system

Abstract

The system according to this embodiment aims to enable anyone to efficiently modify macros and PowerShell files. [Solution] The system according to the embodiment comprises a reading unit, an analysis unit, an explanation unit, an input unit, and an editing unit. The reading unit reads macros and PowerShell files. The analysis unit analyzes the files read by the reading unit. The explanation unit explains the results analyzed by the analysis unit in natural language and illustrates them. The input unit takes input from the user about the content they want to modify. The editing unit edits the code based on the content entered by the input unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the maintenance of macros and PowerShell files depends on personnel with specialized knowledge, and there is a problem that efficient modification is difficult.

[0005] The system according to the embodiment aims to enable anyone to efficiently modify macros and PowerShell files.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, an analysis unit, an explanation unit, an input unit, and an editing unit. The reading unit reads macros and PowerShell files. The analysis unit analyzes the files read by the reading unit. The explanation unit explains the results analyzed by the analysis unit in natural language and illustrates them. The input unit takes the content that the user wants to modify. The editing unit edits the code based on the content entered by the input unit. [Effects of the Invention]

[0007] The system according to this embodiment allows anyone to efficiently modify macros and PowerShell files. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The business efficiency tool maintenance support system according to an embodiment of the present invention is a system that supports the maintenance of Excel macros and PowerShell scripts used in business operations. By loading the target macro or PowerShell file, this system can clearly verbalize and illustrate what kind of processing is being executed by which code. The aim is to enable anyone to modify and create business efficiency tools. For example, a user loads a macro or PowerShell file that they want to modify into this system. Next, the generating AI analyzes the file and explains in natural language what kind of processing is being executed by which code, and illustrates it. For example, "This part of the processing filters the data. This is done with this code. The flow looks like this." Furthermore, by inputting the content that the user wants to modify in a chat format, the generating AI analyzes the content and edits the necessary code. This makes it possible for even people who have never touched macros or PowerShell scripts before to create them. For example, if the user inputs "I want to change the filtering conditions in this part," the generating AI will generate the appropriate code and edit it. This mechanism makes it easy to maintain business efficiency tools and enables anyone to create business efficiency tools. For example, even if a company is using tools created by a former employee but lacks the personnel to maintain them, this system makes it possible to modify and create new tools that are tailored to current operations. This business efficiency tool maintenance support system simplifies the maintenance of Excel macros and PowerShell scripts used in business operations, enabling anyone to modify and create business efficiency tools.

[0029] The business efficiency tool maintenance support system according to the embodiment comprises a reading unit, an analysis unit, an explanation unit, an input unit, and an editing unit. The reading unit reads macros and PowerShell files. The reading unit can read, for example, Excel macro files and PowerShell script files. The reading unit can select the optimal reading method according to the file format. For example, in the case of Excel macro files, the reading unit can read while analyzing the contents of the cells. In the case of PowerShell scripts, the reading unit can read while analyzing the structure of the script. Furthermore, if multiple files are related, the reading unit can read them simultaneously while considering the dependencies. The analysis unit analyzes the files read by the reading unit. The analysis unit can analyze the contents of the files using a generation AI. For example, the analysis unit can analyze the structure and dependencies of the files using a generation AI. Furthermore, the analysis unit can extract information for explaining the contents of the files in natural language using a generation AI. Furthermore, the analysis unit can extract information for visualizing the contents of the files using a generation AI. The explanation unit explains the results analyzed by the analysis unit in natural language and visualizes them. The explanation unit can use generative AI to explain the analysis results in natural language. For example, the explanation unit can use generative AI to generate text to explain the analysis results in natural language. Furthermore, the explanation unit can use generative AI to generate diagrams to visualize the analysis results. In addition, the explanation unit can use generative AI to build a system for explaining and visualizing the analysis results in natural language. The input unit allows the user to input the content they want to modify. For example, the input unit allows the user to input the content they want to modify in a chat format. The input unit can analyze the user's input and extract the necessary information. The editing unit edits the code based on the content entered by the input unit. The editing unit can use generative AI to edit the code based on the input content. For example, the editing unit can use generative AI to generate code based on the input content.Furthermore, the editorial department can use a generation AI to modify the code based on the input content. In addition, the editorial department can use a generation AI to build a system for editing the code based on the input content. As a result, the business efficiency tool maintenance support system according to the embodiment can analyze, explain, and edit macros and PowerShell files, making the maintenance of business efficiency tools easier.

[0030] The reading unit reads macros and PowerShell files. For example, it can read Excel macro files and PowerShell script files. The reading unit can select the optimal reading method depending on the file format. For example, in the case of Excel macro files, the reading unit can read them while analyzing the contents of the cells. Specifically, it can sequentially analyze the contents of each cell in the Excel macro file and accurately read the formulas, functions, and macro code within the cells. This allows for a detailed understanding of the internal structure of the Excel macro file. Furthermore, in the case of PowerShell scripts, the reading unit can read them while analyzing the structure of the script. Specifically, it can analyze each line of the PowerShell script and clarify the dependencies between commands, functions, and variables within the script. In addition, if multiple files are related, the reading unit can read them simultaneously, taking dependencies into consideration. For example, if an Excel macro file and a PowerShell script work together, it can analyze the dependencies between them and read the related files all at once. This allows the reading unit to efficiently maintain business efficiency tools with complex file structures. Furthermore, the reading unit can use optimization algorithms to improve file reading speed and accuracy. For example, parallel processing technology can be introduced to quickly load large Excel macro files or lengthy PowerShell scripts. This allows the loading unit to perform maintenance tasks on business efficiency tools quickly and accurately.

[0031] The analysis unit analyzes the files read by the reading unit. The analysis unit can analyze the contents of the files using a generation AI. Specifically, the generation AI uses natural language processing technology to analyze the contents of the files in detail. For example, the generation AI can analyze the formulas and functions in the cells of an Excel macro file and clarify how they interact with each other. The generation AI can also analyze the role of each command and function in a PowerShell script and understand the operation of the entire script. Furthermore, the analysis unit can use the generation AI to extract information for explaining the contents of the files in natural language. For example, the generation AI can analyze the contents of each cell in an Excel macro file and generate text to explain those contents in natural language. The generation AI can also analyze the role of each command and function in a PowerShell script and extract information for explaining them in natural language. Furthermore, the analysis unit can use the generation AI to extract information for visualizing the contents of the files. For example, the generation AI can analyze the dependencies between cells in an Excel macro file and extract information for visualizing them. Furthermore, the generating AI can analyze the dependencies between commands and functions in a PowerShell script and extract information for visualizing them. This allows the analysis unit to analyze the file contents in detail, explain the contents in natural language, and provide information for visualization.

[0032] The explanation unit explains and visualizes the results analyzed by the analysis unit in natural language. The explanation unit can use generative AI to explain the analysis results in natural language. Specifically, the generative AI can generate text in natural language to explain the contents of Excel macro files and PowerShell scripts based on the information provided by the analysis unit. For example, the generative AI can generate text explaining the contents and dependencies of each cell in an Excel macro file and provide it to the user. It can also generate text explaining the roles of each command and function in a PowerShell script and provide it to the user. Furthermore, the explanation unit can use the generative AI to generate diagrams to visualize the analysis results. For example, the generative AI can generate a diagram showing dependencies based on information for visualizing the dependencies of cells in an Excel macro file. It can also generate a diagram showing dependencies based on information for visualizing the dependencies of commands and functions in a PowerShell script. Moreover, the explanation unit can use the generative AI to build a system for explaining and visualizing the analysis results in natural language. This allows the explanation unit to explain the analysis results to the user in an easy-to-understand and visually comprehensible format.

