system

Abstract

The system according to the embodiment aims to create an automatic processing tool for improving the efficiency of operational tasks, even if the user does not have programming knowledge. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and an execution unit. The collection unit collects operation log data. The analysis unit analyzes the operation log data collected by the collection unit and identifies patterned tasks. The generation unit generates a program for automatic processing from the log data of the tasks identified by the analysis unit. The execution unit executes the program generated by the generation 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, it was difficult to create automated processing tools to improve the efficiency of operational tasks, and there was a problem that they were difficult to execute, especially for staff without programming knowledge.

[0005] The system according to the embodiment aims to create an automatic processing tool for improving the efficiency of operational tasks, even if the user does not have programming knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an execution unit. The collection unit collects operation log data. The analysis unit analyzes the operation log data collected by the collection unit and identifies patterned tasks. The generation unit generates a program for automatic processing from the log data of the tasks identified by the analysis unit. The execution unit executes the program generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment makes it possible to create an automatic processing tool for improving the efficiency of operational tasks, even if the user does not have programming knowledge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automatic processing tool creation system according to an embodiment of the present invention creates an automatic processing tool based on operation log data, thereby improving the efficiency of operational tasks. The automatic processing tool creation system collects and analyzes operation log data, identifies patterned tasks, generates programs corresponding to the identified tasks, and executes the generated programs to create an automatic processing tool. For example, the automatic processing tool creation system collects operation log data, such as PC operation history and application usage. Next, the automatic processing tool creation system analyzes the collected operation log data to identify patterned tasks, such as routine tasks performed at the same time every day or operations repeated under specific conditions. Next, the automatic processing tool creation system generates programs corresponding to the identified tasks. For example, a program that automates file copying and movement, or a program that automates application startup and shutdown, is generated. Finally, the automatic processing tool creation system executes the generated program to create an automatic processing tool. This allows for the efficiency of operational tasks to be improved even without programming knowledge. The automatic processing tool creation system thus improves the efficiency of operational tasks and reduces the burden on personnel. For example, even when routine tasks are detailed and involve a large volume of work, creating an automatic processing tool based on operation log data can efficiently carry out the tasks.

[0029] The automatic processing tool creation system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an execution unit. The collection unit collects operation log data. The operation log data includes, for example, a PC operation history and application usage status, but is not limited to these examples. The collection unit collects, for example, a PC operation history in real time. The collection unit can also collect operation log data periodically. The collection unit can also collect operation log data when a specific event occurs. For example, the collection unit records keyboard input and mouse operations in real time. In periodic collection, operation log data is collected at regular intervals. When a specific event occurs, operation log data is collected when an application is launched or closed. The analysis unit analyzes the operation log data collected by the collection unit and identifies patterned tasks. Patterned tasks include, for example, routine tasks performed at the same time every day and operations repeated under specific conditions, but are not limited to these examples. For example, the analysis unit analyzes the operation log data using data mining technology. The analysis unit can also identify patterned tasks using a machine learning algorithm. The analysis unit can also identify patterned operations using statistical analysis. For example, the analysis unit uses data mining technology to extract frequently performed operations from operation log data. A machine learning algorithm learns from the operation log data and identifies patterned operations. Statistical analysis analyzes the distribution of the operation log data and identifies patterned operations. The generation unit generates a program corresponding to the operations identified by the analysis unit. Examples of the generated program include, but are not limited to, a program that automates copying or moving files, or a program that automates launching and closing applications. For example, the generation unit generates the program using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit can generate, for example, a program that automates copying or moving files. The generation unit can also generate a program that automates launching and closing applications. The generation unit can also generate a program that automates operations under specific conditions.For example, the generation unit generates a program that automates copying and moving files. The generation AI generates a program that automates copying and moving files based on operation log data. The generation unit generates a program that automates launching and closing an application. The generation AI generates a program that automates launching and closing an application based on operation log data. The generation unit generates a program that automates operations under specific conditions. The generation AI generates a program that automates operations under specific conditions based on operation log data. The execution unit executes the program generated by the generation unit. The execution unit executes the generated program, for example, based on a schedule. The execution unit can also execute the generated program in response to a user instruction. The execution unit can also execute the generated program when a specific event occurs. For example, the execution unit executes the generated program based on a schedule. The generated program can also be executed manually in response to a user instruction. When a specific event occurs, the generated program is executed when an application is launched or closed. As a result, the automatic processing tool creation system according to the embodiment can create an automatic processing tool based on operation log data, thereby improving the efficiency of operational tasks. For example, the generation unit generates a program using the generation AI. The generation AI generates a program that automates copying and moving files based on the operation log data. The generation unit generates a program that automates launching and closing an application. The generation AI generates a program that automates launching and closing an application based on the operation log data. The generation unit generates a program that automates operations under specific conditions. The generation AI generates a program that automates operations under specific conditions based on the operation log data. As a result, the automatic processing tool creation system according to the embodiment can create an automatic processing tool based on the operation log data, thereby improving the efficiency of operational tasks.

[0030] The collection unit can collect operation log data of a PC operation history or application usage status. The PC operation history includes, but is not limited to, keyboard input, mouse clicks, window operations, etc. For example, the collection unit records keyboard input in real time. The collection unit can also record mouse clicks in real time. The collection unit can also record window operations in real time. For example, the collection unit records keyboard input in real time and saves it as operation log data. The collection unit records mouse clicks in real time and saves it as operation log data. The collection unit records window operations in real time and saves it as operation log data. The application usage status includes, but is not limited to, the start and end of an application, usage time, operation content, etc. For example, the collection unit records the start and end of an application in real time. The collection unit can also record the usage time of an application in real time. The collection unit can also record the operation content of an application in real time. For example, the collection unit records the start and end of an application in real time and saves it as operation log data. The collection unit records the application usage time in real time and saves it as operation log data. The collection unit records the application operation details in real time and saves it as operation log data. This allows detailed operation log data to be obtained by collecting the PC operation history and application usage status. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input keyboard input and mouse click data to the generation AI and have the generation AI collect the operation log data.

[0031] The analysis unit can identify patterned operations, such as routine operations performed at the same time every day or operations that are repeated under specific conditions. Examples of routine operations include, but are not limited to, backup operations and periodic data entry operations that are performed at the same time every day. For example, the analysis unit can identify backup operations that are performed at the same time every day. The analysis unit can also identify periodic data entry operations. Furthermore, the analysis unit can identify routine operations that are performed on the same day of the week every week. For example, the analysis unit can identify backup operations that are performed at the same time every day. The analysis unit can identify periodic data entry operations. The analysis unit can identify routine operations that are performed on the same day of the week every week. Examples of operations that are repeated under specific conditions include, but are not limited to, recovery operations when a specific error occurs and response operations when a specific event occurs. For example, the analysis unit can identify recovery operations when a specific error occurs. The analysis unit can also identify response operations when a specific event occurs. Furthermore, the analysis unit can identify routine operations under specific conditions. For example, the analysis unit can identify recovery operations when a specific error occurs. The analysis unit can also identify response operations when a specific event occurs. The analysis unit identifies periodic operations under specific conditions. This allows for the identification of routine tasks or operations that are repeated under specific conditions, enabling efficient task automation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input operation log data into a generation AI and have the generation AI identify patterned tasks.

