System

Abstract

An object of the system according to the embodiment is to improve the efficiency of a regulation creation and management process.SOLUTION: A system according to an embodiment includes a generation unit, a comparison unit, a consistency check unit, a detection unit, and a management unit. The generation unit generates a regulation draft. The comparison unit compares the draft of the regulation created by the creation unit. The matching confirmation part analyzes the contents of the regulation compared by the comparison part and confirms the matching with the law or the like. The detection unit analyzes the text of the rule confirmed by the matching confirmation unit, and detects misspellings. The management unit manages the version of the regulation detected by the detection unit and records a change history.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] Conventional technologies have had the problem that the process of creating and managing regulations is complicated and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of the process of creating and managing rules. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a comparison unit, a consistency check unit, a detection unit, and a management unit. The generation unit creates draft regulations. The comparison unit compares the draft regulations created by the generation unit. The consistency check unit analyzes the content of the regulations compared by the comparison unit and checks their consistency with laws and regulations. The detection unit analyzes the text of the regulations checked by the consistency check unit and detects typos and omissions. The management unit manages the versions of the regulations detected by the detection unit and records the change history. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the process of creating and managing rules. [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 regulations management system according to an embodiment of the present invention efficiently drafts regulations, compares them, checks for consistency with laws and regulations, detects typos, and manages versions. In the regulations management system, AI automatically generates draft regulations based on information entered by a user, compares the old and new regulations, and clearly displays changes. AI also analyzes the content of the regulations, checks their consistency with relevant laws and regulations, and automatically detects typos. Furthermore, the system manages versions of regulations and records change history. For example, the regulations management system drafts regulations based on the purpose, scope, and specific content entered by a user. Next, the regulations management system creates a database of relevant laws and regulations, and AI references it to check consistency. Furthermore, the regulations management system uses natural language processing technology to analyze the text of regulations and detect typos. Finally, the regulations management system incorporates a version control system and automatically records change history of regulations. This allows the regulations management process to be more efficient and always kept up-to-date. This allows the regulations management system to streamline the regulations management process and keep it up to date at all times. For example, it allows for efficient drafting of regulations, comparison of previous and subsequent versions, checking for consistency with laws and regulations, detecting typos, and managing versions.

[0029] A regulations management system according to an embodiment includes a generation unit, a comparison unit, a consistency check unit, a detection unit, and a management unit. The generation unit creates a draft of regulations. The generation unit creates the draft of regulations based on, for example, the purpose, scope, and specific content of the regulations input by a user. The generation unit can also select optimal wording by referring to past regulations data. The generation unit can also customize the draft of regulations based on the user's business operations and industry characteristics. The comparison unit compares the previous and next regulations. The comparison unit, for example, compares the new and old regulations and clearly displays the changes. The comparison unit can also determine the display priority based on the importance of the changes. The comparison unit can also apply different comparison algorithms depending on the user's business operations. The consistency check unit analyzes the contents of the regulations and checks their consistency with laws and regulations. The consistency check unit, for example, creates a database of related laws and regulations, and AI references it to check the consistency. The consistency check unit can also apply different consistency check algorithms depending on the user's business operations. Furthermore, the consistency checking unit can improve accuracy by referring to the user's past consistency checking results. The detection unit analyzes the text of the regulations and detects typos and omissions. The detection unit can analyze the text of the regulations using, for example, natural language processing technology and detect typos and omissions. The detection unit can also apply different detection algorithms depending on the user's business operations. Furthermore, the detection unit can improve accuracy by referring to the user's past detection results. The management unit manages versions of the regulations and records change histories. The management unit can, for example, introduce a version control system and automatically record change histories of the regulations. The management unit can also apply different management algorithms depending on the user's business operations. Furthermore, the management unit can improve accuracy by referring to the user's past management results. As a result, the regulations management system according to the embodiment can streamline the regulations management process and always be kept up to date.

[0030] The generation unit can create a draft rule based on the purpose, scope, and specific content of the rule input by the user. The generation unit, for example, creates a draft rule based on the purpose of the rule input by the user. For example, the generation unit can create a draft rule for compliance with laws and regulations. The generation unit can also create a draft rule for improving business efficiency. The generation unit can also create a draft rule based on the scope of the rule input by the user. For example, the generation unit can create a draft rule for a specific business. The generation unit can also create a draft rule for a specific department. The generation unit can also create a draft rule based on the specific content of the rule input by the user. For example, the generation unit can create a draft rule that includes procedures. The generation unit can also create a draft rule that includes rules. This makes it possible to automatically generate draft rules based on information input by the user.

[0031] The consistency confirmation unit stores laws and regulations in a database, and AI can refer to it to confirm consistency. The consistency confirmation unit stores, for example, related laws and regulations in a database, and AI can refer to it to confirm consistency. For example, the consistency confirmation unit stores labor laws in a database and checks whether the contents of regulations comply with labor laws. The consistency confirmation unit can also store environmental regulations in a database and check whether the contents of regulations comply with environmental regulations. Furthermore, the consistency confirmation unit designs the database structure and defines how data is updated. For example, the consistency confirmation unit designs the database table structure and efficiently stores data on laws and regulations. The consistency confirmation unit can also regularly update data on laws and regulations to keep the latest information. This makes it possible to confirm whether regulations comply with laws and regulations.

[0032] The detection unit can analyze the text of the regulations using natural language processing technology and detect typos and omissions. The detection unit can, for example, analyze the text of the regulations using natural language processing technology and detect typos and omissions. For example, the detection unit can divide the text using morphological analysis and detect typos and omissions. The detection unit can also analyze the structure of a sentence using grammatical analysis and detect typos and omissions. Furthermore, the detection unit can analyze the meaning of a sentence using semantic analysis and detect typos and omissions. For example, the detection unit can detect word errors using morphological analysis, detect errors in sentence structure using grammatical analysis, and detect errors in the meaning of a sentence using semantic analysis. In this way, the text of the regulations can be analyzed and typos and omissions can be automatically detected.

[0033] The management department can introduce a version control system and automatically record the change history of the regulations. The management department can, for example, introduce a version control system and automatically record the change history of the regulations. For example, the management department can use Git to manage the versions of the regulations. The management department can also use SVN to manage the versions of the regulations. Furthermore, the management department defines a method for identifying versions and records the change history. For example, the management department can assign a version number to identify the version of the regulations. The management department can also record the changes and maintain the change history. This allows the versions of the regulations to be managed and kept up to date at all times.

[0034] The generation unit can select optimal wording by referring to past regulation data when creating a draft of the regulations. For example, the generation unit selects optimal wording by referring to past regulation data when creating a draft of the regulations. For example, the generation unit extracts frequently used wording from the past regulation data and reflects it in the draft. The generation unit can also select wording that complies with laws and regulations from the past regulation data and reflect it in the draft. Furthermore, the generation unit can select wording that has received good user feedback from the past regulation data and reflect it in the draft. For example, the generation unit selects optimal wording by referring to past regulation data. In this way, optimal wording can be selected by referring to past regulation data.

[0035] The generation unit can customize the draft regulations based on the user's business content and industry characteristics when creating the draft regulations. For example, the generation unit customizes the draft regulations based on the user's business content and industry characteristics when creating the draft regulations. For example, the generation unit uses wording that is appropriate for a specific business process based on the user's business content. The generation unit can also use wording that complies with industry standards based on the user's industry characteristics. Furthermore, the generation unit can combine the user's business content and industry characteristics to select optimal wording. For example, the generation unit customizes the draft regulations based on the user's business content and industry characteristics. This makes it possible to customize the draft regulations based on the user's business content and industry characteristics.

[0036] The generation unit can improve accuracy by reflecting the user's past feedback when creating a draft of the regulations. For example, the generation unit can improve accuracy by reflecting the user's past feedback when creating a draft of the regulations. For example, the generation unit can analyze the user's past feedback and create a draft that reflects improvements. The generation unit can also use wording that was particularly highly rated from the user's past feedback. Furthermore, the generation unit can adjust the way the draft is expressed based on the user's past feedback. For example, the generation unit can refer to the user's past feedback and select the optimal wording. This makes it possible to improve the accuracy of the draft of the regulations by reflecting the user's past feedback.

