System

Abstract

An object of the system according to the embodiment is to analyze capture of an error screen, identify an error, and present a solution.SOLUTION: A system according to an embodiment includes a capture analysis unit, an error identification unit, and a solution presentation unit. The capture analyzer analyzes the capture of the error screen. The error identification unit identifies an error based on the result analyzed by the capture analyzer. The solution presentation unit presents a solution to the error identified by the error identification 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] With conventional technology, identifying errors that occur while using the system and presenting solutions is time-consuming, which reduces business efficiency.

[0005] The system according to the embodiment aims to analyze a capture of an error screen, identify the error, and present a solution. [Means for solving the problem]

[0006] The system according to the embodiment includes a capture analysis unit, an error identification unit, and a solution presentation unit. The capture analysis unit analyzes a capture of an error screen. The error identification unit identifies an error based on the analysis result by the capture analysis unit. The solution presentation unit presents a solution to the error identified by the error identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the capture of the error screen, identify the error, and suggest a solution. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The error resolution system according to the embodiment of the present invention is a system that automatically analyzes a capture of an error screen, identifies the error, and presents a solution to the error, thereby enabling the error resolution system to quickly and efficiently identify the error and present a solution.

[0029] The error resolution system according to the embodiment includes a capture analysis unit, an error identification unit, and a solution presentation unit. The capture analysis unit analyzes a capture of an error screen. For example, the capture analysis unit analyzes a screenshot of the error screen to extract an error message. The capture analysis unit can also analyze a video capture to extract a specific frame. The capture analysis unit can also convert the error message into text data using OCR technology. For example, the capture analysis unit analyzes the screenshot at high resolution to accurately extract the error message. In the case of a video capture, a specific frame is selected and analyzed. The OCR technology is used to save the error message as text data. The error identification unit identifies an error based on the analysis results of the capture analysis unit. For example, the error identification unit analyzes the error message and identifies an error code. The error identification unit can also compare the error message with a past error history to identify similar errors. The error identification unit can also analyze an error log to identify the cause of the error. For example, the error identification unit compares the error message with a database to identify the error code. By comparing with past error history, the same error can be quickly identified if it recurs. By analyzing the error log, the cause of the error can be identified in detail. The solution presentation unit presents a solution for the error identified by the error identification unit. For example, the solution presentation unit presents a solution corresponding to an error code. The solution presentation unit can also present multiple solutions for the user to select. The solution presentation unit can also provide the solution as a visual guide or video tutorial. For example, the solution presentation unit presents a correction procedure corresponding to an error code. By presenting multiple solutions, the user can select the most appropriate solution. The use of a visual guide or video tutorial makes the solutions easier to understand visually. As a result, the error resolution system according to the embodiment can quickly and efficiently identify errors and present solutions.For example, an error resolution system can analyze error screen captures, identify errors, and present solutions to improve business efficiency. It also manages error histories and responds quickly if the same error recurs. It also provides a user interface that allows users to easily report errors and obtain solutions.

[0030] The capture analysis unit analyzes not only the text in the capture but also the icon and color pattern, allowing for more accurate identification of the type of error. For example, when analyzing a capture of an error screen, the capture analysis unit analyzes not only the text information but also the icon shape and color pattern. For example, if a red error message and a specific icon are displayed, the specific error can be identified from this combination. The capture analysis unit can also analyze the icon shape to identify a specific error. The capture analysis unit can also analyze the color pattern to identify the type of error. For example, the specific error can be identified by analyzing the icon shape. The error type can be identified by analyzing the color pattern. This allows for more accurate identification of the type of error.

[0031] When analyzing a capture of an error screen, the capture analysis unit compares it with past error history, allowing it to quickly identify similar errors if they occur. For example, when analyzing a capture of an error screen, the capture analysis unit compares it with past error history. For example, if the same error message as an error that occurred in the past is displayed, the error can be quickly identified. The capture analysis unit can also save past error history in a database and compare it. The capture analysis unit can also identify errors based on past error history. For example, by saving past error history in a database and comparing it, it can quickly identify the same error if it recurs. Identifying errors based on past error history improves the accuracy of error identification. As a result, similar errors can be quickly identified by comparing it with past error history.