[0032] The generation unit can generate a program corresponding to the identified task. Examples of programs corresponding to the identified task include, but are not limited to, programs that automate the copying and moving of files and programs that automate the launching and closing of applications. The generation unit can also generate a program that automates the launching and closing of applications. The generation unit can also generate a program that automates operations under specific conditions. For example, the generation unit generates a program that automates the copying and moving of files. The generation unit generates a program that automates the launching and closing of applications. The generation unit generates a program that automates operations under specific conditions. This facilitates the creation of an automated processing tool by generating a program corresponding to the identified task. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input operation log data into the generation AI and cause the generation AI to generate a program corresponding to the identified task.

[0033] The generating unit can generate a program that automates the copying and moving of files, and a program that automates the launching and closing of applications. Examples of programs that automate the copying and moving of files include, but are not limited to, moving files between specific folders and copying files for backup purposes. For example, the generating unit can generate a program that automates moving files between specific folders. The generating unit can also generate a program that automates copying files for backup purposes. Furthermore, the generating unit can generate a program that automates file operations under specific conditions. For example, the generating unit generates a program that automates moving files between specific folders. The generating unit generates a program that automates copying files for backup purposes. The generating unit can also generate a program that automates file operations under specific conditions. Examples of programs that automate the launching and closing of applications include, but are not limited to, programs that launch and close applications at specific times and programs that launch and close applications when specific events occur. For example, the generating unit generates a program that launches and closes an application at specific times. The generating unit can also generate a program that launches and closes an application when a specific event occurs. Furthermore, the generation unit can generate a program that automates application operations under specific conditions. For example, the generation unit generates a program that starts and stops an application at a specific time. The generation unit generates a program that starts and stops an application when a specific event occurs. The generation unit generates a program that automates application operations under specific conditions. This automates file copying and moving and application startup and shutdown, thereby improving work efficiency. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input operation log data into the generation AI and cause the generation AI to generate a program that automates file copying and movement.

[0034] The execution unit can execute the generated program to create an automatic processing tool. Examples of automatic processing tools include, but are not limited to, scripts for automating specific tasks and macros for sequentially executing multiple operations. The execution unit, for example, executes the generated script. The execution unit can also execute the generated macro. The execution unit can also execute a tool that automates operations under specific conditions. For example, the execution unit executes the generated script to create an automatic processing tool. The execution unit executes the generated macro to create an automatic processing tool. The execution unit executes a tool that automates operations under specific conditions to create an automatic processing tool. By executing the generated program, the automatic processing tool is created and operational efficiency is improved. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the generated program to a generation AI and have the generation AI execute the program.

[0035] The collection unit can analyze the user's past operation history and select an appropriate collection method. For example, the collection unit prioritizes collection of operations frequently performed by the user in the past. The collection unit can also predict operations to be performed during a specific time period from the user's past operation history and concentrate collection during that time period. The collection unit can also analyze the user's operation history and adjust the collection method based on the usage of a specific application. For example, the collection unit prioritizes collection of operations frequently performed by the user in the past. The collection unit predicts operations to be performed during a specific time period from the user's past operation history and concentrates collection during that time period. The collection unit analyzes the user's operation history and adjusts the collection method based on the usage of a specific application. This enables efficient data collection by analyzing the user's past operation history and selecting the optimal collection method. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal collection method.

[0036] When collecting operation log data, the collection unit can filter the operation log data based on the user's current work status and areas of interest. For example, the collection unit collects only operation log data related to the work the user is currently performing. The collection unit can also prioritize collecting related operation log data based on the user's areas of interest. Furthermore, the collection unit can monitor the user's current work status in real time and filter the data to be collected as needed. For example, the collection unit collects only operation log data related to the work the user is currently performing. The collection unit prioritizes collecting related operation log data based on the user's areas of interest. The collection unit monitors the user's current work status in real time and filters the data to be collected as needed. In this way, by filtering the data based on the user's current work status and areas of interest, highly relevant data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current work status data to a generation AI and have the generation AI perform filtering.

[0037] When collecting operation log data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. Furthermore, if the user is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can also prioritize collecting image data. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. If the user is using text input, the collection unit prioritizes collecting text data. If the user is using image input, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting operation log data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting operation log data at that location. The collection unit can also prioritize collecting related operation log data based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can also prioritize collecting operation log data at a destination. For example, when the user is in a specific location, the collection unit prioritizes collecting operation log data at that location. The collection unit prioritizes collecting related operation log data based on the user's geographical location information. When the user is moving, the collection unit prioritizes collecting operation log data at a destination. This allows highly relevant data to be collected efficiently by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0039] When collecting operation log data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit prioritizes collecting operation log data performed by the user on social media. The collection unit can also collect related operation log data based on the user's social media activities. Furthermore, the collection unit can analyze the user's social media posts and collect related operation log data. For example, the collection unit prioritizes collecting operation log data performed by the user on social media. The collection unit collects related operation log data based on the user's social media activities. The collection unit analyzes the user's social media posts and collects related operation log data. In this way, by analyzing the user's social media activities, highly relevant data can be efficiently collected. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0040] When collecting operation log data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and propose an optimal collection method. The collection unit can also adjust the type and amount of operation log data to be collected by reflecting the user's feedback. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit analyzes the user's past feedback and proposes an optimal collection method. The collection unit adjusts the type and amount of operation log data to be collected by reflecting the user's feedback. This enables the optimal collection method to be selected by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data to the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the operation. For example, the analysis unit performs a detailed analysis on important operations. The analysis unit can also perform a simplified analysis on less important operations. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the operation. For example, the analysis unit performs a detailed analysis on important operations. The analysis unit performs a simplified analysis on less important operations. The analysis unit adjusts the depth and scope of the analysis according to the importance of the operation. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the operation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of operation. For example, the analysis unit applies an analysis algorithm dedicated to file operations to file operations. The analysis unit can also apply an analysis algorithm dedicated to application operations to application launches and terminations. The analysis unit can also apply an analysis algorithm dedicated to network operations to network operations. For example, the analysis unit applies an analysis algorithm dedicated to file operations to file operations. The analysis unit applies an analysis algorithm dedicated to application operations to application launches and terminations. The analysis unit applies an analysis algorithm dedicated to network operations to network operations. This enables highly accurate analysis by applying different analysis algorithms depending on the category of operation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and cause the generation AI to apply different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and propose an optimal analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit analyzes the user's past analysis results and proposes an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the operations were performed. For example, the analysis unit prioritizes analysis of recently performed operations. The analysis unit can also prioritize analysis of operations performed in a specific time period. Furthermore, the analysis unit can adjust the analysis priority based on the time when the operations were performed. For example, the analysis unit prioritizes analysis of recently performed operations. The analysis unit prioritizes analysis of operations performed in a specific time period. The analysis unit adjusts the analysis priority based on the time when the operations were performed. This enables efficient analysis by determining the analysis priority based on the time when the operations were performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the operations. For example, the analysis unit prioritizes analysis of highly relevant operations. The analysis unit can also adjust the order of analysis based on the relevance of the operations. Furthermore, the analysis unit can postpone analysis of less relevant operations. For example, the analysis unit prioritizes analysis of highly relevant operations. The analysis unit adjusts the order of analysis based on the relevance of the operations. The analysis unit postpones analysis of less relevant operations. In this way, by adjusting the order of analysis based on the relevance of the operations, highly relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and cause the generation AI to adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. If the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit adjusts the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0047] The generation unit can adjust the level of detail of the program to be generated based on the importance of the operation during generation. For example, the generation unit generates a detailed program for an important operation. The generation unit can also generate a simplified program for an operation with low importance. Furthermore, the generation unit can adjust the level of detail of the program according to the importance of the operation. For example, the generation unit generates a detailed program for an important operation. The generation unit generates a simplified program for an operation with low importance. The generation unit adjusts the level of detail of the program according to the importance of the operation. This enables efficient program generation by adjusting the level of detail of the program according to the importance of the operation. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to adjust the level of detail of the program.

