system

Abstract

We provide the system. [Solution] A means for automatically generating legal documents based on user input conditions using natural language processing, A means for automatically generating documents based on user input conditions using natural language processing, A means of identifying risks by comparing the generated documents with the regulatory data structure, Means for proposing corrective measures for identified risks, A means of instantly searching for and presenting regulatory and case law information to users, Means for streamlining agreement documents used in trading platforms, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult to efficiently utilize the latest legal information because the creation and review of legal documents are very time-consuming and require specialized knowledge. Also, when dealing with legal details related to multiple languages, appropriate responses in international transactions are required, but it is often difficult to respond to this. Therefore, there is a need for a system that can flexibly respond in a legal confirmation, identification of legal risks, and multilingual transaction environment.

Means for Solving the Problems

[0005] This invention significantly streamlines the process of creating legal documents such as contracts by providing a means for automatically generating legal documents based on user input conditions using natural language processing. Furthermore, it provides a system that includes means for identifying legal risks by comparing the generated legal documents with a database of laws and precedents, and proposing revisions, thereby enabling accurate reviews based on the latest legal information. In addition, this system searches for laws and precedents in real time and presents them to the user, quickly providing the information the user requests. Beyond these means, it also includes means for analyzing contract-related risks based on user input and proposing countermeasures, as well as means for providing legal support in multiple languages ​​to handle international transactions, comprehensively solving challenges across the entire legal work process.

[0006] "Natural language processing" is a technology that enables computers to understand and process human language appropriately.

[0007] "Legal documents" refer to documents related to contracts and regulations that have legal effect.

[0008] "Automatic generation" refers to the process by which a system generates data according to a specific algorithm without requiring manual user intervention.

[0009] A "Laws and Precedents Database" is a data management system that collects and centralizes information on laws and precedents.

[0010] "Identifying legal risks" is the process of identifying potential legal risks and clarifying the issues.

[0011] A "proposal for correction" refers to presenting appropriate countermeasures for the identified problems.

[0012] "Real-time search" means searching the database instantly without any time delay and retrieving the necessary information.

[0013] "Multilingual legal support" means providing legal assistance in multiple languages ​​to support international business operations.

[0014] "Supporting international transactions" means providing flexible support that can adapt to the laws and languages ​​of different countries. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple 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), etc.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The present invention is a system for streamlining the creation and review of legal documents, and its specific embodiments are described below.

[0037] This system's main feature is that, based on information entered by the user through a terminal, the server automatically generates legal documents using natural language processing technology. The user enters the necessary conditions and clauses for the contract through the terminal. This input data is sent to the server. The server uses a generation AI to retrieve relevant information from existing laws and past case law databases and generates the optimal legal document. This process allows legal professionals to significantly streamline their work.

[0038] The generated legal documents are compared by the server against a database of laws and precedents to identify potential legal risks. The server then suggests corrections to the user for the identified issues. The user can review these suggestions via their terminal and edit the document as needed.

[0039] Furthermore, the server also has the ability to search for laws and precedents in real time in response to user requests. When a user requests information on a specific law or precedent, the server immediately retrieves the relevant information from the database and sends it to the terminal. This function allows users to quickly access the latest legal information.

[0040] As a concrete example, consider a scenario where a company's legal department uses this system to draft a new business agreement. The user inputs the terms of the transaction, and the server automatically generates an optimal contract based on that information. The server then automatically analyzes the legal risks and presents the user with necessary revisions. The user can then finalize the contract based on these suggestions. In this way, the accuracy and efficiency of the entire legal work process are improved.

[0041] Furthermore, in international transactions where multilingual support is required, this system can provide legal support in multiple languages. If a user needs to generate contracts in different languages, the system can automatically generate contracts in each language, facilitating smooth international business transactions.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uses a device to input the necessary conditions and clauses for a legal document. The device then formats that information and sends it to the server.

[0045] Step 2:

[0046] The server activates the generation AI to create a draft of a legal document based on the input conditions and clauses. The server refers to a database of past laws and precedents to select the most appropriate wording and content to generate the document.

[0047] Step 3:

[0048] The server compares the generated legal documents with a database of laws and precedents. The server identifies legal risks and deficiencies and generates analysis results.

[0049] Step 4:

[0050] The server generates necessary corrective action suggestions based on the identified legal risks. The server then sends these suggestions to the terminal and presents them to the user.

[0051] Step 5:

[0052] The user reviews the proposed revisions via their device and edits the legal document as needed. The device can then resend the revised document to the server.

[0053] Step 6:

[0054] When a user requests specific legal or case law information, they send a search request to the server via their device. The server searches the legal and case law database in real time and retrieves the relevant information.

[0055] Step 7:

[0056] The server sends the search results to the device and presents them to the user. The user reviews the information displayed on the device and obtains the necessary legal information.

[0057] Step 8:

[0058] When a user requires a legal document in multiple languages, they select a language on their device and send a request to the server. The server generates the legal document in the selected language and sends it to the device, allowing the user to view the document in multiple languages.

[0059] (Example 1)

[0060] 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."

[0061] The process of creating legal documents is time-consuming and labor-intensive, and specialized knowledge is particularly required for identifying legal risks and considering countermeasures. This increases the burden on legal departments and makes efficient work difficult. Furthermore, challenges exist in obtaining appropriate legal support in international transactions requiring multilingual support. Therefore, there is a need for a system that automates the generation of legal documents, streamlines the management of legal risks, and is capable of handling international transactions.

[0062] 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.

[0063] This invention includes a server that automatically generates documents based on user requirements using natural language processing technology, a function that compares the generated documents with a database of laws and precedents to identify potential legal risks, and a function that implements proposed modifications to address the identified legal risks. This enables more efficient and accurate document creation processes, allowing users to quickly receive appropriate legal support even in international transactions.

[0064] "Natural language processing technology" refers to a set of technologies that enable computers to understand and process human language.

[0065] A "user" is an individual or organization that uses this system to create and manage legal documents.

[0066] "Conditions" refer to information such as necessary requirements and clauses defined by the user when generating a legal document.

[0067] "Document generation" is the process by which a computer automatically creates appropriate legal documents based on the input conditions.

[0068] A "legal and case law database" is a collection of information that accumulates laws and past case precedents necessary for creating legal documents and identifying risks.

[0069] "Legal risk" refers to any legal problems or deficiencies that may arise when a legal document is examined in light of laws and precedents.

[0070] A "proposal for correction" is a proposal for correction or improvement generated to address identified legal risks.

[0071] "Multilingual support" refers to the ability to operate functions or generate and provide documents in multiple different languages.

[0072] "International trade" refers to commercial transactions conducted between companies or individuals located in different countries.

[0073] This invention is a system for streamlining the creation and review of legal documents, and specifically utilizes natural language processing technology. The system operates in a manner where users input information via a terminal, and a server processes that information based on that input. The implementation of the system depends on the following hardware and software.

[0074] Users input the necessary information for the contract (e.g., names of the contracting parties, contract details, deadline, etc.) from their device (PC, tablet, smartphone, etc.). This information can be easily entered through the device's interface.

[0075] The terminal transmits data entered by the user to the server via the internet. This data transmission is encrypted using a secure protocol (e.g., HTTPS).

[0076] The server uses natural language processing technology based on the received data to generate legal documents using a generative AI model (e.g., GPT-4®). The generative AI model is given prompts such as "A new business agreement draft is needed. The trading partner is Company X, the contract period is one year, and the main transaction is the supply of 10,000 products per year," and creates an appropriate draft of the contract.

[0077] The generated document is cross-referenced with a database of laws and precedents on the server. The server identifies legal risks and creates proposed revisions to mitigate those risks. These proposed revisions are sent to the user's terminal as text.

[0078] Users can review this transmitted information on their device and edit the document as needed. This allows users to quickly complete legally accurate documents.

[0079] Furthermore, the server has multilingual capabilities, allowing it to generate legal documents in different languages ​​to accommodate international transactions. This enables smooth contract signing even in international business settings.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The user uses a terminal to input the necessary conditions and clauses for the contract. The information entered includes specific clauses such as the names of the contracting parties, the contract details, and the contract period. The terminal receives this information as a form and constructs the input data.

[0083] Step 2:

[0084] The terminal sends information entered by the user to the server. The transmitted data is encrypted using a secure communication protocol (e.g., HTTPS). The server prepares this received data for analysis.

[0085] Step 3:

[0086] The server analyzes the received data and generates a prompt statement based on that information. This prompt statement is then input to a generation AI model (e.g., GPT-4) to generate the appropriate legal document. The prompt statement is configured to reflect the input contract terms.

[0087] Step 4:

[0088] The generated legal documents are compared against a database of laws and precedents on the server. The server uses text mining techniques to compare the document content with the database content and identify potential legal risks. This identifies deficiencies and risks within the documents.

[0089] Step 5:

[0090] The server generates corrective action suggestions based on the identified risks. These suggestions include solutions and improvements to the problems found. The suggestions are returned to the user's terminal in text format.

[0091] Step 6:

[0092] The user reviews the proposed revisions sent from the server via their terminal. The user edits the document as needed and makes decisions to finalize the contract. An interface for document editing is provided on the terminal.

[0093] Step 7:

[0094] The server searches for legal and case law information in real time upon user request and immediately transmits the relevant information to the terminal. This function allows users to quickly obtain additional legal information.

[0095] (Application Example 1)

[0096] 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."

[0097] In modern electronic and international transactions, there is a need to generate legal documents quickly and accurately and manage appropriate legal risks. However, traditional methods involve time-consuming manual document creation, which can lead to insufficient identification and management of legal risks. Furthermore, in international transactions requiring multilingual support, the ability to quickly generate legal documents that take into account the regulations of each country becomes an even greater challenge.

[0098] 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.

[0099] In this invention, the server includes means for automatically generating documents based on user input conditions using natural language processing, means for identifying risks by comparing the generated documents with a regulatory data structure, and means for proposing modifications to the identified risks. This enables users to efficiently generate accurate legal documents and quickly manage legal risks. Furthermore, by providing support for multiple languages, legal documents can be generated quickly even in international business.

[0100] "Natural language processing" refers to techniques used by computers to understand and generate natural human language.

[0101] A "user" is an individual or legal entity that uses this system to create or review legal documents or agreements.

[0102] "Input conditions" refer to the requirements and clauses that users provide when generating legal documents.

[0103] A "document" is a written document that contains information related to legal matters or agreements.

[0104] "Automatic generation methods" refer to technologies in which a computer system mechanically creates documents with a specified format and content based on input from a user.

[0105] A "regulatory data structure" is a database that stores information related to laws and precedents.

[0106] "Risk" refers to the potential legal flaws or problems inherent in the generated document.

[0107] A "revision suggestion" refers to specific advice or guidelines for users to resolve problems contained in the generated document.

[0108] A "trading platform" is an electronic system used for exchanging goods and services in commercial and business transactions.

[0109] "Agreement" refers to the terms agreed upon by the parties involved in a transaction or contract.

[0110] "International business" refers to commercial activities and transactions conducted between different countries.

[0111] "Support" means providing users with the information and functions they need when creating documents or conducting transactions.

[0112] To realize this invention, a server, terminals, and a communication network to connect them are necessary. The server uses software equipped with natural language processing technology to automatically generate legal documents based on input conditions. The regulatory data structure also stores a database of laws and precedents, which is used to verify and identify the legal risks of the generated documents.

[0113] The terminal accepts input of conditions and clauses through a user-friendly interface. Furthermore, it displays the generated document and risk analysis results to the user and offers suggestions for revisions. Based on this information, the user can edit the document and complete the final version. Multilingual support enables automatic document generation in the languages ​​required for international business.

[0114] As a concrete example, consider the generation of contract documents on a trading platform. When a user enters specific transaction terms such as payment terms and contract period into a terminal, the server generates an appropriate contract document based on that information. Using a generation AI model, the system refers to relevant past precedents and laws to identify potential legal risks hidden in the document and proposes revisions to minimize those risks. For example, by providing a specific prompt such as, "If the user wishes to generate a contract for use in international transactions, please include clauses based on the laws of a specific country," the AI ​​model effectively extracts the necessary information and responds to the user's request.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The terminal accepts the necessary conditions and clauses for legal documents from the user as input. Specifically, the user fills in information about the contract details into an input form. This input information is then sent to the server.

