system

Abstract

The system according to this embodiment aims to efficiently create correction instructions for web pages and paper documents. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and an output unit. The acquisition unit acquires information to be corrected. The analysis unit analyzes the information acquired by the acquisition unit. The generation unit generates correction instructions based on the information analyzed by the analysis unit. The output unit outputs the correction instructions generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 prior art, it is difficult to efficiently create correction instructions for web pages or paper media, and there is a risk of manual errors and time waste.

[0005] The system according to the embodiment aims to efficiently create correction instructions for web pages or paper media.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, and an output unit. The acquisition unit acquires information to be modified. The analysis unit analyzes the information acquired by the acquisition unit. The generation unit generates modification instructions based on the information analyzed by the analysis unit. The output unit outputs the modification instructions generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create correction instructions for web pages and paper documents. [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, etc. 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 correction instruction creation system according to an embodiment of the present invention is a system for efficiently creating correction instructions for web pages and paper documents. The system works by having a user paste a web page URL or a screenshot of a paper document into the system, allowing the AI ​​to recognize the text and create a correction instruction document based on specified correction rules. This system prevents omissions and inconsistencies in corrections and reduces the human resources required to create correction instructions. For example, a user pastes a web page URL or a screenshot of a paper document into the system. The user only needs to specify the page or screenshot to be corrected. For example, the user enters the URL of a page where a service name change or condition change is necessary. This information is input to the AI. Next, the AI ​​analyzes the input information and recognizes the text. The AI ​​can accurately recognize all text, including text and variations in spelling within images. For example, it recognizes text and identical words with different spellings within an image and identifies the areas to be corrected. The AI ​​then creates a correction instruction document based on specified correction rules. These rules include substitution, addition, deletion, and contextual correction. The AI ​​marks the areas to be corrected and writes the correction instructions according to these rules. Furthermore, when the AI ​​creates revision instructions, it adds symbols before and after the parts to be revised. For example, it lists the revised parts in the format "Before revision → After revision" and the added text in the format "Newly added text". This makes the revised parts clear and the revision instructions easy to understand. This system can reduce the cost of page revisions within the company and reduce the risk of revision errors. For example, even if service names or conditions change frequently, the AI ​​automatically creates revision instructions, significantly reducing human resources. In addition, by preventing omissions of revisions and inconsistencies in terminology, the accuracy of revisions is improved and the risk of revision errors is reduced. In summary, the revision instruction creation system can prevent omissions of revisions and inconsistencies in terminology, and reduce the human resources required to create revision instructions.

[0029] The correction instruction creation system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and an output unit. The acquisition unit acquires information to be corrected. The information to be corrected includes, but is not limited to, text data, image data, and program code. The acquisition unit can acquire information from, for example, a database. The acquisition unit can also acquire information from user input. For example, the acquisition unit can acquire URLs and captured images entered by the user. The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit can perform, for example, text analysis. The analysis unit can also perform image analysis. For example, the analysis unit can recognize characters in an image and convert them into text data. The analysis unit can also perform data mining. For example, the analysis unit can extract useful information from a large amount of data. The generation unit generates correction instructions based on the information analyzed by the analysis unit. The generation unit can, for example, identify the parts to be corrected and generate correction instructions. The generation unit can also generate correction instructions based on correction rules. For example, the generation unit can generate correction instructions according to grammar rules and style guides. The output unit outputs the correction instructions generated by the generation unit. The output unit can, for example, display the correction instructions on the screen. The output unit can also output the correction instructions to a file. For example, the output unit can output the correction instructions as a PDF file. The output unit can also send the correction instructions via email. For example, the output unit can send the correction instructions via email. This allows the correction instruction creation system to efficiently acquire, analyze, generate, and output the information to be corrected. Some or all of the above-described processes in the acquisition unit, analysis unit, generation unit, and output unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input a URL entered by the user into the generation AI and have the generation AI execute the process of acquiring information from the URL.

[0030] The data acquisition unit acquires information to be modified. This information includes, but is not limited to, text data, image data, and program code. The data acquisition unit can acquire information from, for example, a database. Specifically, the unit accesses the database using SQL queries and extracts the necessary information. The data acquisition unit can also acquire information from user input. For example, the unit can acquire URLs and screenshots entered by the user. For URLs entered by the user, it sends an HTTP request and acquires the HTML data returned as a response. For screenshots, it reads the image file and acquires it as binary data. Furthermore, the data acquisition unit can acquire information from external services through APIs. For example, the unit sends a request to a specific API endpoint, parses the returned JSON data, and extracts the necessary information. This allows the data acquisition unit to efficiently collect information from diverse sources and improve the overall data collection capability of the system.

[0031] The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit performs text analysis. Specifically, it analyzes text data using natural language processing technology to detect grammatical errors and style inconsistencies. The analysis unit can also perform image analysis. For example, it uses OCR (optical character recognition) technology to recognize characters in images and convert them into text data. Furthermore, the analysis unit can perform data mining. For example, it applies clustering and classification algorithms to extract useful information from large amounts of data. This allows the analysis unit to analyze acquired information from multiple perspectives and provide the basic data necessary for generating correction instructions. In addition, the analysis unit can perform advanced analysis using AI. For example, it uses deep learning models to perform semantic analysis of text data and feature extraction of image data. This allows the analysis unit to provide more accurate analysis results and improve the overall system performance.

[0032] The generation unit generates correction instructions based on the information analyzed by the analysis unit. For example, the generation unit identifies areas that need correction and generates correction instructions. Specifically, it identifies areas that need correction and generates appropriate correction instructions based on grammatical errors and style mismatch information provided by the analysis unit. The generation unit can also generate correction instructions based on correction rules. For example, the generation unit generates correction instructions according to grammatical rules and style guides. This allows the generation unit to provide consistent correction instructions, enabling users to perform correction work efficiently. Furthermore, the generation unit can also generate correction instructions using AI. For example, it can use generation AI to analyze information obtained from URLs entered by the user and generate appropriate correction instructions. Generation AI can automatically generate grammatically correct correction instructions using natural language generation technology. This allows the generation unit to reduce the burden on the user and significantly improve the efficiency of the correction work.

[0033] The output unit outputs the correction instructions generated by the generation unit. The output unit can, for example, display the correction instructions on the screen. Specifically, it can visually display the correction instructions through the user interface to make them easily understandable to the user. The output unit can also output the correction instructions to a file. For example, the output unit can output the correction instructions as a PDF file so that the user can refer to it later. Furthermore, the output unit can send the correction instructions via email. For example, the output unit can send the correction instructions via email so that the user can receive them remotely. This allows the output unit to provide correction instructions to the user in various ways, improving the convenience of the correction work. In addition, the output unit can optimize the output content using AI. For example, it can automatically select the most appropriate output format based on the user's past correction history. This allows the output unit to provide flexible output tailored to the user's needs, improving the overall usability of the system.

[0034] The system includes a configuration section for setting correction rules. The configuration section sets the correction rules. These correction rules include, but are not limited to, grammar rules, style guides, and coding conventions. For example, the configuration section can set grammar rules. The configuration section can also set style guides. For example, the configuration section can set correction rules according to a specific style. The configuration section can also set coding conventions. For example, the configuration section can set correction rules for program code. This makes the generation of correction instructions more efficient by setting correction rules. Some or all of the above processing in the configuration section may be performed using, for example, AI, or not using AI. For example, the configuration section can input correction rules entered by the user into a generation AI and have the generation AI execute the setting of correction rules.

[0035] The system includes a marking unit for marking the areas to be corrected. The marking unit marks the areas to be corrected. Marking includes, but is not limited to, highlighting, underlining, and inserting comments. For example, the marking unit can highlight the areas to be corrected. The marking unit can also underline the areas to be corrected. For example, the marking unit can draw an underline over the areas to be corrected. The marking unit can also insert comments over the areas to be corrected. For example, the marking unit can add comments over the areas to be corrected. This makes the correction instructions easier to understand by clearly indicating the areas to be corrected. Some or all of the above processing in the marking unit may be performed using, for example, AI, or without using AI. For example, the marking unit can input the areas to be corrected into a generating AI and have the generating AI perform the marking of the areas to be corrected.