[0048] The generation unit can apply different generation algorithms depending on the category of operation during generation. For example, the generation unit applies a generation algorithm dedicated to file operations to file operations. The generation unit can also apply a generation algorithm dedicated to application operations to launching and closing an application. The generation unit can also apply a generation algorithm dedicated to network operations to network operations. For example, the generation unit applies a generation algorithm dedicated to file operations to file operations. The generation unit applies a generation algorithm dedicated to application operations to launching and closing an application. The generation unit applies a generation algorithm dedicated to network operations to network operations. This enables highly accurate program generation by applying different generation algorithms depending on the category of operation. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to apply different generation algorithms.

[0049] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and propose an optimal generation method. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit improves the accuracy of generation by referring to the user's past generation results. The generation unit analyzes the user's past generation results and proposes an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0050] At the time of generation, the generation unit can determine the priority of programs to be generated based on the execution time of operations. For example, the generation unit generates programs with priority for recently executed operations. The generation unit can also generate programs with priority for operations executed during a specific time period. Furthermore, the generation unit can adjust the generation order of programs based on the execution time of operations. For example, the generation unit generates programs with priority for recently executed operations. The generation unit generates programs with priority for operations executed during a specific time period. The generation unit adjusts the generation order of programs based on the execution time of operations. This enables efficient program generation by determining the priority of programs based on the execution time of operations. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to determine the priority of programs.

[0051] The generation unit can adjust the order of programs to be generated based on the relevance of operations during generation. For example, the generation unit generates programs with priority for highly relevant operations. The generation unit can also adjust the order of program generation based on the relevance of operations. Furthermore, the generation unit can postpone generating programs for operations with low relevance. For example, the generation unit generates programs with priority for highly relevant operations. The generation unit adjusts the order of program generation based on the relevance of operations. The generation unit postpones generating programs for operations with low relevance. In this way, by adjusting the order of programs to be generated based on the relevance of operations, highly relevant data can be generated with priority. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to adjust the order of the programs.

[0052] During generation, the generation unit can adjust the use of technical terminology in the program to be generated according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a program that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can also generate a program in simple language. Furthermore, the generation unit can adjust the way the program is expressed according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a program that uses a lot of technical terminology. If the user does not have technical expertise, the generation unit generates a program in simple language. The generation unit adjusts the way the program is expressed according to the user's level of expertise. This allows the use of technical terminology in the program to be adjusted according to the user's level of expertise, thereby generating a program that is easy for the user to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0053] At the time of execution, the execution unit can select the optimal execution method by referring to the user's past execution history. The execution unit, for example, selects the optimal execution method based on methods previously executed by the user. The execution unit can also suggest an efficient execution method from the user's past execution history. The execution unit can also analyze the user's past execution history and select the optimal execution procedure. For example, the execution unit selects the optimal execution method based on methods previously executed by the user. The execution unit suggests an efficient execution method from the user's past execution history. The execution unit analyzes the user's past execution history and selects the optimal execution procedure. In this way, by referring to the user's past execution history, the optimal execution method can be selected and efficient execution is possible. Some or all of the above-described processing in the execution unit may be performed, for example, using AI or without AI. For example, the execution unit can input the user's past execution history data to a generation AI and have the generation AI select the optimal execution method.

[0054] The execution unit can customize the execution means based on the user's current work situation during execution. The execution unit, for example, customizes the execution means to match the work the user is currently performing. The execution unit can also monitor the user's current work situation in real time and propose optimal execution means. The execution unit can also adjust the execution means based on the user's current work situation. For example, the execution unit customizes the execution means to match the work the user is currently performing. The execution unit monitors the user's current work situation in real time and proposes optimal execution means. The execution unit adjusts the execution means based on the user's current work situation. This enables efficient execution by customizing the execution means based on the user's current work situation. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's current work situation data to a generation AI and have the generation AI customize the execution means.

[0055] The execution unit can improve the execution method by reflecting user feedback during execution. The execution unit improves the execution method, for example, based on feedback provided by the user. The execution unit can also analyze the user feedback and propose an optimal execution method. The execution unit can also adjust the execution procedure by reflecting the user feedback. For example, the execution unit improves the execution method based on feedback provided by the user. The execution unit analyzes the user feedback and proposes an optimal execution method. The execution unit adjusts the execution procedure by reflecting the user feedback. In this way, the execution method is improved by reflecting the user feedback, enabling efficient execution. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or without AI. For example, the execution unit can input user feedback data into a generation AI and have the generation AI improve the execution method.

[0056] During execution, the execution unit can select the optimal execution method by taking into account the user's geographical location information. For example, when the user is in a specific location, the execution unit selects an execution method appropriate for that location. The execution unit can also suggest the optimal execution method based on the user's geographical location information. Furthermore, when the user is on the move, the execution unit can select an execution method appropriate for the destination. For example, when the user is in a specific location, the execution unit selects an execution method appropriate for that location. The execution unit suggests the optimal execution method based on the user's geographical location information. When the user is on the move, the execution unit selects an execution method appropriate for the destination. This enables the optimal execution method to be selected by taking into account the user's geographical location information, enabling efficient execution. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's geographical location information data into a generation AI and have the generation AI select the optimal execution method.

[0057] During execution, the execution unit can analyze the user's social media activity and suggest an execution means. The execution unit can, for example, suggest an optimal execution means based on operations performed by the user on social media. The execution unit can also analyze the user's social media activity and suggest related execution means. Furthermore, the execution unit can suggest an optimal execution means based on the content of the user's posts on social media. For example, the execution unit can suggest an optimal execution means based on operations performed by the user on social media. The execution unit can analyze the user's social media activity and suggest related execution means. The execution unit can suggest an optimal execution means based on the content of the user's posts on social media. In this way, by analyzing the user's social media activity, related execution means can be efficiently suggested. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of execution means.

