System

Abstract

To provide a system.SOLUTION: A system comprises: means for a user transmitting input information by using a communication application; means for a server receiving the input information from the user and transmitting it to an AI engine; means for the AI engine analyzing the input information by using a generation AI model; means for, on the basis of an analysis result of the analysis means, the AI engine selecting an optimal contract document template from a contract document database; means for the AI engine customizing the selected contract document template; and means for the server providing the customized contract document template to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Creating and reviewing contracts requires specialized knowledge, which is time-consuming and costly. In addition, because the content of contracts varies greatly depending on the contracting party and the business, general templates are often insufficient. [Means for solving the problem]

[0005] The system connects an AI engine with a contract database to generate optimal contracts based on information input by users. It also includes a function to review contract drafts provided by users and output revision history and comments. This will improve the efficiency of contract creation and review. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention is a system that connects an AI engine with a contract database. This system generates an optimal contract based on information input by the user. Specifically, when the user inputs "this type of business with this type of party" into a messenger app, the AI ​​engine analyzes this input information and selects the optimal contract template from the contract database. The system then generates a contract based on the selected template and provides it to the user.

[0029] "Example 2"

[0030] Furthermore, another embodiment of the present invention is a system that reviews contract drafts provided by users. Specifically, when a user attaches a contract draft file to a messenger app, the system analyzes the draft and identifies any necessary revisions. The system then generates a history of the revisions and comments and provides them to the user. This function allows users to efficiently create and revise contracts without having to review them themselves.

[0031] "Example 3"

[0032] Furthermore, as a further embodiment of the present invention, there is a function for selecting a review level. Specifically, the user can select a review level from three levels: "Serious Negotiation," "Normal," and "Fast Closure Priority." This function allows the user to select the optimal review level depending on the situation and needs of their business.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The user enters information about their business and the person they are contracting with in the messenger app.

[0036] Step 2: The AI ​​engine analyzes this input information.

[0037] Step 3: The AI ​​engine selects the most suitable contract template from the contract database.

[0038] Step 4: Generate the contract based on the selected template.

[0039] Step 5: Provide the generated contract to the user.

[0040] "Example 2"

[0041] Step 1: The user attaches the draft contract file to a messenger app. Step 2: The system analyzes this draft.

[0042] Step 3: Identify the system's required modifications.

[0043] Step 4: Generate revision history and comments.

[0044] Step 5: Provide the generated revision history and comments to the user.

[0045] "Example 3"

[0046] Step 1: The user selects a review level from three levels: "Serious Negotiation," "Normal," and "Fastest Closure Priority."

[0047] Step 2: The system performs the review based on the selected review level.

[0048] Step 3: Provide the review results to the user.

[0049] Example 1

[0050] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0051] Conventional contract generation systems had the problem that it took a lot of time and effort for users to create a contract. In addition, specialized knowledge was required to check whether the content of the contract was appropriate, making it a difficult task for general users. Furthermore, the process of selecting and customizing a contract template was complicated and inefficient.

[0052] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0053] In this invention, the server includes means for a user to send input information using a communication application, means for the server to receive the user's input information and send it to an AI engine, means for the AI ​​engine to analyze the input information using a generative AI model, means for the AI ​​engine to select an optimal contract template from a contract database, means for the AI ​​engine to customize the selected template, and means for the server to provide the customized contract to the user. This enables users to easily generate optimal contracts and create appropriate contracts without specialized knowledge.

[0054] "User" means any person or entity that uses a communication application to generate a contract using the system.

[0055] "Communication application" refers to software that a user uses to send input information, and includes messenger applications, chat applications, and the like.

[0056] A "server" is a computer system that receives input information from users and sends it to the AI ​​engine.

[0057] An "AI engine" is a software component that uses a generative AI model to analyze user input information, select the most appropriate contract template from a contract database, and customize it.

[0058] A "generative AI model" is an artificial intelligence model used to analyze user input information, and uses natural language processing techniques.

[0059] The "contract database" is a database that stores contract templates corresponding to various business scenarios.

[0060] A "contract template" is a contract template that corresponds to a specific business scenario and is customized according to the user's requirements.

[0061] "Customization" is the process of modifying or adding to a selected contract template to reflect the user's specific requirements and conditions.

[0062] "Providing" means the act of sending the customized agreement to the user for review and modification.

[0063] The present invention provides a system for allowing a user to generate a contract using a communication application, the system including a server, an AI engine, a contract database, and a communication application.

[0064] The user uses a communication application to input the contents of the contract. For example, the user enters "I would like to enter into a software development contract with a certain company" and presses the send button. The communication application can be a messenger application or a chat application.

[0065] The server receives the input information sent by the user. The received information is sent to the AI ​​engine. The server converts the user's input information into an appropriate format and generates a prompt that the AI ​​engine can understand. For example, a prompt might be generated that reads, "The user has input that they would like to enter into a software development contract with a certain company. Please select and customize the most appropriate contract template."

[0066] The AI ​​engine uses a generative AI model (e.g., GPT-4 (registered trademark)) to analyze the user's input information. The analysis uses natural language processing technology to understand the user's intent and requirements. Based on the analysis results, the AI ​​engine selects the optimal contract template from a contract database. The contract database stores templates corresponding to various business scenarios.

[0067] The selected template is then customized according to the user's specific requirements. For example, the name of the contracting party or specific contract terms may be added to the template. The customized contract is then provided to the user via the server. The user can then review the generated contract in their communication application and make any necessary modifications.

[0068] As a concrete example, consider the case where a user inputs "I would like to enter into a software development contract with a certain company" into a communication application. The server that receives this input information sends the information to an AI engine. The AI ​​engine analyzes the input information using a generative AI model (e.g., GPT-4) and selects a "software development contract" template from a contract database. The selected template is customized to suit the content of a contract with a certain company and provided to the user.

[0069] In this way, the system quickly generates contracts that meet the specific needs of the user.

[0070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0071] Step 1:

[0072] A user sends input information using a communication application.

[0073] Specifically, the user opens a communication application (e.g., a messenger app), types in "I would like to enter into a software development contract with a certain company," and presses the send button.

[0074] Input: Text information about the user's contract details

[0075] Output: Data sent from the communication application to the server

[0076] Step 2:

[0077] The server receives the user's input.

[0078] In a specific operation, the server receives a message sent from the communication application saying, "I would like to enter into a software development contract with a certain company."

[0079] Input: User input information sent from a communication application

[0080] Output: Received data in the server

[0081] Step 3:

[0082] The server sends the received information to the AI ​​engine.

[0083] Specifically, the server sends the received message to the AI ​​engine, with a prompt such as, "The user has entered that they would like to enter into a software development contract with a certain company. Please select and customize the most appropriate contract template."

[0084] Input: Data received within the server

[0085] Output: Prompt sentence to the AI ​​engine

[0086] Step 4:

[0087] The AI ​​engine analyzes the input information.

[0088] Specifically, the AI ​​engine uses a generative AI model (e.g., GPT-4) to analyze the message "I would like to enter into a software development contract with a certain company" and understand the user's intent.

[0089] Input: Prompt sentence to AI engine

[0090] Output: Analysis results (user intent and requirements)

[0091] Step 5:

[0092] The AI ​​engine selects the optimal template from the contract database.

[0093] Specifically, the AI ​​engine selects a "software development contract" template from the contract database based on the analysis results.

[0094] Input: Analysis results

[0095] Output: Selected Agreement Template

[0096] Step 6:

[0097] The AI ​​engine will customize the selected template.

[0098] Specifically, the AI ​​engine customizes a "Software Development Agreement" template by adding the name of a company and specific contract terms.

[0099] Input: Selected Agreement Template

[0100] Output: Customized contract

[0101] Step 7:

[0102] The server provides the customized contract to the user.

[0103] Specifically, the server sends the customized contract to the user, who can then review it in the communication application and make any necessary modifications.

[0104] Input: Customized Agreement

[0105] Output: Data provided to the user (contract)

[0106] (Application example 1)

[0107] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] In conventional contract generation systems, users had to take time to input business details and counterparty information, and the process of selecting the most suitable contract template was cumbersome. Furthermore, the content of the generated contract often did not fully meet the user's specific needs, resulting in issues such as time-consuming revisions and review of the contract.

[0109] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0110] In this invention, the server includes means for connecting the AI ​​engine to the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for the user to input business details and other party information, means for the AI ​​engine to analyze the input information and select a contract template, means for generating a contract based on the selected template, and means for displaying the generated contract. This enables the user to easily input business details and other party information and quickly generate an optimal contract.

[0111] An "AI engine" is software that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[0112] A "contract database" is a database system that stores various contract templates and past contract data and allows for searching and retrieval.

[0113] "Information input from the user" refers to data such as business details and other party information that the user provides to the system.

[0114] "Means for generating optimal contracts" refers to a function that selects an appropriate template from a contract database based on information entered by the user and creates a customized contract.

[0115] "Means for providing the generated contract to the user" refers to a function that displays the generated contract to the user or provides it in a downloadable format.

[0116] "Business content" refers to the specific content of the transactions or business conducted by the user.

[0117] "Information about the other party" refers to information such as the name and contact details of the other party with whom the user enters into a contract.

[0118] A "contract template" is a format in which the basic structure and content of a contract are predetermined.

[0119] The "means for generating a contract" is a function that creates a contract that reflects the information entered by the user based on the selected contract template.

[0120] "Means for displaying the contract" is a function that displays the generated contract on the user's device.

[0121] A system for implementing this invention includes means for connecting an AI engine to a contract database, means for generating an optimal contract based on information input by a user, means for providing the generated contract to the user, means for the user to input business details and information about the other party, means for the AI ​​engine to analyze the input information and select a contract template, means for generating a contract based on the selected template, and means for displaying the generated contract.

[0122] System program processing

[0123] Hardware and Software Configuration

[0124] Server: Hosts the AI ​​engine (OpenAI® API) and contract database (SQLite).

[0125] User device: A device such as a smartphone or computer on which a user provides input information and checks the generated contract.

[0126] Data processing and calculation

[0127] 1. Getting user input:

[0128] Enter the business details and the other party's information from the user's device.

[0129] For example: "Enter your business: Food supply" or "Enter your contact details: Supplier"

[0130] 2. Template selection by AI engine:

[0131] The server uses the OpenAI API to analyze the user's input information and select the most appropriate contract template.

[0132] Example prompt: "Business: Food supply. Partner: Supplier. Choose the most appropriate contract template."

[0133] 3. Generate the contract:

[0134] The server retrieves the selected template from the contract database and embeds the user's input information to generate a customized contract.

[0135] Example: The template "Agreement regarding {business}\nWith: {partner}" is converted to "Agreement regarding the supply of food\nWith: Supplier".

[0136] 4. Providing the generated contract:

[0137] The server sends the generated contract to the user's terminal and displays it for the user to review.

[0138] Specific examples

[0139] If a user wants to generate a food supply contract using a smartphone, they can use the system by following the steps below.

[0140] 1. The user opens the application on their smartphone and enters "food supply" as the business type and "supply company" as the recipient information.

[0141] 2. The server uses the OpenAI API to analyze the input information and select the most appropriate contract template.

[0142] 3. The server generates a contract based on the selected template, reflecting the information entered by the user.

[0143] 4. The generated contract will be displayed on the user's smartphone, allowing the user to check the contents.

[0144] In this way, users can easily input business details and counterparty information and quickly generate the optimal contract.

[0145] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0146] Step 1:

[0147] The user opens the application on their smartphone and inputs the business details and the other party's information. The input information is "food supply" for the business details and "supply company" for the other party's information. This input information is sent to the server.

[0148] Step 2:

[0149] The server analyzes the information received from the user. Specifically, it processes the business details and the other party's information as text data and passes it to the AI ​​engine. The input data is formatted as a prompt: "Business details: Food supply\nOther party: Supplier\nPlease select the most appropriate contract template."

[0150] Step 3:

[0151] The server sends the prompt text to the AI ​​engine using the OpenAI API. The AI ​​engine analyzes the prompt text and selects the most appropriate contract template from the contract database. The ID of the selected template is returned to the server.

[0152] Step 4:

[0153] The server retrieves the selected template from the contract database. The template is a format that contains placeholders (e.g., "Contract for {business}\nTo: {partner}").

[0154] Step 5:

[0155] The server then fills the template with the user's input, replacing the placeholders "{business}" with "food supply" and "{partner}" with "supplier," generating a customized contract.

[0156] Step 6:

[0157] The server sends the generated contract to the user's smartphone, where the user can view the contract through an application on the smartphone.

[0158] Step 7:

[0159] The user can review the generated contract and make any necessary corrections or add comments. The corrected contract is then sent back to the server and saved as the final contract.

[0160] Example 2

[0161] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0162] Conventional contract drafting systems required users to review draft contracts and identify revisions, which was time-consuming and laborious. There was also a risk that revisions would not be made appropriately from a legal perspective. This could result in a decline in contract quality and the possibility of legal issues arising.

[0163] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for connecting the AI ​​engine and the document database, means for generating an optimal document based on information input by a user, means for providing the generated document to the user, means for the user to attach a document draft to a messenger app, means for the server to receive the attached file, means for the server to convert the file into text format, means for the server to analyze the document using the generated AI model, means for the server to identify corrections and generate a correction history and comments, and means for the server to provide the generated correction history and comments to the user. This allows the user to efficiently review contracts and create high-quality contracts with appropriate corrections from a legal perspective.

[0164] An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions.

[0165] A "document database" is a database system that stores contracts and other document templates and allows them to be searched and retrieved.

[0166] "User input" refers to data or instructions that a user provides to the system, upon which the system operates.

[0167] An "optimal document" is a document that is generated based on user input information and is most suitable for the purpose.

[0168] A "messenger app" is application software that allows users to send and receive text messages and files.

[0169] "Attachment" refers to a document file sent via a messenger app.

[0170] "Text format" is a format in which the contents of a document are expressed as character data.

[0171] A "generative AI model" is a model that uses artificial intelligence technology to analyze and generate documents.

[0172] "Correction" means a portion of a document that has been identified as requiring correction.

[0173] "Revision history" is data that records the revisions made to a document.

[0174] A "comment" is text that includes specific instructions or explanations for the correction.

[0175] MODE FOR CARRYING OUT THE INVENTION

[0176] The present invention provides a system for allowing a user to efficiently review a contract draft and identify parts that need to be revised. A specific embodiment of this system will be described below.

[0177] Hardware and software used

[0178] The system uses the following hardware and software:

[0179] Server: Receives data, analyzes it, runs generative AI models, and generates revision history and comments.

[0180] Messenger App: An application through which a user can attach and send a draft agreement (e.g., Slack, MICROSOFT® TEAMS®).

[0181] Generative AI model: An artificial intelligence model (e.g., GPT-4) that analyzes contracts and identifies areas for revision.