[0037] The generation unit can reflect region-specific regulations by taking into account the user's geographical conditions when creating a draft of the regulations. For example, the generation unit reflects region-specific regulations by taking into account the user's geographical conditions when creating a draft of the regulations. For example, the generation unit creates a draft that reflects region-specific laws and regulations based on the user's location. The generation unit can also use appropriate wording by taking into account the characteristics of the region where the user's business is conducted. Furthermore, the generation unit can create a draft that reflects region-specific regulations based on the user's geographical conditions. For example, the generation unit creates a draft of the regulations by taking into account the user's geographical conditions. This makes it possible to create a draft of the regulations that reflects region-specific regulations based on the user's geographical conditions.

[0038] The generation unit can analyze the user's business process and propose optimal rules when creating a draft of the rules. For example, the generation unit can analyze the user's business process and propose optimal rules when creating a draft of the rules. For example, the generation unit can analyze the user's business process in detail and propose optimal rules based on the results. The generation unit can also use appropriate wording to improve the efficiency of the user's business process. Furthermore, the generation unit can propose rules that are suitable for the user's business process and reflect them in the draft. For example, the generation unit can analyze the user's business process and propose optimal rules. This makes it possible to propose optimal rules based on the user's business process.

[0039] The generation unit can refer to the user's past rule change history to suggest optimal changes when creating a draft of rules. For example, when creating a draft of rules, the generation unit refers to the user's past rule change history to suggest optimal changes. For example, the generation unit analyzes the user's past rule change history to suggest optimal changes. The generation unit can also reflect changes that were particularly highly rated from the user's past rule change history. Furthermore, the generation unit can also suggest changes to the draft based on the user's past rule change history. For example, the generation unit refers to the user's past rule change history to suggest optimal changes. This makes it possible to suggest optimal changes by referring to the user's past rule change history.

[0040] The comparison unit can determine the display priority based on the importance of the changes when comparing the rules before and after. For example, the comparison unit determines the display priority based on the importance of the changes when comparing the rules before and after. For example, the comparison unit preferentially displays important changes to alert the user. The comparison unit can also adjust the display priority based on the importance of the changes. Furthermore, the comparison unit can briefly display changes with low importance and display changes with high importance in detail. For example, the comparison unit determines the display priority based on the importance of the changes. This makes it possible to determine the display priority based on the importance of the changes.

[0041] The comparison unit can apply different comparison algorithms depending on the user's work content when comparing the rules before and after the rule. For example, the comparison unit applies different comparison algorithms depending on the user's work content when comparing the rules before and after the rule. For example, the comparison unit selects an optimal comparison algorithm based on the user's work content. The comparison unit can also apply different comparison algorithms depending on the user's work content. Furthermore, the comparison unit can propose an optimal comparison algorithm taking the user's work content into consideration. For example, the comparison unit applies different comparison algorithms depending on the user's work content. This makes it possible to apply different comparison algorithms depending on the user's work content.

[0042] The comparison unit can improve accuracy by referring to the user's past comparison results when comparing before and after the rule. For example, the comparison unit improves accuracy by referring to the user's past comparison results when comparing before and after the rule. For example, the comparison unit analyzes the user's past comparison results and improves accuracy. The comparison unit can also use an algorithm that was particularly highly rated from the user's past comparison results. Furthermore, the comparison unit can adjust the comparison algorithm based on the user's past comparison results. For example, the comparison unit improves accuracy by referring to the user's past comparison results. In this way, the accuracy of the comparison can be improved by referring to the user's past comparison results.

[0043] The comparison unit can select the optimal comparison method by taking into consideration the user's business process when comparing the rules before and after. For example, the comparison unit selects the optimal comparison method by taking into consideration the user's business process when comparing the rules before and after. For example, the comparison unit selects the optimal comparison method based on the user's business process. The comparison unit can also propose an appropriate comparison method by taking into consideration the user's business process. Furthermore, the comparison unit can apply different comparison methods depending on the user's business process. For example, the comparison unit selects the optimal comparison method by taking into consideration the user's business process. This makes it possible to select the optimal comparison method based on the user's business process.

[0044] The comparison unit can customize the comparison method by reflecting the user's past feedback when comparing before and after the rule. For example, the comparison unit customizes the comparison method by reflecting the user's past feedback when comparing before and after the rule. For example, the comparison unit analyzes the user's past feedback and customizes the comparison method. The comparison unit can also use a comparison method that has received particularly high ratings from the user's past feedback. Furthermore, the comparison unit can adjust the comparison method based on the user's past feedback. For example, the comparison unit customizes the comparison method by reflecting the user's past feedback. In this way, the comparison method can be customized by reflecting the user's past feedback.

[0045] The comparison unit can set optimal comparison criteria taking into account the user's industry characteristics when comparing regulations before and after. For example, the comparison unit sets optimal comparison criteria taking into account the user's industry characteristics when comparing regulations before and after. For example, the comparison unit sets optimal comparison criteria based on the user's industry characteristics. The comparison unit can also propose appropriate comparison criteria taking into account the user's industry characteristics. Furthermore, the comparison unit can apply different comparison criteria depending on the user's industry characteristics. For example, the comparison unit sets optimal comparison criteria based on the user's industry characteristics. This makes it possible to set optimal comparison criteria based on the user's industry characteristics.

[0046] The consistency checking unit can determine the display priority based on the importance of related laws and regulations when checking consistency. The consistency checking unit, for example, determines the display priority based on the importance of related laws and regulations when checking consistency. For example, the consistency checking unit prioritizes display of important laws and regulations to alert the user. The consistency checking unit can also adjust the display priority based on the importance of laws and regulations. Furthermore, the consistency checking unit can briefly display laws and regulations with low importance and display laws and regulations with high importance in detail. For example, the consistency checking unit determines the display priority based on the importance of laws and regulations. This makes it possible to determine the display priority based on the importance of related laws and regulations.

[0047] The consistency checking unit can apply different consistency checking algorithms depending on the user's business content during consistency checking. For example, the consistency checking unit applies different consistency checking algorithms depending on the user's business content during consistency checking. For example, the consistency checking unit selects an optimal consistency checking algorithm based on the user's business content. The consistency checking unit can also apply different consistency checking algorithms depending on the user's business content. Furthermore, the consistency checking unit can also propose an optimal consistency checking algorithm taking the user's business content into consideration. For example, the consistency checking unit applies different consistency checking algorithms depending on the user's business content. This makes it possible to apply different consistency checking algorithms depending on the user's business content.

[0048] The consistency confirmation unit can improve accuracy by referring to the user's past consistency confirmation results when performing consistency confirmation. For example, the consistency confirmation unit improves accuracy by referring to the user's past consistency confirmation results when performing consistency confirmation. For example, the consistency confirmation unit analyzes the user's past consistency confirmation results to improve accuracy. The consistency confirmation unit can also use an algorithm that was particularly highly rated from the user's past consistency confirmation results. Furthermore, the consistency confirmation unit can adjust the consistency confirmation algorithm based on the user's past consistency confirmation results. For example, the consistency confirmation unit improves accuracy by referring to the user's past consistency confirmation results. This makes it possible to improve the accuracy of consistency confirmation by referring to the user's past consistency confirmation results.

[0049] The consistency confirmation unit can select the optimal consistency confirmation method in consideration of the user's business process during consistency confirmation. For example, the consistency confirmation unit selects the optimal consistency confirmation method in consideration of the user's business process during consistency confirmation. For example, the consistency confirmation unit selects the optimal consistency confirmation method based on the user's business process. The consistency confirmation unit can also propose an appropriate consistency confirmation method in consideration of the user's business process. Furthermore, the consistency confirmation unit can apply different consistency confirmation methods depending on the user's business process. For example, the consistency confirmation unit selects the optimal consistency confirmation method in consideration of the user's business process. This makes it possible to select the optimal consistency confirmation method based on the user's business process.