[0118] Step 2:

[0119] The server receives input information sent from the terminal and automatically generates legal documents using natural language processing technology. Based on the entered contract terms, it applies existing standard document templates to construct an appropriate document structure. As a result, an initial contract document is generated.

[0120] Step 3:

[0121] The server compares the generated legal documents against the regulatory data structure. The input here is the generated document, and the output is the identification of legal risks. Specifically, it searches a database based on laws and precedents to find ambiguities and inconsistencies within the document.

[0122] Step 4:

[0123] The server generates proposed revisions based on the identified legal risks. The input is the risks identified in step 3, and the server outputs proposals by processing the data to identify countermeasures for these risks. Specifically, it generates draft document revisions and proposed clauses to be added to mitigate the risks.

[0124] Step 5:

[0125] The terminal displays revision suggestions sent from the server to the user. The input is the revision suggestions, and the output is a visual presentation to the user. The user reviews the revision suggestions and edits the document as needed.

[0126] Step 6:

[0127] Once the user has completed the final document revisions, the terminal outputs the completed legal document and saves or outputs it in the required format. This output becomes a formal contract after being reviewed by the user.

[0128] 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.

[0129] The present invention is a legal document creation and review system that includes an emotion engine for identifying user emotions, and is intended to more effectively support legal work. Specific embodiments thereof are described below.

[0130] This system is configured so that the server automatically generates legal documents using natural language processing technology based on the legal document creation conditions entered by the user through a terminal. An emotion engine is incorporated into the system, analyzing the emotions the user expresses in real time during input. This information is reflected in the tone and style of the generated document. For example, if the user expresses anxiety or anger, the system will select more cautious and calm language.

[0131] The generated legal documents are cross-referenced by the server against a database of laws and precedents to identify legal risks. Here again, an emotion engine is utilized, dynamically adjusting suggested mitigation measures for detected risks based on the user's emotions. For example, if the user expresses strong concerns about a risk, the system provides detailed suggestions to minimize that risk.

[0132] As a concrete example, consider a scenario where a company's legal department uses this system during contract negotiations. The user inputs the contract terms via a terminal, and the server generates a corresponding contract while simultaneously analyzing the user's emotions. If the server detects that the user is experiencing stress, it can create a contract with wording that alleviates those emotions. Furthermore, suggested revisions are adjusted to match the user's preferences, and the suggested evidence and justifications are presented in more detail.

[0133] The emotional data collected by the emotion engine is used to optimize the entire legal support process. This allows the system to provide legal advice tailored to the individual needs of each user and to perform well in a variety of tasks, including international transactions.

[0134] In this way, by combining emotion recognition technology and natural language processing technology, this system achieves even more effective legal support than before.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The user uses a terminal to input the necessary conditions and information for a legal document. The terminal records the user's input data in real time and sends this information to a server. At this time, an emotion engine analyzes the user's emotions at the time of input.

[0138] Step 2:

[0139] Based on the input data received by the server, natural language processing techniques are used to generate an initial legal document. The server also considers the results of the emotion engine and reflects the user's emotions in the tone and style of the document.

[0140] Step 3:

[0141] The generated legal documents are cross-referenced by the server against a database of laws and precedents. The server identifies legal risks and generates revision suggestions tailored to the user's feelings. For example, it provides detailed and reassuring suggestions to alleviate the user's anxiety.

[0142] Step 4:

[0143] The server sends identified legal risks and suggested modifications to the user's device. The user can then use the device to review the suggestions and select the modifications that best suit their needs.

[0144] Step 5:

[0145] Users can make necessary corrections on their device and resend the edited document to the server. The server reviews the final document and re-evaluates it as needed.

[0146] Step 6:

[0147] When a user requests information on laws and precedents, they send a request from their device. The server searches the database in real time and provides the retrieved information to the user.

[0148] Step 7:

[0149] The emotion engine tracks the user's emotional changes, and the server analyzes that data. The server uses the collected emotional data to optimize the legal support process and improve future interactions.

[0150] (Example 2)

[0151] 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 as the "terminal".

[0152] Existing legal document creation systems often fail to adequately address user sentiments and needs through automatically generated documents, potentially resulting in inappropriate identification of legal risks and suggested revisions. Furthermore, they present challenges in providing sufficient support in situations requiring international multilingual capabilities.

[0153] 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.

[0154] In this invention, the server includes means for automatically generating documents based on user input conditions and sentiment data using natural language processing technology, means for identifying risks by comparing the generated documents with a data store of laws and precedents, and means for dynamically adjusting proposed modifications to the identified risks according to the user's sentiment. This enables the automatic generation of legal documents and risk analysis that are adapted to the user's sentiment and input conditions.

[0155] "Natural language processing technology" refers to the technology that enables computers to understand and generate human language, making semantic analysis of text and document generation possible.

[0156] "Sentimental data" refers to information about emotions collected from user input and interactions, and is used to adjust the tone and style of a document.

[0157] "Methods for automatically generating documents" refers to a process that automatically creates legal documents based on user input conditions and sentiment data, and utilizes natural language processing technology.

[0158] A "data store of laws and precedents" refers to a database used to manage and compare legal information and past precedents necessary for generating legal documents and conducting risk analysis.

[0159] "Means of identifying risks" refers to the process of comparing generated documents with legal and case law data to identify potential legal risks.

[0160] "Means for dynamically adjusting proposed modifications" refers to a process that appropriately changes proposed modifications for identified risks in accordance with the user's emotions, in order to provide proposals that meet the user's needs.

[0161] This system automates the creation and review of legal documents and operates based on information entered by users via a terminal. Users can input the requirements and conditions of legal documents, and this input is sent from the terminal to the server. The server analyzes the user's input data using natural language processing technology and sentiment analysis engines and automatically generates the necessary legal documents. Generative AI models such as BERT and GPT are used for natural language processing.

[0162] The server identifies potential legal risks by cross-referencing the input document with a database of laws and precedents. This cross-referencing can utilize a cloud-based database system. The server also adjusts the tone and style of the generated document to reflect sentiment analysis results, optimizing it to reflect the emotions expressed by the user during input.

[0163] Furthermore, the server proposes corrective actions for identified risks. These corrective actions are dynamically adjusted based on the user's sentiment data, allowing users to confidently accept the suggestions.

[0164] A concrete example is a scenario where a company's legal department uses this system when drafting a new contract. For instance, by entering a prompt such as, "Please help me draft a legal document regarding non-disclosure clauses in an inter-company contract, taking into consideration my concerns," the server can generate and propose a document that appropriately addresses these concerns. In this way, it is possible to meet the diverse needs of users through automated legal support.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The user inputs the requirements, conditions, and prompts for legal documents through their terminal. This input data includes contract clauses and associated emotional requests. This information is sent from the terminal to the server. Specifically, the user enters information into a form and presses the "Submit" button. The input data is passed to the server as prompts.

[0168] Step 2:

[0169] The server generates legal documents using natural language processing techniques based on the received prompt text. This process utilizes generative AI models such as BERT and GPT to transform the input information into appropriate legal text. Specific data processing includes analysis of the input text and requirements extraction, resulting in the creation of an initial version of the contract document.

[0170] Step 3:

[0171] The server compares the generated legal document with a database of laws and precedents. This verifies whether the document's content complies with current laws and precedents and identifies potential legal risks. Specifically, it performs data calculations by issuing database queries and comparing the document's content with the information on laws and precedents. The output is the document with identified risks.

[0172] Step 4:

[0173] The server adjusts the tone and style of the generated document based on the user's sentiment data. A sentiment analysis engine analyzes the user's emotions and provides specific guidance for document adjustments. The input here is the user's sentiment data, and the output is a document adapted to those emotions.

[0174] Step 5:

[0175] The server generates proposed solutions for identified legal risks and dynamically adjusts them based on the user's sentiment. Specifically, it adjusts the content and tone of the proposed solutions to make the information more acceptable to the user. The input is risk information and sentiment data, and the output is the adjusted proposed solutions.

[0176] Step 6:

[0177] The terminal presents the user with the completed legal document and suggested revisions returned from the server. The user reviews the document based on the presented information and makes final adjustments as needed. Specifically, the user reviews the document on the screen and proceeds to the next step using "Approve" or "Revise" buttons. The output is the final, reviewed legal document.

[0178] (Application Example 2)

[0179] 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 device 14 will be referred to as the "terminal."

[0180] In drafting and reviewing legal documents, it is necessary to dynamically adjust the tone and style of the document in response to the user's emotions and to provide legal risk correction suggestions tailored to the user's emotional state. Traditional systems have been unable to analyze and reflect the user's emotions in the document, thus failing to alleviate stress and anxiety.

[0181] 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.

[0182] In this invention, the server includes means for automatically generating documents based on user input conditions using natural language processing, means for identifying risks by comparing the generated documents with an information database, and means for adjusting the tone and style of the documents using an emotion engine that identifies the user's emotions. This makes it possible to adjust documents according to the user's emotions and optimize risk correction suggestions.

[0183] "Natural language processing" is the technology that enables computers to understand, generate, and process human language.

[0184] "Users" are individuals or organizations that operate the system and provide or receive information.

[0185] "Means for automatically generating documents" refers to technologies or devices that allow a computer to automatically create documents based on user input.

[0186] An "information database" is a collection of various data, including legal documents and precedents, that can be searched and referenced by computer.

[0187] "Means of identifying risks" refers to techniques or processes that analyze generated documents to identify potential legal risks.

[0188] An "emotion engine" is software or hardware used to identify and analyze a user's emotions in real time.

[0189] "Methods for adjusting tone and style" refer to techniques for changing the atmosphere and expression of a document based on emotion.

[0190] "Means of making corrective suggestions" refer to technologies and processes that provide improvement proposals to mitigate identified risks.

[0191] To implement this invention, the following configuration is necessary. The server first implements a program using the Python programming language and a natural language processing library (e.g., NLTK, spaCy) to utilize natural language processing technology. The user provides input information through a terminal, and the server automatically generates a document based on that information. The generated document is compared with a database of laws and precedents to identify legal risks. A SQL-based database system is used to manage the information database.

[0192] The server further identifies the user's emotions using an emotion engine. By utilizing Microsoft® Azure® Sentiment Analysis APIs, it can analyze the user's voice and text data in real time. Based on the analysis results, the tone and style of the document are adjusted. For example, if the user is feeling anxious, the tone of the text is softened. The generated document and improvement suggestions are further enhanced with natural language processing using Google® Cloud Natural Language APIs.

[0193] For example, if a user enters "I have concerns about the terms of this contract," the system will immediately analyze their emotions and provide support to alleviate their anxiety. In addition to generating a standard contract, the system will automatically generate supplementary explanations and reassuring documents. An example of a prompt used in this process would be: "Please perform a real-time emotional analysis of the contract terms presented by the user and propose improvements in a calming tone."

[0194] Thus, by combining an emotion engine with natural language processing technology, the invention is configured to improve the user experience and provide more personalized and effective support in the creation and review of legal documents.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user enters legal information through their terminal. The entered data is mainly in text format, such as contract terms. The server receives this input data and prepares to begin natural language processing.

[0198] Step 2:

[0199] The server uses natural language processing libraries (e.g., NLTK, spaCy) to analyze text data entered by the user. This analysis helps the server understand the grammatical structure and meaning of the text, and extracts the information necessary for the automatic generation of contract documents. The output of the analysis is a dataset used by the generative AI model.

[0200] Step 3:

[0201] Based on the analyzed dataset, the server accesses a database of laws and precedents and automatically generates corresponding legal documents. This generation process effectively searches the database using a SQL-based database system to collect relevant information. The output is an unedited contract document for presentation to the user.

[0202] Step 4:

[0203] For the generated documents, the server uses an emotion engine to analyze the user's emotions in real time. Leveraging Microsoft Azure's emotion analysis API, it identifies the emotions the user feels towards the input data. The input can be voice or text data, and the output is an indicator of the emotional state.

