system

Abstract

The system according to the embodiment aims to streamline the maintenance of automated tools and reduce the need for manual work. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a correction unit, an optimization unit, a recording unit, and a feedback provision unit. The collection unit collects operation logs and error logs of the automation tool. The analysis unit analyzes the logs collected by the collection unit. The correction unit corrects errors based on the analysis results obtained by the analysis unit. The optimization unit optimizes settings based on the analysis results obtained by the analysis unit. The recording unit records the results of maintenance work performed by the correction unit and the optimization unit. The feedback provision unit provides feedback based on the results recorded by the recording unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that maintaining automated tools requires a lot of man-hours, and manual work is often prioritized.

[0005] The system according to the embodiment aims to streamline the maintenance of automated tools and reduce the need for manual work. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a correction unit, an optimization unit, a recording unit, and a feedback providing unit. The collection unit collects operation logs and error logs of the automation tool. The analysis unit analyzes the logs collected by the collection unit. The correction unit corrects errors based on the analysis results obtained by the analysis unit. The optimization unit optimizes settings based on the analysis results obtained by the analysis unit. The recording unit records the results of the maintenance work performed by the correction unit and the optimization unit. The feedback providing unit provides feedback based on the results recorded by the recording unit. [Effects of the Invention]

[0007] Embodiments of the system can streamline maintenance of automated tools and reduce the need for manual work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An automation tool maintenance system according to an embodiment of the present invention is a system for reducing the labor required for maintaining automation tools and performing the work more efficiently than manual work. This system automates automation tool maintenance using a generation AI. Specifically, the generation AI collects and analyzes operation logs and error logs of the automation tool. Then, based on the analysis results, the generation AI automatically performs maintenance work on the automation tool. For example, the generation AI identifies the cause of an error and makes necessary corrections or optimizes settings. Furthermore, the generation AI records the results of the maintenance work and provides feedback to be used for the next maintenance. This mechanism significantly reduces the labor required for automation tool maintenance and enables work to be performed more efficiently than manual work. For example, the generation AI collects and analyzes operation logs and error logs of the automation tool. Based on the analysis results, the generation AI identifies the cause of an error and makes necessary corrections. The generation AI also optimizes settings and records the results of the maintenance work. Based on the recorded results, it provides feedback to be used for the next maintenance. This significantly reduces the labor required for automation tool maintenance and enables work to be performed more efficiently than manual work. As a result, the automated tool maintenance system reduces the amount of work required to maintain automated tools, allowing work to be performed more efficiently than manual work.

[0029] An automation tool maintenance system according to an embodiment includes a collection unit, an analysis unit, a correction unit, an optimization unit, a recording unit, and a feedback unit. The collection unit collects operation logs and error logs of the automation tool. The collection unit collects operation logs, such as the system's operating status, error messages, and performance data. The collection unit can also collect error logs, such as error codes, error messages, and occurrence times. The analysis unit analyzes the collected logs and identifies the cause of the error. The analysis unit performs analysis using, for example, log pattern matching, statistical analysis, or a machine learning algorithm. The correction unit corrects the error based on the analysis results. The correction unit performs correction using, for example, a correction procedure based on the error code or an automatic correction script. The optimization unit optimizes settings based on the analysis results. The optimization unit performs, for example, performance tuning and resource allocation adjustment. The recording unit records the results of maintenance work performed by the correction unit and the optimization unit. The recording unit stores the results in a log file or records them in a database. The feedback unit provides feedback based on the recorded results. The feedback unit adjusts, for example, the method of notifying the user and the format of the feedback content, thereby enabling the automation tool maintenance system according to the embodiment to reduce the number of steps required for maintenance of the automation tool and perform the work more efficiently than manual work.

[0030] The collection unit can dynamically change the type of logs to be collected depending on the operating status of the automation tool. For example, when the automation tool is operating normally, the collection unit causes the generation AI to collect only basic operation logs. Furthermore, when an abnormality occurs in the automation tool, the collection unit can also cause the generation AI to collect detailed error logs. Furthermore, when the operation of the automation tool is unstable, the collection unit can cause the generation AI to collect both operation logs and error logs to assist in identifying the problem. This enables efficient data collection by changing the type of logs depending on the operating status. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input operating status data of the automation tool to the generation AI and cause the generation AI to change the type of logs to be collected.

[0031] The collection unit can add a trigger function that starts collecting logs immediately when a specific error occurs during log collection. For example, when a specific error message occurs, the collection unit causes the generation AI to immediately start collecting detailed error logs. The collection unit can also cause the generation AI to start collecting operation logs when system performance suddenly drops. The collection unit can also cause the generation AI to start collecting logs related to a specific operation when a user performs that operation. This allows for rapid problem resolution by immediately starting log collection when a specific error occurs. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data that detects a specific error message into the generation AI and cause the generation AI to execute a log collection trigger.

[0032] The collection unit can optimize the collection method by taking into account the network status when collecting logs. For example, when the network is congested, the collection unit causes the generation AI to set a low log collection frequency. Furthermore, when the network is stable, the collection unit can also cause the generation AI to collect detailed logs. Furthermore, when the network is unstable, the collection unit can also cause the generation AI to prioritize the collection of only important logs. This enables efficient data collection by optimizing the collection method according to the network status. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input network status data to the generation AI and cause the generation AI to optimize the collection method.

[0033] The collection unit can also integrate and collect data from other systems when collecting logs. For example, the collection unit integrates and collects operation logs and error logs of automation tools. The collection unit can also collect and integrate performance data from other systems. The collection unit can also collect and integrate user operation logs from other systems. This enables comprehensive data collection by integrating and collecting data from other systems. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from other systems into the generation AI and have the generation AI perform integrated collection of the data.

