System

Abstract

An object of a system according to an embodiment is to improve the efficiency of a process of managing regulations and to automate the confirmation of consistency with laws and the like and the detection of misspellings.SOLUTION: A system includes a consistency confirmation part, a draft preparation part, a before-and-after comparison part, a wrong / omitted character detection part, and a version management part. The consistency confirmation unit confirms consistency with laws and regulations. The draft preparation part prepares a draft of a rule. The before-and-after comparison part compares contents before and after revision. The wrong / omitted character detection unit detects a wrong / omitted character. The version management unit performs version management.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of managing regulations was complex, requiring a great deal of effort to check for compliance with laws and regulations and to detect typos and omissions.

[0005] The system according to the embodiment aims to improve the efficiency of the process of managing regulations and automate the checking of consistency with laws and regulations and the detection of typos and omissions. [Means for solving the problem]

[0006] The system according to the embodiment includes a consistency checking unit, a draft creation unit, a context comparison unit, a typographical error and omission detection unit, and a version management unit. The consistency checking unit checks consistency with laws and regulations. The draft creation unit creates a draft of the regulations. The context comparison unit compares the content before and after the revision. The typographical error and omission detection unit detects typos and omissions. The version management unit manages versions. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the process of managing regulations and automate the checking of consistency with laws and regulations and the detection of typos and omissions. [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) The AI ​​service according to the embodiment of the present invention is a system that streamlines the management of company regulations. This system implements functions such as checking consistency with laws and regulations, creating drafts, comparing previous and next statements, correcting typos and omissions, and managing versions. As a result, the AI ​​service can streamline the management of company regulations and ensure compliance with laws and regulations.

[0029] The AI ​​service according to the embodiment includes a consistency checking unit, a draft creation unit, a context comparison unit, a typo / misspelling detection unit, and a version management unit. The consistency checking unit checks consistency with laws and regulations. For example, the generation AI refers to a laws and regulations database and checks whether the regulations are consistent with the latest laws and regulations. The draft creation unit creates a draft of the regulations. For example, the generation AI generates an appropriate draft based on the content and requirements of the regulations. The context comparison unit compares the content before and after the revision. For example, the generation AI analyzes the regulations before and after the revision and clearly indicates the changes. The typo / misspelling detection unit detects typos. For example, the generation AI analyzes and points out typos in the regulations. The version management unit manages versions. For example, the generation AI manages the versions of the regulations and records the revision history. As a result, the AI ​​service according to the embodiment can improve the efficiency of a company's regulation management and ensure compliance with laws and regulations.

[0030] The consistency confirmation unit can refer to a database of laws and regulations, automatically obtain past revision history, and check consistency by comparing it with the content of the regulations. For example, the generation AI in the consistency confirmation unit refers to a database of laws and regulations and automatically obtains past revision history. For example, it tracks the revision history of the Labor Standards Act and checks consistency by comparing it with the content of the regulations. The consistency confirmation unit also automatically checks whether each provision of the regulations complies with the latest laws and regulations based on the revision history of laws and regulations. For example, it checks whether revised provisions are reflected in the regulations. The generation AI in the consistency confirmation unit also analyzes the revision history of laws and regulations and identifies potential inconsistencies in the regulations. For example, it points out areas of the regulations that need to be revised based on revised laws and regulations. This makes it possible to check the consistency of the regulations based on the revision history of laws and regulations.

[0031] The consistency confirmation unit can point out potential legal risks in regulations based on its interpretation of laws and regulations and make improvement suggestions. In the consistency confirmation unit, for example, the generation AI automatically analyzes potential legal risks in regulations based on its interpretation of laws and regulations. For example, it identifies risky parts of regulations based on its interpretation of laws and regulations. The consistency confirmation unit also automatically generates improvement suggestions for regulations based on its interpretation of laws and regulations. For example, it presents specific amendments to reduce legal risks. The consistency confirmation unit also builds a system in which the generation AI learns the interpretation of laws and regulations and evaluates the legal risks of regulations. For example, it calculates a risk score based on its interpretation of laws and regulations and makes improvement suggestions. This makes it possible to present specific amendments to reduce legal risks.

[0032] The consistency checking unit can simultaneously check consistency with the laws and regulations of different countries and regions, enabling global regulation management. For example, the generation AI in the consistency checking unit references databases of laws and regulations from different countries and regions and automatically checks the consistency of regulations. For example, it compares regulations with the laws and regulations of the United States, Europe, and Asia. The consistency checking unit also automatically generates proposed amendments to regulations based on the laws and regulations of different countries and regions. For example, it presents specific amendment proposals to meet the legal requirements of each region. The consistency checking unit also builds a system in which the generation AI analyzes global legal data and evaluates the international consistency of regulations. For example, it automatically generates regulations that comply with the laws and regulations of each country. This makes it possible to automatically generate regulations that comply with the laws and regulations of different countries and regions.

[0033] The consistency checking unit checks consistency with industry-specific guidelines and best practices, and can provide regulations that comply with industry standards. For example, the generation AI in the consistency checking unit references industry-specific guidelines and best practices and automatically checks the consistency of regulations. For example, it compares regulations with IT industry security guidelines. The consistency checking unit also automatically generates proposed modifications to regulations based on industry guidelines. For example, it presents specific modifications to comply with industry standards. The consistency checking unit also builds a system in which the generation AI analyzes industry-specific data and evaluates the regulations' compliance with industry standards. For example, it automatically generates regulations based on best practices in each industry. This makes it possible to present specific modifications to comply with industry standards.