[0032] The capture analysis unit also supports error screen capture analysis for video-format error reports, and can identify errors by analyzing specific frames within the video. The capture analysis unit also supports error screen capture analysis for video-format error reports, for example. For example, if a user reports an error occurrence using a video, the unit analyzes specific frames within the video to identify the error. The capture analysis unit can also analyze video-format error reports and extract specific frames. The capture analysis unit can also analyze specific frames within the video to identify the error. For example, if a user reports an error occurrence using a video, the unit analyzes specific frames within the video to identify the error. The unit analyzes video-format error reports and extracts specific frames. The unit analyzes specific frames within the video to identify the error. This allows error reports to be handled in video format, and errors can be identified by analyzing specific frames.

[0033] The capture analysis unit can also accommodate voice input in the capture analysis of the error screen, and analyze the voice data when the user verbally explains the error content. The capture analysis unit, for example, can also accommodate voice input in the capture analysis of the error screen. For example, it analyzes the voice data when the user verbally explains the error content and identifies the error based on the content. The capture analysis unit can also analyze the voice input and identify the error. The capture analysis unit can also analyze the voice data and identify the error. For example, it analyzes the voice data when the user verbally explains the error content and identifies the error based on the content. It analyzes the voice input and identifies the error. It analyzes the voice data and identifies the error. This allows it to also accommodate voice input, and analyze the voice data when the user verbally explains the error.

[0034] After identifying an error, the error identification unit can present multiple solutions to the error, allowing the user to select one. For example, after identifying an error, the error identification unit presents multiple solutions to the error. For example, when error code 123 occurs, multiple solutions are presented, such as how to modify a configuration file or a reboot procedure. The error identification unit can also present multiple solutions, allowing the user to select one. The error identification unit can also provide the solutions as a visual guide or a video tutorial. For example, when error code 123 occurs, multiple solutions are presented, such as how to modify a configuration file or a reboot procedure. The multiple solutions are presented, allowing the user to select one. The solutions are provided as a visual guide or a video tutorial. In this way, multiple solutions to the error are presented, allowing the user to select one.

[0035] The error identification unit can refer to the user's past operation history when identifying an error and presenting a solution, and propose the most appropriate solution. The error identification unit, for example, refers to the user's past operation history when identifying an error and presenting a solution. For example, the error identification unit proposes an optimal solution based on solutions used when the same error occurred in the past. The error identification unit can also save the user's operation history in a database and refer to it. The error identification unit can also propose the optimal solution based on the past operation history. For example, by saving the user's operation history in a database and referring to it, the error identification unit proposes an optimal solution based on solutions used when the same error occurred in the past. Proposing the optimal solution based on the past operation history improves the accuracy of error resolution. In this way, the user's past operation history can be referenced to propose the most appropriate solution.

[0036] The solution presentation unit can provide error identification and solution presentation in different languages ​​to accommodate international users. The solution presentation unit, for example, provides error identification and solution presentation in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The solution presentation unit can also provide error identification and solution presentation in different languages ​​to accommodate international users. The solution presentation unit can also provide error identification and solution presentation in different languages ​​to accommodate international users. For example, it supports multiple languages ​​such as English, French, and Chinese. The error identification and solution presentation in different languages ​​to accommodate international users. This allows error identification and solution presentation in different languages ​​to accommodate international users.

[0037] The solution presentation unit can provide the identification of errors and the presentation of solutions as a visual guide or a video tutorial, making it visually easy to understand. The solution presentation unit, for example, provides the identification of errors and the presentation of solutions as a visual guide. For example, the steps for resolving errors are shown in diagrams or illustrations. The solution presentation unit can also provide the identification of errors and the presentation of solutions as a video tutorial. The solution presentation unit can also use a visual guide or a video tutorial to make the identification of errors and the presentation of solutions visually easy to understand. For example, the steps for resolving errors are shown in diagrams or illustrations. The identification of errors and the presentation of solutions are provided as a video tutorial. The visual guide or video tutorial makes it visually easy to understand the identification of errors and the presentation of solutions. In this way, the visual guide or video tutorial makes it visually easy to understand the identification of errors and the presentation of solutions.

