system

Abstract

We provide the system. [Solution] Methods for entering contract information, A means of analyzing the entered contract information and converting it into text data, A means of identifying legal risks based on analyzed text data, Means for generating proposed modifications based on identified risks, A means of creating a revised version of the contract using the generated revised draft, A means of sending the revised version to a device capable of displaying it, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 When concluding a contract with a new business partner, since the contract document is not standardized, it takes a great deal of time and labor for legal review, and the burden on the legal department is large, and it takes time for the approval of contract conclusion. The present invention aims to realize the efficiency of legal review work and accelerate the process of contract conclusion. 【Means for Solving the Problems】 【0005】 This invention aims to reduce the manpower required for legal checks and improve the efficiency of contract conclusion through a system that includes means for inputting contract information, means for analyzing the input contract information and converting it into text data, means for identifying legal risks based on the analyzed text data, means for generating proposed revisions based on the identified risks, means for creating a revised version of the contract using the generated proposed revisions, and means for transmitting the revised version to a terminal capable of displaying it. Furthermore, by adding means for referring to past case law information for risk identification and means for confirming the consistency and legal validity of the entire contract based on the generated proposed revisions, the invention achieves the creation of more accurate contracts. 【0006】 "Contract information" refers to written or digital data that contains details about a contract. 【0007】 "Means of input" refers to interfaces or devices used to transmit or import contract information into a system. 【0008】 "Means of analysis" refer to the processes and techniques for structuring input data and understanding and classifying its content. 【0009】 "Text data" refers to data that can be stored and processed in digital format as character information. 【0010】 "Legal risk" refers to potential legal issues or disadvantageous clauses contained in a contract. 【0011】 "Means of identification" refers to a method or apparatus for finding and classifying specific information or patterns. 【0012】 A "revised version" refers to clauses that have been improved based on the original contract, or to newly proposed documents. 【0013】 "Generative means" refer to processes and tools for automatically creating new data and information. 【0014】 "Revised version" refers to a version that has been corrected or improved from the original version. 【0015】 "Displayable terminal" refers to a device that can visually confirm digital content. 【0016】 "Transmission means" refers to a technology or device that transfers data from one location to another. 【0017】 "Case information" refers to information that records past judicial judgments. 【0018】 "Consistency" refers to a state where there is no contradiction and it is consistent throughout. 【0019】 "Legal validity" refers to a state where legitimacy is recognized in light of the law. 【Brief Explanation of Drawings】 【0020】 [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [[ID=4l]] [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. ]> [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0021】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0022】 First, the terms used in the following description will be described. 【0023】 In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units 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), and the like. 【0024】 In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor. 【0025】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0026】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0027】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0028】 [First Embodiment] 【0029】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0030】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0031】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0032】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0033】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0034】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0035】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0036】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0037】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0038】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0039】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0040】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0041】 This system provides a software platform that uses AI technology to automatically analyze and revise contracts. First, the user uploads contract information from their device to the system. Contract information is typically provided in PDF or Word format. The server receives this information and converts it into text data using Optical Character Recognition (OCR) technology. This makes the contract content available for processing in digital format. 【0042】 Next, the server uses natural language processing (NLP) technology to analyze the text data and structure each contract clause. Based on the analyzed data, the server identifies legal risks within the contract. To identify legal risks, the server refers to past case law information stored in the database and compares it with the contract clauses. 【0043】 Once risks are identified, the server uses a generative AI model to generate proposed revisions. These revisions aim to mitigate the identified risks and provide clauses that are favorable to the contract. The generated revisions may undergo supplementary checks to ensure consistency and legal validity of the entire contract. 【0044】 The server then integrates the proposed revisions and creates a revised version of the contract. This revised version is sent to a displayable terminal so that the user can review and make final adjustments. The user can visually review the revised contract on the terminal and make any necessary adjustments. 【0045】 As a concrete example, if a user is trying to conclude a product sales contract with a new business partner, the user uploads a draft contract to the system. The server determines that the clause regarding "penalties for delayed delivery" is ambiguous. The AI ​​generation model outputs a clear revised version stating, "If delivery is delayed by more than 30 days, a penalty of 10% of the delivery amount will be paid." After the user reviews this revised version, they can proceed with negotiations with the business partner more smoothly and achieve a quick contract conclusion. 【0046】 The following describes the processing flow. 【0047】 Step 1: 【0048】 Users upload contract drafts to the system from their devices. Contracts are usually provided in PDF or Word format. 【0049】 Step 2: 【0050】 The server receives the uploaded contract draft and converts it into text data using OCR technology. This makes the contract content available for processing in digital format. 【0051】 Step 3: 【0052】 The server uses NLP technology to analyze text data, extract contract clauses, and clarify their structure. Based on the analysis results, it identifies which parts correspond to which contract clauses. 【0053】 Step 4: 【0054】 The server compares the analyzed data with past case law information in the database to identify legal risks. It particularly focuses on identifying risks related to ambiguous clauses and unfavorable conditions. 【0055】 Step 5: 【0056】 The server uses a generative AI model to generate proposed amendments and favorable clauses for identified legal risks. These amendments aim to minimize legal risks. 【0057】 Step 6: 【0058】 The server creates a revised version of the contract based on the generated revision proposals. The revised version is then finalized by integrating the proposed changes into the original contract. 【0059】 Step 7: 【0060】 The server sends the completed revised contract to the user's terminal. 【0061】 Step 8: 【0062】 The user reviews the revised contract on their device and performs a final check to ensure the content is correct. If necessary, the user can make minor adjustments to the contract. 【0063】 Step 9: 【0064】 Users can share the revised contract with relevant parties after review and proceed with negotiations with business partners. This allows for a rapid contract signing process. 【0065】 (Example 1) 【0066】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0067】 The process of identifying and correcting legal risks, which requires specialized knowledge, during the drafting and review of contract documents is time-consuming and labor-intensive. Furthermore, human error can lead to serious legal problems. It is necessary to mitigate these challenges and streamline contract processing. 【0068】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0069】 In this invention, the server includes means for receiving contract document information, means for converting the received contract document information into character data using character recognition technology, and means for analyzing the converted character data using natural language processing technology and structuring the document items. This makes it possible to automatically identify the legal risks of the contract document and generate and present proposed revisions using generation AI technology. 【0070】 "Contract document information" refers to data in the form of documents that describe the details of a contract. 【0071】 "Character recognition technology" is a technology that converts text characters from images or scanned data into digital data. 【0072】 "Natural language processing technology" is a technique that uses computers to analyze human language and extract and structure its meaning. 【0073】 A "document item" refers to an individual unit of information that is divided into clauses or sections within a contract document. 【0074】 "Legal risks" refer to legal risks or potential problems associated with a contract. 【0075】 "Generative AI technology" refers to the process of generating new information and data using artificial intelligence. 【0076】 A "revised proposal" refers to changes or policies proposed to solve a specific issue or problem. 【0077】 A "revised version" refers to a new version of a document that has been modified from the original. 【0078】 "Information storage device" refers to a system or device used to store data and information. 【0079】 This invention is a system that automates the analysis and revision of contract documents using AI technology. The system mainly consists of a server, a user terminal, and various software and AI models installed on them. 【0080】 First, the user uploads contract document information to the system using their own device. The contract documents are prepared in common file formats such as PDF and Word. The user's device then sends this to the server via a simple user interface. 【0081】 Upon receiving the uploaded contract document, the server uses optical character recognition (OCR) technology to convert the document content into text data. Existing technologies such as "Tesseract" can be used for this OCR process. The converted text data is promptly stored in the database. 【0082】 Next, the server applies natural language processing (NLP) techniques to the stored text data to identify and structure each clause of the document. Here, NLP libraries such as "spaCy" are used to perform grammatical analysis and semantic extraction. 【0083】 Based on these analysis results, the server identifies potential legal risks within the contract. To identify these risks, it compares them with past case precedents and references information storage devices to provide useful insights. 【0084】 If a risk is detected, the server uses a generative AI model to generate proposed corrections for the relevant section. In this process, predefined prompts are input to the AI ​​to obtain the necessary suggestions. For example, the prompt "Please provide specific proposals for penalty clauses for delivery delays" is used. 【0085】 Upon receiving the output from the AI, the server creates a revised version of the document and performs checks to ensure consistency and legal validity. The completed revised version is sent to the user's terminal for visual review and final adjustments. The user can perform final checks via the interface on their terminal and make minor corrections as needed. 【0086】 In this way, this system significantly reduces the time and expertise required for contract review, enabling efficient contract processing. 【0087】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0088】 Step 1: 【0089】 Users upload contract document information to the system from their own devices. PDF and Word files are accepted as input. This upload process is completed when the user selects the desired file through the device's interface and presses the send button. The output is the server receiving the file. 【0090】 Step 2: 【0091】 The server converts received contract documents into text data using optical character recognition (OCR) technology. The input includes contract documents, and OCR software (e.g., Tesseract) is used to recognize the characters and generate digital text. The output at this stage is the converted text data. 【0092】 Step 3: 【0093】 The server applies natural language processing (NLP) techniques to the converted text data to analyze and structure each clause of the contract. The input is text data generated by OCR, and the data is structured by performing part-of-speech and semantic analysis using an NLP library (e.g., spaCy). The output obtained from this analysis is structured contract clause data. 【0094】 Step 4: 【0095】 The server identifies legal risks based on structured data. The input is parsed clause data. The server compares this data with case law information stored in its data storage device to identify high-risk items. The output is a data list of the risk elements. 【0096】 Step 5: 【0097】 The server uses a generative AI model to generate corrective action proposals for risks. The input is a list of risk elements, and the prompt "Please provide corrective action proposals for this risk" is passed to the AI ​​model. The corrective action proposals generated by the AI ​​are the output. 【0098】 Step 6: 【0099】 The server aggregates the generated revision proposals and creates a revised version of the contract document. The input uses revision proposal data from AI. The server checks the consistency and legal validity of the revisions, ensuring their coherence. The output is the completed revised contract document. 【0100】 Step 7: 【0101】 The server sends the completed revised version to the user's terminal. The user is provided with input methods to review the revised version, allowing them to make final adjustments and confirmations via the interface. The output is the revised contract document actively displayed on the terminal. 【0102】 (Application Example 1) 【0103】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0104】 The present invention aims to provide a system that efficiently analyzes the contents of contract documents and quickly and accurately identifies legal risks. Current contract management processes involve the time-consuming and error-prone process of manually reviewing complex clauses and identifying risks. Furthermore, it is necessary to present analysis results visually so that users can immediately understand the risks and make informed decisions. However, existing solutions do not adequately meet this requirement. 【0105】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0106】 In this invention, the server includes a device for inputting contract information, a device for analyzing the input contract information and converting it into text data, a device for identifying legal risks based on the analyzed text data, a device for creating a revised version of the contract using the generated revision suggestions, a device for transmitting the revised version to a display device capable of displaying it, a device for visually presenting important clauses and risks in the contract information, and a device for structuring and analyzing the contract information using natural language processing technology. This enables the rapid identification of legal risks in the contract process and the automatic generation of revision suggestions, allowing users to easily understand contract clauses and manage risks. 【0107】 "Contract information" refers to various document data, including legal documents such as contracts and terms of service. 【0108】 "Device" refers to the entire set of hardware or software components designed to perform a specific function. 【0109】 "Input" refers to the act of a user supplying data or information to a system, or the result thereof. 【0110】 "Analysis" is the process of examining input data in detail and deriving specific structures or meanings. 【0111】 "Text data" refers to text extracted from documents in various formats, in a state that can be processed by a computer. 【0112】 "Legal risk" refers to factors that may violate contracts or laws and are likely to lead to problems in the future. 【0113】 "Identification" means identifying and clarifying the necessary information or elements from the given data. 【0114】 A "proposal for correction" is a suggested change to mitigate or eliminate identified risks. 【0115】 A "revised version" refers to a newly created document that incorporates proposed revisions to the original contract document. 【0116】 A "display device" refers to equipment or software that displays text data or images on a screen, allowing users to visually confirm them. 【0117】 "Visual presentation" refers to showing information in an easily understandable and visually appealing way using formats such as text and graphs. 【0118】 "Natural language processing technology" is a technology that enables computers to understand and process the language that humans use on a daily basis. 【0119】 "Structuring" refers to organizing data according to certain rules and preparing it in a format that is easy to handle. 【0120】 In this embodiment of the invention, the server first receives contract data from the user through a device for receiving contract information. The received contract information is usually in PDF or Word format, and the server converts it into text data using optical character recognition (OCR) technology. This conversion process uses the open-source OCR software "Tesseract" to extract text from image data. 【0121】 Next, the server uses natural language processing techniques to analyze the text data and structure the contract information. This process utilizes Hugging Face's "transformers" library to identify each clause within the contract. 【0122】 Furthermore, the server identifies legal risks based on the identified clauses. At this stage, it references accumulated past case law information from a database and compares it with the contract clauses. Based on the identified risks, the server uses a generative AI model to generate proposed revisions. These generated proposed revisions may undergo supplementary checks to ensure the overall consistency and legal validity of the contract. 【0123】 Once the revised contract information is complete, the server transmits it to the user's display device. On the user's terminal, important clauses and risks are visually presented, allowing the user to review the revisions and make adjustments as needed. A concrete example is detecting ambiguous parts of the privacy clause in a new electronic payment service contract and providing clear revision suggestions. Based on these revision suggestions, the user can use the service with confidence. 【0124】 An example of a prompt would be, "Analyze the privacy clause in the contract, identify the risks, and provide proposed revisions." This prompt allows the generating AI model to properly process the contract information and make necessary improvements. 【0125】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0126】 Step 1: 【0127】 The user uploads contract information to the server. The input is contract data in PDF or Word format, which the server receives. The received file is converted from image to text data using optical character recognition (OCR) technology. Through this conversion, the contract information is output as computer-processable text data. 