[0034] The draft text creation department can refer to a database of past draft regulations and automatically select the most effective expressions and structures. In the draft text creation department, for example, a generation AI analyzes a database of past draft regulations and automatically selects the most effective expressions and structures. For example, it prioritizes the adoption of expressions that have had many successful examples. The draft text creation department also builds a system that automatically generates optimal expressions and structures based on past draft regulations data. For example, it learns effective expression patterns and reflects them in the draft text. In the draft text creation department, a generation AI analyzes past data and proposes the most effective expressions and structures. For example, it generates optimal draft texts based on past success stories. This makes it possible to automatically select the most effective expressions and structures.

[0035] The draft text creation unit can understand the purpose and background information of the regulations and generate the optimal draft text based on that. For example, the draft text creation unit uses a generation AI to analyze the purpose and background information of the regulations and automatically generate the optimal draft text based on that. For example, it proposes specific expressions that match the purpose of the regulations. The draft text creation unit also builds a system that generates the optimal draft text based on the background information of the regulations. For example, it automatically generates expressions that match the purpose and background of the regulations. The draft text creation unit also uses a generation AI to learn the purpose and background information of the regulations and propose the optimal draft text based on that. For example, it adopts effective expressions that match the purpose of the regulations. This makes it possible to generate the optimal draft text based on the purpose and background information of the regulations.

[0036] The draft text creation unit can create draft texts in different languages ​​and provide regulations that are compatible with multiple languages. For example, the draft text creation unit uses a generation AI to automatically create draft texts in different languages ​​and provide regulations that are compatible with multiple languages. For example, it generates draft texts in multiple languages ​​such as English, French, and Chinese. The draft text creation unit also builds a system that automatically generates regulations that are compatible with multiple languages. For example, it automatically generates expressions that correspond to the characteristics of each language. The draft text creation unit also uses generation AI to learn data in different languages ​​and propose draft texts that are compatible with multiple languages. For example, it adopts expressions that take into consideration the culture and customs of each language. This makes it possible to automatically generate regulations that are compatible with multiple languages.

[0037] The before-and-after comparison unit can simulate the impact of regulations before and after revision and evaluate the impact of the changes on business. In the before-and-after comparison unit, for example, the generation AI simulates regulations before and after revision and automatically evaluates the impact of the changes on business. For example, it analyzes changes to business processes. The before-and-after comparison unit also builds a system that simulates the impact of regulations before and after revision. For example, it visually displays changes to business flows. The before-and-after comparison unit also analyzes regulations before and after revision and evaluates the impact on business. For example, it simulates changes in business efficiency and risks. This makes it possible to simulate the impact before and after revision and evaluate the impact on business.

[0038] The before-and-after comparison unit can compare the legal risks of regulations before and after revision and make proposals to reduce the risks. In the before-and-after comparison unit, for example, the generation AI analyzes regulations before and after revision and automatically compares the legal risks. For example, it calculates a risk score before and after revision and evaluates whether the risk has increased or decreased. The before-and-after comparison unit also builds a system that compares the legal risks of regulations before and after revision. For example, it automatically generates specific revision proposals to reduce the risks. In addition, the before-and-after comparison unit analyzes regulations before and after revision with the generation AI and makes proposals to reduce the risks. For example, it points out points that need to be revised to minimize the legal risks. This makes it possible to compare legal risks and present specific revision proposals to reduce the risks.

[0039] The spelling and typographical error detection unit not only detects spelling and typographical errors, but also checks for consistency in grammar and style, thereby improving overall quality. For example, the generative AI not only detects spelling and typographical errors, but also automatically checks for consistency in grammar and style. For example, it points out grammatical errors and stylistic inconsistencies. The spelling and typographical error detection unit also builds a system that checks for consistency in grammar and style, thereby improving overall quality. For example, it makes suggestions to correct grammatical errors. The spelling and typographical error detection unit also analyzes grammatical and stylistic data and evaluates overall quality based on the results of spelling and typographical error detection. For example, it suggests expressions that comply with a style guide. This checks for consistency in grammar and style and improves overall quality.

[0040] The typo / misspelling detection unit automatically makes correction suggestions based on the results of typo / misspelling detection, thereby reducing the effort required for correction. For example, the generation AI automatically makes correction suggestions based on the results of typo / misspelling detection. For example, it presents specific suggestions for correcting typos. The typo / misspelling detection unit also builds a system that automatically generates correction suggestions for typos, thereby reducing the effort required for correction. For example, it presents specific steps for correcting typos. The typo / misspelling detection unit also analyzes the results of typo / misspelling detection by the generation AI and makes correction suggestions. For example, it proposes the optimal method for correcting typos. In this way, correction suggestions for typos can be automatically generated, reducing the effort required for correction.