[0036] The system includes an adjustment unit that adjusts the description of the correction instructions. The adjustment unit adjusts the description of the correction instructions. Adjustments include, but are not limited to, grammar checks, style adjustments, and format changes. For example, the adjustment unit performs a grammar check on the correction instructions. The adjustment unit can also adjust the style of the correction instructions. For example, the adjustment unit adjusts the correction instructions according to a specific style. The adjustment unit can also change the format of the correction instructions. For example, the adjustment unit changes the format of the correction instructions. By optimizing the description of the correction instructions, the accuracy of the correction instructions is improved. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input the correction instructions to a generating AI and have the generating AI perform the adjustment of the correction instructions.

[0037] The configuration unit is related to the generation unit and sets correction rules that serve as the basis for the generation unit to generate correction instructions. For example, the configuration unit provides correction rules to the generation unit, which then serve as the basis for the generation unit to generate correction instructions. This allows the generation unit to efficiently generate correction instructions by setting correction rules. Some or all of the above-described processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input correction rules to the generation AI and cause the generation AI to execute the basis for the generation unit to generate correction instructions.

[0038] The marking unit is associated with the output unit and marks correction instructions to clarify the areas to be corrected. For example, the marking unit provides the areas to be corrected to the output unit, and marks the areas to be corrected when the output unit outputs the correction instructions. This makes the correction instructions easier to understand by clarifying the areas to be corrected. Some or all of the above processing in the marking unit may be performed using AI, for example, or without AI. For example, the marking unit can input the areas to be corrected to a generating AI, and cause the generating AI to perform the process of marking the areas to be corrected when the output unit outputs the correction instructions.

[0039] The adjustment unit is located between the generation unit and the output unit and optimizes the generated correction instructions. For example, the adjustment unit receives correction instructions from the generation unit, optimizes them, and provides them to the output unit. This improves the accuracy of the correction instructions by optimizing them. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the generated correction instructions to the generation AI and have the generation AI perform the optimization of the correction instructions.

[0040] The acquisition unit analyzes the user's past revision history and selects the optimal acquisition method. For example, the acquisition unit prioritizes acquiring sections that the user has frequently revised in the past. The acquisition unit can also suggest the optimal acquisition method by referring to the revision methods the user has used in the past (manual, voice input, etc.). Furthermore, the acquisition unit can analyze revisions made during specific time periods from the user's past revision history and select the optimal acquisition timing. In this way, the optimal acquisition method can be selected by analyzing the user's past revision history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past revision history data into a generating AI and have the generating AI select the optimal acquisition method.

[0041] The acquisition unit filters the information to be modified based on the user's current projects and areas of interest. For example, the acquisition unit prioritizes acquiring information related to the project the user is currently working on. The acquisition unit can also filter and acquire highly relevant information based on the user's areas of interest. Furthermore, the acquisition unit can filter and acquire information to be modified based on keywords set by the user. This allows for the acquisition of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0042] The data acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location when acquiring information to be corrected. For example, if the user is in a specific region, the data acquisition unit prioritizes acquiring information to be corrected that is related to that region. Furthermore, if the user is traveling, the data acquisition unit can also acquire highly relevant information to be corrected based on their current location. Additionally, if the user is participating in a specific event, the data acquisition unit can prioritize acquiring information to be corrected that is related to that event. This allows for the prioritization of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant information.

[0043] The acquisition unit analyzes the user's social media activity and obtains relevant information when acquiring information to be corrected. For example, the acquisition unit acquires relevant information to be corrected based on information shared by the user on social media. The acquisition unit can also analyze the content of posts from accounts that the user follows and acquire relevant information to be corrected. Furthermore, the acquisition unit can analyze the activities of groups and communities that the user participates in and acquire relevant information to be corrected. This allows for the efficient acquisition of relevant information by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0044] The analysis unit adjusts the level of detail of the analysis based on the importance of the information to be corrected. For example, the analysis unit performs a detailed analysis on information that is of high importance. The analysis unit can also perform a simplified analysis on information that is of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to importance and analyze the information in order from most important to least important. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information to be corrected. 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 importance data of the information to be corrected into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit applies different analysis algorithms depending on the category of information to be corrected during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. It can also apply an image recognition algorithm to image information. Furthermore, the analysis unit can apply a data mining algorithm to tabular data. By applying an appropriate analysis algorithm according to the category of information to be corrected, the accuracy of the analysis is improved. 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 category data of the information to be corrected into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit determines the priority of the analysis based on the submission timing of the information to be corrected. For example, the analysis unit prioritizes the analysis of information to be corrected that has been submitted earlier. The analysis unit can also postpone the analysis of information to be corrected that has been submitted later. Furthermore, the analysis unit can adjust the analysis schedule according to the submission timing. This enables efficient analysis by determining the priority of the analysis based on the submission timing of the information to be corrected. 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 submission timing data of the information to be corrected into a generating AI and have the generating AI perform the determination of the analysis priority.

[0047] The analysis unit adjusts the order of analysis based on the relevance of the information to be corrected during the analysis. For example, the analysis unit prioritizes analyzing information to be corrected that is highly relevant. The analysis unit can also postpone the analysis of information to be corrected that is less relevant. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information to be corrected. 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 relevance data of the information to be corrected into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The generation unit adjusts the level of detail of the generated correction instructions based on the importance of the information to be corrected. For example, the generation unit generates detailed correction instructions for information with high importance. The generation unit can also generate concise correction instructions for information with low importance. Furthermore, the generation unit can determine the priority of the correction instructions according to their importance and generate them in order from most important to least important. This allows for efficient generation of correction instructions by adjusting the level of detail based on the importance of the information to be corrected. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input importance data of the information to be corrected into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated instructions.

[0049] The generation unit applies different generation algorithms depending on the category of information to be corrected when generating correction instructions. For example, the generation unit applies a natural language generation algorithm to text information. It can also apply an image generation algorithm to image information. Furthermore, it can apply a data generation algorithm to tabular data. By applying an appropriate generation algorithm according to the category of information to be corrected, the generation accuracy is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input category data of the information to be corrected into a generation AI and have the generation AI execute the application of the generation algorithm.

[0050] The generation unit determines the generation priority based on the submission timing of the information to be corrected when generating correction instructions. For example, the generation unit prioritizes generating information to be corrected that has an earlier submission date. The generation unit can also postpone the generation of information to be corrected that has a later submission date. Furthermore, the generation unit can adjust the generation schedule of correction instructions according to the submission dates. This enables efficient generation of correction instructions by determining the generation priority based on the submission dates of the information to be corrected. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input submission date data of the information to be corrected into a generation AI and have the generation AI perform the determination of the generation priority.

[0051] The generation unit adjusts the generation order based on the relevance of the information to be corrected when generating correction instructions. For example, the generation unit prioritizes generating information to be corrected that is highly relevant. The generation unit can also postpone the generation of information to be corrected that is less relevant. Furthermore, the generation unit can adjust the generation order of correction instructions according to their relevance. This allows for efficient generation of correction instructions by adjusting the generation order based on the relevance of the information to be corrected. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevance data of the information to be corrected into a generation AI and have the generation AI perform the adjustment of the generation order.

[0052] The output unit selects the optimal output method when outputting correction instructions by referring to the user's past output history. For example, the output unit prioritizes suggesting output methods previously used by the user (PDF, text file, etc.). The output unit can also select the optimal output method by referring to the format of correction instructions previously output by the user. Furthermore, the output unit can predict and suggest an output method to be used during a specific time period based on the user's past output history. This allows the optimal output method to be selected by referring to the user's past output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past output history data into a generating AI and have the generating AI select the optimal output method.

[0053] The output unit selects the optimal output method when outputting correction instructions, taking into account the user's device information. For example, if the user is using a smartphone, the output unit provides an output method that matches the screen size. The output unit can also provide an output method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a desktop, the output unit can provide an output method that includes detailed information. This allows for the selection of the optimal output method by considering the user's device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user device information data into a generating AI and have the generating AI select the optimal output method.

[0054] The output unit selects the optimal output method when outputting correction instructions, taking into account the user's device information. For example, if the user is using a smartphone, the output unit provides an output method that matches the screen size. The output unit can also provide an output method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a desktop, the output unit can provide an output method that includes detailed information. This allows for the selection of the optimal output method by considering the user's device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user device information data into a generating AI and have the generating AI select the optimal output method.