[0033] The input section allows users to input the content they wish to modify. For example, users can input their desired modifications in a chat format. Specifically, users can enter the content they wish to modify into a chat box and send it to the system. The input section can analyze the user's input and extract the necessary information. For example, the input section can analyze the text entered by the user using natural language processing technology to identify the areas and content that need to be modified. Furthermore, the input section can automatically complete the necessary information for modification based on the user's input. For example, if a user inputs "I want to modify the formula in cell A1," the input section can analyze the current formula and dependencies in cell A1 and provide the information necessary for modification. In this way, the input section can accurately understand the content the user wants to modify and provide the necessary information.

[0034] The editorial department edits the code based on the content entered by the input department. The editorial department can use a generation AI to edit the code based on the entered content. Specifically, the generation AI can automatically generate code for Excel macro files and PowerShell scripts based on the modification content entered by the user. For example, if a user enters "I want to modify the formula in cell A1," the generation AI can analyze the current formula in cell A1 and generate a new formula based on the modification content specified by the user. The generation AI can also automatically modify the code of a PowerShell script based on the modification content. For example, if a user enters "I want to add a specific command," the generation AI can analyze the structure of the script and add the new command in the appropriate place. Furthermore, the editorial department can use the generation AI to build a system for editing code based on the entered content. This allows the editorial department to automatically generate and modify the code for Excel macro files and PowerShell scripts based on the modification content entered by the user, enabling efficient maintenance of business efficiency tools.

[0035] The analysis unit includes a storage unit for saving the analysis results. The analysis unit can analyze the contents of a file using a generative AI. For example, the analysis unit can analyze the structure and dependencies of a file using a generative AI. The analysis unit can also extract information to describe the contents of a file in natural language using a generative AI. Furthermore, the analysis unit can extract information to visualize the contents of a file using a generative AI. The storage unit saves the results analyzed by the analysis unit. For example, the storage unit can save the analysis results to a database. The storage unit can also save the analysis results to a file. Furthermore, the storage unit can save the analysis results to cloud storage. This makes it possible to refer to the analysis results later. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can save the analysis results using an AI model for saving analysis results to a database.

[0036] The explanation unit includes a display unit that displays the generated figures. The explanation unit can use a generation AI to explain and illustrate the analysis results in natural language. For example, the explanation unit can use a generation AI to generate text for explaining the analysis results in natural language. The explanation unit can also use a generation AI to generate figures for illustrating the analysis results. Furthermore, the explanation unit can use a generation AI to construct a system for explaining and illustrating the analysis results in natural language. The display unit displays the figures generated by the explanation unit. For example, the display unit can display the generated figures on a monitor. The display unit can also project the generated figures using a projector. Furthermore, the display unit can print the generated figures. This allows for visual confirmation of the analysis results by displaying the generated figures. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can display the figures using an AI model for displaying the generated figures on a monitor.

[0037] The editorial department includes a storage unit for saving edited code. The editorial department can edit code based on input content using a generative AI. For example, the editorial department can generate code based on input content using a generative AI. The editorial department can also modify code based on input content using a generative AI. Furthermore, the editorial department can build a system for editing code based on input content using a generative AI. The storage unit saves the code edited by the editorial department. For example, the storage unit can save the edited code to a database. The storage unit can also save the edited code to a file. Furthermore, the storage unit can save the edited code to cloud storage. This allows for the retention of modifications by saving the edited code. Some or all of the above processes in the storage unit may be performed using, for example, AI, or without AI. For example, the storage unit can save code using an AI model for saving edited code to a database.

[0038] The explanation unit can explain and visualize the analysis results in natural language. The explanation unit can use generative AI to explain the analysis results in natural language. For example, the explanation unit can use generative AI to generate text for explaining the analysis results in natural language. The explanation unit can also use generative AI to generate diagrams for visualizing the analysis results. Furthermore, the explanation unit can use generative AI to construct a system for explaining and visualizing the analysis results in natural language. This makes the analysis results easier to understand by explaining and visualizing them in natural language. Some or all of the above-described processes in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can display the generated diagrams using an AI model for displaying the diagrams on a monitor.

[0039] The input section allows users to input the content they wish to modify in a chat format. For example, the input section allows users to input the content they wish to modify in a chat format. The input section can analyze the user's input and extract the necessary information. This allows users to operate intuitively by inputting modification details in a chat format. Some or all of the above-described processing in the input section may be performed using AI, or not. For example, the input section can extract the necessary information using an AI model to analyze the user's input.

[0040] The reading unit can select the optimal reading method depending on the file type and content. For example, in the case of an Excel macro file, the reading unit can read while analyzing the contents of the cells. In the case of a PowerShell script, the reading unit can read while analyzing the structure of the script. Furthermore, if multiple files are related, the reading unit can read them simultaneously while considering dependencies. This allows for efficient file reading by selecting the optimal reading method according to the file type and content. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the file type and content into a generating AI and have the generating AI select the optimal reading method.

[0041] The reading unit can analyze file dependencies and simultaneously read related files. For example, the reading unit can simultaneously read external data files referenced by macro files. It can also simultaneously read other script files called by PowerShell scripts. Furthermore, if multiple macro files are interdependent, the reading unit can simultaneously read all of them. This allows for accurate analysis by analyzing file dependencies and simultaneously reading related files. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input file dependencies into a generating AI and have the generating AI perform the dependency analysis.

[0042] The reading unit can analyze file metadata to improve reading efficiency. For example, the reading unit can prioritize reading the most recent files based on their creation date and modification date. It can also determine the most efficient reading order based on file size. Furthermore, it can select the optimal reading method based on file type. By analyzing file metadata, files can be read efficiently. Some or all of the above processes in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input file metadata into a generating AI and have the generating AI perform metadata analysis.

[0043] The reading unit can read the optimal version of a file by considering its version control information. For example, the reading unit can automatically select and read the latest version. It can also prioritize reading a version specified by the user. Furthermore, the reading unit can analyze the differences between versions and select the optimal version. In this way, by considering the file's version control information, it can read the optimal version of the file. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the file's version control information into a generating AI and have the generating AI perform the selection of the optimal version.

[0044] The analysis unit can improve the accuracy of its analysis by considering the file structure and dependencies. For example, the analysis unit can analyze the cell reference relationships of macro files to understand the precise dependencies. It can also analyze the module dependencies of PowerShell scripts to provide accurate analysis results. Furthermore, if multiple files are interdependent, the analysis unit can analyze all dependencies to improve accuracy. In this way, the accuracy of the analysis can be improved by considering the file structure and dependencies. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the file structure and dependencies into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0045] The analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit can apply the optimal analysis algorithm to similar files based on past analysis results. The analysis unit can also extract specific patterns from past analysis results to improve analysis accuracy. Furthermore, the analysis unit can refer to past analysis results and perform optimizations to shorten analysis time. In this way, by referring to past analysis results, the analysis algorithm can be optimized and analysis accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis results into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0046] The analysis unit can improve the efficiency of the analysis by considering the file's change history. For example, the analysis unit can prioritize the analysis of changed parts based on the file's change history. Furthermore, the analysis unit can extract important changes from the change history and analyze them efficiently. In addition, the analysis unit can refer to the change history and compare it with past analysis results to improve efficiency. This allows for efficient analysis by considering the file's change history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the file's change history into a generating AI and have the generating AI perform the analysis of the change history.

[0047] The analysis unit can improve the accuracy of its analysis by referring to the relevant documentation for the file. For example, the analysis unit can refer to the relevant documentation for a macro file to provide accurate analysis results. It can also refer to the relevant documentation for a PowerShell script to provide accurate analysis results. Furthermore, the analysis unit can improve the accuracy of its analysis results based on the relevant documentation. Thus, by referring to the relevant documentation for a file, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevant documentation into a generating AI and have the generating AI perform the documentation referencing.