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

[0039] The error resolution system may further include an operation history analysis unit that analyzes the user's operation history. The operation history analysis unit can analyze the operations performed by the user before the error occurred and identify the cause of the error. For example, if an error occurs immediately after the user installs specific software, the operation history analysis unit may identify the possibility that the software is the cause. Furthermore, if an error occurs after the user changes a specific setting, the operation history analysis unit may also identify the possibility that the setting change is the cause. Furthermore, if an error occurs immediately after the user opens a specific file, the operation history analysis unit may identify the possibility that the file is the cause. This allows the error resolution system to analyze the user's operation history and more accurately identify the cause of the error.

[0040] The error resolution system can further include a monitoring unit that monitors the occurrence of errors in real time. The monitoring unit, for example, monitors the performance of the entire system and identifies an error if an abnormality occurs. The monitoring unit can also monitor the network status and identify an error if a communication error occurs. The monitoring unit can also monitor the hardware status and identify an error if a hardware error occurs. This allows the error resolution system to monitor the status of the entire system in real time and quickly identify errors.

[0041] The error resolution system can further include an occurrence frequency analysis unit that analyzes the frequency of error occurrence. For example, if a specific error occurs frequently, the occurrence frequency analysis unit can be set to resolve that error as a priority. The occurrence frequency analysis unit can also analyze the frequency of error occurrence and take preventative measures to prevent the specific error from recurring. Furthermore, the occurrence frequency analysis unit can analyze the frequency of error occurrence and identify the cause of the error. In this way, the error resolution system can analyze the frequency of error occurrence and prevent the error from recurring.

[0042] The error resolution system can further include an impact range analysis unit that analyzes the impact range of an error. The impact range analysis unit analyzes, for example, the extent to which a specific error will affect the entire system. The impact range analysis unit can also analyze the impact range of an error and prioritize resolving errors with a large impact. The impact range analysis unit can also analyze the impact range of an error and postpone resolving errors with a small impact. This allows the error resolution system to analyze the impact range of an error and set priorities.

[0043] The error resolution system can further include a prediction unit that predicts the cause of an error. The prediction unit predicts errors that may occur in the future, for example, based on past error history. The prediction unit can also monitor the system status and predict errors before an abnormality occurs. Furthermore, the prediction unit can analyze the user's operation history and predict the possibility that a specific operation will cause an error. This allows the error resolution system to predict the cause of an error and take measures in advance.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The capture analyzer analyzes the capture of the error screen. For example, the capture analyzer analyzes a screenshot of the error screen and extracts the error message. It can also analyze a video capture and extract a specific frame. It can also convert the error message into text data using OCR technology. Step 2: The error identification unit identifies the error based on the results of the analysis by the capture analysis unit. For example, it analyzes the error message and identifies the error code. It can also compare the error code with past error history to identify similar errors. It can also analyze the error log to identify the cause of the error. Step 3: The solution presenting unit presents solutions to the errors identified by the error identifying unit. For example, it presents solutions corresponding to error codes. It is also possible to present multiple solutions so that the user can select one. It is also possible to provide solutions as visual guides or video tutorials.

[0046] (Example 2) The error resolution system according to the embodiment of the present invention is a system that automatically analyzes a capture of an error screen, identifies the error, and presents a solution to the error, thereby enabling the error resolution system to quickly and efficiently identify the error and present a solution.