[0204] Step 5:

[0205] The server adjusts the tone and style of the generated document based on the sentiment analysis results. Using the Google Cloud Natural Language API, it modifies the wording within the document according to the user's emotions and adds supplementary explanations to reduce stress. The output is an adjusted contract document that is easy for the user to understand and less stressful to read.

[0206] Step 6:

[0207] Finally, the server sends the revised contract document and proposed revisions to the user's terminal. The user can review the received document and provide additional feedback as needed. This feedback may be used to further revise the document.

[0208] Through this series of processes, the system can assist in the creation of legal documents while taking the user's emotions into consideration.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] The present invention is a system for streamlining the creation and review of legal documents, and its specific embodiments are described below.

[0226] This system's main feature is that, based on information entered by the user through a terminal, the server automatically generates legal documents using natural language processing technology. The user enters the necessary conditions and clauses for the contract through the terminal. This input data is sent to the server. The server uses a generation AI to retrieve relevant information from existing laws and past case law databases and generates the optimal legal document. This process allows legal professionals to significantly streamline their work.

[0227] The generated legal documents are compared by the server against a database of laws and precedents to identify potential legal risks. The server then suggests corrections to the user for the identified issues. The user can review these suggestions via their terminal and edit the document as needed.

[0228] Furthermore, the server also has the ability to search for laws and precedents in real time in response to user requests. When a user requests information on a specific law or precedent, the server immediately retrieves the relevant information from the database and sends it to the terminal. This function allows users to quickly access the latest legal information.

[0229] As a concrete example, consider a scenario where a company's legal department uses this system to draft a new business agreement. The user inputs the terms of the transaction, and the server automatically generates an optimal contract based on that information. The server then automatically analyzes the legal risks and presents the user with necessary revisions. The user can then finalize the contract based on these suggestions. In this way, the accuracy and efficiency of the entire legal work process are improved.

[0230] Furthermore, in international transactions where multilingual support is required, this system can provide legal support in multiple languages. If a user needs to generate contracts in different languages, the system can automatically generate contracts in each language, facilitating smooth international business transactions.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The user uses a device to input the necessary conditions and clauses for a legal document. The device then formats that information and sends it to the server.

[0234] Step 2:

[0235] The server activates the generation AI to create a draft of a legal document based on the input conditions and clauses. The server refers to a database of past laws and precedents to select the most appropriate wording and content to generate the document.

[0236] Step 3:

[0237] The server compares the generated legal documents with a database of laws and precedents. The server identifies legal risks and deficiencies and generates analysis results.

[0238] Step 4:

[0239] The server generates necessary corrective action suggestions based on the identified legal risks. The server then sends these suggestions to the terminal and presents them to the user.

[0240] Step 5:

[0241] The user reviews the proposed revisions via their device and edits the legal document as needed. The device can then resend the revised document to the server.

[0242] Step 6:

[0243] When a user requests specific legal or case law information, they send a search request to the server via their device. The server searches the legal and case law database in real time and retrieves the relevant information.

[0244] Step 7:

[0245] The server sends the search results to the device and presents them to the user. The user reviews the information displayed on the device and obtains the necessary legal information.

[0246] Step 8:

[0247] When a user requires a legal document in multiple languages, they select a language on their device and send a request to the server. The server generates the legal document in the selected language and sends it to the device, allowing the user to view the document in multiple languages.

[0248] (Example 1)

[0249] 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 glasses 214 will be referred to as the "terminal."

[0250] The process of creating legal documents is time-consuming and labor-intensive, and specialized knowledge is particularly required for identifying legal risks and considering countermeasures. This increases the burden on legal departments and makes efficient work difficult. Furthermore, challenges exist in obtaining appropriate legal support in international transactions requiring multilingual support. Therefore, there is a need for a system that automates the generation of legal documents, streamlines the management of legal risks, and is capable of handling international transactions.

[0251] 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.

[0252] This invention includes a server that automatically generates documents based on user requirements using natural language processing technology, a function that compares the generated documents with a database of laws and precedents to identify potential legal risks, and a function that implements proposed modifications to address the identified legal risks. This enables more efficient and accurate document creation processes, allowing users to quickly receive appropriate legal support even in international transactions.

[0253] "Natural language processing technology" refers to a set of technologies that enable computers to understand and process human language.

[0254] A "user" is an individual or organization that uses this system to create and manage legal documents.

[0255] "Conditions" refer to information such as necessary requirements and clauses defined by the user when generating a legal document.

[0256] "Document generation" is the process by which a computer automatically creates appropriate legal documents based on the input conditions.

[0257] A "legal and case law database" is a collection of information that accumulates laws and past case precedents necessary for creating legal documents and identifying risks.

[0258] "Legal risk" refers to any legal problems or deficiencies that may arise when a legal document is examined in light of laws and precedents.

[0259] A "proposal for correction" is a proposal for correction or improvement generated to address identified legal risks.

[0260] "Multilingual support" refers to the ability to operate functions or generate and provide documents in multiple different languages.

[0261] "International trade" refers to commercial transactions conducted between companies or individuals located in different countries.

[0262] This invention is a system for streamlining the creation and review of legal documents, and specifically utilizes natural language processing technology. The system operates in a manner where users input information via a terminal, and a server processes that information based on that input. The implementation of the system depends on the following hardware and software.

[0263] Users input the necessary information for the contract (e.g., names of the contracting parties, contract details, deadline, etc.) from their device (PC, tablet, smartphone, etc.). This information can be easily entered through the device's interface.

[0264] The terminal transmits data entered by the user to the server via the internet. This data transmission is encrypted using a secure protocol (e.g., HTTPS).

[0265] The server uses natural language processing technology based on the received data and generates legal documents using a generative AI model (e.g., GPT-4). The generative AI model is given prompts such as "A new business contract draft is needed. The trading partner is Company X, the contract period is one year, and the main transaction is the supply of 10,000 products per year," and creates an appropriate contract draft.

[0266] The generated document is cross-referenced with a database of laws and precedents on the server. The server identifies legal risks and creates proposed revisions to mitigate those risks. These proposed revisions are sent to the user's terminal as text.

[0267] Users can review this transmitted information on their device and edit the document as needed. This allows users to quickly complete legally accurate documents.

[0268] Furthermore, the server has multilingual capabilities, allowing it to generate legal documents in different languages ​​to accommodate international transactions. This enables smooth contract signing even in international business settings.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The user uses a terminal to input the necessary conditions and clauses for the contract. The information entered includes specific clauses such as the names of the contracting parties, the contract details, and the contract period. The terminal receives this information as a form and constructs the input data.

[0272] Step 2:

[0273] The terminal sends information entered by the user to the server. The transmitted data is encrypted using a secure communication protocol (e.g., HTTPS). The server prepares this received data for analysis.

[0274] Step 3:

[0275] The server analyzes the received data and generates a prompt statement based on that information. This prompt statement is then input to a generation AI model (e.g., GPT-4) to generate the appropriate legal document. The prompt statement is configured to reflect the input contract terms.

[0276] Step 4:

[0277] The generated legal documents are cross-referenced with a database of laws and precedents on the server. The server uses text mining techniques to compare the document content with the database content and identify potential legal risks. This identifies deficiencies and risks within the documents.

[0278] Step 5:

[0279] The server generates corrective action suggestions based on the identified risks. These suggestions include solutions and improvements to the problems found. The suggestions are returned to the user's terminal in text format.

[0280] Step 6:

[0281] The user reviews the proposed revisions sent from the server via their terminal. The user edits the document as needed and makes decisions to finalize the contract. An interface for document editing is provided on the terminal.

[0282] Step 7:

[0283] The server searches for laws and case information in real time according to the user's request and immediately sends the relevant information to the terminal. With this function, users can quickly obtain additional legal information.

[0284] (Application Example 1)

[0285] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In modern electronic transactions and international transactions, there is a need to generate legal documents quickly and accurately and manage appropriate legal risks. However, in conventional methods, manual document creation takes a lot of time, and the identification and management of legal risks may not be sufficiently carried out. Also, in international transactions that require support in multiple languages, it has become an even more challenging task to quickly generate legal documents considering the regulations of each country.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0288] In this invention, the server includes means for automatically generating a document based on input conditions from a user using natural language processing, means for identifying risks by comparing the generated document with a regulatory data structure, and means for making a modification proposal for the identified risks. Thereby, the user can efficiently generate accurate legal documents and quickly manage legal risks. Also, by providing support for multiple languages, legal documents can be quickly generated even in international business.

[0289] "Natural language processing" is a method for a computer to understand and generate human natural language.

[0290] The "user" is an individual or a corporation that creates or reviews legal documents or agreement documents using this system.

[0291] "Input conditions" refer to the requirements and clauses that users provide when generating legal documents.

[0292] A "document" is a written document that contains information related to legal matters or agreements.

[0293] "Automatic generation methods" refer to technologies in which a computer system mechanically creates documents with a specified format and content based on input from a user.

[0294] A "regulatory data structure" is a database that stores information related to laws and precedents.

[0295] "Risk" refers to the potential legal flaws or problems inherent in the generated document.

[0296] A "revision suggestion" refers to specific advice or guidelines for users to resolve problems contained in the generated document.

[0297] A "trading platform" is an electronic system used for exchanging goods and services in commercial and business transactions.

[0298] "Agreement" refers to the terms agreed upon by the parties involved in a transaction or contract.

[0299] "International business" refers to commercial activities and transactions conducted between different countries.

[0300] "Support" means providing users with the information and functions they need when creating documents or conducting transactions.

[0301] To realize this invention, a server, terminals, and a communication network to connect them are necessary. The server uses software equipped with natural language processing technology to automatically generate legal documents based on input conditions. The regulatory data structure also stores a database of laws and precedents, which is used to verify and identify the legal risks of the generated documents.

[0302] The terminal accepts input of conditions and clauses through a user-friendly interface. Furthermore, it displays the generated document and risk analysis results to the user and offers suggestions for revisions. Based on this information, the user can edit the document and complete the final version. Multilingual support enables automatic document generation in the languages ​​required for international business.

[0303] As a concrete example, consider the generation of contract documents on a trading platform. When a user enters specific transaction terms such as payment terms and contract period into a terminal, the server generates an appropriate contract document based on that information. Using a generation AI model, the system refers to relevant past precedents and laws to identify potential legal risks hidden in the document and proposes revisions to minimize those risks. For example, by providing a specific prompt such as, "If the user wishes to generate a contract for use in international transactions, please include clauses based on the laws of a specific country," the AI ​​model effectively extracts the necessary information and responds to the user's request.

[0304] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0305] Step 1:

[0306] The terminal accepts the necessary conditions and clauses for legal documents from the user as input. Specifically, the user fills in information about the contract details into an input form. This input information is then sent to the server.

[0307] Step 2:

[0308] The server receives the input information sent from the terminal and automatically generates a legal document using natural language processing technology. Based on the input contract terms, it applies an existing standard document template to construct an appropriate document structure. As a result, an initial contract document is generated.

[0309] Step 3:

[0310] The server compares the generated legal document with the regulatory data structure. Here, the input is the generated document, and the output is the identification of legal risks. As a specific operation, it searches a database based on laws and regulations and precedents to find ambiguities and deficiencies in the document.

[0311] Step 4:

[0312] The server generates a modification proposal based on the identified legal risks. The input is the risk identified in Step 3, and by processing countermeasures for this, it outputs a proposal. Specifically, it generates suggestions for document modification points and additional clauses to reduce risks.

[0313] Step 5:

[0314] The terminal displays the modification proposal sent from the server to the user. This input is the modification proposal, and the output is a visual presentation to the user. The user refers to the amendment and edits the document as needed.

[0315] Step 6:

[0316] When the user's final document modification is completed, the terminal outputs the completed legal document and saves or outputs it in the required format. This output becomes a formal contract after the user's confirmation.

[0317] 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.

[0318] The present invention is a legal document creation and review system that includes an emotion engine for identifying user emotions, and is intended to more effectively support legal work. Specific embodiments thereof are described below.