[0182] PDF parser library: A library for converting PDF files to text format (e.g. PyPDF2).

[0183] Word parser library: A library for converting Word files to text format (e.g. Python-docx).

[0184] System Operation

[0185] A user creates a draft contract and logs in to a messenger app. Next, the user selects the draft contract file, attaches it to the chat window of the messenger app, and sends it. The server receives the file sent from the messenger app and obtains the contents of the file.

[0186] If the received file is in a format such as PDF or Word, the server converts it to text format. Specifically, for PDF files, it uses a PDF parser library (e.g., PyPDF2), and for Word files, it uses the Python-docx library to extract the text.

[0187] Next, the server inputs the converted draft contract into a generative AI model (e.g., GPT-4). The server generates a prompt to instruct the model to analyze the contents of the contract. An example of a prompt is, "Please analyze the following draft contract and identify the necessary revisions. Please provide specific comments for the revisions."

[0188] The server receives the analysis results from the generative AI model and identifies areas to be revised in the contract. For example, it identifies areas to be revised such as "the definition of confidential information is vague." The server then generates a revision history and comments for the identified areas to be revised. The comments include specific instructions such as "The definition of confidential information needs to be clarified."

[0189] Finally, the server sends the generated revision history and comments to the chat window of the messenger app, where the user can review the received revision history and comments and make revisions to the contract.

[0190] Specific examples

[0191] As a concrete example, consider the case where a user wants to review a draft NDA (non-disclosure agreement). The user attaches the draft NDA to Slack and sends it to the server. The server receives this draft and analyzes it using GPT-4. As a result of the analysis, areas that need to be revised, such as "the definition of confidential information is vague," are identified. The server generates a comment for this revision, such as "The definition of confidential information needs to be clarified," and provides it to the user.

[0192] Example prompts to input to a generative AI model:

[0193] Please analyze the NDA draft below and identify any necessary changes. Please provide specific comments for any changes.

[0194] NDA Draft:

[0195] 1. This Agreement is entered into between the Discloser and the Recipient of Confidential Information.

[0196] 2. Confidential Information means all information disclosed by the Disclosing Party to the Receiving Party.

[0197] ...

[0198] "

[0199] In this way, users can efficiently create and modify contracts.

[0200] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0201] Step 1:

[0202] The user attaches the draft contract to a messenger app and sends it.

[0203] Specifically, a user creates a draft contract and logs into a messenger app (e.g., Slack, Microsoft Teams). Next, the user selects the draft contract file, attaches it to the chat window of the messenger app, and sends it. The input is the draft contract file, and the output is the file sent through the messenger app.

[0204] Step 2:

[0205] The server receives the attachment.

[0206] The server receives the file sent from the messenger app. Specifically, the server checks the metadata of the received file (file name, file size, sender information, etc.) and obtains the file contents. The input is the file sent from the messenger app, and the output is the file data stored on the server.

[0207] Step 3:

[0208] The server converts the file into a text format.

[0209] If the received file is in a format such as PDF or Word, the server converts it to text format. Specifically, for PDF files, a PDF parser library (e.g., PyPDF2) is used, and for Word files, the Python-docx library is used to extract the text. The input is a PDF or Word file, and the output is text data.

[0210] Step 4:

[0211] The server analyzes the document using the generative AI model.

[0212] The server inputs the draft contract converted into text format into a generative AI model (e.g., GPT-4). Specifically, the server generates a prompt and instructs the model to analyze the contents of the contract. An example of a prompt would be, "Analyze the following draft contract and identify any necessary revisions. Please provide specific comments for the revisions." The input is the text draft contract and the prompt, and the output is the analysis result from the generative AI model.

[0213] Step 5:

[0214] The server identifies the modifications and generates a modification history and comments.

[0215] The server receives the analysis results from the generative AI model and identifies the areas to be revised in the contract. Specific operations include identifying areas to be revised, such as "the definition of confidential information is unclear." The server then generates a revision history and comments for the identified areas to be revised. The comments include specific instructions such as "The definition of confidential information needs to be clarified." The input is the analysis results from the generative AI model, and the output is a revision history and comments.

[0216] Step 6:

[0217] Providing server-generated revision history and comments to users.

[0218] The server sends the generated revision history and comments to the chat window of the messenger app. Specifically, the server sends the revision history and comments in text format to the messenger app so that the user can view them. The input is the revision history and comments, and the output is the revision history and comments provided to the user through the messenger app.

[0219] In this way, users can efficiently create and modify contracts.

[0220] (Application example 2)

[0221] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0222] In conventional contract creation systems, even if users uploaded a draft contract, they lacked the functionality to automatically analyze the content, identify revisions, and generate comments. This meant that users had to manually review the contract. This resulted in the problem of requiring a great deal of time and effort to create and revise contracts.

[0223] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0224] In this invention, the server includes means for connecting the AI ​​engine with the contract database, means for generating an optimal contract based on information input by a user, means for analyzing a contract draft uploaded by a user, identifying corrections to be made, and generating comments, and means for providing the generated comments to the user. This enables users to automate contract reviews and efficiently create and revise contracts.

[0225] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[0226] A "contract database" is a database system that stores contract templates and past contract data, and allows for searching and retrieval as needed.

[0227] "User input information" refers to information provided by the user to the system, including data and conditions necessary for creating or amending a contract.

[0228] The "means for generating optimal contracts" is a system that has the function of selecting an appropriate template from a contract database based on information input by the user, and filling in the necessary information to create a contract.

[0229] A "means for providing a generated contract to a user" is a system that has the ability to display, transmit, or provide a system-generated contract to a user in a downloadable format.

[0230] A "draft contract" is an incomplete contract created by a user, which is a document that requires final confirmation and revision.

[0231] The "means for identifying the areas to be corrected and generating comments" is a system that has the function of analyzing a contract draft, automatically detecting the areas that need to be corrected, and generating comments and advice regarding them.

[0232] "Means for providing generated comments to users" means a system that has the functionality to provide system-generated comments and advice on corrections to users in a format that can be displayed, sent, or downloaded.

[0233] The system for implementing this invention consists of a server that includes an AI engine, a contract database, and a user interface, and a terminal for user access. The specific operation and usage of the system are explained below.

[0234] System configuration

[0235] 1. AI Engine

[0236] The AI ​​engine installed on the server uses a generative AI model to analyze and generate contracts. Specifically, it analyzes the contract draft provided by the user, identifies areas to be revised, and generates comments.

[0237] 2. Contract Database

[0238] The contract database stores contract templates and past contract data. The AI ​​engine selects the optimal template from this database and generates a contract based on the information entered by the user.

[0239] 3. User Interface

[0240] The user interface provides a screen for users to upload contracts and review generated contracts and comments, and is implemented as a web browser or mobile application.

[0241] System Operation

[0242] 1. Upload the contract

[0243] Users use their devices to upload draft contracts to the system, which are then analyzed by the server's AI engine.

[0244] 2. Contract analysis and comment generation

[0245] The AI ​​engine uses a generative AI model to analyze the contents of the draft contract, identify areas for revision, and generate appropriate comments and advice for the identified areas.

[0246] 3. Submitting Comments

[0247] The generated comments are provided to the user through the user interface, allowing the user to refer to them when revising the contract.

[0248] Hardware and software used

[0249] Hardware: Servers, user devices (PCs, smartphones, tablets, etc.)

[0250] Software: Python, docx library, openai library, web browser or mobile application

[0251] Specific examples

[0252] When a user uploads terms of use for an electronic payment service, the following comment is generated:

[0253] 1. The description of "Payment Method" in Article 3 is unclear. Please specify the specific payment method.

[0254] 2. The description regarding the "Cancellation Policy" in Article 5 is insufficient. Please add the cancellation conditions and procedures.

[0255] 3. The description of "Privacy Policy" in Article 7 is out of date. Please update it to the latest privacy policy.

[0256] Prompt Sentence Examples

[0257] Please analyze the draft contract below, identify any necessary revisions, and generate comments.

[0258] [Contents of the contract draft]

[0259] In this way, users simply upload contracts or terms of use, and the AI ​​engine automatically analyzes the content, identifies areas to be corrected, and generates comments, allowing them to efficiently create and modify contracts.

[0260] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0261] Step 1:

[0262] The user uploads the draft agreement using a device.

[0263] Input: The user selects the contract draft file from the terminal and presses the upload button.

[0264] Output: A draft contract file is sent to the server.

[0265] Specific behavior: The user selects a contract draft file through a web browser or mobile application interface and clicks the upload button, which sends the file to the server via an HTTP request.

[0266] Step 2:

[0267] The server receives the contract draft and passes it to the AI ​​engine.

[0268] Input: The uploaded draft agreement file.

[0269] Output: The contents of the draft contract are passed to the AI ​​engine.

[0270] Specific operation: The server temporarily stores the received contract draft file, reads its contents, and converts it into text format to be passed to the AI ​​engine.

[0271] Step 3:

[0272] The AI ​​engine analyzes the contract draft, identifies areas to be revised, and generates comments.

[0273] Input: The text content of the draft contract.

[0274] Output: Corrections and comments.

[0275] What it does: The AI ​​engine uses the generative AI model to analyze the content of the contract draft, generate prompts and send them to the OpenAI API, analyze the results returned by the API, and extract corrections and comments.

[0276] Step 4:

[0277] The server provides the generated comments to the user.

[0278] Input: Corrections and comments returned by the AI ​​engine.

[0279] Output: Corrections and comments displayed in the user interface.

[0280] Specific operation: The server sends the corrections and comments received from the AI ​​engine to the user interface, where the user can view the corrections and comments through a web browser or mobile application.

[0281] Step 5:

[0282] The user checks the corrections and comments and modifies the contract.

[0283] Input: Corrections and comments provided by the server.

[0284] Output: Amended draft contract.

[0285] Specific operation: The user can revise the contract draft based on the provided revisions and comments. Once the revisions are complete, they can be uploaded back to the system.

[0286] In this way, users can automate contract review and efficiently create and modify contracts.

[0287] Example 3

[0288] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0289] Conventional contract generation systems have made it difficult to flexibly review contracts according to the user's business situation and needs. Furthermore, they lacked the ability to select the appropriate review level for cases where detailed negotiations were required or where quick conclusion was required. This often resulted in users spending a great deal of time and effort reviewing contracts.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0291] In this invention, the server includes a means for allowing a user to select a review level, a means for generating a prompt sentence based on the selected review level, and a means for inputting the generated prompt sentence into a generative AI model to generate a review. This allows a user to select an optimal review level according to their business situation and needs, and efficiently review a contract.

[0292] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze and generate data.

[0293] A "contract database" is a database system that stores contract templates and past contract data and allows for searching and referencing.

[0294] "User input" refers to data or instructions provided by the user to the system, such as the content of the contract or the selection of the review level.

[0295] "Review Level" refers to three options that users can choose from when reviewing a contract, which vary in level of detail and priority: "Serious Negotiation," "Normal," and "Fastest Conclusion Priority."

[0296] A "prompt sentence" is an instruction sentence input to the generative AI model, and is generated according to the review level.

[0297] A "generative AI model" is an artificial intelligence model that generates text based on a given prompt, such as GPT-4.

[0298] A "review" is a document generated by a generative AI model that includes an evaluation of the contract and suggested modifications.

[0299] "Means provided to users" refers to the functionality for displaying or sending generated contracts and reviews to the user's device.

[0300] This invention is a system that allows users to select a contract review level based on their business situation and needs, and efficiently reviews contracts using a generative AI model. Specific embodiments of this system are described below.

[0301] Hardware and software used

[0302] Hardware: Servers, user devices (PCs, smartphones, etc.)

[0303] Software: web applications, generative AI models (e.g., GPT-4)

[0304] System Overview

[0305] The user accesses the web application using a terminal and logs in. After logging in, the user selects one of three options on the review level selection screen: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." The selected review level information is sent from the terminal to the server.

[0306] The server generates a prompt to be input to the generative AI model based on the received review level. For example, if "serious negotiation" is selected, the prompt will be "Please generate a review for detailed contract negotiations." The server inputs the generated prompt into the generative AI model (e.g., GPT-4) to generate a review. The generated review is stored on the server and sent to the user's device. The user can check the review results on their device.

[0307] Specific examples

[0308] If the user selects "Serious Negotiation"

[0309] Example prompt: "Generate a review to facilitate detailed contract negotiations."

[0310] Review produced: "With regard to Article 3 of this Agreement, the following points need to be negotiated..."

[0311] If the user selects "Normal"

[0312] Example prompt: "Generate a standard contract review."

[0313] Produces the following review: "This agreement is general and I have no issues with it, but I need to clarify section 5..."

[0314] If the user selects "Fastest conclusion priority"

[0315] Example prompt: "Generate a quick review to close the deal faster."

[0316] Produces review: "This contract can be executed quickly. No issues..."

[0317] In this way, users can select the optimal review level according to their business situation and needs, and efficiently review contracts using the generative AI model. This system allows users to significantly reduce the time and effort required to review contracts. The flow of the specific processing in Example 3 is explained using Figure 15.

[0318] Step 1:

[0319] User selects review level

[0320] Input: A user accesses a web application using a terminal and logs in.

[0321] Specific operation: The user selects one of the three options on the review level selection screen: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." Clicks the review level radio button and presses the "Submit" button.

[0322] Output: The selected review level information is saved to the device.

[0323] Step 2:

[0324] The device sends the selected review level to the server.

[0325] Input: User-selected review level information

[0326] Specific behavior: The device sends a POST request to the server with review-level data in JSON format.

[0327] Output: The server receives the review level information.

[0328] Step 3:

[0329] The server generates a prompt according to the review level.

[0330] Input: Review level information received by the server

[0331] Specific operation: The server determines the review level using conditional branching and generates the corresponding prompt text as a string.

[0332] Example: If "Serious Negotiation" is selected, the prompt will read "Generate a review to conduct detailed contract negotiations."

[0333] Output: The generated prompt text is saved on the server.

[0334] Step 4:

[0335] The server inputs a prompt into the generative AI model and generates a review.

[0336] Input: Server generated prompt text

[0337] Specific operation: The server sends a prompt to the API endpoint of the generative AI model and receives the generated review.

[0338] Output: The generated reviews are saved on the server.

[0339] Step 5:

[0340] The server sends the generated reviews to the user's device.

[0341] Input: Server-generated review

[0342] Specific behavior: The server sends the generated review in JSON format to the user's device as an HTTP response.

[0343] Output: The user's device receives the review.

[0344] Step 6:

[0345] The user checks the review results

[0346] Input: Review sent to user's device

[0347] Specific behavior: The user scrolls through the review results displayed on the device screen, reads them, and downloads or prints them as needed.

[0348] Output: User checks the review results and decides on the next action.