[0034] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past error data. For example, the analysis unit allows the generation AI to refer to past error data and identify similar errors. The analysis unit can also allow the generation AI to identify the cause of an error based on past error data. The analysis unit can also allow the generation AI to analyze past error data and propose measures to prevent the error from recurring. In this way, by referring to past error data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past error data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0035] During analysis, the analysis unit can combine different analysis algorithms to identify the cause of an error from multiple angles. For example, the generation AI can combine a machine learning algorithm and a rule-based algorithm to identify the cause of an error. The analysis unit can also have the generation AI run different analysis algorithms in parallel and select the most reliable result. The analysis unit can also have the generation AI integrate the results of different analysis algorithms to identify the overall cause of an error. This improves the accuracy of identifying the cause of an error by combining different analysis algorithms. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data from different analysis algorithms into the generation AI and have the generation AI perform a multifaceted identification of the cause of an error.

[0036] During analysis, the analysis unit can also integrate data from other systems. For example, the generation AI can integrate and analyze the operation log and error log of an automation tool. The analysis unit can also integrate and analyze performance data from other systems. The analysis unit can also integrate and analyze user operation logs from other systems. This enables comprehensive analysis by integrating and analyzing data from other systems. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data from other systems into the generation AI and have the generation AI perform an integrated analysis of the data.

[0037] The analysis unit can add a function to provide analysis results in real time during analysis. For example, the analysis unit allows the generation AI to analyze error logs in real time and provide the results immediately. The analysis unit can also allow the generation AI to analyze operation logs in real time and provide the results immediately. The analysis unit can also allow the generation AI to analyze performance data in real time and provide the results immediately. This enables rapid problem resolution by providing analysis results in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input real-time data to the generation AI and have the generation AI provide real-time analysis results.

[0038] When making a correction, the correction unit can select the optimal correction method by referring to past correction history. For example, the correction unit allows the generation AI to refer to past correction history and select the optimal correction method for a similar error. The correction unit can also allow the generation AI to identify the cause of the error based on the past correction history and propose the optimal correction method. The correction unit can also allow the generation AI to analyze the past correction history and propose measures to prevent the error from recurring. In this way, the optimal correction method is selected by referring to the past correction history. Some or all of the above-mentioned processing in the correction unit may be performed using, or without, AI, for example. For example, the correction unit can input past correction history data into the generation AI and have the generation AI select the optimal correction method.

[0039] The correction unit can apply different correction procedures depending on the type of error during correction. For example, the generation AI in the correction unit selects the optimal correction procedure depending on the type of error. The correction unit can also execute different correction procedures in parallel depending on the type of error. The correction unit can also dynamically change the correction procedure depending on the type of error. This enables efficient correction by applying a correction procedure depending on the type of error. Some or all of the above-mentioned processing in the correction unit may be performed using, or without, AI. For example, the correction unit can input error type data to the generation AI and cause the generation AI to apply a correction procedure.

[0040] The correction unit can also refer to data from other systems when making corrections. For example, the generation AI can make corrections by referring to the operation log and error log of the automation tool. The correction unit can also make corrections by referring to performance data from other systems. The correction unit can also make corrections by referring to user operation logs from other systems. This enables comprehensive corrections by making corrections by referring to data from other systems. Some or all of the above-mentioned processing in the correction unit can be performed using, for example, AI, or can be performed without using AI. For example, the correction unit can input data from other systems into the generation AI and have the generation AI perform reference corrections on the data.

[0041] The correction unit can add a function to automatically document the corrections. For example, the correction unit allows the generation AI to automatically document the corrections and save them as a correction history. The correction unit can also allow the generation AI to automatically document the corrections and share them with other systems. The correction unit can also allow the generation AI to automatically document the corrections and use the document for the next correction. This makes it easier to manage the correction history by automatically documenting the corrections. Some or all of the above-mentioned processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can input correction content data into the generation AI and have the generation AI perform documentation.

[0042] During optimization, the optimization unit can select an optimization algorithm by referring to past optimization data. For example, the optimization unit allows the generation AI to refer to past optimization data and select an optimal algorithm. The optimization unit can also allow the generation AI to set optimal parameters based on past optimization data. The optimization unit can also allow the generation AI to analyze past optimization data and propose an optimal optimization method. In this way, the optimal algorithm is selected by referring to past optimization data. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input past optimization data into the generation AI and have the generation AI select an optimization algorithm.

[0043] The optimization unit can perform optimization taking into account the system load status during optimization. For example, the optimization unit performs optimization when the generation AI has a low system load. The optimization unit can also postpone optimization when the generation AI has a high system load. The optimization unit can also adjust the scope of optimization according to the system load status. This enables efficient optimization by performing optimization according to the system load status. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input system load status data to the generation AI and have the generation AI perform optimization.

[0044] The optimization unit can also refer to data from other systems during optimization. For example, the optimization unit may have the generation AI refer to the operation log and error log of an automation tool to perform optimization. The optimization unit may also have the generation AI refer to performance data from other systems to perform optimization. The optimization unit may also have the generation AI refer to user operation logs from other systems to perform optimization. This enables comprehensive optimization by performing optimization with reference to data from other systems. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit may input data from other systems into the generation AI and have the generation AI perform data reference optimization.

[0045] The optimization unit can add a function to visualize the optimization results. For example, the optimization unit causes the generation AI to visualize the optimization results in graphs or charts. The optimization unit can also cause the generation AI to display the optimization results on a dashboard. The optimization unit can also cause the generation AI to provide the optimization results in report format. By visualizing the optimization results, the results can be easily understood. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input optimization result data to the generation AI and have the generation AI perform visualization.