[0048] The explanation unit can adjust the level of detail in its explanations based on the importance of the analysis results. For example, the explanation unit can provide detailed explanations for important analysis results. It can also provide concise explanations for less important analysis results. Furthermore, the explanation unit can adjust the level of detail in its explanations according to the importance of the analysis results. This allows important information to be explained in detail by adjusting the level of detail in the explanations according to the importance of the analysis results. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail in the explanations.

[0049] The explanation unit can apply different explanation algorithms depending on the category of the analysis result. For example, the explanation unit can provide an explanation that emphasizes data flow for analysis results related to data processing. It can also provide an explanation that emphasizes structural diagrams for analysis results related to script structure. Furthermore, it can provide an explanation that emphasizes error flow for analysis results related to error handling. This enables the provision of the most appropriate explanation for each category of analysis result. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the category of the analysis result into a generating AI and have the generating AI apply the explanation algorithm.

[0050] The explanation unit can determine the priority of explanations based on the submission timing of the analysis results. For example, the explanation unit can prioritize explanations for urgent analysis results. It can also prioritize explanations for analysis results with approaching submission deadlines. Furthermore, the explanation unit can adjust the priority of explanations according to the submission timing. This allows for the priority of providing important information by determining the priority of explanations according to the submission timing of the analysis results. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the submission timing of the analysis results into a generating AI and have the generating AI determine the priority of explanations.

[0051] The explanation unit can adjust the order of explanations based on the relevance of the analysis results. For example, the explanation unit can prioritize explaining highly relevant analysis results. It can also postpone explaining less relevant analysis results. Furthermore, the explanation unit can adjust the order of explanations according to the relevance of the analysis results. By adjusting the order of explanations according to the relevance of the analysis results, it becomes possible to provide explanations that are easy for the user to understand. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without using AI. For example, the explanation unit can input the relevance of the analysis results into a generating AI and have the generating AI perform the adjustment of the order of explanations.

[0052] The input unit can select the optimal input method by referring to the user's past input history. For example, the input unit can automatically display as suggestions content that the user has frequently entered in the past. Furthermore, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the input unit can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the system to provide the optimal input method by referring to the user's past input history. Some or all of the above-described processes in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input history into a generating AI and have the generating AI select the optimal input method.

[0053] The input unit can provide real-time feedback based on the user's input. For example, the input unit can provide immediate feedback on the content entered by the user. Furthermore, the input unit can suggest the next content to be entered based on the user's input. In addition, if there is an error in the user's input, the input unit can suggest corrections in real time. This improves input efficiency by providing real-time feedback based on the user's input. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's input into a generating AI and have the generating AI provide real-time feedback.

[0054] The input unit can select the optimal input method by considering the user's device information. For example, if the user is using a smartphone, the input unit can prioritize touch input. If the user is using a desktop computer, the input unit can prioritize keyboard input. Furthermore, if the user is using a voice input device, the input unit can prioritize voice input. In this way, the optimal input method can be provided by considering the user's device information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's device information into a generating AI and have the generating AI select the optimal input method.

[0055] The input unit can improve input efficiency by analyzing the user's operation history. For example, the input unit can suggest the optimal input method based on the user's operation history. Furthermore, the input unit can automatically display frequently used input content from the user's operation history. In addition, the input unit can analyze the user's operation history and provide feedback to improve input efficiency. Thus, input efficiency can be improved by analyzing the user's operation history. Some or all of the above-described processes in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's operation history into a generating AI and have the generating AI perform the analysis of the operation history.

[0056] The editorial team can adjust the level of detail in editing based on the importance of the code. For example, the editorial team can perform detailed editing on important code, and concise editing on less important code. Furthermore, the editorial team can adjust the level of detail in editing according to the importance of the code. This allows important code to be edited in detail by adjusting the level of detail according to the importance of the code. Some or all of the above processes in the editorial team may be performed using AI, for example, or not using AI. For example, the editorial team can input the importance of the code into a generating AI and have the generating AI perform the adjustment of the level of detail in editing.

[0057] The editorial team can apply different editing algorithms depending on the category of the code. For example, the editorial team can perform data flow-focused editing on code related to data processing. They can also perform structural diagram-focused editing on code related to script structure. Furthermore, they can perform error flow-focused editing on code related to error handling. This enables optimal editing according to the code category. Some or all of the above processes in the editorial team may be performed using AI, for example, or without AI. For example, the editorial team can input the code category into a generating AI and have the generating AI apply the editing algorithm.

[0058] The editorial team can determine editing priorities based on the submission date of the code. For example, the editorial team can prioritize editing urgent code. They can also prioritize editing code with an approaching submission deadline. Furthermore, the editorial team can adjust editing priorities according to the submission date. This allows for prioritizing important code by determining editing priorities based on the submission date. Some or all of the above processes in the editorial team may be performed using AI, or not. For example, the editorial team can input the code submission dates into a generating AI and have the generating AI determine the editing priorities.

[0059] The editorial team can adjust the editing order based on the relevance of the code. For example, the editorial team can prioritize editing highly relevant code. They can also postpone editing less relevant code. Furthermore, the editorial team can adjust the editing order according to the relevance of the code. This allows for efficient editing by adjusting the editing order according to the relevance of the code. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input the relevance of the code into a generating AI and have the generating AI perform the adjustment of the editing order.

[0060] The storage unit can adjust the level of detail in saving files based on their importance. For example, the storage unit can perform detailed saving for important files, and simple saving for less important files. Furthermore, the storage unit can adjust the level of detail in saving files according to their importance. This allows important files to be saved in detail by adjusting the level of detail according to their importance. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of files into a generating AI and have the generating AI perform the adjustment of the level of detail in saving files.

[0061] The storage unit can save the optimal version of a file, taking into account its version control information. For example, the storage unit can automatically select and save the latest version. It can also prioritize saving a version specified by the user. Furthermore, the storage unit can analyze the differences between versions and select the optimal version. In this way, by considering the file's version control information, the optimal version of the file can be saved. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the file's version control information into a generating AI and have the generating AI select the optimal version.

[0062] The display unit can adjust the level of detail displayed based on the importance of the analysis results. For example, the display unit can provide detailed displays for important analysis results. Conversely, the display unit can provide concise displays for analysis results of lower importance. Furthermore, the display unit can adjust the level of detail displayed according to the importance of the analysis results. This allows important information to be displayed in detail by adjusting the level of detail displayed according to the importance of the analysis results. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0063] The display unit can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a desktop, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for touch operation. In this way, the optimal display method can be provided by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The analysis unit can improve analysis efficiency by considering the file's change history. For example, it can prioritize analysis of changed parts based on the file's change history. It can also extract important changes from the change history and analyze them efficiently. Furthermore, it can refer to the change history and compare it with past analysis results to improve efficiency. In this way, by considering the file's change history, analysis can be performed efficiently.

[0066] The explanation section can apply different explanation algorithms depending on the category of the analysis results. For example, for analysis results related to data processing, it can provide an explanation that emphasizes data flow. Similarly, for analysis results related to script structure, it can provide an explanation that emphasizes structural diagrams. Furthermore, for analysis results related to error handling, it can provide an explanation that emphasizes error flow. This enables the provision of the most appropriate explanation for each category of analysis result.

[0067] The input unit can select the optimal input method by referring to the user's past input history. For example, it can automatically display as suggestions content that the user has frequently entered in the past. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest input methods that the user will use at specific times based on their past input history. In this way, by referring to the user's past input history, the system can provide the optimal input method.