[0349] (Application example 3)

[0350] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0351] Conventional contract generation systems have difficulty in providing flexible reviews that are tailored to the user's business situation and needs, and have had problems in that they were unable to meet user requirements, particularly in terms of review quality and speed. Furthermore, the system was unable to understand how detailed the user wanted the review to be and provide appropriate guidelines, which sometimes reduced the efficiency and effectiveness of the review.

[0352] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0353] In this invention, the server includes means for connecting the AI ​​engine with the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for the user to select a review level from three levels: "serious negotiation," "normal," and "fastest conclusion priority," and means for providing review guidelines according to the selected review level. This enables flexible and efficient reviews according to the user's business situation and needs.

[0354] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze data and derive optimal results.

[0355] A "contract database" is a database system that stores contract templates and past contract data, and can be searched and retrieved as needed.

[0356] "User Input" means information provided by a user to the system, including data necessary for contract generation and review.

[0357] "Means for generating optimal contracts" refers to a function that selects an appropriate template from a contract database based on information input by the user, and creates a contract by filling in the necessary information.

[0358] The "means for providing the generated contract to the user" is a function for displaying or sending the generated contract to the user.

[0359] The "means of selecting review level" is a function that allows users to select the level of detail and speed of the review from three levels: "serious negotiation," "normal," and "priority for fastest conclusion."

[0360] "Means for providing review guidelines" is a function that presents users with appropriate review methods and points to note depending on the review level selected.

[0361] The system for implementing this invention is composed of a server including an AI engine, a contract database, and a user interface, and a terminal for user access. A specific embodiment of the system is described below.

[0362] System configuration

[0363] The server has a means for connecting the AI ​​engine with the contract database. The AI ​​engine analyzes the information input by the user and selects the optimal contract template from the contract database. The server also has a means for generating the optimal contract based on the selected template. The generated contract is provided to the user.

[0364] Furthermore, the server has a means for the user to select a review level from three levels: "serious negotiation," "normal," and "fastest conclusion priority." It also includes a means for providing review guidelines according to the selected review level.

[0365] Program processing

[0366] The server is implemented using programming languages ​​such as Python and Java (registered trademark). The AI ​​engine uses a generative AI model that uses natural language processing (NLP) technology. Specifically, machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch are used. The contract database is built using SQL or NoSQL databases.

[0367] When a user accesses the system from their device and requests the generation of a contract, the server receives the user's input information, which is then analyzed by the AI ​​engine. Based on the analysis results, the optimal template is selected from the contract database and the contract is generated. The generated contract is then provided to the user's device.

[0368] When a user selects a review level, the server generates review guidelines according to the selected level and provides them to the user. For example, if "serious negotiation" is selected, detailed review guidelines will be displayed. Specifically, a specific review such as "This product is of very high quality, with particularly good battery life. However, I'm concerned about the high price" is expected.

[0369] Prompt Sentence Examples

[0370] "Please select your review level. Choose from hard negotiation, normal, or fastest closing priority."

[0371] In this way, flexible and efficient reviews can be performed according to the user's business situation and needs.

[0372] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0373] Step 1:

[0374] The user accesses the system from a terminal and requests the generation of a contract.

[0375] Input: User's contract generation request information (contract content, conditions, etc.)

[0376] Output: Contract generation request to the server

[0377] Specific operations: The user enters the information required to generate a contract through the terminal interface and presses the send button.

[0378] Step 2:

[0379] The server receives the user's input information and sends it to the AI ​​engine.

[0380] Input: User's contract generation request information

[0381] Output: Analysis request to the AI ​​engine

[0382] Specific operation: The server analyzes the received user input information, converts it into an appropriate format, and sends it to the AI ​​engine.

[0383] Step 3:

[0384] The AI ​​engine analyzes the information entered by the user and selects the most appropriate contract template from the contract database.

[0385] Input: User's contract generation request information

[0386] Output: Optimal contract template

[0387] Specific operation: The AI ​​engine uses natural language processing technology to analyze the information entered by the user and select the most appropriate template from the contract database.

[0388] Step 4:

[0389] The server generates an optimal contract based on the selected contract template.

[0390] Input: Optimal contract template, user contract generation request information

[0391] Output: Generated contract

[0392] Specific operation: The server embeds the user's input information into the template and generates a complete contract.

[0393] Step 5:

[0394] The server provides the generated contract to the user.

[0395] Input: Generated Agreement

[0396] Output: The contract displayed on the user's device

[0397] Specific operation: The server sends the generated contract to the user's terminal and displays it on the user interface.

[0398] Step 6:

[0399] The user selects the review level.

[0400] Input: User review level selection information ("Serious Negotiation", "Normal", "Fastest Conclusion Priority")

[0401] Output: Review level selection information to the server

[0402] Specific operation: The user selects the review level through the terminal interface and presses the send button.

[0403] Step 7:

[0404] The server generates review guidelines according to the selected review level and provides them to the user.

[0405] Input: User review level selection information

[0406] Output: Review guidelines according to review level

[0407] Specific operation: The server generates appropriate review guidelines based on the selected review level and sends them to the user's device.

[0408] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0409] "Example 1"

[0410] In one embodiment of the present invention, the emotion engine recognizes a user's emotions and adjusts the generation and review of contracts based on those emotions. Specifically, when a user enters information about the business and the contracting party in a messaging app, the emotion engine analyzes the user's emotions. For example, if the user is feeling angry or anxious, the emotion engine conveys that information to the AI ​​engine, which then generates and reviews contracts more carefully.

[0411] "Example 2"

[0412] In another embodiment of the present invention, the emotion engine automatically adjusts the review level based on the user's emotions. Specifically, when the user selects a review level from three levels—"hard bargaining," "normal," and "fastest closing priority"—the emotion engine analyzes the user's emotions and recommends the optimal review level based on those emotions. For example, if the user is feeling strong dissatisfaction or anger, the emotion engine recommends "hard bargaining."

[0413] "Example 3"

[0414] In another embodiment of the present invention, the emotion engine analyzes a user's emotions and adjusts the generation and review of the contract based on the emotions. Specifically, when a user attaches a draft contract file to a messenger app, the emotion engine analyzes the user's emotions and identifies necessary revisions based on the emotions. For example, if the user is feeling anxious or scared, the emotion engine conveys that information to the AI ​​engine, which then reviews the contract more carefully.

[0415] The processing flow of each embodiment will be described below.

[0416] "Example 1"

[0417] Step 1: The user enters information about their business and the person they are contracting with in the messenger app.

[0418] Step 2: The emotion engine analyzes the user's emotions.

[0419] Step 3: The emotion engine communicates this information to the AI ​​engine.

[0420] Step 4: The AI ​​engine generates and reviews contracts more carefully.

[0421] "Example 2"

[0422] Step 1: The user selects a review level from three levels: "Serious Negotiation," "Normal," and "Fastest Closure Priority."

[0423] Step 2: The emotion engine analyzes the user's emotions.

[0424] Step 3: The sentiment engine recommends the optimal review level based on the sentiment.

[0425] "Example 3"

[0426] Step 1: The user attaches the draft contract file to the messenger app. Step 2: The emotion engine analyzes the user's emotions.

[0427] Step 3: The emotion engine identifies the necessary modifications based on the emotion.

[0428] Step 4: The emotion engine communicates this information to the AI ​​engine.

[0429] Step 5: The AI ​​engine reviews the contract more carefully.

[0430] Example 1

[0431] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0432] Conventional contract generation systems have the problem that it is difficult to generate optimal contracts based on user input information, and the generation and review of contracts do not take user sentiment into consideration, resulting in low user satisfaction. Furthermore, the contract template selection and generation process is not automated, which creates issues of inefficiency and time.

[0433] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0434] In this invention, the server includes means for receiving input information from a user, means including an artificial intelligence engine for analyzing the received input information, means including an emotion analysis engine for analyzing the user's emotions, means for selecting an optimal contract template from a contract database based on the analysis results, means for generating a contract based on the selected template, and means for providing the generated contract to the user. This makes it possible to automatically generate an optimal contract that takes into account the user's input information and emotions.

[0435] "Means for receiving input information from users" refers to a function that allows the server to receive information entered by users through a messenger app or other interface.

[0436] "Means including an artificial intelligence engine" refers to a function that uses artificial intelligence technology to analyze received user input information and extract information such as the type of business and the characteristics of the contracting party.

[0437] The "means including an emotion analysis engine" is a function for analyzing emotions from information input by a user and extracting the emotion information.

[0438] The "means for selecting the most appropriate contract template from the contract database" is a function for selecting the most appropriate template from the contract database based on the analysis results of the artificial intelligence engine and the sentiment analysis engine.

[0439] The "means for generating a contract based on a selected template" is a function for generating a specific contract by reflecting the information input by the user in the selected contract template.

[0440] "Means for providing the generated contract to the user" refers to a function for sending the generated contract to the user so that the user can review, modify, and approve it.

[0441] MODE FOR CARRYING OUT THE INVENTION

[0442] This invention is a system that generates an optimal contract based on information input by a user and provides it to the user. This system is composed of a server that includes an artificial intelligence engine, an emotion analysis engine, and a contract database, and a terminal used by the user.

[0443] Hardware and software used

[0444] The server uses the following hardware and software:

[0445] Hardware: High-performance server, network interface

[0446] Software: Artificial intelligence engine (e.g., OpenAI GPT-4), sentiment analysis engine (e.g., IBM Watson (registered trademark) Tone Analyzer), contract database, messenger app API

[0447] Users use common devices such as smartphones and PCs, which have messenger apps installed.

[0448] Program processing

[0449] The server receives information entered by the user in the messenger app. For example, the user enters, "I want to launch a new marketing campaign with a partner company." This information is then sent to the server.

[0450] The server sends the received input information to the AI ​​engine, which analyzes the input information and extracts information such as the type of business and characteristics of the contracting party. At the same time, the emotion analysis engine analyzes the emotions from the user's input information and extracts that emotion information.

[0451] Based on the analysis results, the server selects the most appropriate contract template from the contract database. For example, it may select a template suitable for a marketing campaign. Based on the selected template, the AI ​​engine generates a specific contract. In this process, it also takes into account the analysis results of the sentiment analysis engine. If the user is unsure, it will adjust the content to be more detailed and careful.

[0452] The generated contract is then sent from the server to the user's messaging app, where the user can review the contract and make any necessary amendments or approvals.

[0453] Examples of concrete examples and prompts

[0454] For example, consider a case where a user types into a messaging app, "I'd like to launch a new marketing campaign with a partner company." If the sentiment analysis engine detects uncertainty in the user's input, the artificial intelligence engine will take that information into account and generate a more detailed and careful contract.

[0455] Example prompt sentence:

[0456] "We would like to launch a new marketing campaign with a partner company. Please draw up a contract."

[0457] When this prompt sentence is input into the generative AI model, the artificial intelligence engine selects the most appropriate template from the contract database and generates the contract taking into account the analysis results of the sentiment analysis engine.

[0458] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0459] Step 1:

[0460] The user enters information in the messenger app.

[0461] Users open a messenger app on their smartphone or PC and type in, "I want to start a new marketing campaign with a partner company." This input information is sent to the server.

[0462] Step 2:

[0463] The server receives the input information and sends it to the artificial intelligence engine.

[0464] The server receives the information sent from the messenger app and sends an HTTP request to the API endpoint of the AI ​​engine. The input information includes the text data entered by the user. The output is the analysis result returned by the AI ​​engine.

[0465] Step 3:

[0466] An artificial intelligence engine analyzes the input information.

[0467] The AI ​​engine uses natural language processing technology to analyze the received information and extract keywords such as "marketing campaign" and "partner company." Input includes user text data. Output includes the analyzed keywords and business type.

[0468] Step 4:

[0469] The sentiment analysis engine analyzes the user's emotions.

[0470] The emotion analysis engine analyzes emotions from user input sentences and identifies emotions such as "anxiety" or "anger." For example, it can read the user's positive intentions from the expression "I want to do it." The input includes the user's text data. The output includes analyzed emotional information.

[0471] Step 5:

[0472] The server selects the most suitable template from the contract database.

[0473] The server selects a "marketing campaign contract template" from the contract database based on the analysis results from the artificial intelligence engine and the sentiment analysis engine. The input includes the analysis results. The output includes the selected template.

[0474] Step 6:

[0475] An artificial intelligence engine generates contracts based on templates.

[0476] The AI ​​engine generates a contract based on the selected template and reflects the user's input information. It also takes into account the results of the sentiment analysis engine and adjusts the content to be more detailed and cautious if the user feels uneasy. The inputs include the template and the user's input information. The output includes the generated contract.

[0477] Step 7:

[0478] The server provides the generated contract to the user.

[0479] The server sends the generated contract to the user's messenger app, where the user can review the contract and make any necessary amendments or approvals. Inputs include the generated contract. Outputs include the contract provided to the user.

[0480] (Application example 1)

[0481] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0482] While conventional contract generation systems can generate contracts based on user input, they have problems such as being unable to adjust the contract to take user emotions into account or display the contract in real time. Furthermore, they do not support contract generation using voice input, which reduces user convenience.

[0483] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0484] In this invention, the server includes means for connecting the AI ​​engine and the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for acquiring voice input, means for analyzing emotions, means for adjusting the content of the contract based on the analyzed emotions, and means for displaying the generated contract on a display device in real time. This makes it possible to generate and adjust a contract taking the user's emotions into consideration, supports contract generation using voice input, and enables contract display in real time.

[0485] An "AI engine" is an artificial intelligence system that analyzes information input by users and selects the most appropriate contract template from a contract database.

[0486] A "contract database" is a database that stores various contract templates and provides them as needed.

[0487] "Information input by the user" refers to information about the business content and the contracting party that the user provides to generate the contract.

[0488] The "means for generating the optimal contract" is the process of selecting an appropriate template from a contract database based on information input by the user, and filling in the necessary information to create a contract.

[0489] A "means for providing the generated agreement to the user" is a method for displaying or transmitting the generated agreement to the user.

[0490] "Means for acquiring voice input" refers to technology for recognizing the user's voice and acquiring it as text data.

[0491] "Means for analyzing emotions" refers to technology for reading and analyzing emotions from the user's voice and facial expressions.

[0492] The "means for adjusting the content of the contract based on the analyzed emotions" is a process for appropriately changing the content and wording of the contract depending on the emotional state of the user.

[0493] The "means for displaying in real time on a display device" is a technique for displaying the generated contract in real time on the user's display device.

[0494] The system for implementing this invention includes an AI engine, a contract database, a voice input acquisition means, an emotion analysis means, a contract content adjustment means, and a real-time display means. Specific embodiments of each means are described below.

[0495] System configuration

[0496] 1. AI Engine

[0497] The server analyzes the information input by the user and uses an AI engine to select the optimal contract template from the contract database. The AI ​​engine uses natural language processing technology to understand the information input by the user and select the appropriate template.