[0055] The configuration unit, when setting correction rules, selects the optimal configuration method by referring to the user's past configuration history. For example, the configuration unit prioritizes suggesting correction rules that the user has used in the past. The configuration unit can also select the optimal configuration method by referring to the format of correction rules that the user has set in the past. Furthermore, the configuration unit can predict and suggest correction rules to be used during a specific time period based on the user's past configuration history. This allows the optimal configuration method to be selected by referring to the user's past configuration history. Some or all of the above processes in the configuration unit may be performed using AI, for example, or not using AI. For example, the configuration unit can input the user's past configuration history data into a generating AI and have the generating AI perform the selection of the optimal configuration method.

[0056] The configuration unit selects the optimal configuration method when setting correction rules, taking into account the user's industry information. For example, the configuration unit proposes standard correction rules for the industry to which the user belongs. Furthermore, if the user works in a specific industry, the configuration unit can provide industry-specific correction rules. In addition, the configuration unit can set the optimal correction rules based on the user's industry information. This allows for the selection of the optimal configuration method by considering the user's industry information. Some or all of the above processing in the configuration unit may be performed using AI, or not. For example, the configuration unit can input the user's industry information data into a generating AI and have the generating AI select the optimal configuration method.

[0057] The configuration unit selects the optimal configuration method when setting correction rules, taking into account the user's industry information. For example, the configuration unit proposes standard correction rules for the industry to which the user belongs. Furthermore, if the user works in a specific industry, the configuration unit can provide industry-specific correction rules. In addition, the configuration unit can set the optimal correction rules based on the user's industry information. This allows for the selection of the optimal configuration method by considering the user's industry information. Some or all of the above processing in the configuration unit may be performed using AI, or not. For example, the configuration unit can input the user's industry information data into a generating AI and have the generating AI select the optimal configuration method.

[0058] The marking unit selects the optimal marking method when marking correction areas by referring to the user's past marking history. For example, the marking unit may prioritize suggesting marking methods previously used by the user. The marking unit can also select the optimal marking method by referring to the format of correction areas previously marked by the user. Furthermore, the marking unit can predict and suggest marking methods to be used during specific time periods based on the user's past marking history. This allows the optimal marking method to be selected by referring to the user's past marking history. Some or all of the above processing in the marking unit may be performed using AI, for example, or without AI. For example, the marking unit can input the user's past marking history data into a generating AI and have the generating AI select the optimal marking method.

[0059] The marking unit selects the optimal marking method when marking the areas to be corrected, taking into account the user's device information. For example, if the user is using a smartphone, the marking unit provides a marking method that matches the screen size. Furthermore, if the user is using a tablet, the marking unit can also provide a marking method optimized for larger screens. Additionally, if the user is using a desktop, the marking unit can provide a marking method that includes detailed information. This allows the system to select the optimal marking method by considering the user's device information. Some or all of the above processing in the marking unit may be performed using AI, for example, or without AI. For example, the marking unit can input user device information data into a generating AI and have the generating AI select the optimal marking method.

[0060] The marking unit selects the optimal marking method when marking the areas to be corrected, taking into account the user's device information. For example, if the user is using a smartphone, the marking unit provides a marking method that matches the screen size. Furthermore, if the user is using a tablet, the marking unit can also provide a marking method optimized for larger screens. Additionally, if the user is using a desktop, the marking unit can provide a marking method that includes detailed information. This allows the system to select the optimal marking method by considering the user's device information. Some or all of the above processing in the marking unit may be performed using AI, for example, or without AI. For example, the marking unit can input user device information data into a generating AI and have the generating AI select the optimal marking method.

[0061] The adjustment unit selects the optimal adjustment method when adjusting correction instructions by referring to the user's past adjustment history. For example, the adjustment unit may prioritize suggesting adjustment methods previously used by the user. The adjustment unit can also select the optimal adjustment method by referring to the format of correction instructions previously adjusted by the user. Furthermore, the adjustment unit can predict and suggest adjustment methods to be used during specific time periods based on the user's past adjustment history. This allows the optimal adjustment method to be selected by referring to the user's past adjustment history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past adjustment history data into a generating AI and have the generating AI select the optimal adjustment method.

[0062] The adjustment unit selects the optimal adjustment method when adjusting correction instructions, taking into account the user's device information. For example, if the user is using a smartphone, the adjustment unit provides an adjustment method that matches the screen size. The adjustment unit can also provide an adjustment method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a desktop, the adjustment unit can provide an adjustment method that includes detailed information. This allows the system to select the optimal adjustment method by considering the user's device information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user device information data into a generating AI and have the generating AI select the optimal adjustment method.

[0063] The adjustment unit selects the optimal adjustment method when adjusting correction instructions, taking into account the user's device information. For example, if the user is using a smartphone, the adjustment unit provides an adjustment method that matches the screen size. The adjustment unit can also provide an adjustment method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a desktop, the adjustment unit can provide an adjustment method that includes detailed information. This allows the system to select the optimal adjustment method by considering the user's device information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user device information data into a generating AI and have the generating AI select the optimal adjustment method.

[0064] The adjustment unit selects the optimal adjustment method when adjusting correction instructions by referring to the user's past adjustment history. For example, the adjustment unit may prioritize suggesting adjustment methods previously used by the user. The adjustment unit can also select the optimal adjustment method by referring to the format of correction instructions previously adjusted by the user. Furthermore, the adjustment unit can predict and suggest adjustment methods to be used during specific time periods based on the user's past adjustment history. This allows the optimal adjustment method to be selected by referring to the user's past adjustment history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past adjustment history data into a generating AI and have the generating AI select the optimal adjustment method.

[0065] The adjustment unit selects an adjustment method based on the user's current projects and areas of interest when adjusting correction instructions. For example, the adjustment unit prioritizes suggesting adjustment methods related to the project the user is currently working on. The adjustment unit can also select a highly relevant adjustment method based on the user's areas of interest. Furthermore, the adjustment unit can select an adjustment method for correction instructions based on keywords set by the user. This enables efficient adjustment by selecting an adjustment method based on the user's current projects and areas of interest. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's project information and area of ​​interest data into a generating AI and have the generating AI select the optimal adjustment method.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The revision instruction creation system can analyze the user's past revision history and propose the optimal revision rules. For example, it can prioritize suggesting revision rules that the user has frequently used in the past. It can also set optimal revision rules by referring to the content of the user's past revisions. Furthermore, it can predict and suggest revision rules to be used during specific time periods based on the user's past revision history. In this way, the system can set optimal revision rules by referring to the user's past revision history.

[0068] The correction instruction creation system can select the optimal output method for correction instructions by considering the user's device information. For example, if the user is using a smartphone, it can provide an output method that matches the screen size. If the user is using a tablet, it can provide an output method optimized for larger screens. Furthermore, if the user is using a desktop computer, it can provide an output method that includes detailed information. This allows the system to select the optimal output method by considering the user's device information.

[0069] The revision instruction creation system can filter the content of revision instructions based on the user's current projects and areas of interest. For example, it can prioritize including information related to the user's current project in the revision instructions. It can also filter and include highly relevant information based on the user's areas of interest. Furthermore, it can filter the content of revision instructions based on keywords set by the user. This ensures that highly relevant information is included in the revision instructions based on the user's current projects and areas of interest.

[0070] The revision instruction creation system can select the optimal output method by referring to the user's past output history. For example, it prioritizes suggesting output methods the user has used in the past (PDF, text file, etc.). It can also select the optimal output method by referring to the format of revision instructions the user has previously output. Furthermore, it can predict and suggest the output method to be used during a specific time period based on the user's past output history. In this way, the system can select the optimal output method by referring to the user's past output history.

[0071] The correction instruction generation system can prioritize the inclusion of highly relevant information in correction instructions, taking into account the user's geographical location. For example, if the user is in a specific region, the correction instructions will prioritize information related to that region. Similarly, if the user is traveling, the system can prioritize information related to their current location. Furthermore, if the user is participating in a specific event, the system can prioritize information related to that event. This allows the system to prioritize the inclusion of highly relevant information in correction instructions by considering the user's geographical location.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The acquisition unit retrieves the information to be modified. This information includes, for example, text data, image data, and program code. The acquisition unit can retrieve information from a database or from user input. For example, it can retrieve URLs or screenshots entered by the user. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit performs, for example, text analysis and image analysis. For example, it recognizes characters in an image and converts them into text data. It can also perform data mining to extract useful information from large amounts of data. Step 3: The generation unit generates correction instructions based on the information analyzed by the analysis unit. For example, the generation unit identifies the areas to be corrected and generates correction instructions. It can also generate correction instructions according to grammatical rules or style guides. Step 4: The output unit outputs the correction instructions generated by the generation unit. The output unit can, for example, display the correction instructions on the screen. It can also output the correction instructions to a file. For example, it can output the correction instructions as a PDF file. It can also send the correction instructions via email.