[0046] The recording unit can optimize the recorded content by referring to past recorded data when recording. For example, the recording unit allows the generation AI to refer to the past recorded data and select the optimal recorded content. The recording unit can also allow the generation AI to automatically classify the recorded content based on the past recorded data. The recording unit can also allow the generation AI to analyze the past recorded data and optimize the recorded content. In this way, the optimal recorded content is selected by referring to the past recorded data. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past recorded data to the generation AI and cause the generation AI to optimize the recorded content.

[0047] The recording unit can add a function to automatically classify recorded data when recording. For example, the generation AI in the recording unit automatically classifies the recorded data into categories. The recording unit can also have the generation AI automatically tag the recorded data. The recording unit can also have the generation AI automatically organize the recorded data in chronological order. This makes it easier to manage the data by automatically classifying the recorded data. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI, for example. For example, the recording unit can input the recorded data to the generation AI and have the generation AI classify the data.

[0048] The recording unit can also integrate and record data from other systems when recording. For example, the generation AI can integrate and record the operation log and error log of an automation tool in the recording unit. The generation AI can also integrate and record performance data from other systems in the recording unit. The generation AI can also integrate and record user operation logs from other systems in the recording unit. This enables comprehensive recording by integrating and recording data from other systems. Some or all of the above-mentioned processes in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input data from other systems into the generation AI and have the generation AI perform integrated recording of the data.

[0049] The recording unit can be added with a function to automatically back up the recorded data. For example, the generation AI periodically backs up the recorded data. The recording unit can also cause the generation AI to back up the recorded data to the cloud. The recording unit can also cause the generation AI to back up the recorded data to local storage. This makes it easier to protect the data by automatically backing up the recorded data. Some or all of the above-described processing in the recording unit may be performed using AI, for example, or may be performed without using AI. For example, the recording unit can input the recorded data to the generation AI and have the generation AI perform the backup.

[0050] The feedback unit can optimize the feedback content by referring to past feedback data when providing feedback. For example, the feedback unit has a generation AI referencing past feedback data and selecting optimal feedback content. The feedback unit can also automatically classify the feedback content based on past feedback data. The feedback unit can also have the generation AI analyze past feedback data and optimize the feedback content. In this way, optimal feedback content is selected by referring to past feedback data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input past feedback data to the generation AI and cause the generation AI to optimize the feedback content.

[0051] The feedback unit can add a function to automatically classify the feedback content at the time of feedback. For example, the feedback unit has the generation AI automatically classify the feedback content into categories. The feedback unit can also have the generation AI automatically tag the feedback content. The feedback unit can also have the generation AI automatically organize the feedback content in chronological order. This makes it easier to manage data by automatically classifying the feedback content. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the feedback content to the generation AI and have the generation AI classify the data.

[0052] The feedback unit can provide feedback by integrating data from other systems. For example, the feedback unit can provide feedback by having the generation AI integrate the operation log and error log of an automation tool. The feedback unit can also provide feedback by having the generation AI integrate performance data from other systems. The feedback unit can also provide feedback by having the generation AI integrate user operation logs from other systems. This enables comprehensive feedback by integrating data from other systems. Some or all of the above-described processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can input data from other systems into the generation AI and cause the generation AI to perform integrated feedback of the data.

[0053] The feedback unit can add a function to automatically document the feedback content. For example, the feedback unit may have the generation AI automatically document the feedback content and save it as feedback history. The feedback unit may also have the generation AI automatically document the feedback content and share it with other systems. The feedback unit may also have the generation AI automatically document the feedback content and use it for the next feedback. By automatically documenting the feedback content, management of the feedback history becomes easier. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI, for example. For example, the feedback unit may input feedback content data to the generation AI and have the generation AI perform documentation.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When collecting logs, the collection unit can also integrate and collect data from other systems. For example, it can integrate and collect operation logs and error logs of automation tools. It can also collect and integrate performance data from other systems. It can also collect and integrate user operation logs from other systems. This allows for comprehensive data collection by integrating and collecting data from other systems.

[0056] During analysis, the analysis unit can combine different analysis algorithms to identify the cause of an error from multiple angles. For example, the generation AI can combine machine learning algorithms and rule-based algorithms to identify the cause of an error. The generation AI can also run different analysis algorithms in parallel and select the most reliable result. Furthermore, the generation AI can integrate the results of different analysis algorithms to identify the overall cause of an error. This combination of different analysis algorithms improves the accuracy of identifying the cause of an error.

[0057] When making corrections, the correction unit can refer to past correction history to select the optimal correction method. For example, the generation AI can refer to past correction history to select the optimal correction method for a similar error. The generation AI can also identify the cause of the error based on past correction history and propose the optimal correction method. Furthermore, the generation AI can analyze past correction history and propose measures to prevent the error from recurring. In this way, the optimal correction method can be selected by referring to past correction history.

[0058] The optimization unit can perform optimization taking into account the system load status. For example, the generation AI performs optimization when the system load is low. The generation AI can also postpone optimization when the system load is high. Furthermore, the generation AI can adjust the scope of optimization depending on the system load status. This allows for efficient optimization by performing optimization according to the system load status.