[0050] The consistency check unit can customize the consistency check method by reflecting the user's past feedback during consistency check. For example, the consistency check unit customizes the consistency check method by reflecting the user's past feedback during consistency check. For example, the consistency check unit analyzes the user's past feedback and customizes the consistency check method. The consistency check unit can also use a consistency check method that has received particularly high ratings from the user's past feedback. Furthermore, the consistency check unit can adjust the consistency check method based on the user's past feedback. For example, the consistency check unit customizes the consistency check method by reflecting the user's past feedback. In this way, the consistency check method can be customized by reflecting the user's past feedback.

[0051] The consistency checking unit can set optimal consistency checking criteria taking into account the industry characteristics of the user during consistency checking. For example, the consistency checking unit sets optimal consistency checking criteria taking into account the industry characteristics of the user during consistency checking. For example, the consistency checking unit sets optimal consistency checking criteria based on the industry characteristics of the user. The consistency checking unit can also propose appropriate consistency checking criteria taking into account the industry characteristics of the user. Furthermore, the consistency checking unit can apply different consistency checking criteria depending on the industry characteristics of the user. For example, the consistency checking unit sets optimal consistency checking criteria based on the industry characteristics of the user. This makes it possible to set optimal consistency checking criteria based on the industry characteristics of the user.

[0052] The detection unit can determine the detection priority based on the importance of the rule when detecting typos and omissions. For example, the detection unit determines the detection priority based on the importance of the rule when detecting typos and omissions. For example, the detection unit preferentially detects typos and omissions in important rules and alerts the user. The detection unit can also adjust the detection priority based on the importance of the rule. Furthermore, the detection unit can briefly display typos and omissions in rules with low importance and display typos and omissions in rules with high importance in detail. For example, the detection unit determines the detection priority based on the importance of the rule. This makes it possible to determine the detection priority for typos and omissions based on the importance of the rule.

[0053] The detection unit can apply different detection algorithms depending on the user's work content when detecting typos and omissions. For example, the detection unit applies different detection algorithms depending on the user's work content when detecting typos and omissions. For example, the detection unit selects an optimal detection algorithm based on the user's work content. The detection unit can also apply different detection algorithms depending on the user's work content. Furthermore, the detection unit can propose an optimal detection algorithm taking the user's work content into consideration. For example, the detection unit applies different detection algorithms depending on the user's work content. This makes it possible to apply different detection algorithms depending on the user's work content.

[0054] The detection unit can improve accuracy when detecting typos and omissions by referring to the user's past detection results. For example, when detecting typos and omissions, the detection unit improves accuracy by referring to the user's past detection results. For example, the detection unit analyzes the user's past detection results to improve accuracy. The detection unit can also use an algorithm that was particularly highly rated from the user's past detection results. Furthermore, the detection unit can adjust the detection algorithm based on the user's past detection results. For example, the detection unit improves accuracy by referring to the user's past detection results. This makes it possible to improve the accuracy of detecting typos and omissions by referring to the user's past detection results.

[0055] The detection unit can select the optimal detection method in consideration of the user's business process when detecting typos and omissions. For example, the detection unit selects the optimal detection method in consideration of the user's business process when detecting typos and omissions. For example, the detection unit selects the optimal detection method based on the user's business process. The detection unit can also suggest an appropriate detection method in consideration of the user's business process. Furthermore, the detection unit can apply different detection methods depending on the user's business process. For example, the detection unit selects the optimal detection method in consideration of the user's business process. This makes it possible to select the optimal detection method for typos and omissions based on the user's business process.

[0056] The detection unit can customize the detection method by reflecting the user's past feedback when detecting typos and omissions. For example, the detection unit customizes the detection method by reflecting the user's past feedback when detecting typos and omissions. For example, the detection unit analyzes the user's past feedback and customizes the detection method. The detection unit can also use a detection method that has received particularly high ratings from the user's past feedback. Furthermore, the detection unit can adjust the detection method based on the user's past feedback. For example, the detection unit customizes the detection method by reflecting the user's past feedback. In this way, the detection method for detecting typos and omissions can be customized by reflecting the user's past feedback.

[0057] The detection unit can set optimal detection criteria taking into consideration the industry characteristics of the user when detecting typos and omissions. For example, the detection unit sets optimal detection criteria taking into consideration the industry characteristics of the user when detecting typos and omissions. For example, the detection unit sets optimal detection criteria based on the industry characteristics of the user. The detection unit can also propose appropriate detection criteria taking into consideration the industry characteristics of the user. Furthermore, the detection unit can apply different detection criteria depending on the industry characteristics of the user. For example, the detection unit sets optimal detection criteria based on the industry characteristics of the user. This makes it possible to set optimal detection criteria for typos and omissions based on the industry characteristics of the user.

[0058] The management unit can determine the management priority based on the importance of the rules during version management. The management unit, for example, determines the management priority based on the importance of the rules during version management. For example, the management unit prioritizes management of versions of important rules and alerts the user. The management unit can also adjust the management priority based on the importance of the rules. Furthermore, the management unit can simply manage versions of rules with low importance and manage versions of rules with high importance in detail. For example, the management unit determines the management priority based on the importance of the rules. In this way, the priority of version management can be determined based on the importance of the rules.

[0059] The management unit can apply different management algorithms depending on the user's work content during version management. For example, the management unit applies different management algorithms depending on the user's work content during version management. For example, the management unit selects an optimal management algorithm based on the user's work content. The management unit can also apply different management algorithms depending on the user's work content. Furthermore, the management unit can propose an optimal management algorithm taking the user's work content into consideration. For example, the management unit applies different management algorithms depending on the user's work content. This makes it possible to apply different version management algorithms depending on the user's work content.

[0060] The management unit can improve accuracy during version management by referring to the user's past management results. For example, during version management, the management unit improves accuracy by referring to the user's past management results. For example, the management unit analyzes the user's past management results and improves accuracy. The management unit can also use an algorithm that was particularly highly rated from the user's past management results. Furthermore, the management unit can adjust the management algorithm based on the user's past management results. For example, the management unit improves accuracy by referring to the user's past management results. In this way, the accuracy of version management can be improved by referring to the user's past management results.

[0061] The management unit can select the optimal management method in consideration of the user's business process when performing version management. For example, the management unit selects the optimal management method in consideration of the user's business process when performing version management. For example, the management unit selects the optimal management method based on the user's business process. The management unit can also propose an appropriate management method in consideration of the user's business process. Furthermore, the management unit can apply different management methods depending on the user's business process. For example, the management unit selects the optimal management method in consideration of the user's business process. This makes it possible to select the optimal version management method based on the user's business process.

[0062] The management unit can customize the management method by reflecting the user's past feedback during version management. For example, the management unit customizes the management method by reflecting the user's past feedback during version management. For example, the management unit analyzes the user's past feedback and customizes the management method. The management unit can also use a management method that has received particularly high praise from the user's past feedback. Furthermore, the management unit can adjust the management method based on the user's past feedback. For example, the management unit customizes the management method by reflecting the user's past feedback. In this way, the version management method can be customized by reflecting the user's past feedback.

[0063] The management unit can set optimal management standards taking into account the industry characteristics of the user during version management. For example, the management unit sets optimal management standards taking into account the industry characteristics of the user during version management. For example, the management unit sets optimal management standards based on the industry characteristics of the user. The management unit can also propose appropriate management standards taking into account the industry characteristics of the user. Furthermore, the management unit can apply different management standards depending on the industry characteristics of the user. For example, the management unit sets optimal management standards based on the industry characteristics of the user. This makes it possible to set optimal version control standards based on the industry characteristics of the user.

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

[0065] When creating a draft of the regulations, the generation unit can reflect region-specific regulations by taking into account the user's geographical conditions. For example, the generation unit creates a draft that reflects region-specific laws and regulations based on the user's location. The generation unit can also use appropriate wording by taking into account the characteristics of the region where the user's business is conducted. Furthermore, the generation unit can create a draft that reflects region-specific regulations based on the user's geographical conditions. This allows the creation of a draft of the regulations that reflects region-specific regulations based on the user's geographical conditions.

[0066] When comparing before and after regulations, the comparison unit can determine the display priority based on the importance of the changes. For example, the comparison unit can prioritize displaying important changes to alert the user. The comparison unit can also adjust the display priority based on the importance of the changes. Furthermore, the comparison unit can display changes with low importance briefly and changes with high importance in detail. This allows the display priority to be determined based on the importance of the changes.