[0041] The version management department can automatically track the revision history of regulations and analyze the frequency and trends of revisions. For example, the generation AI in the version management department automatically tracks the revision history of regulations and analyzes the frequency and trends of revisions. For example, it identifies revision patterns based on past revision history. The version management department also builds a system that tracks revision history and analyzes frequency and trends. For example, it displays the frequency and trends of revisions in graphs and charts. The generation AI in the version management department also analyzes the revision history and evaluates the frequency and trends of revisions. For example, it identifies areas that are frequently revised and makes suggestions for improvement. This makes it possible to track revision history and analyze frequency and trends.

[0042] The version management department can predict future revisions based on the revision history of regulations and propose countermeasures in advance. In the version management department, for example, a generation AI analyzes the revision history of regulations and automatically predicts future revisions. For example, future revisions are predicted based on past revision patterns. The version management department also builds a system that predicts future revisions based on the revision history and proposes countermeasures. For example, it presents specific countermeasures for predicted revisions. In the version management department, a generation AI analyzes the revision history and predicts future revisions. For example, it predicts future revisions based on the frequency and trends of revisions and proposes countermeasures. This makes it possible to predict future revisions and propose countermeasures in advance.

[0043] The version management unit manages versions for different departments and positions, and can provide a revision history for each department. For example, the generation AI automatically manages versions for different departments and positions, and provides a revision history for each department. For example, it manages the revision history for each department individually. The version management unit also builds a system that provides revision history for each department. For example, it displays the revision history for each department in graphs and charts. The version management unit also allows the generation AI to analyze data for each department and position and provide a revision history. For example, it evaluates the frequency and trends of revisions by department. This makes it possible to provide a revision history for each department.

[0044] The version management department can visualize the revision history of regulations and provide the revision flow in a format that allows for intuitive understanding. For example, the version management department uses a generation AI to visualize the revision history of regulations and provide the revision flow in a format that allows for intuitive understanding. For example, the revision history is displayed in a timeline format. The version management department also builds a system that automatically generates visualized revision history. For example, the revision history is displayed in graphs or charts. The version management department also uses a generation AI to analyze the revision history and propose a visualized revision flow. For example, important revision points are highlighted. This makes it possible to visualize the revision history and provide the revision flow in a format that allows for intuitive understanding.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The consistency check unit can evaluate the legal risks of regulations in more detail by referring not only to the revision history of laws and regulations, but also to related precedents and court interpretations. For example, the generation AI can refer to a precedent database to check whether the regulations are consistent with past precedents. The consistency check unit can also identify potential legal risks in regulations based on court interpretations and make specific improvement proposals. For example, it can make proposals to revise high-risk clauses based on precedents. The consistency check unit can also build a system in which the generation AI analyzes precedent data and scores the legal risks of regulations. This allows for a more detailed evaluation of legal risks and makes specific improvement proposals.

[0047] When drafting regulations, the drafting department can use natural language processing technology to evaluate the readability and understandability of the document and select the most appropriate expressions. For example, a generation AI can analyze the complexity of the document and convert it into easy-to-read expressions. The drafting department can also build a system that suggests the most appropriate expressions for different readership groups. For example, the drafting department can generate drafts that use both a lot of technical jargon and general expressions. The drafting department can also use a generation AI to score the readability of the document and suggest the most appropriate expressions. This can improve the readability and understandability of the document.

[0048] When simulating the impact of regulations before and after revisions, the before-and-after comparison section can evaluate not only changes to business processes but also the impact on costs and resources. For example, the generation AI analyzes business processes before and after revisions and simulates fluctuations in costs and resources. The before-and-after comparison section can also build a system that evaluates the cost reduction effects and resource optimization of revisions. For example, the cost reduction effects of revisions can be displayed in graphs and charts. The before-and-after comparison section can also allow the generation AI to analyze cost and resource data before and after revisions and propose optimal revision proposals. This allows for a comprehensive evaluation of the impact of revisions and propose optimal revision proposals.

[0049] The spelling and typo detection unit not only detects spelling and typo errors, but also checks the consistency and integrity of the document, improving overall quality. For example, the generation AI analyzes the consistency of the document and points out inconsistencies. The spelling and typo detection unit can also build a system to check the consistency of the document and improve overall quality. For example, it checks the consistency of the terminology within the document. The spelling and typo detection unit can also allow the generation AI to score the consistency and integrity of the document and make suggestions for improvement. This allows the consistency and integrity of the document to be checked and overall quality to be improved.

[0050] The version management department can simulate the impact of revisions based on the revision history of regulations and predict the impact that future revisions will have on business operations. For example, a generation AI can analyze past revision history and simulate the impact that future revisions will have on business processes. The version management department can also build a system that predicts the impact of revisions and propose countermeasures in advance. For example, it can present specific countermeasures for the predicted impact. The version management department can also use a generation AI to analyze revision history and score the impact of future revisions. This makes it possible to predict the impact that future revisions will have on business operations and propose countermeasures in advance.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The consistency check unit checks consistency with laws and regulations. For example, the generation AI references a database of laws and regulations to check whether the regulations are consistent with the latest laws and regulations. Step 2: The drafting unit creates a draft of the regulations. For example, a generation AI generates an appropriate draft based on the content and requirements of the regulations. Step 3: The before-and-after comparison unit compares the content before and after the revision. For example, the generation AI analyzes the regulations before and after the revision and clearly indicates the changes. Step 4: The misspelling detection unit detects misspellings. For example, the generation AI analyzes and points out misspellings in the regulations. Step 5: The version management unit manages the version. For example, the generation AI manages the version of the regulations and records the revision history.