【0128】 Step 2: 【0129】 The server analyzes the text data using natural language processing techniques. Through a process of structuring the input text data, each contract clause is identified. Hugging Face's "transformers" library is used for this analysis, and the output identifies key sections within the contract information. 【0130】 Step 3: 【0131】 The server identifies legal risks based on structured data. It references past case law information from the database and compares it with contractual clauses. The input is parsed structured data, which is used to identify legal risks and generate risk information as output. This risk information includes potential legal issues. 【0132】 Step 4: 【0133】 The server generates proposed modifications using a generative AI model. The input is identified risk information, and the generative AI model outputs suggestions. The generated suggestions are proposed modifications to mitigate legal risks. The generative AI model is also prompted with the message, "Analyze the privacy clauses in the contract, clarify the risks, and provide proposed modifications." 【0134】 Step 5: 【0135】 The server integrates the proposed revisions into the contract information and creates a revised version. The input consists of the original contract data and the generated proposed revisions; these are integrated to output the revised contract information. This revised version is designed to mitigate legal risks and ensure consistency and validity. 【0136】 Step 6: 【0137】 The server sends the revised contract information to the user's terminal. On the user's terminal, important clauses and revisions are visually presented. The input is the revised contract information, and by visualizing it, the user can review the risks and revisions as output and make final adjustments based on that. 【0138】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0139】 This invention is a system that uses AI technology to analyze and revise contracts, while simultaneously recognizing the user's emotions using an emotion engine, thereby supporting the contract creation and negotiation process. First, the user uploads a contract draft to the system from their terminal. Contracts are typically provided in PDF or Word format. After receiving this information, the server converts it into text data using OCR technology. This makes the contract content digitally processable within the system. 【0140】 The server uses NLP technology to analyze text data and structure the contract clauses. Based on the analysis results, a risk identification module is activated to identify legal risk elements. In this process, past case law information stored in a database is referenced. Once legal risks are identified, the server uses a generative AI model to generate proposed revisions and create a revised version of the contract. These generated revisions are then verified for consistency and legal validity of the entire contract before being sent to the user's terminal as the final revised version. 【0141】 Furthermore, a key feature of this invention is that the emotion engine provides the ability to recognize the user's emotional state in real time. While the user is reviewing the contract, the emotion engine analyzes the user's facial expressions and tone of voice through the camera and microphone to determine the user's emotions. If the user shows anxiety or dissatisfaction, the system can display supportive information to help them relax through the interface. In addition, the user's emotional data is recorded and can be used as reference information during future contract negotiations. 【0142】 For example, when a user uses the system to conclude a contract with a new business partner, they may have concerns about the contract period or payment terms. The emotion engine detects this concern, and the server explains the terms in language that the user can easily understand, and offers alternatives if necessary. This allows the user to feel at ease and effectively negotiate the contract terms. This system makes the contract creation and negotiation process more humane and efficient. 【0143】 The following describes the processing flow. 【0144】 Step 1: 【0145】 Users upload contract drafts to the system from their devices. Contracts can be in common formats such as PDF or Word. 【0146】 Step 2: 【0147】 The server receives the uploaded contract information and converts it into text data using OCR technology. This makes the contents of the contract available for processing in text format. 【0148】 Step 3: 【0149】 The server analyzes the converted text data using natural language processing (NLP) technology to classify and extract the structure and content of the contract clauses. 【0150】 Step 4: 【0151】 The server compares the analyzed clauses with past case law information in the database to identify legal risk factors. This is done by a risk identification module using a specific algorithm. 【0152】 Step 5: 【0153】 The server uses a generation AI model based on identified legal risks to generate proposed clause revisions. This results in a revised version with clauses that are favorable to the user. 【0154】 Step 6: 【0155】 The server sends the revised contract to the user's terminal. This contract integrates the generated revisions and ensures contractual consistency and legal validity. 【0156】 Step 7: 【0157】 The user's device will display the submitted revised agreement, allowing the user to review it. The user can review the revision and make any necessary adjustments directly. 【0158】 Step 8: 【0159】 While the user is reviewing the contract, an emotion engine activates, analyzing the user's emotions from their facial expressions and voice. If the user shows signs of anxiety or doubt, the system detects it. 【0160】 Step 9: 【0161】 When a user expresses anxiety, the device displays information and support to help them relax. Appropriate assistance is provided based on data from the emotion engine. 【0162】 Step 10: 【0163】 User sentiment data is recorded for use in future contract negotiations. This data may be used to streamline and improve contracts as needed. 【0164】 (Example 2) 【0165】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0166】 Traditional contract drafting processes lack systems that accurately analyze contract content, identify legal risks, and support contract negotiations while considering the user's feelings. As a result, it is difficult to prevent legal problems before they occur, and users often experience anxiety and dissatisfaction. This invention aims to achieve more efficient, safe, and user-friendly contract drafting and negotiation by automating contract analysis and revision suggestions, as well as providing support that takes the user's emotional state into consideration. 【0167】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0168】 In this invention, the server includes a device for receiving contract information, a device for converting the received contract information into character data using optical character recognition technology, and a device for analyzing the converted character data using natural language processing technology and generating structured data. This enables automatic analysis of contracts and risk identification, reduces legal risks by generating revision suggestions using generative AI technology, and allows users to conduct contract negotiations with confidence by providing appropriate support information based on their emotional state. 【0169】 "Contract information" refers to data, including legal contracts and related documents, that are stored or transmitted in digital format. 【0170】 "Optical character recognition technology" is a technology that converts printed or handwritten characters into digital text from image data. 【0171】 "Character data" refers to text information represented in digital format to enable natural language processing and data analysis. 【0172】 "Natural language processing technology" refers to algorithms and techniques for computers to understand and analyze human language. 【0173】 "Structured data" refers to data that is organized in databases or tabular formats and arranged in a way that allows for efficient searching and analysis. 【0174】 "Legal risk" is a concept that refers to the legal risks and problems inherent in the content of a contract. 【0175】 "Generative AI technology" refers to technology that uses artificial intelligence to generate new information and proposals. 【0176】 A "proposal for amendment" is a proposed revision of a contract document generated to reduce or resolve identified legal risks. 【0177】 A "revised version" is a new version of a contract that has been modified or improved from the original. 【0178】 "Information equipment" is a general term referring to electronic devices capable of creating, editing, displaying, or transmitting digital information. 【0179】 "Emotional state" refers to a state that shows the user's psychological reactions and emotional changes in real time. 【0180】 "Support information" refers to helpful guidance and notifications provided according to the user's emotions and circumstances. 【0181】 A "data storage device" refers to equipment or systems for storing information and data, either physically or digitally. 【0182】 "Case law knowledge" refers to information that compiles past legal judgments and the criteria used by courts to make decisions. 【0183】 This system performs contract analysis and provides emotional support to users, and is implemented as follows: 【0184】 First, the user uploads contract information using their device. This contract information is often provided in formats such as PDF or Word. The device is equipped with an interface for sending these files to the system. 【0185】 Next, the server converts the received contract information into text data using OCR technology. Tools such as the Tesseract OCR engine or Adobe Acrobat's OCR function are used for OCR. This conversion makes the contract information available for analysis in digital format. 【0186】 Next, the server uses natural language processing technology to analyze the text data and generate structured data from the clauses within the contract. Natural language processing libraries such as spaCy and NLTK are used for the analysis. 【0187】 Based on the analysis results, the server identifies legal risks and references past case law knowledge from the data storage device. This reference is to improve the accuracy of risk identification. 【0188】 Next, using a generative AI model, the server generates proposed modifications to address the identified legal risks. This involves prompting the generative AI model to suggest appropriate contractual modifications. An example of a prompt would be, "Please suggest what modifications would be appropriate to mitigate this risk." 【0189】 Furthermore, while the user is reviewing the contract, the server uses an emotion engine to detect the user's emotional state in real time. The emotional state is identified by analyzing the user's facial expressions and voice captured through the camera and microphone. If emotions such as anxiety or dissatisfaction are detected, the terminal displays supportive information to help the user relax. Specifically, it reduces user stress by presenting supplementary explanations and options to provide a sense of security. 【0190】 In this way, this system not only automatically analyzes contracts and suggests revisions, but also enables the creation of contracts and negotiations that take into account the user's feelings. 【0191】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0192】 Step 1: 【0193】 The user uploads contract information using a terminal. The input is a contract file in PDF or Word format. The terminal that receives this file relays the data via an interface for sending the file to the server. The output is the raw data received by the server. 【0194】 Step 2: 【0195】 The server converts received contract information into text data using OCR technology. Specifically, OCR software analyzes the characters in the image and converts them into text data. The input is a raw data file, and the output is readable text data. 【0196】 Step 3: 【0197】 The server analyzes the text data using natural language processing techniques. The analysis engine extracts contract clauses from the input text data and generates structured data. For example, it organizes related information using nouns as keys. The output is the structured data resulting from the analysis. 【0198】 Step 4: 【0199】 The server identifies legal risks based on structured data. This involves a process of referencing a database and cross-referencing it with past case law knowledge. The input is structured data, and the output is a list of identified risks. 【0200】 Step 5: 【0201】 The server generates suggested modifications using a generative AI model. In this process, prompts are input to the AI ​​model, and the suggested modifications are obtained as output. Specifically, prompts such as "Please suggest the best wording to mitigate a specific risk" are used. The output is a set of suggested modifications. 【0202】 Step 6: 【0203】 The server creates a revised version of the contract based on the generated revision proposals. The revised version takes shape by incorporating the proposed revisions into the contract. The inputs are the revision proposals and the original contract, and the output is the revised contract. 【0204】 Step 7: 【0205】 While the user reviews the contract, the server activates an emotion engine to read the user's emotional state. Based on data obtained through the camera and microphone, it performs real-time emotion analysis. The input is visual and audio data, and the output is an evaluation of the user's emotional state. 【0206】 Step 8: 【0207】 The server provides support information based on the user's emotional state. If the user expresses anxiety or dissatisfaction, the terminal displays support information and additional explanations to help them relax. The input is the result of the emotional state assessment, and the output is the support information presented. 【0208】 (Application Example 2) 【0209】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0210】 During contract negotiations and drafting, users may feel uneasy about the contract's contents, highlighting the need for support that provides understanding and reassurance. However, conventional systems are limited to contract analysis and drafting, lacking flexible support tailored to the user's emotional state. Therefore, a method is needed to reduce anxiety and misunderstandings during the contract process and realize a more efficient and humane contract experience. 【0211】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0212】 In this invention, the server includes means for inputting contract information, means for analyzing the input contract information and converting it into text data, means for identifying legal risks based on the analyzed text data, means for generating proposed revisions based on the identified risks, means for creating a revised version of the contract using the generated proposed revisions, means for transmitting the revised version to a terminal capable of displaying it, means for identifying the user's emotional state in real time, and means for adjusting suggestions based on the emotional identification and providing information that meets the user's needs. This enables a detailed response that responds to the user's emotions during the contract analysis and revision process, providing support that helps them feel secure and understand the document. 【0213】 "Means for inputting contract information" refers to the method by which users provide contracts to the system and receive them in digital format. 【0214】 "Means of analyzing input contract information and converting it into text data" refers to a method of converting contract data in a certain format into editable text data using OCR or other technologies. 【0215】 "Methods for identifying legal risks based on analyzed text data" refers to the process of analyzing text data within a contract to identify legal issues and risk factors. 【0216】 "Means for generating revised proposals based on identified risks" refers to methods for creating revised proposals using generative AI models or the like to address identified risks. 【0217】 "Methods for creating a revised version of the contract using the generated revisions" refers to the process of creating a new version of the contract based on the proposed revisions. 【0218】 "Means of sending the revised version to a device capable of displaying it" refers to a method of transferring the revised version of the created contract to a device that can display and verify it. 【0219】 "Means for identifying a user's emotional state in real time" refers to technologies that use sensor devices such as cameras and microphones to determine a user's emotions from their facial expressions and tone of voice. 【0220】 "Means of tailoring suggestions based on sentiment identification and providing information that meets user needs" refers to the process of analyzing user sentiment data and providing users with individually customized information and suggestions based on the results. 【0221】 In order to implement this invention, a system combining various technical elements is required. A specific embodiment is shown below. 【0222】 The server is equipped with an input mechanism to receive contract information from users, which is typically provided in PDF or Word format. The entered contract information is converted into text data using OCR technology. Examples of OCR software used include Tesseract. This text data is analyzed within the server using an NLP engine (e.g., spaCy). Through this analysis, the server gains a detailed understanding of the contract and identifies legal risks. 【0223】 Once legal risks are identified, the server references past case law information from its database and generates proposed revisions using a generative AI model (e.g., OpenAI® GPT). After verifying that the revisions maintain the consistency and legal validity of the contract, the revised version is created and sent to the user's accessible device. 【0224】 Furthermore, this system can analyze the user's emotional state in real time. When a user views a contract, the system analyzes the user's facial expressions and tone of voice through the terminal's camera and microphone, and identifies their emotional state using Azure® Face API and other tools. Based on this information, the system can display information to support understanding and alternative suggestions that are appropriate to the user's emotions. 【0225】 A concrete example is providing users who are concerned about payment terms when a family purchases a new home with detailed information about alternatives and payment plans. This can alleviate user anxiety and promote understanding of the contract. 【0226】 An example of a prompt to input into the generating AI model is, "Create a proposal to alleviate the user's concerns regarding the apartment purchase contract." Through this prompt, the necessary support can be provided to the user quickly and accurately. 【0227】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0228】 Step 1: 【0229】 The user uploads the contract from their device. The input is a contract file in PDF or Word format. The server receives this contract information and prepares it for processing. 【0230】 Step 2: 【0231】 The server uses OCR technology to convert the entered contract into text data. Specifically, it uses Tesseract to extract text from PDF or Word documents and converts it into structured digital data. The output is then passed on to the next step as text data. 【0232】 Step 3: 【0233】 The server uses an NLP engine to analyze text data. It uses spaCy as the NLP engine to analyze contract clauses, understand the context, and identify legal risk elements. During this process, it references past case law information from a database. The output is data that identifies the risk elements. 【0234】 Step 4: 【0235】 The server generates proposed solutions using a generative AI model based on the identified risk elements. It uses tools such as OpenAI GPT to create solutions tailored to the identified risks. The input is the identified risk elements, and the output is the text of the proposed solutions. 