system

Abstract

The system according to the embodiment aims to automatically deal with abnormalities that occur after updating business department terminals. [Solution] A system according to an embodiment includes a selection unit, an implementation unit, a detection unit, a debugging unit, and a restoration unit. The selection unit selects whether to automatically perform an update. The implementation unit automatically performs an update based on the settings selected by the selection unit. The detection unit detects an abnormality after the update performed by the implementation unit. The debugging unit automatically debugs the abnormality detected by the detection unit. The restoration unit returns to initial settings based on the results of debugging by the debugging 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] With conventional technology, issues that arise after updating business department terminals have to be dealt with manually, which is inefficient.

[0005] The system according to the embodiment aims to automatically deal with abnormalities that occur after updating business department terminals. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, an implementation unit, a detection unit, a debugging unit, and a restoration unit. The selection unit selects whether to automatically perform an update. The implementation unit automatically performs an update based on the settings selected by the selection unit. The detection unit detects an abnormality after the update performed by the implementation unit. The debugging unit automatically debugs the abnormality detected by the detection unit. The restoration unit returns to the initial settings based on the results of debugging by the debugging unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically handle abnormalities that occur after updating a business department terminal. [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) A system according to an embodiment of the present invention provides a function for selecting whether to automatically perform updates in advance on departmental PCs in operation, and automatically performs updates when OS or security updates are distributed. If an abnormality occurs after an update, the system automatically performs debugging and restores the system to its original state as necessary. This allows users to focus on their work without worrying about OS or security updates. This mechanism is already implemented in certain systems and can be expanded to include departmental PCs. First, the user selects whether to automatically perform updates in advance. Next, when an update is distributed, the system automatically downloads and installs the update. If the system detects an abnormality after the update, the system automatically performs debugging and restores the system to its original state as necessary. This allows users to focus on their work without worrying about OS or security updates.

[0029] A system according to an embodiment includes a selection unit, an implementation unit, a detection unit, a debugging unit, and a restoration unit. The selection unit has a function for a user to select whether to automatically perform updates in advance. For example, the selection unit displays a screen for selecting whether to automatically perform updates when the system starts up. The selection unit can also select whether to automatically perform updates from a settings menu. The selection unit can also suggest optimal options based on the user's past selection history. For example, the selection unit automatically displays update settings that the user has frequently selected in the past as candidates. The implementation unit has a function for automatically downloading and installing updates when they are distributed. For example, the implementation unit can automatically start downloading only when connected to Wi-Fi. The implementation unit can also perform background downloads. The implementation unit can also optimize the order of downloads and installations based on the importance of the updates. For example, the implementation unit downloads and installs important security updates with the highest priority. The detection unit has a function for detecting system anomalies after updates. For example, the detection unit can perform log analysis to detect anomalies. The detection unit can also perform real-time monitoring to detect anomalies. Furthermore, the detection unit can optimize the anomaly detection algorithm by referring to past anomaly data. For example, the detection unit adjusts the anomaly detection threshold based on the past anomaly data. The debugging unit has a function for automatically debugging detected anomalies. For example, the debugging unit executes a script to debug the anomaly. The debugging unit can also analyze an error log to debug the anomaly. Furthermore, the debugging unit can optimize the debugging algorithm by referring to past debugging data. For example, the debugging unit adjusts the debugging threshold based on past debugging data. The restoration unit has a function for restoring the system to its original state based on the debugging results. For example, the restoration unit performs a system restore to restore the system to its original state. The restoration unit can also restore a configuration file to restore the system to its original state. Furthermore, the restoration unit can optimize the restoration algorithm by referring to past restoration data.For example, the restoration unit adjusts the restoration threshold based on past restoration data. This allows the system according to the embodiment to perform automatic update, anomaly detection, debugging, and restoration in a single sequence.

[0030] The selection unit has a function for the user to select whether or not to automatically perform updates in advance. The selection unit, for example, displays a screen for selecting whether or not to automatically perform updates when the system starts up. For example, the selection unit displays a pop-up window when the system starts up, allowing the user to select whether or not to automatically perform updates. The selection unit can also select whether or not to automatically perform updates from a settings menu. For example, the selection unit may provide an "Update Settings" option in the settings menu, allowing the user to manually change the settings. Furthermore, the selection unit can suggest optimal options based on the user's past selection history. For example, the selection unit may automatically display update settings that the user has frequently selected in the past as candidates. This allows the user to select whether or not to automatically perform updates in advance.

[0031] The implementation unit has a function of automatically downloading and installing updates when they are distributed. For example, the implementation unit automatically starts downloading only when a Wi-Fi connection is established. For example, the implementation unit detects a Wi-Fi connection and starts downloading an update file. The implementation unit can also perform background downloads. For example, the implementation unit downloads update files in the background while the user is performing other tasks. Furthermore, the implementation unit can optimize the order of downloads and installations based on the importance of the updates. For example, the implementation unit downloads and installs important security updates with the highest priority. This allows the implementation unit to automatically download and install updates when they are distributed.

[0032] The detection unit has a function to detect abnormalities in the system after an update. The detection unit, for example, performs log analysis to detect abnormalities. For example, the detection unit analyzes system logs to detect error messages and abnormal behavior. The detection unit can also perform real-time monitoring to detect abnormalities. For example, the detection unit monitors system operation in real time to detect abnormal behavior. Furthermore, the detection unit can also optimize the abnormality detection algorithm by referring to past abnormal data. For example, the detection unit adjusts the abnormality detection threshold based on past abnormal data. This allows the detection unit to detect abnormalities in the system after an update.

[0033] The debugging unit has a function to automatically debug detected abnormalities. The debugging unit, for example, executes a script to debug the abnormality. For example, the debugging unit executes a debug script prepared in advance to correct the abnormality. The debugging unit can also analyze an error log to debug the abnormality. For example, the debugging unit analyzes the error log to identify and correct the cause of the abnormality. Furthermore, the debugging unit can also optimize the debugging algorithm by referring to past debugging data. For example, the debugging unit adjusts the debugging threshold based on past debugging data. This allows the debugging unit to automatically debug detected abnormalities.

[0034] The restoration unit has a function of restoring the system to its original state based on the results of debugging. The restoration unit, for example, performs a system restore to restore the system to its original state. For example, the restoration unit uses a system restore point to restore the system to its original state. The restoration unit can also restore a configuration file to restore the system to its original state. For example, the restoration unit uses a backed-up configuration file to restore the system settings to their original state. Furthermore, the restoration unit can optimize the restoration algorithm by referring to past restoration data. For example, the restoration unit adjusts the restoration threshold based on the past restoration data. This allows the restoration unit to restore the system to its original state based on the results of debugging.

[0035] The selection unit can analyze past update history and automatically suggest appropriate options. For example, the selection unit automatically displays update settings that the user has frequently selected in the past as candidates. For example, the selection unit stores past update history in a database and preferentially displays frequently selected settings. The selection unit can also suggest optimal options based on the update history that the user has previously selected. For example, the selection unit analyzes past update history and suggests settings with a high success rate. Furthermore, the selection unit can predict and suggest optimal options for specific time periods based on the user's past update history. For example, the selection unit identifies the time period during which the user performed the most updates based on the past update history and suggests the optimal option for that time period. This allows the selection unit to suggest optimal options based on the past update history.