[0047] The error resolution system according to the embodiment includes a capture analysis unit, an error identification unit, and a solution presentation unit. The capture analysis unit analyzes a capture of an error screen. For example, the capture analysis unit analyzes a screenshot of the error screen to extract an error message. The capture analysis unit can also analyze a video capture to extract a specific frame. The capture analysis unit can also convert the error message into text data using OCR technology. For example, the capture analysis unit analyzes the screenshot at high resolution to accurately extract the error message. In the case of a video capture, a specific frame is selected and analyzed. The OCR technology is used to save the error message as text data. The error identification unit identifies an error based on the analysis results of the capture analysis unit. For example, the error identification unit analyzes the error message and identifies an error code. The error identification unit can also compare the error message with a past error history to identify similar errors. The error identification unit can also analyze an error log to identify the cause of the error. For example, the error identification unit compares the error message with a database to identify the error code. By comparing with past error history, the same error can be quickly identified if it recurs. By analyzing the error log, the cause of the error can be identified in detail. The solution presentation unit presents a solution for the error identified by the error identification unit. For example, the solution presentation unit presents a solution corresponding to an error code. The solution presentation unit can also present multiple solutions for the user to select. The solution presentation unit can also provide the solution as a visual guide or video tutorial. For example, the solution presentation unit presents a correction procedure corresponding to an error code. By presenting multiple solutions, the user can select the most appropriate solution. The use of a visual guide or video tutorial makes the solutions easier to understand visually. As a result, the error resolution system according to the embodiment can quickly and efficiently identify errors and present solutions.For example, an error resolution system can analyze error screen captures, identify errors, and present solutions to improve business efficiency. It also manages error histories and responds quickly if the same error recurs. It also provides a user interface that allows users to easily report errors and obtain solutions.

[0048] The capture analysis unit analyzes not only the text in the capture but also the icon and color pattern, allowing for more accurate identification of the type of error. For example, when analyzing a capture of an error screen, the capture analysis unit analyzes not only the text information but also the icon shape and color pattern. For example, if a red error message and a specific icon are displayed, the specific error can be identified from this combination. The capture analysis unit can also analyze the icon shape to identify a specific error. The capture analysis unit can also analyze the color pattern to identify the type of error. For example, the specific error can be identified by analyzing the icon shape. The error type can be identified by analyzing the color pattern. This allows for more accurate identification of the type of error.

[0049] When analyzing a capture of an error screen, the capture analysis unit compares it with past error history, allowing it to quickly identify similar errors if they occur. For example, when analyzing a capture of an error screen, the capture analysis unit compares it with past error history. For example, if the same error message as an error that occurred in the past is displayed, the error can be quickly identified. The capture analysis unit can also save past error history in a database and compare it. The capture analysis unit can also identify errors based on past error history. For example, by saving past error history in a database and comparing it, it can quickly identify the same error if it recurs. Identifying errors based on past error history improves the accuracy of error identification. As a result, similar errors can be quickly identified by comparing it with past error history.

[0050] The capture analysis unit uses the emotion estimation function to estimate the emotion of a user who has viewed the error screen, and can prioritize responses to errors that are particularly stressful. The capture analysis unit, for example, uses the emotion estimation function to estimate the emotion of a user who has viewed the error screen. For example, it analyzes the user's facial expression and voice to determine whether the user is feeling stressed. The capture analysis unit can also analyze the user's emotion in real time and prioritize responses to errors that are particularly stressful. The capture analysis unit can also use the emotion estimation function to analyze the user's emotion and set error priorities. For example, it analyzes the user's facial expression and voice to determine whether the user is feeling stressed. The user's emotion is analyzed in real time and prioritize responses to errors that are particularly stressful. The emotion estimation function is used to analyze the user's emotion and set error priorities. In this way, it is possible to take the user's emotion into consideration and prioritize responses to errors that are particularly stressful.

[0051] The capture analysis unit also supports error screen capture analysis for video-format error reports, and can identify errors by analyzing specific frames within the video. The capture analysis unit also supports error screen capture analysis for video-format error reports, for example. For example, if a user reports an error occurrence using a video, the unit analyzes specific frames within the video to identify the error. The capture analysis unit can also analyze video-format error reports and extract specific frames. The capture analysis unit can also analyze specific frames within the video to identify the error. For example, if a user reports an error occurrence using a video, the unit analyzes specific frames within the video to identify the error. The unit analyzes video-format error reports and extracts specific frames. The unit analyzes specific frames within the video to identify the error. This allows error reports to be handled in video format, and errors can be identified by analyzing specific frames.