[0498] 2. Contract Database

[0499] The contract database stores various contract templates, and the AI ​​engine provides the selected template. The database is built using a database management system such as SQL or NoSQL.

[0500] 3. Voice input acquisition method

[0501] The device receives user voice input using voice recognition technology, which can be achieved using voice recognition services such as Google® Speech Recognition API or IBM Watson.

[0502] 4. Emotion analysis method

[0503] The server uses an emotion analysis engine to analyze emotions from the user's voice and facial expressions. For emotion analysis, services such as Microsoft Azure (registered trademark) Emotion API and Amazon Rekognition can be used.

[0504] 5. Means for adjusting contract contents

[0505] The server then adjusts the content of the contract based on the analyzed emotions. For example, if the user is feeling anxious, the server will make the contract more polite.

[0506] 6. Real-time display means

[0507] The terminal displays the generated contract in real time. The display device can be a smart eyeglass or a head-mounted display.

[0508] Specific examples

[0509] When a store staff member signs a new service contract with a customer, they can say "I would like to sign a new service contract" through the smart glasses. The terminal will recognize the voice, and the server will analyze the customer's emotions, generate the most appropriate contract, and display it on the smart glasses.

[0510] Prompt Sentence Examples

[0511] "We'd like to enter into a new service contract. The customer seems a little anxious."

[0512] By inputting this prompt into a generative AI model, the AI ​​engine generates the optimal contract, and the emotion engine adjusts the contract content to take into account the customer's concerns.

[0513] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0514] Step 1:

[0515] The user inputs voice through the smart glasses. The user says, "I would like to sign up for a new service contract." The device uses voice recognition technology to convert this voice into text data. The input is voice data, and the output is text data. Specifically, the device calls the Google Speech Recognition API to convert the voice data into text data.

[0516] Step 2:

[0517] The device sends the acquired text data to the server. The server receives this text data and passes it to the AI ​​engine. The input is text data, and the output is input data to the AI ​​engine. Specifically, the device sends the text data to the server using an HTTP request.

[0518] Step 3:

[0519] The server uses an AI engine to analyze the text data and select the most appropriate contract template from the contract database. The input is text data and the output is a contract template. Specifically, the AI ​​engine uses natural language processing technology to analyze the text data and select an appropriate template from the contract database.

[0520] Step 4:

[0521] The server uses an emotion analysis engine to analyze the user's emotions. The input is text data and the output is emotion data. Specifically, the emotion analysis engine uses the Microsoft Azure Emotion API to read emotions from the text data.

[0522] Step 5:

[0523] The server adjusts the content of the contract based on the analyzed emotion data. The input is a contract template and emotion data, and the output is the adjusted contract. Specifically, the server changes the wording and content of the contract taking the emotion data into account.

[0524] Step 6:

[0525] The server sends the generated contract to the terminal. The input is the adjusted contract, and the output is the data to be sent to the terminal. Specifically, the server sends the contract data to the terminal using an HTTP response.

[0526] Step 7:

[0527] The terminal displays the received contract on the smart glasses display in real time. The input is the contract data, and the output is the contract displayed on the display. Specifically, the terminal uses the smart glasses' display control API to display the contract.

[0528] Example 2

[0529] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0530] Conventional contract drafting systems require users to manually review contract drafts and identify areas for revision, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to automatically adjust the optimal review level based on user sentiment, making it difficult to create contracts that meet the user's intent.

[0531] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0532] In this invention, the server includes means for connecting the AI ​​engine and the database, means for generating an optimal document based on information input by a user, means for providing the generated document to the user, means for reviewing the document draft provided by the user and outputting a revision history and comments, and means for analyzing the user's emotions and recommending an optimal review level. This allows the user to efficiently review and revise the contract, and furthermore, the optimal review level is automatically adjusted based on the user's emotions, making it possible to create a contract that meets the user's intentions.

[0533] An "AI engine" is a computer program that uses artificial intelligence technology to analyze and process data.

[0534] A "database" is a system for efficiently storing, searching, and managing data.

[0535] "User" means any individual or legal entity that uses this system to create or review contracts.

[0536] "Input information" means data or instructions provided by a user to a system.

[0537] "Document" refers to a contract or other official document.

[0538] A "Document Draft" is an incomplete document created by a User.

[0539] "Review" is the process of checking the contents of a document draft and identifying any necessary revisions.

[0540] A "revision history" is a record of revisions made to a document draft.

[0541] A "comment" is text that includes explanations and instructions for the correction.

[0542] "Emotion" refers to the user's psychological state and is the subject of analysis by the system.

[0543] "Review level" is an indicator of the rigor and detail of the document review.

[0544] "Recommendation" is the act of the system presenting the best option to the user.

[0545] This invention is a system for allowing users to efficiently review and revise contract drafts. The system includes means for connecting an AI engine with a database, means for generating an optimal document based on information input by the user, means for providing the generated document to the user, means for reviewing the document draft provided by the user and outputting a revision history and comments, and means for analyzing user sentiment and recommending an optimal review level.

[0546] The server uses natural language processing technology as its AI engine, specifically generative AI models such as Google Cloud Natural Language API and OpenAI's GPT-4, to analyze the text of the contract draft and identify necessary revisions.

[0547] The user sends a draft contract file to the server using a messenger app. The server analyzes the received draft and identifies areas for revision. For example, it generates a comment such as, "The wording of Article 3 is ambiguous, so specific numerical values ​​need to be added." This allows the user to check the revision history and comments provided, enabling efficient revision of the contract.

[0548] The server then uses an emotion engine to analyze the user's emotions. Using an emotion analysis API (e.g., IBM Watson Tone Analyzer or Microsoft Azure Text Analytics), the server analyzes the user's messages and selections and recommends the optimal review level based on their emotions. For example, if the user is feeling very dissatisfied or angry, the server recommends "hard negotiation."

[0549] For example, if a user sends a "Contract Draft.docx" file attached to a messaging app, the server receives the file and analyzes it using the Google Cloud Natural Language API. As a result of the analysis, a comment such as "The wording of Article 3 is ambiguous, so specific numerical values ​​should be added" is generated. The user can then use this comment to revise the contract.

[0550] If the user attempts to select "Normal" but the emotion engine detects strong dissatisfaction in the user's message, the server will recommend "Serious Negotiation." The user can adjust the review level accordingly.

[0551] Examples of prompts include:

[0552] "Please review the draft contract. Analyze the draft below, identify any necessary revisions, and generate revision history and comments."

[0553] "Analyze user sentiment and recommend the optimal contract review level. Please obtain user sentiment from the following message."

[0554] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0555] Step 1:

[0556] The user submits the draft agreement.

[0557] The user opens a messenger app, attaches a contract draft file, and sends it. The input is the contract draft file, and the output is sending the file to the server. Specifically, the user uploads "ContractDraft.docx" to the messenger app and presses the send button.

[0558] Step 2:

[0559] The server receives the draft.

[0560] The server receives the contract draft file sent from the messenger app. The input is the file sent by the user, and the output is a file saved in temporary storage on the server. Specifically, the server saves the received file in a specific directory.

[0561] Step 3:

[0562] The server parses the draft.

[0563] The server calls a generative AI model, such as Google Cloud Natural Language API or OpenAI's GPT-4, to analyze the received contract draft. The input is the saved contract draft text, and the output is the analysis result. Specifically, the server sends the draft text to the API and obtains the analysis result.

[0564] Step 4:

[0565] The server identifies the corrections.

[0566] Based on the analysis results, the server identifies the necessary revisions to the contract. The input is the analysis results, and the output is a list of revisions. Specifically, the server identifies revisions such as "the wording of Article 3 is ambiguous, so specific numerical values ​​need to be added."

[0567] Step 5:

[0568] The server generates revision history and comments.

[0569] The server generates a revision history and comments for the identified revisions. The input is a list of revisions, and the output is a revision history and comments. Specifically, the server generates a comment containing an explanation or instruction for the revisions.

[0570] Step 6:

[0571] The server provides the results to the user.

[0572] The server sends the generated revision history and comments to the user. The input is the revision history and comments, and the output is a notification to the user. Specifically, the server sends the revision history and comments to the user through a messenger app.

[0573] Step 7:

[0574] The user selects the review level.

[0575] The user opens an interface to select the review level of the contract. The input is the user's selection, and the output is the selected review level. In concrete terms, the user selects from three options: "hardcore negotiation," "normal," and "fastest closing priority."

[0576] Step 8:

[0577] The server analyzes the user's emotions.

[0578] The server calls the sentiment analysis API to analyze the user's sentiment. The input is the user's message and selections, and the output is the sentiment analysis result. Specifically, the server sends the user's message to the API and obtains the sentiment analysis result.

[0579] Step 9:

[0580] The server will recommend the optimal review level.

[0581] The server recommends the optimal review level for the user based on the sentiment analysis results. The input is the sentiment analysis results, and the output is the recommended review level. Specifically, if the server recommends "serious negotiation," it notifies the user.

[0582] Step 10:

[0583] The user confirms the recommended review level.

[0584] The user checks the recommendation from the server and adjusts the review level as needed. The input is the recommended review level, and the output is the final review level selection. As a specific operation, the user adjusts the review level according to the recommendation.

[0585] (Application example 2)

[0586] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0587] Conventional contract review systems require users to manually revise contract drafts, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to automatically adjust the optimal review level based on user sentiment, making it difficult to flexibly respond to user needs. This has led to issues with the inefficiency of contract creation and revision.

[0588] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0589] In this invention, the server includes means for connecting the AI ​​engine with the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for analyzing the contract draft provided by the user and identifying necessary revisions, means for generating a revision history and comments and providing them to the user, and means for analyzing the user's emotions and recommending a review level based on the emotions. This enables the user to review contracts efficiently and makes it possible to recommend an optimal review level according to the user's emotions.

[0590] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[0591] A "contract database" is a data storage system that stores contract templates and past contract data, allowing them to be searched and referenced as needed.

[0592] "User input information" refers to information such as text data, files, and emotional state that a user provides to the system.

[0593] "Draft Agreement" means a draft or initial version of the Agreement created by a User.

[0594] "Amendments" are the parts of the draft contract that require amendment.

[0595] "Amendment history" is a record of amendments made to a draft contract.

[0596] "Comments" are explanations or suggestions given regarding amendments to the draft contract.

[0597] An "emotion engine" is a system that analyzes a user's emotions and recommends appropriate actions based on the results.

[0598] "Review level" is an indicator of the rigor and detail of the contract review.

[0599] A system for carrying out the present invention is configured as follows: The server includes means for connecting an AI engine with a contract database, means for generating an optimal contract based on information input by a user, means for providing the generated contract to the user, means for analyzing the contract draft provided by the user and identifying necessary revisions, means for generating a revision history and comments and providing them to the user, and means for analyzing user sentiment and recommending a review level based on the sentiment.

[0600] Specifically, the server runs a Python program that uses the TextBlob library and a custom sentiment analysis engine to analyze user sentiment. Regular expressions are used to analyze the draft contract and identify necessary revisions. This allows users to efficiently review the contract and recommends the optimal review level based on the user's sentiment.

[0601] For example, when a user sends a draft contract to the server using a smartphone messenger app, the server analyzes the draft contract, generates corrections and comments, and analyzes the user's sentiment and recommends a review level based on the sentiment: "serious negotiation," "normal," or "fastest conclusion priority."

[0602] For example, if a user enters "I'm not satisfied with this contract," the emotion engine will recommend "serious negotiation." Also, if the contract draft does not include a "deadline," the engine will suggest "The deadline is not specified" as a correction.

[0603] An example of a prompt for the generative AI model is as follows:

[0604] Enter the text of the draft agreement.

[0605] Enter the user's feelings.

[0606] In this way, users can efficiently review contracts and receive recommendations for the optimal review level based on sentiment.

[0607] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0608] Step 1:

[0609] The user sends the contract draft to the server using a messenger app on their smartphone.

[0610] Input: Contract draft text file

[0611] Output: A draft contract is uploaded to the server

[0612] Specific operation: The user opens the messenger app, selects the contract draft file, and presses the send button. The file is then uploaded to the server.

[0613] Step 2:

[0614] The server receives the contract draft and begins text analysis.

[0615] Input: Uploaded draft agreement text file

[0616] Output: List of modifications and comments

[0617] What happens: The server reads the text of the draft contract and uses regular expressions to identify the necessary corrections. For example, if a "deadline" is missing, it adds "No deadline is specified" to the list of corrections.

[0618] Step 3:

[0619] The server analyzes the user's emotions.

[0620] Input: User sentiment text

[0621] Output: Sentiment analysis result (positive, negative, neutral)

[0622] Specific operation: The server uses the TextBlob library to analyze the user's sentiment text. For example, if the text is "I am dissatisfied with this contract," the sentiment analysis result will be "negative."

[0623] Step 4:

[0624] The server recommends a review level based on the sentiment analysis results.

[0625] Input: Sentiment analysis results

[0626] Output: Recommended review level (hard bargaining, normal, fastest conclusion priority)

[0627] Specific operation: The server determines the review level based on the sentiment analysis result. For example, if the sentiment analysis result is "negative," it recommends "serious negotiation."

[0628] Step 5:

[0629] The server provides the user with the corrections, comments, and recommended review level.

[0630] Input: List of corrections, comments, recommended review level

[0631] Output: Corrections, comments, and recommended review level provided to the user

[0632] What it does: The server sends a list of corrections, comments, and a recommended review level to the user, who can receive this information in their messenger app.

[0633] Through the above steps, the user can efficiently review the contract and receive recommendations for the optimal review level based on their sentiment.

[0634] Example 3

[0635] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0636] Conventional contract review systems were unable to provide optimal contract reviews because they struggled to respond flexibly to user emotions and business situations. Furthermore, because they conducted uniform reviews without considering user emotions, they were unable to fully address users' anxieties and concerns. This resulted in delays in the contract signing process and reduced business efficiency.

[0637] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0638] In this invention, the server includes means for a user to select a review level, means for a user to upload a contract draft, means for analyzing the user's emotions using an emotion analysis engine, means for adjusting the contract review based on the analyzed emotion information, means for reviewing the contents of the contract using an AI engine and identifying areas to be revised, and means for providing the review results to the user, thereby enabling an optimal contract review according to the user's emotions and business situation.

[0639] "Means for users to select review levels" refers to a function that provides an interface for users to select the rigor and speed of contract review.

[0640] "Means for users to upload draft contracts" refers to a function that allows users to send draft contracts they have created to the system.

[0641] An "emotion analysis engine" is a combination of software or hardware for analyzing a user's emotions and providing data based on those emotions.

[0642] The "means for adjusting the review of a contract based on analyzed emotional information" is a function for adjusting the method and content of reviewing a contract based on the emotional information of the user obtained from the emotion analysis engine.

[0643] An "AI engine" is a combination of software and hardware that uses artificial intelligence technology to analyze the contents of a contract and identify any necessary revisions.