【0236】 Step 5: 【0237】 The server uses the generated revisions to create a revised version of the contract. The revisions are integrated into the existing contract structure, and consistency and legal validity are verified. The output is the revised contract, ready to be sent to the user. 【0238】 Step 6: 【0239】 The system uses the device's camera and microphone to analyze the user's emotional state in real time. It leverages the Azure Face API to analyze facial expressions and voice tone, identifying emotions. Input consists of real-time video and audio data. 【0240】 Step 7: 【0241】 Based on the results of emotion recognition, the server provides the user with tailored information. For example, if the user shows anxiety, it presents details of the contract and alternatives to deepen the user's understanding. It also displays information to promote calmness. The output consists of customized suggestions and information. 【0242】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0243】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0244】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0245】 [Second Embodiment] 【0246】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0247】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0248】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0249】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0250】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0251】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0252】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0253】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0254】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0255】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0256】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0257】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0258】 This system provides a software platform that uses AI technology to automatically analyze and revise contracts. First, the user uploads contract information from their device to the system. Contract information is typically provided in PDF or Word format. The server receives this information and converts it into text data using Optical Character Recognition (OCR) technology. This makes the contract content available for processing in digital format. 【0259】 Next, the server uses natural language processing (NLP) technology to analyze the text data and structure each contract clause. Based on the analyzed data, the server identifies legal risks within the contract. To identify legal risks, the server refers to past case law information stored in the database and compares it with the contract clauses. 【0260】 Once risks are identified, the server uses a generative AI model to generate proposed revisions. These revisions aim to mitigate the identified risks and provide clauses that are favorable to the contract. The generated revisions may undergo supplementary checks to ensure consistency and legal validity of the entire contract. 【0261】 The server then integrates the proposed revisions and creates a revised version of the contract. This revised version is sent to a displayable terminal so that the user can review and make final adjustments. The user can visually review the revised contract on the terminal and make any necessary adjustments. 【0262】 As a concrete example, if a user is trying to conclude a product sales contract with a new business partner, the user uploads a draft contract to the system. The server determines that the clause regarding "penalties for delayed delivery" is ambiguous. The AI ​​generation model outputs a clear revised version stating, "If delivery is delayed by more than 30 days, a penalty of 10% of the delivery amount will be paid." After the user reviews this revised version, they can proceed with negotiations with the business partner more smoothly and achieve a quick contract conclusion. 【0263】 The following describes the processing flow. 【0264】 Step 1: 【0265】 Users upload contract drafts to the system from their devices. Contracts are usually provided in PDF or Word format. 【0266】 Step 2: 【0267】 The server receives the uploaded contract draft and converts it into text data using OCR technology. This makes the contract content available for processing in digital format. 【0268】 Step 3: 【0269】 The server uses NLP technology to analyze text data, extract contract clauses, and clarify their structure. Based on the analysis results, it identifies which parts correspond to which contract clauses. 【0270】 Step 4: 【0271】 The server compares the analyzed data with past case law information in the database to identify legal risks. It particularly focuses on identifying risks related to ambiguous clauses and unfavorable conditions. 【0272】 Step 5: 【0273】 The server uses a generative AI model to generate proposed amendments and favorable clauses for identified legal risks. These amendments aim to minimize legal risks. 【0274】 Step 6: 【0275】 The server creates a revised version of the contract based on the generated revision proposals. The revised version is then finalized by integrating the proposed changes into the original contract. 【0276】 Step 7: 【0277】 The server sends the completed revised contract to the user's terminal. 【0278】 Step 8: 【0279】 The user reviews the revised contract on their device and performs a final check to ensure the content is correct. If necessary, the user can make minor adjustments to the contract. 【0280】 Step 9: 【0281】 The user shares the confirmed revised contract document with the relevant parties and proceeds with negotiations with the business partner. It is possible to quickly conduct the process towards contract conclusion. 【0282】 (Example 1) 【0283】 Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal". 【0284】 In the creation and review of contract documents, the identification and correction process of legal risks that require expertise is time-consuming and labor-intensive. Also, there is a possibility of causing significant legal problems due to human error. It is necessary to reduce such issues and streamline the contract process. 【0285】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0286】 In this invention, the server includes means for receiving contract document information, means for converting the received contract document information into character data by character recognition technology, and means for analyzing the converted character data using natural language processing technology to structure document items. Thereby, it becomes possible to automatically identify the legal risks of contract documents and generate and present amendments using generative AI technology. 【0287】 "Contract document information" refers to data as a document in which the contents of the contract are described in detail. 【0288】 "Character recognition technology" is a technology for converting text characters from images or scan data into digital data. 【0289】 "Natural language processing technology" is a technology for analyzing human language by a computer to extract and structure meaning. 【0290】 A "document item" refers to an individual unit of information that is divided into clauses or sections within a contract document. 【0291】 "Legal risks" refer to legal risks or potential problems associated with a contract. 【0292】 "Generative AI technology" refers to the process of generating new information and data using artificial intelligence. 【0293】 A "revised proposal" refers to changes or policies proposed to solve a specific issue or problem. 【0294】 A "revised version" refers to a new version of a document that has been modified from the original. 【0295】 "Information storage device" refers to a system or device used to store data and information. 【0296】 This invention is a system that automates the analysis and revision of contract documents using AI technology. The system mainly consists of a server, a user terminal, and various software and AI models installed on them. 【0297】 First, the user uploads contract document information to the system using their own device. The contract documents are prepared in common file formats such as PDF and Word. The user's device then sends this to the server via a simple user interface. 【0298】 Upon receiving the uploaded contract document, the server uses optical character recognition (OCR) technology to convert the document content into text data. Existing technologies such as "Tesseract" can be used for this OCR process. The converted text data is promptly stored in the database. 【0299】 Next, the server applies natural language processing (NLP) techniques to the stored text data to identify and structure each clause of the document. Here, NLP libraries such as "spaCy" are used to perform grammatical analysis and semantic extraction. 【0300】 Based on these analysis results, the server identifies potential legal risks within the contract. To identify these risks, it compares them with past case precedents and references information storage devices to provide useful insights. 【0301】 If a risk is detected, the server uses a generative AI model to generate proposed corrections for the relevant section. In this process, predefined prompts are input to the AI ​​to obtain the necessary suggestions. For example, the prompt "Please provide specific proposals for penalty clauses for delivery delays" is used. 【0302】 Upon receiving the output from the AI, the server creates a revised version of the document and performs checks to ensure consistency and legal validity. The completed revised version is sent to the user's terminal for visual review and final adjustments. The user can perform final checks via the interface on their terminal and make minor corrections as needed. 【0303】 In this way, this system significantly reduces the time and expertise required for contract review, enabling efficient contract processing. 【0304】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0305】 Step 1: 【0306】 Users upload contract document information to the system from their own devices. PDF and Word files are accepted as input. This upload process is completed when the user selects the desired file through the device's interface and presses the send button. The output is the server receiving the file. 【0307】 Step 2: 【0308】 The server converts the received contract document into text data using optical character recognition (OCR) technology. The input includes the contract document, and characters are recognized using OCR software (e.g., Tesseract) to generate digital text. The output at this stage is the converted text data. 【0309】 Step 3: 【0310】 The server applies natural language processing (NLP) technology to the converted text data to analyze and structure each clause of the contract. The input is the text data from OCR, and the data is structured by performing part-of-speech analysis and semantic analysis using an NLP library (e.g., spaCy). The output obtained from this analysis is the structured contract clause data. 【0311】 Step 4: 【0312】 The server identifies legal risks based on the structured data. The input is the analyzed clause data. The server compares it with the case information stored in the information storage device to identify high-risk items. The output is data listing the risk factors. 【0313】 Step 5: 【0314】 The server uses a generative AI model to generate amendments to the risks. The input is a list of risk factors, and the prompt sentence "Please provide a proposed amendment to this risk" is passed to the AI model. The amendment generated by the AI is the output. 【0315】 Step 6: 【0316】 The server aggregates the generated revision proposals and creates a revised version of the contract document. The input uses revision proposal data from AI. The server checks the consistency and legal validity of the revisions, ensuring their coherence. The output is the completed revised contract document. 【0317】 Step 7: 【0318】 The server sends the completed revised version to the user's terminal. The user is provided with input methods to review the revised version, allowing them to make final adjustments and confirmations via the interface. The output is the revised contract document actively displayed on the terminal. 【0319】 (Application Example 1) 【0320】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0321】 The present invention aims to provide a system that efficiently analyzes the contents of contract documents and quickly and accurately identifies legal risks. Current contract management processes involve the time-consuming and error-prone process of manually reviewing complex clauses and identifying risks. Furthermore, it is necessary to present analysis results visually so that users can immediately understand the risks and make informed decisions. However, existing solutions do not adequately meet this requirement. 【0322】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0323】 In this invention, the server includes a device for inputting contract information, a device for analyzing the input contract information and converting it into text data, a device for identifying legal risks based on the analyzed text data, a device for creating a revised version of the contract using the generated revision suggestions, a device for transmitting the revised version to a display device capable of displaying it, a device for visually presenting important clauses and risks in the contract information, and a device for structuring and analyzing the contract information using natural language processing technology. This enables the rapid identification of legal risks in the contract process and the automatic generation of revision suggestions, allowing users to easily understand contract clauses and manage risks. 【0324】 "Contract information" refers to various document data, including legal documents such as contracts and terms of service. 【0325】 "Device" refers to the entire set of hardware or software components designed to perform a specific function. 【0326】 "Input" refers to the act of a user supplying data or information to a system, or the result thereof. 【0327】 "Analysis" is the process of examining input data in detail and deriving specific structures or meanings. 【0328】 "Text data" refers to text extracted from documents in various formats, in a state that can be processed by a computer. 【0329】 "Legal risk" refers to factors that may violate contracts or laws and are likely to lead to problems in the future. 【0330】 "Identification" means identifying and clarifying the necessary information or elements from the given data. 【0331】 A "proposal for correction" is a suggested change to mitigate or eliminate identified risks. 【0332】 A "revised version" refers to a newly created document that incorporates proposed revisions to the original contract document. 【0333】 A "display device" refers to equipment or software that displays text data or images on a screen, allowing users to visually confirm them. 【0334】 "Visual presentation" refers to showing information in an easily understandable and visually appealing way using formats such as text and graphs. 【0335】 "Natural language processing technology" is a technology that enables computers to understand and process the language that humans use on a daily basis. 【0336】 "Structuring" refers to organizing data according to certain rules and preparing it in a format that is easy to handle. 【0337】 In this embodiment of the invention, the server first receives contract data from the user through a device for receiving contract information. The received contract information is usually in PDF or Word format, and the server converts it into text data using optical character recognition (OCR) technology. This conversion process uses the open-source OCR software "Tesseract" to extract text from image data. 【0338】 Next, the server uses natural language processing techniques to analyze the text data and structure the contract information. This process utilizes Hugging Face's "transformers" library to identify each clause within the contract. 【0339】 Furthermore, the server identifies legal risks based on the identified clauses. At this stage, it references accumulated past case law information from a database and compares it with the contract clauses. Based on the identified risks, the server uses a generative AI model to generate proposed revisions. These generated proposed revisions may undergo supplementary checks to ensure the overall consistency and legal validity of the contract. 【0340】 Once the revised contract information is complete, the server transmits it to the user's display device. On the user's terminal, important clauses and risks are visually presented, allowing the user to review the revisions and make adjustments as needed. A concrete example is detecting ambiguous parts of the privacy clause in a new electronic payment service contract and providing clear revision suggestions. Based on these revision suggestions, the user can use the service with confidence. 【0341】 An example of a prompt would be, "Analyze the privacy clause in the contract, identify the risks, and provide proposed revisions." This prompt allows the generating AI model to properly process the contract information and make necessary improvements. 【0342】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0343】 Step 1: 【0344】 The user uploads contract information to the server. The input is contract data in PDF or Word format, which the server receives. The received file is converted from image to text data using optical character recognition (OCR) technology. Through this conversion, the contract information is output as computer-processable text data. 【0345】 Step 2: 【0346】 The server analyzes the text data using natural language processing techniques. Through a process of structuring the input text data, each contract clause is identified. Hugging Face's "transformers" library is used for this analysis, and the output identifies key sections within the contract information. 【0347】 Step 3: 【0348】 The server identifies legal risks based on structured data. It references past case law information from the database and compares it with contractual clauses. The input is parsed structured data, which is used to identify legal risks and generate risk information as output. This risk information includes potential legal issues. 【0349】 Step 4: 【0350】 The server generates proposed modifications using a generative AI model. The input is identified risk information, and the generative AI model outputs suggestions. The generated suggestions are proposed modifications to mitigate legal risks. The generative AI model is also prompted with the message, "Analyze the privacy clauses in the contract, clarify the risks, and provide proposed modifications." 【0351】 Step 5: 【0352】 The server integrates the proposed revisions into the contract information and creates a revised version. The input consists of the original contract data and the generated proposed revisions; these are integrated to output the revised contract information. This revised version is designed to mitigate legal risks and ensure consistency and validity. 【0353】 Step 6: 【0354】 The server sends the revised contract information to the user's terminal. On the user's terminal, important clauses and revisions are visually presented. The input is the revised contract information, and by visualizing it, the user can review the risks and revisions as output and make final adjustments based on that. 【0355】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0356】 This invention is a system that uses AI technology to analyze and revise contracts, while simultaneously recognizing the user's emotions using an emotion engine, thereby supporting the contract creation and negotiation process. First, the user uploads a contract draft to the system from their terminal. Contracts are typically provided in PDF or Word format. After receiving this information, the server converts it into text data using OCR technology. This makes the contract content digitally processable within the system. 【0357】 The server uses NLP technology to analyze text data and structure the contract clauses. Based on the analysis results, a risk identification module is activated to identify legal risk elements. In this process, past case law information stored in a database is referenced. Once legal risks are identified, the server uses a generative AI model to generate proposed revisions and create a revised version of the contract. These generated revisions are then verified for consistency and legal validity of the entire contract before being sent to the user's terminal as the final revised version. 