[0053] (Example 2) The AI ​​service according to the embodiment of the present invention is a system that streamlines the management of company regulations. This system implements functions such as checking consistency with laws and regulations, creating drafts, comparing previous and next statements, correcting typos and omissions, and managing versions. As a result, the AI ​​service can streamline the management of company regulations and ensure compliance with laws and regulations.

[0054] The AI ​​service according to the embodiment includes a consistency checking unit, a draft creation unit, a context comparison unit, a typo / misspelling detection unit, and a version management unit. The consistency checking unit checks consistency with laws and regulations. For example, the generation AI refers to a laws and regulations database and checks whether the regulations are consistent with the latest laws and regulations. The draft creation unit creates a draft of the regulations. For example, the generation AI generates an appropriate draft based on the content and requirements of the regulations. The context comparison unit compares the content before and after the revision. For example, the generation AI analyzes the regulations before and after the revision and clearly indicates the changes. The typo / misspelling detection unit detects typos. For example, the generation AI analyzes and points out typos in the regulations. The version management unit manages versions. For example, the generation AI manages the versions of the regulations and records the revision history. As a result, the AI ​​service according to the embodiment can improve the efficiency of a company's regulation management and ensure compliance with laws and regulations.

[0055] The consistency confirmation unit can refer to a database of laws and regulations, automatically obtain past revision history, and check consistency by comparing it with the content of the regulations. For example, the generation AI in the consistency confirmation unit refers to a database of laws and regulations and automatically obtains past revision history. For example, it tracks the revision history of the Labor Standards Act and checks consistency by comparing it with the content of the regulations. The consistency confirmation unit also automatically checks whether each provision of the regulations complies with the latest laws and regulations based on the revision history of laws and regulations. For example, it checks whether revised provisions are reflected in the regulations. The generation AI in the consistency confirmation unit also analyzes the revision history of laws and regulations and identifies potential inconsistencies in the regulations. For example, it points out areas of the regulations that need to be revised based on revised laws and regulations. This makes it possible to check the consistency of the regulations based on the revision history of laws and regulations.

[0056] The consistency confirmation unit can point out potential legal risks in regulations based on its interpretation of laws and regulations and make improvement suggestions. In the consistency confirmation unit, for example, the generation AI automatically analyzes potential legal risks in regulations based on its interpretation of laws and regulations. For example, it identifies risky parts of regulations based on its interpretation of laws and regulations. The consistency confirmation unit also automatically generates improvement suggestions for regulations based on its interpretation of laws and regulations. For example, it presents specific amendments to reduce legal risks. The consistency confirmation unit also builds a system in which the generation AI learns the interpretation of laws and regulations and evaluates the legal risks of regulations. For example, it calculates a risk score based on its interpretation of laws and regulations and makes improvement suggestions. This makes it possible to present specific amendments to reduce legal risks.

[0057] The consistency checking unit can simultaneously check consistency with the laws and regulations of different countries and regions, enabling global regulation management. For example, the generation AI in the consistency checking unit references databases of laws and regulations from different countries and regions and automatically checks the consistency of regulations. For example, it compares regulations with the laws and regulations of the United States, Europe, and Asia. The consistency checking unit also automatically generates proposed amendments to regulations based on the laws and regulations of different countries and regions. For example, it presents specific amendment proposals to meet the legal requirements of each region. The consistency checking unit also builds a system in which the generation AI analyzes global legal data and evaluates the international consistency of regulations. For example, it automatically generates regulations that comply with the laws and regulations of each country. This makes it possible to automatically generate regulations that comply with the laws and regulations of different countries and regions.

[0058] The consistency checking unit checks consistency with industry-specific guidelines and best practices, and can provide regulations that comply with industry standards. For example, the generation AI in the consistency checking unit references industry-specific guidelines and best practices and automatically checks the consistency of regulations. For example, it compares regulations with IT industry security guidelines. The consistency checking unit also automatically generates proposed modifications to regulations based on industry guidelines. For example, it presents specific modifications to comply with industry standards. The consistency checking unit also builds a system in which the generation AI analyzes industry-specific data and evaluates the regulations' compliance with industry standards. For example, it automatically generates regulations based on best practices in each industry. This makes it possible to present specific modifications to comply with industry standards.

[0059] The draft text creation department can refer to a database of past draft regulations and automatically select the most effective expressions and structures. In the draft text creation department, for example, a generation AI analyzes a database of past draft regulations and automatically selects the most effective expressions and structures. For example, it prioritizes the adoption of expressions that have had many successful examples. The draft text creation department also builds a system that automatically generates optimal expressions and structures based on past draft regulations data. For example, it learns effective expression patterns and reflects them in the draft text. In the draft text creation department, a generation AI analyzes past data and proposes the most effective expressions and structures. For example, it generates optimal draft texts based on past success stories. This makes it possible to automatically select the most effective expressions and structures.

