System

Abstract

Systems according to embodiments aim to automate the evaluation and correction of documents to reduce quality variations.SOLUTION: A system according to an embodiment includes an analysis unit, an evaluation unit, a generation unit, and a regeneration unit. The analysis unit analyzes the content of the document. The evaluation unit evaluates the document based on a specific evaluation criterion of the document analyzed by the analysis unit. The generation unit generates a correction proposal of the document based on the evaluation result obtained by the evaluation unit. The regeneration unit regenerates the document based on a specific regeneration method based on the correction plan generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, document evaluation and correction may vary from person to person, resulting in variations in quality.

[0005] The system according to the embodiment aims to automate document evaluation and correction to reduce quality variations. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an evaluation unit, a generation unit, and a regeneration unit. The analysis unit analyzes the content of a document. The evaluation unit evaluates the document analyzed by the analysis unit based on specific evaluation criteria. The generation unit generates a revision proposal for the document based on the evaluation result obtained by the evaluation unit. The regeneration unit regenerates the document based on the revision proposal generated by the generation unit in accordance with a specific regeneration method. [Effects of the Invention]

[0007] The system according to the embodiment automates document evaluation and correction, and can reduce quality variability. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generation AI to eliminate variations in document difficulty and quality that occur during transfers and handovers. In this system, documents created by each member are input into the generation AI, which analyzes the document's content and evaluates its difficulty and quality. Evaluation criteria include, for example, the frequency of use of technical terminology, grammatical accuracy, and content consistency. Based on the evaluation results, the generation AI then generates revision suggestions to standardize the document's difficulty and quality. For example, it replaces technical terms with simpler terms and corrects grammatical errors. It also adds necessary information and deletes unnecessary information to maintain content consistency. The generation AI then regenerates the document based on the revision suggestions. In this process, the generation AI customizes the document according to each member's skill level and role. For example, it adds simple explanations for new employees and provides detailed technical information for experienced members. Finally, the generation AI provides the revised document to each member. This eliminates variations in document difficulty and quality that occur during transfers and handovers, enabling a smooth handover. This allows the system to handle sudden transfers of skilled members, preventing inadequate handovers. It also standardizes the quality of deliverables from vendors, eliminating variations in language and quality.

[0029] A document management system according to an embodiment includes an analysis unit, an evaluation unit, a generation unit, and a regeneration unit. The analysis unit analyzes the content of a document using a generation AI. The analysis unit performs analysis using, for example, evaluation criteria such as the frequency of use of technical terms, grammatical accuracy, and content consistency. The evaluation unit uses the generation AI to evaluate the document analyzed by the analysis unit based on specific evaluation criteria. The evaluation unit performs evaluation using, for example, evaluation criteria such as grammatical accuracy, appropriateness of terminology, and content consistency. The generation unit uses the generation AI to generate suggested revisions to the document based on the evaluation results obtained by the evaluation unit. The generation unit generates suggested revisions, such as replacing technical terms with simpler terms, correcting grammatical errors, adding information necessary to maintain content consistency, and deleting unnecessary information. The regeneration unit uses the generation AI to regenerate the document based on the suggested revisions generated by the generation unit. The regeneration unit customizes the document according to, for example, the skill level and role of each member. The regeneration unit adds simple explanations for new employees and provides detailed technical information for experienced members. As a result, the document management system according to the embodiment eliminates variations in the difficulty and quality of documents, enabling a smooth handover. Some or all of the above-described processing by the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit may use a generative AI model that regenerates a document using the revision proposal generated by the generation unit as input.

[0030] The analysis unit can evaluate the frequency of use of technical terms, grammatical accuracy, and content consistency based on specific criteria. The frequency of use of technical terms can be measured, for example, by the number of occurrences or the percentage of the entire document. The accuracy of grammar can be evaluated, for example, by using a grammar checker or applying specific grammar rules. The consistency of content can be evaluated, for example, by thematic unity or logical flow. This improves the accuracy of analysis by using the frequency of use of technical terms, grammatical accuracy, and content consistency as evaluation criteria. Some or all of the above-mentioned processing in the analysis unit can be performed using or without a generative AI. For example, the analysis unit can input a document into a generative AI and perform evaluation using a generative AI model that evaluates the frequency of use of technical terms, grammatical accuracy, and content consistency.

[0031] The generation unit can replace technical terms with simpler terms based on specific criteria. Examples of technical terms include specialized terms and industry jargon. Examples of simpler terms include general terms and everyday words. For example, the generation unit can replace technical terms with simpler terms, making the document easier to understand. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the generation unit can perform the replacement using a generative AI model that replaces technical terms with simpler terms.

[0032] The generation unit can correct specific grammatical errors. Specific grammatical errors include, for example, subject-verb agreement and tense agreement. For example, the generation unit corrects grammatical errors, thereby improving the quality of the document. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the generation unit can perform the correction using a generative AI model that corrects grammatical errors.

[0033] The generation unit can add specific information to maintain consistency of the content. Specific information includes, for example, background information and supplemental explanations. For example, the generation unit improves the consistency of the document by adding information necessary to maintain consistency of the content. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the generation unit can add information using a generative AI model that adds information necessary to maintain consistency of the content.

[0034] The generation unit can remove specific unnecessary information to maintain content consistency. Specific unnecessary information includes, for example, duplicate information and irrelevant information. For example, the generation unit can improve document quality by removing unnecessary information to maintain content consistency. Some or all of the above-described processing in the generation unit can be performed using a generative AI, or can be performed without using a generative AI. For example, the generation unit can remove information using a generative AI model that removes unnecessary information to maintain content consistency.

[0035] The regeneration unit can perform customization according to the specific skill level and role of each member. Specific skill levels include, for example, beginner, intermediate, and advanced. Roles include, for example, project manager, developer, and designer. The regeneration unit can improve the applicability of the document by performing customization according to, for example, the skill level and role of each member. Some or all of the above-described processing in the regeneration unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the regeneration unit can perform customization using a generative AI model that performs customization according to the skill level and role of each member.

[0036] The regeneration unit can add specific explanations for new employees. The specific explanations include, for example, explanations of basic concepts and detailed procedures. For example, the regeneration unit can add simple explanations for new employees to make them easier to understand. Some or all of the above-described processing in the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit can add explanations using a generation AI model that adds specific explanations for new employees.

[0037] The regeneration unit can provide specific technical information for experienced members. Specific technical information includes, for example, technical specifications, blueprints, and code samples. For example, the regeneration unit can provide detailed technical information for experienced members, thereby enabling them to obtain specialized information. Some or all of the above-described processing in the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit can provide information using a generation AI model that provides specific technical information for experienced members.

[0038] When analyzing a document, the analysis unit can optimize a specific analysis algorithm by referring to past analysis results. For example, the analysis unit selects the optimal analysis algorithm for a similar document based on past analysis results by the generation AI. The analysis unit can also adjust the analysis algorithm by having the generation AI analyze past analysis results and taking into account frequent errors and problems. The analysis unit can also automatically adjust parameters to improve the accuracy of the analysis by having the generation AI refer to past analysis results. In this way, the accuracy of the analysis algorithm is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis results into the generation AI and perform analysis using a generation AI model that optimizes the analysis algorithm.