【0358】 Furthermore, a key feature of this invention is that the emotion engine provides the ability to recognize the user's emotional state in real time. While the user is reviewing the contract, the emotion engine analyzes the user's facial expressions and tone of voice through the camera and microphone to determine the user's emotions. If the user shows anxiety or dissatisfaction, the system can display supportive information to help them relax through the interface. In addition, the user's emotional data is recorded and can be used as reference information during future contract negotiations. 【0359】 For example, when a user uses the system to conclude a contract with a new business partner, they may have concerns about the contract period or payment terms. The emotion engine detects this concern, and the server explains the terms in language that the user can easily understand, and offers alternatives if necessary. This allows the user to feel at ease and effectively negotiate the contract terms. This system makes the contract creation and negotiation process more humane and efficient. 【0360】 The following describes the processing flow. 【0361】 Step 1: 【0362】 Users upload contract drafts to the system from their devices. Contracts can be in common formats such as PDF or Word. 【0363】 Step 2: 【0364】 The server receives the uploaded contract information and converts it into text data using OCR technology. This makes the contents of the contract available for processing in text format. 【0365】 Step 3: 【0366】 The server analyzes the converted text data using natural language processing (NLP) technology to classify and extract the structure and content of the contract clauses. 【0367】 Step 4: 【0368】 The server compares the analyzed clauses with past case law information in the database to identify legal risk factors. This is done by a risk identification module using a specific algorithm. 【0369】 Step 5: 【0370】 The server uses a generation AI model based on identified legal risks to generate proposed clause revisions. This results in a revised version with clauses that are favorable to the user. 【0371】 Step 6: 【0372】 The server sends the revised contract to the user's terminal. This contract integrates the generated revisions and ensures contractual consistency and legal validity. 【0373】 Step 7: 【0374】 The user's device will display the submitted revised agreement, allowing the user to review it. The user can review the revision and make any necessary adjustments directly. 【0375】 Step 8: 【0376】 While the user is reviewing the contract, an emotion engine activates, analyzing the user's emotions from their facial expressions and voice. If the user shows signs of anxiety or doubt, the system detects it. 【0377】 Step 9: 【0378】 When a user expresses anxiety, the device displays information and support to help them relax. Appropriate assistance is provided based on data from the emotion engine. 【0379】 Step 10: 【0380】 User sentiment data is recorded for use in future contract negotiations. This data may be used to streamline and improve contracts as needed. 【0381】 (Example 2) 【0382】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0383】 Traditional contract drafting processes lack systems that accurately analyze contract content, identify legal risks, and support contract negotiations while considering the user's feelings. As a result, it is difficult to prevent legal problems before they occur, and users often experience anxiety and dissatisfaction. This invention aims to achieve more efficient, safe, and user-friendly contract drafting and negotiation by automating contract analysis and revision suggestions, as well as providing support that takes the user's emotional state into consideration. 【0384】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0385】 In this invention, the server includes a device for receiving contract information, a device for converting the received contract information into character data using optical character recognition technology, and a device for analyzing the converted character data using natural language processing technology and generating structured data. This enables automatic analysis of contracts and risk identification, reduces legal risks by generating revision suggestions using generative AI technology, and allows users to conduct contract negotiations with confidence by providing appropriate support information based on their emotional state. 【0386】 "Contract information" refers to data, including legal contracts and related documents, that are stored or transmitted in digital format. 【0387】 "Optical character recognition technology" is a technology that converts printed or handwritten characters into digital text from image data. 【0388】 "Character data" refers to text information represented in digital format to enable natural language processing and data analysis. 【0389】 "Natural language processing technology" refers to algorithms and techniques for computers to understand and analyze human language. 【0390】 "Structured data" refers to data that is organized in databases or tabular formats and arranged in a way that allows for efficient searching and analysis. 【0391】 "Legal risk" is a concept that refers to the legal risks and problems inherent in the content of a contract. 【0392】 "Generative AI technology" refers to technology that uses artificial intelligence to generate new information and proposals. 【0393】 A "proposal for amendment" is a proposed revision of a contract document generated to reduce or resolve identified legal risks. 【0394】 A "revised version" is a new version of a contract that has been modified or improved from the original. 【0395】 "Information equipment" is a general term referring to electronic devices capable of creating, editing, displaying, or transmitting digital information. 【0396】 "Emotional state" refers to a state that shows the user's psychological reactions and emotional changes in real time. 【0397】 "Support information" refers to helpful guidance and notifications provided according to the user's emotions and circumstances. 【0398】 A "data storage device" refers to equipment or systems for storing information and data, either physically or digitally. 【0399】 "Case law knowledge" refers to information that compiles past legal judgments and the criteria used by courts to make decisions. 【0400】 This system performs contract analysis and provides emotional support to users, and is implemented as follows: 【0401】 First, the user uploads contract information using their device. This contract information is often provided in formats such as PDF or Word. The device is equipped with an interface for sending these files to the system. 【0402】 Next, the server converts the received contract information into text data using OCR technology. Tools such as the Tesseract OCR engine or Adobe Acrobat's OCR function are used for OCR. This conversion makes the contract information available for analysis in digital format. 【0403】 Next, the server uses natural language processing technology to analyze the text data and generate structured data from the clauses within the contract. Natural language processing libraries such as spaCy and NLTK are used for the analysis. 【0404】 Based on the analysis results, the server identifies legal risks and references past case law knowledge from the data storage device. This reference is to improve the accuracy of risk identification. 【0405】 Next, using a generative AI model, the server generates proposed modifications to address the identified legal risks. This involves prompting the generative AI model to suggest appropriate contractual modifications. An example of a prompt would be, "Please suggest what modifications would be appropriate to mitigate this risk." 【0406】 Furthermore, while the user is reviewing the contract, the server uses an emotion engine to detect the user's emotional state in real time. The emotional state is identified by analyzing the user's facial expressions and voice captured through the camera and microphone. If emotions such as anxiety or dissatisfaction are detected, the terminal displays supportive information to help the user relax. Specifically, it reduces user stress by presenting supplementary explanations and options to provide a sense of security. 【0407】 In this way, this system not only automatically analyzes contracts and suggests revisions, but also enables the creation of contracts and negotiations that take into account the user's feelings. 【0408】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0409】 Step 1: 【0410】 The user uploads contract information using a terminal. The input is a contract file in PDF or Word format. The terminal that receives this file relays the data via an interface for sending the file to the server. The output is the raw data received by the server. 【0411】 Step 2: 【0412】 The server converts received contract information into text data using OCR technology. Specifically, OCR software analyzes the characters in the image and converts them into text data. The input is a raw data file, and the output is readable text data. 【0413】 Step 3: 【0414】 The server analyzes the text data using natural language processing techniques. The analysis engine extracts contract clauses from the input text data and generates structured data. For example, it organizes related information using nouns as keys. The output is the structured data resulting from the analysis. 【0415】 Step 4: 【0416】 The server identifies legal risks based on structured data. This involves a process of referencing a database and cross-referencing it with past case law knowledge. The input is structured data, and the output is a list of identified risks. 【0417】 Step 5: 【0418】 The server generates suggested modifications using a generative AI model. In this process, prompts are input to the AI ​​model, and the suggested modifications are obtained as output. Specifically, prompts such as "Please suggest the best wording to mitigate a specific risk" are used. The output is a set of suggested modifications. 【0419】 Step 6: 【0420】 The server creates a revised version of the contract based on the generated revision proposals. The revised version takes shape by incorporating the proposed revisions into the contract. The inputs are the revision proposals and the original contract, and the output is the revised contract. 【0421】 Step 7: 【0422】 While the user reviews the contract, the server activates an emotion engine to read the user's emotional state. Based on data obtained through the camera and microphone, it performs real-time emotion analysis. The input is visual and audio data, and the output is an evaluation of the user's emotional state. 【0423】 Step 8: 【0424】 The server provides support information based on the user's emotional state. If the user expresses anxiety or dissatisfaction, the terminal displays support information and additional explanations to help them relax. The input is the result of the emotional state assessment, and the output is the support information presented. 【0425】 (Application Example 2) 【0426】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0427】 During contract negotiations and drafting, users may feel uneasy about the contract's contents, highlighting the need for support that provides understanding and reassurance. However, conventional systems are limited to contract analysis and drafting, lacking flexible support tailored to the user's emotional state. Therefore, a method is needed to reduce anxiety and misunderstandings during the contract process and realize a more efficient and humane contract experience. 【0428】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0429】 In this invention, the server includes means for inputting contract information, means for analyzing the input contract information and converting it into text data, means for identifying legal risks based on the analyzed text data, means for generating proposed revisions based on the identified risks, means for creating a revised version of the contract using the generated proposed revisions, means for transmitting the revised version to a terminal capable of displaying it, means for identifying the user's emotional state in real time, and means for adjusting suggestions based on the emotional identification and providing information that meets the user's needs. This enables a detailed response that responds to the user's emotions during the contract analysis and revision process, providing support that helps them feel secure and understand the document. 【0430】 "Means for inputting contract information" refers to the method by which users provide contracts to the system and receive them in digital format. 【0431】 "Means of analyzing input contract information and converting it into text data" refers to a method of converting contract data in a certain format into editable text data using OCR or other technologies. 【0432】 "Methods for identifying legal risks based on analyzed text data" refers to the process of analyzing text data within a contract to identify legal issues and risk factors. 【0433】 "Means for generating revised proposals based on identified risks" refers to methods for creating revised proposals using generative AI models or the like to address identified risks. 【0434】 "Methods for creating a revised version of the contract using the generated revisions" refers to the process of creating a new version of the contract based on the proposed revisions. 【0435】 "Means of sending the revised version to a device capable of displaying it" refers to a method of transferring the revised version of the created contract to a device that can display and verify it. 【0436】 "Means for identifying a user's emotional state in real time" refers to technologies that use sensor devices such as cameras and microphones to determine a user's emotions from their facial expressions and tone of voice. 【0437】 "Means of tailoring suggestions based on sentiment identification and providing information that meets user needs" refers to the process of analyzing user sentiment data and providing users with individually customized information and suggestions based on the results. 【0438】 In order to implement this invention, a system combining various technical elements is required. A specific embodiment is shown below. 【0439】 The server is equipped with an input mechanism to receive contract information from users, which is typically provided in PDF or Word format. The entered contract information is converted into text data using OCR technology. Examples of OCR software used include Tesseract. This text data is analyzed within the server using an NLP engine (e.g., spaCy). Through this analysis, the server gains a detailed understanding of the contract and identifies legal risks. 【0440】 Once legal risks are identified, the server references past case law information from its database and generates proposed revisions using a generative AI model (e.g., OpenAI GPT). After verifying that the revisions maintain the consistency and legal validity of the contract, they are created as a revised version and sent to the user's accessible device. 【0441】 Furthermore, this system can analyze the user's emotional state in real time. When a user views a contract, the system analyzes the user's facial expressions and tone of voice through the terminal's camera and microphone, and identifies their emotional state using the Azure Face API and other tools. Based on this information, the system can display information to support understanding and alternative suggestions that are appropriate to the user's emotions. 【0442】 A concrete example is providing users who are concerned about payment terms when a family purchases a new home with detailed information about alternatives and payment plans. This can alleviate user anxiety and promote understanding of the contract. 【0443】 An example of a prompt to input into the generating AI model is, "Create a proposal to alleviate the user's concerns regarding the apartment purchase contract." Through this prompt, the necessary support can be provided to the user quickly and accurately. 【0444】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0445】 Step 1: 【0446】 The user uploads the contract from their device. The input is a contract file in PDF or Word format. The server receives this contract information and prepares it for processing. 【0447】 Step 2: 【0448】 The server uses OCR technology to convert the entered contract into text data. Specifically, it uses Tesseract to extract text from PDF or Word documents and converts it into structured digital data. The output is then passed on to the next step as text data. 【0449】 Step 3: 【0450】 The server uses an NLP engine to analyze text data. It uses spaCy as the NLP engine to analyze contract clauses, understand the context, and identify legal risk elements. During this process, it references past case law information from a database. The output is data that identifies the risk elements. 【0451】 Step 4: 【0452】 The server generates proposed solutions using a generative AI model based on the identified risk elements. It uses tools such as OpenAI GPT to create solutions tailored to the identified risks. The input is the identified risk elements, and the output is the text of the proposed solutions. 【0453】 Step 5: 【0454】 The server uses the generated revisions to create a revised version of the contract. The revisions are integrated into the existing contract structure, and consistency and legal validity are verified. The output is the revised contract, ready to be sent to the user. 【0455】 Step 6: 【0456】 The system uses the device's camera and microphone to analyze the user's emotional state in real time. It leverages the Azure Face API to analyze facial expressions and voice tone, identifying emotions. Input consists of real-time video and audio data. 【0457】 Step 7: 【0458】 Based on the results of emotion recognition, the server provides the user with tailored information. For example, if the user shows anxiety, it presents details of the contract and alternatives to deepen the user's understanding. It also displays information to promote calmness. The output consists of customized suggestions and information. 【0459】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0460】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0461】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0462】 [Third Embodiment] 【0463】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0464】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0465】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0466】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0467】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0468】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0469】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0470】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0471】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0472】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0473】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0474】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0475】 This system provides a software platform that uses AI technology to automatically analyze and revise contracts. First, the user uploads contract information from their device to the system. Contract information is typically provided in PDF or Word format. The server receives this information and converts it into text data using Optical Character Recognition (OCR) technology. This makes the contract content available for processing in digital format. 【0476】 Next, the server uses natural language processing (NLP) technology to analyze the text data and structure each contract clause. Based on the analyzed data, the server identifies legal risks within the contract. To identify legal risks, the server refers to past case law information stored in the database and compares it with the contract clauses. 【0477】 Once risks are identified, the server uses a generative AI model to generate proposed revisions. These revisions aim to mitigate the identified risks and provide clauses that are favorable to the contract. The generated revisions may undergo supplementary checks to ensure consistency and legal validity of the entire contract. 