[0644] "Means for providing review results to users" refers to a function that notifies users of the review results of the contract and provides a report including corrections and comments.

[0645] The present invention provides a system for enabling a user to review a contract efficiently and effectively. A specific embodiment of this system will be described below.

[0646] First, the user accesses the system using a terminal, and selects one of three options from the review level interface: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." This selection information is sent to the server.

[0647] Next, the user uploads the contract draft file to the server using a messenger app. For example, the user attaches a file called "ContractDraft.docx" and sends it. This file is saved on the server.

[0648] The server uses a sentiment analysis engine to analyze the user's emotions. The sentiment analysis engine can use IBM Watson or Microsoft Azure's sentiment analysis API. The sentiment analysis engine obtains emotional data from the user's messages and behavior, and identifies emotions such as "anxiety" or "relief."

[0649] The analyzed emotional information is returned to the server, which then adjusts the review of the contract based on this information. Specifically, the AI ​​engine carefully considers each clause of the contract based on the user's emotional information obtained from the emotion analysis engine. OpenAI's GPT-4 can be used as the AI ​​engine.

[0650] The AI ​​engine analyzes the contents of the contract and identifies the necessary revisions. For example, if the user is unsure, the AI ​​engine will examine each clause in the contract in more detail and identify the necessary revisions.

[0651] Finally, the server generates the review results and provides them to the user. Specifically, it generates a contract with the revisions highlighted and a report explaining the reasons for the revisions. For example, the server generates "Contract Draft_Revisions.docx" and "Revision Report.pdf" and sends them to the user.

[0652] Examples:

[0653] A user uses this system when signing a new business contract. The user selects the "Normal" review level and attaches "Contract Draft.docx" to a messenger app and sends it. The sentiment analysis engine determines that the user is feeling a little uneasy and passes that information on to the AI ​​engine. The AI ​​engine carefully reviews each clause of the contract and identifies any necessary revisions. The server generates "Contract Draft_Amendments.docx" and "Amendment Report.pdf" with the revisions highlighted, and sends them to the user.

[0654] Example prompt sentence:

[0655] "Please review the draft new business contract. The review level is 'Normal' and the user is feeling a bit uneasy. Please carefully review each clause in the contract and identify any necessary revisions."

[0656] In this way, the user can receive the most suitable contract review according to his / her business situation and feelings. The flow of the specification process in the third embodiment will be described with reference to FIG.

[0657] Step 1:

[0658] The user accesses the system using a terminal and selects a review level. The user selects one of three options: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." This selection information is sent to the server. The input is the user's review level selection, and the output is the review level information sent to the server.

[0659] Step 2:

[0660] A user uploads a contract draft file to the server using a messenger app. For example, the user sends a message with a file named "ContractDraft.docx" attached. This file is saved on the server. The input is the contract draft file uploaded by the user, and the output is the contract draft file saved on the server.

[0661] Step 3:

[0662] The server uses a sentiment analysis engine to analyze the user's emotions. A general sentiment analysis API can be used as the sentiment analysis engine. The server obtains emotional data from the user's messages and actions, and identifies emotions such as "anxiety" or "relief." The input is the user's message and behavior data, and the output is analyzed emotional information.

[0663] Step 4:

[0664] The server adjusts the contract review based on the analyzed emotional information. Specifically, the AI ​​engine carefully reviews each clause of the contract based on the user's emotional information obtained from the emotion analysis engine. A general generative AI model can be used as the AI ​​engine. The input is the analyzed emotional information and the contract draft file, and the output is a contract draft with identified revisions.

[0665] Step 5:

[0666] The server uses an AI engine to review the contents of the contract and identify any necessary revisions. For example, if the user is feeling uneasy, the AI ​​engine will examine each clause of the contract in more detail and identify any necessary revisions. The input is the draft contract file and the analyzed emotional information, and the output is a draft contract with identified revisions.

[0667] Step 6:

[0668] The server generates the review results and provides them to the user. Specifically, it generates a contract with the revisions highlighted and a report explaining the reasons for the revisions. For example, the server generates "Contract Draft_Revisions.docx" and "Revision Report.pdf" and sends them to the user. The input is the contract draft with the revisions identified, and the output is the revisions and report provided to the user.

[0669] (Application example 3)

[0670] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0671] Conventional contract generation systems have difficulty responding flexibly to user emotions and business situations, and have had issues in not being able to fully meet user needs when reviewing and generating contracts. Furthermore, when reviewing contracts, careful consideration that takes user emotions into account is sometimes required, and there has been no system that can do this automatically.

[0672] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0673] In this invention, the server includes means for connecting the AI ​​engine and the contract database, means for generating an optimal contract based on information input by a user, means for providing the generated contract to the user, means including an emotion engine for analyzing the user's emotions, means for adjusting the review of the contract based on the emotions analyzed by the emotion engine, means for the user to select a review level, and means for reviewing the contract based on the selected review level. This enables flexible generation and review of contracts according to the user's emotions and business circumstances.

[0674] An "AI engine" is a system that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[0675] A "contract database" is a database that stores contract templates and past contract data, and can be searched and retrieved as needed.

[0676] "User Input" means data or instructions provided by a user to the system, including information required for contract generation or review.

[0677] The "emotion engine" is an engine that analyzes the user's emotions and adjusts the system's behavior based on the results.

[0678] "Review Level" refers to the multiple levels of caution or speed that users can select when reviewing a contract.

[0679] "Contract review" is the process of checking the contents of a contract and making any necessary corrections or comments.

[0680] "Revision history" is a record of changes made to a contract, showing who revised what part and how.

[0681] "Comments" are explanations or opinions added to a particular part of the contract and are used in the review process.

[0682] A system for carrying out the present invention has the following configuration: a server includes means for connecting an AI engine to a contract database, means for generating an optimal contract based on information input by a user, means for providing the generated contract to the user, means including an emotion engine for analyzing user emotions, means for adjusting review of the contract based on the emotions analyzed by the emotion engine, means for the user to select a review level, and means for reviewing the contract based on the selected review level.

[0683] Hardware and software used

[0684] Hardware: Smartphones, servers

[0685] Software: Python, sentiment analysis library (e.g., emotion_analysis), contract review library (e.g., contract_review)

[0686] Data processing and calculation

[0687] Emotion analysis

[0688] When a user uploads a contract draft using their smartphone, the emotion engine analyzes the user's emotions in real time. It uses an emotion analysis library (e.g., emotion_analysis) to obtain emotion data from the user's facial expressions and voice and generates analysis results.

[0689] Determining the review level

[0690] Based on the analyzed emotional data, the server determines the appropriate review level, for example, selecting "hard bargaining" if the user is feeling anxious or scared, or "normal" if the user is calm.

[0691] Contract review

[0692] Use the contract review library (e.g., contract_review) to review the contract based on the selected review level. During the review, revision history and comments are generated and provided to the user.

[0693] Specific examples

[0694] User A uploads a contract to use an electronic payment service. The emotion engine analyzes User A's emotions and detects that User A is feeling anxious. The server selects the "serious negotiation" review level and carefully reviews the contract. As a result of the review, a revision history and comments are generated and provided to User A.

[0695] Prompt Sentence Examples

[0696] A user has uploaded a draft contract. Use the sentiment engine to analyze the user's sentiment and select the appropriate review level. If the user is feeling anxious or scared, select "Serious Negotiation" and carefully review the contract.

[0697] In this way, flexible contract generation and review can be achieved according to the user's emotions and business situation.

[0698] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0699] Step 1:

[0700] A user uploads a draft agreement using their smartphone.

[0701] Input: User selects contract draft file and presses upload button

[0702] Output: A draft contract file is sent to the server.

[0703] Specific operation: The user opens the application on their smartphone, selects the contract draft file, and presses the upload button, which sends the file to the server via the Internet.

[0704] Step 2:

[0705] The server uses an emotion engine to analyze the user's emotions.

[0706] Input: The uploaded contract draft file and the user's facial expression and voice data.

[0707] Output: User emotion data.

[0708] Specific operation: The server uses an emotion analysis library (e.g., emotion_analysis) to analyze the user's facial expressions and voice data and generate emotion data.

[0709] Step 3:

[0710] The server determines the review level based on the emotion data.

[0711] Input: User emotion data.

[0712] Output: Appropriate review level ("Serious Negotiation", "Normal", "Fastest Closure First").

[0713] Specific operation: The server analyzes the emotional data and selects "serious negotiation" if the user is feeling anxious or scared, "normal" if the user is calm, and "prioritize the fastest conclusion" if the user is in a hurry.

[0714] Step 4:

[0715] The server reviews the contract using a contract review library.

[0716] Input: The uploaded draft agreement file and the selected review level.

[0717] Output: The agreement with revision history and comments.

[0718] Specific Behavior: The server uses a contract review library (e.g., contract_review) to review the contract based on the selected review level and generate an amendment history and comments.

[0719] Step 5:

[0720] The server provides the review results to the user.

[0721] Input: The agreement with amendment history and comments.

[0722] Output: Review results sent to the user's smartphone.

[0723] Specific operation: The server sends the contract with the revision history and comments to the user's smartphone, allowing the user to check the review results.

[0724] In this way, flexible contracts can be generated and reviewed according to the user's emotions and business situations.

[0725] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0726] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0727] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0728] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0729] [Second embodiment]

[0730] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0731] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0732] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0733] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0734] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0735] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0736] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0737] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0738] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0739] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0740] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0741] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0742] "Example 1"

[0743] One embodiment of the present invention is a system that connects an AI engine with a contract database. This system generates an optimal contract based on information input by the user. Specifically, when the user inputs "this type of business with this type of party" into a messenger app, the AI ​​engine analyzes this input information and selects the optimal contract template from the contract database. The system then generates a contract based on the selected template and provides it to the user.

[0744] "Example 2"

[0745] Furthermore, another embodiment of the present invention is a system that reviews contract drafts provided by users. Specifically, when a user attaches a contract draft file to a messenger app, the system analyzes the draft and identifies any necessary revisions. The system then generates a history of the revisions and comments and provides them to the user. This function allows users to efficiently create and revise contracts without having to review them themselves.

[0746] "Example 3"

[0747] Furthermore, as a further embodiment of the present invention, there is a function for selecting a review level. Specifically, the user can select a review level from three levels: "Serious Negotiation," "Normal," and "Fast Closure Priority." This function allows the user to select the optimal review level depending on the situation and needs of their business.

[0748] The processing flow of each embodiment will be described below.

[0749] "Example 1"

[0750] Step 1: The user enters information about their business and the person they are contracting with in the messenger app.

[0751] Step 2: The AI ​​engine analyzes this input information.

[0752] Step 3: The AI ​​engine selects the most suitable contract template from the contract database.

[0753] Step 4: Generate the contract based on the selected template.

[0754] Step 5: Provide the generated contract to the user.

[0755] "Example 2"

[0756] Step 1: The user attaches the draft contract file to a messenger app. Step 2: The system analyzes this draft.

[0757] Step 3: Identify the system's required modifications.

[0758] Step 4: Generate revision history and comments.

[0759] Step 5: Provide the generated revision history and comments to the user.

[0760] "Example 3"

[0761] Step 1: The user selects a review level from three levels: "Serious Negotiation," "Normal," and "Fastest Closure Priority."

[0762] Step 2: The system performs the review based on the selected review level.

[0763] Step 3: Provide the review results to the user.

[0764] Example 1

[0765] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0766] Conventional contract generation systems had the problem that it took a lot of time and effort for users to create a contract. In addition, specialized knowledge was required to check whether the content of the contract was appropriate, making it a difficult task for general users. Furthermore, the process of selecting and customizing a contract template was complicated and inefficient.

[0767] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0768] In this invention, the server includes means for a user to send input information using a communication application, means for the server to receive the user's input information and send it to an AI engine, means for the AI ​​engine to analyze the input information using a generative AI model, means for the AI ​​engine to select an optimal contract template from a contract database, means for the AI ​​engine to customize the selected template, and means for the server to provide the customized contract to the user. This enables users to easily generate optimal contracts and create appropriate contracts without specialized knowledge.

[0769] "User" means any person or entity that uses a communication application to generate a contract using the system.

[0770] "Communication application" refers to software that a user uses to send input information, and includes messenger applications, chat applications, and the like.

[0771] A "server" is a computer system that receives input information from users and sends it to the AI ​​engine.

[0772] An "AI engine" is a software component that uses a generative AI model to analyze user input information, select the most appropriate contract template from a contract database, and customize it.

[0773] A "generative AI model" is an artificial intelligence model used to analyze user input information, and uses natural language processing techniques.

[0774] The "contract database" is a database that stores contract templates corresponding to various business scenarios.

[0775] A "contract template" is a contract template that corresponds to a specific business scenario and is customized according to the user's requirements.

[0776] "Customization" is the process of modifying or adding to a selected contract template to reflect the user's specific requirements and conditions.

[0777] "Providing" means the act of sending the customized agreement to the user for review and modification.

[0778] The present invention provides a system for allowing a user to generate a contract using a communication application, the system including a server, an AI engine, a contract database, and a communication application.

[0779] The user uses a communication application to input the contents of the contract. For example, the user enters "I would like to enter into a software development contract with a certain company" and presses the send button. The communication application can be a messenger application or a chat application.

[0780] The server receives the input information sent by the user. The received information is sent to the AI ​​engine. The server converts the user's input information into an appropriate format and generates a prompt that the AI ​​engine can understand. For example, a prompt might be generated that reads, "The user has input that they would like to enter into a software development contract with a certain company. Please select and customize the most appropriate contract template."

[0781] The AI ​​engine uses a generative AI model (e.g., GPT-4) to analyze the user's input information. Natural language processing technology is used for the analysis to understand the user's intent and requirements. Based on the analysis results, the AI ​​engine selects the optimal contract template from a contract database, which stores templates corresponding to various business scenarios.

[0782] The selected template is then customized according to the user's specific requirements. For example, the name of the contracting party or specific contract terms may be added to the template. The customized contract is then provided to the user via the server. The user can then review the generated contract in their communication application and make any necessary modifications.

[0783] As a concrete example, consider the case where a user inputs "I would like to enter into a software development contract with a certain company" into a communication application. The server that receives this input information sends the information to an AI engine. The AI ​​engine analyzes the input information using a generative AI model (e.g., GPT-4) and selects a "software development contract" template from a contract database. The selected template is customized to suit the content of a contract with a certain company and provided to the user.

[0784] In this way, the system quickly generates contracts that meet the specific needs of the user.

[0785] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0786] Step 1:

[0787] A user sends input information using a communication application.

[0788] Specifically, the user opens a communication application (e.g., a messenger app), types in "I would like to enter into a software development contract with a certain company," and presses the send button.

[0789] Input: Text information about the user's contract details

[0790] Output: Data sent from the communication application to the server

[0791] Step 2:

[0792] The server receives the user's input.