[0039] When analyzing a document, the analysis unit can apply a specific analysis method depending on the document category. For example, in the case of a technical document, the generation AI can apply a method specialized for analyzing technical terms. In the case of a manual, the analysis unit can also apply an analysis method that emphasizes clarity and consistency of procedures. In the case of a report, the generation AI can also apply an analysis method that emphasizes accuracy of data and logical consistency. In this way, by applying an analysis method depending on the document category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the document category into the generation AI and perform analysis using a generation AI model that applies an analysis method depending on the category.

[0040] When analyzing a document, the analysis unit can adjust the level of analysis detail according to the user's specific skill level. For example, for a document intended for beginners, the analysis unit may cause the generation AI to reduce the level of analysis detail and focus on basic information. For a document intended for intermediate users, the analysis unit may cause the generation AI to reduce the level of analysis detail and include technical details. For a document intended for advanced users, the analysis unit may increase the level of analysis detail and analyze specialized information in detail. This allows appropriate analysis results to be obtained by adjusting the level of analysis detail according to the user's skill level. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's skill level into the generation AI and perform analysis using a generation AI model that adjusts the level of analysis detail according to the skill level.

[0041] When analyzing a document, the analysis unit can determine specific analysis priorities based on the submission date of the document. For example, the analysis unit prioritizes analysis of documents with an upcoming deadline. The analysis unit can also postpone documents with a more distant submission date. When documents have the same submission date, the analysis unit can also determine priorities based on importance. This enables efficient analysis by determining analysis priorities based on the submission date of the document. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs the submission date of the document into the generation AI and determines the analysis priority based on the submission date.

[0042] When analyzing documents, the analysis unit can adjust the specific analysis order based on the relevance of the documents. For example, the analysis unit prioritizes analysis of highly relevant documents. The analysis unit can also postpone analysis of less relevant documents. When relevance is the same, the analysis unit can also determine the analysis order based on importance. This enables efficient analysis by adjusting the analysis order based on the relevance of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the analysis unit can perform analysis using a generative AI model that inputs the relevance of documents into the generative AI and adjusts the analysis order based on the relevance.

[0043] When analyzing a document, the analysis unit can adjust the use of technical terms in the analysis according to the user's specific level of expertise. For example, if the document is intended for beginners, the generation AI can avoid technical terms and use simple language. For a document intended for intermediate users, the analysis unit can also use some technical terms. For a document intended for advanced users, the generation AI can use a lot of technical terms and perform a detailed analysis. This allows appropriate analysis results to be obtained by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and perform the analysis using a generation AI model that adjusts the use of technical terms in the analysis according to the level of expertise.

[0044] When evaluating a document, the evaluation unit can optimize a specific evaluation algorithm by referring to past evaluation results. For example, the evaluation unit selects the optimal evaluation algorithm for similar documents based on past evaluation results by the generation AI. The evaluation unit can also adjust the evaluation algorithm by having the generation AI analyze past evaluation results and take frequent errors and problems into consideration. The evaluation unit can also automatically adjust parameters for improving the accuracy of the evaluation by having the generation AI refer to past evaluation results. In this way, the accuracy of the evaluation algorithm is improved by referring to past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input past evaluation results into the generation AI and perform evaluation using a generation AI model that optimizes the evaluation algorithm.

[0045] When evaluating a document, the evaluation unit can apply a specific evaluation method depending on the document category. For example, in the case of a technical document, the generation AI can use the frequency of use of technical terms and technical accuracy as evaluation criteria. In the case of a manual, the generation AI can also use the clarity and consistency of procedures as evaluation criteria. In the case of a report, the generation AI can also use the accuracy of data and logical consistency as evaluation criteria. In this way, by applying an evaluation method depending on the document category, the accuracy of the evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the evaluation unit can input the document category into the generation AI and perform the evaluation using a generation AI model that applies an evaluation method depending on the category.

[0046] When evaluating a document, the evaluation unit can adjust the level of detail in the evaluation according to the user's specific skill level. For example, for a document intended for beginners, the generation AI can reduce the level of detail in the evaluation and focus on basic information. For a document intended for intermediate users, the evaluation unit can adjust the level of detail in the evaluation to medium levels, including technical details. For a document intended for advanced users, the generation AI can increase the level of detail in the evaluation and evaluate specialized information in detail. This allows appropriate evaluation results to be obtained by adjusting the level of detail in the evaluation according to the user's skill level. Some or all of the above-described processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can input the user's skill level into the generation AI and perform the evaluation using a generation AI model that adjusts the level of detail in the evaluation according to the skill level.

[0047] When evaluating documents, the evaluation unit can determine specific evaluation priorities based on the time of document submission. For example, the evaluation unit prioritizes evaluation of documents with upcoming deadlines. The evaluation unit can also postpone documents with more distant submission dates. When documents have the same submission date, the evaluation unit can also determine priorities based on importance. This enables efficient evaluation by determining evaluation priorities based on the time of document submission. Some or all of the above-mentioned processing in the evaluation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the evaluation unit can input the time of document submission into the generation AI and perform evaluation using a generation AI model that determines evaluation priorities based on the time of submission.

[0048] When evaluating documents, the evaluation unit can adjust the specific evaluation order based on the relevance of the documents. For example, the evaluation unit prioritizes evaluation of highly relevant documents. The evaluation unit can also postpone evaluation of less relevant documents. When relevance is the same, the evaluation unit can also determine the evaluation order based on importance. This enables efficient evaluation by adjusting the evaluation order based on the relevance of the documents. Some or all of the above-mentioned processing in the evaluation unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the evaluation unit can perform evaluation using a generative AI model that inputs the relevance of documents into the generative AI and adjusts the evaluation order based on the relevance.

[0049] When evaluating a document, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's specific level of expertise. For example, for a document intended for beginners, the evaluation unit may have the generation AI avoid technical terminology and use simple language. For a document intended for intermediate users, the evaluation unit may have the generation AI use some technical terminology. For a document intended for advanced users, the evaluation unit may have the generation AI use more technical terminology and perform a detailed evaluation. This allows for appropriate evaluation results to be obtained by adjusting the use of technical terminology in the evaluation according to the user's level of expertise. Some or all of the above-described processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit may input the user's level of expertise into the generation AI and perform the evaluation using a generation AI model that adjusts the use of technical terminology in the evaluation according to the level of expertise.

[0050] When generating revision suggestions, the generation unit can optimize a specific generation algorithm by referring to past revision suggestions. For example, the generation unit generates optimal revision suggestions for similar documents based on past revision suggestions by the generation AI. The generation unit can also adjust the generation algorithm by having the generation AI analyze past revision suggestions and taking frequent errors and problems into consideration. The generation unit can also automatically adjust parameters for improving the accuracy of revisions by having the generation AI refer to past revision suggestions. In this way, the accuracy of the generation algorithm is improved by referring to past revision suggestions. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input past revision suggestions into the generation AI and generate revision suggestions using a generation AI model that optimizes the generation algorithm.