[0058] The execution unit can customize the execution method by reflecting the user's past feedback during execution. The execution unit customizes the execution method based on, for example, feedback provided by the user in the past. The execution unit can also analyze the user's past feedback and propose an optimal execution method. The execution unit can also adjust the execution procedure by reflecting the user's feedback. For example, the execution unit customizes the execution method based on feedback provided by the user in the past. The execution unit analyzes the user's past feedback and proposes an optimal execution method. The execution unit adjusts the execution procedure by reflecting the user's feedback. In this way, by reflecting the user's past feedback, an optimal execution method can be selected and efficient execution is possible. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's past feedback data into a generation AI and have the generation AI customize the execution method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can analyze the user's past operation history and select an appropriate collection method. For example, it can prioritize collection of operations that the user frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and concentrate collection during that time period. Furthermore, the collection unit can analyze the user's operation history and adjust the collection method based on the usage status of a specific application. This allows the analysis of the user's past operation history to select the optimal collection method and enable efficient data collection. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal collection method.

[0061] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the operation. For example, a detailed analysis is performed for an important operation. The analysis unit can also perform a simplified analysis for an operation with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the operation. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the operation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0062] When collecting operation log data, the collection unit can filter the data based on the user's current work status and areas of interest. For example, the collection unit can collect only operation log data related to the work the user is currently performing. The collection unit can also prioritize the collection of related operation log data based on the user's areas of interest. Furthermore, the collection unit can monitor the user's current work status in real time and filter the data to be collected as needed. This allows for efficient collection of highly relevant data by filtering data based on the user's current work status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current work status data to a generation AI and have the generation AI perform filtering.

[0063] During generation, the generation unit can apply different generation algorithms depending on the category of operation. For example, for file operations, the generation unit can apply a generation algorithm dedicated to file operations. Furthermore, the generation unit can also apply a generation algorithm dedicated to application operations for launching or closing an application. Furthermore, the generation unit can apply a generation algorithm dedicated to network operations for network operations. In this way, by applying different generation algorithms depending on the category of operation, highly accurate program generation becomes possible. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to apply different generation algorithms.

[0064] At the time of execution, the execution unit can select the optimal execution method by referring to the user's past execution history. For example, the execution unit selects the optimal execution method based on methods the user has executed in the past. The execution unit can also suggest an efficient execution method from the user's past execution history. Furthermore, the execution unit can analyze the user's past execution history and select the optimal execution procedure. This allows the optimal execution method to be selected by referring to the user's past execution history, enabling efficient execution. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's past execution history data into the generation AI and have the generation AI select the optimal execution method.

[0065] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0066] When collecting operation log data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user uses text input, the collection unit can also prioritize collecting text data. Furthermore, if the user uses image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects operation log data. Operation log data includes PC operation history and application usage status. The collection unit can collect PC operation history in real time, and can also collect operation log data periodically. It can also collect operation log data when a specific event occurs. For example, it can record keyboard input and mouse operations in real time, collect operation log data at regular intervals, and collect operation log data when an application is started or closed. Step 2: The analysis unit analyzes the operation log data collected by the collection unit and identifies patterned tasks. Patterned tasks include routine tasks performed at the same time every day and operations that are repeated under specific conditions. The analysis unit analyzes the operation log data using data mining technology, machine learning algorithms, and statistical analysis to extract frequently performed operations and identify patterned tasks. Step 3: The generation unit generates a program corresponding to the tasks identified by the analysis unit. The generated programs include programs that automate file copying and moving, and programs that automate application startup and shutdown. The generation unit uses a generation AI to generate a program that automates operations under specific conditions based on operation log data. Step 4: The execution unit executes the program generated by the generation unit. The execution unit can execute the generated program based on a schedule, or can execute the generated program at the user's instruction. It can also execute the generated program when a specific event occurs. For example, the execution unit executes the generated program based on a schedule, manually at the user's instruction, or when the application is started or terminated.

[0069] (Example 2) An automatic processing tool creation system according to an embodiment of the present invention creates an automatic processing tool based on operation log data, thereby improving the efficiency of operational tasks. The automatic processing tool creation system collects and analyzes operation log data, identifies patterned tasks, generates programs corresponding to the identified tasks, and executes the generated programs to create an automatic processing tool. For example, the automatic processing tool creation system collects operation log data, such as PC operation history and application usage. Next, the automatic processing tool creation system analyzes the collected operation log data to identify patterned tasks, such as routine tasks performed at the same time every day or operations repeated under specific conditions. Next, the automatic processing tool creation system generates programs corresponding to the identified tasks. For example, a program that automates file copying and movement, or a program that automates application startup and shutdown, is generated. Finally, the automatic processing tool creation system executes the generated program to create an automatic processing tool. This allows for the efficiency of operational tasks to be improved even without programming knowledge. The automatic processing tool creation system thus improves the efficiency of operational tasks and reduces the burden on personnel. For example, even when routine tasks are detailed and involve a large volume of work, creating an automatic processing tool based on operation log data can efficiently carry out the tasks.

[0070] The automatic processing tool creation system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an execution unit. The collection unit collects operation log data. The operation log data includes, for example, a PC operation history and application usage status, but is not limited to these examples. The collection unit collects, for example, a PC operation history in real time. The collection unit can also collect operation log data periodically. The collection unit can also collect operation log data when a specific event occurs. For example, the collection unit records keyboard input and mouse operations in real time. In periodic collection, operation log data is collected at regular intervals. When a specific event occurs, operation log data is collected when an application is launched or closed. The analysis unit analyzes the operation log data collected by the collection unit and identifies patterned tasks. Patterned tasks include, for example, routine tasks performed at the same time every day and operations repeated under specific conditions, but are not limited to these examples. For example, the analysis unit analyzes the operation log data using data mining technology. The analysis unit can also identify patterned tasks using a machine learning algorithm. The analysis unit can also identify patterned operations using statistical analysis. For example, the analysis unit uses data mining technology to extract frequently performed operations from operation log data. A machine learning algorithm learns from the operation log data and identifies patterned operations. Statistical analysis analyzes the distribution of the operation log data and identifies patterned operations. The generation unit generates a program corresponding to the operations identified by the analysis unit. Examples of the generated program include, but are not limited to, a program that automates copying or moving files, or a program that automates launching and closing applications. For example, the generation unit generates the program using a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit can generate, for example, a program that automates copying or moving files. The generation unit can also generate a program that automates launching and closing applications. The generation unit can also generate a program that automates operations under specific conditions.For example, the generation unit generates a program that automates copying and moving files. The generation AI generates a program that automates copying and moving files based on operation log data. The generation unit generates a program that automates launching and closing an application. The generation AI generates a program that automates launching and closing an application based on operation log data. The generation unit generates a program that automates operations under specific conditions. The generation AI generates a program that automates operations under specific conditions based on operation log data. The execution unit executes the program generated by the generation unit. The execution unit executes the generated program, for example, based on a schedule. The execution unit can also execute the generated program in response to a user instruction. The execution unit can also execute the generated program when a specific event occurs. For example, the execution unit executes the generated program based on a schedule. The generated program can also be executed manually in response to a user instruction. When a specific event occurs, the generated program is executed when an application is launched or closed. As a result, the automatic processing tool creation system according to the embodiment can create an automatic processing tool based on operation log data, thereby improving the efficiency of operational tasks. For example, the generation unit generates a program using the generation AI. The generation AI generates a program that automates copying and moving files based on the operation log data. The generation unit generates a program that automates launching and closing an application. The generation AI generates a program that automates launching and closing an application based on the operation log data. The generation unit generates a program that automates operations under specific conditions. The generation AI generates a program that automates operations under specific conditions based on the operation log data. As a result, the automatic processing tool creation system according to the embodiment can create an automatic processing tool based on the operation log data, thereby improving the efficiency of operational tasks.