[0067] During consistency check, the consistency check unit can determine the display priority based on the importance of the relevant laws and regulations. For example, the consistency check unit can prioritize the display of important laws and regulations to alert the user. The consistency check unit can also adjust the display priority based on the importance of the laws and regulations. Furthermore, the consistency check unit can display laws and regulations with low importance in a concise manner and laws and regulations with high importance in detail. This allows the display priority to be determined based on the importance of the relevant laws and regulations.

[0068] When detecting typos and omissions, the detection unit can determine the priority of detection based on the importance of the rule. For example, the detection unit can preferentially detect typos and omissions in important rules and alert the user. The detection unit can also adjust the priority of detection based on the importance of the rule. Furthermore, the detection unit can briefly display typos and omissions in rules with low importance and display typos and omissions in rules with high importance in detail. In this way, the priority of detecting typos and omissions can be determined based on the importance of the rule.

[0069] The management unit can determine the management priority based on the importance of the rules when managing versions. For example, the management unit can prioritize managing versions of important rules and alert the user. The management unit can also adjust the management priority based on the importance of the rules. Furthermore, the management unit can manage versions of rules with low importance simply and manage versions of rules with high importance in detail. This makes it possible to determine the priority of version management based on the importance of the rules.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The generator creates a draft of the regulations. The generator creates a draft of the regulations based on the purpose, scope, and specific content of the regulations entered by the user. The generator can also select the most appropriate wording by referencing past regulations data. Furthermore, the generator can customize the draft of the regulations based on the user's work content and industry characteristics. Step 2: The comparison unit compares the old and new rules. The comparison unit compares the old and new rules and clearly displays the changes. The comparison unit can also determine the display priority based on the importance of the changes. Furthermore, the comparison unit can apply different comparison algorithms depending on the user's business operations. Step 3: The consistency check unit analyzes the contents of the regulations and checks their consistency with laws and regulations. The consistency check unit creates a database of relevant laws and regulations, which the AI ​​references to check consistency. The consistency check unit can also apply different consistency check algorithms depending on the user's business operations. Furthermore, the consistency check unit can also improve accuracy by referring to the user's past consistency check results. Step 4: The detection unit analyzes the text of the regulations and detects typos. The detection unit uses natural language processing technology to analyze the text of the regulations and detect typos. The detection unit can also apply different detection algorithms depending on the user's work. Furthermore, the detection unit can improve accuracy by referring to the user's past detection results. Step 5: The management department manages the version of the regulations and records the change history. The management department introduces a version control system and automatically records the change history of the regulations. The management department can also apply different management algorithms depending on the user's business content. Furthermore, the management department can improve accuracy by referring to the user's past management results.

[0072] (Example 2) A regulations management system according to an embodiment of the present invention efficiently drafts regulations, compares them, checks for consistency with laws and regulations, detects typos, and manages versions. In the regulations management system, AI automatically generates draft regulations based on information entered by a user, compares the old and new regulations, and clearly displays changes. AI also analyzes the content of the regulations, checks their consistency with relevant laws and regulations, and automatically detects typos. Furthermore, the system manages versions of regulations and records change history. For example, the regulations management system drafts regulations based on the purpose, scope, and specific content entered by a user. Next, the regulations management system creates a database of relevant laws and regulations, and AI references it to check consistency. Furthermore, the regulations management system uses natural language processing technology to analyze the text of regulations and detect typos. Finally, the regulations management system incorporates a version control system and automatically records change history of regulations. This allows the regulations management process to be more efficient and always kept up-to-date. This allows the regulations management system to streamline the regulations management process and keep it up to date at all times. For example, it allows for efficient drafting of regulations, comparison of previous and subsequent versions, checking for consistency with laws and regulations, detecting typos, and managing versions.

[0073] A regulations management system according to an embodiment includes a generation unit, a comparison unit, a consistency check unit, a detection unit, and a management unit. The generation unit creates a draft of regulations. The generation unit creates the draft of regulations based on, for example, the purpose, scope, and specific content of the regulations input by a user. The generation unit can also select optimal wording by referring to past regulations data. The generation unit can also customize the draft of regulations based on the user's business operations and industry characteristics. The comparison unit compares the previous and next regulations. The comparison unit, for example, compares the new and old regulations and clearly displays the changes. The comparison unit can also determine the display priority based on the importance of the changes. The comparison unit can also apply different comparison algorithms depending on the user's business operations. The consistency check unit analyzes the contents of the regulations and checks their consistency with laws and regulations. The consistency check unit, for example, creates a database of related laws and regulations, and AI references it to check the consistency. The consistency check unit can also apply different consistency check algorithms depending on the user's business operations. Furthermore, the consistency checking unit can improve accuracy by referring to the user's past consistency checking results. The detection unit analyzes the text of the regulations and detects typos and omissions. The detection unit can analyze the text of the regulations using, for example, natural language processing technology and detect typos and omissions. The detection unit can also apply different detection algorithms depending on the user's business operations. Furthermore, the detection unit can improve accuracy by referring to the user's past detection results. The management unit manages versions of the regulations and records change histories. The management unit can, for example, introduce a version control system and automatically record change histories of the regulations. The management unit can also apply different management algorithms depending on the user's business operations. Furthermore, the management unit can improve accuracy by referring to the user's past management results. As a result, the regulations management system according to the embodiment can streamline the regulations management process and always be kept up to date.

[0074] The generation unit can create a draft rule based on the purpose, scope, and specific content of the rule input by the user. The generation unit, for example, creates a draft rule based on the purpose of the rule input by the user. For example, the generation unit can create a draft rule for compliance with laws and regulations. The generation unit can also create a draft rule for improving business efficiency. The generation unit can also create a draft rule based on the scope of the rule input by the user. For example, the generation unit can create a draft rule for a specific business. The generation unit can also create a draft rule for a specific department. The generation unit can also create a draft rule based on the specific content of the rule input by the user. For example, the generation unit can create a draft rule that includes procedures. The generation unit can also create a draft rule that includes rules. This makes it possible to automatically generate draft rules based on information input by the user.

[0075] The consistency confirmation unit stores laws and regulations in a database, and AI can refer to it to confirm consistency. The consistency confirmation unit stores, for example, related laws and regulations in a database, and AI can refer to it to confirm consistency. For example, the consistency confirmation unit stores labor laws in a database and checks whether the contents of regulations comply with labor laws. The consistency confirmation unit can also store environmental regulations in a database and check whether the contents of regulations comply with environmental regulations. Furthermore, the consistency confirmation unit designs the database structure and defines how data is updated. For example, the consistency confirmation unit designs the database table structure and efficiently stores data on laws and regulations. The consistency confirmation unit can also regularly update data on laws and regulations to keep the latest information. This makes it possible to confirm whether regulations comply with laws and regulations.

[0076] The detection unit can analyze the text of the regulations using natural language processing technology and detect typos and omissions. The detection unit can, for example, analyze the text of the regulations using natural language processing technology and detect typos and omissions. For example, the detection unit can divide the text using morphological analysis and detect typos and omissions. The detection unit can also analyze the structure of a sentence using grammatical analysis and detect typos and omissions. Furthermore, the detection unit can analyze the meaning of a sentence using semantic analysis and detect typos and omissions. For example, the detection unit can detect word errors using morphological analysis, detect errors in sentence structure using grammatical analysis, and detect errors in the meaning of a sentence using semantic analysis. In this way, the text of the regulations can be analyzed and typos and omissions can be automatically detected.

[0077] The management department can introduce a version control system and automatically record the change history of the regulations. The management department can, for example, introduce a version control system and automatically record the change history of the regulations. For example, the management department can use Git to manage the versions of the regulations. The management department can also use SVN to manage the versions of the regulations. Furthermore, the management department defines a method for identifying versions and records the change history. For example, the management department can assign a version number to identify the version of the regulations. The management department can also record the changes and maintain the change history. This allows the versions of the regulations to be managed and kept up to date at all times.