[0051] When generating revision suggestions, the generation unit can apply a specific generation method depending on the document category. For example, in the case of a technical document, the generation AI generates revision suggestions that emphasize the frequency of use of technical terms and technical accuracy. In the case of a manual, the generation AI can also generate revision suggestions that emphasize clarity and consistency of procedures. In the case of a report, the generation AI can also generate revision suggestions that emphasize data accuracy and logical consistency. In this way, by applying a generation method depending on the document category, the accuracy of the revision suggestions is improved. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the document category into the generation AI and generate revision suggestions using a generation AI model that applies a generation method depending on the category.

[0052] When generating revision suggestions, the generation unit can adjust the level of detail of the generation according to the user's specific skill level. For example, for a document aimed at beginners, the generation AI generates concise and easy-to-understand revision suggestions. For a document aimed at intermediate users, the generation AI can also generate revision suggestions that include technical details. For a document aimed at advanced users, the generation AI can also generate revision suggestions that include detailed specialized information. In this way, by adjusting the level of detail of the generation according to the user's skill level, appropriate revision suggestions can be obtained. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's skill level into the generation AI and generate revision suggestions using a generation AI model that adjusts the level of detail of the generation according to the skill level.

[0053] When generating revision proposals, the generation unit can determine specific generation priorities based on the submission dates of the documents. For example, the generation unit prioritizes revision of documents with upcoming deadlines. The generation unit can also postpone documents with more distant submission dates. When documents have the same submission date, the generation unit can also determine priorities based on importance. This enables efficient revisions by determining generation priorities based on the submission dates of the documents. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission dates of documents into the generation AI and generate revision proposals using a generation AI model that determines generation priorities based on the submission dates.

[0054] When generating revision suggestions, the generation unit can adjust the specific generation order based on the relevance of the documents. For example, the generation unit prioritizes revising highly relevant documents. The generation unit can also postpone revising less relevant documents. When relevance is the same, the generation unit can also determine the generation order based on importance. This enables efficient revision by adjusting the generation order based on the relevance of the documents. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input document relevance into the generation AI and generate revision suggestions using a generation AI model that adjusts the generation order based on relevance.

[0055] When generating revision suggestions, the generation unit can adjust the use of technical terminology in the generation according to the user's specific level of expertise. For example, for a document aimed at beginners, the generation AI can avoid technical terminology and use simple words. For a document aimed at intermediate users, the generation unit can also use some technical terminology. For a document aimed at advanced users, the generation unit can also use a lot of technical terminology to generate detailed revision suggestions. In this way, appropriate revision suggestions can be obtained by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and generate revision suggestions using a generation AI model that adjusts the use of technical terminology in the generation according to the level of expertise.

[0056] During regeneration, the regeneration unit can optimize a specific regeneration algorithm by referring to past regeneration results. For example, the regeneration unit performs optimal regeneration for similar documents based on past regeneration results by the generation AI. The regeneration unit can also analyze past regeneration results by the generation AI and adjust the regeneration algorithm by taking frequent errors and problems into consideration. The regeneration unit can also automatically adjust parameters for improving the accuracy of regeneration by the generation AI by referring to past regeneration results. In this way, the accuracy of the regeneration algorithm is improved by referring to past regeneration results. Some or all of the above-mentioned processing in the regeneration unit may be performed using or without the generation AI. For example, the regeneration unit can input past regeneration results into the generation AI and perform regeneration using a generation AI model that optimizes the regeneration algorithm.

[0057] During regeneration, the regeneration unit can apply a specific regeneration method depending on the document category. For example, in the case of a technical document, the generation AI can perform regeneration with an emphasis on the frequency of use of technical terms and technical accuracy. In the case of a manual, the regeneration unit can also perform regeneration with an emphasis on clarity and consistency of procedures. In the case of a report, the regeneration unit can also perform regeneration with an emphasis on data accuracy and logical consistency. In this way, by applying a regeneration method depending on the document category, the accuracy of regeneration is improved. Some or all of the above-mentioned processing in the regeneration unit may be performed using or without the generation AI. For example, the regeneration unit can input the document category into the generation AI and perform regeneration using a generation AI model that applies a regeneration method depending on the category.

[0058] During regeneration, the regeneration unit can adjust the level of detail of the regeneration according to the user's specific skill level. For example, in the case of a document for beginners, the regeneration unit's generation AI can perform concise and easy-to-understand regeneration. In the case of a document for intermediate users, the regeneration unit's generation AI can also perform regeneration that includes technical details. In the case of a document for advanced users, the regeneration unit's generation AI can also perform regeneration that includes detailed specialized information. In this way, by adjusting the level of detail of the regeneration according to the user's skill level, appropriate regeneration can be obtained. Some or all of the above-described processing in the regeneration unit may be performed using or without the generation AI. For example, the regeneration unit can perform regeneration using a generation AI model that inputs the user's skill level into the generation AI and adjusts the level of detail of the regeneration according to the skill level.

[0059] During regeneration, the regeneration unit can determine specific regeneration priorities based on the submission dates of the documents. For example, the regeneration unit prioritizes the regeneration of documents with upcoming deadlines. The regeneration unit can also postpone documents with more distant submission dates. When documents have the same submission date, the regeneration unit can also determine priorities based on importance. This enables efficient regeneration by determining the regeneration priority based on the submission date of the document. Some or all of the above-described processing in the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit can input the submission date of the document into the generation AI and perform regeneration using a generation AI model that determines the regeneration priority based on the submission date.

[0060] During regeneration, the regeneration unit can adjust the specific order of regeneration based on the relevance of the documents. For example, the regeneration unit prioritizes the regeneration of highly relevant documents. The regeneration unit can also postpone documents with low relevance. When the relevance is the same, the regeneration unit can also determine the order of regeneration based on importance. This enables efficient regeneration by adjusting the order of regeneration based on the relevance of the documents. Some or all of the above-mentioned processing in the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit can perform regeneration using a generation AI model that inputs the relevance of documents into the generation AI and adjusts the order of regeneration based on the relevance.

[0061] During regeneration, the regeneration unit can adjust the use of technical terminology in the regeneration according to the user's specific level of expertise. For example, for a document aimed at beginners, the regeneration unit causes the generation AI to avoid technical terminology and use simple words. For a document aimed at intermediate users, the regeneration unit can also cause the generation AI to use some technical terminology. For a document aimed at advanced users, the regeneration unit can also cause the generation AI to use a lot of technical terminology and perform detailed regeneration. This allows appropriate regeneration to be obtained by adjusting the use of technical terminology in the regeneration according to the user's level of expertise. Some or all of the above-described processing in the regeneration unit may be performed using or without the generation AI. For example, the regeneration unit can input the user's level of expertise into the generation AI and perform regeneration using a generation AI model that adjusts the use of technical terminology in the regeneration according to the level of expertise.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] When analyzing the contents of a document, the analysis unit can improve the accuracy of the analysis by referring to the document creator's past creation history. For example, by learning the trends and characteristics of documents created by the same creator in the past and performing analysis based on that, more accurate evaluation is possible. Also, by performing analysis taking into account the creator's past mistakes and areas for improvement, it is possible to prevent the same mistakes from being repeated. Furthermore, it is possible to adjust the level of detail of the analysis depending on the creator's skill level and expertise. This allows the analysis unit to utilize the creator's past creation history to perform more accurate analysis.