[0071] The collection unit can collect operation log data of a PC operation history or application usage status. The PC operation history includes, but is not limited to, keyboard input, mouse clicks, window operations, etc. For example, the collection unit records keyboard input in real time. The collection unit can also record mouse clicks in real time. The collection unit can also record window operations in real time. For example, the collection unit records keyboard input in real time and saves it as operation log data. The collection unit records mouse clicks in real time and saves it as operation log data. The collection unit records window operations in real time and saves it as operation log data. The application usage status includes, but is not limited to, the start and end of an application, usage time, operation content, etc. For example, the collection unit records the start and end of an application in real time. The collection unit can also record the usage time of an application in real time. The collection unit can also record the operation content of an application in real time. For example, the collection unit records the start and end of an application in real time and saves it as operation log data. The collection unit records the application usage time in real time and saves it as operation log data. The collection unit records the application operation details in real time and saves it as operation log data. This allows detailed operation log data to be obtained by collecting the PC operation history and application usage status. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input keyboard input and mouse click data to the generation AI and have the generation AI collect the operation log data.

[0072] The analysis unit can identify patterned operations, such as routine operations performed at the same time every day or operations that are repeated under specific conditions. Examples of routine operations include, but are not limited to, backup operations and periodic data entry operations that are performed at the same time every day. For example, the analysis unit can identify backup operations that are performed at the same time every day. The analysis unit can also identify periodic data entry operations. Furthermore, the analysis unit can identify routine operations that are performed on the same day of the week every week. For example, the analysis unit can identify backup operations that are performed at the same time every day. The analysis unit can identify periodic data entry operations. The analysis unit can identify routine operations that are performed on the same day of the week every week. Examples of operations that are repeated under specific conditions include, but are not limited to, recovery operations when a specific error occurs and response operations when a specific event occurs. For example, the analysis unit can identify recovery operations when a specific error occurs. The analysis unit can also identify response operations when a specific event occurs. Furthermore, the analysis unit can identify routine operations under specific conditions. For example, the analysis unit can identify recovery operations when a specific error occurs. The analysis unit can also identify response operations when a specific event occurs. The analysis unit identifies periodic operations under specific conditions. This allows for the identification of routine tasks or operations that are repeated under specific conditions, enabling efficient task automation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input operation log data into a generation AI and have the generation AI identify patterned tasks.

[0073] The generation unit can generate a program corresponding to the identified task. Examples of programs corresponding to the identified task include, but are not limited to, programs that automate the copying and moving of files and programs that automate the launching and closing of applications. The generation unit can also generate a program that automates the launching and closing of applications. The generation unit can also generate a program that automates operations under specific conditions. For example, the generation unit generates a program that automates the copying and moving of files. The generation unit generates a program that automates the launching and closing of applications. The generation unit generates a program that automates operations under specific conditions. This facilitates the creation of an automated processing tool by generating a program corresponding to the identified task. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input operation log data into the generation AI and cause the generation AI to generate a program corresponding to the identified task.

[0074] The generating unit can generate a program that automates the copying and moving of files, and a program that automates the launching and closing of applications. Examples of programs that automate the copying and moving of files include, but are not limited to, moving files between specific folders and copying files for backup purposes. For example, the generating unit can generate a program that automates moving files between specific folders. The generating unit can also generate a program that automates copying files for backup purposes. Furthermore, the generating unit can generate a program that automates file operations under specific conditions. For example, the generating unit generates a program that automates moving files between specific folders. The generating unit generates a program that automates copying files for backup purposes. The generating unit can also generate a program that automates file operations under specific conditions. Examples of programs that automate the launching and closing of applications include, but are not limited to, programs that launch and close applications at specific times and programs that launch and close applications when specific events occur. For example, the generating unit generates a program that launches and closes an application at specific times. The generating unit can also generate a program that launches and closes an application when a specific event occurs. Furthermore, the generation unit can generate a program that automates application operations under specific conditions. For example, the generation unit generates a program that starts and stops an application at a specific time. The generation unit generates a program that starts and stops an application when a specific event occurs. The generation unit generates a program that automates application operations under specific conditions. This automates file copying and moving and application startup and shutdown, thereby improving work efficiency. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input operation log data into the generation AI and cause the generation AI to generate a program that automates file copying and movement.

[0075] The execution unit can execute the generated program to create an automatic processing tool. Examples of automatic processing tools include, but are not limited to, scripts for automating specific tasks and macros for sequentially executing multiple operations. The execution unit, for example, executes the generated script. The execution unit can also execute the generated macro. The execution unit can also execute a tool that automates operations under specific conditions. For example, the execution unit executes the generated script to create an automatic processing tool. The execution unit executes the generated macro to create an automatic processing tool. The execution unit executes a tool that automates operations under specific conditions to create an automatic processing tool. By executing the generated program, the automatic processing tool is created and operational efficiency is improved. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the generated program to a generation AI and have the generation AI execute the program.

[0076] The collection unit can estimate the user's emotions and adjust the timing of collecting operation log data based on the estimated user emotions. For example, when the user is stressed, the collection unit reduces the collection of operation log data, thereby reducing the burden on the user. Furthermore, when the user is relaxed, the collection unit can collect detailed operation log data and acquire more information. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting only important operations and acquire data efficiently. For example, when the user is stressed, the collection unit reduces the collection of operation log data, thereby reducing the burden on the user. When the user is relaxed, the collection unit collects detailed operation log data and acquires more information. When the user is in a hurry, the collection unit prioritizes collecting only important operations and acquires data efficiently. In this way, the timing of collecting operation log data is adjusted according to the user's emotions, thereby reducing the burden on the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0077] The collection unit can analyze the user's past operation history and select an appropriate collection method. For example, the collection unit prioritizes collection of operations frequently performed by the user in the past. The collection unit can also predict operations to be performed during a specific time period from the user's past operation history and concentrate collection during that time period. The collection unit can also analyze the user's operation history and adjust the collection method based on the usage of a specific application. For example, the collection unit prioritizes collection of operations frequently performed by the user in the past. The collection unit predicts operations to be performed during a specific time period from the user's past operation history and concentrates collection during that time period. The collection unit analyzes the user's operation history and adjusts the collection method based on the usage of a specific application. This enables efficient data collection by analyzing the user's past operation history and selecting the optimal collection method. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal collection method.

[0078] When collecting operation log data, the collection unit can filter the operation log data based on the user's current work status and areas of interest. For example, the collection unit collects only operation log data related to the work the user is currently performing. The collection unit can also prioritize collecting related operation log data based on the user's areas of interest. Furthermore, the collection unit can monitor the user's current work status in real time and filter the data to be collected as needed. For example, the collection unit collects only operation log data related to the work the user is currently performing. The collection unit prioritizes collecting related operation log data based on the user's areas of interest. The collection unit monitors the user's current work status in real time and filters the data to be collected as needed. In this way, by filtering the data based on the user's current work status and areas of interest, highly relevant data can be efficiently collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current work status data to a generation AI and have the generation AI perform filtering.