[0060] The draft text creation unit can understand the purpose and background information of the regulations and generate the optimal draft text based on that. For example, the draft text creation unit uses a generation AI to analyze the purpose and background information of the regulations and automatically generate the optimal draft text based on that. For example, it proposes specific expressions that match the purpose of the regulations. The draft text creation unit also builds a system that generates the optimal draft text based on the background information of the regulations. For example, it automatically generates expressions that match the purpose and background of the regulations. The draft text creation unit also uses a generation AI to learn the purpose and background information of the regulations and propose the optimal draft text based on that. For example, it adopts effective expressions that match the purpose of the regulations. This makes it possible to generate the optimal draft text based on the purpose and background information of the regulations.

[0061] The draft text creation unit can create draft texts in different languages ​​and provide regulations that are compatible with multiple languages. For example, the draft text creation unit uses a generation AI to automatically create draft texts in different languages ​​and provide regulations that are compatible with multiple languages. For example, it generates draft texts in multiple languages ​​such as English, French, and Chinese. The draft text creation unit also builds a system that automatically generates regulations that are compatible with multiple languages. For example, it automatically generates expressions that correspond to the characteristics of each language. The draft text creation unit also uses generation AI to learn data in different languages ​​and propose draft texts that are compatible with multiple languages. For example, it adopts expressions that take into consideration the culture and customs of each language. This makes it possible to automatically generate regulations that are compatible with multiple languages.

[0062] The before-and-after comparison unit can simulate the impact of regulations before and after revision and evaluate the impact of the changes on business. In the before-and-after comparison unit, for example, the generation AI simulates regulations before and after revision and automatically evaluates the impact of the changes on business. For example, it analyzes changes to business processes. The before-and-after comparison unit also builds a system that simulates the impact of regulations before and after revision. For example, it visually displays changes to business flows. The before-and-after comparison unit also analyzes regulations before and after revision and evaluates the impact on business. For example, it simulates changes in business efficiency and risks. This makes it possible to simulate the impact before and after revision and evaluate the impact on business.

[0063] The before-and-after comparison unit can compare the legal risks of regulations before and after revision and make proposals to reduce the risks. In the before-and-after comparison unit, for example, the generation AI analyzes regulations before and after revision and automatically compares the legal risks. For example, it calculates a risk score before and after revision and evaluates whether the risk has increased or decreased. The before-and-after comparison unit also builds a system that compares the legal risks of regulations before and after revision. For example, it automatically generates specific revision proposals to reduce the risks. In addition, the before-and-after comparison unit analyzes regulations before and after revision with the generation AI and makes proposals to reduce the risks. For example, it points out points that need to be revised to minimize the legal risks. This makes it possible to compare legal risks and present specific revision proposals to reduce the risks.

[0064] The spelling and typographical error detection unit not only detects spelling and typographical errors, but also checks for consistency in grammar and style, thereby improving overall quality. For example, the generative AI not only detects spelling and typographical errors, but also automatically checks for consistency in grammar and style. For example, it points out grammatical errors and stylistic inconsistencies. The spelling and typographical error detection unit also builds a system that checks for consistency in grammar and style, thereby improving overall quality. For example, it makes suggestions to correct grammatical errors. The spelling and typographical error detection unit also analyzes grammatical and stylistic data and evaluates overall quality based on the results of spelling and typographical error detection. For example, it suggests expressions that comply with a style guide. This checks for consistency in grammar and style and improves overall quality.

[0065] The typo / misspelling detection unit automatically makes correction suggestions based on the results of typo / misspelling detection, thereby reducing the effort required for correction. For example, the generation AI automatically makes correction suggestions based on the results of typo / misspelling detection. For example, it presents specific suggestions for correcting typos. The typo / misspelling detection unit also builds a system that automatically generates correction suggestions for typos, thereby reducing the effort required for correction. For example, it presents specific steps for correcting typos. The typo / misspelling detection unit also analyzes the results of typo / misspelling detection by the generation AI and makes correction suggestions. For example, it proposes the optimal method for correcting typos. In this way, correction suggestions for typos can be automatically generated, reducing the effort required for correction.

[0066] The version management department can automatically track the revision history of regulations and analyze the frequency and trends of revisions. For example, the generation AI in the version management department automatically tracks the revision history of regulations and analyzes the frequency and trends of revisions. For example, it identifies revision patterns based on past revision history. The version management department also builds a system that tracks revision history and analyzes frequency and trends. For example, it displays the frequency and trends of revisions in graphs and charts. The generation AI in the version management department also analyzes the revision history and evaluates the frequency and trends of revisions. For example, it identifies areas that are frequently revised and makes suggestions for improvement. This makes it possible to track revision history and analyze frequency and trends.

[0067] The version management department can predict future revisions based on the revision history of regulations and propose countermeasures in advance. In the version management department, for example, a generation AI analyzes the revision history of regulations and automatically predicts future revisions. For example, future revisions are predicted based on past revision patterns. The version management department also builds a system that predicts future revisions based on the revision history and proposes countermeasures. For example, it presents specific countermeasures for predicted revisions. In the version management department, a generation AI analyzes the revision history and predicts future revisions. For example, it predicts future revisions based on the frequency and trends of revisions and proposes countermeasures. This makes it possible to predict future revisions and propose countermeasures in advance.