【0478】 The server then integrates the proposed revisions and creates a revised version of the contract. This revised version is sent to a displayable terminal so that the user can review and make final adjustments. The user can visually review the revised contract on the terminal and make any necessary adjustments. 【0479】 As a concrete example, if a user is trying to conclude a product sales contract with a new business partner, the user uploads a draft contract to the system. The server determines that the clause regarding "penalties for delayed delivery" is ambiguous. The AI ​​generation model outputs a clear revised version stating, "If delivery is delayed by more than 30 days, a penalty of 10% of the delivery amount will be paid." After the user reviews this revised version, they can proceed with negotiations with the business partner more smoothly and achieve a quick contract conclusion. 【0480】 The following describes the processing flow. 【0481】 Step 1: 【0482】 Users upload contract drafts to the system from their devices. Contracts are usually provided in PDF or Word format. 【0483】 Step 2: 【0484】 The server receives the uploaded contract draft and converts it into text data using OCR technology. This makes the contract content available for processing in digital format. 【0485】 Step 3: 【0486】 The server uses NLP technology to analyze text data, extract contract clauses, and clarify their structure. Based on the analysis results, it identifies which parts correspond to which contract clauses. 【0487】 Step 4: 【0488】 The server compares the analyzed data with past case law information in the database to identify legal risks. It particularly focuses on identifying risks related to ambiguous clauses and unfavorable conditions. 【0489】 Step 5: 【0490】 The server uses a generative AI model to generate proposed amendments and favorable clauses for identified legal risks. These amendments aim to minimize legal risks. 【0491】 Step 6: 【0492】 The server creates a revised version of the contract based on the generated revision proposals. The revised version is then finalized by integrating the proposed changes into the original contract. 【0493】 Step 7: 【0494】 The server sends the completed revised contract to the user's terminal. 【0495】 Step 8: 【0496】 The user reviews the revised contract on their device and performs a final check to ensure the content is correct. If necessary, the user can make minor adjustments to the contract. 【0497】 Step 9: 【0498】 Users can share the revised contract with relevant parties after review and proceed with negotiations with business partners. This allows for a rapid contract signing process. 【0499】 (Example 1) 【0500】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0501】 The process of identifying and correcting legal risks, which requires specialized knowledge, during the drafting and review of contract documents is time-consuming and labor-intensive. Furthermore, human error can lead to serious legal problems. It is necessary to mitigate these challenges and streamline contract processing. 【0502】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0503】 In this invention, the server includes means for receiving contract document information, means for converting the received contract document information into character data using character recognition technology, and means for analyzing the converted character data using natural language processing technology and structuring the document items. This makes it possible to automatically identify the legal risks of the contract document and generate and present proposed revisions using generation AI technology. 【0504】 "Contract document information" refers to data in the form of documents that describe the details of a contract. 【0505】 "Character recognition technology" is a technology that converts text characters from images or scanned data into digital data. 【0506】 "Natural language processing technology" is a technique that uses computers to analyze human language and extract and structure its meaning. 【0507】 A "document item" refers to an individual unit of information that is divided into clauses or sections within a contract document. 【0508】 "Legal risks" refer to legal risks or potential problems associated with a contract. 【0509】 "Generative AI technology" refers to the process of generating new information and data using artificial intelligence. 【0510】 A "revised proposal" refers to changes or policies proposed to solve a specific issue or problem. 【0511】 A "revised version" refers to a new version of a document that has been modified from the original. 【0512】 "Information storage device" refers to a system or device used to store data and information. 【0513】 This invention is a system that automates the analysis and revision of contract documents using AI technology. The system mainly consists of a server, a user terminal, and various software and AI models installed on them. 【0514】 First, the user uploads contract document information to the system using their own device. The contract documents are prepared in common file formats such as PDF and Word. The user's device then sends this to the server via a simple user interface. 【0515】 Upon receiving the uploaded contract document, the server uses optical character recognition (OCR) technology to convert the document content into text data. Existing technologies such as "Tesseract" can be used for this OCR process. The converted text data is promptly stored in the database. 【0516】 Next, the server applies natural language processing (NLP) techniques to the stored text data to identify and structure each clause of the document. Here, NLP libraries such as "spaCy" are used to perform grammatical analysis and semantic extraction. 【0517】 Based on these analysis results, the server identifies potential legal risks within the contract. To identify these risks, it compares them with past case precedents and references information storage devices to provide useful insights. 【0518】 If a risk is detected, the server uses a generative AI model to generate proposed corrections for the relevant section. In this process, predefined prompts are input to the AI ​​to obtain the necessary suggestions. For example, the prompt "Please provide specific proposals for penalty clauses for delivery delays" is used. 【0519】 Upon receiving the output from the AI, the server creates a revised version of the document and performs checks to ensure consistency and legal validity. The completed revised version is sent to the user's terminal for visual review and final adjustments. The user can perform final checks via the interface on their terminal and make minor corrections as needed. 【0520】 In this way, this system significantly reduces the time and expertise required for contract review, enabling efficient contract processing. 【0521】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0522】 Step 1: 【0523】 Users upload contract document information to the system from their own devices. PDF and Word files are accepted as input. This upload process is completed when the user selects the desired file through the device's interface and presses the send button. The output is the server receiving the file. 【0524】 Step 2: 【0525】 The server converts received contract documents into text data using optical character recognition (OCR) technology. The input includes contract documents, and OCR software (e.g., Tesseract) is used to recognize the characters and generate digital text. The output at this stage is the converted text data. 【0526】 Step 3: 【0527】 The server applies natural language processing (NLP) techniques to the converted text data to analyze and structure each clause of the contract. The input is text data generated by OCR, and the data is structured by performing part-of-speech and semantic analysis using an NLP library (e.g., spaCy). The output obtained from this analysis is structured contract clause data. 【0528】 Step 4: 【0529】 The server identifies legal risks based on structured data. The input is parsed clause data. The server compares this data with case law information stored in its data storage device to identify high-risk items. The output is a data list of the risk elements. 【0530】 Step 5: 【0531】 The server uses a generative AI model to generate corrective action proposals for risks. The input is a list of risk elements, and the prompt "Please provide corrective action proposals for this risk" is passed to the AI ​​model. The corrective action proposals generated by the AI ​​are the output. 【0532】 Step 6: 【0533】 The server aggregates the generated revision proposals and creates a revised version of the contract document. The input uses revision proposal data from AI. The server checks the consistency and legal validity of the revisions, ensuring their coherence. The output is the completed revised contract document. 【0534】 Step 7: 【0535】 The server sends the completed revised version to the user's terminal. The user is provided with input methods to review the revised version, allowing them to make final adjustments and confirmations via the interface. The output is the revised contract document actively displayed on the terminal. 【0536】 (Application Example 1) 【0537】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0538】 The present invention aims to provide a system that efficiently analyzes the contents of contract documents and quickly and accurately identifies legal risks. Current contract management processes involve the time-consuming and error-prone process of manually reviewing complex clauses and identifying risks. Furthermore, it is necessary to present analysis results visually so that users can immediately understand the risks and make informed decisions. However, existing solutions do not adequately meet this requirement. 【0539】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0540】 In this invention, the server includes a device for inputting contract information, a device for analyzing the input contract information and converting it into text data, a device for identifying legal risks based on the analyzed text data, a device for creating a revised version of the contract using the generated revision suggestions, a device for transmitting the revised version to a display device capable of displaying it, a device for visually presenting important clauses and risks in the contract information, and a device for structuring and analyzing the contract information using natural language processing technology. This enables the rapid identification of legal risks in the contract process and the automatic generation of revision suggestions, allowing users to easily understand contract clauses and manage risks. 【0541】 "Contract information" refers to various document data, including legal documents such as contracts and terms of service. 【0542】 "Device" refers to the entire set of hardware or software components designed to perform a specific function. 【0543】 "Input" refers to the act of a user supplying data or information to a system, or the result thereof. 【0544】 "Analysis" is the process of examining input data in detail and deriving specific structures or meanings. 【0545】 "Text data" refers to text extracted from documents in various formats, in a state that can be processed by a computer. 【0546】 "Legal risk" refers to factors that may violate contracts or laws and are likely to lead to problems in the future. 【0547】 "Identification" means identifying and clarifying the necessary information or elements from the given data. 【0548】 A "proposal for correction" is a suggested change to mitigate or eliminate identified risks. 【0549】 A "revised version" refers to a newly created document that incorporates proposed revisions to the original contract document. 【0550】 A "display device" refers to equipment or software that displays text data or images on a screen, allowing users to visually confirm them. 【0551】 "Visual presentation" refers to showing information in an easily understandable and visually appealing way using formats such as text and graphs. 【0552】 "Natural language processing technology" is a technology that enables computers to understand and process the language that humans use on a daily basis. 【0553】 "Structuring" refers to organizing data according to certain rules and preparing it in a format that is easy to handle. 【0554】 In this embodiment of the invention, the server first receives contract data from the user through a device for receiving contract information. The received contract information is usually in PDF or Word format, and the server converts it into text data using optical character recognition (OCR) technology. This conversion process uses the open-source OCR software "Tesseract" to extract text from image data. 【0555】 Next, the server uses natural language processing techniques to analyze the text data and structure the contract information. This process utilizes Hugging Face's "transformers" library to identify each clause within the contract. 【0556】 Furthermore, the server identifies legal risks based on the identified clauses. At this stage, it references accumulated past case law information from a database and compares it with the contract clauses. Based on the identified risks, the server uses a generative AI model to generate proposed revisions. These generated proposed revisions may undergo supplementary checks to ensure the overall consistency and legal validity of the contract. 【0557】 Once the revised contract information is complete, the server transmits it to the user's display device. On the user's terminal, important clauses and risks are visually presented, allowing the user to review the revisions and make adjustments as needed. A concrete example is detecting ambiguous parts of the privacy clause in a new electronic payment service contract and providing clear revision suggestions. Based on these revision suggestions, the user can use the service with confidence. 【0558】 An example of a prompt would be, "Analyze the privacy clause in the contract, identify the risks, and provide proposed revisions." This prompt allows the generating AI model to properly process the contract information and make necessary improvements. 【0559】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0560】 Step 1: 【0561】 The user uploads contract information to the server. The input is contract data in PDF or Word format, which the server receives. The received file is converted from image to text data using optical character recognition (OCR) technology. Through this conversion, the contract information is output as computer-processable text data. 【0562】 Step 2: 【0563】 The server analyzes the text data using natural language processing techniques. Through a process of structuring the input text data, each contract clause is identified. Hugging Face's "transformers" library is used for this analysis, and the output identifies key sections within the contract information. 【0564】 Step 3: 【0565】 The server identifies legal risks based on structured data. It references past case law information from the database and compares it with contractual clauses. The input is parsed structured data, which is used to identify legal risks and generate risk information as output. This risk information includes potential legal issues. 【0566】 Step 4: 【0567】 The server generates proposed modifications using a generative AI model. The input is identified risk information, and the generative AI model outputs suggestions. The generated suggestions are proposed modifications to mitigate legal risks. The generative AI model is also prompted with the message, "Analyze the privacy clauses in the contract, clarify the risks, and provide proposed modifications." 【0568】 Step 5: 【0569】 The server integrates the proposed revisions into the contract information and creates a revised version. The input consists of the original contract data and the generated proposed revisions; these are integrated to output the revised contract information. This revised version is designed to mitigate legal risks and ensure consistency and validity. 【0570】 Step 6: 【0571】 The server sends the revised contract information to the user's terminal. On the user's terminal, important clauses and revisions are visually presented. The input is the revised contract information, and by visualizing it, the user can review the risks and revisions as output and make final adjustments based on that. 【0572】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0573】 This invention is a system that uses AI technology to analyze and revise contracts, while simultaneously recognizing the user's emotions using an emotion engine, thereby supporting the contract creation and negotiation process. First, the user uploads a contract draft to the system from their terminal. Contracts are typically provided in PDF or Word format. After receiving this information, the server converts it into text data using OCR technology. This makes the contract content digitally processable within the system. 【0574】 The server uses NLP technology to analyze text data and structure the contract clauses. Based on the analysis results, a risk identification module is activated to identify legal risk elements. In this process, past case law information stored in a database is referenced. Once legal risks are identified, the server uses a generative AI model to generate proposed revisions and create a revised version of the contract. These generated revisions are then verified for consistency and legal validity of the entire contract before being sent to the user's terminal as the final revised version. 【0575】 Furthermore, a key feature of this invention is that the emotion engine provides the ability to recognize the user's emotional state in real time. While the user is reviewing the contract, the emotion engine analyzes the user's facial expressions and tone of voice through the camera and microphone to determine the user's emotions. If the user shows anxiety or dissatisfaction, the system can display supportive information to help them relax through the interface. In addition, the user's emotional data is recorded and can be used as reference information during future contract negotiations. 【0576】 For example, when a user uses the system to conclude a contract with a new business partner, they may have concerns about the contract period or payment terms. The emotion engine detects this concern, and the server explains the terms in language that the user can easily understand, and offers alternatives if necessary. This allows the user to feel at ease and effectively negotiate the contract terms. This system makes the contract creation and negotiation process more humane and efficient. 【0577】 The following describes the processing flow. 【0578】 Step 1: 【0579】 Users upload contract drafts to the system from their devices. Contracts can be in common formats such as PDF or Word. 【0580】 Step 2: 【0581】 The server receives the uploaded contract information and converts it into text data using OCR technology. This makes the contents of the contract available for processing in text format. 【0582】 Step 3: 【0583】 The server analyzes the converted text data using natural language processing (NLP) technology to classify and extract the structure and content of the contract clauses. 【0584】 Step 4: 【0585】 The server compares the analyzed clauses with past case law information in the database to identify legal risk factors. This is done by a risk identification module using a specific algorithm. 【0586】 Step 5: 【0587】 The server uses a generation AI model based on identified legal risks to generate proposed clause revisions. This results in a revised version with clauses that are favorable to the user. 【0588】 Step 6: 【0589】 The server sends the revised contract to the user's terminal. This contract integrates the generated revisions and ensures contractual consistency and legal validity. 【0590】 Step 7: 【0591】 The user's device will display the submitted revised agreement, allowing the user to review it. The user can review the revision and make any necessary adjustments directly. 【0592】 Step 8: 【0593】 While the user is reviewing the contract, an emotion engine activates, analyzing the user's emotions from their facial expressions and voice. If the user shows signs of anxiety or doubt, the system detects it. 【0594】 Step 9: 【0595】 When a user expresses anxiety, the device displays information and support to help them relax. Appropriate assistance is provided based on data from the emotion engine. 