[0064] The evaluation department can dynamically change the evaluation criteria when evaluating a document. For example, the evaluation criteria can be customized according to the requirements of a specific project or client. This allows for more appropriate evaluations by applying different evaluation criteria to each project. Furthermore, when changing the evaluation criteria, the evaluation department can refer to past evaluation results to select the most appropriate criteria. Furthermore, the evaluation criteria can be changed in real time, and the criteria can also be adjusted during the evaluation. This allows the evaluation department to flexibly change the evaluation criteria and perform more appropriate evaluations.

[0065] When generating proposed revisions for a document, the generation unit can improve the accuracy of the proposed revisions by referring to an external database or knowledge base. For example, the generation unit can obtain information on technical terms and expertise from an external database and generate proposed revisions based on that information. It is also possible to generate optimal proposed revisions by referring to past proposed revisions and proposed revisions used in other projects. Furthermore, the generation unit can incorporate user feedback during the proposed revision generation process and generate proposed revisions that reflect the user's opinions. This allows the generation unit to generate more accurate proposed revisions by utilizing an external database or knowledge base.

[0066] When regenerating a document, the regeneration unit can regenerate it in a different language. For example, a document created in English can be translated and regenerated into Japanese. The regeneration unit can also regenerate documents taking into account the consistency of terminology and grammatical accuracy between different languages. Furthermore, the regeneration unit can also regenerate documents taking into account the characteristics of different cultures and regions. This allows the regeneration unit to regenerate documents that are compatible with different languages ​​and cultures, making it applicable to global projects.

[0067] When analyzing a document, the analysis unit can estimate the intention of the document creator and adjust the level of detail of the analysis based on the estimated intention. For example, if the creator intends to provide a concise explanation, the analysis unit can avoid detailed analysis and provide concise analysis results. Alternatively, if the creator intends to provide a detailed explanation, the analysis unit can perform detailed analysis and provide detailed analysis results. Furthermore, it is also possible to adjust the evaluation criteria of the analysis according to the creator's intention. This allows the analysis unit to adjust the level of detail of the analysis according to the creator's intention and provide more appropriate analysis results.

[0068] When evaluating a document, the evaluation unit can provide a dashboard for visually displaying the evaluation results. For example, the evaluation results can be displayed in graphs or charts to make them easier to understand visually. Details of the evaluation results can also be displayed by clicking them, allowing the user to quickly obtain the information they need. Furthermore, the evaluation results can be updated in real time, and the latest evaluation results can always be displayed. In this way, the evaluation unit can visually display the evaluation results to make it easier for the user to understand the evaluation results.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The analysis unit uses generative AI to analyze the content of the document. The analysis unit evaluates the frequency of use of technical terms, grammatical accuracy, and content consistency. Step 2: The evaluation unit uses the generative AI to evaluate the document analyzed by the analysis unit based on specific evaluation criteria, such as grammatical accuracy, appropriateness of terminology, and consistency of content. Step 3: The generator uses generative AI to generate suggested revisions to the document based on the evaluation results obtained by the evaluator, such as replacing technical terms with simpler terms, correcting grammatical errors, adding information necessary to maintain consistency, and deleting unnecessary information. Step 4: The regeneration unit uses the generative AI to regenerate the document based on the proposed revisions generated by the generation unit. The regeneration unit customizes the document based on each member's skill level and role. For example, it adds simple explanations for new employees and provides detailed technical information for experienced members.

[0071] (Example 2) A system according to an embodiment of the present invention uses a generation AI to eliminate variations in document difficulty and quality that occur during transfers and handovers. In this system, documents created by each member are input into the generation AI, which analyzes the document's content and evaluates its difficulty and quality. Evaluation criteria include, for example, the frequency of use of technical terminology, grammatical accuracy, and content consistency. Based on the evaluation results, the generation AI then generates revision suggestions to standardize the document's difficulty and quality. For example, it replaces technical terms with simpler terms and corrects grammatical errors. It also adds necessary information and deletes unnecessary information to maintain content consistency. The generation AI then regenerates the document based on the revision suggestions. In this process, the generation AI customizes the document according to each member's skill level and role. For example, it adds simple explanations for new employees and provides detailed technical information for experienced members. Finally, the generation AI provides the revised document to each member. This eliminates variations in document difficulty and quality that occur during transfers and handovers, enabling a smooth handover. This allows the system to handle sudden transfers of skilled members, preventing inadequate handovers. It also standardizes the quality of deliverables from vendors, eliminating variations in language and quality.

[0072] A document management system according to an embodiment includes an analysis unit, an evaluation unit, a generation unit, and a regeneration unit. The analysis unit analyzes the content of a document using a generation AI. The analysis unit performs analysis using, for example, evaluation criteria such as the frequency of use of technical terms, grammatical accuracy, and content consistency. The evaluation unit uses the generation AI to evaluate the document analyzed by the analysis unit based on specific evaluation criteria. The evaluation unit performs evaluation using, for example, evaluation criteria such as grammatical accuracy, appropriateness of terminology, and content consistency. The generation unit uses the generation AI to generate suggested revisions to the document based on the evaluation results obtained by the evaluation unit. The generation unit generates suggested revisions, such as replacing technical terms with simpler terms, correcting grammatical errors, adding information necessary to maintain content consistency, and deleting unnecessary information. The regeneration unit uses the generation AI to regenerate the document based on the suggested revisions generated by the generation unit. The regeneration unit customizes the document according to, for example, the skill level and role of each member. The regeneration unit adds simple explanations for new employees and provides detailed technical information for experienced members. As a result, the document management system according to the embodiment eliminates variations in the difficulty and quality of documents, enabling a smooth handover. Some or all of the above-described processing by the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit may use a generative AI model that regenerates a document using the revision proposal generated by the generation unit as input.

[0073] The analysis unit can evaluate the frequency of use of technical terms, grammatical accuracy, and content consistency based on specific criteria. The frequency of use of technical terms can be measured, for example, by the number of occurrences or the percentage of the entire document. The accuracy of grammar can be evaluated, for example, by using a grammar checker or applying specific grammar rules. The consistency of content can be evaluated, for example, by thematic unity or logical flow. This improves the accuracy of analysis by using the frequency of use of technical terms, grammatical accuracy, and content consistency as evaluation criteria. Some or all of the above-mentioned processing in the analysis unit can be performed using or without a generative AI. For example, the analysis unit can input a document into a generative AI and perform evaluation using a generative AI model that evaluates the frequency of use of technical terms, grammatical accuracy, and content consistency.

[0074] The generation unit can replace technical terms with simpler terms based on specific criteria. Examples of technical terms include specialized terms and industry jargon. Examples of simpler terms include general terms and everyday words. For example, the generation unit can replace technical terms with simpler terms, making the document easier to understand. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the generation unit can perform the replacement using a generative AI model that replaces technical terms with simpler terms.

[0075] The generation unit can correct specific grammatical errors. Specific grammatical errors include, for example, subject-verb agreement and tense agreement. For example, the generation unit corrects grammatical errors, thereby improving the quality of the document. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the generation unit can perform the correction using a generative AI model that corrects grammatical errors.