[0059] When recording, the recording unit can also integrate and record data from other systems. For example, the generation AI can integrate and record the operation log and error log of an automation tool. The generation AI can also integrate and record performance data from other systems. Furthermore, the generation AI can also integrate and record user operation logs from other systems. This allows for comprehensive recording by integrating and recording data from other systems.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects operation logs and error logs of the automation tool. The collection unit collects operation logs such as the system's operating status, error messages, and performance data. The collection unit can also collect error logs such as error codes, error messages, and the time of occurrence. Step 2: The analysis unit analyzes the collected logs and identifies the cause of the error. The analysis unit performs the analysis using, for example, log pattern matching, statistical analysis, machine learning algorithms, etc. Step 3: The correction unit corrects the error based on the analysis result. For example, the correction unit performs the correction using a correction procedure based on the error code or an automatic correction script. Step 4: The optimization unit optimizes the settings based on the analysis results. The optimization unit performs, for example, performance tuning and resource allocation adjustment. Step 5: The recording unit records the results of the maintenance work performed by the correction unit and the optimization unit, for example, by saving the results in a log file or recording them in a database. Step 6: The feedback unit provides feedback based on the recorded results. For example, the feedback unit adjusts the notification method to the user and the format of the feedback content.

[0062] (Example 2) An automation tool maintenance system according to an embodiment of the present invention is a system for reducing the labor required for maintaining automation tools and performing the work more efficiently than manual work. This system automates automation tool maintenance using a generation AI. Specifically, the generation AI collects and analyzes operation logs and error logs of the automation tool. Then, based on the analysis results, the generation AI automatically performs maintenance work on the automation tool. For example, the generation AI identifies the cause of an error and makes necessary corrections or optimizes settings. Furthermore, the generation AI records the results of the maintenance work and provides feedback to be used for the next maintenance. This mechanism significantly reduces the labor required for automation tool maintenance and enables work to be performed more efficiently than manual work. For example, the generation AI collects and analyzes operation logs and error logs of the automation tool. Based on the analysis results, the generation AI identifies the cause of an error and makes necessary corrections. The generation AI also optimizes settings and records the results of the maintenance work. Based on the recorded results, it provides feedback to be used for the next maintenance. This significantly reduces the labor required for automation tool maintenance and enables work to be performed more efficiently than manual work. As a result, the automated tool maintenance system reduces the amount of work required to maintain automated tools, allowing work to be performed more efficiently than manual work.

[0063] An automation tool maintenance system according to an embodiment includes a collection unit, an analysis unit, a correction unit, an optimization unit, a recording unit, and a feedback unit. The collection unit collects operation logs and error logs of the automation tool. The collection unit collects operation logs, such as the system's operating status, error messages, and performance data. The collection unit can also collect error logs, such as error codes, error messages, and occurrence times. The analysis unit analyzes the collected logs and identifies the cause of the error. The analysis unit performs analysis using, for example, log pattern matching, statistical analysis, or a machine learning algorithm. The correction unit corrects the error based on the analysis results. The correction unit performs correction using, for example, a correction procedure based on the error code or an automatic correction script. The optimization unit optimizes settings based on the analysis results. The optimization unit performs, for example, performance tuning and resource allocation adjustment. The recording unit records the results of maintenance work performed by the correction unit and the optimization unit. The recording unit stores the results in a log file or records them in a database. The feedback unit provides feedback based on the recorded results. The feedback unit adjusts, for example, the method of notifying the user and the format of the feedback content, thereby enabling the automation tool maintenance system according to the embodiment to reduce the number of steps required for maintenance of the automation tool and perform the work more efficiently than manual work.

[0064] The collection unit can analyze the user's emotions and adjust the timing of log collection based on the analyzed user's emotions. For example, if the user is feeling stressed, the collection unit can cause the generation AI to set the log collection frequency low, thereby reducing the user's burden. Furthermore, if the user is relaxed, the collection unit can cause the generation AI to set the log collection frequency high to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can cause the generation AI to prioritize collecting only important logs and perform analysis quickly. This allows for more appropriate data collection by adjusting the log collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0065] The collection unit can dynamically change the type of logs to be collected depending on the operating status of the automation tool. For example, when the automation tool is operating normally, the collection unit causes the generation AI to collect only basic operation logs. Furthermore, when an abnormality occurs in the automation tool, the collection unit can also cause the generation AI to collect detailed error logs. Furthermore, when the operation of the automation tool is unstable, the collection unit can cause the generation AI to collect both operation logs and error logs to assist in identifying the problem. This enables efficient data collection by changing the type of logs depending on the operating status. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input operating status data of the automation tool to the generation AI and cause the generation AI to change the type of logs to be collected.

[0066] The collection unit can add a trigger function that starts collecting logs immediately when a specific error occurs during log collection. For example, when a specific error message occurs, the collection unit causes the generation AI to immediately start collecting detailed error logs. The collection unit can also cause the generation AI to start collecting operation logs when system performance suddenly drops. The collection unit can also cause the generation AI to start collecting logs related to a specific operation when a user performs that operation. This allows for rapid problem resolution by immediately starting log collection when a specific error occurs. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data that detects a specific error message into the generation AI and cause the generation AI to execute a log collection trigger.

[0067] The collection unit can analyze the user's emotions and determine the priority of logs to be collected based on the analyzed user's emotions. For example, when the user is stressed, the collection unit causes the generation AI to prioritize collecting important error logs. Furthermore, when the user is relaxed, the collection unit can also cause the generation AI to prioritize collecting detailed operation logs. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting logs that the generation AI can analyze quickly. This allows important data to be collected preferentially by determining the priority of logs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the log priorities.

[0068] The collection unit can optimize the collection method by taking into account the network status when collecting logs. For example, when the network is congested, the collection unit causes the generation AI to set a low log collection frequency. Furthermore, when the network is stable, the collection unit can also cause the generation AI to collect detailed logs. Furthermore, when the network is unstable, the collection unit can also cause the generation AI to prioritize the collection of only important logs. This enables efficient data collection by optimizing the collection method according to the network status. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input network status data to the generation AI and cause the generation AI to optimize the collection method.