[0052] The capture analysis unit can also accommodate voice input in the capture analysis of the error screen, and analyze the voice data when the user verbally explains the error content. The capture analysis unit, for example, can also accommodate voice input in the capture analysis of the error screen. For example, it analyzes the voice data when the user verbally explains the error content and identifies the error based on the content. The capture analysis unit can also analyze the voice input and identify the error. The capture analysis unit can also analyze the voice data and identify the error. For example, it analyzes the voice data when the user verbally explains the error content and identifies the error based on the content. It analyzes the voice input and identifies the error. It analyzes the voice data and identifies the error. This allows it to also accommodate voice input, and analyze the voice data when the user verbally explains the error.

[0053] The capture analysis unit uses the emotion estimation function to analyze the user's emotion in real time when uploading a capture of the error screen and can provide positive feedback. The capture analysis unit, for example, uses the emotion estimation function to analyze the user's emotion in real time when uploading a capture of the error screen. For example, it analyzes the user's facial expression and voice to estimate the emotion. The capture analysis unit can also analyze the user's emotion in real time and provide positive feedback. The capture analysis unit can also use the emotion estimation function to analyze the user's emotion and provide feedback. For example, it analyzes the user's facial expression and voice to estimate the emotion. The user's emotion is analyzed in real time and positive feedback is provided. The emotion estimation function is used to analyze the user's emotion and provide feedback. In this way, the user's emotion is analyzed in real time and positive feedback is provided.

[0054] After identifying an error, the error identification unit can present multiple solutions to the error, allowing the user to select one. For example, after identifying an error, the error identification unit presents multiple solutions to the error. For example, when error code 123 occurs, multiple solutions are presented, such as how to modify a configuration file or a reboot procedure. The error identification unit can also present multiple solutions, allowing the user to select one. The error identification unit can also provide the solutions as a visual guide or a video tutorial. For example, when error code 123 occurs, multiple solutions are presented, such as how to modify a configuration file or a reboot procedure. The multiple solutions are presented, allowing the user to select one. The solutions are provided as a visual guide or a video tutorial. In this way, multiple solutions to the error are presented, allowing the user to select one.

[0055] The error identification unit can refer to the user's past operation history when identifying an error and presenting a solution, and propose the most appropriate solution. The error identification unit, for example, refers to the user's past operation history when identifying an error and presenting a solution. For example, the error identification unit proposes an optimal solution based on solutions used when the same error occurred in the past. The error identification unit can also save the user's operation history in a database and refer to it. The error identification unit can also propose the optimal solution based on the past operation history. For example, by saving the user's operation history in a database and referring to it, the error identification unit proposes an optimal solution based on solutions used when the same error occurred in the past. Proposing the optimal solution based on the past operation history improves the accuracy of error resolution. In this way, the user's past operation history can be referenced to propose the most appropriate solution.

[0056] The error identification unit can use the emotion estimation function to consider the user's emotion when presenting a solution to the error and also provide advice to reduce stress. The error identification unit, for example, uses the emotion estimation function to consider the user's emotion when presenting a solution to the error. For example, if the user is feeling stressed, the error identification unit briefly explains the solution. The error identification unit can also analyze the user's emotion in real time and provide advice to reduce stress. The error identification unit can also use the emotion estimation function to analyze the user's emotion and provide advice to reduce stress. For example, if the user is feeling stressed, the error identification unit briefly explains the solution. The error identification unit can analyze the user's emotion in real time and provide advice to reduce stress. The error identification unit can use the emotion estimation function to analyze the user's emotion and provide advice to reduce stress. In this way, the user's emotion can be considered and advice to reduce stress can be provided.

[0057] The solution presentation unit can provide error identification and solution presentation in different languages ​​to accommodate international users. The solution presentation unit, for example, provides error identification and solution presentation in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The solution presentation unit can also provide error identification and solution presentation in different languages ​​to accommodate international users. The solution presentation unit can also provide error identification and solution presentation in different languages ​​to accommodate international users. For example, it supports multiple languages ​​such as English, French, and Chinese. The error identification and solution presentation in different languages ​​to accommodate international users. This allows error identification and solution presentation in different languages ​​to accommodate international users.