[0319] This system is configured so that the server automatically generates legal documents using natural language processing technology based on the legal document creation conditions entered by the user through a terminal. An emotion engine is incorporated into the system, analyzing the emotions the user expresses in real time during input. This information is reflected in the tone and style of the generated document. For example, if the user expresses anxiety or anger, the system will select more cautious and calm language.

[0320] The generated legal documents are cross-referenced by the server against a database of laws and precedents to identify legal risks. Here again, an emotion engine is utilized, dynamically adjusting suggested mitigation measures for detected risks based on the user's emotions. For example, if the user expresses strong concerns about a risk, the system provides detailed suggestions to minimize that risk.

[0321] As a concrete example, consider a scenario where a company's legal department uses this system during contract negotiations. The user inputs the contract terms via a terminal, and the server generates a corresponding contract while simultaneously analyzing the user's emotions. If the server detects that the user is experiencing stress, it can create a contract with wording that alleviates those emotions. Furthermore, suggested revisions are adjusted to match the user's preferences, and the suggested evidence and justifications are presented in more detail.

[0322] The emotional data collected by the emotion engine is used to optimize the entire legal support process. This allows the system to provide legal advice tailored to the individual needs of each user and to perform well in a variety of tasks, including international transactions.

[0323] In this way, by combining emotion recognition technology and natural language processing technology, this system achieves even more effective legal support than before.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The user uses a terminal to input the necessary conditions and information for a legal document. The terminal records the user's input data in real time and sends this information to a server. At this time, an emotion engine analyzes the user's emotions at the time of input.

[0327] Step 2:

[0328] Based on the input data received by the server, natural language processing techniques are used to generate an initial legal document. The server also considers the results of the emotion engine and reflects the user's emotions in the tone and style of the document.

[0329] Step 3:

[0330] The generated legal documents are cross-referenced by the server against a database of laws and precedents. The server identifies legal risks and generates revision suggestions tailored to the user's feelings. For example, it provides detailed and reassuring suggestions to alleviate the user's anxiety.

[0331] Step 4:

[0332] The server sends identified legal risks and suggested modifications to the user's device. The user can then use the device to review the suggestions and select the modifications that best suit their needs.

[0333] Step 5:

[0334] Users can make necessary corrections on their device and resend the edited document to the server. The server reviews the final document and re-evaluates it as needed.

[0335] Step 6:

[0336] When a user requests information on laws and precedents, they send a request from their device. The server searches the database in real time and provides the retrieved information to the user.

[0337] Step 7:

[0338] The emotion engine tracks the user's emotional changes, and the server analyzes that data. The server uses the collected emotional data to optimize the legal support process and improve future interactions.

[0339] (Example 2)

[0340] 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".

[0341] Existing legal document creation systems often fail to adequately address user sentiments and needs through automatically generated documents, potentially resulting in inappropriate identification of legal risks and suggested revisions. Furthermore, they present challenges in providing sufficient support in situations requiring international multilingual capabilities.

[0342] 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.

[0343] In this invention, the server includes means for automatically generating documents based on user input conditions and sentiment data using natural language processing technology, means for identifying risks by comparing the generated documents with a data store of laws and precedents, and means for dynamically adjusting proposed modifications to the identified risks according to the user's sentiment. This enables the automatic generation of legal documents and risk analysis that are adapted to the user's sentiment and input conditions.

[0344] "Natural language processing technology" refers to the technology that enables computers to understand and generate human language, making semantic analysis of text and document generation possible.

[0345] "Sentimental data" refers to information about emotions collected from user input and interactions, and is used to adjust the tone and style of a document.

[0346] "Methods for automatically generating documents" refers to a process that automatically creates legal documents based on user input conditions and sentiment data, and utilizes natural language processing technology.

[0347] A "data store of laws and precedents" refers to a database used to manage and compare legal information and past precedents necessary for generating legal documents and conducting risk analysis.

[0348] "Means of identifying risks" refers to the process of comparing generated documents with legal and case law data to identify potential legal risks.

[0349] "Means for dynamically adjusting proposed modifications" refers to a process that appropriately changes proposed modifications for identified risks in accordance with the user's emotions, in order to provide proposals that meet the user's needs.

[0350] This system automates the creation and review of legal documents and operates based on information entered by users via a terminal. Users can input the requirements and conditions of legal documents, and this input is sent from the terminal to the server. The server analyzes the user's input data using natural language processing technology and sentiment analysis engines and automatically generates the necessary legal documents. Generative AI models such as BERT and GPT are used for natural language processing.

[0351] The server identifies potential legal risks by cross-referencing the input document with a database of laws and precedents. This cross-referencing can utilize a cloud-based database system. The server also adjusts the tone and style of the generated document to reflect sentiment analysis results, optimizing it to reflect the emotions expressed by the user during input.

[0352] Furthermore, the server proposes corrective actions for identified risks. These corrective actions are dynamically adjusted based on the user's sentiment data, allowing users to confidently accept the suggestions.

[0353] A concrete example is a scenario where a company's legal department uses this system when drafting a new contract. For instance, by entering a prompt such as, "Please help me draft a legal document regarding non-disclosure clauses in an inter-company contract, taking into consideration my concerns," the server can generate and propose a document that appropriately addresses these concerns. In this way, it is possible to meet the diverse needs of users through automated legal support.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The user inputs the requirements, conditions, and prompts for legal documents through their terminal. This input data includes contract clauses and associated emotional requests. This information is sent from the terminal to the server. Specifically, the user enters information into a form and presses the "Submit" button. The input data is passed to the server as prompts.

[0357] Step 2:

[0358] The server generates legal documents using natural language processing techniques based on the received prompt text. This process utilizes generative AI models such as BERT and GPT to transform the input information into appropriate legal text. Specific data processing includes analysis of the input text and requirements extraction, resulting in the creation of an initial version of the contract document.

[0359] Step 3:

[0360] The server compares the generated legal document with a database of laws and precedents. This verifies whether the document's content complies with current laws and precedents and identifies potential legal risks. Specifically, it performs data calculations by issuing database queries and comparing the document's content with the information on laws and precedents. The output is the document with identified risks.

[0361] Step 4:

[0362] The server adjusts the tone and style of the generated document based on the user's sentiment data. A sentiment analysis engine analyzes the user's emotions and provides specific guidance for document adjustments. The input here is the user's sentiment data, and the output is a document adapted to those emotions.

[0363] Step 5:

[0364] The server generates proposed solutions for identified legal risks and dynamically adjusts them based on the user's sentiment. Specifically, it adjusts the content and tone of the proposed solutions to make the information more acceptable to the user. The input is risk information and sentiment data, and the output is the adjusted proposed solutions.

[0365] Step 6:

[0366] The terminal presents the user with the completed legal document and suggested revisions returned from the server. The user reviews the document based on the presented information and makes final adjustments as needed. Specifically, the user reviews the document on the screen and proceeds to the next step using "Approve" or "Revise" buttons. The output is the final, reviewed legal document.

[0367] (Application Example 2)

[0368] 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."

[0369] In drafting and reviewing legal documents, it is necessary to dynamically adjust the tone and style of the document in response to the user's emotions and to provide legal risk correction suggestions tailored to the user's emotional state. Traditional systems have been unable to analyze and reflect the user's emotions in the document, thus failing to alleviate stress and anxiety.

[0370] 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.

[0371] In this invention, the server includes means for automatically generating documents based on user input conditions using natural language processing, means for identifying risks by comparing the generated documents with an information database, and means for adjusting the tone and style of the documents using an emotion engine that identifies the user's emotions. This makes it possible to adjust documents according to the user's emotions and optimize risk correction suggestions.

[0372] "Natural language processing" is the technology that enables computers to understand, generate, and process human language.

[0373] "Users" are individuals or organizations that operate the system and provide or receive information.

[0374] "Means for automatically generating documents" refers to technologies or devices that allow a computer to automatically create documents based on user input.

[0375] An "information database" is a collection of various data, including legal documents and precedents, that can be searched and referenced by computer.

[0376] "Means of identifying risks" refers to techniques or processes that analyze generated documents to identify potential legal risks.

[0377] An "emotion engine" is software or hardware used to identify and analyze a user's emotions in real time.

[0378] "Methods for adjusting tone and style" refer to techniques for changing the atmosphere and expression of a document based on emotion.

[0379] "Means of making corrective suggestions" refer to technologies and processes that provide improvement proposals to mitigate identified risks.

[0380] To implement this invention, the following configuration is necessary. The server first implements a program using the Python programming language and a natural language processing library (e.g., NLTK, spaCy) to utilize natural language processing technology. The user provides input information through a terminal, and the server automatically generates a document based on that information. The generated document is compared with a database of laws and precedents to identify legal risks. A SQL-based database system is used to manage the information database.

[0381] The server further identifies the user's emotions using an emotion engine. By utilizing Microsoft Azure's emotion analysis API, it can analyze the user's voice and text data in real time. Based on the analysis results, it adjusts the tone and style of the document. For example, if the user is feeling anxious, it adjusts the tone of the text to soften it. The generated document and improvement suggestions are further supplemented with natural language processing using the Google Cloud Natural Language API.

[0382] For example, if a user enters "I have concerns about the terms of this contract," the system will immediately analyze their emotions and provide support to alleviate their anxiety. In addition to generating a standard contract, the system will automatically generate supplementary explanations and reassuring documents. An example of a prompt used in this process would be: "Please perform a real-time emotional analysis of the contract terms presented by the user and propose improvements in a calming tone."

[0383] Thus, by combining an emotion engine with natural language processing technology, the invention is configured to improve the user experience and provide more personalized and effective support in the creation and review of legal documents.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The user enters legal information through their terminal. The entered data is mainly in text format, such as contract terms. The server receives this input data and prepares to begin natural language processing.

[0387] Step 2:

[0388] The server uses natural language processing libraries (e.g., NLTK, spaCy) to analyze text data entered by the user. This analysis helps the server understand the grammatical structure and meaning of the text, and extracts the information necessary for the automatic generation of contract documents. The output of the analysis is a dataset used by the generative AI model.

[0389] Step 3:

[0390] Based on the analyzed dataset, the server accesses a database of laws and precedents and automatically generates corresponding legal documents. This generation process effectively searches the database using a SQL-based database system to collect relevant information. The output is an unedited contract document for presentation to the user.

[0391] Step 4:

[0392] For the generated documents, the server uses an emotion engine to analyze the user's emotions in real time. Leveraging Microsoft Azure's emotion analysis API, it identifies the emotions the user feels towards the input data. The input can be voice or text data, and the output is an indicator of the emotional state.

[0393] Step 5:

[0394] The server adjusts the tone and style of the generated document based on the sentiment analysis results. Using the Google Cloud Natural Language API, it modifies the wording within the document according to the user's emotions and adds supplementary explanations to reduce stress. The output is an adjusted contract document that is easy for the user to understand and less stressful to read.

[0395] Step 6:

[0396] Finally, the server sends the revised contract document and proposed revisions to the user's terminal. The user can review the received document and provide additional feedback as needed. This feedback may be used to further revise the document.

[0397] Through this series of processes, the system can assist in the creation of legal documents while taking the user's emotions into consideration.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] The present invention is a system for streamlining the creation and review of legal documents, and its specific embodiments are described below.

[0415] This system's main feature is that, based on information entered by the user through a terminal, the server automatically generates legal documents using natural language processing technology. The user enters the necessary conditions and clauses for the contract through the terminal. This input data is sent to the server. The server uses a generation AI to retrieve relevant information from existing laws and past case law databases and generates the optimal legal document. This process allows legal professionals to significantly streamline their work.

[0416] The generated legal documents are compared by the server against a database of laws and precedents to identify potential legal risks. The server then suggests corrections to the user for the identified issues. The user can review these suggestions via their terminal and edit the document as needed.

[0417] Furthermore, the server also has the ability to search for laws and precedents in real time in response to user requests. When a user requests information on a specific law or precedent, the server immediately retrieves the relevant information from the database and sends it to the terminal. This function allows users to quickly access the latest legal information.

