develops a system in which a generative AI provides visual instructions using videos and images in response to questions about how to operate an application. For example, in response to a question such as, "I don't know how to save a file," an image showing the operating procedures is provided. The analysis unit also builds a system in which a generative AI provides visual instructions using videos and images in response to questions about how to operate an application. For example, in response to a question such as, "I don't know how to print," a video showing the operating procedures is provided. In this way, visual instructions can be provided in response to questions about how to operate an application.

[0083] The analysis unit can customize solutions regarding application operation methods according to the user's technical level and provide them in an easy-to-understand format. The analysis unit, for example, builds a system that customizes solutions regarding application operation methods according to the user's technical level and provides them in an easy-to-understand format. For example, it provides simple procedures for beginners and detailed procedures for advanced users. The analysis unit also develops a system that customizes solutions regarding application operation methods according to the user's technical level and provides them in an easy-to-understand format. For example, it provides procedures using diagrams or videos for beginners and command line instructions for advanced users. The analysis unit also builds a system that customizes solutions regarding application operation methods according to the user's technical level and provides them in an easy-to-understand format. For example, it provides a step-by-step guide for beginners and technical details for advanced users. In this way, it is possible to provide solutions regarding application operation methods according to the user's technical level.

[0084] The analysis unit can automatically translate questions about how to operate an application into different languages, making it possible to accommodate international users. The analysis unit, for example, uses a generation AI to automatically translate questions about how to operate an application into different languages, building a system that can accommodate international users. For example, for a question such as "I don't know how to create a new project," the system automatically translates the procedure into English, French, Chinese, etc. and provides it. The analysis unit also develops a system in which a generation AI automatically translates questions about how to operate an application into different languages, making it possible to accommodate international users. For example, for a question such as "I don't know how to save a file," the system automatically translates the procedure into English, French, Chinese, etc. The analysis unit also develops a system in which a generation AI automatically translates questions about how to operate an application into different languages, making it possible to accommodate international users. For example, for a question such as "I don't know how to print," the system automatically translates the procedure into English, French, Chinese, etc. and provides it. This allows questions about how to operate an application to be automatically translated into different languages, making it possible to accommodate international users.

[0085] The analysis unit uses the emotion estimation function to analyze a user's emotional response to an application operation method in real time and provide positive feedback. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes a user's emotional response to an application operation method in real time and provides positive feedback. For example, in response to a question such as "I don't know how to create a new project," a message such as "Don't worry, we'll provide a solution right away" is displayed. The analysis unit also develops a system that analyzes a user's emotional response to an application operation method in real time and provides positive feedback using the emotion estimation function. For example, in response to a question such as "I don't know how to save a file," a message such as "I see you're having trouble. We'll find a solution right away" is displayed. The analysis unit also uses the emotion estimation function to build a system that analyzes a user's emotional response to an application operation method in real time and provides positive feedback. For example, in response to a question such as "I don't know how to print," a message such as "Don't worry, we'll provide a solution right away" is displayed. In this way, a user's emotional response to an application operation method can be analyzed in real time and positive feedback can be provided.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The TechHelp ChatBot system can also be customized to suit the user's skill level. For example, it can provide simple instructions to novice users and detailed technical explanations to advanced users. The analysis unit can also automatically determine the user's skill level and provide solutions accordingly. For example, it can provide illustrated or video instructions to beginners and command line instructions to advanced users. This allows it to provide optimal support according to the user's skill level.

[0088] The TechHelp ChatBot system can also integrate knowledge from different fields to propose comprehensive solutions. For example, it can analyze problems from both hardware and software perspectives and propose solutions. It can also integrate knowledge from other related fields, such as network settings and security measures, to provide more comprehensive solutions. For example, if a printer is not working, it will propose solutions from multiple perspectives, such as checking the network settings and reinstalling the driver. This allows users to solve multiple problems at once.

[0089] The TechHelp ChatBot system can also analyze user operation logs to identify operational errors and suggest solutions. For example, if a user repeatedly makes a mistake in a particular operation, it can provide specific instructions for that operation. It can also predict common user mistakes based on the operation logs and provide advance warnings. For example, if a user does not know how to save a file, it can display a message explaining the saving procedure. This reduces user operational errors and supports efficient operation.