[0076] The generation unit can add specific information to maintain consistency of the content. Specific information includes, for example, background information and supplemental explanations. For example, the generation unit improves the consistency of the document by adding information necessary to maintain consistency of the content. Some or all of the above-described processing in the generation unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the generation unit can add information using a generative AI model that adds information necessary to maintain consistency of the content.

[0077] The generation unit can remove specific unnecessary information to maintain content consistency. Specific unnecessary information includes, for example, duplicate information and irrelevant information. For example, the generation unit can improve document quality by removing unnecessary information to maintain content consistency. Some or all of the above-described processing in the generation unit can be performed using a generative AI, or can be performed without using a generative AI. For example, the generation unit can remove information using a generative AI model that removes unnecessary information to maintain content consistency.

[0078] The regeneration unit can perform customization according to the specific skill level and role of each member. Specific skill levels include, for example, beginner, intermediate, and advanced. Roles include, for example, project manager, developer, and designer. The regeneration unit can improve the applicability of the document by performing customization according to, for example, the skill level and role of each member. Some or all of the above-described processing in the regeneration unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the regeneration unit can perform customization using a generative AI model that performs customization according to the skill level and role of each member.

[0079] The regeneration unit can add specific explanations for new employees. The specific explanations include, for example, explanations of basic concepts and detailed procedures. For example, the regeneration unit can add simple explanations for new employees to make them easier to understand. Some or all of the above-described processing in the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit can add explanations using a generation AI model that adds specific explanations for new employees.

[0080] The regeneration unit can provide specific technical information for experienced members. Specific technical information includes, for example, technical specifications, blueprints, and code samples. For example, the regeneration unit can provide detailed technical information for experienced members, thereby enabling them to obtain specialized information. Some or all of the above-described processing in the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit can provide information using a generation AI model that provides specific technical information for experienced members.

[0081] The analysis unit can estimate the user's emotions and adjust the specific analysis method based on the estimated user emotions. For example, if the user is stressed, the generation AI can reduce the level of analysis detail and provide a concise analysis result. If the user is relaxed, the generation AI can increase the level of analysis detail and provide a detailed analysis result. If the user is in a hurry, the generation AI can prioritize the speed of the analysis and provide a quick analysis result. This allows for more appropriate analysis results to be obtained by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input user emotion data into the generation AI and perform analysis using a generation AI model that adjusts the analysis method based on the emotion.

[0082] When analyzing a document, the analysis unit can optimize a specific analysis algorithm by referring to past analysis results. For example, the analysis unit selects the optimal analysis algorithm for a similar document based on past analysis results by the generation AI. The analysis unit can also adjust the analysis algorithm by having the generation AI analyze past analysis results and taking into account frequent errors and problems. The analysis unit can also automatically adjust parameters to improve the accuracy of the analysis by having the generation AI refer to past analysis results. In this way, the accuracy of the analysis algorithm is improved by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input past analysis results into the generation AI and perform analysis using a generation AI model that optimizes the analysis algorithm.

[0083] When analyzing a document, the analysis unit can apply a specific analysis method depending on the document category. For example, in the case of a technical document, the generation AI can apply a method specialized for analyzing technical terms. In the case of a manual, the analysis unit can also apply an analysis method that emphasizes clarity and consistency of procedures. In the case of a report, the generation AI can also apply an analysis method that emphasizes accuracy of data and logical consistency. In this way, by applying an analysis method depending on the document category, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the document category into the generation AI and perform analysis using a generation AI model that applies an analysis method depending on the category.

[0084] When analyzing a document, the analysis unit can adjust the level of analysis detail according to the user's specific skill level. For example, for a document intended for beginners, the analysis unit may cause the generation AI to reduce the level of analysis detail and focus on basic information. For a document intended for intermediate users, the analysis unit may cause the generation AI to reduce the level of analysis detail and include technical details. For a document intended for advanced users, the analysis unit may increase the level of analysis detail and analyze specialized information in detail. This allows appropriate analysis results to be obtained by adjusting the level of analysis detail according to the user's skill level. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit may input the user's skill level into the generation AI and perform analysis using a generation AI model that adjusts the level of analysis detail according to the skill level.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the specific analysis results based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. If the user is relaxed, the generation AI can also provide a display method that includes detailed information. If the user is in a hurry, the generation AI can also provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using the generation AI, or can be performed without the generation AI. For example, the analysis unit can input user emotion data into the generation AI and display the analysis results using a generation AI model that adjusts the display method of the analysis results based on the emotion.

[0086] When analyzing a document, the analysis unit can determine specific analysis priorities based on the submission date of the document. For example, the analysis unit prioritizes analysis of documents with an upcoming deadline. The analysis unit can also postpone documents with a more distant submission date. When documents have the same submission date, the analysis unit can also determine priorities based on importance. This enables efficient analysis by determining analysis priorities based on the submission date of the document. Some or all of the above-mentioned processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit can perform analysis using a generation AI model that inputs the submission date of the document into the generation AI and determines the analysis priority based on the submission date.

[0087] When analyzing documents, the analysis unit can adjust the specific analysis order based on the relevance of the documents. For example, the analysis unit prioritizes analysis of highly relevant documents. The analysis unit can also postpone analysis of less relevant documents. When relevance is the same, the analysis unit can also determine the analysis order based on importance. This enables efficient analysis by adjusting the analysis order based on the relevance of the documents. Some or all of the above-mentioned processing in the analysis unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the analysis unit can perform analysis using a generative AI model that inputs the relevance of documents into the generative AI and adjusts the analysis order based on the relevance.

[0088] When analyzing a document, the analysis unit can adjust the use of technical terms in the analysis according to the user's specific level of expertise. For example, if the document is intended for beginners, the generation AI can avoid technical terms and use simple language. For a document intended for intermediate users, the analysis unit can also use some technical terms. For a document intended for advanced users, the generation AI can use a lot of technical terms and perform a detailed analysis. This allows appropriate analysis results to be obtained by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and perform the analysis using a generation AI model that adjusts the use of technical terms in the analysis according to the level of expertise.

[0089] The evaluation unit can estimate the user's emotions and adjust specific evaluation criteria based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can relax the evaluation criteria and perform a simple evaluation. If the user is relaxed, the evaluation unit can also tighten the evaluation criteria and perform a detailed evaluation. If the user is in a hurry, the evaluation unit can also simplify the evaluation criteria and perform a quick evaluation. This enables appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the evaluation unit can input user emotion data into the generation AI and perform evaluation using a generation AI model that adjusts the evaluation criteria based on the emotion.

[0090] When evaluating a document, the evaluation unit can optimize a specific evaluation algorithm by referring to past evaluation results. For example, the evaluation unit selects the optimal evaluation algorithm for similar documents based on past evaluation results by the generation AI. The evaluation unit can also adjust the evaluation algorithm by having the generation AI analyze past evaluation results and take frequent errors and problems into consideration. The evaluation unit can also automatically adjust parameters for improving the accuracy of the evaluation by having the generation AI refer to past evaluation results. In this way, the accuracy of the evaluation algorithm is improved by referring to past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input past evaluation results into the generation AI and perform evaluation using a generation AI model that optimizes the evaluation algorithm.