[0078] The generation unit can analyze the user's emotions and adjust the expression style of the draft rules based on the analyzed user emotions. The generation unit, for example, analyzes the user's emotions and adjusts the expression style of the draft rules based on the analyzed user emotions. For example, if the user is feeling stressed, the generation unit can create draft rules using concise and clear expressions. Also, if the user is relaxed, the generation unit can create draft rules using detailed and polite expressions. Furthermore, if the user is in a hurry, the generation unit can create draft rules using short sentences that get to the point. For example, the generation unit can estimate the user's emotions and adjust the expression style of the draft rules based on the estimated user emotions. In this way, the expression style of the draft rules can be adjusted according to the user's emotions.

[0079] The generation unit can select optimal wording by referring to past regulation data when creating a draft of the regulations. For example, the generation unit selects optimal wording by referring to past regulation data when creating a draft of the regulations. For example, the generation unit extracts frequently used wording from the past regulation data and reflects it in the draft. The generation unit can also select wording that complies with laws and regulations from the past regulation data and reflect it in the draft. Furthermore, the generation unit can select wording that has received good user feedback from the past regulation data and reflect it in the draft. For example, the generation unit selects optimal wording by referring to past regulation data. In this way, optimal wording can be selected by referring to past regulation data.

[0080] The generation unit can customize the draft regulations based on the user's business content and industry characteristics when creating the draft regulations. For example, the generation unit customizes the draft regulations based on the user's business content and industry characteristics when creating the draft regulations. For example, the generation unit uses wording that is appropriate for a specific business process based on the user's business content. The generation unit can also use wording that complies with industry standards based on the user's industry characteristics. Furthermore, the generation unit can combine the user's business content and industry characteristics to select optimal wording. For example, the generation unit customizes the draft regulations based on the user's business content and industry characteristics. This makes it possible to customize the draft regulations based on the user's business content and industry characteristics.

[0081] The generation unit can improve accuracy by reflecting the user's past feedback when creating a draft of the regulations. For example, the generation unit can improve accuracy by reflecting the user's past feedback when creating a draft of the regulations. For example, the generation unit can analyze the user's past feedback and create a draft that reflects improvements. The generation unit can also use wording that was particularly highly rated from the user's past feedback. Furthermore, the generation unit can adjust the way the draft is expressed based on the user's past feedback. For example, the generation unit can refer to the user's past feedback and select the optimal wording. This makes it possible to improve the accuracy of the draft of the regulations by reflecting the user's past feedback.

[0082] The generation unit can analyze the user's emotions and adjust the length of the draft rules based on the analyzed user's emotions. The generation unit, for example, analyzes the user's emotions and adjusts the length of the draft rules based on the analyzed user's emotions. For example, if the user is feeling stressed, the generation unit creates a short, concise draft rule that focuses on the main points. Also, if the user is relaxed, the generation unit can create a longer draft rule that includes detailed explanations. Furthermore, if the user is in a hurry, the generation unit can create a concise, short draft rule. For example, the generation unit estimates the user's emotions and adjusts the length of the draft rules based on the estimated user's emotions. In this way, the length of the draft rules can be adjusted according to the user's emotions.

[0083] The generation unit can reflect region-specific regulations by taking into account the user's geographical conditions when creating a draft of the regulations. For example, the generation unit reflects region-specific regulations by taking into account the user's geographical conditions when creating a draft of the regulations. For example, the generation unit creates a draft that reflects region-specific laws and regulations based on the user's location. The generation unit can also use appropriate wording by taking into account the characteristics of the region where the user's business is conducted. Furthermore, the generation unit can create a draft that reflects region-specific regulations based on the user's geographical conditions. For example, the generation unit creates a draft of the regulations by taking into account the user's geographical conditions. This makes it possible to create a draft of the regulations that reflects region-specific regulations based on the user's geographical conditions.

[0084] The generation unit can analyze the user's business process and propose optimal rules when creating a draft of the rules. For example, the generation unit can analyze the user's business process and propose optimal rules when creating a draft of the rules. For example, the generation unit can analyze the user's business process in detail and propose optimal rules based on the results. The generation unit can also use appropriate wording to improve the efficiency of the user's business process. Furthermore, the generation unit can propose rules that are suitable for the user's business process and reflect them in the draft. For example, the generation unit can analyze the user's business process and propose optimal rules. This makes it possible to propose optimal rules based on the user's business process.

[0085] The generation unit can refer to the user's past rule change history to suggest optimal changes when creating a draft of rules. For example, when creating a draft of rules, the generation unit refers to the user's past rule change history to suggest optimal changes. For example, the generation unit analyzes the user's past rule change history to suggest optimal changes. The generation unit can also reflect changes that were particularly highly rated from the user's past rule change history. Furthermore, the generation unit can also suggest changes to the draft based on the user's past rule change history. For example, the generation unit refers to the user's past rule change history to suggest optimal changes. This makes it possible to suggest optimal changes by referring to the user's past rule change history.

[0086] The comparison unit can analyze the user's emotions and adjust the display method of the comparison results based on the analyzed user's emotions. For example, the comparison unit can analyze the user's emotions and adjust the display method of the comparison results based on the analyzed user's emotions. For example, if the user is feeling stressed, the comparison unit can provide a concise and highly visible display method. Furthermore, if the user is relaxed, the comparison unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the comparison unit can provide a display method that focuses on the main points. For example, the comparison unit can estimate the user's emotions and adjust the display method of the comparison results based on the estimated user's emotions. In this way, the display method of the comparison results can be adjusted according to the user's emotions.

[0087] The comparison unit can determine the display priority based on the importance of the changes when comparing the rules before and after. For example, the comparison unit determines the display priority based on the importance of the changes when comparing the rules before and after. For example, the comparison unit preferentially displays important changes to alert the user. The comparison unit can also adjust the display priority based on the importance of the changes. Furthermore, the comparison unit can briefly display changes with low importance and display changes with high importance in detail. For example, the comparison unit determines the display priority based on the importance of the changes. This makes it possible to determine the display priority based on the importance of the changes.

[0088] The comparison unit can apply different comparison algorithms depending on the user's work content when comparing the rules before and after the rule. For example, the comparison unit applies different comparison algorithms depending on the user's work content when comparing the rules before and after the rule. For example, the comparison unit selects an optimal comparison algorithm based on the user's work content. The comparison unit can also apply different comparison algorithms depending on the user's work content. Furthermore, the comparison unit can propose an optimal comparison algorithm taking the user's work content into consideration. For example, the comparison unit applies different comparison algorithms depending on the user's work content. This makes it possible to apply different comparison algorithms depending on the user's work content.

[0089] The comparison unit can improve accuracy by referring to the user's past comparison results when comparing before and after the rule. For example, the comparison unit improves accuracy by referring to the user's past comparison results when comparing before and after the rule. For example, the comparison unit analyzes the user's past comparison results and improves accuracy. The comparison unit can also use an algorithm that was particularly highly rated from the user's past comparison results. Furthermore, the comparison unit can adjust the comparison algorithm based on the user's past comparison results. For example, the comparison unit improves accuracy by referring to the user's past comparison results. In this way, the accuracy of the comparison can be improved by referring to the user's past comparison results.

[0090] The comparison unit can analyze the user's emotions and adjust the level of detail of the comparison results based on the analyzed user's emotions. For example, the comparison unit can analyze the user's emotions and adjust the level of detail of the comparison results based on the analyzed user's emotions. For example, if the user is feeling stressed, the comparison unit can display a concise and to-the-point comparison result. Also, if the user is relaxed, the comparison unit can display a detailed comparison result. Furthermore, if the user is in a hurry, the comparison unit can display only important changes. For example, the comparison unit can estimate the user's emotions and adjust the level of detail of the comparison results based on the estimated user's emotions. In this way, the level of detail of the comparison results can be adjusted according to the user's emotions.

[0091] The comparison unit can select the optimal comparison method by taking into consideration the user's business process when comparing the rules before and after. For example, the comparison unit selects the optimal comparison method by taking into consideration the user's business process when comparing the rules before and after. For example, the comparison unit selects the optimal comparison method based on the user's business process. The comparison unit can also propose an appropriate comparison method by taking into consideration the user's business process. Furthermore, the comparison unit can apply different comparison methods depending on the user's business process. For example, the comparison unit selects the optimal comparison method by taking into consideration the user's business process. This makes it possible to select the optimal comparison method based on the user's business process.