[0069] The collection unit can also integrate and collect data from other systems when collecting logs. For example, the collection unit integrates and collects operation logs and error logs of automation tools. The collection unit can also collect and integrate performance data from other systems. The collection unit can also collect and integrate user operation logs from other systems. This enables comprehensive data collection by integrating and collecting data from other systems. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data from other systems into the generation AI and have the generation AI perform integrated collection of the data.

[0070] The analysis unit can analyze the user's emotions and adjust the display method of the analysis results based on the analyzed user's emotions. For example, if the user is nervous, the analysis unit can have the generation AI display a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can have the generation AI display a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can have the generation AI display a summary of the key points. This allows for highly visible results to be displayed by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0071] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past error data. For example, the analysis unit allows the generation AI to refer to past error data and identify similar errors. The analysis unit can also allow the generation AI to identify the cause of an error based on past error data. The analysis unit can also allow the generation AI to analyze past error data and propose measures to prevent the error from recurring. In this way, by referring to past error data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input past error data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0072] During analysis, the analysis unit can combine different analysis algorithms to identify the cause of an error from multiple angles. For example, the generation AI can combine a machine learning algorithm and a rule-based algorithm to identify the cause of an error. The analysis unit can also have the generation AI run different analysis algorithms in parallel and select the most reliable result. The analysis unit can also have the generation AI integrate the results of different analysis algorithms to identify the overall cause of an error. This improves the accuracy of identifying the cause of an error by combining different analysis algorithms. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data from different analysis algorithms into the generation AI and have the generation AI perform a multifaceted identification of the cause of an error.

[0073] The analysis unit can analyze the user's emotions and determine the analysis priority based on the analyzed user's emotions. For example, if the user is stressed, the analysis unit can cause the generation AI to prioritize analysis of important errors. Furthermore, if the user is relaxed, the analysis unit can also cause the generation AI to prioritize detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can also prioritize errors that the generation AI can analyze quickly. Thus, by determining the analysis priority according to the user's emotions, the analysis of important errors can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0074] During analysis, the analysis unit can also integrate data from other systems. For example, the generation AI can integrate and analyze the operation log and error log of an automation tool. The analysis unit can also integrate and analyze performance data from other systems. The analysis unit can also integrate and analyze user operation logs from other systems. This enables comprehensive analysis by integrating and analyzing data from other systems. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data from other systems into the generation AI and have the generation AI perform an integrated analysis of the data.

[0075] The analysis unit can add a function to provide analysis results in real time during analysis. For example, the analysis unit allows the generation AI to analyze error logs in real time and provide the results immediately. The analysis unit can also allow the generation AI to analyze operation logs in real time and provide the results immediately. The analysis unit can also allow the generation AI to analyze performance data in real time and provide the results immediately. This enables rapid problem resolution by providing analysis results in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input real-time data to the generation AI and have the generation AI provide real-time analysis results.

[0076] The correction unit can analyze the user's emotions and adjust the correction method based on the analyzed user's emotions. For example, if the user is nervous, the correction unit can cause the generation AI to provide a simple and highly visible correction method. Furthermore, if the user is relaxed, the correction unit can cause the generation AI to provide a detailed correction method. Furthermore, if the user is in a hurry, the correction unit can cause the generation AI to provide a method that allows for quick correction. Thus, by adjusting the correction method according to the user's emotions, a highly visible correction method is provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the correction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the correction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the correction method.

[0077] When making a correction, the correction unit can select the optimal correction method by referring to past correction history. For example, the correction unit allows the generation AI to refer to past correction history and select the optimal correction method for a similar error. The correction unit can also allow the generation AI to identify the cause of the error based on the past correction history and propose the optimal correction method. The correction unit can also allow the generation AI to analyze the past correction history and propose measures to prevent the error from recurring. In this way, the optimal correction method is selected by referring to the past correction history. Some or all of the above-mentioned processing in the correction unit may be performed using, or without, AI, for example. For example, the correction unit can input past correction history data into the generation AI and have the generation AI select the optimal correction method.

[0078] The correction unit can apply different correction procedures depending on the type of error during correction. For example, the generation AI in the correction unit selects the optimal correction procedure depending on the type of error. The correction unit can also execute different correction procedures in parallel depending on the type of error. The correction unit can also dynamically change the correction procedure depending on the type of error. This enables efficient correction by applying a correction procedure depending on the type of error. Some or all of the above-mentioned processing in the correction unit may be performed using, or without, AI. For example, the correction unit can input error type data to the generation AI and cause the generation AI to apply a correction procedure.

[0079] The correction unit can analyze the user's emotions and determine the priority of corrections based on the analyzed user's emotions. For example, if the user is stressed, the correction unit can cause the generation AI to prioritize correcting important errors. Furthermore, if the user is relaxed, the correction unit can also cause the generation AI to prioritize detailed corrections. Furthermore, if the user is in a hurry, the correction unit can also prioritize errors that can be quickly corrected. Thus, by determining the priority of corrections according to the user's emotions, the correction of important errors can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the correction unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the correction unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of corrections.

[0080] The correction unit can also refer to data from other systems when making corrections. For example, the generation AI can make corrections by referring to the operation log and error log of the automation tool. The correction unit can also make corrections by referring to performance data from other systems. The correction unit can also make corrections by referring to user operation logs from other systems. This enables comprehensive corrections by making corrections by referring to data from other systems. Some or all of the above-mentioned processing in the correction unit can be performed using, for example, AI, or can be performed without using AI. For example, the correction unit can input data from other systems into the generation AI and have the generation AI perform reference corrections on the data.