[0068] The editorial team can apply different editing algorithms depending on the category of the code. For example, code related to data processing can be edited with an emphasis on data flow. Code related to script structure can be edited with an emphasis on structural diagrams. Furthermore, code related to error handling can be edited with an emphasis on error flow. This allows for optimal editing according to the category of the code.

[0069] The storage unit can save the optimal version of a file, taking into account its version control information. For example, it can automatically select and save the latest version. It can also prioritize saving a version specified by the user. Furthermore, it can analyze the differences between versions and select the optimal version. In this way, by considering the file's version control information, it can save the optimal version of the file.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The loading section loads macros and PowerShell files. For example, it can load Excel macro files and PowerShell script files, and selects the optimal loading method depending on the file format. In the case of Excel macro files, it loads them while analyzing the contents of the cells, and in the case of PowerShell scripts, it loads them while analyzing the structure of the script. Furthermore, if multiple files are related, it can load them simultaneously while considering dependencies. Step 2: The analysis unit analyzes the file read by the reading unit. Using the generation AI, it analyzes the file's content, structure, and dependencies, and extracts information for explanation in natural language and information for visualization. Step 3: The explanation unit explains and visualizes the results analyzed by the analysis unit in natural language. Using generative AI, a system is built to generate text and diagrams to explain the analysis results in natural language and to explain and visualize the analysis results. Step 4: The input section is where the user enters the content they want to modify. For example, the user enters the content they want to modify in a chat format, and the system analyzes the entered content to extract the necessary information. Step 5: The editorial department edits the code based on the input entered by the input department. Using generation AI, a system is built to generate and modify code based on the input content, and to edit the code.

[0072] (Example of form 2) The business efficiency tool maintenance support system according to an embodiment of the present invention is a system that supports the maintenance of Excel macros and PowerShell scripts used in business operations. By loading the target macro or PowerShell file, this system can clearly verbalize and illustrate what kind of processing is being executed by which code. The aim is to enable anyone to modify and create business efficiency tools. For example, a user loads a macro or PowerShell file that they want to modify into this system. Next, the generating AI analyzes the file and explains in natural language what kind of processing is being executed by which code, and illustrates it. For example, "This part of the processing filters the data. This is done with this code. The flow looks like this." Furthermore, by inputting the content that the user wants to modify in a chat format, the generating AI analyzes the content and edits the necessary code. This makes it possible for even people who have never touched macros or PowerShell scripts before to create them. For example, if the user inputs "I want to change the filtering conditions in this part," the generating AI will generate the appropriate code and edit it. This mechanism makes it easy to maintain business efficiency tools and enables anyone to create business efficiency tools. For example, even if a company is using tools created by a former employee but lacks the personnel to maintain them, this system makes it possible to modify and create new tools that are tailored to current operations. This business efficiency tool maintenance support system simplifies the maintenance of Excel macros and PowerShell scripts used in business operations, enabling anyone to modify and create business efficiency tools.

[0073] The business efficiency tool maintenance support system according to the embodiment comprises a reading unit, an analysis unit, an explanation unit, an input unit, and an editing unit. The reading unit reads macros and PowerShell files. The reading unit can read, for example, Excel macro files and PowerShell script files. The reading unit can select the optimal reading method according to the file format. For example, in the case of Excel macro files, the reading unit can read while analyzing the contents of the cells. In the case of PowerShell scripts, the reading unit can read while analyzing the structure of the script. Furthermore, if multiple files are related, the reading unit can read them simultaneously while considering the dependencies. The analysis unit analyzes the files read by the reading unit. The analysis unit can analyze the contents of the files using a generation AI. For example, the analysis unit can analyze the structure and dependencies of the files using a generation AI. Furthermore, the analysis unit can extract information for explaining the contents of the files in natural language using a generation AI. Furthermore, the analysis unit can extract information for visualizing the contents of the files using a generation AI. The explanation unit explains the results analyzed by the analysis unit in natural language and visualizes them. The explanation unit can use generative AI to explain the analysis results in natural language. For example, the explanation unit can use generative AI to generate text to explain the analysis results in natural language. Furthermore, the explanation unit can use generative AI to generate diagrams to visualize the analysis results. In addition, the explanation unit can use generative AI to build a system for explaining and visualizing the analysis results in natural language. The input unit allows the user to input the content they want to modify. For example, the input unit allows the user to input the content they want to modify in a chat format. The input unit can analyze the user's input and extract the necessary information. The editing unit edits the code based on the content entered by the input unit. The editing unit can use generative AI to edit the code based on the input content. For example, the editing unit can use generative AI to generate code based on the input content.Furthermore, the editorial department can use a generation AI to modify the code based on the input content. In addition, the editorial department can use a generation AI to build a system for editing the code based on the input content. As a result, the business efficiency tool maintenance support system according to the embodiment can analyze, explain, and edit macros and PowerShell files, making the maintenance of business efficiency tools easier.

[0074] The reading unit reads macros and PowerShell files. For example, it can read Excel macro files and PowerShell script files. The reading unit can select the optimal reading method depending on the file format. For example, in the case of Excel macro files, the reading unit can read them while analyzing the contents of the cells. Specifically, it can sequentially analyze the contents of each cell in the Excel macro file and accurately read the formulas, functions, and macro code within the cells. This allows for a detailed understanding of the internal structure of the Excel macro file. Furthermore, in the case of PowerShell scripts, the reading unit can read them while analyzing the structure of the script. Specifically, it can analyze each line of the PowerShell script and clarify the dependencies between commands, functions, and variables within the script. In addition, if multiple files are related, the reading unit can read them simultaneously, taking dependencies into consideration. For example, if an Excel macro file and a PowerShell script work together, it can analyze the dependencies between them and read the related files all at once. This allows the reading unit to efficiently maintain business efficiency tools with complex file structures. Furthermore, the reading unit can use optimization algorithms to improve file reading speed and accuracy. For example, parallel processing technology can be introduced to quickly load large Excel macro files or lengthy PowerShell scripts. This allows the loading unit to perform maintenance tasks on business efficiency tools quickly and accurately.

[0075] The analysis unit analyzes the files read by the reading unit. The analysis unit can analyze the contents of the files using a generation AI. Specifically, the generation AI uses natural language processing technology to analyze the contents of the files in detail. For example, the generation AI can analyze the formulas and functions in the cells of an Excel macro file and clarify how they interact with each other. The generation AI can also analyze the role of each command and function in a PowerShell script and understand the operation of the entire script. Furthermore, the analysis unit can use the generation AI to extract information for explaining the contents of the files in natural language. For example, the generation AI can analyze the contents of each cell in an Excel macro file and generate text to explain those contents in natural language. The generation AI can also analyze the role of each command and function in a PowerShell script and extract information for explaining them in natural language. Furthermore, the analysis unit can use the generation AI to extract information for visualizing the contents of the files. For example, the generation AI can analyze the dependencies between cells in an Excel macro file and extract information for visualizing them. Furthermore, the generating AI can analyze the dependencies between commands and functions in a PowerShell script and extract information for visualizing them. This allows the analysis unit to analyze the file contents in detail, explain the contents in natural language, and provide information for visualization.