[0092] The comparison unit can customize the comparison method by reflecting the user's past feedback when comparing before and after the rule. For example, the comparison unit customizes the comparison method by reflecting the user's past feedback when comparing before and after the rule. For example, the comparison unit analyzes the user's past feedback and customizes the comparison method. The comparison unit can also use a comparison method that has received particularly high ratings from the user's past feedback. Furthermore, the comparison unit can adjust the comparison method based on the user's past feedback. For example, the comparison unit customizes the comparison method by reflecting the user's past feedback. In this way, the comparison method can be customized by reflecting the user's past feedback.

[0093] The comparison unit can set optimal comparison criteria taking into account the user's industry characteristics when comparing regulations before and after. For example, the comparison unit sets optimal comparison criteria taking into account the user's industry characteristics when comparing regulations before and after. For example, the comparison unit sets optimal comparison criteria based on the user's industry characteristics. The comparison unit can also propose appropriate comparison criteria taking into account the user's industry characteristics. Furthermore, the comparison unit can apply different comparison criteria depending on the user's industry characteristics. For example, the comparison unit sets optimal comparison criteria based on the user's industry characteristics. This makes it possible to set optimal comparison criteria based on the user's industry characteristics.

[0094] The consistency confirmation unit can analyze the user's emotions and adjust the display method of the consistency confirmation based on the analyzed user's emotions. The consistency confirmation unit, for example, analyzes the user's emotions and adjusts the display method of the consistency confirmation based on the analyzed user's emotions. For example, if the user is feeling stressed, the consistency confirmation unit provides a concise and highly visible display method. Furthermore, if the user is relaxed, the consistency confirmation unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the consistency confirmation unit can also provide a display method that focuses on the main points. For example, the consistency confirmation unit estimates the user's emotions and adjusts the display method of the consistency confirmation based on the estimated user's emotions. In this way, the display method of the consistency confirmation can be adjusted according to the user's emotions.

[0095] The consistency checking unit can determine the display priority based on the importance of related laws and regulations when checking consistency. The consistency checking unit, for example, determines the display priority based on the importance of related laws and regulations when checking consistency. For example, the consistency checking unit prioritizes display of important laws and regulations to alert the user. The consistency checking unit can also adjust the display priority based on the importance of laws and regulations. Furthermore, the consistency checking unit can briefly display laws and regulations with low importance and display laws and regulations with high importance in detail. For example, the consistency checking unit determines the display priority based on the importance of laws and regulations. This makes it possible to determine the display priority based on the importance of related laws and regulations.

[0096] The consistency checking unit can apply different consistency checking algorithms depending on the user's business content during consistency checking. For example, the consistency checking unit applies different consistency checking algorithms depending on the user's business content during consistency checking. For example, the consistency checking unit selects an optimal consistency checking algorithm based on the user's business content. The consistency checking unit can also apply different consistency checking algorithms depending on the user's business content. Furthermore, the consistency checking unit can also propose an optimal consistency checking algorithm taking the user's business content into consideration. For example, the consistency checking unit applies different consistency checking algorithms depending on the user's business content. This makes it possible to apply different consistency checking algorithms depending on the user's business content.

[0097] The consistency confirmation unit can improve accuracy by referring to the user's past consistency confirmation results when performing consistency confirmation. For example, the consistency confirmation unit improves accuracy by referring to the user's past consistency confirmation results when performing consistency confirmation. For example, the consistency confirmation unit analyzes the user's past consistency confirmation results to improve accuracy. The consistency confirmation unit can also use an algorithm that was particularly highly rated from the user's past consistency confirmation results. Furthermore, the consistency confirmation unit can adjust the consistency confirmation algorithm based on the user's past consistency confirmation results. For example, the consistency confirmation unit improves accuracy by referring to the user's past consistency confirmation results. This makes it possible to improve the accuracy of consistency confirmation by referring to the user's past consistency confirmation results.

[0098] The consistency checking unit can analyze the user's emotions and adjust the level of detail of the consistency checking based on the analyzed user's emotions. The consistency checking unit, for example, analyzes the user's emotions and adjusts the level of detail of the consistency checking based on the analyzed user's emotions. For example, if the user is feeling stressed, the consistency checking unit displays a concise and to-the-point consistency checking result. Furthermore, if the user is relaxed, the consistency checking unit can also display a detailed consistency checking result. Furthermore, if the user is in a hurry, the consistency checking unit can display only important laws and regulations. For example, the consistency checking unit estimates the user's emotions and adjusts the level of detail of the consistency checking based on the estimated user's emotions. In this way, the level of detail of the consistency checking can be adjusted according to the user's emotions.

[0099] The consistency confirmation unit can select the optimal consistency confirmation method in consideration of the user's business process during consistency confirmation. For example, the consistency confirmation unit selects the optimal consistency confirmation method in consideration of the user's business process during consistency confirmation. For example, the consistency confirmation unit selects the optimal consistency confirmation method based on the user's business process. The consistency confirmation unit can also propose an appropriate consistency confirmation method in consideration of the user's business process. Furthermore, the consistency confirmation unit can apply different consistency confirmation methods depending on the user's business process. For example, the consistency confirmation unit selects the optimal consistency confirmation method in consideration of the user's business process. This makes it possible to select the optimal consistency confirmation method based on the user's business process.

[0100] The consistency check unit can customize the consistency check method by reflecting the user's past feedback during consistency check. For example, the consistency check unit customizes the consistency check method by reflecting the user's past feedback during consistency check. For example, the consistency check unit analyzes the user's past feedback and customizes the consistency check method. The consistency check unit can also use a consistency check method that has received particularly high ratings from the user's past feedback. Furthermore, the consistency check unit can adjust the consistency check method based on the user's past feedback. For example, the consistency check unit customizes the consistency check method by reflecting the user's past feedback. In this way, the consistency check method can be customized by reflecting the user's past feedback.

[0101] The consistency checking unit can set optimal consistency checking criteria taking into account the industry characteristics of the user during consistency checking. For example, the consistency checking unit sets optimal consistency checking criteria taking into account the industry characteristics of the user during consistency checking. For example, the consistency checking unit sets optimal consistency checking criteria based on the industry characteristics of the user. The consistency checking unit can also propose appropriate consistency checking criteria taking into account the industry characteristics of the user. Furthermore, the consistency checking unit can apply different consistency checking criteria depending on the industry characteristics of the user. For example, the consistency checking unit sets optimal consistency checking criteria based on the industry characteristics of the user. This makes it possible to set optimal consistency checking criteria based on the industry characteristics of the user.

[0102] The detection unit can analyze the user's emotions and adjust the method for detecting typos and omissions based on the analyzed user's emotions. The detection unit, for example, analyzes the user's emotions and adjusts the method for detecting typos and omissions based on the analyzed user's emotions. For example, the detection unit displays concise and highly visible typo detection results when the user is stressed. The detection unit can also display detailed typo detection results when the user is relaxed. Furthermore, the detection unit can display only important typos and omissions when the user is in a hurry. For example, the detection unit estimates the user's emotions and adjusts the method for detecting typos and omissions based on the estimated user's emotions. This makes it possible to adjust the method for detecting typos and omissions according to the user's emotions.

[0103] The detection unit can determine the detection priority based on the importance of the rule when detecting typos and omissions. For example, the detection unit determines the detection priority based on the importance of the rule when detecting typos and omissions. For example, the detection unit preferentially detects typos and omissions in important rules and alerts the user. The detection unit can also adjust the detection priority based on the importance of the rule. Furthermore, the detection unit can briefly display typos and omissions in rules with low importance and display typos and omissions in rules with high importance in detail. For example, the detection unit determines the detection priority based on the importance of the rule. This makes it possible to determine the detection priority for typos and omissions based on the importance of the rule.

[0104] The detection unit can apply different detection algorithms depending on the user's work content when detecting typos and omissions. For example, the detection unit applies different detection algorithms depending on the user's work content when detecting typos and omissions. For example, the detection unit selects an optimal detection algorithm based on the user's work content. The detection unit can also apply different detection algorithms depending on the user's work content. Furthermore, the detection unit can propose an optimal detection algorithm taking the user's work content into consideration. For example, the detection unit applies different detection algorithms depending on the user's work content. This makes it possible to apply different detection algorithms depending on the user's work content.