[0418] As a concrete example, consider a scenario where a company's legal department uses this system to draft a new business agreement. The user inputs the terms of the transaction, and the server automatically generates an optimal contract based on that information. The server then automatically analyzes the legal risks and presents the user with necessary revisions. The user can then finalize the contract based on these suggestions. In this way, the accuracy and efficiency of the entire legal work process are improved.

[0419] Furthermore, in international transactions where multilingual support is required, this system can provide legal support in multiple languages. If a user needs to generate contracts in different languages, the system can automatically generate contracts in each language, facilitating smooth international business transactions.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The user uses a device to input the necessary conditions and clauses for a legal document. The device then formats that information and sends it to the server.

[0423] Step 2:

[0424] The server activates the generation AI to create a draft of a legal document based on the input conditions and clauses. The server refers to a database of past laws and precedents to select the most appropriate wording and content to generate the document.

[0425] Step 3:

[0426] The server compares the generated legal documents with a database of laws and precedents. The server identifies legal risks and deficiencies and generates analysis results.

[0427] Step 4:

[0428] The server generates necessary corrective action suggestions based on the identified legal risks. The server then sends these suggestions to the terminal and presents them to the user.

[0429] Step 5:

[0430] The user reviews the proposed revisions via their device and edits the legal document as needed. The device can then resend the revised document to the server.

[0431] Step 6:

[0432] When a user requests specific legal or case law information, they send a search request to the server via their device. The server searches the legal and case law database in real time and retrieves the relevant information.

[0433] Step 7:

[0434] The server sends the search results to the device and presents them to the user. The user reviews the information displayed on the device and obtains the necessary legal information.

[0435] Step 8:

[0436] When a user requires a legal document in multiple languages, they select a language on their device and send a request to the server. The server generates the legal document in the selected language and sends it to the device, allowing the user to view the document in multiple languages.

[0437] (Example 1)

[0438] 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."

[0439] The process of creating legal documents is time-consuming and labor-intensive, and specialized knowledge is particularly required for identifying legal risks and considering countermeasures. This increases the burden on legal departments and makes efficient work difficult. Furthermore, challenges exist in obtaining appropriate legal support in international transactions requiring multilingual support. Therefore, there is a need for a system that automates the generation of legal documents, streamlines the management of legal risks, and is capable of handling international transactions.

[0440] 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.

[0441] This invention includes a server that automatically generates documents based on user requirements using natural language processing technology, a function that compares the generated documents with a database of laws and precedents to identify potential legal risks, and a function that implements proposed modifications to address the identified legal risks. This enables more efficient and accurate document creation processes, allowing users to quickly receive appropriate legal support even in international transactions.

[0442] "Natural language processing technology" refers to a set of technologies that enable computers to understand and process human language.

[0443] A "user" is an individual or organization that uses this system to create and manage legal documents.

[0444] "Conditions" refer to information such as necessary requirements and clauses defined by the user when generating a legal document.

[0445] "Document generation" is the process by which a computer automatically creates appropriate legal documents based on the input conditions.

[0446] A "legal and case law database" is a collection of information that accumulates laws and past case precedents necessary for creating legal documents and identifying risks.

[0447] "Legal risk" refers to any legal problems or deficiencies that may arise when a legal document is examined in light of laws and precedents.

[0448] A "proposal for correction" is a proposal for correction or improvement generated to address identified legal risks.

[0449] "Multilingual support" refers to the ability to operate functions or generate and provide documents in multiple different languages.

[0450] "International trade" refers to commercial transactions conducted between companies or individuals located in different countries.

[0451] This invention is a system for streamlining the creation and review of legal documents, and specifically utilizes natural language processing technology. The system operates in a manner where users input information via a terminal, and a server processes that information based on that input. The implementation of the system depends on the following hardware and software.

[0452] Users input the necessary information for the contract (e.g., names of the contracting parties, contract details, deadline, etc.) from their device (PC, tablet, smartphone, etc.). This information can be easily entered through the device's interface.

[0453] The terminal transmits data entered by the user to the server via the internet. This data transmission is encrypted using a secure protocol (e.g., HTTPS).

[0454] The server uses natural language processing technology based on the received data and generates legal documents using a generative AI model (e.g., GPT-4). The generative AI model is given prompts such as "A new business contract draft is needed. The trading partner is Company X, the contract period is one year, and the main transaction is the supply of 10,000 products per year," and creates an appropriate contract draft.

[0455] The generated document is cross-referenced with a database of laws and precedents on the server. The server identifies legal risks and creates proposed revisions to mitigate those risks. These proposed revisions are sent to the user's terminal as text.

[0456] Users can review this transmitted information on their device and edit the document as needed. This allows users to quickly complete legally accurate documents.

[0457] Furthermore, the server has multilingual capabilities, allowing it to generate legal documents in different languages ​​to accommodate international transactions. This enables smooth contract signing even in international business settings.

[0458] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0459] Step 1:

[0460] The user uses a terminal to input the necessary conditions and clauses for the contract. The information entered includes specific clauses such as the names of the contracting parties, the contract details, and the contract period. The terminal receives this information as a form and constructs the input data.

[0461] Step 2:

[0462] The terminal sends information entered by the user to the server. The transmitted data is encrypted using a secure communication protocol (e.g., HTTPS). The server prepares this received data for analysis.

[0463] Step 3:

[0464] The server analyzes the received data and generates a prompt statement based on that information. This prompt statement is then input to a generation AI model (e.g., GPT-4) to generate the appropriate legal document. The prompt statement is configured to reflect the input contract terms.

[0465] Step 4:

[0466] The generated legal documents are cross-referenced with a database of laws and precedents on the server. The server uses text mining techniques to compare the document content with the database content and identify potential legal risks. This identifies deficiencies and risks within the documents.

[0467] Step 5:

[0468] The server generates corrective action suggestions based on the identified risks. These suggestions include solutions and improvements to the problems found. The suggestions are returned to the user's terminal in text format.

[0469] Step 6:

[0470] The user reviews the proposed revisions sent from the server via their terminal. The user edits the document as needed and makes decisions to finalize the contract. An interface for document editing is provided on the terminal.

[0471] Step 7:

[0472] The server searches for legal and case law information in real time upon user request and immediately transmits the relevant information to the terminal. This function allows users to quickly obtain additional legal information.

[0473] (Application Example 1)

[0474] 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."

[0475] In modern electronic and international transactions, there is a need to generate legal documents quickly and accurately and manage appropriate legal risks. However, traditional methods involve time-consuming manual document creation, which can lead to insufficient identification and management of legal risks. Furthermore, in international transactions requiring multilingual support, the ability to quickly generate legal documents that take into account the regulations of each country becomes an even greater challenge.

[0476] 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.

[0477] In this invention, the server includes means for automatically generating documents based on user input conditions using natural language processing, means for identifying risks by comparing the generated documents with a regulatory data structure, and means for proposing modifications to the identified risks. This enables users to efficiently generate accurate legal documents and quickly manage legal risks. Furthermore, by providing support for multiple languages, legal documents can be generated quickly even in international business.

[0478] "Natural language processing" refers to techniques used by computers to understand and generate natural human language.

[0479] A "user" is an individual or legal entity that uses this system to create or review legal documents or agreements.

[0480] "Input conditions" refer to the requirements and clauses that users provide when generating legal documents.

[0481] A "document" is a written document that contains information related to legal matters or agreements.

[0482] "Automatic generation methods" refer to technologies in which a computer system mechanically creates documents with a specified format and content based on input from a user.

[0483] A "regulatory data structure" is a database that stores information related to laws and precedents.

[0484] "Risk" refers to the potential legal flaws or problems inherent in the generated document.

[0485] A "revision suggestion" refers to specific advice or guidelines for users to resolve problems contained in the generated document.

[0486] A "trading platform" is an electronic system used for exchanging goods and services in commercial and business transactions.

[0487] "Agreement" refers to the terms agreed upon by the parties involved in a transaction or contract.

[0488] "International business" refers to commercial activities and transactions conducted between different countries.

[0489] "Support" means providing users with the information and functions they need when creating documents or conducting transactions.

[0490] To realize this invention, a server, terminals, and a communication network to connect them are necessary. The server uses software equipped with natural language processing technology to automatically generate legal documents based on input conditions. The regulatory data structure also stores a database of laws and precedents, which is used to verify and identify the legal risks of the generated documents.

[0491] The terminal accepts input of conditions and clauses through a user-friendly interface. Furthermore, it displays the generated document and risk analysis results to the user and offers suggestions for revisions. Based on this information, the user can edit the document and complete the final version. Multilingual support enables automatic document generation in the languages ​​required for international business.

[0492] As a concrete example, consider the generation of contract documents on a trading platform. When a user enters specific transaction terms such as payment terms and contract period into a terminal, the server generates an appropriate contract document based on that information. Using a generation AI model, the system refers to relevant past precedents and laws to identify potential legal risks hidden in the document and proposes revisions to minimize those risks. For example, by providing a specific prompt such as, "If the user wishes to generate a contract for use in international transactions, please include clauses based on the laws of a specific country," the AI ​​model effectively extracts the necessary information and responds to the user's request.

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] The terminal accepts the necessary conditions and clauses for legal documents from the user as input. Specifically, the user fills in information about the contract details into an input form. This input information is then sent to the server.

[0496] Step 2:

[0497] The server receives input information sent from the terminal and automatically generates legal documents using natural language processing technology. Based on the entered contract terms, it applies existing standard document templates to construct an appropriate document structure. As a result, an initial contract document is generated.

[0498] Step 3:

[0499] The server compares the generated legal documents against the regulatory data structure. The input here is the generated document, and the output is the identification of legal risks. Specifically, it searches a database based on laws and precedents to find ambiguities and inconsistencies within the document.

[0500] Step 4:

[0501] The server generates proposed revisions based on the identified legal risks. The input is the risks identified in step 3, and the server outputs proposals by processing the data to identify countermeasures for these risks. Specifically, it generates draft document revisions and proposed clauses to be added to mitigate the risks.

[0502] Step 5:

[0503] The terminal displays revision suggestions sent from the server to the user. The input is the revision suggestions, and the output is a visual presentation to the user. The user reviews the revision suggestions and edits the document as needed.

[0504] Step 6:

[0505] Once the user has completed the final document revisions, the terminal outputs the completed legal document and saves or outputs it in the required format. This output becomes a formal contract after being reviewed by the user.

[0506] 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.

[0507] This invention relates to a legal document creation and review system that includes an emotion engine for identifying user emotions, and is intended to more effectively support legal work. Specific embodiments thereof are described below.

[0508] This system is configured so that the server automatically generates legal documents using natural language processing technology based on the legal document creation conditions entered by the user through a terminal. An emotion engine is incorporated into the system, analyzing the emotions the user expresses in real time during input. This information is reflected in the tone and style of the generated document. For example, if the user expresses anxiety or anger, the system will select more cautious and calm language.

[0509] The generated legal documents are cross-referenced by the server against a database of laws and precedents to identify legal risks. Here again, an emotion engine is utilized, dynamically adjusting suggested mitigation measures for detected risks based on the user's emotions. For example, if the user expresses strong concerns about a risk, the system provides detailed suggestions to minimize that risk.

[0510] As a concrete example, consider a scenario where a company's legal department uses this system during contract negotiations. The user inputs the contract terms via a terminal, and the server generates a corresponding contract while simultaneously analyzing the user's emotions. If the server detects that the user is experiencing stress, it can create a contract with wording that alleviates those emotions. Furthermore, suggested revisions are adjusted to match the user's preferences, and the suggested evidence and justifications are presented in more detail.

[0511] The emotional data collected by the emotion engine is used to optimize the entire legal support process. This allows the system to provide legal advice tailored to the individual needs of each user and to perform well in a variety of tasks, including international transactions.

[0512] In this way, by combining emotion recognition technology and natural language processing technology, this system achieves even more effective legal support than before.

[0513] The following describes the processing flow.

[0514] Step 1:

[0515] The user uses a terminal to input the necessary conditions and information for a legal document. The terminal records the user's input data in real time and sends this information to a server. At this time, an emotion engine analyzes the user's emotions at the time of input.