[0058] The solution presentation unit can provide the identification of errors and the presentation of solutions as a visual guide or a video tutorial, making it visually easy to understand. The solution presentation unit, for example, provides the identification of errors and the presentation of solutions as a visual guide. For example, the steps for resolving errors are shown in diagrams or illustrations. The solution presentation unit can also provide the identification of errors and the presentation of solutions as a video tutorial. The solution presentation unit can also use a visual guide or a video tutorial to make the identification of errors and the presentation of solutions visually easy to understand. For example, the steps for resolving errors are shown in diagrams or illustrations. The identification of errors and the presentation of solutions are provided as a video tutorial. The visual guide or video tutorial makes it visually easy to understand the identification of errors and the presentation of solutions. In this way, the visual guide or video tutorial makes it visually easy to understand the identification of errors and the presentation of solutions.

[0059] The solution presentation unit uses the emotion estimation function to monitor the user's emotional reaction in real time when an error solution is presented, and can continuously provide an optimal solution. The solution presentation unit, for example, uses the emotion estimation function to monitor the user's emotional reaction in real time when an error solution is presented. For example, it analyzes the user's facial expression and voice to estimate their emotion. The solution presentation unit can also monitor the user's emotional reaction in real time and continuously provide an optimal solution. The solution presentation unit can also use the emotion estimation function to analyze the user's emotion and continuously provide an optimal solution. For example, it analyzes the user's facial expression and voice to estimate their emotion. The user's emotional reaction is monitored in real time and continuously provides an optimal solution. The emotion estimation function is used to analyze the user's emotion and continuously provide an optimal solution. In this way, the user's emotional reaction can be monitored in real time and an optimal solution can be continuously provided.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The error resolution system may further include an operation history analysis unit that analyzes the user's operation history. The operation history analysis unit can analyze the operations performed by the user before the error occurred and identify the cause of the error. For example, if an error occurs immediately after the user installs specific software, the operation history analysis unit may identify the possibility that the software is the cause. Furthermore, if an error occurs after the user changes a specific setting, the operation history analysis unit may also identify the possibility that the setting change is the cause. Furthermore, if an error occurs immediately after the user opens a specific file, the operation history analysis unit may identify the possibility that the file is the cause. This allows the error resolution system to analyze the user's operation history and more accurately identify the cause of the error.

[0062] The error resolution system may further include an emotion priority setting unit that estimates a user's emotion and sets an error priority based on the estimated user's emotion. For example, if the user feels stressed when viewing an error screen, the emotion priority setting unit may set the error to be resolved with priority. The emotion priority setting unit may also set the error to be resolved quickly if the user feels anxious when viewing an error screen. The emotion priority setting unit may also set the error to be resolved with top priority if the user feels irritated when viewing an error screen. This allows the error resolution system to set error priorities taking the user's emotion into consideration.

[0063] The error resolution system can further include a monitoring unit that monitors the occurrence of errors in real time. The monitoring unit, for example, monitors the performance of the entire system and identifies an error if an abnormality occurs. The monitoring unit can also monitor the network status and identify an error if a communication error occurs. The monitoring unit can also monitor the hardware status and identify an error if a hardware error occurs. This allows the error resolution system to monitor the status of the entire system in real time and quickly identify errors.

[0064] The error resolution system may further include a customization unit that estimates the user's emotions and customizes an error resolution method based on the estimated user emotions. For example, if the user feels stressed when viewing an error screen, the customization unit may briefly explain a solution. Furthermore, if the user feels anxious when viewing an error screen, the customization unit may also explain a solution in detail. Furthermore, if the user feels irritated when viewing an error screen, the customization unit may also quickly present a solution. In this way, the error resolution system can customize an error resolution method taking the user's emotions into consideration.

[0065] The error resolution system can further include an occurrence frequency analysis unit that analyzes the frequency of error occurrence. For example, if a specific error occurs frequently, the occurrence frequency analysis unit can be set to resolve that error as a priority. The occurrence frequency analysis unit can also analyze the frequency of error occurrence and take preventative measures to prevent the specific error from recurring. Furthermore, the occurrence frequency analysis unit can analyze the frequency of error occurrence and identify the cause of the error. In this way, the error resolution system can analyze the frequency of error occurrence and prevent the error from recurring.