[0068] The version management unit manages versions for different departments and positions, and can provide a revision history for each department. For example, the generation AI automatically manages versions for different departments and positions, and provides a revision history for each department. For example, it manages the revision history for each department individually. The version management unit also builds a system that provides revision history for each department. For example, it displays the revision history for each department in graphs and charts. The version management unit also allows the generation AI to analyze data for each department and position and provide a revision history. For example, it evaluates the frequency and trends of revisions by department. This makes it possible to provide a revision history for each department.

[0069] The version management department can visualize the revision history of regulations and provide the revision flow in a format that allows for intuitive understanding. For example, the version management department uses a generation AI to visualize the revision history of regulations and provide the revision flow in a format that allows for intuitive understanding. For example, the revision history is displayed in a timeline format. The version management department also builds a system that automatically generates visualized revision history. For example, the revision history is displayed in graphs or charts. The version management department also uses a generation AI to analyze the revision history and propose a visualized revision flow. For example, important revision points are highlighted. This makes it possible to visualize the revision history and provide the revision flow in a format that allows for intuitive understanding.

[0070] The consistency confirmation unit can use the emotion estimation function to analyze employees' emotional reactions to legal amendments and evaluate the acceptability of regulations. The consistency confirmation unit, for example, uses the emotion estimation function to collect employees' emotional reactions to legal amendments in real time. For example, it analyzes employees' facial expressions and voices and calculates an emotion score. The consistency confirmation unit also builds a system to evaluate the acceptability of regulations based on employees' emotional reaction data. For example, it prioritizes the adoption of regulations that evoke a high level of positive emotions. The consistency confirmation unit also identifies areas in the regulations that need improvement based on the emotion estimation data and makes suggestions to improve employee acceptability. For example, it modifies areas that evoke a high level of negative emotions. In this way, it is possible to analyze employees' emotional reactions and evaluate the acceptability of regulations.

[0071] The draft text creation unit can use the emotion estimation function to evaluate the emotional impact that the tone and style of the draft text have on employees and propose the optimal draft text. The draft text creation unit, for example, uses the emotion estimation function to evaluate the emotional impact that the tone and style of the draft text have on employees in real time. For example, it prioritizes the adoption of expressions that elicit positive emotions. The draft text creation unit also builds a system that proposes optimal draft texts based on employee emotional response data. For example, it reflects expressions with high emotion scores in the draft text. The draft text creation unit also adjusts the tone and style of the draft text based on the emotion estimation data and makes suggestions to minimize the emotional impact on employees. For example, it modifies expressions that evoke negative emotions. In this way, it is possible to evaluate the emotional impact that the tone and style of the draft text have on employees and propose the optimal draft text.

[0072] The before-and-after comparison unit can use the emotion estimation function to compare employees' emotional reactions to the regulations before and after the revision, and evaluate the acceptability of the revision. The before-and-after comparison unit, for example, uses the emotion estimation function to collect and compare employees' emotional reactions to the regulations before and after the revision in real time. For example, it calculates emotion scores before and after the revision and evaluates the acceptability. The before-and-after comparison unit also builds a system to evaluate the acceptability of the regulations before and after the revision based on the employee emotion reaction data. For example, it prioritizes the adoption of revision proposals that have a high proportion of positive emotions. The before-and-after comparison unit also identifies areas of improvement in the regulations before and after the revision based on the emotion estimation data, and makes suggestions to improve employee acceptability. For example, it modifies parts that have a high proportion of negative emotions. In this way, it is possible to compare employees' emotional reactions to the regulations before and after the revision and evaluate the acceptability of the revision.

[0073] The typo / misspelling detection unit uses the emotion estimation function to evaluate the impact of typos on employees' trust and identify important areas to correct. The typo / misspelling detection unit, for example, uses the emotion estimation function to evaluate the impact of typos on employees' trust in real time. For example, it calculates an emotion score for areas with a high number of typos. The typo / misspelling detection unit also builds a system that identifies important areas to correct based on employees' emotional response data. For example, it prioritizes correcting typos that damage trust. The typo / misspelling detection unit also identifies areas to correct for typos based on the emotion estimation data and makes suggestions to improve employees' trust. For example, it corrects areas that evoke a high number of negative emotions. This makes it possible to evaluate the impact of typos on employees' trust and identify important areas to correct.

[0074] The version management department can use the emotion estimation function to evaluate the impact of the revision history of regulations on employee emotions and optimize the timing of revisions. For example, the version management department uses the emotion estimation function to evaluate the impact of the revision history of regulations on employee emotions in real time. For example, it analyzes the impact of the frequency and timing of revisions on employee emotions. The version management department also builds a system that optimizes the timing of revisions based on employee emotional response data. For example, it makes revisions when positive emotions are most prevalent. The version management department also uses the emotion estimation data to evaluate the impact of the revision history of regulations on employee emotions and makes suggestions to adjust the timing of revisions. For example, it makes revisions when negative emotions are least prevalent. This makes it possible to evaluate the impact of the revision history on employee emotions and optimize the timing of revisions.