[0793] In a specific operation, the server receives a message sent from the communication application saying, "I would like to enter into a software development contract with a certain company."

[0794] Input: User input information sent from a communication application

[0795] Output: Received data in the server

[0796] Step 3:

[0797] The server sends the received information to the AI ​​engine.

[0798] Specifically, the server sends the received message to the AI ​​engine, with a prompt such as, "The user has entered that they would like to enter into a software development contract with a certain company. Please select and customize the most appropriate contract template."

[0799] Input: Data received within the server

[0800] Output: Prompt sentence to the AI ​​engine

[0801] Step 4:

[0802] The AI ​​engine analyzes the input information.

[0803] Specifically, the AI ​​engine uses a generative AI model (e.g., GPT-4) to analyze the message "I would like to enter into a software development contract with a certain company" and understand the user's intent.

[0804] Input: Prompt sentence to AI engine

[0805] Output: Analysis results (user intent and requirements)

[0806] Step 5:

[0807] The AI ​​engine selects the optimal template from the contract database.

[0808] Specifically, the AI ​​engine selects a "software development contract" template from the contract database based on the analysis results.

[0809] Input: Analysis results

[0810] Output: Selected Agreement Template

[0811] Step 6:

[0812] The AI ​​engine will customize the selected template.

[0813] Specifically, the AI ​​engine customizes a "Software Development Agreement" template by adding the name of a company and specific contract terms.

[0814] Input: Selected Agreement Template

[0815] Output: Customized contract

[0816] Step 7:

[0817] The server provides the customized contract to the user.

[0818] Specifically, the server sends the customized contract to the user, who can then review it in the communication application and make any necessary modifications.

[0819] Input: Customized Agreement

[0820] Output: Data provided to the user (contract)

[0821] (Application example 1)

[0822] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0823] In conventional contract generation systems, users had to take time to input business details and counterparty information, and the process of selecting the most suitable contract template was cumbersome. Furthermore, the content of the generated contract often did not fully meet the user's specific needs, resulting in issues such as time-consuming revisions and review of the contract.

[0824] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0825] In this invention, the server includes means for connecting the AI ​​engine to the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for the user to input business details and other party information, means for the AI ​​engine to analyze the input information and select a contract template, means for generating a contract based on the selected template, and means for displaying the generated contract. This enables the user to easily input business details and other party information and quickly generate an optimal contract.

[0826] An "AI engine" is software that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[0827] A "contract database" is a database system that stores various contract templates and past contract data and allows for searching and retrieval.

[0828] "Information input from the user" refers to data such as business details and other party information that the user provides to the system.

[0829] "Means for generating optimal contracts" refers to a function that selects an appropriate template from a contract database based on information entered by the user and creates a customized contract.

[0830] "Means for providing the generated contract to the user" refers to a function that displays the generated contract to the user or provides it in a downloadable format.

[0831] "Business content" refers to the specific content of the transactions or business conducted by the user.

[0832] "Information about the other party" refers to information such as the name and contact details of the other party with whom the user enters into a contract.

[0833] A "contract template" is a format in which the basic structure and content of a contract are predetermined.

[0834] The "means for generating a contract" is a function that creates a contract that reflects the information entered by the user based on the selected contract template.

[0835] "Means for displaying the contract" is a function that displays the generated contract on the user's device.

[0836] A system for implementing this invention includes means for connecting an AI engine to a contract database, means for generating an optimal contract based on information input by a user, means for providing the generated contract to the user, means for the user to input business details and information about the other party, means for the AI ​​engine to analyze the input information and select a contract template, means for generating a contract based on the selected template, and means for displaying the generated contract.

[0837] System program processing

[0838] Hardware and Software Configuration

[0839] Server: Hosts the AI ​​engine (OpenAI API) and contract database (SQLite).

[0840] User device: A device such as a smartphone or computer on which a user provides input information and checks the generated contract.

[0841] Data processing and calculation

[0842] 1. Getting user input:

[0843] Enter the business details and the other party's information from the user's device.

[0844] For example: "Enter your business: Food supply" or "Enter your contact details: Supplier"

[0845] 2. Template selection by AI engine:

[0846] The server uses the OpenAI API to analyze the user's input information and select the most appropriate contract template.

[0847] Example prompt: "Business: Food supply. Partner: Supplier. Choose the most appropriate contract template."

[0848] 3. Generate the contract:

[0849] The server retrieves the selected template from the contract database and embeds the user's input information to generate a customized contract.

[0850] Example: The template "Agreement regarding {business}\nWith: {partner}" is converted to "Agreement regarding the supply of food\nWith: Supplier".

[0851] 4. Providing the generated contract:

[0852] The server sends the generated contract to the user's terminal and displays it for the user to review.

[0853] Specific examples

[0854] If a user wants to generate a food supply contract using a smartphone, they can use the system by following the steps below.

[0855] 1. The user opens the application on their smartphone and enters "food supply" as the business type and "supply company" as the recipient information.

[0856] 2. The server uses the OpenAI API to analyze the input information and select the most appropriate contract template.

[0857] 3. The server generates a contract based on the selected template, reflecting the information entered by the user.

[0858] 4. The generated contract will be displayed on the user's smartphone, allowing the user to check the contents.

[0859] In this way, users can easily input business details and counterparty information and quickly generate the optimal contract.

[0860] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0861] Step 1:

[0862] The user opens the application on their smartphone and inputs the business details and the other party's information. The input information is "food supply" for the business details and "supply company" for the other party's information. This input information is sent to the server.

[0863] Step 2:

[0864] The server analyzes the information received from the user. Specifically, it processes the business details and the other party's information as text data and passes it to the AI ​​engine. The input data is formatted as a prompt: "Business details: Food supply\nOther party: Supplier\nPlease select the most appropriate contract template."

[0865] Step 3:

[0866] The server sends the prompt text to the AI ​​engine using the OpenAI API. The AI ​​engine analyzes the prompt text and selects the most appropriate contract template from the contract database. The ID of the selected template is returned to the server.

[0867] Step 4:

[0868] The server retrieves the selected template from the contract database. The template is a format that contains placeholders (e.g., "Contract for {business}\nTo: {partner}").

[0869] Step 5:

[0870] The server then fills the template with the user's input, replacing the placeholders "{business}" with "food supply" and "{partner}" with "supplier," generating a customized contract.

[0871] Step 6:

[0872] The server sends the generated contract to the user's smartphone, where the user can view the contract through an application on the smartphone.

[0873] Step 7:

[0874] The user can review the generated contract and make any necessary corrections or add comments. The corrected contract is then sent back to the server and saved as the final contract.

[0875] Example 2

[0876] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0877] Conventional contract drafting systems required users to review draft contracts and identify revisions, which was time-consuming and laborious. There was also a risk that revisions would not be made appropriately from a legal perspective. This could result in a decline in contract quality and the possibility of legal issues arising.

[0878] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for connecting the AI ​​engine and the document database, means for generating an optimal document based on information input by a user, means for providing the generated document to the user, means for the user to attach a document draft to a messenger app, means for the server to receive the attached file, means for the server to convert the file into text format, means for the server to analyze the document using the generated AI model, means for the server to identify corrections and generate a correction history and comments, and means for the server to provide the generated correction history and comments to the user. This allows the user to efficiently review contracts and create high-quality contracts with appropriate corrections from a legal perspective.

[0879] An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions.

[0880] A "document database" is a database system that stores contracts and other document templates and allows them to be searched and retrieved.

[0881] "User input" refers to data or instructions that a user provides to the system, upon which the system operates.

[0882] An "optimal document" is a document that is generated based on user input information and is most suitable for the purpose.

[0883] A "messenger app" is application software that allows users to send and receive text messages and files.

[0884] "Attachment" refers to a document file sent via a messenger app.

[0885] "Text format" is a format in which the contents of a document are expressed as character data.

[0886] A "generative AI model" is a model that uses artificial intelligence technology to analyze and generate documents.

[0887] "Correction" means a portion of a document that has been identified as requiring correction.

[0888] "Revision history" is data that records the revisions made to a document.

[0889] A "comment" is text that includes specific instructions or explanations for the correction.

[0890] MODE FOR CARRYING OUT THE INVENTION

[0891] The present invention provides a system for allowing a user to efficiently review a contract draft and identify parts that need to be revised. A specific embodiment of this system will be described below.

[0892] Hardware and software used

[0893] The system uses the following hardware and software:

[0894] Server: Receives data, analyzes it, runs generative AI models, and generates revision history and comments.

[0895] Messenger app: The application through which the user attaches and sends the contract draft (e.g., Slack, Microsoft Teams).

[0896] Generative AI model: An artificial intelligence model (e.g., GPT-4) that analyzes contracts and identifies areas for revision.

[0897] PDF parser library: A library for converting PDF files to text format (e.g. PyPDF2).

[0898] Word parser library: A library for converting Word files to text format (e.g. Python-docx).

[0899] System Operation

[0900] A user creates a draft contract and logs in to a messenger app. Next, the user selects the draft contract file, attaches it to the chat window of the messenger app, and sends it. The server receives the file sent from the messenger app and obtains the contents of the file.

[0901] If the received file is in a format such as PDF or Word, the server converts it to text format. Specifically, for PDF files, it uses a PDF parser library (e.g., PyPDF2), and for Word files, it uses the Python-docx library to extract the text.

[0902] Next, the server inputs the converted draft contract into a generative AI model (e.g., GPT-4). The server generates a prompt to instruct the model to analyze the contents of the contract. An example of a prompt is, "Please analyze the following draft contract and identify the necessary revisions. Please provide specific comments for the revisions."

[0903] The server receives the analysis results from the generative AI model and identifies areas to be revised in the contract. For example, it identifies areas to be revised such as "the definition of confidential information is vague." The server then generates a revision history and comments for the identified areas to be revised. The comments include specific instructions such as "The definition of confidential information needs to be clarified."

[0904] Finally, the server sends the generated revision history and comments to the chat window of the messenger app, where the user can review the received revision history and comments and make revisions to the contract.

[0905] Specific examples

[0906] As a concrete example, consider the case where a user wants to review a draft NDA (non-disclosure agreement). The user attaches the draft NDA to Slack and sends it to the server. The server receives this draft and analyzes it using GPT-4. As a result of the analysis, areas that need to be revised, such as "the definition of confidential information is vague," are identified. The server generates a comment for this revision, such as "The definition of confidential information needs to be clarified," and provides it to the user.

[0907] Example prompts to input to a generative AI model:

[0908] Please analyze the NDA draft below and identify any necessary changes. Please provide specific comments for any changes.

[0909] NDA Draft:

[0910] 1. This Agreement is entered into between the Discloser and the Recipient of Confidential Information.

[0911] 2. Confidential Information means all information disclosed by the Disclosing Party to the Receiving Party.

[0912] ...

[0913] "

[0914] In this way, users can efficiently create and modify contracts.

[0915] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0916] Step 1:

[0917] The user attaches the draft contract to a messenger app and sends it.

[0918] Specifically, a user creates a draft contract and logs into a messenger app (e.g., Slack, Microsoft Teams). Next, the user selects the draft contract file, attaches it to the chat window of the messenger app, and sends it. The input is the draft contract file, and the output is the file sent through the messenger app.

[0919] Step 2:

[0920] The server receives the attachment.

[0921] The server receives the file sent from the messenger app. Specifically, the server checks the metadata of the received file (file name, file size, sender information, etc.) and obtains the file contents. The input is the file sent from the messenger app, and the output is the file data stored on the server.

[0922] Step 3:

[0923] The server converts the file into a text format.

[0924] If the received file is in a format such as PDF or Word, the server converts it to text format. Specifically, for PDF files, a PDF parser library (e.g., PyPDF2) is used, and for Word files, the Python-docx library is used to extract the text. The input is a PDF or Word file, and the output is text data.

[0925] Step 4:

[0926] The server analyzes the document using the generative AI model.

[0927] The server inputs the draft contract converted into text format into a generative AI model (e.g., GPT-4). Specifically, the server generates a prompt and instructs the model to analyze the contents of the contract. An example of a prompt would be, "Analyze the following draft contract and identify any necessary revisions. Please provide specific comments for the revisions." The input is the text draft contract and the prompt, and the output is the analysis result from the generative AI model.

[0928] Step 5:

[0929] The server identifies the modifications and generates a modification history and comments.

[0930] The server receives the analysis results from the generative AI model and identifies the areas to be revised in the contract. Specific operations include identifying areas to be revised, such as "the definition of confidential information is unclear." The server then generates a revision history and comments for the identified areas to be revised. The comments include specific instructions such as "The definition of confidential information needs to be clarified." The input is the analysis results from the generative AI model, and the output is a revision history and comments.

[0931] Step 6:

[0932] Providing server-generated revision history and comments to users.

[0933] The server sends the generated revision history and comments to the chat window of the messenger app. Specifically, the server sends the revision history and comments in text format to the messenger app so that the user can view them. The input is the revision history and comments, and the output is the revision history and comments provided to the user through the messenger app.

[0934] In this way, users can efficiently create and modify contracts.

[0935] (Application example 2)

[0936] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0937] In conventional contract creation systems, even if users uploaded a draft contract, they lacked the functionality to automatically analyze the content, identify revisions, and generate comments. This meant that users had to manually review the contract. This resulted in the problem of requiring a great deal of time and effort to create and revise contracts.

[0938] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0939] In this invention, the server includes means for connecting the AI ​​engine with the contract database, means for generating an optimal contract based on information input by a user, means for analyzing a contract draft uploaded by a user, identifying corrections to be made, and generating comments, and means for providing the generated comments to the user. This enables users to automate contract reviews and efficiently create and revise contracts.

[0940] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[0941] A "contract database" is a database system that stores contract templates and past contract data, and allows for searching and retrieval as needed.

[0942] "User input information" refers to information provided by the user to the system, including data and conditions necessary for creating or amending a contract.

[0943] The "means for generating optimal contracts" is a system that has the function of selecting an appropriate template from a contract database based on information input by the user, and filling in the necessary information to create a contract.

[0944] A "means for providing a generated contract to a user" is a system that has the ability to display, transmit, or provide a system-generated contract to a user in a downloadable format.

[0945] A "draft contract" is an incomplete contract created by a user, which is a document that requires final confirmation and revision.

[0946] The "means for identifying the areas to be corrected and generating comments" is a system that has the function of analyzing a contract draft, automatically detecting the areas that need to be corrected, and generating comments and advice regarding them.

[0947] "Means for providing generated comments to users" means a system that has the functionality to provide system-generated comments and advice on corrections to users in a format that can be displayed, sent, or downloaded.

[0948] The system for implementing this invention consists of a server that includes an AI engine, a contract database, and a user interface, and a terminal for user access. The specific operation and usage of the system are explained below.