[0516] Step 2:

[0517] Based on the input data received by the server, natural language processing technology is used to generate an initial legal document. The server also considers the results of the emotion engine and reflects the tone and style of the document according to the user's emotions.

[0518] Step 3:

[0519] The generated legal documents are cross-referenced by the server against a database of laws and precedents. The server identifies legal risks and generates revision suggestions tailored to the user's feelings. For example, it provides detailed and reassuring suggestions to alleviate the user's anxiety.

[0520] Step 4:

[0521] The server sends identified legal risks and suggested modifications to the user's device. The user can then use the device to review the suggestions and select the modifications that best suit their needs.

[0522] Step 5:

[0523] Users can make necessary corrections on their device and resend the edited document to the server. The server reviews the final document and re-evaluates it as needed.

[0524] Step 6:

[0525] When a user requests information on laws and precedents, they send a request from their device. The server searches the database in real time and provides the retrieved information to the user.

[0526] Step 7:

[0527] The emotion engine tracks the user's emotional changes, and the server analyzes that data. The server uses the collected emotional data to optimize the legal support process and improve future interactions.

[0528] (Example 2)

[0529] 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."

[0530] Existing legal document creation systems often fail to adequately address user sentiments and needs through automatically generated documents, potentially resulting in inappropriate identification of legal risks and suggested revisions. Furthermore, they present challenges in providing sufficient support in situations requiring international multilingual capabilities.

[0531] 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.

[0532] In this invention, the server includes means for automatically generating documents based on user input conditions and sentiment data using natural language processing technology, means for identifying risks by comparing the generated documents with a data store of laws and precedents, and means for dynamically adjusting proposed modifications to the identified risks according to the user's sentiment. This enables the automatic generation of legal documents and risk analysis that are adapted to the user's sentiment and input conditions.

[0533] "Natural language processing technology" refers to the technology that enables computers to understand and generate human language, making semantic analysis of text and document generation possible.

[0534] "Sentimental data" refers to information about emotions collected from user input and interactions, and is used to adjust the tone and style of a document.

[0535] "Methods for automatically generating documents" refers to a process that automatically creates legal documents based on user input conditions and sentiment data, and utilizes natural language processing technology.

[0536] A "data store of laws and precedents" refers to a database used to manage and compare legal information and past precedents necessary for generating legal documents and conducting risk analysis.

[0537] "Means of identifying risks" refers to the process of comparing generated documents with legal and case law data to identify potential legal risks.

[0538] "Means for dynamically adjusting proposed modifications" refers to a process that appropriately changes proposed modifications for identified risks in accordance with the user's emotions, in order to provide proposals that meet the user's needs.

[0539] This system automates the creation and review of legal documents and operates based on information entered by users via a terminal. Users can input the requirements and conditions of legal documents, and this input is sent from the terminal to the server. The server analyzes the user's input data using natural language processing technology and sentiment analysis engines and automatically generates the necessary legal documents. Generative AI models such as BERT and GPT are used for natural language processing.

[0540] The server identifies potential legal risks by cross-referencing the input document with a database of laws and precedents. This cross-referencing can utilize a cloud-based database system. The server also adjusts the tone and style of the generated document to reflect sentiment analysis results, optimizing it to reflect the emotions expressed by the user during input.

[0541] Furthermore, the server proposes corrective actions for identified risks. These corrective actions are dynamically adjusted based on the user's sentiment data, allowing users to confidently accept the suggestions.

[0542] A concrete example is a scenario where a company's legal department uses this system when drafting a new contract. For instance, by entering a prompt such as, "Please help me draft a legal document regarding non-disclosure clauses in an inter-company contract, taking into consideration my concerns," the server can generate and propose a document that appropriately addresses these concerns. In this way, it is possible to meet the diverse needs of users through automated legal support.

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The user inputs the requirements, conditions, and prompts for legal documents through their terminal. This input data includes contract clauses and associated emotional requests. This information is sent from the terminal to the server. Specifically, the user enters information into a form and presses the "Submit" button. The input data is passed to the server as prompts.

[0546] Step 2:

[0547] The server generates legal documents using natural language processing techniques based on the received prompt text. This process utilizes generative AI models such as BERT and GPT to transform the input information into appropriate legal text. Specific data processing includes analysis of the input text and requirements extraction, resulting in the creation of an initial version of the contract document.

[0548] Step 3:

[0549] The server compares the generated legal document with a database of laws and precedents. This verifies whether the document's content complies with current laws and precedents and identifies potential legal risks. Specifically, it performs data calculations by issuing database queries and comparing the document's content with the information on laws and precedents. The output is the document with identified risks.

[0550] Step 4:

[0551] The server adjusts the tone and style of generated documents based on the user's sentiment data. A sentiment analysis engine analyzes the user's emotions and provides specific guidance for document adjustments. The input here is the user's sentiment data, and the output is a document adapted to those emotions.

[0552] Step 5:

[0553] The server generates proposed solutions for identified legal risks and dynamically adjusts them based on the user's sentiment. Specifically, it adjusts the content and tone of the proposed solutions to make the information more acceptable to the user. The input is risk information and sentiment data, and the output is the adjusted proposed solutions.

[0554] Step 6:

[0555] The terminal presents the user with the completed legal document and suggested revisions returned from the server. The user reviews the document based on the presented information and makes final adjustments as needed. Specifically, the user reviews the document on the screen and proceeds to the next step using "Approve" or "Revise" buttons. The output is the final, reviewed legal document.

[0556] (Application Example 2)

[0557] 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."

[0558] In drafting and reviewing legal documents, it is necessary to dynamically adjust the tone and style of the document in response to the user's emotions and to provide legal risk correction suggestions tailored to the user's emotional state. Traditional systems have been unable to analyze and reflect the user's emotions in the document, thus failing to alleviate stress and anxiety.

[0559] 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.

[0560] In this invention, the server includes means for automatically generating documents based on user input conditions using natural language processing, means for identifying risks by comparing the generated documents with an information database, and means for adjusting the tone and style of the documents using an emotion engine that identifies the user's emotions. This makes it possible to adjust documents according to the user's emotions and optimize risk correction suggestions.

[0561] "Natural language processing" is the technology that enables computers to understand, generate, and process human language.

[0562] "Users" are individuals or organizations that operate the system and provide or receive information.

[0563] "Means for automatically generating documents" refers to technologies or devices that allow a computer to automatically create documents based on user input.

[0564] An "information database" is a collection of various data, including legal documents and precedents, that can be searched and referenced by computer.

[0565] "Means of identifying risks" refers to techniques or processes that analyze generated documents to identify potential legal risks.

[0566] An "emotion engine" is software or hardware used to identify and analyze a user's emotions in real time.

[0567] "Methods for adjusting tone and style" refer to techniques for changing the atmosphere and expression of a document based on emotion.

[0568] "Means of making corrective suggestions" refer to technologies and processes that provide improvement proposals to mitigate identified risks.

[0569] To implement this invention, the following configuration is necessary. The server first implements a program using the Python programming language and a natural language processing library (e.g., NLTK, spaCy) to utilize natural language processing technology. The user provides input information through a terminal, and the server automatically generates a document based on that information. The generated document is compared with a database of laws and precedents to identify legal risks. A SQL-based database system is used to manage the information database.

[0570] The server further identifies the user's emotions using an emotion engine. By utilizing Microsoft Azure's emotion analysis API, it can analyze the user's voice and text data in real time. Based on the analysis results, it adjusts the tone and style of the document. For example, if the user is feeling anxious, it adjusts the tone of the text to soften it. The generated document and improvement suggestions are further supplemented with natural language processing using the Google Cloud Natural Language API.

[0571] For example, if a user enters "I have concerns about the terms of this contract," the system will immediately analyze their emotions and provide support to alleviate their anxiety. In addition to generating a standard contract, the system will automatically generate supplementary explanations and reassuring documents. An example of a prompt used in this process would be: "Please perform a real-time emotional analysis of the contract terms presented by the user and propose improvements in a calming tone."

[0572] Thus, by combining an emotion engine with natural language processing technology, the invention is configured to improve the user experience and provide more personalized and effective support in the creation and review of legal documents.

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The user enters legal information through their terminal. The entered data is mainly in text format, such as contract terms and conditions. The server receives this input data and prepares to begin natural language processing.

[0576] Step 2:

[0577] The server uses natural language processing libraries (e.g., NLTK, spaCy) to analyze text data entered by the user. This analysis helps the server understand the grammatical structure and meaning of the text, and extracts the information necessary for the automatic generation of contract documents. The output of the analysis is a dataset used by the generative AI model.

[0578] Step 3:

[0579] Based on the analyzed dataset, the server accesses a database of laws and precedents and automatically generates corresponding legal documents. This generation process effectively searches the database using a SQL-based database system to collect relevant information. The output is an unedited contract document for presentation to the user.

[0580] Step 4:

[0581] For the generated documents, the server uses an emotion engine to analyze the user's emotions in real time. Leveraging Microsoft Azure's emotion analysis API, it identifies the emotions the user feels towards the input data. The input can be voice or text data, and the output is an indicator of the emotional state.

[0582] Step 5:

[0583] The server adjusts the tone and style of the generated document based on the sentiment analysis results. Using the Google Cloud Natural Language API, it modifies the wording within the document according to the user's emotions and adds supplementary explanations to reduce stress. The output is an adjusted contract document that is easy for the user to understand and less stressful to read.

[0584] Step 6:

[0585] Finally, the server sends the revised contract document and proposed revisions to the user's terminal. The user can review the received document and provide additional feedback as needed. This feedback may be used to further revise the document.

[0586] Through this series of processes, the system can assist in the creation of legal documents while taking the user's emotions into consideration.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] [Fourth Embodiment]

[0591] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0592] 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.

[0593] 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).

[0594] 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.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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".

[0604] The present invention is a system for streamlining the creation and review of legal documents, and its specific embodiments are described below.

[0605] This system's main feature is that, based on information entered by the user through a terminal, the server automatically generates legal documents using natural language processing technology. The user enters the necessary conditions and clauses for the contract through the terminal. This input data is sent to the server. The server uses a generation AI to retrieve relevant information from existing laws and past case law databases and generates the optimal legal document. This process allows legal professionals to significantly streamline their work.

[0606] The generated legal documents are compared by the server against a database of laws and precedents to identify potential legal risks. The server then suggests corrections to the user for the identified issues. The user can review these suggestions via their terminal and edit the document as needed.

[0607] Furthermore, the server also has the ability to search for laws and precedents in real time in response to user requests. When a user requests information on a specific law or precedent, the server immediately retrieves the relevant information from the database and sends it to the terminal. This function allows users to quickly access the latest legal information.

[0608] As a concrete example, consider a scenario where a company's legal department uses this system to draft a new business agreement. The user inputs the terms of the transaction, and the server automatically generates an optimal contract based on that information. The server then automatically analyzes the legal risks and presents the user with necessary revisions. The user can then finalize the contract based on these suggestions. In this way, the accuracy and efficiency of the entire legal work process are improved.

[0609] Furthermore, in international transactions where multilingual support is required, this system can provide legal support in multiple languages. If a user needs to generate contracts in different languages, the system can automatically generate contracts in each language, facilitating smooth international business transactions.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The user uses a device to input the necessary conditions and clauses for a legal document. The device then formats that information and sends it to the server.

[0613] Step 2:

[0614] The server activates the generation AI to create a draft of a legal document based on the input conditions and clauses. The server refers to a database of past laws and precedents to select the most appropriate wording and content to generate the document.

[0615] Step 3:

[0616] The server compares the generated legal documents with a database of laws and precedents. The server identifies legal risks and deficiencies and generates analysis results.

[0617] Step 4:

[0618] The server generates necessary corrective action suggestions based on the identified legal risks. The server then sends these suggestions to the terminal and presents them to the user.

[0619] Step 5:

[0620] The user reviews the proposed revisions via their device and edits the legal document as needed. The device can then resend the revised document to the server.