[0036] The selection unit can dynamically change whether to automatically perform updates based on the user's work schedule. For example, the selection unit references the user's calendar information and proposes a setting to automatically perform updates outside of work hours. For example, the selection unit obtains data from a calendar app and automatically proposes a setting to perform updates outside of work hours. The selection unit can also propose a setting to refrain from updating before important meetings or presentations based on the user's work schedule. For example, the selection unit analyzes calendar information and proposes a setting to postpone updates before important events. Furthermore, the selection unit can analyze the user's work schedule and dynamically change the optimal update timing. For example, the selection unit monitors the work schedule in real time and performs updates at the optimal timing. This allows the selection unit to dynamically change the implementation of updates based on the user's work schedule.

[0037] The selection unit can present appropriate options taking into account the user's geographical location information. For example, if the user is in the office, the selection unit presents an option to not perform updates during business hours. For example, the selection unit acquires GPS data to detect that the user is in the office and proposes a setting to not perform updates during business hours. The selection unit can also present an option to perform updates outside of business hours if the user is at home. For example, the selection unit estimates the user's location from an IP address, detects that the user is at home, and proposes a setting to perform updates outside of business hours. Furthermore, the selection unit can present an option to postpone updates if the user is on a business trip. For example, the selection unit combines the user's calendar information and location information to detect that the user is on a business trip and proposes a setting to postpone updates. This allows the selection unit to present optimal options based on the user's geographical location information.

[0038] The selection unit can analyze the user's device usage history and suggest the optimal option. The selection unit can suggest the optimal option based on, for example, device settings that the user has frequently used in the past. For example, the selection unit can store the device usage history in a database and preferentially display frequently used settings. The selection unit can also suggest the optimal update timing based on the user's device usage history. For example, the selection unit can analyze the device usage history to identify the time period when the user least uses the device and suggest settings to perform the update during that time period. Furthermore, the selection unit can analyze the user's device usage history and suggest the most efficient option. For example, the selection unit can suggest settings that allow the user to perform the update most efficiently based on the device usage history. This allows the selection unit to suggest the optimal option based on the user's device usage history.

[0039] The implementation unit can adjust the download and installation order based on the importance of the update. For example, the implementation unit downloads and installs important security updates with the highest priority. For example, the implementation unit evaluates the importance of the update and downloads and installs security patches with the highest priority. The implementation unit can also download and install less important updates outside of business hours. For example, the implementation unit downloads and installs feature-add updates outside of business hours. Furthermore, the implementation unit can dynamically change the download and installation order based on the importance of the update. For example, the implementation unit evaluates the importance of the update in real time and downloads and installs them in the optimal order. This allows the implementation unit to optimize the download and installation order based on the importance of the update.

[0040] The implementation unit can apply different installation methods depending on the type of update. For example, the implementation unit installs security updates immediately. For example, the implementation unit installs security patches immediately to ensure system security. The implementation unit can also install feature addition updates outside of business hours. For example, the implementation unit installs feature addition updates outside of business hours to minimize the impact on business operations. Furthermore, the implementation unit can install bug fix updates according to the user's work schedule. For example, the implementation unit references the user's calendar information and installs the bug fix update at the optimal timing. This allows the implementation unit to apply an appropriate installation method depending on the type of update.

[0041] The implementation unit can select an appropriate download timing by taking into account the user's network status. For example, if the user is connected to a high-speed network, the implementation unit immediately starts the download. For example, the implementation unit measures the network bandwidth and starts the download when a high-speed connection is detected. Furthermore, if the user is connected to a slow network, the implementation unit can postpone the download until the network stabilizes. For example, the implementation unit monitors the network connection status in real time and postpones the download when a slow connection is detected. Furthermore, the implementation unit can monitor the user's network status in real time and select the optimal download timing. For example, the implementation unit continuously monitors the network connection status and starts the download at the optimal timing. This allows the implementation unit to select the optimal download timing based on the user's network status.

[0042] The implementation unit can adjust the timing of installation taking into account the battery status of the user's device. For example, if the user's device is charging, the implementation unit immediately starts installation. For example, the implementation unit detects the charging status of the device and starts installation after confirming that it is charging. Furthermore, if the battery of the user's device is low, the implementation unit can postpone installation until charging is complete. For example, the implementation unit monitors the remaining battery level and postpones installation if the battery level is low. Furthermore, the implementation unit can monitor the battery status of the user's device in real time and select the optimal installation timing. For example, the implementation unit continuously monitors the battery charging status and starts installation at the optimal timing. This allows the implementation unit to select the optimal installation timing based on the battery status of the user's device.

[0043] The detection unit can adjust the anomaly detection algorithm by referring to past abnormal data. The detection unit, for example, adjusts the anomaly detection threshold based on the past abnormal data. For example, the detection unit analyzes past error logs and optimizes the anomaly detection threshold. The detection unit can also analyze past abnormal data and optimize the anomaly detection pattern. For example, the detection unit adjusts the anomaly detection algorithm based on past system crash data. Furthermore, the detection unit can also improve the accuracy of anomaly detection by referring to past abnormal data. For example, the detection unit adjusts the parameters of the anomaly detection algorithm based on the past abnormal data to improve accuracy. This allows the detection unit to optimize the anomaly detection algorithm based on the past abnormal data.

[0044] The detection unit can apply different detection methods depending on the type of anomaly. For example, in the case of a security anomaly, the detection unit immediately detects and notifies the user. For example, the detection unit monitors security logs in real time and immediately notifies the user if an anomaly is detected. In addition, in the case of a system anomaly, the detection unit can also acquire and analyze detailed logs. For example, the detection unit analyzes system logs in detail and identifies the cause of the anomaly. Furthermore, in the case of an application anomaly, the detection unit can also apply an appropriate detection method depending on the type of anomaly. For example, the detection unit analyzes application logs and applies a detection method depending on the type of anomaly. This allows the detection unit to apply an appropriate detection method depending on the type of anomaly.

[0045] The detection unit can adjust the frequency of anomaly detection taking into account the user's device usage status. For example, the detection unit increases the frequency of anomaly detection when the user uses the device frequently. For example, the detection unit monitors the device usage status in real time and increases the frequency of anomaly detection when the device is used frequently. The detection unit can also decrease the frequency of anomaly detection when the user does not use the device very often. For example, the detection unit monitors the device usage status and decreases the frequency of anomaly detection when the device is not used very often. Furthermore, the detection unit can monitor the user's device usage status in real time and dynamically adjust the frequency of anomaly detection. For example, the detection unit continuously monitors the device usage status and performs anomaly detection at an optimal frequency. This allows the detection unit to adjust the frequency of anomaly detection based on the user's device usage status.

[0046] The detection unit can adjust the timing of anomaly detection taking into account the user's network status. For example, if the user is connected to a high-speed network, the detection unit immediately detects anomalies. For example, the detection unit measures the network bandwidth and detects anomalies when a high-speed connection is detected. Furthermore, if the user is connected to a slow network, the detection unit can postpone anomaly detection until the network stabilizes. For example, the detection unit monitors the network connection status in real time and postpones anomaly detection when a slow connection is detected. Furthermore, the detection unit can monitor the user's network status in real time and select the optimal timing for anomaly detection. For example, the detection unit continuously monitors the network connection status and detects anomalies at the optimal timing. This allows the detection unit to optimize the timing of anomaly detection based on the user's network status.