[0949] System configuration

[0950] 1. AI Engine

[0951] The AI ​​engine installed on the server uses a generative AI model to analyze and generate contracts. Specifically, it analyzes the contract draft provided by the user, identifies areas to be revised, and generates comments.

[0952] 2. Contract Database

[0953] The contract database stores contract templates and past contract data. The AI ​​engine selects the optimal template from this database and generates a contract based on the information entered by the user.

[0954] 3. User Interface

[0955] The user interface provides a screen for users to upload contracts and review generated contracts and comments, and is implemented as a web browser or mobile application.

[0956] System Operation

[0957] 1. Upload the contract

[0958] Users use their devices to upload draft contracts to the system, which are then analyzed by the server's AI engine.

[0959] 2. Contract analysis and comment generation

[0960] The AI ​​engine uses a generative AI model to analyze the contents of the draft contract, identify areas for revision, and generate appropriate comments and advice for the identified areas.

[0961] 3. Submitting Comments

[0962] The generated comments are provided to the user through the user interface, allowing the user to refer to them when revising the contract.

[0963] Hardware and software used

[0964] Hardware: Servers, user devices (PCs, smartphones, tablets, etc.)

[0965] Software: Python, docx library, openai library, web browser or mobile application

[0966] Specific examples

[0967] When a user uploads terms of use for an electronic payment service, the following comment is generated:

[0968] 1. The description of "Payment Method" in Article 3 is unclear. Please specify the specific payment method.

[0969] 2. The description regarding the "Cancellation Policy" in Article 5 is insufficient. Please add the cancellation conditions and procedures.

[0970] 3. The description of "Privacy Policy" in Article 7 is out of date. Please update it to the latest privacy policy.

[0971] Prompt Sentence Examples

[0972] Please analyze the draft contract below, identify any necessary revisions, and generate comments.

[0973] [Contents of the contract draft]

[0974] In this way, users simply upload contracts or terms of use, and the AI ​​engine automatically analyzes the content, identifies areas to be corrected, and generates comments, allowing them to efficiently create and modify contracts.

[0975] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0976] Step 1:

[0977] The user uploads the draft agreement using a device.

[0978] Input: The user selects the contract draft file from the terminal and presses the upload button.

[0979] Output: A draft contract file is sent to the server.

[0980] Specific behavior: The user selects a contract draft file through a web browser or mobile application interface and clicks the upload button, which sends the file to the server via an HTTP request.

[0981] Step 2:

[0982] The server receives the contract draft and passes it to the AI ​​engine.

[0983] Input: The uploaded draft agreement file.

[0984] Output: The contents of the draft contract are passed to the AI ​​engine.

[0985] Specific operation: The server temporarily stores the received contract draft file, reads its contents, and converts it into text format to be passed to the AI ​​engine.

[0986] Step 3:

[0987] The AI ​​engine analyzes the contract draft, identifies areas to be revised, and generates comments.

[0988] Input: The text content of the draft contract.

[0989] Output: Corrections and comments.

[0990] What it does: The AI ​​engine uses the generative AI model to analyze the content of the contract draft, generate prompts and send them to the OpenAI API, analyze the results returned by the API, and extract corrections and comments.

[0991] Step 4:

[0992] The server provides the generated comments to the user.

[0993] Input: Corrections and comments returned by the AI ​​engine.

[0994] Output: Corrections and comments displayed in the user interface.

[0995] Specific operation: The server sends the corrections and comments received from the AI ​​engine to the user interface, where the user can view the corrections and comments through a web browser or mobile application.

[0996] Step 5:

[0997] The user checks the corrections and comments and modifies the contract.

[0998] Input: Corrections and comments provided by the server.

[0999] Output: Amended draft contract.

[1000] Specific operation: The user can revise the contract draft based on the provided revisions and comments. Once the revisions are complete, they can be uploaded back to the system.

[1001] In this way, users can automate contract review and efficiently create and modify contracts.

[1002] Example 3

[1003] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1004] Conventional contract generation systems have made it difficult to flexibly review contracts according to the user's business situation and needs. Furthermore, they lacked the ability to select the appropriate review level for cases where detailed negotiations were required or where quick conclusion was required. This often resulted in users spending a great deal of time and effort reviewing contracts.

[1005] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1006] In this invention, the server includes a means for allowing a user to select a review level, a means for generating a prompt sentence based on the selected review level, and a means for inputting the generated prompt sentence into a generative AI model to generate a review. This allows a user to select an optimal review level according to their business situation and needs, and efficiently review a contract.

[1007] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze and generate data.

[1008] A "contract database" is a database system that stores contract templates and past contract data and allows for searching and referencing.

[1009] "User input" refers to data or instructions provided by the user to the system, such as the content of the contract or the selection of the review level.

[1010] "Review Level" refers to three options that users can choose from when reviewing a contract, which vary in level of detail and priority: "Serious Negotiation," "Normal," and "Fastest Conclusion Priority."

[1011] A "prompt sentence" is an instruction sentence input to the generative AI model, and is generated according to the review level.

[1012] A "generative AI model" is an artificial intelligence model that generates text based on a given prompt, such as GPT-4.

[1013] A "review" is a document generated by a generative AI model that includes an evaluation of the contract and suggested modifications.

[1014] "Means provided to users" refers to the functionality for displaying or sending generated contracts and reviews to the user's device.

[1015] This invention is a system that allows users to select a contract review level based on their business situation and needs, and efficiently reviews contracts using a generative AI model. Specific embodiments of this system are described below.

[1016] Hardware and software used

[1017] Hardware: Servers, user devices (PCs, smartphones, etc.)

[1018] Software: web applications, generative AI models (e.g., GPT-4)

[1019] System Overview

[1020] The user accesses the web application using a terminal and logs in. After logging in, the user selects one of three options on the review level selection screen: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." The selected review level information is sent from the terminal to the server.

[1021] The server generates a prompt to be input to the generative AI model based on the received review level. For example, if "serious negotiation" is selected, the prompt will be "Please generate a review for detailed contract negotiations." The server inputs the generated prompt into the generative AI model (e.g., GPT-4) to generate a review. The generated review is stored on the server and sent to the user's device. The user can check the review results on their device.

[1022] Specific examples

[1023] If the user selects "Serious Negotiation"

[1024] Example prompt: "Generate a review to facilitate detailed contract negotiations."

[1025] Review produced: "With regard to Article 3 of this Agreement, the following points need to be negotiated..."

[1026] If the user selects "Normal"

[1027] Example prompt: "Generate a standard contract review."

[1028] Produces the following review: "This agreement is general and I have no issues with it, but I need to clarify section 5..."

[1029] If the user selects "Fastest conclusion priority"

[1030] Example prompt: "Generate a quick review to close the deal faster."

[1031] Produces review: "This contract can be executed quickly. No issues..."

[1032] In this way, users can select the optimal review level according to their business situation and needs, and efficiently review contracts using the generative AI model. This system allows users to significantly reduce the time and effort required to review contracts. The flow of the specific processing in Example 3 is explained using Figure 15.

[1033] Step 1:

[1034] User selects review level

[1035] Input: A user accesses a web application using a terminal and logs in.

[1036] Specific operation: The user selects one of the three options on the review level selection screen: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." Clicks the review level radio button and presses the "Submit" button.

[1037] Output: The selected review level information is saved to the device.

[1038] Step 2:

[1039] The device sends the selected review level to the server.

[1040] Input: User-selected review level information

[1041] Specific behavior: The device sends a POST request to the server with review-level data in JSON format.

[1042] Output: The server receives the review level information.

[1043] Step 3:

[1044] The server generates a prompt according to the review level.

[1045] Input: Review level information received by the server

[1046] Specific operation: The server determines the review level using conditional branching and generates the corresponding prompt text as a string.

[1047] Example: If "Serious Negotiation" is selected, the prompt will read "Generate a review to conduct detailed contract negotiations."

[1048] Output: The generated prompt text is saved on the server.

[1049] Step 4:

[1050] The server inputs a prompt into the generative AI model and generates a review.

[1051] Input: Server generated prompt text

[1052] Specific operation: The server sends a prompt to the API endpoint of the generative AI model and receives the generated review.

[1053] Output: The generated reviews are saved on the server.

[1054] Step 5:

[1055] The server sends the generated reviews to the user's device.

[1056] Input: Server-generated review

[1057] Specific behavior: The server sends the generated review in JSON format to the user's device as an HTTP response.

[1058] Output: The user's device receives the review.

[1059] Step 6:

[1060] The user checks the review results

[1061] Input: Review sent to user's device

[1062] Specific behavior: The user scrolls through the review results displayed on the device screen, reads them, and downloads or prints them as needed.

[1063] Output: User checks the review results and decides on the next action.

[1064] (Application example 3)

[1065] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1066] Conventional contract generation systems have difficulty in providing flexible reviews that are tailored to the user's business situation and needs, and have had problems in that they were unable to meet user requirements, particularly in terms of review quality and speed. Furthermore, the system was unable to understand how detailed the user wanted the review to be and provide appropriate guidelines, which sometimes reduced the efficiency and effectiveness of the review.

[1067] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1068] In this invention, the server includes means for connecting the AI ​​engine with the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for the user to select a review level from three levels: "serious negotiation," "normal," and "fastest conclusion priority," and means for providing review guidelines according to the selected review level. This enables flexible and efficient reviews according to the user's business situation and needs.

[1069] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze data and derive optimal results.

[1070] A "contract database" is a database system that stores contract templates and past contract data, and can be searched and retrieved as needed.

[1071] "User Input" means information provided by a user to the system, including data necessary for contract generation and review.

[1072] "Means for generating optimal contracts" refers to a function that selects an appropriate template from a contract database based on information input by the user, and creates a contract by filling in the necessary information.

[1073] The "means for providing the generated contract to the user" is a function for displaying or sending the generated contract to the user.

[1074] The "means of selecting review level" is a function that allows users to select the level of detail and speed of the review from three levels: "serious negotiation," "normal," and "priority for fastest conclusion."

[1075] "Means for providing review guidelines" is a function that presents users with appropriate review methods and points to note depending on the review level selected.

[1076] The system for implementing this invention is composed of a server including an AI engine, a contract database, and a user interface, and a terminal for user access. A specific embodiment of the system is described below.

[1077] System configuration

[1078] The server has a means for connecting the AI ​​engine with the contract database. The AI ​​engine analyzes the information input by the user and selects the optimal contract template from the contract database. The server also has a means for generating the optimal contract based on the selected template. The generated contract is provided to the user.

[1079] Furthermore, the server has a means for the user to select a review level from three levels: "serious negotiation," "normal," and "fastest conclusion priority." It also includes a means for providing review guidelines according to the selected review level.

[1080] Program processing

[1081] The server is implemented using programming languages ​​such as Python and Java. The AI ​​engine uses a generative AI model that uses natural language processing (NLP) technology. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used. The contract database is built using SQL or NoSQL databases.

[1082] When a user accesses the system from their device and requests the generation of a contract, the server receives the user's input information, which is then analyzed by the AI ​​engine. Based on the analysis results, the optimal template is selected from the contract database and the contract is generated. The generated contract is then provided to the user's device.

[1083] When a user selects a review level, the server generates review guidelines according to the selected level and provides them to the user. For example, if "serious negotiation" is selected, detailed review guidelines will be displayed. Specifically, a specific review such as "This product is of very high quality, with particularly good battery life. However, I'm concerned about the high price" is expected.

[1084] Prompt Sentence Examples

[1085] "Please select your review level. Choose from hard negotiation, normal, or fastest closing priority."

[1086] In this way, flexible and efficient reviews can be performed according to the user's business situation and needs.

[1087] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1088] Step 1:

[1089] The user accesses the system from a terminal and requests the generation of a contract.

[1090] Input: User's contract generation request information (contract content, conditions, etc.)

[1091] Output: Contract generation request to the server

[1092] Specific operations: The user enters the information required to generate a contract through the terminal interface and presses the send button.

[1093] Step 2:

[1094] The server receives the user's input information and sends it to the AI ​​engine.

[1095] Input: User's contract generation request information

[1096] Output: Analysis request to the AI ​​engine

[1097] Specific operation: The server analyzes the received user input information, converts it into an appropriate format, and sends it to the AI ​​engine.

[1098] Step 3:

[1099] The AI ​​engine analyzes the information entered by the user and selects the most appropriate contract template from the contract database.

[1100] Input: User's contract generation request information

[1101] Output: Optimal contract template

[1102] Specific operation: The AI ​​engine uses natural language processing technology to analyze the information entered by the user and select the most appropriate template from the contract database.

[1103] Step 4:

[1104] The server generates an optimal contract based on the selected contract template.

[1105] Input: Optimal contract template, user contract generation request information

[1106] Output: Generated contract

[1107] Specific operation: The server embeds the user's input information into the template and generates a complete contract.

[1108] Step 5:

[1109] The server provides the generated contract to the user.

[1110] Input: Generated Agreement

[1111] Output: The contract displayed on the user's device

[1112] Specific operation: The server sends the generated contract to the user's terminal and displays it on the user interface.

[1113] Step 6:

[1114] The user selects the review level.

[1115] Input: User review level selection information ("Serious Negotiation", "Normal", "Fastest Conclusion Priority")

[1116] Output: Review level selection information to the server

[1117] Specific operation: The user selects the review level through the terminal interface and presses the send button.

[1118] Step 7:

[1119] The server generates review guidelines according to the selected review level and provides them to the user.

[1120] Input: User review level selection information

[1121] Output: Review guidelines according to review level

[1122] Specific operation: The server generates appropriate review guidelines based on the selected review level and sends them to the user's device.

[1123] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1124] "Example 1"

[1125] In one embodiment of the present invention, the emotion engine recognizes a user's emotions and adjusts the generation and review of contracts based on those emotions. Specifically, when a user enters information about the business and the contracting party in a messaging app, the emotion engine analyzes the user's emotions. For example, if the user is feeling angry or anxious, the emotion engine conveys that information to the AI ​​engine, which then generates and reviews contracts more carefully.

[1126] "Example 2"

[1127] In another embodiment of the present invention, the emotion engine automatically adjusts the review level based on the user's emotions. Specifically, when the user selects a review level from three levels—"hard bargaining," "normal," and "fastest closing priority"—the emotion engine analyzes the user's emotions and recommends the optimal review level based on those emotions. For example, if the user is feeling strong dissatisfaction or anger, the emotion engine recommends "hard bargaining."

[1128] "Example 3"

[1129] In another embodiment of the present invention, the emotion engine analyzes a user's emotions and adjusts the generation and review of the contract based on the emotions. Specifically, when a user attaches a draft contract file to a messenger app, the emotion engine analyzes the user's emotions and identifies necessary revisions based on the emotions. For example, if the user is feeling anxious or scared, the emotion engine conveys that information to the AI ​​engine, which then reviews the contract more carefully.

[1130] The processing flow of each embodiment will be described below.