[0074] (Example of form 2) The correction instruction creation system according to an embodiment of the present invention is a system for efficiently creating correction instructions for web pages and paper documents. The system works by having a user paste a web page URL or a screenshot of a paper document into the system, allowing the AI ​​to recognize the text and create a correction instruction document based on specified correction rules. This system prevents omissions and inconsistencies in corrections and reduces the human resources required to create correction instructions. For example, a user pastes a web page URL or a screenshot of a paper document into the system. The user only needs to specify the page or screenshot to be corrected. For example, the user enters the URL of a page where a service name change or condition change is necessary. This information is input to the AI. Next, the AI ​​analyzes the input information and recognizes the text. The AI ​​can accurately recognize all text, including text and variations in spelling within images. For example, it recognizes text and identical words with different spellings within an image and identifies the areas to be corrected. The AI ​​then creates a correction instruction document based on specified correction rules. These rules include substitution, addition, deletion, and contextual correction. The AI ​​marks the areas to be corrected and writes the correction instructions according to these rules. Furthermore, when the AI ​​creates revision instructions, it adds symbols before and after the parts to be revised. For example, it will indicate the revised part in the format "■■Before revision → After revision■■" and the added text in the format "??Newly added text??". This makes the revised parts clear and the revision instructions easy to understand. This system can reduce the cost of page revisions within the company and reduce the risk of revision errors. For example, even if service names or conditions change frequently, the AI ​​can automatically create revision instructions, significantly reducing human resources. In addition, by preventing omissions of revisions and inconsistencies in terminology, the accuracy of revisions is improved and the risk of revision errors is reduced. In summary, the revision instruction creation system can prevent omissions of revisions and inconsistencies in terminology, and reduce the human resources required to create revision instructions.

[0075] The correction instruction creation system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, and an output unit. The acquisition unit acquires information to be corrected. The information to be corrected includes, but is not limited to, text data, image data, and program code. The acquisition unit can acquire information from, for example, a database. The acquisition unit can also acquire information from user input. For example, the acquisition unit can acquire URLs and captured images entered by the user. The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit can perform, for example, text analysis. The analysis unit can also perform image analysis. For example, the analysis unit can recognize characters in an image and convert them into text data. The analysis unit can also perform data mining. For example, the analysis unit can extract useful information from a large amount of data. The generation unit generates correction instructions based on the information analyzed by the analysis unit. The generation unit can, for example, identify the parts to be corrected and generate correction instructions. The generation unit can also generate correction instructions based on correction rules. For example, the generation unit can generate correction instructions according to grammar rules and style guides. The output unit outputs the correction instructions generated by the generation unit. The output unit can, for example, display the correction instructions on the screen. The output unit can also output the correction instructions to a file. For example, the output unit can output the correction instructions as a PDF file. The output unit can also send the correction instructions via email. For example, the output unit can send the correction instructions via email. This allows the correction instruction creation system to efficiently acquire, analyze, generate, and output the information to be corrected. Some or all of the above-described processes in the acquisition unit, analysis unit, generation unit, and output unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input a URL entered by the user into the generation AI and have the generation AI execute the process of acquiring information from the URL.

[0076] The data acquisition unit acquires information to be modified. This information includes, but is not limited to, text data, image data, and program code. The data acquisition unit can acquire information from, for example, a database. Specifically, the unit accesses the database using SQL queries and extracts the necessary information. The data acquisition unit can also acquire information from user input. For example, the unit can acquire URLs and screenshots entered by the user. For URLs entered by the user, it sends an HTTP request and acquires the HTML data returned as a response. For screenshots, it reads the image file and acquires it as binary data. Furthermore, the data acquisition unit can acquire information from external services through APIs. For example, the unit sends a request to a specific API endpoint, parses the returned JSON data, and extracts the necessary information. This allows the data acquisition unit to efficiently collect information from diverse sources and improve the overall data collection capability of the system.

[0077] The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit performs text analysis. Specifically, it analyzes text data using natural language processing technology to detect grammatical errors and style inconsistencies. The analysis unit can also perform image analysis. For example, it uses OCR (optical character recognition) technology to recognize characters in images and convert them into text data. Furthermore, the analysis unit can perform data mining. For example, it applies clustering and classification algorithms to extract useful information from large amounts of data. This allows the analysis unit to analyze acquired information from multiple perspectives and provide the basic data necessary for generating correction instructions. In addition, the analysis unit can perform advanced analysis using AI. For example, it uses deep learning models to perform semantic analysis of text data and feature extraction of image data. This allows the analysis unit to provide more accurate analysis results and improve the overall system performance.

[0078] The generation unit generates correction instructions based on the information analyzed by the analysis unit. For example, the generation unit identifies areas that need correction and generates correction instructions. Specifically, it identifies areas that need correction and generates appropriate correction instructions based on grammatical errors and style mismatch information provided by the analysis unit. The generation unit can also generate correction instructions based on correction rules. For example, the generation unit generates correction instructions according to grammatical rules and style guides. This allows the generation unit to provide consistent correction instructions, enabling users to perform correction work efficiently. Furthermore, the generation unit can also generate correction instructions using AI. For example, it can use generation AI to analyze information obtained from URLs entered by the user and generate appropriate correction instructions. Generation AI can automatically generate grammatically correct correction instructions using natural language generation technology. This allows the generation unit to reduce the burden on the user and significantly improve the efficiency of the correction work.

[0079] The output unit outputs the correction instructions generated by the generation unit. The output unit can, for example, display the correction instructions on the screen. Specifically, it can visually display the correction instructions through the user interface to make them easily understandable to the user. The output unit can also output the correction instructions to a file. For example, the output unit can output the correction instructions as a PDF file so that the user can refer to it later. Furthermore, the output unit can send the correction instructions via email. For example, the output unit can send the correction instructions via email so that the user can receive them remotely. This allows the output unit to provide correction instructions to the user in various ways, improving the convenience of the correction work. In addition, the output unit can optimize the output content using AI. For example, it can automatically select the most appropriate output format based on the user's past correction history. This allows the output unit to provide flexible output tailored to the user's needs, improving the overall usability of the system.

[0080] The system includes a configuration section for setting correction rules. The configuration section sets the correction rules. These correction rules include, but are not limited to, grammar rules, style guides, and coding conventions. For example, the configuration section can set grammar rules. The configuration section can also set style guides. For example, the configuration section can set correction rules according to a specific style. The configuration section can also set coding conventions. For example, the configuration section can set correction rules for program code. This makes the generation of correction instructions more efficient by setting correction rules. Some or all of the above processing in the configuration section may be performed using, for example, AI, or not using AI. For example, the configuration section can input correction rules entered by the user into a generation AI and have the generation AI execute the setting of correction rules.

[0081] The system includes a marking unit for marking the areas to be corrected. The marking unit marks the areas to be corrected. Marking includes, but is not limited to, highlighting, underlining, and inserting comments. For example, the marking unit can highlight the areas to be corrected. The marking unit can also underline the areas to be corrected. For example, the marking unit can draw an underline over the areas to be corrected. The marking unit can also insert comments over the areas to be corrected. For example, the marking unit can add comments over the areas to be corrected. This makes the correction instructions easier to understand by clearly indicating the areas to be corrected. Some or all of the above processing in the marking unit may be performed using, for example, AI, or without using AI. For example, the marking unit can input the areas to be corrected into a generating AI and have the generating AI perform the marking of the areas to be corrected.