[0047] The debugging unit can optimize the debugging algorithm by referring to past debugging data. The debugging unit, for example, adjusts the debugging threshold based on the past debugging data. For example, the debugging unit analyzes past error logs and optimizes the debugging threshold. The debugging unit can also analyze past debugging data and optimize the debugging pattern. For example, the debugging unit adjusts the debugging algorithm based on past correction history. Furthermore, the debugging unit can also improve the accuracy of debugging by referring to past debugging data. For example, the debugging unit adjusts parameters of the debugging algorithm based on past debugging data to improve accuracy. This allows the debugging unit to optimize the debugging algorithm based on past debugging data.

[0048] The debugging unit can apply different debugging techniques depending on the type of anomaly. For example, in the case of a security anomaly, the debugging unit immediately performs debugging. For example, the debugging unit monitors security logs in real time and immediately performs debugging when an anomaly is detected. The debugging unit can also acquire and analyze detailed logs in the case of a system anomaly. For example, the debugging unit can analyze system logs in detail, identify the cause of the anomaly, and apply an appropriate debugging technique. Furthermore, in the case of an application anomaly, the debugging unit can also apply an appropriate debugging technique depending on the type of anomaly. For example, the debugging unit analyzes application logs and applies a debugging technique depending on the type of anomaly. This allows the debugging unit to apply an appropriate debugging technique depending on the type of anomaly.

[0049] The debugging unit can adjust the timing of debugging taking into account the user's device usage. For example, if the user uses the device frequently, the debugging unit adjusts the timing of debugging to outside of business hours. For example, the debugging unit monitors the device usage in real time, and adjusts the timing of debugging to outside of business hours if the device is being used frequently. The debugging unit can also perform debugging immediately if the user does not use the device very often. For example, the debugging unit monitors the device usage and performs debugging immediately if the device is not being used very often. Furthermore, the debugging unit can monitor the user's device usage in real time and select the optimal timing for debugging. For example, the debugging unit continuously monitors the device usage and performs debugging at the optimal timing. This allows the debugging unit to adjust the timing of debugging based on the user's device usage.

[0050] The debugging unit can optimize the method of notifying the user of the debugging progress by taking into account the user's network status. For example, the debugging unit notifies the user of detailed debugging progress when the user is connected to a high-speed network. For example, the debugging unit measures the network bandwidth and notifies the user of detailed debugging progress when a high-speed connection is detected. The debugging unit can also notify the user of brief debugging progress when the user is connected to a slow network. For example, the debugging unit monitors the network connection status in real time and notifies the user of brief debugging progress when a slow connection is detected. Furthermore, the debugging unit can monitor the user's network status in real time and select an optimal method of notifying the user of the debugging progress. For example, the debugging unit continuously monitors the network connection status and notifies the user of the debugging progress at an optimal timing. In this way, the debugging unit can provide an appropriate method of notifying the user of the debugging progress based on the user's network status.

[0051] The restoration unit can adjust the restoration algorithm by referring to past restoration data. The restoration unit, for example, adjusts the restoration threshold based on the past restoration data. For example, the restoration unit analyzes past restoration history and optimizes the restoration threshold. The restoration unit can also analyze past restoration data and optimize the restoration pattern. For example, the restoration unit adjusts the restoration algorithm based on past error logs. Furthermore, the restoration unit can improve the accuracy of restoration by referring to the past restoration data. For example, the restoration unit adjusts parameters of the restoration algorithm based on the past restoration data to improve accuracy. This allows the restoration unit to optimize the restoration algorithm based on the past restoration data.

[0052] The restoration unit can apply different restoration methods depending on the type of anomaly. For example, in the case of a security anomaly, the restoration unit immediately performs restoration. For example, the restoration unit monitors security logs in real time and immediately performs restoration when an anomaly is detected. In addition, in the case of a system anomaly, the restoration unit can also acquire and analyze detailed logs. For example, the restoration unit can analyze system logs in detail, identify the cause of the anomaly, and apply an appropriate restoration method. Furthermore, in the case of an application anomaly, the restoration unit can also apply an appropriate restoration method depending on the type of anomaly. For example, the restoration unit analyzes application logs and applies a restoration method depending on the type of anomaly. This allows the restoration unit to apply an appropriate restoration method depending on the type of anomaly.

[0053] The restoration unit can adjust the timing of restoration taking into account the user's device usage status. For example, if the user uses the device frequently, the restoration unit adjusts the timing of restoration to outside of business hours. For example, the restoration unit monitors the device usage status in real time, and adjusts the timing of restoration to outside of business hours if the device is used frequently. The restoration unit can also immediately perform restoration if the user does not use the device very often. For example, the restoration unit monitors the device usage status and immediately performs restoration if the device is not used very often. Furthermore, the restoration unit can monitor the user's device usage status in real time and select the optimal timing of restoration. For example, the restoration unit continuously monitors the device usage status and performs restoration at the optimal timing. This allows the restoration unit to adjust the timing of restoration based on the user's device usage status.

[0054] The restoration unit can optimize the method of notifying the user of the restoration progress by taking into account the user's network status. For example, the restoration unit notifies the user of detailed restoration progress when the user is connected to a high-speed network. For example, the restoration unit measures the network bandwidth and notifies the user of detailed restoration progress when a high-speed connection is detected. The restoration unit can also notify the user of brief restoration progress when the user is connected to a slow network. For example, the restoration unit monitors the network connection status in real time and notifies the user of brief restoration progress when a slow connection is detected. Furthermore, the restoration unit can monitor the user's network status in real time and select an optimal method of notifying the user of the restoration progress. For example, the restoration unit continuously monitors the network connection status and notifies the user of the restoration progress at an optimal timing. In this way, the restoration unit can provide an appropriate method of notifying the user of the restoration progress based on the user's network status.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The selection unit can analyze not only the user's past selection history but also the selection history of other users to propose optimal options. For example, the selection unit proposes the most common settings based on the selection history of other users in the same work environment. The selection unit can also present options according to the user's work. For example, if the user works in sales, the selection unit proposes settings that avoid updates while the user is out. Furthermore, the selection unit can analyze the user's device usage patterns to propose optimal options. For example, if the user often does not use the device at night, the selection unit proposes settings that perform updates at night. In this way, the selection unit can propose optimal options by utilizing the user's past selection history and data from other users.

[0057] The implementation unit can select the optimal timing for downloading and installing updates, taking into account the performance of the user's device. For example, the implementation unit can start downloading when the device's CPU usage is low. The implementation unit can also monitor the device's memory usage and install when there is sufficient memory available. Furthermore, the implementation unit can monitor the temperature of the user's device and perform the update when the temperature is low. This allows the implementation unit to perform the update while optimizing the device's performance.

[0058] When detecting an anomaly, the detection unit can adjust the frequency of anomaly detection taking into account the user's device usage. For example, the detection unit can increase the frequency of anomaly detection when the user uses the device frequently. Alternatively, the detection unit can decrease the frequency of anomaly detection when the user uses the device infrequently. Furthermore, the detection unit can monitor the user's device usage in real time and dynamically adjust the frequency of anomaly detection. This allows the detection unit to optimize the frequency of anomaly detection based on the user's device usage.

[0059] When debugging an abnormality, the debugging unit can adjust the timing of debugging by taking into account the resource status of the user's device. For example, the debugging unit can start debugging when the device's CPU usage is low. The debugging unit can also monitor the device's memory usage and perform debugging when there is sufficient memory available. Furthermore, the debugging unit can monitor the temperature of the user's device and perform debugging when the temperature is low. This allows the debugging unit to perform debugging while optimizing the device's resources.