[0081] The correction unit can add a function to automatically document the corrections. For example, the correction unit allows the generation AI to automatically document the corrections and save them as a correction history. The correction unit can also allow the generation AI to automatically document the corrections and share them with other systems. The correction unit can also allow the generation AI to automatically document the corrections and use the document for the next correction. This makes it easier to manage the correction history by automatically documenting the corrections. Some or all of the above-mentioned processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can input correction content data into the generation AI and have the generation AI perform documentation.

[0082] The optimization unit can analyze the user's emotions and adjust the optimization method based on the analyzed user's emotions. For example, if the user is nervous, the optimization unit can cause the generation AI to provide a simple, highly visible optimization method. Furthermore, if the user is relaxed, the optimization unit can cause the generation AI to provide a detailed optimization method. Furthermore, if the user is in a hurry, the optimization unit can cause the generation AI to provide a method that allows for quick optimization. This allows the optimization method to be adjusted according to the user's emotions, thereby providing a highly visible optimization method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the optimization unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the optimization unit can input the user's emotion data into the generation AI and have the generation AI adjust the optimization method.

[0083] During optimization, the optimization unit can select an optimization algorithm by referring to past optimization data. For example, the optimization unit allows the generation AI to refer to past optimization data and select an optimal algorithm. The optimization unit can also allow the generation AI to set optimal parameters based on past optimization data. The optimization unit can also allow the generation AI to analyze past optimization data and propose an optimal optimization method. In this way, the optimal algorithm is selected by referring to past optimization data. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input past optimization data into the generation AI and have the generation AI select an optimization algorithm.

[0084] The optimization unit can perform optimization taking into account the system load status during optimization. For example, the optimization unit performs optimization when the generation AI has a low system load. The optimization unit can also postpone optimization when the generation AI has a high system load. The optimization unit can also adjust the scope of optimization according to the system load status. This enables efficient optimization by performing optimization according to the system load status. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input system load status data to the generation AI and have the generation AI perform optimization.

[0085] The optimization unit can analyze the user's emotions and determine the priority of optimization based on the analyzed user's emotions. For example, if the user is feeling stressed, the optimization unit can cause the generation AI to prioritize important optimization. Furthermore, if the user is relaxed, the optimization unit can also cause the generation AI to prioritize detailed optimization. Furthermore, if the user is in a hurry, the optimization unit can prioritize items that can be quickly optimized. Thus, by determining the priority of optimization according to the user's emotions, important optimization can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the optimization unit can input the user's emotion data into the generation AI and have the generation AI determine the optimization priority.

[0086] The optimization unit can also refer to data from other systems during optimization. For example, the optimization unit may have the generation AI refer to the operation log and error log of an automation tool to perform optimization. The optimization unit may also have the generation AI refer to performance data from other systems to perform optimization. The optimization unit may also have the generation AI refer to user operation logs from other systems to perform optimization. This enables comprehensive optimization by performing optimization with reference to data from other systems. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit may input data from other systems into the generation AI and have the generation AI perform data reference optimization.

[0087] The optimization unit can add a function to visualize the optimization results. For example, the optimization unit causes the generation AI to visualize the optimization results in graphs or charts. The optimization unit can also cause the generation AI to display the optimization results on a dashboard. The optimization unit can also cause the generation AI to provide the optimization results in report format. By visualizing the optimization results, the results can be easily understood. Some or all of the above-described processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input optimization result data to the generation AI and have the generation AI perform visualization.

[0088] The recording unit can analyze the user's emotions and adjust the recording method based on the analyzed user's emotions. For example, if the user is nervous, the recording unit can provide the generation AI with a simple, highly visible recording method. Furthermore, if the user is relaxed, the recording unit can also provide the generation AI with a detailed recording method. Furthermore, if the user is in a hurry, the recording unit can also provide the generation AI with a method that allows for quick recording. This allows the recording method to be adjusted according to the user's emotions, thereby providing a highly visible recording method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI adjust the recording method.

[0089] The recording unit can optimize the recorded content by referring to past recorded data when recording. For example, the recording unit allows the generation AI to refer to the past recorded data and select the optimal recorded content. The recording unit can also allow the generation AI to automatically classify the recorded content based on the past recorded data. The recording unit can also allow the generation AI to analyze the past recorded data and optimize the recorded content. In this way, the optimal recorded content is selected by referring to the past recorded data. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past recorded data to the generation AI and cause the generation AI to optimize the recorded content.

[0090] The recording unit can add a function to automatically classify recorded data when recording. For example, the generation AI in the recording unit automatically classifies the recorded data into categories. The recording unit can also have the generation AI automatically tag the recorded data. The recording unit can also have the generation AI automatically organize the recorded data in chronological order. This makes it easier to manage the data by automatically classifying the recorded data. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI, for example. For example, the recording unit can input the recorded data to the generation AI and have the generation AI classify the data.

[0091] The recording unit can analyze the user's emotions and determine the priority of recording based on the analyzed user's emotions. For example, if the user is feeling stressed, the recording unit can cause the generation AI to prioritize important records. Furthermore, if the user is relaxed, the recording unit can also cause the generation AI to prioritize detailed records. Furthermore, if the user is in a hurry, the recording unit can also prioritize items that the generation AI can quickly record. Thus, by determining the priority of recording according to the user's emotions, important records can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recording unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of recording.

[0092] The recording unit can also integrate and record data from other systems when recording. For example, the generation AI can integrate and record the operation log and error log of an automation tool in the recording unit. The generation AI can also integrate and record performance data from other systems in the recording unit. The generation AI can also integrate and record user operation logs from other systems in the recording unit. This enables comprehensive recording by integrating and recording data from other systems. Some or all of the above-mentioned processes in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input data from other systems into the generation AI and have the generation AI perform integrated recording of the data.