[0090] The TechHelp ChatBot system can also automatically translate error messages into different languages, making it suitable for international users. For example, when an error message is displayed, the message is automatically translated into English, French, Chinese, etc. The analysis unit can also analyze the error message and automatically search for and provide related software bug reports and patch information. This allows for fast and appropriate support for international users.

[0091] The TechHelp ChatBot system can also analyze hardware diagnostic logs to identify the cause of the problem and suggest solutions. For example, if a printer isn't working, it will analyze the diagnostic logs and suggest reinstalling the driver or checking the connection cable. If a computer won't start, it will suggest checking the power cable or starting in safe mode. This allows the system to quickly identify hardware problems and provide appropriate solutions.

[0092] The TechHelp ChatBot system also uses emotion estimation to analyze the user's emotions in real time and provide positive feedback. For example, if a user types "My computer won't start," the system displays a message such as "Don't worry, we'll find a solution right away." Similarly, if a user types "My printer isn't working," the system displays a message such as "I see you're having trouble. We'll find a solution right away." This allows the system to provide positive feedback that reflects the user's emotions.

[0093] The TechHelp ChatBot system also uses emotion estimation to analyze the user's emotional response to error messages and provide support messages to reduce stress. For example, when "Error 404" is displayed, it displays a message such as "Don't worry, we'll provide a solution right away." Similarly, when "Error 500" is displayed, it displays a message such as "We see you're having trouble, we'll find a solution right away." This allows the system to analyze the user's emotional response to error messages and provide support messages.

[0094] The TechHelp ChatBot system can also use emotion estimation to prioritize solutions based on the user's emotions and provide the most appropriate solution. For example, if a user types "my computer won't start," and is emotionally unstable, the system will prioritize the easiest and quickest solution. Also, if a user types "my printer won't work," and is emotionally unstable, the system will prioritize the easiest and quickest solution. This allows the system to prioritize solutions based on the user's emotions.

[0095] The TechHelp ChatBot system also uses emotion estimation to analyze users' emotional responses to hardware problems in real time and provide positive feedback. For example, if the problem is "your printer isn't working," the system displays a message like "Don't worry, we'll find a solution right away." Similarly, if the problem is "your computer won't start," the system displays a message like "We hear you're having trouble, we'll find a solution right away." This allows the system to analyze users' emotional responses to hardware problems in real time and provide positive feedback.

[0096] The TechHelp ChatBot system also uses emotion estimation to analyze the user's emotional response to how the application is operated in real time and provide positive feedback. For example, in response to a question such as "I don't know how to create a new project," the system displays a message such as "Don't worry, we'll provide a solution right away." Similarly, in response to a question such as "I don't know how to save a file," the system displays a message such as "I see you're having trouble, we'll find a solution right away." This allows the system to analyze the user's emotional response to how the application is operated in real time and provide positive feedback.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The input unit accepts questions from the user. For example, a user can enter a question such as, "My computer won't start. What should I do?" The input unit can also accept voice input and convert it into text using voice recognition technology. Step 2: The analysis unit analyzes the question received by the input unit. For example, the generation AI uses natural language processing technology to analyze the user's question and understand its content. The generation AI can also suggest the optimal solution by referring to the history of solving similar problems in the past. Furthermore, the generation AI can automatically determine the urgency of the problem and take action according to the urgency. Step 3: The provision unit provides solutions based on the results of the analysis by the analysis unit. For example, the generation AI suggests steps such as "Make sure the power cable is connected properly" or "Try booting in safe mode." The generation AI can also analyze the user's emotions using its emotion estimation function and provide support messages to reduce stress and anxiety.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

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

Claims

1. an input unit for accepting questions from a user; an analysis unit that analyzes the question received by the input unit; a providing unit that provides a solution based on the results of the analysis by the analyzing unit. A system characterized by:

2. The analysis unit Propose optimal solutions by referring to the history of solving similar problems in the past 2. The system of claim 1.

3. The analysis unit Automatically determine the urgency of the problem and respond accordingly 2. The system of claim 1.

4. The analysis unit Analyzes user emotions and provides supportive messages to reduce stress and anxiety 2. The system of claim 1.

5. The input unit Accepts voice input and converts it into text using voice recognition technology 2. The system of claim 1.

6. The input unit Accepts images and videos and analyzes problems based on visual information 2. The system of claim 1.

Images