[0082] The system includes an adjustment unit that adjusts the description of the correction instructions. The adjustment unit adjusts the description of the correction instructions. Adjustments include, but are not limited to, grammar checks, style adjustments, and format changes. For example, the adjustment unit performs a grammar check on the correction instructions. The adjustment unit can also adjust the style of the correction instructions. For example, the adjustment unit adjusts the correction instructions according to a specific style. The adjustment unit can also change the format of the correction instructions. For example, the adjustment unit changes the format of the correction instructions. By optimizing the description of the correction instructions, the accuracy of the correction instructions is improved. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or without AI. For example, the adjustment unit can input the correction instructions to a generating AI and have the generating AI perform the adjustment of the correction instructions.

[0083] The configuration unit is related to the generation unit and sets correction rules that serve as the basis for the generation unit to generate correction instructions. For example, the configuration unit provides correction rules to the generation unit, which then serve as the basis for the generation unit to generate correction instructions. This allows the generation unit to efficiently generate correction instructions by setting correction rules. Some or all of the above-described processes in the configuration unit may be performed using AI, for example, or without AI. For example, the configuration unit can input correction rules to the generation AI and cause the generation AI to execute the basis for the generation unit to generate correction instructions.

[0084] The marking unit is associated with the output unit and marks correction instructions to clarify the areas to be corrected. For example, the marking unit provides the areas to be corrected to the output unit, and marks the areas to be corrected when the output unit outputs the correction instructions. This makes the correction instructions easier to understand by clarifying the areas to be corrected. Some or all of the above processing in the marking unit may be performed using AI, for example, or without AI. For example, the marking unit can input the areas to be corrected to a generating AI, and cause the generating AI to perform the process of marking the areas to be corrected when the output unit outputs the correction instructions.

[0085] The adjustment unit is located between the generation unit and the output unit and optimizes the generated correction instructions. For example, the adjustment unit receives correction instructions from the generation unit, optimizes them, and provides them to the output unit. This improves the accuracy of the correction instructions by optimizing them. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the generated correction instructions to the generation AI and have the generation AI perform the optimization of the correction instructions.

[0086] The acquisition unit estimates the user's emotions and adjusts the timing of acquiring the information to be corrected based on the estimated emotions. For example, if the user is stressed, the system will immediately acquire the information to be corrected and begin processing quickly. If the user is relaxed, the system can acquire the information to be corrected at the user's pace. Furthermore, if the user is focused, the system can acquire the information to be corrected at an appropriate time to avoid interrupting the user's work. This allows for efficient information acquisition by adjusting the timing of acquiring the information to be corrected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of information acquisition based on emotions.

[0087] The acquisition unit analyzes the user's past revision history and selects the optimal acquisition method. For example, the acquisition unit prioritizes acquiring sections that the user has frequently revised in the past. The acquisition unit can also suggest the optimal acquisition method by referring to the revision methods the user has used in the past (manual, voice input, etc.). Furthermore, the acquisition unit can analyze revisions made during specific time periods from the user's past revision history and select the optimal acquisition timing. In this way, the optimal acquisition method can be selected by analyzing the user's past revision history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past revision history data into a generating AI and have the generating AI select the optimal acquisition method.

[0088] The acquisition unit filters the information to be modified based on the user's current projects and areas of interest. For example, the acquisition unit prioritizes acquiring information related to the project the user is currently working on. The acquisition unit can also filter and acquire highly relevant information based on the user's areas of interest. Furthermore, the acquisition unit can filter and acquire information to be modified based on keywords set by the user. This allows for the acquisition of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's project information and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0089] The acquisition unit estimates the user's emotions and determines the priority of the information to be corrected based on the estimated emotions. For example, if the user is stressed, the acquisition unit will prioritize acquiring information that is of high importance. If the user is relaxed, the acquisition unit can also acquire information that is of lower importance. Furthermore, if the user is focused, the acquisition unit can prioritize acquiring information that is of interest to the current task. This enables efficient information acquisition by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input user emotion data into the generative AI and have the generative AI perform the determination of information priority based on emotions.

[0090] The data acquisition unit prioritizes acquiring highly relevant information by considering the user's geographical location when acquiring information to be corrected. For example, if the user is in a specific region, the data acquisition unit prioritizes acquiring information to be corrected that is related to that region. Furthermore, if the user is traveling, the data acquisition unit can also acquire highly relevant information to be corrected based on their current location. Additionally, if the user is participating in a specific event, the data acquisition unit can prioritize acquiring information to be corrected that is related to that event. This allows for the prioritization of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location data into a generating AI and have the generating AI acquire highly relevant information.

[0091] The acquisition unit analyzes the user's social media activity and obtains relevant information when acquiring information to be corrected. For example, the acquisition unit acquires relevant information to be corrected based on information shared by the user on social media. The acquisition unit can also analyze the content of posts from accounts that the user follows and acquire relevant information to be corrected. Furthermore, the acquisition unit can analyze the activities of groups and communities that the user participates in and acquire relevant information to be corrected. This allows for the efficient acquisition of relevant information by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0092] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide highly easy-to-understand analysis results. 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 user emotion data into the generative AI and have the generative AI perform adjustments to the presentation of the analysis based on emotions.

[0093] The analysis unit adjusts the level of detail of the analysis based on the importance of the information to be corrected. For example, the analysis unit performs a detailed analysis on information that is of high importance. The analysis unit can also perform a simplified analysis on information that is of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to importance and analyze the information in order from most important to least important. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information to be corrected. 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 importance data of the information to be corrected into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0094] The analysis unit applies different analysis algorithms depending on the category of information to be corrected during analysis. For example, the analysis unit applies a natural language processing algorithm to text information. It can also apply an image recognition algorithm to image information. Furthermore, the analysis unit can apply a data mining algorithm to tabular data. By applying an appropriate analysis algorithm according to the category of information to be corrected, the accuracy of the analysis is improved. 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 category data of the information to be corrected into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0095] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows for efficient analysis results by adjusting the length of the analysis 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 may be, 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 or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis based on the emotions.

[0096] The analysis unit determines the priority of the analysis based on the submission timing of the information to be corrected. For example, the analysis unit prioritizes the analysis of information to be corrected that has been submitted earlier. The analysis unit can also postpone the analysis of information to be corrected that has been submitted later. Furthermore, the analysis unit can adjust the analysis schedule according to the submission timing. This enables efficient analysis by determining the priority of the analysis based on the submission timing of the information to be corrected. 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 submission timing data of the information to be corrected into a generating AI and have the generating AI perform the determination of the analysis priority.

[0097] The analysis unit adjusts the order of analysis based on the relevance of the information to be corrected during the analysis. For example, the analysis unit prioritizes analyzing information to be corrected that is highly relevant. The analysis unit can also postpone the analysis of information to be corrected that is less relevant. Furthermore, the analysis unit can adjust the order of analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information to be corrected. 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 relevance data of the information to be corrected into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0098] The generation unit estimates the user's emotions and adjusts the method of generating corrective instructions based on the estimated emotions. For example, if the user is relaxed, the generation unit generates detailed corrective instructions. If the user is in a hurry, the generation unit can also generate concise corrective instructions. Furthermore, if the user is excited, the generation unit can generate corrective instructions with visually stimulating effects. This allows for efficient generation of corrective instructions by adjusting the method of generating corrective instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the method of generating corrective instructions based on emotions.

[0099] The generation unit adjusts the level of detail of the generated correction instructions based on the importance of the information to be corrected. For example, the generation unit generates detailed correction instructions for information with high importance. The generation unit can also generate concise correction instructions for information with low importance. Furthermore, the generation unit can determine the priority of the correction instructions according to their importance and generate them in order from most important to least important. This allows for efficient generation of correction instructions by adjusting the level of detail based on the importance of the information to be corrected. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input importance data of the information to be corrected into a generation AI and have the generation AI perform the adjustment of the level of detail of the generated instructions.

[0100] The generation unit applies different generation algorithms depending on the category of information to be corrected when generating correction instructions. For example, the generation unit applies a natural language generation algorithm to text information. It can also apply an image generation algorithm to image information. Furthermore, it can apply a data generation algorithm to tabular data. By applying an appropriate generation algorithm according to the category of information to be corrected, the generation accuracy is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input category data of the information to be corrected into a generation AI and have the generation AI execute the application of the generation algorithm.

[0101] The generation unit estimates the user's emotions and adjusts the length of the correction instructions based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates short, concise correction instructions. If the user is relaxed, the generation unit can also generate detailed correction instructions. Furthermore, if the user is excited, the generation unit can generate correction instructions with visually stimulating effects. This allows for efficient generation of correction instructions by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the correction instructions based on the emotions.