[0093] The recording unit can be added with a function to automatically back up the recorded data. For example, the generation AI periodically backs up the recorded data. The recording unit can also cause the generation AI to back up the recorded data to the cloud. The recording unit can also cause the generation AI to back up the recorded data to local storage. This makes it easier to protect the data by automatically backing up the recorded data. Some or all of the above-described processing in the recording unit may be performed using AI, for example, or may be performed without using AI. For example, the recording unit can input the recorded data to the generation AI and have the generation AI perform the backup.

[0094] The feedback unit can analyze the user's emotions and adjust the content of the feedback based on the analyzed user's emotions. For example, if the user is nervous, the feedback unit can cause the generation AI to provide simple, highly visible feedback. Furthermore, if the user is relaxed, the feedback unit can also cause the generation AI to provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can provide a method for the generation AI to provide quick feedback. This allows the feedback content to be adjusted according to the user's emotions, thereby providing highly visible feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI adjust the feedback content.

[0095] The feedback unit can optimize the feedback content by referring to past feedback data when providing feedback. For example, the feedback unit has a generation AI referencing past feedback data and selecting optimal feedback content. The feedback unit can also automatically classify the feedback content based on past feedback data. The feedback unit can also have the generation AI analyze past feedback data and optimize the feedback content. In this way, optimal feedback content is selected by referring to past feedback data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input past feedback data to the generation AI and cause the generation AI to optimize the feedback content.

[0096] The feedback unit can add a function to automatically classify the feedback content at the time of feedback. For example, the feedback unit has the generation AI automatically classify the feedback content into categories. The feedback unit can also have the generation AI automatically tag the feedback content. The feedback unit can also have the generation AI automatically organize the feedback content in chronological order. This makes it easier to manage data by automatically classifying the feedback content. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the feedback content to the generation AI and have the generation AI classify the data.

[0097] The feedback unit can analyze the user's emotions and determine the priority of feedback based on the analyzed user's emotions. For example, if the user is feeling stressed, the feedback unit can cause the generation AI to prioritize important feedback. Furthermore, if the user is relaxed, the feedback unit can also cause the generation AI to prioritize detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can also prioritize items for which the generation AI can provide quick feedback. Thus, by determining the priority of feedback according to the user's emotions, important feedback can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of feedback.

[0098] The feedback unit can provide feedback by integrating data from other systems. For example, the feedback unit can provide feedback by having the generation AI integrate the operation log and error log of an automation tool. The feedback unit can also provide feedback by having the generation AI integrate performance data from other systems. The feedback unit can also provide feedback by having the generation AI integrate user operation logs from other systems. This enables comprehensive feedback by integrating data from other systems. Some or all of the above-described processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can input data from other systems into the generation AI and cause the generation AI to perform integrated feedback of the data.

[0099] The feedback unit can add a function to automatically document the feedback content. For example, the feedback unit may have the generation AI automatically document the feedback content and save it as feedback history. The feedback unit may also have the generation AI automatically document the feedback content and share it with other systems. The feedback unit may also have the generation AI automatically document the feedback content and use it for the next feedback. By automatically documenting the feedback content, management of the feedback history becomes easier. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI, for example. For example, the feedback unit may input feedback content data to the generation AI and have the generation AI perform documentation. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, correction unit, optimization unit, recording unit, and feedback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects operation logs and error logs using the camera 42 and microphone 38B of the smart device 14 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected logs and identifies the cause of errors. The correction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, corrects errors based on the analysis results. The optimization unit, realized, for example, by the specific processing unit 290 of the data processing device 12, optimizes settings. The recording unit, realized, for example, by the specific processing unit 290 of the data processing device 12, records the results of maintenance work. The feedback unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides feedback based on the recorded results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, correction unit, optimization unit, recording unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects operation logs and error logs using the camera 42 and microphone 238 of the smart glasses 214 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected logs and identifies the cause of errors. The correction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, corrects errors based on the analysis results. The optimization unit, realized, for example, by the specific processing unit 290 of the data processing device 12, optimizes settings. The recording unit, realized, for example, by the specific processing unit 290 of the data processing device 12, records the results of maintenance work. The feedback unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides feedback based on the recorded results. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, correction unit, optimization unit, recording unit, and feedback unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects operation logs and error logs using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected logs and identifies the cause of errors. The correction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, corrects errors based on the analysis results. The optimization unit, realized, for example, by the specific processing unit 290 of the data processing device 12, optimizes settings. The recording unit, realized, for example, by the specific processing unit 290 of the data processing device 12, records the results of maintenance work. The feedback unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides feedback based on the recorded results. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, correction unit, optimization unit, recording unit, and feedback unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects operation logs and error logs using the camera 42 and microphone 238 of the robot 414 and transmits them to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected logs and identifies the cause of the error. The correction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, corrects the error based on the analysis result. The optimization unit, realized, for example, by the specific processing unit 290 of the data processing device 12, optimizes settings. The recording unit, realized, for example, by the specific processing unit 290 of the data processing device 12, records the results of the maintenance work. The feedback unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides feedback based on the recorded results.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The analysis unit can analyze the user's emotions and adjust the depth of the analysis based on the analyzed user emotions. For example, if the user is feeling stressed, the generation AI will perform a simple analysis to avoid placing a burden on the user. Alternatively, if the user is relaxed, the generation AI can perform a detailed analysis and provide more information. Furthermore, if the user is in a hurry, the generation AI can perform a quick analysis and provide only the necessary information. This allows the depth of analysis to be adjusted according to the user's emotions, making it possible to provide the most appropriate information for the user.