[0105] The detection unit can improve accuracy when detecting typos and omissions by referring to the user's past detection results. For example, when detecting typos and omissions, the detection unit improves accuracy by referring to the user's past detection results. For example, the detection unit analyzes the user's past detection results to improve accuracy. The detection unit can also use an algorithm that was particularly highly rated from the user's past detection results. Furthermore, the detection unit can adjust the detection algorithm based on the user's past detection results. For example, the detection unit improves accuracy by referring to the user's past detection results. This makes it possible to improve the accuracy of detecting typos and omissions by referring to the user's past detection results.

[0106] The detection unit can analyze the user's emotions and adjust the display method of the typographical error detection results based on the analyzed user's emotions. The detection unit, for example, analyzes the user's emotions and adjusts the display method of the typographical error detection results based on the analyzed user's emotions. For example, the detection unit provides a concise and highly visible display method when the user is feeling stressed. The detection unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the detection unit can also provide a display method that focuses on the main points when the user is in a hurry. For example, the detection unit estimates the user's emotions and adjusts the display method of the typographical error detection results based on the estimated user's emotions. In this way, the display method of the typographical error detection results can be adjusted according to the user's emotions.

[0107] The detection unit can select the optimal detection method in consideration of the user's business process when detecting typos and omissions. For example, the detection unit selects the optimal detection method in consideration of the user's business process when detecting typos and omissions. For example, the detection unit selects the optimal detection method based on the user's business process. The detection unit can also suggest an appropriate detection method in consideration of the user's business process. Furthermore, the detection unit can apply different detection methods depending on the user's business process. For example, the detection unit selects the optimal detection method in consideration of the user's business process. This makes it possible to select the optimal detection method for typos and omissions based on the user's business process.

[0108] The detection unit can customize the detection method by reflecting the user's past feedback when detecting typos and omissions. For example, the detection unit customizes the detection method by reflecting the user's past feedback when detecting typos and omissions. For example, the detection unit analyzes the user's past feedback and customizes the detection method. The detection unit can also use a detection method that has received particularly high ratings from the user's past feedback. Furthermore, the detection unit can adjust the detection method based on the user's past feedback. For example, the detection unit customizes the detection method by reflecting the user's past feedback. In this way, the detection method for detecting typos and omissions can be customized by reflecting the user's past feedback.

[0109] The detection unit can set optimal detection criteria taking into consideration the industry characteristics of the user when detecting typos and omissions. For example, the detection unit sets optimal detection criteria taking into consideration the industry characteristics of the user when detecting typos and omissions. For example, the detection unit sets optimal detection criteria based on the industry characteristics of the user. The detection unit can also propose appropriate detection criteria taking into consideration the industry characteristics of the user. Furthermore, the detection unit can apply different detection criteria depending on the industry characteristics of the user. For example, the detection unit sets optimal detection criteria based on the industry characteristics of the user. This makes it possible to set optimal detection criteria for typos and omissions based on the industry characteristics of the user.

[0110] The management unit can analyze the user's emotions and adjust the version control method based on the analyzed user's emotions. For example, the management unit can analyze the user's emotions and adjust the version control method based on the analyzed user's emotions. For example, if the user is feeling stressed, the management unit can provide a concise and highly visible version control method. Furthermore, if the user is relaxed, the management unit can provide a version control method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can provide a version control method that focuses on the main points. For example, the management unit can estimate the user's emotions and adjust the version control method based on the estimated user's emotions. In this way, the version control method can be adjusted according to the user's emotions.

[0111] The management unit can determine the management priority based on the importance of the rules during version management. The management unit, for example, determines the management priority based on the importance of the rules during version management. For example, the management unit prioritizes management of versions of important rules and alerts the user. The management unit can also adjust the management priority based on the importance of the rules. Furthermore, the management unit can simply manage versions of rules with low importance and manage versions of rules with high importance in detail. For example, the management unit determines the management priority based on the importance of the rules. In this way, the priority of version management can be determined based on the importance of the rules.

[0112] The management unit can apply different management algorithms depending on the user's work content during version management. For example, the management unit applies different management algorithms depending on the user's work content during version management. For example, the management unit selects an optimal management algorithm based on the user's work content. The management unit can also apply different management algorithms depending on the user's work content. Furthermore, the management unit can propose an optimal management algorithm taking the user's work content into consideration. For example, the management unit applies different management algorithms depending on the user's work content. This makes it possible to apply different version management algorithms depending on the user's work content.

[0113] The management unit can improve accuracy during version management by referring to the user's past management results. For example, during version management, the management unit improves accuracy by referring to the user's past management results. For example, the management unit analyzes the user's past management results and improves accuracy. The management unit can also use an algorithm that was particularly highly rated from the user's past management results. Furthermore, the management unit can adjust the management algorithm based on the user's past management results. For example, the management unit improves accuracy by referring to the user's past management results. In this way, the accuracy of version management can be improved by referring to the user's past management results.

[0114] The management unit can analyze the user's emotions and adjust the level of detail of version management based on the analyzed user's emotions. The management unit, for example, analyzes the user's emotions and adjusts the level of detail of version management based on the analyzed user's emotions. For example, if the user is feeling stressed, the management unit provides a concise and highly visible version management method. Furthermore, if the user is relaxed, the management unit can provide a version management method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can provide a version management method that focuses on the main points. For example, the management unit estimates the user's emotions and adjusts the level of detail of version management based on the estimated user's emotions. In this way, the level of detail of version management can be adjusted according to the user's emotions.

[0115] The management unit can select the optimal management method in consideration of the user's business process when performing version management. For example, the management unit selects the optimal management method in consideration of the user's business process when performing version management. For example, the management unit selects the optimal management method based on the user's business process. The management unit can also propose an appropriate management method in consideration of the user's business process. Furthermore, the management unit can apply different management methods depending on the user's business process. For example, the management unit selects the optimal management method in consideration of the user's business process. This makes it possible to select the optimal version management method based on the user's business process.

[0116] The management unit can customize the management method by reflecting the user's past feedback during version management. For example, the management unit customizes the management method by reflecting the user's past feedback during version management. For example, the management unit analyzes the user's past feedback and customizes the management method. The management unit can also use a management method that has received particularly high praise from the user's past feedback. Furthermore, the management unit can adjust the management method based on the user's past feedback. For example, the management unit customizes the management method by reflecting the user's past feedback. In this way, the version management method can be customized by reflecting the user's past feedback.