[0079] When collecting operation log data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. Furthermore, if the user is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can also prioritize collecting image data. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. If the user is using text input, the collection unit prioritizes collecting text data. If the user is using image input, the collection unit prioritizes collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and cause the generation AI to select the optimal collection means.

[0080] The collection unit can estimate the user's emotions and determine the priority of operation log data to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting only important operation log data. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting detailed operation log data. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting operation log data that can be collected quickly. For example, when the user is stressed, the collection unit prioritizes collecting only important operation log data. When the user is relaxed, the collection unit prioritizes collecting detailed operation log data. When the user is in a hurry, the collection unit prioritizes collecting operation log data that can be collected quickly. Thus, by determining the priority of operation log data according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0081] When collecting operation log data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting operation log data at that location. The collection unit can also prioritize collecting related operation log data based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can also prioritize collecting operation log data at a destination. For example, when the user is in a specific location, the collection unit prioritizes collecting operation log data at that location. The collection unit prioritizes collecting related operation log data based on the user's geographical location information. When the user is moving, the collection unit prioritizes collecting operation log data at a destination. This allows highly relevant data to be collected efficiently by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0082] When collecting operation log data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit prioritizes collecting operation log data performed by the user on social media. The collection unit can also collect related operation log data based on the user's social media activities. Furthermore, the collection unit can analyze the user's social media posts and collect related operation log data. For example, the collection unit prioritizes collecting operation log data performed by the user on social media. The collection unit collects related operation log data based on the user's social media activities. The collection unit analyzes the user's social media posts and collects related operation log data. In this way, by analyzing the user's social media activities, highly relevant data can be efficiently collected. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data.

[0083] When collecting operation log data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, adjusts the collection method based on feedback provided by the user in the past. The collection unit can also analyze the user's past feedback and propose an optimal collection method. The collection unit can also adjust the type and amount of operation log data to be collected by reflecting the user's feedback. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit analyzes the user's past feedback and proposes an optimal collection method. The collection unit adjusts the type and amount of operation log data to be collected by reflecting the user's feedback. This enables the optimal collection method to be selected by reflecting the user's past feedback, enabling efficient data collection. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data to the generation AI and cause the generation AI to customize the collection method.

[0084] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is in a hurry, the analysis unit provides a concise analysis result that focuses on the main points. This allows the analysis result to be easily understood by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the operation. For example, the analysis unit performs a detailed analysis on important operations. The analysis unit can also perform a simplified analysis on less important operations. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the operation. For example, the analysis unit performs a detailed analysis on important operations. The analysis unit performs a simplified analysis on less important operations. The analysis unit adjusts the depth and scope of the analysis according to the importance of the operation. This enables efficient analysis by adjusting the level of detail of the analysis according to the importance of the operation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0086] During analysis, the analysis unit can apply different analysis algorithms depending on the category of operation. For example, the analysis unit applies an analysis algorithm dedicated to file operations to file operations. The analysis unit can also apply an analysis algorithm dedicated to application operations to application launches and terminations. The analysis unit can also apply an analysis algorithm dedicated to network operations to network operations. For example, the analysis unit applies an analysis algorithm dedicated to file operations to file operations. The analysis unit applies an analysis algorithm dedicated to application operations to application launches and terminations. The analysis unit applies an analysis algorithm dedicated to network operations to network operations. This enables highly accurate analysis by applying different analysis algorithms depending on the category of operation. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and cause the generation AI to apply different analysis algorithms.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and propose an optimal analysis method. For example, the analysis unit adjusts the analysis algorithm based on the user's past analysis results. The analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit analyzes the user's past analysis results and proposes an optimal analysis method. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. If the user is relaxed, the analysis unit provides a detailed analysis result. If the user is excited, the analysis unit provides a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0089] During analysis, the analysis unit can determine the analysis priority based on the time when the operations were performed. For example, the analysis unit prioritizes analysis of recently performed operations. The analysis unit can also prioritize analysis of operations performed in a specific time period. Furthermore, the analysis unit can adjust the analysis priority based on the time when the operations were performed. For example, the analysis unit prioritizes analysis of recently performed operations. The analysis unit prioritizes analysis of operations performed in a specific time period. The analysis unit adjusts the analysis priority based on the time when the operations were performed. This enables efficient analysis by determining the analysis priority based on the time when the operations were performed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and have the generation AI determine the analysis priority.

[0090] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the operations. For example, the analysis unit prioritizes analysis of highly relevant operations. The analysis unit can also adjust the order of analysis based on the relevance of the operations. Furthermore, the analysis unit can postpone analysis of less relevant operations. For example, the analysis unit prioritizes analysis of highly relevant operations. The analysis unit adjusts the order of analysis based on the relevance of the operations. The analysis unit postpones analysis of less relevant operations. In this way, by adjusting the order of analysis based on the relevance of the operations, highly relevant data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and cause the generation AI to adjust the order of analysis.

[0091] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. If the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit adjusts the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0092] The generation unit can estimate the user's emotions and adjust the expression method of the generated program based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a program that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate a program that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a program that adds visually stimulating effects. For example, if the user is relaxed, the generation unit generates a program that progresses at a leisurely pace. If the user is in a hurry, the generation unit generates a program that emphasizes the shortest route. If the user is excited, the generation unit generates a program that adds visually stimulating effects. This allows the generation of a program that is easy for the user to understand by adjusting the expression method of the program according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotional data into the generation AI and have the generation AI adjust the program's expression method.

[0093] The generation unit can adjust the level of detail of the program to be generated based on the importance of the operation during generation. For example, the generation unit generates a detailed program for an important operation. The generation unit can also generate a simplified program for an operation with low importance. Furthermore, the generation unit can adjust the level of detail of the program according to the importance of the operation. For example, the generation unit generates a detailed program for an important operation. The generation unit generates a simplified program for an operation with low importance. The generation unit adjusts the level of detail of the program according to the importance of the operation. This enables efficient program generation by adjusting the level of detail of the program according to the importance of the operation. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to adjust the level of detail of the program.

[0094] The generation unit can apply different generation algorithms depending on the category of operation during generation. For example, the generation unit applies a generation algorithm dedicated to file operations to file operations. The generation unit can also apply a generation algorithm dedicated to application operations to launching and closing an application. The generation unit can also apply a generation algorithm dedicated to network operations to network operations. For example, the generation unit applies a generation algorithm dedicated to file operations to file operations. The generation unit applies a generation algorithm dedicated to application operations to launching and closing an application. The generation unit applies a generation algorithm dedicated to network operations to network operations. This enables highly accurate program generation by applying different generation algorithms depending on the category of operation. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to apply different generation algorithms.

[0095] During generation, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit, for example, adjusts the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and propose an optimal generation method. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit improves the accuracy of generation by referring to the user's past generation results. The generation unit analyzes the user's past generation results and proposes an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0096] The generation unit can estimate the user's emotions and adjust the length of the generated program based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a short, to-the-point program. Furthermore, if the user is relaxed, the generation unit can generate a longer program with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a program with visually stimulating effects. For example, if the user is in a hurry, the generation unit generates a short, to-the-point program. If the user is relaxed, the generation unit generates a longer program with detailed explanations. If the user is excited, the generation unit generates a program with visually stimulating effects. By adjusting the program length according to the user's emotions, it is possible to generate an optimal program for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the program.