[0102] The generation unit determines the generation priority based on the submission timing of the information to be corrected when generating correction instructions. For example, the generation unit prioritizes generating information to be corrected that has an earlier submission date. The generation unit can also postpone the generation of information to be corrected that has a later submission date. Furthermore, the generation unit can adjust the generation schedule of correction instructions according to the submission dates. This enables efficient generation of correction instructions by determining the generation priority based on the submission dates of the information to be corrected. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input submission date data of the information to be corrected into a generation AI and have the generation AI perform the determination of the generation priority.

[0103] The generation unit adjusts the generation order based on the relevance of the information to be corrected when generating correction instructions. For example, the generation unit prioritizes generating information to be corrected that is highly relevant. The generation unit can also postpone the generation of information to be corrected that is less relevant. Furthermore, the generation unit can adjust the generation order of correction instructions according to their relevance. This allows for efficient generation of correction instructions by adjusting the generation order based on the relevance of the information to be corrected. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevance data of the information to be corrected into a generation AI and have the generation AI perform the adjustment of the generation order.

[0104] The output unit estimates the user's emotions and adjusts the output method of the correction instructions based on the estimated user emotions. For example, if the user is tense, the output unit provides a simple and highly visible output method. If the user is relaxed, the output unit can also provide an output method that includes detailed information. Furthermore, if the user is in a hurry, the output unit can provide a concise output method. By adjusting the output method of the correction instructions according to the user's emotions, highly visible output is possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI, for example, or not using AI. For example, the output unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the output method based on the emotions.

[0105] The output unit selects the optimal output method when outputting correction instructions by referring to the user's past output history. For example, the output unit prioritizes suggesting output methods previously used by the user (PDF, text file, etc.). The output unit can also select the optimal output method by referring to the format of correction instructions previously output by the user. Furthermore, the output unit can predict and suggest an output method to be used during a specific time period based on the user's past output history. This allows the optimal output method to be selected by referring to the user's past output history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's past output history data into a generating AI and have the generating AI select the optimal output method.

[0106] The output unit selects the optimal output method when outputting correction instructions, taking into account the user's device information. For example, if the user is using a smartphone, the output unit provides an output method that matches the screen size. The output unit can also provide an output method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a desktop, the output unit can provide an output method that includes detailed information. This allows for the selection of the optimal output method by considering the user's device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user device information data into a generating AI and have the generating AI select the optimal output method.

[0107] The output unit estimates the user's emotions and adjusts the output order of correction instructions based on the estimated emotions. For example, if the user is tense, the output unit prioritizes outputting important correction instructions. If the user is relaxed, the output unit can also output all correction instructions in order. Furthermore, if the user is in a hurry, the output unit can prioritize outputting concise correction instructions. This allows for efficient output by adjusting the output order of correction instructions 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 output unit may be performed using AI, or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the output order based on emotions.

[0108] The output unit selects the optimal output method when outputting correction instructions, taking into account the user's device information. For example, if the user is using a smartphone, the output unit provides an output method that matches the screen size. The output unit can also provide an output method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a desktop, the output unit can provide an output method that includes detailed information. This allows for the selection of the optimal output method by considering the user's device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input user device information data into a generating AI and have the generating AI select the optimal output method.

[0109] The settings unit estimates the user's emotions and adjusts the method of setting the modification rules based on the estimated emotions. For example, if the user is nervous, the settings unit provides simple and easy-to-understand modification rules. If the user is relaxed, the settings unit can also provide detailed modification rules. Furthermore, if the user is in a hurry, the settings unit can provide modification rules that can be set quickly. This allows for efficient settings by adjusting the method of setting the modification rules 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 settings unit may be performed using AI, or not using AI. For example, the settings unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the settings method based on emotions.

[0110] The configuration unit, when setting correction rules, selects the optimal configuration method by referring to the user's past configuration history. For example, the configuration unit prioritizes suggesting correction rules that the user has used in the past. The configuration unit can also select the optimal configuration method by referring to the format of correction rules that the user has set in the past. Furthermore, the configuration unit can predict and suggest correction rules to be used during a specific time period based on the user's past configuration history. This allows the optimal configuration method to be selected by referring to the user's past configuration history. Some or all of the above processes in the configuration unit may be performed using AI, for example, or not using AI. For example, the configuration unit can input the user's past configuration history data into a generating AI and have the generating AI perform the selection of the optimal configuration method.

[0111] The configuration unit selects the optimal configuration method when setting correction rules, taking into account the user's industry information. For example, the configuration unit proposes standard correction rules for the industry to which the user belongs. Furthermore, if the user works in a specific industry, the configuration unit can provide industry-specific correction rules. In addition, the configuration unit can set the optimal correction rules based on the user's industry information. This allows for the selection of the optimal configuration method by considering the user's industry information. Some or all of the above processing in the configuration unit may be performed using AI, or not. For example, the configuration unit can input the user's industry information data into a generating AI and have the generating AI select the optimal configuration method.

[0112] The settings unit estimates the user's emotions and determines the priority of the correction rules based on the estimated emotions. For example, if the user is stressed, the settings unit will prioritize important correction rules. If the user is relaxed, the settings unit can also prioritize all correction rules in order. Furthermore, if the user is in a hurry, the settings unit can prioritize concise correction rules. This allows for efficient settings by determining the priority of correction rules according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI or not. For example, the settings unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0113] The configuration unit selects the optimal configuration method when setting correction rules, taking into account the user's industry information. For example, the configuration unit proposes standard correction rules for the industry to which the user belongs. Furthermore, if the user works in a specific industry, the configuration unit can provide industry-specific correction rules. In addition, the configuration unit can set the optimal correction rules based on the user's industry information. This allows for the selection of the optimal configuration method by considering the user's industry information. Some or all of the above processing in the configuration unit may be performed using AI, or not. For example, the configuration unit can input the user's industry information data into a generating AI and have the generating AI select the optimal configuration method.

[0114] The marking unit estimates the user's emotions and adjusts the marking method for corrections based on the estimated emotions. For example, if the user is tense, the marking unit provides a simple and highly visible marking method. If the user is relaxed, the marking unit can also provide a detailed marking method. Furthermore, if the user is in a hurry, the marking unit can provide a concise marking method. By adjusting the marking method for corrections according to the user's emotions, highly visible markings are possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the marking unit may be performed using AI, for example, or without AI. For example, the marking unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the marking method based on the emotions.

[0115] The marking unit selects the optimal marking method when marking correction areas by referring to the user's past marking history. For example, the marking unit may prioritize suggesting marking methods previously used by the user. The marking unit can also select the optimal marking method by referring to the format of correction areas previously marked by the user. Furthermore, the marking unit can predict and suggest marking methods to be used during specific time periods based on the user's past marking history. This allows the optimal marking method to be selected by referring to the user's past marking history. Some or all of the above processing in the marking unit may be performed using AI, for example, or without AI. For example, the marking unit can input the user's past marking history data into a generating AI and have the generating AI select the optimal marking method.

[0116] The marking unit selects the optimal marking method when marking the areas to be corrected, taking into account the user's device information. For example, if the user is using a smartphone, the marking unit provides a marking method that matches the screen size. Furthermore, if the user is using a tablet, the marking unit can also provide a marking method optimized for larger screens. Additionally, if the user is using a desktop, the marking unit can provide a marking method that includes detailed information. This allows the system to select the optimal marking method by considering the user's device information. Some or all of the above processing in the marking unit may be performed using AI, for example, or without AI. For example, the marking unit can input user device information data into a generating AI and have the generating AI select the optimal marking method.

[0117] The marking unit estimates the user's emotions and adjusts the marking order of corrections based on the estimated emotions. For example, if the user is tense, the marking unit will prioritize marking important corrections. If the user is relaxed, the marking unit can also mark all corrections in order. Furthermore, if the user is in a hurry, the marking unit can prioritize marking corrections that highlight the key points. This allows for efficient marking by adjusting the marking order of corrections 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the marking unit may be performed using AI or not. For example, the marking unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the marking order based on emotions.