[0060] When performing a system restore, the restore unit can adjust the timing of the restore taking into account the storage status of the user's device. For example, the restore unit can start the restore when the storage usage of the device is low. The restore unit can also monitor the storage usage of the device and perform the restore when there is sufficient free storage. Furthermore, the restore unit can monitor the storage status of the user's device in real time and perform the restore at the optimal timing. This allows the restore unit to optimize the timing of the restore based on the storage status of the device.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The selection unit has a function that allows the user to select in advance whether or not to automatically perform updates. For example, a screen for selecting whether or not to automatically perform updates can be displayed when the system starts up, or the user can select from a settings menu. It can also suggest the optimal option based on past selection history. Step 2: The implementation unit has the ability to automatically download and install updates when they are released. For example, it can automatically start downloading only when connected to Wi-Fi, or download in the background. It can also optimize the download and installation order based on the importance of the update. Step 3: The detection unit is equipped with a function that allows the system to detect anomalies after the update. For example, it performs log analysis and real-time monitoring to detect anomalies. It can also optimize the anomaly detection algorithm by referencing past anomaly data. Step 4: The debugging unit has the function to automatically debug detected anomalies. For example, it executes scripts and analyzes error logs to debug the anomalies. It can also optimize the debugging algorithm by referencing past debugging data. Step 5: The restoration unit has the function of restoring the system to its original state based on the results of the debugging. For example, it performs a system restore or restores configuration files. It can also optimize the restoration algorithm by referencing past restoration data.

[0063] (Example 2) A system according to an embodiment of the present invention provides a function for selecting whether to automatically perform updates in advance on departmental PCs in operation, and automatically performs updates when OS or security updates are distributed. If an abnormality occurs after an update, the system automatically performs debugging and restores the system to its original state as necessary. This allows users to focus on their work without worrying about OS or security updates. This mechanism is already implemented in certain systems and can be expanded to include departmental PCs. First, the user selects whether to automatically perform updates in advance. Next, when an update is distributed, the system automatically downloads and installs the update. If the system detects an abnormality after the update, the system automatically performs debugging and restores the system to its original state as necessary. This allows users to focus on their work without worrying about OS or security updates.

[0064] A system according to an embodiment includes a selection unit, an implementation unit, a detection unit, a debugging unit, and a restoration unit. The selection unit has a function for a user to select whether to automatically perform updates in advance. For example, the selection unit displays a screen for selecting whether to automatically perform updates when the system starts up. The selection unit can also select whether to automatically perform updates from a settings menu. The selection unit can also suggest optimal options based on the user's past selection history. For example, the selection unit automatically displays update settings that the user has frequently selected in the past as candidates. The implementation unit has a function for automatically downloading and installing updates when they are distributed. For example, the implementation unit can automatically start downloading only when connected to Wi-Fi. The implementation unit can also perform background downloads. The implementation unit can also optimize the order of downloads and installations based on the importance of the updates. For example, the implementation unit downloads and installs important security updates with the highest priority. The detection unit has a function for detecting system anomalies after updates. For example, the detection unit can perform log analysis to detect anomalies. The detection unit can also perform real-time monitoring to detect anomalies. Furthermore, the detection unit can optimize the anomaly detection algorithm by referring to past anomaly data. For example, the detection unit adjusts the anomaly detection threshold based on the past anomaly data. The debugging unit has a function for automatically debugging detected anomalies. For example, the debugging unit executes a script to debug the anomaly. The debugging unit can also analyze an error log to debug the anomaly. Furthermore, the debugging unit can optimize the debugging algorithm by referring to past debugging data. For example, the debugging unit adjusts the debugging threshold based on past debugging data. The restoration unit has a function for restoring the system to its original state based on the debugging results. For example, the restoration unit performs a system restore to restore the system to its original state. The restoration unit can also restore a configuration file to restore the system to its original state. Furthermore, the restoration unit can optimize the restoration algorithm by referring to past restoration data.For example, the restoration unit adjusts the restoration threshold based on past restoration data. This allows the system according to the embodiment to perform automatic update, anomaly detection, debugging, and restoration in a single sequence.

[0065] The selection unit has a function for the user to select whether or not to automatically perform updates in advance. The selection unit, for example, displays a screen for selecting whether or not to automatically perform updates when the system starts up. For example, the selection unit displays a pop-up window when the system starts up, allowing the user to select whether or not to automatically perform updates. The selection unit can also select whether or not to automatically perform updates from a settings menu. For example, the selection unit may provide an "Update Settings" option in the settings menu, allowing the user to manually change the settings. Furthermore, the selection unit can suggest optimal options based on the user's past selection history. For example, the selection unit may automatically display update settings that the user has frequently selected in the past as candidates. This allows the user to select whether or not to automatically perform updates in advance.

[0066] The implementation unit has a function of automatically downloading and installing updates when they are distributed. For example, the implementation unit automatically starts downloading only when a Wi-Fi connection is established. For example, the implementation unit detects a Wi-Fi connection and starts downloading an update file. The implementation unit can also perform background downloads. For example, the implementation unit downloads update files in the background while the user is performing other tasks. Furthermore, the implementation unit can optimize the order of downloads and installations based on the importance of the updates. For example, the implementation unit downloads and installs important security updates with the highest priority. This allows the implementation unit to automatically download and install updates when they are distributed.

[0067] The detection unit has a function to detect abnormalities in the system after an update. The detection unit, for example, performs log analysis to detect abnormalities. For example, the detection unit analyzes system logs to detect error messages and abnormal behavior. The detection unit can also perform real-time monitoring to detect abnormalities. For example, the detection unit monitors system operation in real time to detect abnormal behavior. Furthermore, the detection unit can also optimize the abnormality detection algorithm by referring to past abnormal data. For example, the detection unit adjusts the abnormality detection threshold based on past abnormal data. This allows the detection unit to detect abnormalities in the system after an update.

[0068] The debugging unit has a function to automatically debug detected abnormalities. The debugging unit, for example, executes a script to debug the abnormality. For example, the debugging unit executes a debug script prepared in advance to correct the abnormality. The debugging unit can also analyze an error log to debug the abnormality. For example, the debugging unit analyzes the error log to identify and correct the cause of the abnormality. Furthermore, the debugging unit can also optimize the debugging algorithm by referring to past debugging data. For example, the debugging unit adjusts the debugging threshold based on past debugging data. This allows the debugging unit to automatically debug detected abnormalities.

[0069] The restoration unit has a function of restoring the system to its original state based on the results of debugging. The restoration unit, for example, performs a system restore to restore the system to its original state. For example, the restoration unit uses a system restore point to restore the system to its original state. The restoration unit can also restore a configuration file to restore the system to its original state. For example, the restoration unit uses a backed-up configuration file to restore the system settings to their original state. Furthermore, the restoration unit can optimize the restoration algorithm by referring to past restoration data. For example, the restoration unit adjusts the restoration threshold based on the past restoration data. This allows the restoration unit to restore the system to its original state based on the results of debugging.