[0091] When evaluating a document, the evaluation unit can apply a specific evaluation method depending on the document category. For example, in the case of a technical document, the generation AI can use the frequency of use of technical terms and technical accuracy as evaluation criteria. In the case of a manual, the generation AI can also use the clarity and consistency of procedures as evaluation criteria. In the case of a report, the generation AI can also use the accuracy of data and logical consistency as evaluation criteria. In this way, by applying an evaluation method depending on the document category, the accuracy of the evaluation can be improved. Some or all of the above-mentioned processing in the evaluation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the evaluation unit can input the document category into the generation AI and perform the evaluation using a generation AI model that applies an evaluation method depending on the category.

[0092] When evaluating a document, the evaluation unit can adjust the level of detail in the evaluation according to the user's specific skill level. For example, for a document intended for beginners, the generation AI can reduce the level of detail in the evaluation and focus on basic information. For a document intended for intermediate users, the evaluation unit can adjust the level of detail in the evaluation to medium levels, including technical details. For a document intended for advanced users, the generation AI can increase the level of detail in the evaluation and evaluate specialized information in detail. This allows appropriate evaluation results to be obtained by adjusting the level of detail in the evaluation according to the user's skill level. Some or all of the above-described processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can input the user's skill level into the generation AI and perform the evaluation using a generation AI model that adjusts the level of detail in the evaluation according to the skill level.

[0093] The evaluation unit can estimate the user's emotions and adjust the display method of the specific evaluation results based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. If the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. If the user is in a hurry, the evaluation unit can also provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the evaluation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the evaluation unit can input user emotion data into the generation AI and display the evaluation results using a generation AI model that adjusts the display method of the evaluation results based on the emotion.

[0094] When evaluating documents, the evaluation unit can determine specific evaluation priorities based on the time of document submission. For example, the evaluation unit prioritizes evaluation of documents with upcoming deadlines. The evaluation unit can also postpone documents with more distant submission dates. When documents have the same submission date, the evaluation unit can also determine priorities based on importance. This enables efficient evaluation by determining evaluation priorities based on the time of document submission. Some or all of the above-mentioned processing in the evaluation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the evaluation unit can input the time of document submission into the generation AI and perform evaluation using a generation AI model that determines evaluation priorities based on the time of submission.

[0095] When evaluating documents, the evaluation unit can adjust the specific evaluation order based on the relevance of the documents. For example, the evaluation unit prioritizes evaluation of highly relevant documents. The evaluation unit can also postpone evaluation of less relevant documents. When relevance is the same, the evaluation unit can also determine the evaluation order based on importance. This enables efficient evaluation by adjusting the evaluation order based on the relevance of the documents. Some or all of the above-mentioned processing in the evaluation unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the evaluation unit can perform evaluation using a generative AI model that inputs the relevance of documents into the generative AI and adjusts the evaluation order based on the relevance.

[0096] When evaluating a document, the evaluation unit can adjust the use of technical terminology in the evaluation according to the user's specific level of expertise. For example, for a document intended for beginners, the evaluation unit may have the generation AI avoid technical terminology and use simple language. For a document intended for intermediate users, the evaluation unit may have the generation AI use some technical terminology. For a document intended for advanced users, the evaluation unit may have the generation AI use more technical terminology and perform a detailed evaluation. This allows for appropriate evaluation results to be obtained by adjusting the use of technical terminology in the evaluation according to the user's level of expertise. Some or all of the above-described processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit may input the user's level of expertise into the generation AI and perform the evaluation using a generation AI model that adjusts the use of technical terminology in the evaluation according to the level of expertise.

[0097] The generation unit can estimate the user's emotions and adjust the method for generating specific revision suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can generate concise and easy-to-understand revision suggestions. If the user is relaxed, the generation AI can also generate detailed revision suggestions. If the user is in a hurry, the generation AI can also quickly generate revision suggestions. This allows for adjusting the method for generating revision suggestions according to the user's emotions to obtain more appropriate revision suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the generation unit can input user emotion data into the generation AI and generate revision suggestions using a generation AI model that adjusts the method for generating revision suggestions based on the emotion.

[0098] When generating revision suggestions, the generation unit can optimize a specific generation algorithm by referring to past revision suggestions. For example, the generation unit generates optimal revision suggestions for similar documents based on past revision suggestions by the generation AI. The generation unit can also adjust the generation algorithm by having the generation AI analyze past revision suggestions and taking frequent errors and problems into consideration. The generation unit can also automatically adjust parameters for improving the accuracy of revisions by having the generation AI refer to past revision suggestions. In this way, the accuracy of the generation algorithm is improved by referring to past revision suggestions. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input past revision suggestions into the generation AI and generate revision suggestions using a generation AI model that optimizes the generation algorithm.

[0099] When generating revision suggestions, the generation unit can apply a specific generation method depending on the document category. For example, in the case of a technical document, the generation AI generates revision suggestions that emphasize the frequency of use of technical terms and technical accuracy. In the case of a manual, the generation AI can also generate revision suggestions that emphasize clarity and consistency of procedures. In the case of a report, the generation AI can also generate revision suggestions that emphasize data accuracy and logical consistency. In this way, by applying a generation method depending on the document category, the accuracy of the revision suggestions is improved. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can input the document category into the generation AI and generate revision suggestions using a generation AI model that applies a generation method depending on the category.

[0100] When generating revision suggestions, the generation unit can adjust the level of detail of the generation according to the user's specific skill level. For example, for a document aimed at beginners, the generation AI generates concise and easy-to-understand revision suggestions. For a document aimed at intermediate users, the generation AI can also generate revision suggestions that include technical details. For a document aimed at advanced users, the generation AI can also generate revision suggestions that include detailed specialized information. In this way, by adjusting the level of detail of the generation according to the user's skill level, appropriate revision suggestions can be obtained. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's skill level into the generation AI and generate revision suggestions using a generation AI model that adjusts the level of detail of the generation according to the skill level.

[0101] The generation unit can estimate the user's emotions and adjust the display method of specific revision suggestions based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. If the user is relaxed, the generation AI can also provide a display method that includes detailed information. If the user is in a hurry, the generation AI can also provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of revision suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using the generation AI, or can be performed without the generation AI. For example, the generation unit can input user emotion data into the generation AI and perform display using a generation AI model that adjusts the display method of revision suggestions based on the emotion.

[0102] When generating revision proposals, the generation unit can determine specific generation priorities based on the submission dates of the documents. For example, the generation unit prioritizes revision of documents with upcoming deadlines. The generation unit can also postpone documents with more distant submission dates. When documents have the same submission date, the generation unit can also determine priorities based on importance. This enables efficient revisions by determining generation priorities based on the submission dates of the documents. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission dates of documents into the generation AI and generate revision proposals using a generation AI model that determines generation priorities based on the submission dates.

[0103] When generating revision suggestions, the generation unit can adjust the specific generation order based on the relevance of the documents. For example, the generation unit prioritizes revising highly relevant documents. The generation unit can also postpone revising less relevant documents. When relevance is the same, the generation unit can also determine the generation order based on importance. This enables efficient revision by adjusting the generation order based on the relevance of the documents. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input document relevance into the generation AI and generate revision suggestions using a generation AI model that adjusts the generation order based on relevance.