[0118] The marking unit selects the optimal marking method when marking the areas to be corrected, taking into account the user's device information. For example, if the user is using a smartphone, the marking unit provides a marking method that matches the screen size. Furthermore, if the user is using a tablet, the marking unit can also provide a marking method optimized for larger screens. Additionally, if the user is using a desktop, the marking unit can provide a marking method that includes detailed information. This allows the system to select the optimal marking method by considering the user's device information. Some or all of the above processing in the marking unit may be performed using AI, for example, or without AI. For example, the marking unit can input user device information data into a generating AI and have the generating AI select the optimal marking method.

[0119] The adjustment unit estimates the user's emotions and adjusts the method of adjusting the correction instructions based on the estimated user emotions. For example, if the user is tense, the adjustment unit provides simple and easily visible correction instructions. The adjustment unit can also provide detailed correction instructions if the user is relaxed. Furthermore, if the user is in a hurry, the adjustment unit can provide concise correction instructions. This allows for efficient adjustment by adjusting the method of adjusting the correction instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the adjustment method based on emotions.

[0120] The adjustment unit selects the optimal adjustment method when adjusting correction instructions by referring to the user's past adjustment history. For example, the adjustment unit may prioritize suggesting adjustment methods previously used by the user. The adjustment unit can also select the optimal adjustment method by referring to the format of correction instructions previously adjusted by the user. Furthermore, the adjustment unit can predict and suggest adjustment methods to be used during specific time periods based on the user's past adjustment history. This allows the optimal adjustment method to be selected by referring to the user's past adjustment history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past adjustment history data into a generating AI and have the generating AI select the optimal adjustment method.

[0121] The adjustment unit selects the optimal adjustment method when adjusting correction instructions, taking into account the user's device information. For example, if the user is using a smartphone, the adjustment unit provides an adjustment method that matches the screen size. The adjustment unit can also provide an adjustment method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a desktop, the adjustment unit can provide an adjustment method that includes detailed information. This allows the system to select the optimal adjustment method by considering the user's device information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user device information data into a generating AI and have the generating AI select the optimal adjustment method.

[0122] The adjustment unit estimates the user's emotions and adjusts the order of correction instructions based on the estimated emotions. For example, if the user is tense, the adjustment unit prioritizes important correction instructions. If the user is relaxed, the adjustment unit can also adjust all correction instructions in order. Furthermore, if the user is in a hurry, the adjustment unit can prioritize concise correction instructions. This allows for efficient adjustment by adjusting the order of correction instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the order of corrections based on emotions.

[0123] The adjustment unit selects the optimal adjustment method when adjusting correction instructions, taking into account the user's device information. For example, if the user is using a smartphone, the adjustment unit provides an adjustment method that matches the screen size. The adjustment unit can also provide an adjustment method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a desktop, the adjustment unit can provide an adjustment method that includes detailed information. This allows the system to select the optimal adjustment method by considering the user's device information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user device information data into a generating AI and have the generating AI select the optimal adjustment method.

[0124] The adjustment unit selects the optimal adjustment method when adjusting correction instructions by referring to the user's past adjustment history. For example, the adjustment unit may prioritize suggesting adjustment methods previously used by the user. The adjustment unit can also select the optimal adjustment method by referring to the format of correction instructions previously adjusted by the user. Furthermore, the adjustment unit can predict and suggest adjustment methods to be used during specific time periods based on the user's past adjustment history. This allows the optimal adjustment method to be selected by referring to the user's past adjustment history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past adjustment history data into a generating AI and have the generating AI select the optimal adjustment method.

[0125] The adjustment unit selects an adjustment method based on the user's current projects and areas of interest when adjusting correction instructions. For example, the adjustment unit prioritizes suggesting adjustment methods related to the project the user is currently working on. The adjustment unit can also select a highly relevant adjustment method based on the user's areas of interest. Furthermore, the adjustment unit can select an adjustment method for correction instructions based on keywords set by the user. This enables efficient adjustment by selecting an adjustment method based on the user's current projects and areas of interest. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's project information and area of ​​interest data into a generating AI and have the generating AI select the optimal adjustment method.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The revision instruction creation system can estimate the user's emotions and adjust the revision instruction format based on those emotions. For example, if the user is stressed, the system provides a simple and highly readable format. If the user is relaxed, it can provide a format that includes detailed information. Furthermore, if the user is in a hurry, it can provide a concise format that gets straight to the point. In this way, by adjusting the revision instruction format according to the user's emotions, the system can provide highly readable revision instructions.

[0128] The revision instruction creation system can analyze the user's past revision history and propose the optimal revision rules. For example, it can prioritize suggesting revision rules that the user has frequently used in the past. It can also set optimal revision rules by referring to the content of the user's past revisions. Furthermore, it can predict and suggest revision rules to be used during specific time periods based on the user's past revision history. In this way, the system can set optimal revision rules by referring to the user's past revision history.

[0129] The revision instruction system can estimate the user's emotions and adjust the marking method for revisions based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible marking method. If the user is relaxed, it can provide a more detailed marking method. Furthermore, if the user is in a hurry, it can provide a concise marking method. By adjusting the marking method for revisions according to the user's emotions, highly visible markings can be achieved.

[0130] The correction instruction creation system can select the optimal output method for correction instructions by considering the user's device information. For example, if the user is using a smartphone, it can provide an output method that matches the screen size. If the user is using a tablet, it can provide an output method optimized for larger screens. Furthermore, if the user is using a desktop computer, it can provide an output method that includes detailed information. This allows the system to select the optimal output method by considering the user's device information.

[0131] The revision instruction generation system can estimate the user's emotions and adjust the output order of revision instructions based on those emotions. For example, if the user is stressed, important revision instructions will be output first. If the user is relaxed, all revision instructions can be output in order. Furthermore, if the user is in a hurry, concise revision instructions can be output first. By adjusting the output order of revision instructions according to the user's emotions, efficient output becomes possible.

[0132] The revision instruction creation system can filter the content of revision instructions based on the user's current projects and areas of interest. For example, it can prioritize including information related to the user's current project in the revision instructions. It can also filter and include highly relevant information based on the user's areas of interest. Furthermore, it can filter the content of revision instructions based on keywords set by the user. This ensures that highly relevant information is included in the revision instructions based on the user's current projects and areas of interest.

[0133] The revision instruction generation system can estimate the user's emotions and adjust the method of generating revision instructions based on those emotions. For example, if the user is relaxed, it can generate detailed revision instructions. If the user is in a hurry, it can generate concise revision instructions. Furthermore, if the user is excited, it can generate revision instructions with visually stimulating effects. By adjusting the method of generating revision instructions according to the user's emotions, efficient revision instruction generation becomes possible.

[0134] The revision instruction creation system can select the optimal output method by referring to the user's past output history. For example, it prioritizes suggesting output methods the user has used in the past (PDF, text file, etc.). It can also select the optimal output method by referring to the format of revision instructions the user has previously output. Furthermore, it can predict and suggest the output method to be used during a specific time period based on the user's past output history. In this way, the system can select the optimal output method by referring to the user's past output history.

[0135] The revision instruction generation system can estimate the user's emotions and adjust the length of the revision instructions based on those emotions. For example, if the user is in a hurry, it can generate short, concise revision instructions. If the user is relaxed, it can generate detailed revision instructions. Furthermore, if the user is excited, it can generate revision instructions with visually stimulating effects. By adjusting the length of revision instructions according to the user's emotions, the system enables the efficient generation of revision instructions.