[0076] The explanation unit explains and visualizes the results analyzed by the analysis unit in natural language. The explanation unit can use generative AI to explain the analysis results in natural language. Specifically, the generative AI can generate text in natural language to explain the contents of Excel macro files and PowerShell scripts based on the information provided by the analysis unit. For example, the generative AI can generate text explaining the contents and dependencies of each cell in an Excel macro file and provide it to the user. It can also generate text explaining the roles of each command and function in a PowerShell script and provide it to the user. Furthermore, the explanation unit can use the generative AI to generate diagrams to visualize the analysis results. For example, the generative AI can generate a diagram showing dependencies based on information for visualizing the dependencies of cells in an Excel macro file. It can also generate a diagram showing dependencies based on information for visualizing the dependencies of commands and functions in a PowerShell script. Moreover, the explanation unit can use the generative AI to build a system for explaining and visualizing the analysis results in natural language. This allows the explanation unit to explain the analysis results to the user in an easy-to-understand and visually comprehensible format.

[0077] The input section allows users to input the content they wish to modify. For example, users can input their desired modifications in a chat format. Specifically, users can enter the content they wish to modify into a chat box and send it to the system. The input section can analyze the user's input and extract the necessary information. For example, the input section can analyze the text entered by the user using natural language processing technology to identify the areas and content that need to be modified. Furthermore, the input section can automatically complete the necessary information for modification based on the user's input. For example, if a user inputs "I want to modify the formula in cell A1," the input section can analyze the current formula and dependencies in cell A1 and provide the information necessary for modification. In this way, the input section can accurately understand the content the user wants to modify and provide the necessary information.

[0078] The editorial department edits the code based on the content entered by the input department. The editorial department can use a generation AI to edit the code based on the entered content. Specifically, the generation AI can automatically generate code for Excel macro files and PowerShell scripts based on the modification content entered by the user. For example, if a user enters "I want to modify the formula in cell A1," the generation AI can analyze the current formula in cell A1 and generate a new formula based on the modification content specified by the user. The generation AI can also automatically modify the code of a PowerShell script based on the modification content. For example, if a user enters "I want to add a specific command," the generation AI can analyze the structure of the script and add the new command in the appropriate place. Furthermore, the editorial department can use the generation AI to build a system for editing code based on the entered content. This allows the editorial department to automatically generate and modify the code for Excel macro files and PowerShell scripts based on the modification content entered by the user, enabling efficient maintenance of business efficiency tools.

[0079] The analysis unit includes a storage unit for saving the analysis results. The analysis unit can analyze the contents of a file using a generative AI. For example, the analysis unit can analyze the structure and dependencies of a file using a generative AI. The analysis unit can also extract information to describe the contents of a file in natural language using a generative AI. Furthermore, the analysis unit can extract information to visualize the contents of a file using a generative AI. The storage unit saves the results analyzed by the analysis unit. For example, the storage unit can save the analysis results to a database. The storage unit can also save the analysis results to a file. Furthermore, the storage unit can save the analysis results to cloud storage. This makes it possible to refer to the analysis results later. Some or all of the above-described processes in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can save the analysis results using an AI model for saving analysis results to a database.

[0080] The explanation unit includes a display unit that displays the generated figures. The explanation unit can use a generation AI to explain and illustrate the analysis results in natural language. For example, the explanation unit can use a generation AI to generate text for explaining the analysis results in natural language. The explanation unit can also use a generation AI to generate figures for illustrating the analysis results. Furthermore, the explanation unit can use a generation AI to construct a system for explaining and illustrating the analysis results in natural language. The display unit displays the figures generated by the explanation unit. For example, the display unit can display the generated figures on a monitor. The display unit can also project the generated figures using a projector. Furthermore, the display unit can print the generated figures. This allows for visual confirmation of the analysis results by displaying the generated figures. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can display the figures using an AI model for displaying the generated figures on a monitor.

[0081] The editorial department includes a storage unit for saving edited code. The editorial department can edit code based on input content using a generative AI. For example, the editorial department can generate code based on input content using a generative AI. The editorial department can also modify code based on input content using a generative AI. Furthermore, the editorial department can build a system for editing code based on input content using a generative AI. The storage unit saves the code edited by the editorial department. For example, the storage unit can save the edited code to a database. The storage unit can also save the edited code to a file. Furthermore, the storage unit can save the edited code to cloud storage. This allows for the retention of modifications by saving the edited code. Some or all of the above processes in the storage unit may be performed using, for example, AI, or without AI. For example, the storage unit can save code using an AI model for saving edited code to a database.

[0082] The explanation unit can explain and visualize the analysis results in natural language. The explanation unit can use generative AI to explain the analysis results in natural language. For example, the explanation unit can use generative AI to generate text for explaining the analysis results in natural language. The explanation unit can also use generative AI to generate diagrams for visualizing the analysis results. Furthermore, the explanation unit can use generative AI to construct a system for explaining and visualizing the analysis results in natural language. This makes the analysis results easier to understand by explaining and visualizing them in natural language. Some or all of the above-described processes in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can display the generated diagrams using an AI model for displaying the diagrams on a monitor.

[0083] The input section allows users to input the content they wish to modify in a chat format. For example, the input section allows users to input the content they wish to modify in a chat format. The input section can analyze the user's input and extract the necessary information. This allows users to operate intuitively by inputting modification details in a chat format. Some or all of the above-described processing in the input section may be performed using AI, or not. For example, the input section can extract the necessary information using an AI model to analyze the user's input.

[0084] The reading unit can estimate the user's emotions and adjust the file reading timing based on the estimated emotions. For example, if the user is stressed, the reading unit can speed up the reading to reduce the user's burden. If the user is relaxed, the reading unit can slow down the reading to give the user more time. Furthermore, if the user is in a hurry, the reading unit can read immediately to enable a quick response. In this way, the user's burden can be reduced by adjusting the file reading timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI, or not using AI. For example, the reading unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The reading unit can select the optimal reading method depending on the file type and content. For example, in the case of an Excel macro file, the reading unit can read while analyzing the contents of the cells. In the case of a PowerShell script, the reading unit can read while analyzing the structure of the script. Furthermore, if multiple files are related, the reading unit can read them simultaneously while considering dependencies. This allows for efficient file reading by selecting the optimal reading method according to the file type and content. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the file type and content into a generating AI and have the generating AI select the optimal reading method.

[0086] The reading unit can analyze file dependencies and simultaneously read related files. For example, the reading unit can simultaneously read external data files referenced by macro files. It can also simultaneously read other script files called by PowerShell scripts. Furthermore, if multiple macro files are interdependent, the reading unit can simultaneously read all of them. This allows for accurate analysis by analyzing file dependencies and simultaneously reading related files. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input file dependencies into a generating AI and have the generating AI perform the dependency analysis.

[0087] The reading unit can estimate the user's emotions and determine the priority of files to read based on the estimated emotions. For example, if the user is in a hurry, the reading unit can prioritize reading the most important files. If the user is relaxed, the reading unit can read files in order. Furthermore, if the user is stressed, the reading unit can read files starting with the easiest ones. This allows for file reading that meets the user's needs by determining the priority of files to read according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using AI or not using AI. For example, the reading unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The reading unit can analyze file metadata to improve reading efficiency. For example, the reading unit can prioritize reading the most recent files based on their creation date and modification date. It can also determine the most efficient reading order based on file size. Furthermore, it can select the optimal reading method based on file type. By analyzing file metadata, files can be read efficiently. Some or all of the above processes in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input file metadata into a generating AI and have the generating AI perform metadata analysis.

[0089] The reading unit can read the optimal version of a file by considering its version control information. For example, the reading unit can automatically select and read the latest version. It can also prioritize reading a version specified by the user. Furthermore, the reading unit can analyze the differences between versions and select the optimal version. In this way, by considering the file's version control information, it can read the optimal version of the file. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the file's version control information into a generating AI and have the generating AI perform the selection of the optimal version.