[0117] The management unit can set optimal management standards taking into account the industry characteristics of the user during version management. For example, the management unit sets optimal management standards taking into account the industry characteristics of the user during version management. For example, the management unit sets optimal management standards based on the industry characteristics of the user. The management unit can also propose appropriate management standards taking into account the industry characteristics of the user. Furthermore, the management unit can apply different management standards depending on the industry characteristics of the user. For example, the management unit sets optimal management standards based on the industry characteristics of the user. This makes it possible to set optimal version control standards based on the industry characteristics of the user. === Hard Collateral 1-1 === Each of the multiple elements, including the generation unit, comparison unit, consistency check unit, detection unit, and management unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and creates a draft of the regulations based on the purpose, scope, and specific content of the regulations entered by the user. The comparison unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and compares the old and new regulations to clearly display changes. The consistency check unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and checks consistency with relevant laws and regulations. The detection unit is implemented, for example, by the control unit 46A of the smart device 14 and analyzes the text of the regulations using natural language processing technology to detect typos and omissions. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and manages the versions of the regulations and records change history. === Hard Collateral 1-2 === Each of the multiple elements, including the generation unit, comparison unit, consistency check unit, detection unit, and management unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and creates a draft of the regulations based on the purpose, scope, and specific content of the regulations input by the user. The comparison unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and compares the old and new regulations to clearly display changes. The consistency check unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and checks consistency with relevant laws and regulations. The detection unit is implemented, for example, by the control unit 46A of the smart glasses 214 and analyzes the text of the regulations using natural language processing technology to detect typos. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and manages the versions of the regulations and records change history. === Hard Collateral 1-3 === Each of the multiple elements, including the generation unit, comparison unit, consistency check unit, detection unit, and management unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the headset-type terminal 314 and creates a draft of the regulations based on the purpose, scope, and specific content of the regulations entered by the user. The comparison unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and compares the old and new regulations to clearly display changes. The consistency check unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and checks consistency with relevant laws and regulations. The detection unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and analyzes the text of the regulations using natural language processing technology to detect typos and omissions. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and manages the versions of the regulations and records change history. === Hard Collateral 1-4 === Each of the multiple elements, including the generation unit, comparison unit, consistency check unit, detection unit, and management unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and creates a draft of the regulations based on the purpose, scope, and specific content of the regulations input by the user. The comparison unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and compares the old and new regulations to clearly display changes. The consistency check unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and checks consistency with relevant laws and regulations. The detection unit is implemented, for example, by the control unit 46A of the robot 414 and analyzes the text of the regulations using natural language processing technology to detect typos and omissions. The management unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and manages the versions of the regulations and records change history.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The generation unit can analyze the user's emotions and adjust the expression style of the draft rules based on the analyzed user's emotions. For example, if the user is feeling stressed, the generation unit can create draft rules using concise and clear expressions. If the user is relaxed, the generation unit can also create draft rules using detailed and polite expressions. Furthermore, if the user is in a hurry, the generation unit can create draft rules using short sentences that get to the point. In this way, the expression style of the draft rules can be adjusted according to the user's emotions.

[0120] The comparison unit can analyze the user's emotions and adjust the display method of the comparison results based on the analyzed user's emotions. For example, if the user is feeling stressed, the comparison unit can provide a simple and highly visible display method. If the user is relaxed, the comparison unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the comparison unit can also provide a display method that focuses on the main points. In this way, the display method of the comparison results can be adjusted according to the user's emotions.

[0121] The consistency confirmation unit can analyze the user's emotions and adjust the display method of the consistency confirmation based on the analyzed user's emotions. For example, if the user is feeling stressed, the consistency confirmation unit can provide a simple and highly visible display method. If the user is relaxed, the consistency confirmation unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the consistency confirmation unit can also provide a display method that focuses on the main points. In this way, the display method of the consistency confirmation can be adjusted according to the user's emotions.

[0122] The detection unit can analyze the user's emotions and adjust the method for detecting typos and omissions based on the analyzed user's emotions. For example, if the user is feeling stressed, the detection unit can display a concise and highly visible detection result for typos and omissions. Also, if the user is relaxed, the detection unit can display a detailed detection result for typos and omissions. Furthermore, if the user is in a hurry, the detection unit can display only important typos and omissions. In this way, the method for detecting typos and omissions can be adjusted according to the user's emotions.

[0123] The management unit can analyze the user's emotions and adjust the version control method based on the analyzed user's emotions. For example, if the user is feeling stressed, the management unit can provide a concise and highly visible version control method. If the user is feeling relaxed, the management unit can also provide a version control method that includes detailed information. Furthermore, if the user is in a hurry, the management unit can also provide a version control method that focuses on the main points. In this way, the version control method can be adjusted according to the user's emotions.

[0124] When creating a draft of the regulations, the generation unit can reflect region-specific regulations by taking into account the user's geographical conditions. For example, the generation unit creates a draft that reflects region-specific laws and regulations based on the user's location. The generation unit can also use appropriate wording by taking into account the characteristics of the region where the user's business is conducted. Furthermore, the generation unit can create a draft that reflects region-specific regulations based on the user's geographical conditions. This allows the creation of a draft of the regulations that reflects region-specific regulations based on the user's geographical conditions.

[0125] When comparing before and after regulations, the comparison unit can determine the display priority based on the importance of the changes. For example, the comparison unit can prioritize displaying important changes to alert the user. The comparison unit can also adjust the display priority based on the importance of the changes. Furthermore, the comparison unit can display changes with low importance briefly and changes with high importance in detail. This allows the display priority to be determined based on the importance of the changes.

[0126] During consistency check, the consistency check unit can determine the display priority based on the importance of the relevant laws and regulations. For example, the consistency check unit can prioritize the display of important laws and regulations to alert the user. The consistency check unit can also adjust the display priority based on the importance of the laws and regulations. Furthermore, the consistency check unit can display laws and regulations with low importance in a concise manner and laws and regulations with high importance in detail. This allows the display priority to be determined based on the importance of the relevant laws and regulations.

[0127] When detecting typos and omissions, the detection unit can determine the priority of detection based on the importance of the rule. For example, the detection unit can preferentially detect typos and omissions in important rules and alert the user. The detection unit can also adjust the priority of detection based on the importance of the rule. Furthermore, the detection unit can briefly display typos and omissions in rules with low importance and display typos and omissions in rules with high importance in detail. In this way, the priority of detecting typos and omissions can be determined based on the importance of the rule.

[0128] The management unit can determine the management priority based on the importance of the rules when managing versions. For example, the management unit can prioritize managing versions of important rules and alert the user. The management unit can also adjust the management priority based on the importance of the rules. Furthermore, the management unit can manage versions of rules with low importance simply and manage versions of rules with high importance in detail. This makes it possible to determine the priority of version management based on the importance of the rules.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The generator creates a draft of the regulations. The generator creates a draft of the regulations based on the purpose, scope, and specific content of the regulations entered by the user. The generator can also select the most appropriate wording by referencing past regulations data. Furthermore, the generator can customize the draft of the regulations based on the user's work content and industry characteristics. Step 2: The comparison unit compares the old and new rules. The comparison unit compares the old and new rules and clearly displays the changes. The comparison unit can also determine the display priority based on the importance of the changes. Furthermore, the comparison unit can apply different comparison algorithms depending on the user's business operations. Step 3: The consistency check unit analyzes the contents of the regulations and checks their consistency with laws and regulations. The consistency check unit creates a database of relevant laws and regulations, which the AI ​​references to check consistency. The consistency check unit can also apply different consistency check algorithms depending on the user's business operations. Furthermore, the consistency check unit can also improve accuracy by referring to the user's past consistency check results. Step 4: The detection unit analyzes the text of the regulations and detects typos. The detection unit uses natural language processing technology to analyze the text of the regulations and detect typos. The detection unit can also apply different detection algorithms depending on the user's work. Furthermore, the detection unit can improve accuracy by referring to the user's past detection results. Step 5: The management department manages the version of the regulations and records the change history. The management department introduces a version control system and automatically records the change history of the regulations. The management department can also apply different management algorithms depending on the user's business content. Furthermore, the management department can improve accuracy by referring to the user's past management results.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that drafts the regulations; a comparison unit that compares drafts of rules created by the generation unit; a consistency confirmation unit that analyzes the content of the regulations compared by the comparison unit and confirms consistency with laws and regulations, etc.; a detection unit that analyzes the text of the regulations confirmed by the consistency confirmation unit and detects typos and omissions; a management unit that manages the version of the rule detected by the detection unit and records a change history. A system characterized by:

2. The generation unit Create a draft of the regulations based on the purpose, scope, and specific content of the regulations entered by the user 2. The system of claim 1.

3. The consistency checking unit Laws and regulations are compiled into a database, and AI references it to check for consistency.

2. The system of claim 1.

4. The detection unit Use natural language processing technology to analyze the text of the policy and detect typos 2. The system of claim 1.

5. The management unit Implement a version control system to automatically record the history of changes to regulations.

2. The system of claim 1.

6. The generation unit Analyze user sentiment and adjust the wording of draft regulations based on the analyzed user sentiment.

2. The system of claim 1.

7. The generation unit When drafting regulations, we refer to past regulations data to select the most appropriate wording.

2. The system of claim 1.

8. The generation unit When drafting regulations, customize them based on the user's business operations and industry characteristics.

2. The system of claim 1.

9. The generation unit When drafting regulations, incorporate past user feedback to improve accuracy.

2. The system of claim 1.

10. The generation unit Analyze user sentiment and adjust the length of the draft regulations based on the analyzed user sentiment.

2. The system of claim 1.

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