[0070] The selection unit can estimate the user's emotions using a machine learning algorithm and present options for automatically performing updates based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit presents simple options and minimizes the selection procedure. For example, the selection unit captures the user's facial expressions with a camera and detects stress using an emotion estimation algorithm. Furthermore, if the user is relaxed, the selection unit can present detailed options and suggest a customizable selection method. For example, the selection unit records the user's voice and detects the relaxed state using voice analysis technology. Furthermore, if the user is in a hurry, the selection unit can prioritize voice input and quickly present options. For example, the selection unit measures the user's heart rate with a sensor and detects the state of hurry. This allows the selection unit to present appropriate options based on the user's emotions.

[0071] The selection unit can analyze past update history and automatically suggest appropriate options. For example, the selection unit automatically displays update settings that the user has frequently selected in the past as candidates. For example, the selection unit stores past update history in a database and preferentially displays frequently selected settings. The selection unit can also suggest optimal options based on the update history that the user has previously selected. For example, the selection unit analyzes past update history and suggests settings with a high success rate. Furthermore, the selection unit can predict and suggest optimal options for specific time periods based on the user's past update history. For example, the selection unit identifies the time period during which the user performed the most updates based on the past update history and suggests the optimal option for that time period. This allows the selection unit to suggest optimal options based on the past update history.

[0072] The selection unit can dynamically change whether to automatically perform updates based on the user's work schedule. For example, the selection unit references the user's calendar information and proposes a setting to automatically perform updates outside of work hours. For example, the selection unit obtains data from a calendar app and automatically proposes a setting to perform updates outside of work hours. The selection unit can also propose a setting to refrain from updating before important meetings or presentations based on the user's work schedule. For example, the selection unit analyzes calendar information and proposes a setting to postpone updates before important events. Furthermore, the selection unit can analyze the user's work schedule and dynamically change the optimal update timing. For example, the selection unit monitors the work schedule in real time and performs updates at the optimal timing. This allows the selection unit to dynamically change the implementation of updates based on the user's work schedule.

[0073] The selection unit can estimate the user's emotions using a machine learning algorithm and adjust the display order of options based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit displays the simplest options first. For example, the selection unit captures the user's facial expression with a camera, detects stress using an emotion estimation algorithm, and prioritizes displaying simple options. The selection unit can also display detailed options first if the user is relaxed. For example, the selection unit records the user's voice, detects the user's relaxed state using voice analysis technology, and displays detailed options. Furthermore, if the user is in a hurry, the selection unit can display options that can be selected quickly first. For example, the selection unit measures the user's heart rate with a sensor, detects the user's state of hurry, and displays options that can be selected quickly. This allows the selection unit to adjust the display order of options based on the user's emotions.

[0074] The selection unit can present appropriate options taking into account the user's geographical location information. For example, if the user is in the office, the selection unit presents an option to not perform updates during business hours. For example, the selection unit acquires GPS data to detect that the user is in the office and proposes a setting to not perform updates during business hours. The selection unit can also present an option to perform updates outside of business hours if the user is at home. For example, the selection unit estimates the user's location from an IP address, detects that the user is at home, and proposes a setting to perform updates outside of business hours. Furthermore, the selection unit can present an option to postpone updates if the user is on a business trip. For example, the selection unit combines the user's calendar information and location information to detect that the user is on a business trip and proposes a setting to postpone updates. This allows the selection unit to present optimal options based on the user's geographical location information.

[0075] The selection unit can analyze the user's device usage history and suggest the optimal option. The selection unit can suggest the optimal option based on, for example, device settings that the user has frequently used in the past. For example, the selection unit can store the device usage history in a database and preferentially display frequently used settings. The selection unit can also suggest the optimal update timing based on the user's device usage history. For example, the selection unit can analyze the device usage history to identify the time period when the user least uses the device and suggest settings to perform the update during that time period. Furthermore, the selection unit can analyze the user's device usage history and suggest the most efficient option. For example, the selection unit can suggest settings that allow the user to perform the update most efficiently based on the device usage history. This allows the selection unit to suggest the optimal option based on the user's device usage history.

[0076] The implementation unit can estimate the user's emotions using a machine learning algorithm and adjust the timing of update implementation based on the estimated user emotions. For example, the implementation unit immediately implements an update if the user is relaxed. For example, the implementation unit captures the user's facial expression with a camera, detects the relaxed state using an emotion estimation algorithm, and immediately implements an update. The implementation unit can also implement an update outside of business hours if the user is feeling stressed. For example, the implementation unit can record the user's voice, detect the stress state using voice analysis technology, and implement an update outside of business hours. Furthermore, the implementation unit can postpone the update if the user is in a hurry. For example, the implementation unit can measure the user's heart rate with a sensor, detect the user's state of hurry, and postpone the update. This allows the implementation unit to adjust the timing of update implementation based on the user's emotions.

[0077] The implementation unit can adjust the download and installation order based on the importance of the update. For example, the implementation unit downloads and installs important security updates with the highest priority. For example, the implementation unit evaluates the importance of the update and downloads and installs security patches with the highest priority. The implementation unit can also download and install less important updates outside of business hours. For example, the implementation unit downloads and installs feature-add updates outside of business hours. Furthermore, the implementation unit can dynamically change the download and installation order based on the importance of the update. For example, the implementation unit evaluates the importance of the update in real time and downloads and installs them in the optimal order. This allows the implementation unit to optimize the download and installation order based on the importance of the update.

[0078] The implementation unit can apply different installation methods depending on the type of update. For example, the implementation unit installs security updates immediately. For example, the implementation unit installs security patches immediately to ensure system security. The implementation unit can also install feature addition updates outside of business hours. For example, the implementation unit installs feature addition updates outside of business hours to minimize the impact on business operations. Furthermore, the implementation unit can install bug fix updates according to the user's work schedule. For example, the implementation unit references the user's calendar information and installs the bug fix update at the optimal timing. This allows the implementation unit to apply an appropriate installation method depending on the type of update.

[0079] The implementation unit can estimate a user's emotions using a machine learning algorithm and adjust a method for notifying the user of the update progress based on the estimated user emotions. For example, if the user is nervous, the implementation unit provides a simple, highly visible notification method. For example, the implementation unit captures the user's facial expression with a camera, detects the nervous state using an emotion estimation algorithm, and displays a simple notification. The implementation unit can also provide a detailed notification method if the user is relaxed. For example, the implementation unit records the user's voice, detects the relaxed state using voice analysis technology, and displays a detailed notification. Furthermore, the implementation unit can provide a notification method that focuses on the main points if the user is in a hurry. For example, the implementation unit measures the user's heart rate with a sensor, detects the user's state of hurry, and displays a notification that focuses on the main points. This allows the implementation unit to provide an appropriate notification method based on the user's emotions.

[0080] The implementation unit can select an appropriate download timing by taking into account the user's network status. For example, if the user is connected to a high-speed network, the implementation unit immediately starts the download. For example, the implementation unit measures the network bandwidth and starts the download when a high-speed connection is detected. Furthermore, if the user is connected to a slow network, the implementation unit can postpone the download until the network stabilizes. For example, the implementation unit monitors the network connection status in real time and postpones the download when a slow connection is detected. Furthermore, the implementation unit can monitor the user's network status in real time and select the optimal download timing. For example, the implementation unit continuously monitors the network connection status and starts the download at the optimal timing. This allows the implementation unit to select the optimal download timing based on the user's network status.