[0090] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is stressed, the analysis unit can analyze and provide only the important parts. In this way, by adjusting the level of detail of the analysis according to the user's emotions, analysis results that meet the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can improve the accuracy of its analysis by considering the file structure and dependencies. For example, the analysis unit can analyze the cell reference relationships of macro files to understand the precise dependencies. It can also analyze the module dependencies of PowerShell scripts to provide accurate analysis results. Furthermore, if multiple files are interdependent, the analysis unit can analyze all dependencies to improve accuracy. In this way, the accuracy of the analysis can be improved by considering the file structure and dependencies. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the file structure and dependencies into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0092] The analysis unit can optimize the analysis algorithm by referring to past analysis results. For example, the analysis unit can apply the optimal analysis algorithm to similar files based on past analysis results. The analysis unit can also extract specific patterns from past analysis results to improve analysis accuracy. Furthermore, the analysis unit can refer to past analysis results and perform optimizations to shorten analysis time. In this way, by referring to past analysis results, the analysis algorithm can be optimized and analysis accuracy can be improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis results into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is 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 processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can improve the efficiency of the analysis by considering the file's change history. For example, the analysis unit can prioritize the analysis of changed parts based on the file's change history. Furthermore, the analysis unit can extract important changes from the change history and analyze them efficiently. In addition, the analysis unit can refer to the change history and compare it with past analysis results to improve efficiency. This allows for efficient analysis by considering the file's change history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the file's change history into a generating AI and have the generating AI perform the analysis of the change history.

[0095] The analysis unit can improve the accuracy of its analysis by referring to the relevant documentation for the file. For example, the analysis unit can refer to the relevant documentation for a macro file to provide accurate analysis results. It can also refer to the relevant documentation for a PowerShell script to provide accurate analysis results. Furthermore, the analysis unit can improve the accuracy of its analysis results based on the relevant documentation. Thus, by referring to the relevant documentation for a file, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevant documentation into a generating AI and have the generating AI perform the documentation referencing.

[0096] The explanation unit can estimate the user's emotions and adjust the way the explanation is presented based on the estimated emotions. For example, if the user is nervous, the explanation unit can provide a simple and easy-to-understand explanation. If the user is relaxed, the explanation unit can provide a detailed explanation. Furthermore, if the user is in a hurry, the explanation unit can provide a concise explanation. By adjusting the way the explanation is presented according to the user's emotions, it becomes possible to provide explanations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The explanation unit can adjust the level of detail in its explanations based on the importance of the analysis results. For example, the explanation unit can provide detailed explanations for important analysis results. It can also provide concise explanations for less important analysis results. Furthermore, the explanation unit can adjust the level of detail in its explanations according to the importance of the analysis results. This allows important information to be explained in detail by adjusting the level of detail in the explanations according to the importance of the analysis results. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail in the explanations.

[0098] The explanation unit can apply different explanation algorithms depending on the category of the analysis result. For example, the explanation unit can provide an explanation that emphasizes data flow for analysis results related to data processing. It can also provide an explanation that emphasizes structural diagrams for analysis results related to script structure. Furthermore, it can provide an explanation that emphasizes error flow for analysis results related to error handling. This enables the provision of the most appropriate explanation for each category of analysis result. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the category of the analysis result into a generating AI and have the generating AI apply the explanation algorithm.

[0099] The explanation unit can estimate the user's emotions and adjust the length of the explanation based on the estimated emotions. For example, if the user is in a hurry, the explanation unit can provide a short, concise explanation. If the user is relaxed, the explanation unit can provide a detailed explanation. Furthermore, if the user is stressed, the explanation unit can provide a simple and easy-to-understand explanation. By adjusting the length of the explanation according to the user's emotions, it becomes possible to provide explanations that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the explanation unit may be performed using AI, for example, or not using AI. For example, the explanation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The explanation unit can determine the priority of explanations based on the submission timing of the analysis results. For example, the explanation unit can prioritize explanations for urgent analysis results. It can also prioritize explanations for analysis results with approaching submission deadlines. Furthermore, the explanation unit can adjust the priority of explanations according to the submission timing. This allows for the priority of providing important information by determining the priority of explanations according to the submission timing of the analysis results. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without AI. For example, the explanation unit can input the submission timing of the analysis results into a generating AI and have the generating AI determine the priority of explanations.

[0101] The explanation unit can adjust the order of explanations based on the relevance of the analysis results. For example, the explanation unit can prioritize explaining highly relevant analysis results. It can also postpone explaining less relevant analysis results. Furthermore, the explanation unit can adjust the order of explanations according to the relevance of the analysis results. By adjusting the order of explanations according to the relevance of the analysis results, it becomes possible to provide explanations that are easy for the user to understand. Some or all of the above processing in the explanation unit may be performed using AI, for example, or without using AI. For example, the explanation unit can input the relevance of the analysis results into a generating AI and have the generating AI perform the adjustment of the order of explanations.

[0102] The input unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is tense, the input unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the input unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the input unit can provide a simple and highly visible interface to facilitate the input process. In this way, by adjusting the display method of the input interface according to the user's emotions, a user-friendly interface can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The input unit can select the optimal input method by referring to the user's past input history. For example, the input unit can automatically display as suggestions content that the user has frequently entered in the past. Furthermore, the input unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. In addition, the input unit can predict and suggest input methods to be used during specific time periods based on the user's past input history. This allows the system to provide the optimal input method by referring to the user's past input history. Some or all of the above-described processes in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input history into a generating AI and have the generating AI select the optimal input method.

[0104] The input unit can provide real-time feedback based on the user's input. For example, the input unit can provide immediate feedback on the content entered by the user. Furthermore, the input unit can suggest the next content to be entered based on the user's input. In addition, if there is an error in the user's input, the input unit can suggest corrections in real time. This improves input efficiency by providing real-time feedback based on the user's input. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's input into a generating AI and have the generating AI provide real-time feedback.

[0105] The input unit can estimate the user's emotions and determine the priority of input content based on the estimated emotions. For example, if the user is in a hurry, the input unit can prioritize displaying important input content. If the user is relaxed, the input unit can display input content in order. Furthermore, if the user is stressed, the input unit can display simpler input content first. This allows important input content to be processed preferentially by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI, or not using AI. For example, the input unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The input unit can select the optimal input method by considering the user's device information. For example, if the user is using a smartphone, the input unit can prioritize touch input. If the user is using a desktop computer, the input unit can prioritize keyboard input. Furthermore, if the user is using a voice input device, the input unit can prioritize voice input. In this way, the optimal input method can be provided by considering the user's device information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's device information into a generating AI and have the generating AI select the optimal input method.

[0107] The input unit can improve input efficiency by analyzing the user's operation history. For example, the input unit can suggest the optimal input method based on the user's operation history. Furthermore, the input unit can automatically display frequently used input content from the user's operation history. In addition, the input unit can analyze the user's operation history and provide feedback to improve input efficiency. Thus, input efficiency can be improved by analyzing the user's operation history. Some or all of the above-described processes in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's operation history into a generating AI and have the generating AI perform the analysis of the operation history.

[0108] The editorial team can estimate the user's emotions and adjust the editorial presentation based on the estimated emotions. For example, if the user is stressed, the editorial team can provide a simple and highly visual presentation. If the user is relaxed, the editorial team can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the editorial team can provide a concise presentation. By adjusting the editorial presentation according to the user's emotions, an easy-to-use presentation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI, or not using AI. For example, the editorial team can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The editorial team can adjust the level of detail in editing based on the importance of the code. For example, the editorial team can perform detailed editing on important code, and concise editing on less important code. Furthermore, the editorial team can adjust the level of detail in editing according to the importance of the code. This allows important code to be edited in detail by adjusting the level of detail according to the importance of the code. Some or all of the above processes in the editorial team may be performed using AI, for example, or not using AI. For example, the editorial team can input the importance of the code into a generating AI and have the generating AI perform the adjustment of the level of detail in editing.