[0621] Step 6:

[0622] When a user requests specific legal or case law information, they send a search request to the server via their device. The server searches the legal and case law database in real time and retrieves the relevant information.

[0623] Step 7:

[0624] The server sends the search results to the device and presents them to the user. The user reviews the information displayed on the device and obtains the necessary legal information.

[0625] Step 8:

[0626] When a user requires a legal document in multiple languages, they select a language on their device and send a request to the server. The server generates the legal document in the selected language and sends it to the device, allowing the user to view the document in multiple languages.

[0627] (Example 1)

[0628] 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".

[0629] The process of creating legal documents is time-consuming and labor-intensive, and specialized knowledge is particularly required for identifying legal risks and considering countermeasures. This increases the burden on legal departments and makes efficient work difficult. Furthermore, challenges exist in obtaining appropriate legal support in international transactions requiring multilingual support. Therefore, there is a need for a system that automates the generation of legal documents, streamlines the management of legal risks, and is capable of handling international transactions.

[0630] 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.

[0631] This invention includes a server that automatically generates documents based on user requirements using natural language processing technology, a function that compares the generated documents with a database of laws and precedents to identify potential legal risks, and a function that implements proposed modifications to address the identified legal risks. This enables more efficient and accurate document creation processes, allowing users to quickly receive appropriate legal support even in international transactions.

[0632] "Natural language processing technology" refers to a set of technologies that enable computers to understand and process human language.

[0633] A "user" is an individual or organization that uses this system to create and manage legal documents.

[0634] "Conditions" refer to information such as necessary requirements and clauses defined by the user when generating a legal document.

[0635] "Document generation" is the process by which a computer automatically creates appropriate legal documents based on the input conditions.

[0636] A "legal and case law database" is a collection of information that accumulates laws and past case precedents necessary for creating legal documents and identifying risks.

[0637] "Legal risk" refers to any legal problems or deficiencies that may arise when a legal document is examined in light of laws and precedents.

[0638] A "proposal for correction" is a proposal for correction or improvement generated to address identified legal risks.

[0639] "Multilingual support" refers to the ability to operate functions or generate and provide documents in multiple different languages.

[0640] "International trade" refers to commercial transactions conducted between companies or individuals located in different countries.

[0641] This invention is a system for streamlining the creation and review of legal documents, and specifically utilizes natural language processing technology. The system operates in a manner where users input information via a terminal, and a server processes that information based on that input. The implementation of the system depends on the following hardware and software.

[0642] Users input the necessary information for the contract (e.g., names of the contracting parties, contract details, deadline, etc.) from their device (PC, tablet, smartphone, etc.). This information can be easily entered through the device's interface.

[0643] The terminal transmits data entered by the user to the server via the internet. This data transmission is encrypted using a secure protocol (e.g., HTTPS).

[0644] The server uses natural language processing technology based on the received data and generates legal documents using a generative AI model (e.g., GPT-4). The generative AI model is given prompts such as "A new business contract draft is needed. The trading partner is Company X, the contract period is one year, and the main transaction is the supply of 10,000 products per year," and creates an appropriate contract draft.

[0645] The generated document is cross-referenced with a database of laws and precedents on the server. The server identifies legal risks and creates proposed revisions to mitigate those risks. These proposed revisions are sent to the user's terminal as text.

[0646] Users can review this transmitted information on their device and edit the document as needed. This allows users to quickly complete legally accurate documents.

[0647] Furthermore, the server has multilingual capabilities, allowing it to generate legal documents in different languages ​​to accommodate international transactions. This enables smooth contract signing even in international business settings.

[0648] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0649] Step 1:

[0650] The user uses a terminal to input the necessary conditions and clauses for the contract. The information entered includes specific clauses such as the names of the contracting parties, the contract details, and the contract period. The terminal receives this information as a form and constructs the input data.

[0651] Step 2:

[0652] The terminal sends information entered by the user to the server. The transmitted data is encrypted using a secure communication protocol (e.g., HTTPS). The server prepares this received data for analysis.

[0653] Step 3:

[0654] The server analyzes the received data and generates a prompt statement based on that information. This prompt statement is then input to a generation AI model (e.g., GPT-4) to generate the appropriate legal document. The prompt statement is configured to reflect the input contract terms.

[0655] Step 4:

[0656] The generated legal documents are cross-referenced with a database of laws and precedents on the server. The server uses text mining techniques to compare the document content with the database content and identify potential legal risks. This identifies deficiencies and risks within the documents.

[0657] Step 5:

[0658] The server generates corrective action suggestions based on the identified risks. These suggestions include solutions and improvements to the problems found. The suggestions are returned to the user's terminal in text format.

[0659] Step 6:

[0660] The user reviews the proposed revisions sent from the server via their terminal. The user edits the document as needed and makes decisions to finalize the contract. An interface for document editing is provided on the terminal.

[0661] Step 7:

[0662] The server searches for legal and case law information in real time upon user request and immediately transmits the relevant information to the terminal. This function allows users to quickly obtain additional legal information.

[0663] (Application Example 1)

[0664] 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".

[0665] In modern electronic and international transactions, there is a need to generate legal documents quickly and accurately and manage appropriate legal risks. However, traditional methods involve time-consuming manual document creation, which can lead to insufficient identification and management of legal risks. Furthermore, in international transactions requiring multilingual support, the ability to quickly generate legal documents that take into account the regulations of each country becomes an even greater challenge.

[0666] 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.

[0667] In this invention, the server includes means for automatically generating documents based on user input conditions using natural language processing, means for identifying risks by comparing the generated documents with a regulatory data structure, and means for proposing modifications to the identified risks. This enables users to efficiently generate accurate legal documents and quickly manage legal risks. Furthermore, by providing support for multiple languages, legal documents can be generated quickly even in international business.

[0668] "Natural language processing" refers to techniques used by computers to understand and generate natural human language.

[0669] A "user" is an individual or legal entity that uses this system to create or review legal documents or agreements.

[0670] "Input conditions" refer to the requirements and clauses that users provide when generating legal documents.

[0671] A "document" is a written document that contains information related to legal matters or agreements.

[0672] "Automatic generation methods" refer to technologies in which a computer system mechanically creates documents with a specified format and content based on input from a user.

[0673] A "regulatory data structure" is a database that stores information related to laws and precedents.

[0674] "Risk" refers to the potential legal flaws or problems inherent in the generated document.

[0675] A "revision suggestion" refers to specific advice or guidelines for users to resolve problems contained in the generated document.

[0676] A "trading platform" is an electronic system used for exchanging goods and services in commercial and business transactions.

[0677] "Agreement" refers to the terms agreed upon by the parties involved in a transaction or contract.

[0678] "International business" refers to commercial activities and transactions conducted between different countries.

[0679] "Support" means providing users with the information and functions they need when creating documents or conducting transactions.

[0680] To realize this invention, a server, terminals, and a communication network to connect them are necessary. The server uses software equipped with natural language processing technology to automatically generate legal documents based on input conditions. The regulatory data structure also stores a database of laws and precedents, which is used to verify and identify the legal risks of the generated documents.

[0681] The terminal accepts input of conditions and clauses through a user-friendly interface. Furthermore, it displays the generated document and risk analysis results to the user and offers suggestions for revisions. Based on this information, the user can edit the document and complete the final version. Multilingual support enables automatic document generation in the languages ​​required for international business.

[0682] As a concrete example, consider the generation of contract documents on a trading platform. When a user enters specific transaction terms such as payment terms and contract period into a terminal, the server generates an appropriate contract document based on that information. Using a generation AI model, the system refers to relevant past precedents and laws to identify potential legal risks hidden in the document and proposes revisions to minimize those risks. For example, by providing a specific prompt such as, "If the user wishes to generate a contract for use in international transactions, please include clauses based on the laws of a specific country," the AI ​​model effectively extracts the necessary information and responds to the user's request.

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The terminal accepts the necessary conditions and clauses for legal documents from the user as input. Specifically, the user fills in information about the contract details into an input form. This input information is then sent to the server.

[0686] Step 2:

[0687] The server receives input information sent from the terminal and automatically generates legal documents using natural language processing technology. Based on the entered contract terms, it applies existing standard document templates to construct an appropriate document structure. As a result, an initial contract document is generated.

[0688] Step 3:

[0689] The server compares the generated legal documents against the regulatory data structure. The input here is the generated document, and the output is the identification of legal risks. Specifically, it searches a database based on laws and precedents to find ambiguities and inconsistencies within the document.

[0690] Step 4:

[0691] The server generates proposed revisions based on the identified legal risks. The input is the risks identified in step 3, and the server outputs proposals by processing the data to identify countermeasures for these risks. Specifically, it generates draft document revisions and proposed clauses to be added to mitigate the risks.

[0692] Step 5:

[0693] The terminal displays revision suggestions sent from the server to the user. The input is the revision suggestions, and the output is a visual presentation to the user. The user reviews the revision suggestions and edits the document as needed.

[0694] Step 6:

[0695] Once the user has completed the final document revisions, the terminal outputs the completed legal document and saves or outputs it in the required format. This output becomes a formal contract after being reviewed by the user.

[0696] 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.

[0697] This invention relates to a legal document creation and review system that includes an emotion engine for identifying user emotions, and is intended to more effectively support legal work. Specific embodiments thereof are described below.

[0698] This system is configured so that the server automatically generates legal documents using natural language processing technology based on the legal document creation conditions entered by the user through a terminal. An emotion engine is incorporated into the system, analyzing the emotions the user expresses in real time during input. This information is reflected in the tone and style of the generated document. For example, if the user expresses anxiety or anger, the system will select more cautious and calm language.

[0699] The generated legal documents are cross-referenced by the server against a database of laws and precedents to identify legal risks. Here again, an emotion engine is utilized, dynamically adjusting suggested mitigation measures for detected risks based on the user's emotions. For example, if the user expresses strong concerns about a risk, the system provides detailed suggestions to minimize that risk.

[0700] As a concrete example, consider a scenario where a company's legal department uses this system during contract negotiations. The user inputs the contract terms via a terminal, and the server generates a corresponding contract while simultaneously analyzing the user's emotions. If the server detects that the user is experiencing stress, it can create a contract with wording that alleviates those emotions. Furthermore, suggested revisions are adjusted to match the user's preferences, and the suggested evidence and justifications are presented in more detail.

[0701] The emotional data collected by the emotion engine is used to optimize the entire legal support process. This allows the system to provide legal advice tailored to the individual needs of each user and to perform well in a variety of tasks, including international transactions.

[0702] In this way, by combining emotion recognition technology and natural language processing technology, this system achieves even more effective legal support than before.

[0703] The following describes the processing flow.

[0704] Step 1:

[0705] The user uses a terminal to input the necessary conditions and information for a legal document. The terminal records the user's input data in real time and sends this information to a server. At this time, an emotion engine analyzes the user's emotions at the time of input.

[0706] Step 2:

[0707] Based on the input data received by the server, natural language processing technology is used to generate an initial legal document. The server also considers the results of the emotion engine and reflects the tone and style of the document according to the user's emotions.

[0708] Step 3:

[0709] The generated legal documents are cross-referenced by the server against a database of laws and precedents. The server identifies legal risks and generates revision suggestions tailored to the user's feelings. For example, it provides detailed and reassuring suggestions to alleviate the user's anxiety.

[0710] Step 4:

[0711] The server sends identified legal risks and suggested modifications to the user's device. The user can then use the device to review the suggestions and select the modifications that best suit their needs.

[0712] Step 5:

[0713] Users can make necessary corrections on their device and resend the edited document to the server. The server reviews the final document and re-evaluates it as needed.

[0714] Step 6:

[0715] When a user requests information on laws and precedents, they send a request from their device. The server searches the database in real time and provides the retrieved information to the user.

[0716] Step 7:

[0717] The emotion engine tracks the user's emotional changes, and the server analyzes that data. The server uses the collected emotional data to optimize the legal support process and improve future interactions.

[0718] (Example 2)

[0719] 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".

[0720] Existing legal document creation systems often fail to adequately address user sentiments and needs through automatically generated documents, potentially resulting in inappropriate identification of legal risks and suggested revisions. Furthermore, they present challenges in providing sufficient support in situations requiring international multilingual capabilities.