[0136] The correction instruction generation system can prioritize the inclusion of highly relevant information in correction instructions, taking into account the user's geographical location. For example, if the user is in a specific region, the correction instructions will prioritize information related to that region. Similarly, if the user is traveling, the system can prioritize information related to their current location. Furthermore, if the user is participating in a specific event, the system can prioritize information related to that event. This allows the system to prioritize the inclusion of highly relevant information in correction instructions by considering the user's geographical location.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The acquisition unit retrieves the information to be modified. This information includes, for example, text data, image data, and program code. The acquisition unit can retrieve information from a database or from user input. For example, it can retrieve URLs or screenshots entered by the user. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. The analysis unit performs, for example, text analysis and image analysis. For example, it recognizes characters in an image and converts them into text data. It can also perform data mining to extract useful information from large amounts of data. Step 3: The generation unit generates correction instructions based on the information analyzed by the analysis unit. For example, the generation unit identifies the areas to be corrected and generates correction instructions. It can also generate correction instructions according to grammatical rules or style guides. Step 4: The output unit outputs the correction instructions generated by the generation unit. The output unit can, for example, display the correction instructions on the screen. It can also output the correction instructions to a file. For example, it can output the correction instructions as a PDF file. It can also send the correction instructions via email.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, output unit, setting unit, marking unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can acquire URLs and captured images entered by the user via the control unit 46A of the smart device 14. The analysis unit analyzes the acquired information via the identification processing unit 290 of the data processing unit 12 and performs character recognition and data mining. The generation unit generates correction instructions via the identification processing unit 290 of the data processing unit 12 and identifies the correction locations based on the correction rules. The output unit can display the correction instructions on the screen via the output device 40 of the smart device 14. The setting unit sets the correction rules via the identification processing unit 290 of the data processing unit 12 and generates correction instructions according to grammar rules and style guides. The marking unit can highlight or underline the correction locations via the control unit 46A of the smart device 14. The adjustment unit performs grammar checks and style adjustments on the correction instructions via the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, output unit, setting unit, marking unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can acquire URLs and captured images entered by the user by the control unit 46A of the smart glasses 214. The analysis unit analyzes the acquired information by the identification processing unit 290 of the data processing unit 12 and performs character recognition and data mining. The generation unit generates correction instructions by the identification processing unit 290 of the data processing unit 12 and identifies the correction locations based on the correction rules. The output unit can display the correction instructions on the screen by the output device 40 of the smart glasses 214. The setting unit sets the correction rules by the identification processing unit 290 of the data processing unit 12 and generates correction instructions according to grammar rules and style guides. The marking unit can highlight or underline the correction locations by the control unit 46A of the smart glasses 214. The adjustment unit performs grammar checks and style adjustments of the correction instructions by the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, output unit, setting unit, marking unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can acquire URLs and captured images entered by the user via the control unit 46A of the headset terminal 314. The analysis unit analyzes the acquired information via the identification processing unit 290 of the data processing unit 12 and performs character recognition and data mining. The generation unit generates correction instructions via the identification processing unit 290 of the data processing unit 12 and identifies the correction locations based on the correction rules. The output unit can display the correction instructions on the screen via the output device 40 of the headset terminal 314. The setting unit sets the correction rules via the identification processing unit 290 of the data processing unit 12 and generates correction instructions according to grammar rules and style guides. The marking unit can highlight or underline the correction locations via the control unit 46A of the headset terminal 314. The adjustment unit performs grammar checks and style adjustments of the correction instructions using the specific processing unit 290 of the data processing device 12. 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.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, output unit, setting unit, marking unit, and adjustment unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire URLs and captured images entered by the user via the control unit 46A of the robot 414. The analysis unit analyzes the acquired information via the identification processing unit 290 of the data processing unit 12 and performs character recognition and data mining. The generation unit generates correction instructions via the identification processing unit 290 of the data processing unit 12 and identifies the correction locations based on the correction rules. The output unit can display the correction instructions on the screen via the output device 40 of the robot 414. The setting unit sets the correction rules via the identification processing unit 290 of the data processing unit 12 and generates correction instructions according to grammar rules and style guides. The marking unit can highlight or underline the correction locations via the control unit 46A of the robot 414. The adjustment unit performs grammar checks and style adjustments on the correction instructions via the identification processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] (Note 1) An acquisition unit that acquires the information to be corrected, An analysis unit analyzes the information acquired by the acquisition unit, A generation unit that generates correction instructions based on the information analyzed by the analysis unit, The system includes an output unit that outputs the correction instructions generated by the generation unit. A system characterized by the following features. (Note 2) It includes a setting section for configuring correction rules. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a marking section for marking the areas to be corrected. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an adjustment unit for adjusting the description of correction instructions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned setting unit is, In relation to the generation unit, setting correction rules provides the basis for the generation unit to generate correction instructions. The system described in Appendix 2, characterized by the features described herein. (Note 6) The marking portion is, Mark the correction instructions to clearly indicate the areas to be corrected in relation to the output section. The system described in Appendix 3, characterized by the features described herein. (Note 7) The adjustment unit is, Located between the generation and output sections, it optimizes the generated correction instructions. The system described in Appendix 4, characterized by the features described herein. (Note 8) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring information to be modified based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, Analyze the user's past revision history and select the optimal method for obtaining it. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When retrieving information to be modified, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, It estimates the user's emotions and determines the priority of information to be corrected based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When retrieving information to be corrected, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, When acquiring information to be corrected, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the information to be corrected. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information to be corrected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information to be corrected was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information to be corrected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the method of generating correction instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating correction instructions, adjust the level of detail based on the importance of the information being corrected. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating correction instructions, different generation algorithms are applied depending on the category of information to be corrected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of the correction instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating correction instructions, the priority of generation is determined based on when the information to be corrected was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating correction instructions, the generation order is adjusted based on the relevance of the information being corrected. The system described in Appendix 1, characterized by the features described herein. (Note 26) The output unit is, It estimates the user's emotions and adjusts the output method of correction instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The output unit is, When outputting correction instructions, the system selects the optimal output method by referring to the user's past output history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The output unit is, When outputting correction instructions, the system selects the optimal output method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The output unit is, It estimates the user's emotions and adjusts the output order of correction instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The output unit is, When outputting correction instructions, the system selects the optimal output method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned setting unit is, It estimates the user's emotions and adjusts how the correction rules are set based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned setting unit is, When setting correction rules, the system refers to the user's past setting history to select the optimal setting method. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned setting unit is, When setting correction rules, the optimal setting method is selected by considering the user's industry information. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned setting unit is, The system estimates the user's emotions and prioritizes the correction rules based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned setting unit is, When setting correction rules, the optimal setting method is selected by considering the user's industry information. The system described in Appendix 2, characterized by the features described herein. (Note 36) The marking portion is, The system estimates the user's emotions and adjusts the marking method for corrections based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The marking portion is, When marking correction areas, the system selects the optimal marking method by referring to the user's past marking history. The system described in Appendix 3, characterized by the features described herein. (Note 38) The marking portion is, When marking the areas to be corrected, the optimal marking method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 39) The marking portion is, The system estimates the user's emotions and adjusts the marking order of the correction areas based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The marking portion is, When marking the areas to be corrected, the optimal marking method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 41) The adjustment unit is, The system estimates the user's emotions and adjusts the method of adjusting correction instructions based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The adjustment unit is, When adjusting correction instructions, the system selects the optimal adjustment method by referring to the user's past adjustment history. The system described in Appendix 4, characterized by the features described herein. (Note 43) The adjustment unit is, When adjusting correction instructions, the optimal adjustment method is selected considering the user's device information. The system described in Appendix 4, characterized by the features described herein. (Note 44) The adjustment unit is, The system estimates the user's emotions and adjusts the order of correction instructions based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 45) The adjustment unit is, When adjusting correction instructions, the optimal adjustment method is selected considering the user's device information. The system described in Appendix 4, characterized by the features described herein. (Note 46) The adjustment unit is, When adjusting correction instructions, the system selects the optimal adjustment method by referring to the user's past adjustment history. The system described in Appendix 4, characterized by the features described herein. (Note 47) The adjustment unit is, When adjusting revision instructions, the adjustment method is selected based on the user's current project and areas of interest. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0211] 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. An acquisition unit that acquires the information to be corrected, An analysis unit analyzes the information acquired by the acquisition unit, A generation unit that generates correction instructions based on the information analyzed by the analysis unit, The system includes an output unit that outputs the correction instructions generated by the generation unit. A system characterized by the following features.

2. It includes a setting section for configuring correction rules. The system according to feature 1.

3. It has a marking section for marking the areas to be corrected. The system according to feature 1.

4. It includes an adjustment unit for adjusting the description of correction instructions. The system according to feature 1.

5. The aforementioned setting unit is, Setting correction rules provides a basis for the generation unit to generate correction instructions. The system according to feature 2.

6. The marking portion is, Mark the correction instructions to clearly indicate the areas that need correction. The system according to claim 3.

7. The adjustment unit is, Located between the generation unit and the output unit, it optimizes the generated correction instructions. The system according to feature 4.

8. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring information to be modified based on the estimated emotions. The system according to feature 1.

9. The acquisition unit is, Analyze the user's past revision history and select the optimal method for obtaining it. The system according to feature 1.

Images