[0110] The editorial team can apply different editing algorithms depending on the category of the code. For example, the editorial team can perform data flow-focused editing on code related to data processing. They can also perform structural diagram-focused editing on code related to script structure. Furthermore, they can perform error flow-focused editing on code related to error handling. This enables optimal editing according to the code category. Some or all of the above processes in the editorial team may be performed using AI, for example, or without AI. For example, the editorial team can input the code category into a generating AI and have the generating AI apply the editing algorithm.

[0111] The editorial team can estimate the user's emotions and adjust the length of the edit based on the estimated emotions. For example, if the user is in a hurry, the editorial team can create a short, concise edit. If the user is relaxed, the editorial team can create a detailed edit. Furthermore, if the user is stressed, the editorial team can create a simple and easy-to-understand edit. By adjusting the length of the edit according to the user's emotions, it becomes possible to create edits that are easy for the user to use. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using AI, for example, or not using AI. For example, the editorial team can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The editorial team can determine editing priorities based on the submission date of the code. For example, the editorial team can prioritize editing urgent code. They can also prioritize editing code with an approaching submission deadline. Furthermore, the editorial team can adjust editing priorities according to the submission date. This allows for prioritizing important code by determining editing priorities based on the submission date. Some or all of the above processes in the editorial team may be performed using AI, or not. For example, the editorial team can input the code submission dates into a generating AI and have the generating AI determine the editing priorities.

[0113] The editorial team can adjust the editing order based on the relevance of the code. For example, the editorial team can prioritize editing highly relevant code. They can also postpone editing less relevant code. Furthermore, the editorial team can adjust the editing order according to the relevance of the code. This allows for efficient editing by adjusting the editing order according to the relevance of the code. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input the relevance of the code into a generating AI and have the generating AI perform the adjustment of the editing order.

[0114] The storage unit can estimate the user's emotions and adjust the timing of saving based on the estimated emotions. For example, if the user is in a hurry, the storage unit can save immediately. If the user is relaxed, the storage unit can save at an appropriate time. Furthermore, if the user is stressed, the storage unit can save frequently. In this way, by adjusting the timing of saving according to the user's emotions, saving can be done at the optimal time for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, or not using AI. For example, the storage unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0115] The storage unit can adjust the level of detail in saving files based on their importance. For example, the storage unit can perform detailed saving for important files, and simple saving for less important files. Furthermore, the storage unit can adjust the level of detail in saving files according to their importance. This allows important files to be saved in detail by adjusting the level of detail according to their importance. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of files into a generating AI and have the generating AI perform the adjustment of the level of detail in saving files.

[0116] The storage unit can estimate the user's emotions and determine the priority of saving based on the estimated emotions. For example, if the user is in a hurry, the storage unit can prioritize saving important files. If the user is relaxed, the storage unit can save files in order. Furthermore, if the user is stressed, the storage unit can save files starting with the simplest ones. In this way, by determining the priority of saving according to the user's emotions, important files can be saved preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, or not using AI. For example, the storage unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0117] The storage unit can save the optimal version of a file, taking into account its version control information. For example, the storage unit can automatically select and save the latest version. It can also prioritize saving a version specified by the user. Furthermore, the storage unit can analyze the differences between versions and select the optimal version. In this way, by considering the file's version control information, the optimal version of the file can be saved. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the file's version control information into a generating AI and have the generating AI select the optimal version.

[0118] The display unit can estimate the user's emotions and adjust the display's presentation based on the estimated emotions. For example, if the user is tense, the display unit can provide a simple and highly visible display. If the user is relaxed, the display unit can provide a display that includes detailed information. Furthermore, if the user is in a hurry, the display unit can provide a concise display. By adjusting the display's presentation according to the user's emotions, a user-friendly display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not using AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0119] The display unit can adjust the level of detail displayed based on the importance of the analysis results. For example, the display unit can provide detailed displays for important analysis results. Conversely, the display unit can provide concise displays for analysis results of lower importance. Furthermore, the display unit can adjust the level of detail displayed according to the importance of the analysis results. This allows important information to be displayed in detail by adjusting the level of detail displayed according to the importance of the analysis results. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail displayed.

[0120] The display unit can estimate the user's emotions and determine the display priority based on the estimated emotions. For example, if the user is in a hurry, the display unit can prioritize displaying important analysis results. If the user is relaxed, the display unit can display analysis results in order. Furthermore, if the user is stressed, the display unit can display simpler analysis results first. In this way, by determining the display priority according to the user's emotions, important information can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, or not using AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0121] The display unit can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. If the user is using a desktop, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a tablet, the display unit can provide a display method optimized for touch operation. In this way, the optimal display method can be provided by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on those emotions. For example, if the user is in a hurry, it can prioritize the analysis of important parts. If the user is relaxed, it can analyze the entire data evenly. Furthermore, if the user is stressed, it can analyze the data starting with the easiest parts. By determining the priority of the analysis according to the user's emotions, it becomes possible to perform analysis that meets the user's needs.

[0124] The explanation section can estimate the user's emotions and adjust the tone of the explanation based on those emotions. For example, if the user is nervous, the explanation can be delivered in a calm tone. If the user is relaxed, the explanation can be delivered in a friendly tone. Furthermore, if the user is in a hurry, the explanation can be delivered in a concise and direct tone. By adjusting the tone of the explanation according to the user's emotions, it becomes possible to provide explanations that are easy for the user to understand.

[0125] The input unit can estimate the user's emotions and adjust the level of input support based on those emotions. For example, if the user is nervous, it can provide detailed guidance. If the user is relaxed, it can provide minimal guidance. Furthermore, if the user is in a hurry, it can provide shortcuts to support rapid input. In this way, by adjusting the level of input support according to the user's emotions, a user-friendly input environment can be provided.

[0126] The editorial team can estimate the user's emotions and adjust the way they provide feedback based on that estimation. For example, if the user is stressed, they can emphasize positive feedback. If the user is relaxed, they can provide detailed feedback. Furthermore, if the user is in a hurry, they can provide concise feedback. By adjusting the editorial feedback method according to the user's emotions, they can provide feedback that is effective for the user.

[0127] The storage unit can estimate the user's emotions and adjust the notification method based on those emotions. For example, if the user is stressed, a quiet notification sound can be used. If the user is relaxed, a normal notification sound can be used. Furthermore, if the user is in a hurry, a prominent notification sound can be used. In this way, by adjusting the notification method according to the user's emotions, the system can provide notifications that are appropriate for the user.

[0128] The analysis unit can improve analysis efficiency by considering the file's change history. For example, it can prioritize analysis of changed parts based on the file's change history. It can also extract important changes from the change history and analyze them efficiently. Furthermore, it can refer to the change history and compare it with past analysis results to improve efficiency. In this way, by considering the file's change history, analysis can be performed efficiently.

[0129] The explanation section can apply different explanation algorithms depending on the category of the analysis results. For example, for analysis results related to data processing, it can provide an explanation that emphasizes data flow. Similarly, for analysis results related to script structure, it can provide an explanation that emphasizes structural diagrams. Furthermore, for analysis results related to error handling, it can provide an explanation that emphasizes error flow. This enables the provision of the most appropriate explanation for each category of analysis result.

[0130] The input unit can select the optimal input method by referring to the user's past input history. For example, it can automatically display as suggestions content that the user has frequently entered in the past. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest input methods that the user will use at specific times based on their past input history. In this way, by referring to the user's past input history, the system can provide the optimal input method.