[0075] The consistency checking unit uses the emotion estimation function to analyze the emotional reactions of customers and business partners to legal amendments and can perform external evaluation of the regulations. The consistency checking unit, for example, uses the emotion estimation function to collect the emotional reactions of customers and business partners to legal amendments in real time. For example, it analyzes the customer's facial expressions and voice and calculates an emotion score. The consistency checking unit also builds a system for external evaluation of regulations based on the emotional reaction data of customers and business partners. For example, it prioritizes the adoption of regulations that have a high proportion of positive emotions. The consistency checking unit also identifies areas for improvement in the regulations based on the emotion estimation data and makes suggestions to improve their acceptability to customers and business partners. For example, it modifies areas that have a high proportion of negative emotions. In this way, the emotional reactions of customers and business partners can be analyzed and external evaluation of the regulations can be performed.

[0076] The typo / misspelling detection unit uses the emotion estimation function to evaluate the impact of typos on the trust of customers and business partners, and can perform external evaluation. The typo / misspelling detection unit, for example, uses the emotion estimation function to evaluate in real time the impact of typos on the trust of customers and business partners. For example, it calculates an emotion score for sections with many typos. The typo / misspelling detection unit also builds a system for external evaluation of typos based on emotional response data of customers and business partners. For example, it prioritizes correcting typos that damage trust. The typo / misspelling detection unit also identifies points to correct for typos based on the emotion estimation data, and makes suggestions to improve the trust of customers and business partners. For example, it corrects sections that are associated with many negative emotions. This makes it possible to evaluate the impact of typos on the trust of customers and business partners, and perform external evaluation.

[0077] The version management unit can use the emotion estimation function to evaluate the impact of the revision history of regulations on the trust of customers and business partners, and perform external evaluation. The version management unit, for example, uses the emotion estimation function to evaluate in real time the impact of the revision history of regulations on the trust of customers and business partners. For example, it analyzes the impact of the revision history on trust. The version management unit also builds a system for external evaluation of the revision history based on emotional response data of customers and business partners. For example, it prioritizes correcting revisions that damage trust. The version management unit also evaluates the impact of the revision history of regulations on the trust of customers and business partners based on the emotion estimation data, and performs external evaluation. For example, it corrects revisions that have a high number of negative emotions. In this way, it is possible to evaluate the impact of the revision history on the trust of customers and business partners, and perform external evaluation.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The consistency check unit can evaluate the legal risks of regulations in more detail by referring not only to the revision history of laws and regulations, but also to related precedents and court interpretations. For example, the generation AI can refer to a precedent database to check whether the regulations are consistent with past precedents. The consistency check unit can also identify potential legal risks in regulations based on court interpretations and make specific improvement proposals. For example, it can make proposals to revise high-risk clauses based on precedents. The consistency check unit can also build a system in which the generation AI analyzes precedent data and scores the legal risks of regulations. This allows for a more detailed evaluation of legal risks and makes specific improvement proposals.

[0080] When drafting regulations, the drafting department can use natural language processing technology to evaluate the readability and understandability of the document and select the most appropriate expressions. For example, a generation AI can analyze the complexity of the document and convert it into easy-to-read expressions. The drafting department can also build a system that suggests the most appropriate expressions for different readership groups. For example, the drafting department can generate drafts that use both a lot of technical jargon and general expressions. The drafting department can also use a generation AI to score the readability of the document and suggest the most appropriate expressions. This can improve the readability and understandability of the document.

[0081] When simulating the impact of regulations before and after revisions, the before-and-after comparison section can evaluate not only changes to business processes but also the impact on costs and resources. For example, the generation AI analyzes business processes before and after revisions and simulates fluctuations in costs and resources. The before-and-after comparison section can also build a system that evaluates the cost reduction effects and resource optimization of revisions. For example, the cost reduction effects of revisions can be displayed in graphs and charts. The before-and-after comparison section can also allow the generation AI to analyze cost and resource data before and after revisions and propose optimal revision proposals. This allows for a comprehensive evaluation of the impact of revisions and propose optimal revision proposals.

[0082] The spelling and typo detection unit not only detects spelling and typo errors, but also checks the consistency and integrity of the document, improving overall quality. For example, the generation AI analyzes the consistency of the document and points out inconsistencies. The spelling and typo detection unit can also build a system to check the consistency of the document and improve overall quality. For example, it checks the consistency of the terminology within the document. The spelling and typo detection unit can also allow the generation AI to score the consistency and integrity of the document and make suggestions for improvement. This allows the consistency and integrity of the document to be checked and overall quality to be improved.

[0083] The version management department can simulate the impact of revisions based on the revision history of regulations and predict the impact that future revisions will have on business operations. For example, a generation AI can analyze past revision history and simulate the impact that future revisions will have on business processes. The version management department can also build a system that predicts the impact of revisions and propose countermeasures in advance. For example, it can present specific countermeasures for the predicted impact. The version management department can also use a generation AI to analyze revision history and score the impact of future revisions. This makes it possible to predict the impact that future revisions will have on business operations and propose countermeasures in advance.

[0084] The consistency confirmation unit can use the emotion estimation function to analyze employees' emotional reactions to legal amendments and evaluate the acceptability of regulations. For example, the emotion estimation function can be used to collect employees' emotional reactions to legal amendments in real time. For example, the emotion estimation function can analyze employees' facial expressions and voices to calculate an emotion score. The consistency confirmation unit can also build a system that evaluates the acceptability of regulations based on employees' emotional reaction data. For example, it can prioritize the adoption of regulations that evoke a high level of positive emotions. The consistency confirmation unit can also identify areas for improvement in regulations based on the emotion estimation data and make suggestions to improve employee acceptability. For example, it can revise areas that evoke a high level of negative emotions. In this way, it is possible to analyze employees' emotional reactions and evaluate the acceptability of regulations.