[0097] At the time of generation, the generation unit can determine the priority of programs to be generated based on the execution time of operations. For example, the generation unit generates programs with priority for recently executed operations. The generation unit can also generate programs with priority for operations executed during a specific time period. Furthermore, the generation unit can adjust the generation order of programs based on the execution time of operations. For example, the generation unit generates programs with priority for recently executed operations. The generation unit generates programs with priority for operations executed during a specific time period. The generation unit adjusts the generation order of programs based on the execution time of operations. This enables efficient program generation by determining the priority of programs based on the execution time of operations. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to determine the priority of programs.

[0098] The generation unit can adjust the order of programs to be generated based on the relevance of operations during generation. For example, the generation unit generates programs with priority for highly relevant operations. The generation unit can also adjust the order of program generation based on the relevance of operations. Furthermore, the generation unit can postpone generating programs for operations with low relevance. For example, the generation unit generates programs with priority for highly relevant operations. The generation unit adjusts the order of program generation based on the relevance of operations. The generation unit postpones generating programs for operations with low relevance. In this way, by adjusting the order of programs to be generated based on the relevance of operations, highly relevant data can be generated with priority. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to adjust the order of the programs.

[0099] During generation, the generation unit can adjust the use of technical terminology in the program to be generated according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a program that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can also generate a program in simple language. Furthermore, the generation unit can adjust the way the program is expressed according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates a program that uses a lot of technical terminology. If the user does not have technical expertise, the generation unit generates a program in simple language. The generation unit adjusts the way the program is expressed according to the user's level of expertise. This allows the use of technical terminology in the program to be adjusted according to the user's level of expertise, thereby generating a program that is easy for the user to understand. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0100] The execution unit can estimate the user's emotions and adjust the program execution method based on the estimated user's emotions. For example, if the user is relaxed, the execution unit executes the program at a leisurely pace. Furthermore, if the user is in a hurry, the execution unit can also execute the program quickly. Furthermore, if the user is excited, the execution unit can also execute a program with visually stimulating effects. For example, if the user is relaxed, the execution unit executes the program at a leisurely pace. If the user is in a hurry, the execution unit executes the program quickly. If the user is excited, the execution unit executes the program with visually stimulating effects. This allows the program execution method to be adjusted according to the user's emotions, thereby providing an optimal execution method for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input user emotion data into the generation AI and have the generation AI adjust the way the program is executed.

[0101] At the time of execution, the execution unit can select the optimal execution method by referring to the user's past execution history. The execution unit, for example, selects the optimal execution method based on methods previously executed by the user. The execution unit can also suggest an efficient execution method from the user's past execution history. The execution unit can also analyze the user's past execution history and select the optimal execution procedure. For example, the execution unit selects the optimal execution method based on methods previously executed by the user. The execution unit suggests an efficient execution method from the user's past execution history. The execution unit analyzes the user's past execution history and selects the optimal execution procedure. In this way, by referring to the user's past execution history, the optimal execution method can be selected and efficient execution is possible. Some or all of the above-described processing in the execution unit may be performed, for example, using AI or without AI. For example, the execution unit can input the user's past execution history data to a generation AI and have the generation AI select the optimal execution method.

[0102] The execution unit can customize the execution means based on the user's current work situation during execution. The execution unit, for example, customizes the execution means to match the work the user is currently performing. The execution unit can also monitor the user's current work situation in real time and propose optimal execution means. The execution unit can also adjust the execution means based on the user's current work situation. For example, the execution unit customizes the execution means to match the work the user is currently performing. The execution unit monitors the user's current work situation in real time and proposes optimal execution means. The execution unit adjusts the execution means based on the user's current work situation. This enables efficient execution by customizing the execution means based on the user's current work situation. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's current work situation data to a generation AI and have the generation AI customize the execution means.

[0103] The execution unit can improve the execution method by reflecting user feedback during execution. The execution unit improves the execution method, for example, based on feedback provided by the user. The execution unit can also analyze the user feedback and propose an optimal execution method. The execution unit can also adjust the execution procedure by reflecting the user feedback. For example, the execution unit improves the execution method based on feedback provided by the user. The execution unit analyzes the user feedback and proposes an optimal execution method. The execution unit adjusts the execution procedure by reflecting the user feedback. In this way, the execution method is improved by reflecting the user feedback, enabling efficient execution. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or without AI. For example, the execution unit can input user feedback data into a generation AI and have the generation AI improve the execution method.

[0104] The execution unit can estimate the user's emotions and determine the execution priority of programs based on the estimated user's emotions. For example, if the user is in a hurry, the execution unit can prioritize executing important programs. Furthermore, if the user is relaxed, the execution unit can also prioritize executing detailed programs. Furthermore, if the user is excited, the execution unit can also prioritize executing visually stimulating programs. For example, if the user is in a hurry, the execution unit can prioritize executing important programs. If the user is relaxed, the execution unit can prioritize executing detailed programs. If the user is excited, the execution unit can prioritize executing visually stimulating programs. In this way, by determining the program execution priority according to the user's emotions, important programs can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the execution unit can be performed, for example, using AI or without AI. For example, the execution unit can input user emotion data into the generation AI and have the generation AI determine the execution priority of the program.

[0105] During execution, the execution unit can select the optimal execution method by taking into account the user's geographical location information. For example, when the user is in a specific location, the execution unit selects an execution method appropriate for that location. The execution unit can also suggest the optimal execution method based on the user's geographical location information. Furthermore, when the user is on the move, the execution unit can select an execution method appropriate for the destination. For example, when the user is in a specific location, the execution unit selects an execution method appropriate for that location. The execution unit suggests the optimal execution method based on the user's geographical location information. When the user is on the move, the execution unit selects an execution method appropriate for the destination. This enables the optimal execution method to be selected by taking into account the user's geographical location information, enabling efficient execution. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's geographical location information data into a generation AI and have the generation AI select the optimal execution method.

[0106] During execution, the execution unit can analyze the user's social media activity and suggest an execution means. The execution unit can, for example, suggest an optimal execution means based on operations performed by the user on social media. The execution unit can also analyze the user's social media activity and suggest related execution means. Furthermore, the execution unit can suggest an optimal execution means based on the content of the user's posts on social media. For example, the execution unit can suggest an optimal execution means based on operations performed by the user on social media. The execution unit can analyze the user's social media activity and suggest related execution means. The execution unit can suggest an optimal execution means based on the content of the user's posts on social media. In this way, by analyzing the user's social media activity, related execution means can be efficiently suggested. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of execution means.