[0081] The implementation unit can adjust the timing of installation taking into account the battery status of the user's device. For example, if the user's device is charging, the implementation unit immediately starts installation. For example, the implementation unit detects the charging status of the device and starts installation after confirming that it is charging. Furthermore, if the battery of the user's device is low, the implementation unit can postpone installation until charging is complete. For example, the implementation unit monitors the remaining battery level and postpones installation if the battery level is low. Furthermore, the implementation unit can monitor the battery status of the user's device in real time and select the optimal installation timing. For example, the implementation unit continuously monitors the battery charging status and starts installation at the optimal timing. This allows the implementation unit to select the optimal installation timing based on the battery status of the user's device.

[0082] The detection unit can estimate the user's emotions using a machine learning algorithm and adjust the notification method for anomaly detection based on the estimated user emotions. For example, if the user is nervous, the detection unit provides a simple and highly visible notification method. For example, the detection unit captures the user's facial expression with a camera, detects the nervous state using an emotion estimation algorithm, and displays a simple notification. The detection unit can also provide a detailed notification method if the user is relaxed. For example, the detection unit records the user's voice, detects the relaxed state using voice analysis technology, and displays a detailed notification. Furthermore, the detection unit can provide a notification method that focuses on the main points if the user is in a hurry. For example, the detection unit measures the user's heart rate with a sensor, detects the user's state of hurry, and displays a notification that focuses on the main points. This allows the detection unit to provide an appropriate notification method for anomaly detection based on the user's emotions.

[0083] The detection unit can adjust the anomaly detection algorithm by referring to past abnormal data. The detection unit, for example, adjusts the anomaly detection threshold based on the past abnormal data. For example, the detection unit analyzes past error logs and optimizes the anomaly detection threshold. The detection unit can also analyze past abnormal data and optimize the anomaly detection pattern. For example, the detection unit adjusts the anomaly detection algorithm based on past system crash data. Furthermore, the detection unit can also improve the accuracy of anomaly detection by referring to past abnormal data. For example, the detection unit adjusts the parameters of the anomaly detection algorithm based on the past abnormal data to improve accuracy. This allows the detection unit to optimize the anomaly detection algorithm based on the past abnormal data.

[0084] The detection unit can apply different detection methods depending on the type of anomaly. For example, in the case of a security anomaly, the detection unit immediately detects and notifies the user. For example, the detection unit monitors security logs in real time and immediately notifies the user if an anomaly is detected. In addition, in the case of a system anomaly, the detection unit can also acquire and analyze detailed logs. For example, the detection unit analyzes system logs in detail and identifies the cause of the anomaly. Furthermore, in the case of an application anomaly, the detection unit can also apply an appropriate detection method depending on the type of anomaly. For example, the detection unit analyzes application logs and applies a detection method depending on the type of anomaly. This allows the detection unit to apply an appropriate detection method depending on the type of anomaly.

[0085] The detection unit can estimate the user's emotions using a machine learning algorithm and determine the priority of anomaly detection based on the estimated user emotions. For example, if the user is nervous, the detection unit detects and notifies the user of important anomalies with the highest priority. For example, the detection unit captures the user's facial expressions with a camera, detects the user's nervous state using an emotion estimation algorithm, and notifies the user of important anomalies with the highest priority. The detection unit can also detect and notify detailed anomalies when the user is relaxed. For example, the detection unit can record the user's voice, detect the user's relaxed state using voice analysis technology, and notify the user of detailed anomalies. Furthermore, the detection unit can detect and notify the user of key anomalies with the highest priority when the user is in a hurry. For example, the detection unit can measure the user's heart rate with a sensor, detect the user's state of hurry, and notify the user of key anomalies. This allows the detection unit to determine the priority of anomaly detection based on the user's emotions.

[0086] The detection unit can adjust the frequency of anomaly detection taking into account the user's device usage status. For example, the detection unit increases the frequency of anomaly detection when the user uses the device frequently. For example, the detection unit monitors the device usage status in real time and increases the frequency of anomaly detection when the device is used frequently. The detection unit can also decrease the frequency of anomaly detection when the user does not use the device very often. For example, the detection unit monitors the device usage status and decreases the frequency of anomaly detection when the device is not used very often. Furthermore, the detection unit can monitor the user's device usage status in real time and dynamically adjust the frequency of anomaly detection. For example, the detection unit continuously monitors the device usage status and performs anomaly detection at an optimal frequency. This allows the detection unit to adjust the frequency of anomaly detection based on the user's device usage status.

[0087] The detection unit can adjust the timing of anomaly detection taking into account the user's network status. For example, if the user is connected to a high-speed network, the detection unit immediately detects anomalies. For example, the detection unit measures the network bandwidth and detects anomalies when a high-speed connection is detected. Furthermore, if the user is connected to a slow network, the detection unit can postpone anomaly detection until the network stabilizes. For example, the detection unit monitors the network connection status in real time and postpones anomaly detection when a slow connection is detected. Furthermore, the detection unit can monitor the user's network status in real time and select the optimal timing for anomaly detection. For example, the detection unit continuously monitors the network connection status and detects anomalies at the optimal timing. This allows the detection unit to optimize the timing of anomaly detection based on the user's network status.

[0088] The debugging unit can estimate the user's emotions using a machine learning algorithm and adjust the method of notifying the user of the progress of debugging based on the estimated user emotions. For example, if the user is nervous, the debugging unit provides a simple and highly visible notification method. For example, the debugging unit captures the user's facial expressions with a camera, detects the nervous state using an emotion estimation algorithm, and displays a simple notification. The debugging unit can also provide a detailed notification method if the user is relaxed. For example, the debugging unit records the user's voice, detects the relaxed state using voice analysis technology, and displays a detailed notification. Furthermore, the debugging unit can provide a more concise notification method if the user is in a hurry. For example, the debugging unit measures the user's heart rate with a sensor, detects the user's state of hurry, and displays a more concise notification. This allows the debugging unit to provide an appropriate notification method of the progress of debugging based on the user's emotions.

[0089] The debugging unit can optimize the debugging algorithm by referring to past debugging data. The debugging unit, for example, adjusts the debugging threshold based on the past debugging data. For example, the debugging unit analyzes past error logs and optimizes the debugging threshold. The debugging unit can also analyze past debugging data and optimize the debugging pattern. For example, the debugging unit adjusts the debugging algorithm based on past correction history. Furthermore, the debugging unit can also improve the accuracy of debugging by referring to past debugging data. For example, the debugging unit adjusts parameters of the debugging algorithm based on past debugging data to improve accuracy. This allows the debugging unit to optimize the debugging algorithm based on past debugging data.

[0090] The debugging unit can apply different debugging techniques depending on the type of anomaly. For example, in the case of a security anomaly, the debugging unit immediately performs debugging. For example, the debugging unit monitors security logs in real time and immediately performs debugging when an anomaly is detected. The debugging unit can also acquire and analyze detailed logs in the case of a system anomaly. For example, the debugging unit can analyze system logs in detail, identify the cause of the anomaly, and apply an appropriate debugging technique. Furthermore, in the case of an application anomaly, the debugging unit can also apply an appropriate debugging technique depending on the type of anomaly. For example, the debugging unit analyzes application logs and applies a debugging technique depending on the type of anomaly. This allows the debugging unit to apply an appropriate debugging technique depending on the type of anomaly.