【0596】 Step 10: 【0597】 User sentiment data is recorded for use in future contract negotiations. This data may be used to streamline and improve contracts as needed. 【0598】 (Example 2) 【0599】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0600】 Traditional contract drafting processes lack systems that accurately analyze contract content, identify legal risks, and support contract negotiations while considering the user's feelings. As a result, it is difficult to prevent legal problems before they occur, and users often experience anxiety and dissatisfaction. This invention aims to achieve more efficient, safe, and user-friendly contract drafting and negotiation by automating contract analysis and revision suggestions, as well as providing support that takes the user's emotional state into consideration. 【0601】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0602】 In this invention, the server includes a device for receiving contract information, a device for converting the received contract information into character data using optical character recognition technology, and a device for analyzing the converted character data using natural language processing technology and generating structured data. This enables automatic analysis of contracts and risk identification, reduces legal risks by generating revision suggestions using generative AI technology, and allows users to conduct contract negotiations with confidence by providing appropriate support information based on their emotional state. 【0603】 "Contract information" refers to data, including legal contracts and related documents, that are stored or transmitted in digital format. 【0604】 "Optical character recognition technology" is a technology that converts printed or handwritten characters into digital text from image data. 【0605】 "Character data" refers to text information represented in digital format to enable natural language processing and data analysis. 【0606】 "Natural language processing technology" refers to algorithms and techniques for computers to understand and analyze human language. 【0607】 "Structured data" refers to data that is organized in databases or tabular formats and arranged in a way that allows for efficient searching and analysis. 【0608】 "Legal risk" is a concept that refers to the legal risks and problems inherent in the content of a contract. 【0609】 "Generative AI technology" refers to technology that uses artificial intelligence to generate new information and proposals. 【0610】 A "proposal for amendment" is a proposed revision of a contract document generated to reduce or resolve identified legal risks. 【0611】 A "revised version" is a new version of a contract that has been modified or improved from the original. 【0612】 "Information equipment" is a general term referring to electronic devices capable of creating, editing, displaying, or transmitting digital information. 【0613】 "Emotional state" refers to a state that shows the user's psychological reactions and emotional changes in real time. 【0614】 "Support information" refers to helpful guidance and notifications provided according to the user's emotions and circumstances. 【0615】 A "data storage device" refers to equipment or systems for storing information and data, either physically or digitally. 【0616】 "Case law knowledge" refers to information that compiles past legal judgments and the criteria used by courts to make decisions. 【0617】 This system performs contract analysis and provides emotional support to users, and is implemented as follows: 【0618】 First, the user uploads contract information using their device. This contract information is often provided in formats such as PDF or Word. The device is equipped with an interface for sending these files to the system. 【0619】 Next, the server converts the received contract information into text data using OCR technology. Tools such as the Tesseract OCR engine or Adobe Acrobat's OCR function are used for OCR. This conversion makes the contract information available for analysis in digital format. 【0620】 Next, the server uses natural language processing technology to analyze the text data and generate structured data from the clauses within the contract. Natural language processing libraries such as spaCy and NLTK are used for the analysis. 【0621】 Based on the analysis results, the server identifies legal risks and references past case law knowledge from the data storage device. This reference is to improve the accuracy of risk identification. 【0622】 Next, using a generative AI model, the server generates proposed modifications to address the identified legal risks. This involves prompting the generative AI model to suggest appropriate contractual modifications. An example of a prompt would be, "Please suggest what modifications would be appropriate to mitigate this risk." 【0623】 Furthermore, while the user is reviewing the contract, the server uses an emotion engine to detect the user's emotional state in real time. The emotional state is identified by analyzing the user's facial expressions and voice captured through the camera and microphone. If emotions such as anxiety or dissatisfaction are detected, the terminal displays supportive information to help the user relax. Specifically, it reduces user stress by presenting supplementary explanations and options to provide a sense of security. 【0624】 In this way, this system not only automatically analyzes contracts and suggests revisions, but also enables the creation of contracts and negotiations that take into account the user's feelings. 【0625】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0626】 Step 1: 【0627】 The user uploads contract information using a terminal. The input is a contract file in PDF or Word format. The terminal that receives this file relays the data via an interface for sending the file to the server. The output is the raw data received by the server. 【0628】 Step 2: 【0629】 The server converts received contract information into text data using OCR technology. Specifically, OCR software analyzes the characters in the image and converts them into text data. The input is a raw data file, and the output is readable text data. 【0630】 Step 3: 【0631】 The server analyzes the text data using natural language processing techniques. The analysis engine extracts contract clauses from the input text data and generates structured data. For example, it organizes related information using nouns as keys. The output is the structured data resulting from the analysis. 【0632】 Step 4: 【0633】 The server identifies legal risks based on structured data. This involves a process of referencing a database and cross-referencing it with past case law knowledge. The input is structured data, and the output is a list of identified risks. 【0634】 Step 5: 【0635】 The server generates suggested modifications using a generative AI model. In this process, prompts are input to the AI ​​model, and the suggested modifications are obtained as output. Specifically, prompts such as "Please suggest the best wording to mitigate a specific risk" are used. The output is a set of suggested modifications. 【0636】 Step 6: 【0637】 The server creates a revised version of the contract based on the generated revision proposals. The revised version takes shape by incorporating the proposed revisions into the contract. The inputs are the revision proposals and the original contract, and the output is the revised contract. 【0638】 Step 7: 【0639】 While the user reviews the contract, the server activates an emotion engine to read the user's emotional state. Based on data obtained through the camera and microphone, it performs real-time emotion analysis. The input is visual and audio data, and the output is an evaluation of the user's emotional state. 【0640】 Step 8: 【0641】 The server provides support information based on the user's emotional state. If the user expresses anxiety or dissatisfaction, the terminal displays support information and additional explanations to help them relax. The input is the result of the emotional state assessment, and the output is the support information presented. 【0642】 (Application Example 2) 【0643】 Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0644】 During contract negotiations and drafting, users may feel uneasy about the contract's contents, highlighting the need for support that provides understanding and reassurance. However, conventional systems are limited to contract analysis and drafting, lacking flexible support tailored to the user's emotional state. Therefore, a method is needed to reduce anxiety and misunderstandings during the contract process and realize a more efficient and humane contract experience. 【0645】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0646】 In this invention, the server includes means for inputting contract information, means for analyzing the input contract information and converting it into text data, means for identifying legal risks based on the analyzed text data, means for generating proposed revisions based on the identified risks, means for creating a revised version of the contract using the generated proposed revisions, means for transmitting the revised version to a terminal capable of displaying it, means for identifying the user's emotional state in real time, and means for adjusting suggestions based on the emotional identification and providing information that meets the user's needs. This enables a detailed response that responds to the user's emotions during the contract analysis and revision process, providing support that helps them feel secure and understand the document. 【0647】 "Means for inputting contract information" refers to the method by which users provide contracts to the system and receive them in digital format. 【0648】 "Means of analyzing input contract information and converting it into text data" refers to a method of converting contract data in a certain format into editable text data using OCR or other technologies. 【0649】 "Methods for identifying legal risks based on analyzed text data" refers to the process of analyzing text data within a contract to identify legal issues and risk factors. 【0650】 "Means for generating revised proposals based on identified risks" refers to methods for creating revised proposals using generative AI models or the like to address identified risks. 【0651】 "Methods for creating a revised version of the contract using the generated revisions" refers to the process of creating a new version of the contract based on the proposed revisions. 【0652】 "Means of sending the revised version to a device capable of displaying it" refers to a method of transferring the revised version of the created contract to a device that can display and verify it. 【0653】 "Means for identifying a user's emotional state in real time" refers to technologies that use sensor devices such as cameras and microphones to determine a user's emotions from their facial expressions and tone of voice. 【0654】 "Means of tailoring suggestions based on sentiment identification and providing information that meets user needs" refers to the process of analyzing user sentiment data and providing users with individually customized information and suggestions based on the results. 【0655】 In order to implement this invention, a system combining various technical elements is required. A specific embodiment is shown below. 【0656】 The server is equipped with an input mechanism to receive contract information from users, which is typically provided in PDF or Word format. The entered contract information is converted into text data using OCR technology. Examples of OCR software used include Tesseract. This text data is analyzed within the server using an NLP engine (e.g., spaCy). Through this analysis, the server gains a detailed understanding of the contract and identifies legal risks. 【0657】 Once legal risks are identified, the server references past case law information from its database and generates proposed revisions using a generative AI model (e.g., OpenAI GPT). After verifying that the revisions maintain the consistency and legal validity of the contract, they are created as a revised version and sent to the user's accessible device. 【0658】 Furthermore, this system can analyze the user's emotional state in real time. When a user views a contract, the system analyzes the user's facial expressions and tone of voice through the terminal's camera and microphone, and identifies their emotional state using the Azure Face API and other tools. Based on this information, the system can display information to support understanding and alternative suggestions that are appropriate to the user's emotions. 【0659】 A concrete example is providing users who are concerned about payment terms when a family purchases a new home with detailed information about alternatives and payment plans. This can alleviate user anxiety and promote understanding of the contract. 【0660】 An example of a prompt to input into the generating AI model is, "Create a proposal to alleviate the user's concerns regarding the apartment purchase contract." Through this prompt, the necessary support can be provided to the user quickly and accurately. 【0661】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0662】 Step 1: 【0663】 The user uploads the contract from their device. The input is a contract file in PDF or Word format. The server receives this contract information and prepares it for processing. 【0664】 Step 2: 【0665】 The server uses OCR technology to convert the entered contract into text data. Specifically, it uses Tesseract to extract text from PDF or Word documents and converts it into structured digital data. The output is then passed on to the next step as text data. 【0666】 Step 3: 【0667】 The server uses an NLP engine to analyze text data. It uses spaCy as the NLP engine to analyze contract clauses, understand the context, and identify legal risk elements. During this process, it references past case law information from a database. The output is data that identifies the risk elements. 【0668】 Step 4: 【0669】 The server generates proposed solutions using a generative AI model based on the identified risk elements. It uses tools such as OpenAI GPT to create solutions tailored to the identified risks. The input is the identified risk elements, and the output is the text of the proposed solutions. 【0670】 Step 5: 【0671】 The server uses the generated revisions to create a revised version of the contract. The revisions are integrated into the existing contract structure, and consistency and legal validity are verified. The output is the revised contract, ready to be sent to the user. 【0672】 Step 6: 【0673】 The system uses the device's camera and microphone to analyze the user's emotional state in real time. It leverages the Azure Face API to analyze facial expressions and voice tone, identifying emotions. Input consists of real-time video and audio data. 【0674】 Step 7: 【0675】 Based on the results of emotion recognition, the server provides the user with tailored information. For example, if the user shows anxiety, it presents details of the contract and alternatives to deepen the user's understanding. It also displays information to promote calmness. The output consists of customized suggestions and information. 【0676】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0677】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0678】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0679】 [Fourth Embodiment] 【0680】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0681】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0682】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0683】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0684】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0685】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0686】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0687】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0688】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0689】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0690】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0691】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0692】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0693】 This system provides a software platform that uses AI technology to automatically analyze and revise contracts. First, the user uploads contract information from their device to the system. Contract information is typically provided in PDF or Word format. The server receives this information and converts it into text data using Optical Character Recognition (OCR) technology. This makes the contract content available for processing in digital format. 【0694】 Next, the server uses natural language processing (NLP) technology to analyze the text data and structure each contract clause. Based on the analyzed data, the server identifies legal risks within the contract. To identify legal risks, the server refers to past case law information stored in the database and compares it with the contract clauses. 【0695】 Once risks are identified, the server uses a generative AI model to generate proposed revisions. These revisions aim to mitigate the identified risks and provide clauses that are favorable to the contract. The generated revisions may undergo supplementary checks to ensure consistency and legal validity of the entire contract. 【0696】 The server then integrates the proposed revisions and creates a revised version of the contract. This revised version is sent to a displayable terminal so that the user can review and make final adjustments. The user can visually review the revised contract on the terminal and make any necessary adjustments. 【0697】 As a concrete example, if a user is trying to conclude a product sales contract with a new business partner, the user uploads a draft contract to the system. The server determines that the clause regarding "penalties for delayed delivery" is ambiguous. The AI ​​generation model outputs a clear revised version stating, "If delivery is delayed by more than 30 days, a penalty of 10% of the delivery amount will be paid." After the user reviews this revised version, they can proceed with negotiations with the business partner more smoothly and achieve a quick contract conclusion. 【0698】 The following describes the processing flow. 【0699】 Step 1: 【0700】 Users upload contract drafts to the system from their devices. Contracts are usually provided in PDF or Word format. 【0701】 Step 2: 【0702】 The server receives the uploaded contract draft and converts it into text data using OCR technology. This makes the contract content available for processing in digital format. 【0703】 Step 3: 【0704】 The server uses NLP technology to analyze text data, extract contract clauses, and clarify their structure. Based on the analysis results, it identifies which parts correspond to which contract clauses. 【0705】 Step 4: 【0706】 The server compares the analyzed data with past case law information in the database to identify legal risks. It particularly focuses on identifying risks related to ambiguous clauses and unfavorable conditions. 【0707】 Step 5: 【0708】 The server uses a generative AI model to generate proposed amendments and favorable clauses for identified legal risks. These amendments aim to minimize legal risks. 【0709】 Step 6: 【0710】 The server creates a revised version of the contract based on the generated revision proposals. The revised version is then finalized by integrating the proposed changes into the original contract. 【0711】 Step 7: 【0712】 The server sends the completed revised contract to the user's terminal. 【0713】 Step 8: 【0714】 The user reviews the revised contract on their device and performs a final check to ensure the content is correct. If necessary, the user can make minor adjustments to the contract. 【0715】 Step 9: 【0716】 Users can share the revised contract with relevant parties after review and proceed with negotiations with business partners. This allows for a rapid contract signing process. 【0717】 (Example 1) 【0718】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0719】 The process of identifying and correcting legal risks, which requires specialized knowledge, during the drafting and review of contract documents is time-consuming and labor-intensive. Furthermore, human error can lead to serious legal problems. It is necessary to mitigate these challenges and streamline contract processing. 