[0085] The draft text creation unit can use the emotion estimation function to evaluate the emotional impact that the tone and style of the draft text have on employees and propose the optimal draft text. For example, the emotion estimation function can be used to evaluate the emotional impact that the tone and style of the draft text have on employees in real time. For example, expressions that elicit positive emotions can be preferentially adopted. The draft text creation unit can also build a system that proposes optimal draft text based on employee emotional response data. For example, expressions with high emotion scores can be reflected in the draft text. The draft text creation unit can also adjust the tone and style of the draft text based on the emotion estimation data and make suggestions to minimize the emotional impact on employees. For example, expressions that evoke negative emotions can be modified. In this way, the emotional impact that the tone and style of the draft text have on employees can be evaluated and the optimal draft text can be proposed.

[0086] The before-and-after comparison unit can use the emotion estimation function to compare employees' emotional reactions to the regulations before and after the revision, and evaluate the acceptability of the revision. For example, the emotion estimation function can be used to collect and compare employees' emotional reactions to the regulations before and after the revision in real time. For example, emotion scores before and after the revision can be calculated and the acceptability evaluated. The before-and-after comparison unit can also build a system that evaluates the acceptability of regulations before and after the revision, based on employee emotional reaction data. For example, it can prioritize the adoption of revision proposals that have a high proportion of positive emotions. The before-and-after comparison unit can also identify areas for improvement in the regulations before and after the revision, based on the emotion estimation data, and make suggestions to improve employee acceptability. For example, it can revise parts that have a high proportion of negative emotions. In this way, it is possible to compare employees' emotional reactions to the regulations before and after the revision and evaluate the acceptability of the revision.

[0087] The typo detection unit can use the emotion estimation function to evaluate the impact of typos on employees' trust and identify important areas to correct. For example, the emotion estimation function can be used to evaluate the impact of typos on employees' trust in real time. For example, an emotion score can be calculated for areas with a high number of typos. The typo detection unit can also build a system that identifies important areas to correct based on employees' emotional response data. For example, it can prioritize correcting typos that damage trust. The typo detection unit can also identify areas to correct for typos based on the emotion estimation data and make suggestions to improve employees' trust. For example, it can correct areas that cause a high number of negative emotions. This makes it possible to evaluate the impact of typos on employees' trust and identify important areas to correct.

[0088] The version management department can use the emotion estimation function to evaluate the impact of the revision history of regulations on employee emotions and optimize the timing of revisions. For example, the emotion estimation function can be used to evaluate the impact of the revision history of regulations on employee emotions in real time. For example, the impact of the frequency and timing of revisions on employee emotions can be analyzed. The version management department can also build a system that optimizes the timing of revisions based on employee emotional response data. For example, revisions can be made when positive emotions are most prevalent. The version management department can also evaluate the impact of the revision history of regulations on employee emotions based on the emotion estimation data and make suggestions to adjust the timing of revisions. For example, revisions can be made when negative emotions are least prevalent. In this way, the impact of the revision history on employee emotions can be evaluated and the timing of revisions can be optimized.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The consistency check unit checks consistency with laws and regulations. For example, the generation AI references a database of laws and regulations to check whether the regulations are consistent with the latest laws and regulations. Step 2: The drafting unit creates a draft of the regulations. For example, a generation AI generates an appropriate draft based on the content and requirements of the regulations. Step 3: The before-and-after comparison unit compares the content before and after the revision. For example, the generation AI analyzes the regulations before and after the revision and clearly indicates the changes. Step 4: The misspelling detection unit detects misspellings. For example, the generation AI analyzes and points out misspellings in the regulations. Step 5: The version management unit manages the version. For example, the generation AI manages the version of the regulations and records the revision history.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0139] The data processing system 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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. [Explanation of symbols]

[0158] 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 consistency confirmation department that checks for consistency with laws and regulations, etc.; A drafting department that prepares draft regulations; A before-and-after comparison section that compares the content before and after the revision; a typographical error detection unit that detects typographical errors; A version management unit that performs version management. A system characterized by:

2. The consistency checking unit Refer to the legal database to automatically retrieve the history of past amendments and check the consistency by comparing it with the contents of the regulations.

2. The system of claim 1.

3. The consistency checking unit Based on the interpretation of laws and regulations, we will point out potential legal risks in the above regulations and propose improvements.

2. The system of claim 1.

4. The consistency checking unit Simultaneously check for consistency with the laws and regulations of different countries and regions, and achieve global regulation management.

2. The system of claim 1.

5. The consistency checking unit Ensure consistency with industry-specific guidelines and best practices, and provide industry-standard compliant regulations.

2. The system of claim 1.

6. The drafting department Refer to a database of past draft regulations to automatically select the most effective expressions and structure.

2. The system of claim 1.

7. The drafting department Understand the purpose and background of the regulations and generate the most appropriate draft based on that 2. The system of claim 1.

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