[0091] The debugging unit can estimate the user's emotions using a machine learning algorithm and determine the priority of debugging based on the estimated user emotions. For example, if the user is nervous, the debugging unit performs important debugging with the highest priority. For example, the debugging unit captures the user's facial expressions with a camera, detects the nervous state using an emotion estimation algorithm, and performs important debugging with the highest priority. The debugging unit can also perform detailed debugging if the user is relaxed. For example, the debugging unit can record the user's voice, detect the relaxed state using voice analysis technology, and perform detailed debugging. Furthermore, if the user is in a hurry, the debugging unit can also perform debugging that focuses on the main points with the highest priority. For example, the debugging unit can measure the user's heart rate with a sensor, detect the user's state of hurry, and perform debugging that focuses on the main points. This allows the debugging unit to determine the priority of debugging based on the user's emotions.

[0092] The debugging unit can adjust the timing of debugging taking into account the user's device usage. For example, if the user uses the device frequently, the debugging unit adjusts the timing of debugging to outside of business hours. For example, the debugging unit monitors the device usage in real time, and adjusts the timing of debugging to outside of business hours if the device is being used frequently. The debugging unit can also perform debugging immediately if the user does not use the device very often. For example, the debugging unit monitors the device usage and performs debugging immediately if the device is not being used very often. Furthermore, the debugging unit can monitor the user's device usage in real time and select the optimal timing for debugging. For example, the debugging unit continuously monitors the device usage and performs debugging at the optimal timing. This allows the debugging unit to adjust the timing of debugging based on the user's device usage.

[0093] The debugging unit can optimize the method of notifying the user of the debugging progress by taking into account the user's network status. For example, the debugging unit notifies the user of detailed debugging progress when the user is connected to a high-speed network. For example, the debugging unit measures the network bandwidth and notifies the user of detailed debugging progress when a high-speed connection is detected. The debugging unit can also notify the user of brief debugging progress when the user is connected to a slow network. For example, the debugging unit monitors the network connection status in real time and notifies the user of brief debugging progress when a slow connection is detected. Furthermore, the debugging unit can monitor the user's network status in real time and select an optimal method of notifying the user of the debugging progress. For example, the debugging unit continuously monitors the network connection status and notifies the user of the debugging progress at an optimal timing. In this way, the debugging unit can provide an appropriate method of notifying the user of the debugging progress based on the user's network status.

[0094] The restoration unit can estimate the user's emotions using a machine learning algorithm and adjust the method of notifying the restoration progress based on the estimated user emotions. For example, if the user is nervous, the restoration unit provides a simple and highly visible notification method. For example, the restoration unit captures the user's facial expression with a camera, detects the nervous state using an emotion estimation algorithm, and displays a simple notification. The restoration unit can also provide a detailed notification method if the user is relaxed. For example, the restoration unit records the user's voice, detects the relaxed state using voice analysis technology, and displays a detailed notification. Furthermore, the restoration unit can provide a notification method that focuses on the main points if the user is in a hurry. For example, the restoration unit measures the user's heart rate with a sensor, detects the user's state of hurry, and displays a notification that focuses on the main points. This allows the restoration unit to provide an appropriate notification method of the restoration progress based on the user's emotions.

[0095] The restoration unit can adjust the restoration algorithm by referring to past restoration data. The restoration unit, for example, adjusts the restoration threshold based on the past restoration data. For example, the restoration unit analyzes past restoration history and optimizes the restoration threshold. The restoration unit can also analyze past restoration data and optimize the restoration pattern. For example, the restoration unit adjusts the restoration algorithm based on past error logs. Furthermore, the restoration unit can improve the accuracy of restoration by referring to the past restoration data. For example, the restoration unit adjusts parameters of the restoration algorithm based on the past restoration data to improve accuracy. This allows the restoration unit to optimize the restoration algorithm based on the past restoration data.

[0096] The restoration unit can apply different restoration methods depending on the type of anomaly. For example, in the case of a security anomaly, the restoration unit immediately performs restoration. For example, the restoration unit monitors security logs in real time and immediately performs restoration when an anomaly is detected. In addition, in the case of a system anomaly, the restoration unit can also acquire and analyze detailed logs. For example, the restoration unit can analyze system logs in detail, identify the cause of the anomaly, and apply an appropriate restoration method. Furthermore, in the case of an application anomaly, the restoration unit can also apply an appropriate restoration method depending on the type of anomaly. For example, the restoration unit analyzes application logs and applies a restoration method depending on the type of anomaly. This allows the restoration unit to apply an appropriate restoration method depending on the type of anomaly.

[0097] The restoration unit can estimate the user's emotions using a machine learning algorithm and determine restoration priorities based on the estimated user emotions. For example, if the user is nervous, the restoration unit performs important restorations with the highest priority. For example, the restoration unit captures the user's facial expressions with a camera, detects the nervous state using an emotion estimation algorithm, and performs important restorations with the highest priority. The restoration unit can also perform detailed restorations when the user is relaxed. For example, the restoration unit records the user's voice, detects the relaxed state using voice analysis technology, and performs detailed restorations. Furthermore, if the user is in a hurry, the restoration unit can also perform restorations that emphasize the main points with the highest priority. For example, the restoration unit measures the user's heart rate with a sensor, detects the user's state of hurry, and performs restorations that emphasize the main points. This allows the restoration unit to determine restoration priorities based on the user's emotions.

[0098] The restoration unit can adjust the timing of restoration taking into account the user's device usage status. For example, if the user uses the device frequently, the restoration unit adjusts the timing of restoration to outside of business hours. For example, the restoration unit monitors the device usage status in real time, and adjusts the timing of restoration to outside of business hours if the device is used frequently. The restoration unit can also immediately perform restoration if the user does not use the device very often. For example, the restoration unit monitors the device usage status and immediately performs restoration if the device is not used very often. Furthermore, the restoration unit can monitor the user's device usage status in real time and select the optimal timing of restoration. For example, the restoration unit continuously monitors the device usage status and performs restoration at the optimal timing. This allows the restoration unit to adjust the timing of restoration based on the user's device usage status.

[0099] The restoration unit can optimize the method of notifying the user of the restoration progress by taking into account the user's network status. For example, the restoration unit notifies the user of detailed restoration progress when the user is connected to a high-speed network. For example, the restoration unit measures the network bandwidth and notifies the user of detailed restoration progress when a high-speed connection is detected. The restoration unit can also notify the user of brief restoration progress when the user is connected to a slow network. For example, the restoration unit monitors the network connection status in real time and notifies the user of brief restoration progress when a slow connection is detected. Furthermore, the restoration unit can monitor the user's network status in real time and select an optimal method of notifying the user of the restoration progress. For example, the restoration unit continuously monitors the network connection status and notifies the user of the restoration progress at an optimal timing. In this way, the restoration unit can provide an appropriate method of notifying the user of the restoration progress based on the user's network status. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, implementation unit, detection unit, debugging unit, and restoration unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14 and provides a function for the user to select whether or not to automatically perform an update in advance. The implementation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically downloads and installs an update when it is distributed. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects an abnormality in the system after an update. The debugging unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically debugs the detected abnormality. The restoration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and restores the original state based on the debugging results. === Hard Collateral 1-2 === Each of the multiple elements, including the selection unit, implementation unit, detection unit, debugging unit, and restoration 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 selection unit is realized by the control unit 46A of the smart glasses 214 and provides a function for the user to select in advance whether or not to automatically perform an update. The implementation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically downloads and installs an update when it is distributed. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects an abnormality in the system after an update. The debugging unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically debugs the detected abnormality. The restoration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and restores the original state based on the debugging results. === Hard Collateral 1-3 === Each of the multiple elements including the selection unit, implementation unit, detection unit, debugging unit, and restoration unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314 and provides a function for the user to select in advance whether or not to automatically perform an update. The implementation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically downloads and installs an update when it is distributed. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects an abnormality in the system after an update. The debugging unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically debugs the detected abnormality. The restoration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and restores the original state based on the debugging results. === Hard Collateral 1-4 === Each of the multiple elements, including the selection unit, implementation unit, detection unit, debugging unit, and restoration unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and provides a function that allows the user to select in advance whether or not to automatically perform an update. The implementation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically downloads and installs an update when it is distributed. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects an abnormality in the system after an update. The debugging unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically debugs the detected abnormality. The restoration unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and restores the original state based on the debugging 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 selection unit can analyze not only the user's past selection history but also the selection history of other users to propose optimal options. For example, the selection unit proposes the most common settings based on the selection history of other users in the same work environment. The selection unit can also present options according to the user's work. For example, if the user works in sales, the selection unit proposes settings that avoid updates while the user is out. Furthermore, the selection unit can analyze the user's device usage patterns to propose optimal options. For example, if the user often does not use the device at night, the selection unit proposes settings that perform updates at night. In this way, the selection unit can propose optimal options by utilizing the user's past selection history and data from other users.