[0721] 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.

[0722] In this invention, the server includes means for automatically generating documents based on user input conditions and sentiment data using natural language processing technology, means for identifying risks by comparing the generated documents with a data store of laws and precedents, and means for dynamically adjusting proposed modifications to the identified risks according to the user's sentiment. This enables the automatic generation of legal documents and risk analysis that are adapted to the user's sentiment and input conditions.

[0723] "Natural language processing technology" refers to the technology that enables computers to understand and generate human language, making semantic analysis of text and document generation possible.

[0724] "Sentimental data" refers to information about emotions collected from user input and interactions, and is used to adjust the tone and style of a document.

[0725] "Methods for automatically generating documents" refers to a process that automatically creates legal documents based on user input conditions and sentiment data, and utilizes natural language processing technology.

[0726] A "data store of laws and precedents" refers to a database used to manage and compare legal information and past precedents necessary for generating legal documents and conducting risk analysis.

[0727] "Means of identifying risks" refers to the process of comparing generated documents with legal and case law data to identify potential legal risks.

[0728] "Means for dynamically adjusting proposed modifications" refers to a process that appropriately changes proposed modifications for identified risks in accordance with the user's emotions, in order to provide proposals that meet the user's needs.

[0729] This system automates the creation and review of legal documents and operates based on information entered by users via a terminal. Users can input the requirements and conditions of legal documents, and this input is sent from the terminal to the server. The server analyzes the user's input data using natural language processing technology and sentiment analysis engines and automatically generates the necessary legal documents. Generative AI models such as BERT and GPT are used for natural language processing.

[0730] The server identifies potential legal risks by cross-referencing the input document with a database of laws and precedents. This cross-referencing can utilize a cloud-based database system. The server also adjusts the tone and style of the generated document to reflect sentiment analysis results, optimizing it to reflect the emotions expressed by the user during input.

[0731] Furthermore, the server proposes corrective actions for identified risks. These corrective actions are dynamically adjusted based on the user's sentiment data, allowing users to confidently accept the suggestions.

[0732] A concrete example is a scenario where a company's legal department uses this system when drafting a new contract. For instance, by entering a prompt such as, "Please help me draft a legal document regarding non-disclosure clauses in an inter-company contract, taking into consideration my concerns," the server can generate and propose a document that appropriately addresses these concerns. In this way, it is possible to meet the diverse needs of users through automated legal support.

[0733] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0734] Step 1:

[0735] The user inputs the requirements, conditions, and prompts for legal documents through their terminal. This input data includes contract clauses and associated emotional requests. This information is sent from the terminal to the server. Specifically, the user enters information into a form and presses the "Submit" button. The input data is passed to the server as prompts.

[0736] Step 2:

[0737] The server generates legal documents using natural language processing techniques based on the received prompt text. This process utilizes generative AI models such as BERT and GPT to transform the input information into appropriate legal text. Specific data processing includes analysis of the input text and requirements extraction, resulting in the creation of an initial version of the contract document.

[0738] Step 3:

[0739] The server compares the generated legal document with a database of laws and precedents. This verifies whether the document's content complies with current laws and precedents and identifies potential legal risks. Specifically, it performs data calculations by issuing database queries and comparing the document's content with the information on laws and precedents. The output is the document with identified risks.

[0740] Step 4:

[0741] The server adjusts the tone and style of generated documents based on the user's sentiment data. A sentiment analysis engine analyzes the user's emotions and provides specific guidance for document adjustments. The input here is the user's sentiment data, and the output is a document adapted to those emotions.

[0742] Step 5:

[0743] The server generates proposed solutions for identified legal risks and dynamically adjusts them based on the user's sentiment. Specifically, it adjusts the content and tone of the proposed solutions to make the information more acceptable to the user. The input is risk information and sentiment data, and the output is the adjusted proposed solutions.

[0744] Step 6:

[0745] The terminal presents the user with the completed legal document and suggested revisions returned from the server. The user reviews the document based on the presented information and makes final adjustments as needed. Specifically, the user reviews the document on the screen and proceeds to the next step using "Approve" or "Revise" buttons. The output is the final, reviewed legal document.

[0746] (Application Example 2)

[0747] 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".

[0748] In drafting and reviewing legal documents, it is necessary to dynamically adjust the tone and style of the document in response to the user's emotions and to provide legal risk correction suggestions tailored to the user's emotional state. Traditional systems have been unable to analyze and reflect the user's emotions in the document, thus failing to alleviate stress and anxiety.

[0749] 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.

[0750] In this invention, the server includes means for automatically generating documents based on user input conditions using natural language processing, means for identifying risks by comparing the generated documents with an information database, and means for adjusting the tone and style of the documents using an emotion engine that identifies the user's emotions. This makes it possible to adjust documents according to the user's emotions and optimize risk correction suggestions.

[0751] "Natural language processing" is the technology that enables computers to understand, generate, and process human language.

[0752] "Users" are individuals or organizations that operate the system and provide or receive information.

[0753] "Means for automatically generating documents" refers to technologies or devices that allow a computer to automatically create documents based on user input.

[0754] An "information database" is a collection of various data, including legal documents and precedents, that can be searched and referenced by computer.

[0755] "Means of identifying risks" refers to techniques or processes that analyze generated documents to identify potential legal risks.

[0756] An "emotion engine" is software or hardware used to identify and analyze a user's emotions in real time.

[0757] "Methods for adjusting tone and style" refer to techniques for changing the atmosphere and expression of a document based on emotion.

[0758] "Means of making corrective suggestions" refer to technologies and processes that provide improvement proposals to mitigate identified risks.

[0759] To implement this invention, the following configuration is necessary. The server first implements a program using the Python programming language and a natural language processing library (e.g., NLTK, spaCy) to utilize natural language processing technology. The user provides input information through a terminal, and the server automatically generates a document based on that information. The generated document is compared with a database of laws and precedents to identify legal risks. A SQL-based database system is used to manage the information database.

[0760] The server further identifies the user's emotions using an emotion engine. By utilizing Microsoft Azure's emotion analysis API, it can analyze the user's voice and text data in real time. Based on the analysis results, it adjusts the tone and style of the document. For example, if the user is feeling anxious, it adjusts the tone of the text to soften it. The generated document and improvement suggestions are further supplemented with natural language processing using the Google Cloud Natural Language API.

[0761] For example, if a user enters "I have concerns about the terms of this contract," the system will immediately analyze their emotions and provide support to alleviate their anxiety. In addition to generating a standard contract, the system will automatically generate supplementary explanations and reassuring documents. An example of a prompt used in this process would be: "Please perform a real-time emotional analysis of the contract terms presented by the user and propose improvements in a calming tone."

[0762] Thus, by combining an emotion engine with natural language processing technology, the invention is configured to improve the user experience and provide more personalized and effective support in the creation and review of legal documents.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The user enters legal information through their terminal. The entered data is mainly in text format, such as contract terms and conditions. The server receives this input data and prepares to begin natural language processing.

[0766] Step 2:

[0767] The server uses natural language processing libraries (e.g., NLTK, spaCy) to analyze text data entered by the user. This analysis helps the server understand the grammatical structure and meaning of the text, and extracts the information necessary for the automatic generation of contract documents. The output of the analysis is a dataset used by the generative AI model.

[0768] Step 3:

[0769] Based on the analyzed dataset, the server accesses a database of laws and precedents and automatically generates corresponding legal documents. This generation process effectively searches the database using a SQL-based database system to collect relevant information. The output is an unedited contract document for presentation to the user.

[0770] Step 4:

[0771] For the generated documents, the server uses an emotion engine to analyze the user's emotions in real time. Leveraging Microsoft Azure's emotion analysis API, it identifies the emotions the user feels towards the input data. The input can be voice or text data, and the output is an indicator of the emotional state.

[0772] Step 5:

[0773] The server adjusts the tone and style of the generated document based on the sentiment analysis results. Using the Google Cloud Natural Language API, it modifies the wording within the document according to the user's emotions and adds supplementary explanations to reduce stress. The output is an adjusted contract document that is easy for the user to understand and less stressful to read.

[0774] Step 6:

[0775] Finally, the server sends the revised contract document and proposed revisions to the user's terminal. The user can review the received document and provide additional feedback as needed. This feedback may be used to further revise the document.

[0776] Through this series of processes, the system can assist in the creation of legal documents while taking the user's emotions into consideration.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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."

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0798] The following is further disclosed regarding the embodiments described above.

[0799] (Claim 1)

[0800] A means of automatically generating legal documents based on user input conditions using natural language processing,

[0801] A means of identifying legal risks by comparing the generated legal documents with legal and case law databases,

[0802] Means for proposing modifications to identified legal risks,

[0803] A means of searching for and presenting legal and case law information to users in real time,

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, which includes means for analyzing contract-related risks based on user input information and proposing countermeasures.

[0807] (Claim 3)

[0808] The system according to claim 1, which includes means to provide legal support in multiple languages ​​and to handle international transactions.

[0809] "Example 1"

[0810] (Claim 1)

[0811] A function that automatically generates documents based on conditions set by the user using natural language processing technology,

[0812] The function includes the ability to identify potential legal risks by cross-referencing the generated documents with legal and case law databases,

[0813] The ability to implement corrective action proposals for identified legal risks,

[0814] A function to search for laws and precedents in real time and present them to users,

[0815] A function that acquires and provides information on laws and precedents to the terminal in response to user requests,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, which includes a function to analyze contract-related risks based on user input information and to suggest countermeasures.

[0819] (Claim 3)

[0820] The system according to claim 1, which includes the ability to provide legal support in multiple languages ​​and to handle international negotiations.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A means for automatically generating documents based on user input conditions using natural language processing,

[0824] A means of identifying risks by comparing the generated documents with the regulatory data structure,

[0825] Means for proposing corrective measures for identified risks,

[0826] A means of instantly searching for and presenting regulatory and case law information to users,

[0827] Means for streamlining agreement documents used in trading platforms,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, which includes means for analyzing risks related to agreements based on user input information and proposing countermeasures.

[0831] (Claim 3)

[0832] The system according to claim 1, which includes means for providing support in multiple languages ​​and for handling international business.

[0833] "Example 2 of combining an emotion engine"

[0834] (Claim 1)

[0835] A means for automatically generating documents based on user input conditions and sentiment data using natural language processing technology,

[0836] A means of identifying risks by comparing the generated documents with a data store of laws and precedents,

[0837] A means of dynamically adjusting corrective suggestions for identified risks in accordance with user sentiment,

[0838] A means of performing sentiment analysis and adaptively changing the style and tone of generated documents,

[0839] A means of searching for and providing users with legal and case law information in real time,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, which analyzes contract-related risks based on user sentiment data and proposes countermeasures on an individual basis.

[0843] (Claim 3)

[0844] The system described in claim 1 provides multilingual legal support and accommodates international transactions.

[0845] "Application example 2 of combining emotional engines"

[0846] (Claim 1)

[0847] A means for automatically generating documents based on user input conditions using natural language processing,

[0848] A means of identifying risks by comparing the generated documents with an information database,

[0849] Means for proposing corrective measures for identified risks,

[0850] A means of searching for information in real time and presenting it to users,

[0851] A means of adjusting the tone and style of a document using an emotion engine that identifies the user's emotions,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising means for analyzing the emotional state of a user and dynamically adjusting suggestions according to that state.

[0855] (Claim 3)

[0856] The system according to claim 1, which includes means for providing support in multiple languages ​​and for handling international business. [Explanation of symbols]

[0857] 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

1. A means for automatically generating documents based on user input conditions using natural language processing, A means of identifying risks by comparing the generated documents with the regulatory data structure, Means for proposing corrective measures for identified risks, A means of instantly searching for and presenting regulatory and case law information to users, Means for streamlining agreement documents used in trading platforms, A system that includes this.

2. The system according to claim 1, which includes means for analyzing risks related to agreements based on user input information and proposing countermeasures.

3. The system according to claim 1, which includes means for providing support in multiple languages ​​and for handling international business.

Images