【0720】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0721】 In this invention, the server includes means for receiving contract document information, means for converting the received contract document information into character data using character recognition technology, and means for analyzing the converted character data using natural language processing technology and structuring the document items. This makes it possible to automatically identify the legal risks of the contract document and generate and present proposed revisions using generation AI technology. 【0722】 "Contract document information" refers to data in the form of documents that describe the details of a contract. 【0723】 "Character recognition technology" is a technology that converts text characters from images or scanned data into digital data. 【0724】 "Natural language processing technology" is a technique that uses computers to analyze human language and extract and structure its meaning. 【0725】 A "document item" refers to an individual unit of information that is divided into clauses or sections within a contract document. 【0726】 "Legal risks" refer to legal risks or potential problems associated with a contract. 【0727】 "Generative AI technology" refers to the process of generating new information and data using artificial intelligence. 【0728】 A "revised proposal" refers to changes or policies proposed to solve a specific issue or problem. 【0729】 A "revised version" refers to a new version of a document that has been modified from the original. 【0730】 "Information storage device" refers to a system or device used to store data and information. 【0731】 This invention is a system that automates the analysis and revision of contract documents using AI technology. The system mainly consists of a server, a user terminal, and various software and AI models installed on them. 【0732】 First, the user uploads contract document information to the system using their own device. The contract documents are prepared in common file formats such as PDF and Word. The user's device then sends this to the server via a simple user interface. 【0733】 Upon receiving the uploaded contract document, the server uses optical character recognition (OCR) technology to convert the document content into text data. Existing technologies such as "Tesseract" can be used for this OCR process. The converted text data is promptly stored in the database. 【0734】 Next, the server applies natural language processing (NLP) techniques to the stored text data to identify and structure each clause of the document. Here, NLP libraries such as "spaCy" are used to perform grammatical analysis and semantic extraction. 【0735】 Based on these analysis results, the server identifies potential legal risks within the contract. To identify these risks, it compares them with past case precedents and references information storage devices to provide useful insights. 【0736】 If a risk is detected, the server uses a generative AI model to generate proposed corrections for the relevant section. In this process, predefined prompts are input to the AI ​​to obtain the necessary suggestions. For example, the prompt "Please provide specific proposals for penalty clauses for delivery delays" is used. 【0737】 Upon receiving the output from the AI, the server creates a revised version of the document and performs checks to ensure consistency and legal validity. The completed revised version is sent to the user's terminal for visual review and final adjustments. The user can perform final checks via the interface on their terminal and make minor corrections as needed. 【0738】 In this way, this system significantly reduces the time and expertise required for contract review, enabling efficient contract processing. 【0739】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0740】 Step 1: 【0741】 Users upload contract document information to the system from their own devices. PDF and Word files are accepted as input. This upload process is completed when the user selects the desired file through the device's interface and presses the send button. The output is the server receiving the file. 【0742】 Step 2: 【0743】 The server converts received contract documents into text data using optical character recognition (OCR) technology. The input includes contract documents, and OCR software (e.g., Tesseract) is used to recognize the characters and generate digital text. The output at this stage is the converted text data. 【0744】 Step 3: 【0745】 The server applies natural language processing (NLP) techniques to the converted text data to analyze and structure each clause of the contract. The input is text data generated by OCR, and the data is structured by performing part-of-speech and semantic analysis using an NLP library (e.g., spaCy). The output obtained from this analysis is structured contract clause data. 【0746】 Step 4: 【0747】 The server identifies legal risks based on structured data. The input is parsed clause data. The server compares this data with case law information stored in its data storage device to identify high-risk items. The output is a data list of the risk elements. 【0748】 Step 5: 【0749】 The server uses a generative AI model to generate corrective action proposals for risks. The input is a list of risk elements, and the prompt "Please provide corrective action proposals for this risk" is passed to the AI ​​model. The corrective action proposals generated by the AI ​​are the output. 【0750】 Step 6: 【0751】 The server aggregates the generated revision proposals and creates a revised version of the contract document. The input uses revision proposal data from AI. The server checks the consistency and legal validity of the revisions, ensuring their coherence. The output is the completed revised contract document. 【0752】 Step 7: 【0753】 The server sends the completed revised version to the user's terminal. The user is provided with input methods to review the revised version, allowing them to make final adjustments and confirmations via the interface. The output is the revised contract document actively displayed on the terminal. 【0754】 (Application Example 1) 【0755】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0756】 The present invention aims to provide a system that efficiently analyzes the contents of contract documents and quickly and accurately identifies legal risks. Current contract management processes involve the time-consuming and error-prone process of manually reviewing complex clauses and identifying risks. Furthermore, it is necessary to present analysis results visually so that users can immediately understand the risks and make informed decisions. However, existing solutions do not adequately meet this requirement. 【0757】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0758】 In this invention, the server includes a device for inputting contract information, a device for analyzing the input contract information and converting it into text data, a device for identifying legal risks based on the analyzed text data, a device for creating a revised version of the contract using the generated revision suggestions, a device for transmitting the revised version to a display device capable of displaying it, a device for visually presenting important clauses and risks in the contract information, and a device for structuring and analyzing the contract information using natural language processing technology. This enables the rapid identification of legal risks in the contract process and the automatic generation of revision suggestions, allowing users to easily understand contract clauses and manage risks. 【0759】 "Contract information" refers to various document data, including legal documents such as contracts and terms of service. 【0760】 "Device" refers to the entire set of hardware or software components designed to perform a specific function. 【0761】 "Input" refers to the act of a user supplying data or information to a system, or the result thereof. 【0762】 "Analysis" is the process of examining input data in detail and deriving specific structures or meanings. 【0763】 "Text data" refers to text extracted from documents in various formats, in a state that can be processed by a computer. 【0764】 "Legal risk" refers to factors that may violate contracts or laws and are likely to lead to problems in the future. 【0765】 "Identification" means identifying and clarifying the necessary information or elements from the given data. 【0766】 A "proposal for correction" is a suggested change to mitigate or eliminate identified risks. 【0767】 A "revised version" refers to a newly created document that incorporates proposed revisions to the original contract document. 【0768】 A "display device" refers to equipment or software that displays text data or images on a screen, allowing users to visually confirm them. 【0769】 "Visual presentation" refers to showing information in an easily understandable and visually appealing way using formats such as text and graphs. 【0770】 "Natural language processing technology" is a technology that enables computers to understand and process the language that humans use on a daily basis. 【0771】 "Structuring" refers to organizing data according to certain rules and preparing it in a format that is easy to handle. 【0772】 In this embodiment of the invention, the server first receives contract data from the user through a device for receiving contract information. The received contract information is usually in PDF or Word format, and the server converts it into text data using optical character recognition (OCR) technology. This conversion process uses the open-source OCR software "Tesseract" to extract text from image data. 【0773】 Next, the server uses natural language processing techniques to analyze the text data and structure the contract information. This process utilizes Hugging Face's "transformers" library to identify each clause within the contract. 【0774】 Furthermore, the server identifies legal risks based on the identified clauses. At this stage, it references accumulated past case law information from a database and compares it with the contract clauses. Based on the identified risks, the server uses a generative AI model to generate proposed revisions. These generated proposed revisions may undergo supplementary checks to ensure the overall consistency and legal validity of the contract. 【0775】 Once the revised contract information is complete, the server transmits it to the user's display device. On the user's terminal, important clauses and risks are visually presented, allowing the user to review the revisions and make adjustments as needed. A concrete example is detecting ambiguous parts of the privacy clause in a new electronic payment service contract and providing clear revision suggestions. Based on these revision suggestions, the user can use the service with confidence. 【0776】 An example of a prompt would be, "Analyze the privacy clause in the contract, identify the risks, and provide proposed revisions." This prompt allows the generating AI model to properly process the contract information and make necessary improvements. 【0777】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0778】 Step 1: 【0779】 The user uploads contract information to the server. The input is contract data in PDF or Word format, which the server receives. The received file is converted from image to text data using optical character recognition (OCR) technology. Through this conversion, the contract information is output as computer-processable text data. 【0780】 Step 2: 【0781】 The server analyzes the text data using natural language processing techniques. Through a process of structuring the input text data, each contract clause is identified. Hugging Face's "transformers" library is used for this analysis, and the output identifies key sections within the contract information. 【0782】 Step 3: 【0783】 The server identifies legal risks based on structured data. It references past case law information from the database and compares it with contractual clauses. The input is parsed structured data, which is used to identify legal risks and generate risk information as output. This risk information includes potential legal issues. 【0784】 Step 4: 【0785】 The server generates proposed modifications using a generative AI model. The input is identified risk information, and the generative AI model outputs suggestions. The generated suggestions are proposed modifications to mitigate legal risks. The generative AI model is also prompted with the message, "Analyze the privacy clauses in the contract, clarify the risks, and provide proposed modifications." 【0786】 Step 5: 【0787】 The server integrates the proposed revisions into the contract information and creates a revised version. The input consists of the original contract data and the generated proposed revisions; these are integrated to output the revised contract information. This revised version is designed to mitigate legal risks and ensure consistency and validity. 【0788】 Step 6: 【0789】 The server sends the revised contract information to the user's terminal. On the user's terminal, important clauses and revisions are visually presented. The input is the revised contract information, and by visualizing it, the user can review the risks and revisions as output and make final adjustments based on that. 【0790】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0791】 This invention is a system that uses AI technology to analyze and revise contracts, while simultaneously recognizing the user's emotions using an emotion engine, thereby supporting the contract creation and negotiation process. First, the user uploads a contract draft to the system from their terminal. Contracts are typically provided in PDF or Word format. After receiving this information, the server converts it into text data using OCR technology. This makes the contract content digitally processable within the system. 【0792】 The server uses NLP technology to analyze text data and structure the contract clauses. Based on the analysis results, a risk identification module is activated to identify legal risk elements. In this process, past case law information stored in a database is referenced. Once legal risks are identified, the server uses a generative AI model to generate proposed revisions and create a revised version of the contract. These generated revisions are then verified for consistency and legal validity of the entire contract before being sent to the user's terminal as the final revised version. 【0793】 Furthermore, a key feature of this invention is that the emotion engine provides the ability to recognize the user's emotional state in real time. While the user is reviewing the contract, the emotion engine analyzes the user's facial expressions and tone of voice through the camera and microphone to determine the user's emotions. If the user shows anxiety or dissatisfaction, the system can display supportive information to help them relax through the interface. In addition, the user's emotional data is recorded and can be used as reference information during future contract negotiations. 【0794】 For example, when a user uses the system to conclude a contract with a new business partner, they may have concerns about the contract period or payment terms. The emotion engine detects this concern, and the server explains the terms in language that the user can easily understand, and offers alternatives if necessary. This allows the user to feel at ease and effectively negotiate the contract terms. This system makes the contract creation and negotiation process more humane and efficient. 【0795】 The following describes the processing flow. 【0796】 Step 1: 【0797】 Users upload contract drafts to the system from their devices. Contracts can be in common formats such as PDF or Word. 【0798】 Step 2: 【0799】 The server receives the uploaded contract information and converts it into text data using OCR technology. This makes the contents of the contract available for processing in text format. 【0800】 Step 3: 【0801】 The server analyzes the converted text data using natural language processing (NLP) technology to classify and extract the structure and content of the contract clauses. 【0802】 Step 4: 【0803】 The server compares the analyzed clauses with past case law information in the database to identify legal risk factors. This is done by a risk identification module using a specific algorithm. 【0804】 Step 5: 【0805】 The server uses a generation AI model based on identified legal risks to generate proposed clause revisions. This results in a revised version with clauses that are favorable to the user. 【0806】 Step 6: 【0807】 The server sends the revised contract to the user's terminal. This contract integrates the generated revisions and ensures contractual consistency and legal validity. 【0808】 Step 7: 【0809】 The user's device will display the submitted revised agreement, allowing the user to review it. The user can review the revision and make any necessary adjustments directly. 【0810】 Step 8: 【0811】 While the user is reviewing the contract, an emotion engine activates, analyzing the user's emotions from their facial expressions and voice. If the user shows signs of anxiety or doubt, the system detects it. 【0812】 Step 9: 【0813】 When a user expresses anxiety, the device displays information and support to help them relax. Appropriate assistance is provided based on data from the emotion engine. 【0814】 Step 10: 【0815】 User sentiment data is recorded for use in future contract negotiations. This data may be used to streamline and improve contracts as needed. 【0816】 (Example 2) 【0817】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0818】 Traditional contract drafting processes lack systems that accurately analyze contract content, identify legal risks, and support contract negotiations while considering the user's feelings. As a result, it is difficult to prevent legal problems before they occur, and users often experience anxiety and dissatisfaction. This invention aims to achieve more efficient, safe, and user-friendly contract drafting and negotiation by automating contract analysis and revision suggestions, as well as providing support that takes the user's emotional state into consideration. 【0819】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0820】 In this invention, the server includes a device for receiving contract information, a device for converting the received contract information into character data using optical character recognition technology, and a device for analyzing the converted character data using natural language processing technology and generating structured data. This enables automatic analysis of contracts and risk identification, reduces legal risks by generating revision suggestions using generative AI technology, and allows users to conduct contract negotiations with confidence by providing appropriate support information based on their emotional state. 【0821】 "Contract information" refers to data, including legal contracts and related documents, that are stored or transmitted in digital format. 【0822】 "Optical character recognition technology" is a technology that converts printed or handwritten characters into digital text from image data. 【0823】 "Character data" refers to text information represented in digital format to enable natural language processing and data analysis. 【0824】 "Natural language processing technology" refers to algorithms and techniques for computers to understand and analyze human language. 【0825】 "Structured data" refers to data that is organized in databases or tabular formats and arranged in a way that allows for efficient searching and analysis. 【0826】 "Legal risk" is a concept that refers to the legal risks and problems inherent in the content of a contract. 【0827】 "Generative AI technology" refers to technology that uses artificial intelligence to generate new information and proposals. 【0828】 A "proposal for amendment" is a proposed revision of a contract document generated to reduce or resolve identified legal risks. 【0829】 A "revised version" is a new version of a contract that has been modified or improved from the original. 【0830】 "Information equipment" is a general term referring to electronic devices capable of creating, editing, displaying, or transmitting digital information. 