[0102] The implementation unit can select the optimal timing for downloading and installing updates, taking into account the performance of the user's device. For example, the implementation unit can start downloading when the device's CPU usage is low. The implementation unit can also monitor the device's memory usage and install when there is sufficient memory available. Furthermore, the implementation unit can monitor the temperature of the user's device and perform the update when the temperature is low. This allows the implementation unit to perform the update while optimizing the device's performance.

[0103] When detecting an anomaly, the detection unit can adjust the frequency of anomaly detection taking into account the user's device usage. For example, the detection unit can increase the frequency of anomaly detection when the user uses the device frequently. Alternatively, the detection unit can decrease the frequency of anomaly detection when the user uses the device infrequently. Furthermore, the detection unit can monitor the user's device usage in real time and dynamically adjust the frequency of anomaly detection. This allows the detection unit to optimize the frequency of anomaly detection based on the user's device usage.

[0104] When debugging an abnormality, the debugging unit can adjust the timing of debugging by taking into account the resource status of the user's device. For example, the debugging unit can start debugging when the device's CPU usage is low. The debugging unit can also monitor the device's memory usage and perform debugging when there is sufficient memory available. Furthermore, the debugging unit can monitor the temperature of the user's device and perform debugging when the temperature is low. This allows the debugging unit to perform debugging while optimizing the device's resources.

[0105] When performing a system restore, the restore unit can adjust the timing of the restore taking into account the storage status of the user's device. For example, the restore unit can start the restore when the storage usage of the device is low. The restore unit can also monitor the storage usage of the device and perform the restore when there is sufficient free storage. Furthermore, the restore unit can monitor the storage status of the user's device in real time and perform the restore at the optimal timing. This allows the restore unit to optimize the timing of the restore based on the storage status of the device.

[0106] The selection unit can estimate the user's emotions and customize update options based on the estimated emotions. For example, if the user is feeling stressed, the selection unit can present simple options and minimize selection steps. If the user is relaxed, the selection unit can present detailed options and suggest a customizable selection method. Furthermore, if the user is in a hurry, the selection unit can prioritize voice input and quickly present options. This allows the selection unit to present appropriate options based on the user's emotions.

[0107] The implementation unit can estimate the user's emotions and adjust the timing of update implementation based on the estimated emotions. For example, if the user is relaxed, the implementation unit can immediately implement the update. Also, if the user is feeling stressed, the implementation unit can implement the update outside of business hours. Furthermore, if the user is in a hurry, the implementation unit can postpone the update. In this way, the implementation unit can adjust the timing of update implementation based on the user's emotions.

[0108] The detection unit can estimate the user's emotion and adjust the notification method for anomaly detection based on the estimated emotion. For example, if the user is nervous, the detection unit can provide a simple, highly visible notification method. If the user is relaxed, the detection unit can also provide a detailed notification method. Furthermore, if the user is in a hurry, the detection unit can also provide a notification method that focuses on the main points. In this way, the detection unit can provide an appropriate notification method for anomaly detection based on the user's emotion.

[0109] The debugging unit can estimate the user's emotions and adjust the method of notifying the progress of debugging based on the estimated emotions. For example, if the user is nervous, the debugging unit can provide a simple and highly visible notification method. If the user is relaxed, the debugging unit can also provide a detailed notification method. Furthermore, if the user is in a hurry, the debugging unit can also provide a notification method that focuses on the main points. In this way, the debugging unit can provide an appropriate method of notifying the progress of debugging based on the user's emotions.

[0110] The restoration unit can estimate the user's emotion and adjust the method of notifying the restoration progress status based on the estimated emotion. For example, if the user is nervous, the restoration unit can provide a simple and highly visible notification method. If the user is relaxed, the restoration unit can also provide a detailed notification method. Furthermore, if the user is in a hurry, the restoration unit can also provide a notification method that focuses on the main points. In this way, the restoration unit can provide an appropriate notification method of the restoration progress status based on the user's emotion.

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

[0112] Step 1: The selection unit has a function that allows the user to select in advance whether or not to automatically perform updates. For example, a screen for selecting whether or not to automatically perform updates can be displayed when the system starts up, or the user can select from a settings menu. It can also suggest the optimal option based on past selection history. Step 2: The implementation unit has the ability to automatically download and install updates when they are released. For example, it can automatically start downloading only when connected to Wi-Fi, or download in the background. It can also optimize the download and installation order based on the importance of the update. Step 3: The detection unit is equipped with a function that allows the system to detect anomalies after the update. For example, it performs log analysis and real-time monitoring to detect anomalies. It can also optimize the anomaly detection algorithm by referencing past anomaly data. Step 4: The debugging unit has the function to automatically debug detected anomalies. For example, it executes scripts and analyzes error logs to debug the anomalies. It can also optimize the debugging algorithm by referencing past debugging data. Step 5: The restoration unit has the function of restoring the system to its original state based on the results of the debugging. For example, it performs a system restore or restores configuration files. It can also optimize the restoration algorithm by referencing past restoration data.

[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 the 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 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 type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[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, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[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 selection section for selecting whether or not to automatically perform updates; an implementation unit that automatically implements an update based on the setting selected by the selection unit; a detection unit that detects an abnormality after the update performed by the implementation unit; a debugging unit that automatically debugs the abnormality detected by the detection unit; a restoration unit that restores the initial setting based on the result of debugging by the debugging unit. A system characterized by:

2. The selection unit The user has the ability to choose whether or not to automatically update in advance.

2. The system of claim 1.

3. The implementation unit: Automatically downloads and installs updates when they are released 2. The system of claim 1.

4. The detection unit The system has the ability to detect abnormalities after updates 2. The system of claim 1.

5. The debugging unit Equipped with a function to automatically debug detected abnormalities 2. The system of claim 1.

6. The restoration unit is Ability to restore to original state based on debugging results 2. The system of claim 1.

7. The selection unit The system uses a machine learning algorithm to estimate user sentiment and presents options for automatic update implementation based on the estimated user sentiment.

2. The system of claim 1.

8. The selection unit Analyzes past update history and automatically suggests appropriate options 2. The system of claim 1.

Images