[0104] When generating revision suggestions, the generation unit can adjust the use of technical terminology in the generation according to the user's specific level of expertise. For example, for a document aimed at beginners, the generation AI can avoid technical terminology and use simple words. For a document aimed at intermediate users, the generation unit can also use some technical terminology. For a document aimed at advanced users, the generation unit can also use a lot of technical terminology to generate detailed revision suggestions. In this way, appropriate revision suggestions can be obtained by adjusting the use of technical terminology in the generation according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and generate revision suggestions using a generation AI model that adjusts the use of technical terminology in the generation according to the level of expertise.

[0105] The regeneration unit can estimate the user's emotions and adjust the specific regeneration method based on the estimated user emotions. For example, if the user is stressed, the regeneration unit can cause the generation AI to perform concise and easy-to-understand regeneration. If the user is relaxed, the regeneration unit can also cause the generation AI to perform detailed regeneration. If the user is in a hurry, the regeneration unit can also cause the generation AI to perform quick regeneration. This allows for more appropriate regeneration by adjusting the regeneration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the regeneration unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the regeneration unit can input user emotion data into the generation AI and perform regeneration using a generation AI model that adjusts the regeneration method based on the emotion.

[0106] During regeneration, the regeneration unit can optimize a specific regeneration algorithm by referring to past regeneration results. For example, the regeneration unit performs optimal regeneration for similar documents based on past regeneration results by the generation AI. The regeneration unit can also analyze past regeneration results by the generation AI and adjust the regeneration algorithm by taking frequent errors and problems into consideration. The regeneration unit can also automatically adjust parameters for improving the accuracy of regeneration by the generation AI by referring to past regeneration results. In this way, the accuracy of the regeneration algorithm is improved by referring to past regeneration results. Some or all of the above-mentioned processing in the regeneration unit may be performed using or without the generation AI. For example, the regeneration unit can input past regeneration results into the generation AI and perform regeneration using a generation AI model that optimizes the regeneration algorithm.

[0107] During regeneration, the regeneration unit can apply a specific regeneration method depending on the document category. For example, in the case of a technical document, the generation AI can perform regeneration with an emphasis on the frequency of use of technical terms and technical accuracy. In the case of a manual, the regeneration unit can also perform regeneration with an emphasis on clarity and consistency of procedures. In the case of a report, the regeneration unit can also perform regeneration with an emphasis on data accuracy and logical consistency. In this way, by applying a regeneration method depending on the document category, the accuracy of regeneration is improved. Some or all of the above-mentioned processing in the regeneration unit may be performed using or without the generation AI. For example, the regeneration unit can input the document category into the generation AI and perform regeneration using a generation AI model that applies a regeneration method depending on the category.

[0108] During regeneration, the regeneration unit can adjust the level of detail of the regeneration according to the user's specific skill level. For example, in the case of a document for beginners, the regeneration unit's generation AI can perform concise and easy-to-understand regeneration. In the case of a document for intermediate users, the regeneration unit's generation AI can also perform regeneration that includes technical details. In the case of a document for advanced users, the regeneration unit's generation AI can also perform regeneration that includes detailed specialized information. In this way, by adjusting the level of detail of the regeneration according to the user's skill level, appropriate regeneration can be obtained. Some or all of the above-described processing in the regeneration unit may be performed using or without the generation AI. For example, the regeneration unit can perform regeneration using a generation AI model that inputs the user's skill level into the generation AI and adjusts the level of detail of the regeneration according to the skill level.

[0109] The regeneration unit can estimate the user's emotions and adjust the display method of the specific regenerated results based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. If the user is relaxed, the generation AI can also provide a display method that includes detailed information. If the user is in a hurry, the generation AI can also provide a display method that focuses on the main points. This allows for a more appropriate display by adjusting the display method of the regenerated results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the regeneration unit can be performed using the generation AI, or can be performed without the generation AI. For example, the regeneration unit can input user emotion data into the generation AI and display the regenerated results using a generation AI model that adjusts the display method of the regenerated results based on the emotion.

[0110] During regeneration, the regeneration unit can determine specific regeneration priorities based on the submission dates of the documents. For example, the regeneration unit prioritizes the regeneration of documents with upcoming deadlines. The regeneration unit can also postpone documents with more distant submission dates. When documents have the same submission date, the regeneration unit can also determine priorities based on importance. This enables efficient regeneration by determining the regeneration priority based on the submission date of the document. Some or all of the above-described processing in the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit can input the submission date of the document into the generation AI and perform regeneration using a generation AI model that determines the regeneration priority based on the submission date.

[0111] During regeneration, the regeneration unit can adjust the specific order of regeneration based on the relevance of the documents. For example, the regeneration unit prioritizes the regeneration of highly relevant documents. The regeneration unit can also postpone documents with low relevance. When the relevance is the same, the regeneration unit can also determine the order of regeneration based on importance. This enables efficient regeneration by adjusting the order of regeneration based on the relevance of the documents. Some or all of the above-mentioned processing in the regeneration unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the regeneration unit can perform regeneration using a generation AI model that inputs the relevance of documents into the generation AI and adjusts the order of regeneration based on the relevance.

[0112] During regeneration, the regeneration unit can adjust the use of technical terminology in the regeneration according to the user's specific level of expertise. For example, for a document aimed at beginners, the regeneration unit causes the generation AI to avoid technical terminology and use simple words. For a document aimed at intermediate users, the regeneration unit can also cause the generation AI to use some technical terminology. For a document aimed at advanced users, the regeneration unit can also cause the generation AI to use a lot of technical terminology and perform detailed regeneration. This allows appropriate regeneration to be obtained by adjusting the use of technical terminology in the regeneration according to the user's level of expertise. Some or all of the above-described processing in the regeneration unit may be performed using or without the generation AI. For example, the regeneration unit can input the user's level of expertise into the generation AI and perform regeneration using a generation AI model that adjusts the use of technical terminology in the regeneration according to the level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, evaluation unit, generation unit, and regeneration unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12 and analyzes the content of the document. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the analyzed document. The generation unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and generates a revision proposal based on the evaluation result. The regeneration unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and regenerates the document based on the revision proposal. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, evaluation unit, generation unit, and regeneration unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12 and analyzes the content of the document. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the analyzed document. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and generates a revision proposal based on the evaluation result. The regeneration unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and regenerates the document based on the revision proposal. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, evaluation unit, generation unit, and regeneration unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12 and analyzes the content of the document. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the analyzed document. The generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and generates a revision proposal based on the evaluation result. The regeneration unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 and regenerates the document based on the revision proposal. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, evaluation unit, generation unit, and regeneration unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 or the processor 28 of the data processing device 12 and analyzes the contents of the document. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the analyzed document. The generation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and generates a revision proposal based on the evaluation result. The regeneration unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and regenerates the document based on the revision proposal.