[0131] The editorial team can apply different editing algorithms depending on the category of the code. For example, code related to data processing can be edited with an emphasis on data flow. Code related to script structure can be edited with an emphasis on structural diagrams. Furthermore, code related to error handling can be edited with an emphasis on error flow. This allows for optimal editing according to the category of the code.

[0132] The storage unit can save the optimal version of a file, taking into account its version control information. For example, it can automatically select and save the latest version. It can also prioritize saving a version specified by the user. Furthermore, it can analyze the differences between versions and select the optimal version. In this way, by considering the file's version control information, it can save the optimal version of the file.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The loading section loads macros and PowerShell files. For example, it can load Excel macro files and PowerShell script files, and selects the optimal loading method depending on the file format. In the case of Excel macro files, it loads them while analyzing the contents of the cells, and in the case of PowerShell scripts, it loads them while analyzing the structure of the script. Furthermore, if multiple files are related, it can load them simultaneously while considering dependencies. Step 2: The analysis unit analyzes the file read by the reading unit. Using the generation AI, it analyzes the file's content, structure, and dependencies, and extracts information for explanation in natural language and information for visualization. Step 3: The explanation unit explains and visualizes the results analyzed by the analysis unit in natural language. Using generative AI, a system is built to generate text and diagrams to explain the analysis results in natural language and to explain and visualize the analysis results. Step 4: The input section is where the user enters the content they want to modify. For example, the user enters the content they want to modify in a chat format, and the system analyzes the entered content to extract the necessary information. Step 5: The editorial department edits the code based on the input entered by the input department. Using generation AI, a system is built to generate and modify code based on the input content, and to edit the code.

[0135] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0136] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0137] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0138] Each of the multiple elements described above, including the reading unit, analysis unit, explanation unit, input unit, and editing unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the smart device 14 and reads Excel macro files and PowerShell script files. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the file using generation AI. The explanation unit is implemented by the control unit 46A of the smart device 14 and explains the analysis results in natural language and illustrates them. The input unit is implemented by the receiving device 38 of the smart device 14 and the user inputs the content they want to modify. The editing unit is implemented by the specific processing unit 290 of the data processing unit 12 and edits the code based on the input content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reading unit, analysis unit, explanation unit, input unit, and editing unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the smart glasses 214 and reads Excel macro files and PowerShell script files. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the file using generation AI. The explanation unit is implemented by the control unit 46A of the smart glasses 214 and explains the analysis results in natural language and illustrates them. The input unit is implemented by the microphone 238 of the smart glasses 214 and inputs the content that the user wants to modify. The editing unit is implemented by the specific processing unit 290 of the data processing unit 12 and edits the code based on the input content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the reading unit, analysis unit, explanation unit, input unit, and editing unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the headset terminal 314 and reads Excel macro files and PowerShell script files. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the file using generation AI. The explanation unit is implemented by the control unit 46A of the headset terminal 314 and explains the analysis results in natural language and illustrates them. The input unit is implemented by the microphone 238 of the headset terminal 314 and inputs the content that the user wants to modify. The editing unit is implemented by the specific processing unit 290 of the data processing unit 12 and edits the code based on the input content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] As shown in Figure 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.

[0173] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0177] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0178] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0179] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0180] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0181] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0182] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0183] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0184] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0185] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0187] Each of the multiple elements described above, including the reading unit, analysis unit, explanation unit, input unit, and editing unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the robot 414 and reads Excel macro files and PowerShell script files. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the file using generation AI. The explanation unit is implemented by the control unit 46A of the robot 414 and explains the analysis results in natural language and illustrates them. The input unit is implemented by the microphone 238 of the robot 414 and inputs the content that the user wants to modify. The editing unit is implemented by the specific processing unit 290 of the data processing unit 12 and edits the code based on the input content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0188] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0190] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0191] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0192] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0196] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0197] 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.

[0198] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0199] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0200] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0201] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0203] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0204] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0205] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0206] (Note 1) The loading section reads macros and PowerShell files, An analysis unit that analyzes the file read by the aforementioned reading unit, An explanation unit that explains and illustrates the results of the analysis performed by the aforementioned analysis unit in natural language, An input section where the user enters the details of the modifications they want to make, The system comprises an editing unit that edits the code based on the content entered by the input unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, It includes a storage unit for saving the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The above explanatory section is, It includes a display unit that displays the generated diagram. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned editorial department, It includes a storage section for saving edited code. The system described in Appendix 1, characterized by the features described herein. (Note 5) The above explanatory section is, Explain the analysis results in natural language and visualize them. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned input unit is Users enter the details of the modifications they want to make in a chat format. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reading unit, It estimates the user's emotions and adjusts the file loading timing based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reading unit, Select the optimal reading method depending on the file type and content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reading unit, Analyze file dependencies and read related files simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reading unit, It estimates the user's emotions and determines the priority of files to load based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reading unit, Analyze file metadata to improve reading efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reading unit, Load the optimal version considering the file's version control information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Improve analysis accuracy by considering file structure and dependencies. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Optimize the analysis algorithm by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Improve analysis efficiency by considering the file's change history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Refer to the related documentation for the file to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The above explanatory section is, It estimates the user's emotions and adjusts the way explanations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The above explanatory section is, Adjust the level of detail in the explanation based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The above explanatory section is, Apply different explanatory algorithms depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The above explanatory section is, It estimates the user's emotions and adjusts the length of the explanation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The above explanatory section is, Prioritize explanations based on the timing of submission of analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The above explanatory section is, Adjust the order of explanations based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned input unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned input unit is The system selects the optimal input method by referring to the user's past input history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned input unit is Provides real-time feedback based on user input. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned input unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned input unit is The optimal input method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned input unit is Analyze user activity history to improve input efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned editorial department, It estimates the user's emotions and adjusts the editing style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned editorial department, Adjust the level of detail of edits based on the importance of the code. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned editorial department, Apply different editing algorithms depending on the code category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned editorial department, It estimates the user's emotions and adjusts the length of the edit based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned editorial department, Prioritize edits based on when the code was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned editorial department, Adjust the editing order based on the relevance of the code. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned storage unit is It estimates the user's emotions and adjusts the timing of saving based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned storage unit is Adjust the level of detail in saving based on the importance of the file. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned storage unit is The system estimates the user's emotions and determines the priority of saving based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned storage unit is Save the optimal version, taking into account file version control information. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned display unit is Adjust the level of detail displayed based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned display unit is It estimates the user's emotions and determines the display priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned display unit is The optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The loading section reads macros and PowerShell files, An analysis unit that analyzes the file read by the aforementioned reading unit, An explanation unit that explains and illustrates the results of the analysis performed by the aforementioned analysis unit in natural language, An input section where the user enters the details of the modifications they want to make, The system comprises an editing unit that edits the code based on the content entered by the input unit. A system characterized by the following features.

2. The aforementioned analysis unit, It includes a storage unit for saving the analysis results. The system according to feature 1.

3. The above explanatory section is, It includes a display unit that displays the generated diagram. The system according to feature 1.

4. The aforementioned editorial department, It includes a storage section for saving edited code. The system according to feature 1.

5. The above explanatory section is, Explain the analysis results in natural language and visualize them. The system according to feature 1.

6. The aforementioned input unit is Users enter the details of the modifications they want to make in a chat format. The system according to feature 1.

7. The aforementioned reading unit, It estimates the user's emotions and adjusts the file loading timing based on the estimated emotions. The system according to feature 1.

8. The aforementioned reading unit, Select the optimal reading method depending on the file type and content. The system according to feature 1.

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