[0102] The correction unit can analyze the user's emotions when making corrections and adjust the timing of the corrections based on the analyzed user emotions. For example, if the user is feeling stressed, the generation AI can postpone the correction work and make the corrections when the user is relaxed. Also, if the user is in a hurry, the generation AI can make the corrections quickly so as not to interrupt the user's work. In this way, the burden on the user can be reduced by adjusting the timing of corrections according to the user's emotions.

[0103] During optimization, the optimization unit can analyze the user's emotions and adjust the frequency of optimization based on the analyzed user emotions. For example, if the user is feeling stressed, the generation AI can set the optimization frequency low to reduce the user's burden. Also, if the user is relaxed, the generation AI can set the optimization frequency high to maximize system performance. Furthermore, if the user is in a hurry, the generation AI can prioritize only important optimizations. This allows for efficient system operation by adjusting the optimization frequency according to the user's emotions.

[0104] The recording unit can analyze the user's emotions during recording and adjust the level of detail of the recording based on the analyzed user emotions. For example, if the user is feeling stressed, the generation AI will record briefly, reducing the user's burden. If the user is relaxed, the generation AI will record in detail and provide more information. Furthermore, if the user is in a hurry, the generation AI can record quickly and provide only the necessary information. This allows the level of detail of the recording to be adjusted according to the user's emotions, making it possible to provide the user with the most appropriate information.

[0105] The feedback unit can analyze the user's emotions when providing feedback and adjust the timing of the feedback based on the analyzed user emotions. For example, if the user is feeling stressed, the generation AI can postpone providing feedback until a time when the user is relaxed. Also, if the user is in a hurry, the generation AI can provide feedback quickly so as not to interrupt the user's work. This makes it possible to reduce the burden on the user by adjusting the timing of feedback according to the user's emotions.

[0106] When collecting logs, the collection unit can also integrate and collect data from other systems. For example, it can integrate and collect operation logs and error logs of automation tools. It can also collect and integrate performance data from other systems. It can also collect and integrate user operation logs from other systems. This allows for comprehensive data collection by integrating and collecting data from other systems.

[0107] During analysis, the analysis unit can combine different analysis algorithms to identify the cause of an error from multiple angles. For example, the generation AI can combine machine learning algorithms and rule-based algorithms to identify the cause of an error. The generation AI can also run different analysis algorithms in parallel and select the most reliable result. Furthermore, the generation AI can integrate the results of different analysis algorithms to identify the overall cause of an error. This combination of different analysis algorithms improves the accuracy of identifying the cause of an error.

[0108] When making corrections, the correction unit can refer to past correction history to select the optimal correction method. For example, the generation AI can refer to past correction history to select the optimal correction method for a similar error. The generation AI can also identify the cause of the error based on past correction history and propose the optimal correction method. Furthermore, the generation AI can analyze past correction history and propose measures to prevent the error from recurring. In this way, the optimal correction method can be selected by referring to past correction history.

[0109] The optimization unit can perform optimization taking into account the system load status. For example, the generation AI performs optimization when the system load is low. The generation AI can also postpone optimization when the system load is high. Furthermore, the generation AI can adjust the scope of optimization depending on the system load status. This allows for efficient optimization by performing optimization according to the system load status.

[0110] When recording, the recording unit can also integrate and record data from other systems. For example, the generation AI can integrate and record the operation log and error log of an automation tool. The generation AI can also integrate and record performance data from other systems. Furthermore, the generation AI can also integrate and record user operation logs from other systems. This allows for comprehensive recording by integrating and recording data from other systems.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection unit collects operation logs and error logs of the automation tool. The collection unit collects operation logs such as the system's operating status, error messages, and performance data. The collection unit can also collect error logs such as error codes, error messages, and the time of occurrence. Step 2: The analysis unit analyzes the collected logs and identifies the cause of the error. The analysis unit performs the analysis using, for example, log pattern matching, statistical analysis, machine learning algorithms, etc. Step 3: The correction unit corrects the error based on the analysis result. For example, the correction unit performs the correction using a correction procedure based on the error code or an automatic correction script. Step 4: The optimization unit optimizes the settings based on the analysis results. The optimization unit performs, for example, performance tuning and resource allocation adjustment. Step 5: The recording unit records the results of the maintenance work performed by the correction unit and the optimization unit, for example, by saving the results in a log file or recording them in a database. Step 6: The feedback unit provides feedback based on the recorded results. For example, the feedback unit adjusts the notification method to the user and the format of the feedback content.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

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

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[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 headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

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

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 collection unit that collects operation logs and error logs of the automation tool; an analysis unit that analyzes the logs collected by the collection unit; a correction unit that corrects errors based on the analysis results obtained by the analysis unit; a unit for optimizing settings based on the analysis results obtained by the analysis unit; a recording unit that records the results of the maintenance work performed by the correction unit and the optimization unit; a unit for providing feedback based on the results recorded by the recording unit. A system characterized by:

2. The collecting unit Analyze user sentiment and adjust the timing of log collection based on the analyzed user sentiment.

2. The system of claim 1.

3. The collecting unit Dynamically change the type of logs collected depending on the operation status of the automation tool 2. The system of claim 1.

4. The collecting unit Add a trigger function to start collecting logs immediately when a specific error occurs.

2. The system of claim 1.

5. The collecting unit Analyze user sentiment and prioritize logs to be collected based on the analyzed user sentiment.

2. The system of claim 1.

6. The collecting unit Optimize the log collection method by taking into account the network status when collecting logs 2. The system of claim 1.

7. The collecting unit When collecting logs, also integrate and collect data from other systems.

2. The system of claim 1.

8. The analysis unit Analyze user emotions and adjust the display method of the analysis results based on the analyzed user emotions 2. The system of claim 1.

Images