[0066] The error resolution system may further include a selection unit that estimates the user's emotions and selects an error resolution method based on the estimated user emotions. For example, if the user feels stressed when viewing the error screen, the selection unit may present the simplest solution. Alternatively, if the user feels anxious when viewing the error screen, the selection unit may present the most detailed solution. Alternatively, if the user feels irritated when viewing the error screen, the selection unit may present the quickest solution. This allows the error resolution system to select the optimal error resolution method by taking the user's emotions into consideration.

[0067] The error resolution system can further include an impact range analysis unit that analyzes the impact range of an error. The impact range analysis unit analyzes, for example, the extent to which a specific error will affect the entire system. The impact range analysis unit can also analyze the impact range of an error and prioritize resolving errors with a large impact. The impact range analysis unit can also analyze the impact range of an error and postpone resolving errors with a small impact. This allows the error resolution system to analyze the impact range of an error and set priorities.

[0068] The error resolution system may further include a providing unit that estimates the user's emotions and provides an error resolution method based on the estimated user emotions. For example, if the user feels stressed when viewing the error screen, the providing unit may briefly explain the solution. Furthermore, if the user feels anxious when viewing the error screen, the providing unit may also explain the solution in detail. Furthermore, if the user feels irritated when viewing the error screen, the providing unit may also quickly present the solution. In this way, the error resolution system can take the user's emotions into consideration and provide the optimal error resolution method.

[0069] The error resolution system can further include a prediction unit that predicts the cause of an error. The prediction unit predicts errors that may occur in the future, for example, based on past error history. The prediction unit can also monitor the system status and predict errors before an abnormality occurs. Furthermore, the prediction unit can analyze the user's operation history and predict the possibility that a specific operation will cause an error. This allows the error resolution system to predict the cause of an error and take measures in advance.

[0070] The error resolution system may further include an evaluation unit that estimates a user's emotions and evaluates an error resolution method based on the estimated user emotions. The evaluation unit may, for example, analyze the user's emotions after performing an error resolution method and evaluate whether the solution was effective. The evaluation unit may also analyze the user's emotions in real time and evaluate the effectiveness of the solution. Furthermore, the evaluation unit may use the emotion estimation function to analyze the user's emotions and identify areas for improvement in the solution. This allows the error resolution system to evaluate and improve the error resolution method while taking the user's emotions into consideration.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The capture analyzer analyzes the capture of the error screen. For example, the capture analyzer analyzes a screenshot of the error screen and extracts the error message. It can also analyze a video capture and extract a specific frame. It can also convert the error message into text data using OCR technology. Step 2: The error identification unit identifies the error based on the results of the analysis by the capture analysis unit. For example, it analyzes the error message and identifies the error code. It can also compare the error code with past error history to identify similar errors. It can also analyze the error log to identify the cause of the error. Step 3: The solution presenting unit presents solutions to the errors identified by the error identifying unit. For example, it presents solutions corresponding to error codes. It is also possible to present multiple solutions so that the user can select one. It is also possible to provide solutions as visual guides or video tutorials.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a capture analysis unit that analyzes a capture of an error screen; an error identification unit that identifies an error based on the analysis result by the capture analysis unit; a solution presentation unit that presents a solution to the error identified by the error identification unit. A system characterized by:

2. The capture analysis unit Analyzes not only the text in the capture, but also the icon and color patterns to more accurately identify the type of error 2. The system of claim 1.

3. The capture analysis unit The error screen capture analysis can now also be used for video error reports, allowing you to identify errors by analyzing specific frames within the video.

2. The system of claim 1.

4. The error identification unit After identifying the error, present multiple solutions to the error for the user to choose from 2. The system of claim 1.

5. The solution presentation unit Identifying errors and providing solutions in different languages ​​to accommodate international users 2. The system of claim 1.

6. The capture analysis unit Estimate the emotions of users who see an error screen and prioritize dealing with errors that cause them particular stress 2. The system of claim 1.

7. The error identification unit Consider the user's emotions when presenting solutions to errors and provide advice to reduce stress.

2. The system of claim 1.

8. The solution presentation unit Monitors the user's emotional response in real time when presenting solutions to errors, and continuously provides optimal solutions 2. The system of claim 1.

Images