【0831】 "Emotional state" refers to a state that shows the user's psychological reactions and emotional changes in real time. 【0832】 "Support information" refers to helpful guidance and notifications provided according to the user's emotions and circumstances. 【0833】 A "data storage device" refers to equipment or systems for storing information and data, either physically or digitally. 【0834】 "Case law knowledge" refers to information that compiles past legal judgments and the criteria used by courts to make decisions. 【0835】 This system performs contract analysis and provides emotional support to users, and is implemented as follows: 【0836】 First, the user uploads contract information using their device. This contract information is often provided in formats such as PDF or Word. The device is equipped with an interface for sending these files to the system. 【0837】 Next, the server converts the received contract information into text data using OCR technology. Tools such as the Tesseract OCR engine or Adobe Acrobat's OCR function are used for OCR. This conversion makes the contract information available for analysis in digital format. 【0838】 Next, the server uses natural language processing technology to analyze the text data and generate structured data from the clauses within the contract. Natural language processing libraries such as spaCy and NLTK are used for the analysis. 【0839】 Based on the analysis results, the server identifies legal risks and references past case law knowledge from the data storage device. This reference is to improve the accuracy of risk identification. 【0840】 Next, using a generative AI model, the server generates proposed modifications to address the identified legal risks. This involves prompting the generative AI model to suggest appropriate contractual modifications. An example of a prompt would be, "Please suggest what modifications would be appropriate to mitigate this risk." 【0841】 Furthermore, while the user is reviewing the contract, the server uses an emotion engine to detect the user's emotional state in real time. The emotional state is identified by analyzing the user's facial expressions and voice captured through the camera and microphone. If emotions such as anxiety or dissatisfaction are detected, the terminal displays supportive information to help the user relax. Specifically, it reduces user stress by presenting supplementary explanations and options to provide a sense of security. 【0842】 In this way, this system not only automatically analyzes contracts and suggests revisions, but also enables the creation of contracts and negotiations that take into account the user's feelings. 【0843】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0844】 Step 1: 【0845】 The user uploads contract information using a terminal. The input is a contract file in PDF or Word format. The terminal that receives this file relays the data via an interface for sending the file to the server. The output is the raw data received by the server. 【0846】 Step 2: 【0847】 The server converts received contract information into text data using OCR technology. Specifically, OCR software analyzes the characters in the image and converts them into text data. The input is a raw data file, and the output is readable text data. 【0848】 Step 3: 【0849】 The server analyzes the text data using natural language processing techniques. The analysis engine extracts contract clauses from the input text data and generates structured data. For example, it organizes related information using nouns as keys. The output is the structured data resulting from the analysis. 【0850】 Step 4: 【0851】 The server identifies legal risks based on structured data. This involves a process of referencing a database and cross-referencing it with past case law knowledge. The input is structured data, and the output is a list of identified risks. 【0852】 Step 5: 【0853】 The server generates suggested modifications using a generative AI model. In this process, prompts are input to the AI ​​model, and the suggested modifications are obtained as output. Specifically, prompts such as "Please suggest the best wording to mitigate a specific risk" are used. The output is a set of suggested modifications. 【0854】 Step 6: 【0855】 The server creates a revised version of the contract based on the generated revision proposals. The revised version takes shape by incorporating the proposed revisions into the contract. The inputs are the revision proposals and the original contract, and the output is the revised contract. 【0856】 Step 7: 【0857】 While the user reviews the contract, the server activates an emotion engine to read the user's emotional state. Based on data obtained through the camera and microphone, it performs real-time emotion analysis. The input is visual and audio data, and the output is an evaluation of the user's emotional state. 【0858】 Step 8: 【0859】 The server provides support information based on the user's emotional state. If the user expresses anxiety or dissatisfaction, the terminal displays support information and additional explanations to help them relax. The input is the result of the emotional state assessment, and the output is the support information presented. 【0860】 (Application Example 2) 【0861】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0862】 During contract negotiations and drafting, users may feel uneasy about the contract's contents, highlighting the need for support that provides understanding and reassurance. However, conventional systems are limited to contract analysis and drafting, lacking flexible support tailored to the user's emotional state. Therefore, a method is needed to reduce anxiety and misunderstandings during the contract process and realize a more efficient and humane contract experience. 【0863】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0864】 In this invention, the server includes means for inputting contract information, means for analyzing the input contract information and converting it into text data, means for identifying legal risks based on the analyzed text data, means for generating proposed revisions based on the identified risks, means for creating a revised version of the contract using the generated proposed revisions, means for transmitting the revised version to a terminal capable of displaying it, means for identifying the user's emotional state in real time, and means for adjusting suggestions based on the emotional identification and providing information that meets the user's needs. This enables a detailed response that responds to the user's emotions during the contract analysis and revision process, providing support that helps them feel secure and understand the document. 【0865】 "Means for inputting contract information" refers to the method by which users provide contracts to the system and receive them in digital format. 【0866】 "Means of analyzing input contract information and converting it into text data" refers to a method of converting contract data in a certain format into editable text data using OCR or other technologies. 【0867】 "Methods for identifying legal risks based on analyzed text data" refers to the process of analyzing text data within a contract to identify legal issues and risk factors. 【0868】 "Means for generating revised proposals based on identified risks" refers to methods for creating revised proposals using generative AI models or the like to address identified risks. 【0869】 "Methods for creating a revised version of the contract using the generated revisions" refers to the process of creating a new version of the contract based on the proposed revisions. 【0870】 "Means of sending the revised version to a device capable of displaying it" refers to a method of transferring the revised version of the created contract to a device that can display and verify it. 【0871】 "Means for identifying a user's emotional state in real time" refers to technologies that use sensor devices such as cameras and microphones to determine a user's emotions from their facial expressions and tone of voice. 【0872】 "Means of tailoring suggestions based on sentiment identification and providing information that meets user needs" refers to the process of analyzing user sentiment data and providing users with individually customized information and suggestions based on the results. 【0873】 In order to implement this invention, a system combining various technical elements is required. A specific embodiment is shown below. 【0874】 The server is equipped with an input mechanism to receive contract information from users, which is typically provided in PDF or Word format. The entered contract information is converted into text data using OCR technology. Examples of OCR software used include Tesseract. This text data is analyzed within the server using an NLP engine (e.g., spaCy). Through this analysis, the server gains a detailed understanding of the contract and identifies legal risks. 【0875】 Once legal risks are identified, the server references past case law information from its database and generates proposed revisions using a generative AI model (e.g., OpenAI GPT). After verifying that the revisions maintain the consistency and legal validity of the contract, they are created as a revised version and sent to the user's accessible device. 【0876】 Furthermore, this system can analyze the user's emotional state in real time. When a user views a contract, the system analyzes the user's facial expressions and tone of voice through the terminal's camera and microphone, and identifies their emotional state using the Azure Face API and other tools. Based on this information, the system can display information to support understanding and alternative suggestions that are appropriate to the user's emotions. 【0877】 A concrete example is providing users who are concerned about payment terms when a family purchases a new home with detailed information about alternatives and payment plans. This can alleviate user anxiety and promote understanding of the contract. 【0878】 An example of a prompt to input into the generating AI model is, "Create a proposal to alleviate the user's concerns regarding the apartment purchase contract." Through this prompt, the necessary support can be provided to the user quickly and accurately. 【0879】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0880】 Step 1: 【0881】 The user uploads the contract from their device. The input is a contract file in PDF or Word format. The server receives this contract information and prepares it for processing. 【0882】 Step 2: 【0883】 The server uses OCR technology to convert the entered contract into text data. Specifically, it uses Tesseract to extract text from PDF or Word documents and converts it into structured digital data. The output is then passed on to the next step as text data. 【0884】 Step 3: 【0885】 The server uses an NLP engine to analyze text data. It uses spaCy as the NLP engine to analyze contract clauses, understand the context, and identify legal risk elements. During this process, it references past case law information from a database. The output is data that identifies the risk elements. 【0886】 Step 4: 【0887】 The server generates proposed solutions using a generative AI model based on the identified risk elements. It uses tools such as OpenAI GPT to create solutions tailored to the identified risks. The input is the identified risk elements, and the output is the text of the proposed solutions. 【0888】 Step 5: 【0889】 The server uses the generated revisions to create a revised version of the contract. The revisions are integrated into the existing contract structure, and consistency and legal validity are verified. The output is the revised contract, ready to be sent to the user. 【0890】 Step 6: 【0891】 The system uses the device's camera and microphone to analyze the user's emotional state in real time. It leverages the Azure Face API to analyze facial expressions and voice tone, identifying emotions. Input consists of real-time video and audio data. 【0892】 Step 7: 【0893】 Based on the results of emotion recognition, the server provides the user with tailored information. For example, if the user shows anxiety, it presents details of the contract and alternatives to deepen the user's understanding. It also displays information to promote calmness. The output consists of customized suggestions and information. 【0894】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0895】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0896】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0897】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0898】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0899】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0900】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0901】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0902】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0903】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0904】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0905】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0906】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0907】 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. 【0908】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0909】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0910】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0911】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0912】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0913】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0914】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0915】 The following is further disclosed regarding the embodiments described above. 【0916】 (Claim 1) 【0917】 Methods for entering contract information, 【0918】 A means of analyzing the entered contract information and converting it into text data, 【0919】 A means of identifying legal risks based on analyzed text data, 【0920】 Means for generating proposed modifications based on identified risks, 【0921】 A means of creating a revised version of the contract using the generated revised draft, 【0922】 A means of sending the revised version to a device capable of displaying it, 【0923】 A system that includes this. 【0924】 (Claim 2) 【0925】 The system according to claim 1, further comprising means for referencing past case law information from a database for risk identification. 【0926】 (Claim 3) 【0927】 The system according to claim 1, further comprising means for verifying the consistency and legal validity of the generated revised drafts with respect to the entire contract. 【0928】 "Example 1" 【0929】 (Claim 1) 【0930】 Means for receiving contract document information, 【0931】 A means of converting received contract document information into character data using character recognition technology, 【0932】 A means for analyzing the converted character data using natural language processing technology and structuring document items, 【0933】 A means of identifying legal risks based on the analyzed document items, 【0934】 A means of generating revised plans using AI technology based on identified risks, 【0935】 A means of creating a revised version of the document based on the generated revision proposals, 【0936】 A means of transmitting the revised version to a device capable of displaying it, 【0937】 A system that includes this. 【0938】 (Claim 2) 【0939】 The system according to claim 1, further comprising means for referencing past case law information from an information storage device in order to identify risks. 【0940】 (Claim 3) 【0941】 The system according to claim 1, further comprising means for verifying the consistency and legal validity of the generated revised draft for the entire document. 【0942】 "Application Example 1" 【0943】 (Claim 1) 【0944】 A device for entering contract information, 【0945】 A device that analyzes input contract information and converts it into text data, 【0946】 A device that identifies legal risks based on analyzed text data, 【0947】 A device that generates corrective action proposals based on identified risks, 【0948】 A device for creating a revised version of the contract using the generated revision proposals, 【0949】 A device that transmits the revised version to a display device capable of displaying it, 【0950】 A device that visually presents important clauses and risks in contract information, 【0951】 A device that structures and analyzes contract information using natural language processing technology, 【0952】 A system that includes this. 【0953】 (Claim 2) 【0954】 The system according to claim 1, further comprising a device for referencing past case law information from a data collection medium for risk identification. 【0955】 (Claim 3) 【0956】 The system according to claim 1, further comprising a device for verifying the consistency and legal acceptability of the generated proposed revisions to the overall contract. 【0957】 "Example 2 of combining an emotion engine" 【0958】 (Claim 1) 【0959】 A device that receives contract information, 【0960】 A device that converts received contract information into character data using optical character recognition technology, 【0961】 A device that analyzes converted character data using natural language processing technology and generates structured data, 【0962】 A device that detects legal risks based on structured data, 【0963】 A device that generates corrective suggestions using AI technology based on detected risks, 【0964】 A device that uses the generated revision proposals to create a revised version of the contract, 【0965】 A device for transmitting the revised version to an information device capable of displaying it, 【0966】 A device that senses the user's emotional state, 【0967】 A device that provides support information based on emotional state, 【0968】 A system that includes this. 【0969】 (Claim 2) 【0970】 The system according to claim 1, further comprising a device for referencing past case law knowledge from a data storage device for the detection of legal risks. 【0971】 (Claim 3) 【0972】 The system according to claim 1, further comprising a device for verifying the consistency and legal validity of the proposed revisions to the entire contract. 【0973】 "Application example 2 when combining with an emotional engine" 【0974】 (Claim 1) 【0975】 Methods for entering contract information, 【0976】 A means of analyzing the entered contract information and converting it into text data, 【0977】 A means of identifying legal risks based on analyzed text data, 【0978】 Means for generating proposed modifications based on identified risks, 【0979】 A means of creating a revised version of the contract using the generated revised draft, 【0980】 A means of sending the revised version to a device capable of displaying it, 【0981】 A means of identifying the emotional state of users in real time, 【0982】 A means of adjusting suggestions based on sentiment recognition and providing information that meets the user's needs, 【0983】 A system that includes this. 【0984】 (Claim 2) 【0985】 The system according to claim 1, further comprising means for referencing past case information from a database for risk identification, and further comprising means for analyzing image data and audio data for emotion identification. 【0986】 (Claim 3) 【0987】 The system according to claim 1, further comprising means for verifying the consistency and legal validity of the generated revised drafts of the contract as a whole, and for providing information based on the user's sentiment. [Explanation of symbols] 【0988】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Methods for entering contract information, A means of analyzing the entered contract information and converting it into text data, A means of identifying legal risks based on analyzed text data, Means for generating proposed modifications based on identified risks, A means of creating a revised version of the contract using the generated revised draft, A means of sending the revised version to a device capable of displaying it, A system that includes this. [Claim 2] The system according to claim 1, further comprising means for referencing past case law information from a database for risk identification. [Claim 3] The system according to claim 1, further comprising means for verifying the consistency and legal validity of the generated revised draft as an entire contract.

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