[0107] The execution unit can customize the execution method by reflecting the user's past feedback during execution. The execution unit customizes the execution method based on, for example, feedback provided by the user in the past. The execution unit can also analyze the user's past feedback and propose an optimal execution method. The execution unit can also adjust the execution procedure by reflecting the user's feedback. For example, the execution unit customizes the execution method based on feedback provided by the user in the past. The execution unit analyzes the user's past feedback and proposes an optimal execution method. The execution unit adjusts the execution procedure by reflecting the user's feedback. In this way, by reflecting the user's past feedback, an optimal execution method can be selected and efficient execution is possible. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's past feedback data into a generation AI and have the generation AI customize the execution method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and execution 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 collection unit is realized by the computer 36 of the smart device 14 and collects operation log data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected operation log data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a program corresponding to the identified task. The execution unit is realized by the control unit 46A of the smart device 14 and executes the generated program. The collection unit can estimate the user's emotion and adjust the timing of collecting operation log data based on the estimated user's emotion. The emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and execution unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and collects operation log data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected operation log data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a program corresponding to the identified task. The execution unit is realized by the control unit 46A of the smart glasses 214 and executes the generated program. The collection unit can estimate the user's emotion and adjust the timing of collecting operation log data based on the estimated user's emotion. The emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and execution 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 collection unit is realized by the computer 36 of the headset type terminal 314 and collects operation log data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected operation log data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a program corresponding to the identified task. The execution unit is realized by the control unit 46A of the headset type terminal 314 and executes the generated program. The collection unit can estimate the user's emotion and adjust the timing of collecting operation log data based on the estimated user's emotion. The emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and execution unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and collects operation log data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected operation log data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a program corresponding to the identified task. The execution unit is realized by the control unit 46A of the robot 414 and executes the generated program. The collection unit can estimate the user's emotion and adjust the timing of collecting operation log data based on the estimated user's emotion. The emotion estimation is realized, for example, by the specific processing unit 290 of the data processing device 12.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is stressed, only important operation log data can be analyzed preferentially, reducing the user's burden. Also, if the user is relaxed, detailed operation log data can be analyzed preferentially to obtain more information. Furthermore, if the user is in a hurry, operation log data that can be analyzed quickly can be analyzed preferentially. This enables efficient data analysis by determining the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0110] The collection unit can analyze the user's past operation history and select an appropriate collection method. For example, it can prioritize collection of operations that the user frequently performed in the past. The collection unit can also predict operations that will be performed during a specific time period from the user's past operation history and concentrate collection during that time period. Furthermore, the collection unit can analyze the user's operation history and adjust the collection method based on the usage status of a specific application. This allows the analysis of the user's past operation history to select the optimal collection method and enable efficient data collection. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past operation history data into a generation AI and have the generation AI select the optimal collection method.

[0111] The generation unit can estimate the user's emotions and adjust the expression method of the generated program based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a program that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a program that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a program that adds visually stimulating effects. By adjusting the program's expression method according to the user's emotions, a program that is easy for the user to understand can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the program's expression method.

[0112] The execution unit can estimate the user's emotions and adjust the program execution method based on the estimated user emotions. For example, if the user is relaxed, the program can be executed at a leisurely pace. If the user is in a hurry, the program can be executed quickly. Furthermore, if the user is excited, the program can be executed with visually stimulating effects. By adjusting the program execution method according to the user's emotions, the optimal execution method for the user can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the execution unit can be performed using, for example, an AI, or without an AI. For example, the execution unit can input the user's emotion data into the generation AI and have the generation AI adjust the program execution method.

[0113] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the operation. For example, a detailed analysis is performed for an important operation. The analysis unit can also perform a simplified analysis for an operation with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the operation. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the operation. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input operation log data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0114] When collecting operation log data, the collection unit can filter the data based on the user's current work status and areas of interest. For example, the collection unit can collect only operation log data related to the work the user is currently performing. The collection unit can also prioritize the collection of related operation log data based on the user's areas of interest. Furthermore, the collection unit can monitor the user's current work status in real time and filter the data to be collected as needed. This allows for efficient collection of highly relevant data by filtering data based on the user's current work status and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's current work status data to a generation AI and have the generation AI perform filtering.

[0115] During generation, the generation unit can apply different generation algorithms depending on the category of operation. For example, for file operations, the generation unit can apply a generation algorithm dedicated to file operations. Furthermore, the generation unit can also apply a generation algorithm dedicated to application operations for launching or closing an application. Furthermore, the generation unit can apply a generation algorithm dedicated to network operations for network operations. In this way, by applying different generation algorithms depending on the category of operation, highly accurate program generation becomes possible. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input operation log data to the generation AI and cause the generation AI to apply different generation algorithms.

[0116] At the time of execution, the execution unit can select the optimal execution method by referring to the user's past execution history. For example, the execution unit selects the optimal execution method based on methods the user has executed in the past. The execution unit can also suggest an efficient execution method from the user's past execution history. Furthermore, the execution unit can analyze the user's past execution history and select the optimal execution procedure. This allows the optimal execution method to be selected by referring to the user's past execution history, enabling efficient execution. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the user's past execution history data into the generation AI and have the generation AI select the optimal execution method.

[0117] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide analysis results in simple language. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0118] When collecting operation log data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user uses text input, the collection unit can also prioritize collecting text data. Furthermore, if the user uses image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects operation log data. Operation log data includes PC operation history and application usage status. The collection unit can collect PC operation history in real time, and can also collect operation log data periodically. It can also collect operation log data when a specific event occurs. For example, it can record keyboard input and mouse operations in real time, collect operation log data at regular intervals, and collect operation log data when an application is started or closed. Step 2: The analysis unit analyzes the operation log data collected by the collection unit and identifies patterned tasks. Patterned tasks include routine tasks performed at the same time every day and operations that are repeated under specific conditions. The analysis unit analyzes the operation log data using data mining technology, machine learning algorithms, and statistical analysis to extract frequently performed operations and identify patterned tasks. Step 3: The generation unit generates a program corresponding to the tasks identified by the analysis unit. The generated programs include programs that automate file copying and moving, and programs that automate application startup and shutdown. The generation unit uses a generation AI to generate a program that automates operations under specific conditions based on operation log data. Step 4: The execution unit executes the program generated by the generation unit. The execution unit can execute the generated program based on a schedule, or can execute the generated program at the user's instruction. It can also execute the generated program when a specific event occurs. For example, the execution unit executes the generated program based on a schedule, manually at the user's instruction, or when the application is started or terminated.

[0121] 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.

[0122] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects operation log data; an analysis unit that analyzes the operation log data collected by the collection unit and identifies patterned operations; a generation unit that generates an automatic processing program from the log data of the work identified by the analysis unit; an execution unit that executes the program generated by the generation unit; A system characterized by:

2. The collecting unit Collect operation log data on PC operation history or application usage 2. The system of claim 1.

3. The analysis unit Identify routine tasks that are performed at the same time every day or patterns of operations that are repeated under specific conditions 2. The system of claim 1.

4. The generation unit Generate a program that corresponds to the identified tasks 2. The system of claim 1.

5. The generation unit Generate programs to automate copying and moving files, and to automate starting and closing applications.

2. The system of claim 1.

6. The execution unit: Execute the generated program and create an automated processing tool 2. The system of claim 1.

7. The collecting unit The user's emotions are estimated, and the timing of collecting operation log data is adjusted based on the estimated user's emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past operation history and select the appropriate collection method 2. The system of claim 1.

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