[0113] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0114] When analyzing the contents of a document, the analysis unit can improve the accuracy of the analysis by referring to the document creator's past creation history. For example, by learning the trends and characteristics of documents created by the same creator in the past and performing analysis based on that, more accurate evaluation is possible. Also, by performing analysis taking into account the creator's past mistakes and areas for improvement, it is possible to prevent the same mistakes from being repeated. Furthermore, it is possible to adjust the level of detail of the analysis depending on the creator's skill level and expertise. This allows the analysis unit to utilize the creator's past creation history to perform more accurate analysis.

[0115] The evaluation department can dynamically change the evaluation criteria when evaluating a document. For example, the evaluation criteria can be customized according to the requirements of a specific project or client. This allows for more appropriate evaluations by applying different evaluation criteria to each project. Furthermore, when changing the evaluation criteria, the evaluation department can refer to past evaluation results to select the most appropriate criteria. Furthermore, the evaluation criteria can be changed in real time, and the criteria can also be adjusted during the evaluation. This allows the evaluation department to flexibly change the evaluation criteria and perform more appropriate evaluations.

[0116] When generating proposed revisions for a document, the generation unit can improve the accuracy of the proposed revisions by referring to an external database or knowledge base. For example, the generation unit can obtain information on technical terms and expertise from an external database and generate proposed revisions based on that information. It is also possible to generate optimal proposed revisions by referring to past proposed revisions and proposed revisions used in other projects. Furthermore, the generation unit can incorporate user feedback during the proposed revision generation process and generate proposed revisions that reflect the user's opinions. This allows the generation unit to generate more accurate proposed revisions by utilizing an external database or knowledge base.

[0117] When regenerating a document, the regeneration unit can regenerate it in a different language. For example, a document created in English can be translated and regenerated into Japanese. The regeneration unit can also regenerate documents taking into account the consistency of terminology and grammatical accuracy between different languages. Furthermore, the regeneration unit can also regenerate documents taking into account the characteristics of different cultures and regions. This allows the regeneration unit to regenerate documents that are compatible with different languages ​​and cultures, making it applicable to global projects.

[0118] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can temporarily delay the analysis and perform the analysis when the user is relaxed. Also, if the user is in a hurry, the analysis unit can prioritize the speed of the analysis and provide analysis results quickly. Furthermore, it is possible to adjust the level of detail of the analysis according to the user's emotions. This allows the analysis unit to adjust the timing of the analysis according to the user's emotions and provide more appropriate analysis results.

[0119] The evaluation unit can estimate the user's emotions and adjust the feedback method of the evaluation result based on the estimated user's emotions. For example, if the user is feeling stressed, the evaluation unit can prioritize positive feedback to increase the user's motivation. Also, if the user is relaxed, the evaluation unit can provide detailed feedback and specifically indicate areas for improvement. Furthermore, if the user is in a hurry, the evaluation unit can provide concise feedback that hits the main points. In this way, the evaluation unit can adjust the feedback method according to the user's emotions and provide more effective evaluation results.

[0120] The generation unit can estimate the user's emotions and adjust the priority of suggested revisions based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can prioritize and provide more important suggested revisions, thereby reducing the user's burden. If the user is relaxed, the generation unit can provide detailed suggested revisions, allowing the user to take their time with the revisions. Furthermore, if the user is in a hurry, the generation unit can quickly provide suggested revisions, allowing the user to complete the revisions in a short amount of time. In this way, the generation unit can adjust the priority of suggested revisions according to the user's emotions and provide more appropriate suggested revisions.

[0121] The regeneration unit can estimate the user's emotions and adjust the frequency of regeneration based on the estimated user's emotions. For example, if the user is feeling stressed, the regeneration unit can reduce the frequency of regeneration, and perform regeneration when the user is relaxed. Also, if the user is relaxed, the regeneration unit can increase the frequency of regeneration and perform detailed regeneration. Furthermore, if the user is in a hurry, the regeneration unit can adjust the frequency of regeneration and perform quick regeneration. In this way, the regeneration unit can adjust the frequency of regeneration according to the user's emotions and perform more appropriate regeneration.

[0122] When analyzing a document, the analysis unit can estimate the intention of the document creator and adjust the level of detail of the analysis based on the estimated intention. For example, if the creator intends to provide a concise explanation, the analysis unit can avoid detailed analysis and provide concise analysis results. Alternatively, if the creator intends to provide a detailed explanation, the analysis unit can perform detailed analysis and provide detailed analysis results. Furthermore, it is also possible to adjust the evaluation criteria of the analysis according to the creator's intention. This allows the analysis unit to adjust the level of detail of the analysis according to the creator's intention and provide more appropriate analysis results.

[0123] When evaluating a document, the evaluation unit can provide a dashboard for visually displaying the evaluation results. For example, the evaluation results can be displayed in graphs or charts to make them easier to understand visually. Details of the evaluation results can also be displayed by clicking them, allowing the user to quickly obtain the information they need. Furthermore, the evaluation results can be updated in real time, and the latest evaluation results can always be displayed. In this way, the evaluation unit can visually display the evaluation results to make it easier for the user to understand the evaluation results.

[0124] The processing flow of the second embodiment will be briefly explained below.

[0125] Step 1: The analysis unit uses generative AI to analyze the content of the document. The analysis unit evaluates the frequency of use of technical terms, grammatical accuracy, and content consistency. Step 2: The evaluation unit uses the generative AI to evaluate the document analyzed by the analysis unit based on specific evaluation criteria, such as grammatical accuracy, appropriateness of terminology, and consistency of content. Step 3: The generator uses generative AI to generate suggested revisions to the document based on the evaluation results obtained by the evaluator, such as replacing technical terms with simpler terms, correcting grammatical errors, adding information necessary to maintain consistency, and deleting unnecessary information. Step 4: The regeneration unit uses the generative AI to regenerate the document based on the proposed revisions generated by the generation unit. The regeneration unit customizes the document based on each member's skill level and role. For example, it adds simple explanations for new employees and provides detailed technical information for experienced members.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0144] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0147] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0163] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0164] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0166] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0168] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0169] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0170] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0172] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0173] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0177] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0181] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0182] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0183] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0187] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0188] 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.

[0189] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0190] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0191] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0192] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0194] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0195] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0196] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0197] [Explanation of symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the content of the document; an evaluation unit that evaluates the document analyzed by the analysis unit based on specific evaluation criteria; a generation unit that generates a revision proposal for the document based on the evaluation result obtained by the evaluation unit; a regeneration unit that regenerates the document based on a specific regeneration method on the basis of the revision proposal generated by the generation unit; Equipped with A system characterized by:

2. The analysis unit Evaluate the frequency of technical terminology, grammatical accuracy, and content consistency based on specific criteria 2. The system of claim 1.

3. The generation unit Replace technical terms with simpler terms based on specific criteria 2. The system of claim 1.

4. The generation unit Correcting specific grammar errors 2. The system of claim 1.

5. The generation unit Add specific details to ensure consistency 2. The system of claim 1.

6. The generation unit Remove unnecessary specific information to maintain consistency 2. The system of claim 1.

7. The regeneration unit Customize according to each member's specific skill level and role 2. The system of claim 1.

8. The regeneration unit Add specific instructions for new employees 2. The system of claim 1.

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