[1131] "Example 1"

[1132] Step 1: The user enters information about their business and the person they are contracting with in the messenger app.

[1133] Step 2: The emotion engine analyzes the user's emotions.

[1134] Step 3: The emotion engine communicates this information to the AI ​​engine.

[1135] Step 4: The AI ​​engine generates and reviews contracts more carefully.

[1136] "Example 2"

[1137] Step 1: The user selects a review level from three levels: "Serious Negotiation," "Normal," and "Fastest Closure Priority."

[1138] Step 2: The emotion engine analyzes the user's emotions.

[1139] Step 3: The sentiment engine recommends the optimal review level based on the sentiment.

[1140] "Example 3"

[1141] Step 1: The user attaches the draft contract file to the messenger app. Step 2: The emotion engine analyzes the user's emotions.

[1142] Step 3: The emotion engine identifies the necessary modifications based on the emotion.

[1143] Step 4: The emotion engine communicates this information to the AI ​​engine.

[1144] Step 5: The AI ​​engine reviews the contract more carefully.

[1145] Example 1

[1146] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1147] Conventional contract generation systems have the problem that it is difficult to generate optimal contracts based on user input information, and the generation and review of contracts do not take user sentiment into consideration, resulting in low user satisfaction. Furthermore, the contract template selection and generation process is not automated, which creates issues of inefficiency and time.

[1148] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1149] In this invention, the server includes means for receiving input information from a user, means including an artificial intelligence engine for analyzing the received input information, means including an emotion analysis engine for analyzing the user's emotions, means for selecting an optimal contract template from a contract database based on the analysis results, means for generating a contract based on the selected template, and means for providing the generated contract to the user. This makes it possible to automatically generate an optimal contract that takes into account the user's input information and emotions.

[1150] "Means for receiving input information from users" refers to a function that allows the server to receive information entered by users through a messenger app or other interface.

[1151] "Means including an artificial intelligence engine" refers to a function that uses artificial intelligence technology to analyze received user input information and extract information such as the type of business and the characteristics of the contracting party.

[1152] The "means including an emotion analysis engine" is a function for analyzing emotions from information input by a user and extracting the emotion information.

[1153] The "means for selecting the most appropriate contract template from the contract database" is a function for selecting the most appropriate template from the contract database based on the analysis results of the artificial intelligence engine and the sentiment analysis engine.

[1154] The "means for generating a contract based on a selected template" is a function for generating a specific contract by reflecting the information input by the user in the selected contract template.

[1155] "Means for providing the generated contract to the user" refers to a function for sending the generated contract to the user so that the user can review, modify, and approve it.

[1156] MODE FOR CARRYING OUT THE INVENTION

[1157] This invention is a system that generates an optimal contract based on information input by a user and provides it to the user. This system is composed of a server that includes an artificial intelligence engine, an emotion analysis engine, and a contract database, and a terminal used by the user.

[1158] Hardware and software used

[1159] The server uses the following hardware and software:

[1160] Hardware: High-performance server, network interface

[1161] Software: Artificial intelligence engine (e.g., OpenAI GPT-4), sentiment analysis engine (e.g., IBM Watson Tone Analyzer), contract database, messenger app API

[1162] Users use common devices such as smartphones and PCs, which have messenger apps installed.

[1163] Program processing

[1164] The server receives information entered by the user in the messenger app. For example, the user enters, "I want to launch a new marketing campaign with a partner company." This information is then sent to the server.

[1165] The server sends the received input information to the AI ​​engine, which analyzes the input information and extracts information such as the type of business and characteristics of the contracting party. At the same time, the emotion analysis engine analyzes the emotions from the user's input information and extracts that emotion information.

[1166] Based on the analysis results, the server selects the most appropriate contract template from the contract database. For example, it may select a template suitable for a marketing campaign. Based on the selected template, the AI ​​engine generates a specific contract. In this process, it also takes into account the analysis results of the sentiment analysis engine. If the user is unsure, it will adjust the content to be more detailed and careful.

[1167] The generated contract is then sent from the server to the user's messaging app, where the user can review the contract and make any necessary amendments or approvals.

[1168] Examples of concrete examples and prompts

[1169] For example, consider a case where a user types into a messaging app, "I'd like to launch a new marketing campaign with a partner company." If the sentiment analysis engine detects uncertainty in the user's input, the artificial intelligence engine will take that information into account and generate a more detailed and careful contract.

[1170] Example prompt sentence:

[1171] "We would like to launch a new marketing campaign with a partner company. Please draw up a contract."

[1172] When this prompt sentence is input into the generative AI model, the artificial intelligence engine selects the most appropriate template from the contract database and generates the contract taking into account the analysis results of the sentiment analysis engine.

[1173] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1174] Step 1:

[1175] The user enters information in the messenger app.

[1176] Users open a messenger app on their smartphone or PC and type in, "I want to start a new marketing campaign with a partner company." This input information is sent to the server.

[1177] Step 2:

[1178] The server receives the input information and sends it to the artificial intelligence engine.

[1179] The server receives the information sent from the messenger app and sends an HTTP request to the API endpoint of the AI ​​engine. The input information includes the text data entered by the user. The output is the analysis result returned by the AI ​​engine.

[1180] Step 3:

[1181] An artificial intelligence engine analyzes the input information.

[1182] The AI ​​engine uses natural language processing technology to analyze the received information and extract keywords such as "marketing campaign" and "partner company." Input includes user text data. Output includes the analyzed keywords and business type.

[1183] Step 4:

[1184] The sentiment analysis engine analyzes the user's emotions.

[1185] The emotion analysis engine analyzes emotions from user input sentences and identifies emotions such as "anxiety" or "anger." For example, it can read the user's positive intentions from the expression "I want to do it." The input includes the user's text data. The output includes analyzed emotional information.

[1186] Step 5:

[1187] The server selects the most suitable template from the contract database.

[1188] The server selects a "marketing campaign contract template" from the contract database based on the analysis results from the artificial intelligence engine and the sentiment analysis engine. The input includes the analysis results. The output includes the selected template.

[1189] Step 6:

[1190] An artificial intelligence engine generates contracts based on templates.

[1191] The AI ​​engine generates a contract based on the selected template and reflects the user's input information. It also takes into account the results of the sentiment analysis engine and adjusts the content to be more detailed and cautious if the user feels uneasy. The inputs include the template and the user's input information. The output includes the generated contract.

[1192] Step 7:

[1193] The server provides the generated contract to the user.

[1194] The server sends the generated contract to the user's messenger app, where the user can review the contract and make any necessary amendments or approvals. Inputs include the generated contract. Outputs include the contract provided to the user.

[1195] (Application example 1)

[1196] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1197] While conventional contract generation systems can generate contracts based on user input, they have problems such as being unable to adjust the contract to take user emotions into account or display the contract in real time. Furthermore, they do not support contract generation using voice input, which reduces user convenience.

[1198] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1199] In this invention, the server includes means for connecting the AI ​​engine and the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for acquiring voice input, means for analyzing emotions, means for adjusting the content of the contract based on the analyzed emotions, and means for displaying the generated contract on a display device in real time. This makes it possible to generate and adjust a contract taking the user's emotions into consideration, supports contract generation using voice input, and enables contract display in real time.

[1200] An "AI engine" is an artificial intelligence system that analyzes information input by users and selects the most appropriate contract template from a contract database.

[1201] A "contract database" is a database that stores various contract templates and provides them as needed.

[1202] "Information input by the user" refers to information about the business content and the contracting party that the user provides to generate the contract.

[1203] The "means for generating the optimal contract" is the process of selecting an appropriate template from a contract database based on information input by the user, and filling in the necessary information to create a contract.

[1204] A "means for providing the generated agreement to the user" is a method for displaying or transmitting the generated agreement to the user.

[1205] "Means for acquiring voice input" refers to technology for recognizing the user's voice and acquiring it as text data.

[1206] "Means for analyzing emotions" refers to technology for reading and analyzing emotions from the user's voice and facial expressions.

[1207] The "means for adjusting the content of the contract based on the analyzed emotions" is a process for appropriately changing the content and wording of the contract depending on the emotional state of the user.

[1208] The "means for displaying in real time on a display device" is a technique for displaying the generated contract in real time on the user's display device.

[1209] The system for implementing this invention includes an AI engine, a contract database, a voice input acquisition means, an emotion analysis means, a contract content adjustment means, and a real-time display means. Specific embodiments of each means are described below.

[1210] System configuration

[1211] 1. AI Engine

[1212] The server analyzes the information input by the user and uses an AI engine to select the optimal contract template from the contract database. The AI ​​engine uses natural language processing technology to understand the information input by the user and select the appropriate template.

[1213] 2. Contract Database

[1214] The contract database stores various contract templates, and the AI ​​engine provides the selected template. The database is built using a database management system such as SQL or NoSQL.

[1215] 3. Voice input acquisition method

[1216] The device receives user voice input using voice recognition technology, which can be achieved using voice recognition services such as Google Speech Recognition API or IBM Watson.

[1217] 4. Emotion analysis method

[1218] The server uses an emotion analysis engine to analyze emotions from the user's voice and facial expressions. For emotion analysis, services such as Microsoft Azure Emotion API and Amazon Rekognition can be used.

[1219] 5. Means for adjusting contract contents

[1220] The server then adjusts the content of the contract based on the analyzed emotions. For example, if the user is feeling anxious, the server will make the contract more polite.

[1221] 6. Real-time display means

[1222] The terminal displays the generated contract in real time. The display device can be a smart eyeglass or a head-mounted display.

[1223] Specific examples

[1224] When a store staff member signs a new service contract with a customer, they can say "I would like to sign a new service contract" through the smart glasses. The terminal will recognize the voice, and the server will analyze the customer's emotions, generate the most appropriate contract, and display it on the smart glasses.

[1225] Prompt Sentence Examples

[1226] "We'd like to enter into a new service contract. The customer seems a little anxious."

[1227] By inputting this prompt into a generative AI model, the AI ​​engine generates the optimal contract, and the emotion engine adjusts the contract content to take into account the customer's concerns.

[1228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1229] Step 1:

[1230] The user inputs voice through the smart glasses. The user says, "I would like to sign up for a new service contract." The device uses voice recognition technology to convert this voice into text data. The input is voice data, and the output is text data. Specifically, the device calls the Google Speech Recognition API to convert the voice data into text data.

[1231] Step 2:

[1232] The device sends the acquired text data to the server. The server receives this text data and passes it to the AI ​​engine. The input is text data, and the output is input data to the AI ​​engine. Specifically, the device sends the text data to the server using an HTTP request.

[1233] Step 3:

[1234] The server uses an AI engine to analyze the text data and select the most appropriate contract template from the contract database. The input is text data and the output is a contract template. Specifically, the AI ​​engine uses natural language processing technology to analyze the text data and select an appropriate template from the contract database.

[1235] Step 4:

[1236] The server uses an emotion analysis engine to analyze the user's emotions. The input is text data and the output is emotion data. Specifically, the emotion analysis engine uses the Microsoft Azure Emotion API to read emotions from the text data.

[1237] Step 5:

[1238] The server adjusts the content of the contract based on the analyzed emotion data. The input is a contract template and emotion data, and the output is the adjusted contract. Specifically, the server changes the wording and content of the contract taking the emotion data into account.

[1239] Step 6:

[1240] The server sends the generated contract to the terminal. The input is the adjusted contract, and the output is the data to be sent to the terminal. Specifically, the server sends the contract data to the terminal using an HTTP response.

[1241] Step 7:

[1242] The terminal displays the received contract on the smart glasses display in real time. The input is the contract data, and the output is the contract displayed on the display. Specifically, the terminal uses the smart glasses' display control API to display the contract.

[1243] Example 2

[1244] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1245] Conventional contract drafting systems require users to manually review contract drafts and identify areas for revision, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to automatically adjust the optimal review level based on user sentiment, making it difficult to create contracts that meet the user's intent.

[1246] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1247] In this invention, the server includes means for connecting the AI ​​engine and the database, means for generating an optimal document based on information input by a user, means for providing the generated document to the user, means for reviewing the document draft provided by the user and outputting a revision history and comments, and means for analyzing the user's emotions and recommending an optimal review level. This allows the user to efficiently review and revise the contract, and furthermore, the optimal review level is automatically adjusted based on the user's emotions, making it possible to create a contract that meets the user's intentions.

[1248] An "AI engine" is a computer program that uses artificial intelligence technology to analyze and process data.

[1249] A "database" is a system for efficiently storing, searching, and managing data.

[1250] "User" means any individual or legal entity that uses this system to create or review contracts.

[1251] "Input information" means data or instructions provided by a user to a system.

[1252] "Document" refers to a contract or other official document.

[1253] A "Document Draft" is an incomplete document created by a User.

[1254] "Review" is the process of checking the contents of a document draft and identifying any necessary revisions.

[1255] A "revision history" is a record of revisions made to a document draft.

[1256] A "comment" is text that includes explanations and instructions for the correction.

[1257] "Emotion" refers to the user's psychological state and is the subject of analysis by the system.

[1258] "Review level" is an indicator of the rigor and detail of the document review.

[1259] "Recommendation" is the act of the system presenting the best option to the user.

[1260] This invention is a system for allowing users to efficiently review and revise contract drafts. The system includes means for connecting an AI engine with a database, means for generating an optimal document based on information input by the user, means for providing the generated document to the user, means for reviewing the document draft provided by the user and outputting a revision history and comments, and means for analyzing user sentiment and recommending an optimal review level.

[1261] The server uses natural language processing technology as its AI engine, specifically generative AI models such as Google Cloud Natural Language API and OpenAI's GPT-4, to analyze the text of the contract draft and identify necessary revisions.

[1262] The user sends a draft contract file to the server using a messenger app. The server analyzes the received draft and identifies areas for revision. For example, it generates a comment such as, "The wording of Article 3 is ambiguous, so specific numerical values ​​need to be added." This allows the user to check the revision history and comments provided, enabling efficient revision of the contract.

[1263] The server then uses an emotion engine to analyze the user's emotions. Using an emotion analysis API (e.g., IBM Watson Tone Analyzer or Microsoft Azure Text Analytics), the server analyzes the user's messages and selections and recommends the optimal review level based on their emotions. For example, if the user is feeling very dissatisfied or angry, the server recommends "hard negotiation."

[1264] For example, if a user sends a "Contract Draft.docx" file attached to a messaging app, the server receives the file and analyzes it using the Google Cloud Natural Language API. As a result of the analysis, a comment such as "The wording of Article 3 is ambiguous, so specific numerical values ​​should be added" is generated. The user can then use this comment to revise the contract.

[1265] If the user attempts to select "Normal" but the emotion engine detects strong dissatisfaction in the user's message, the server will recommend "Serious Negotiation." The user can adjust the review level accordingly.

[1266] Examples of prompts include:

[1267] "Please review the draft contract. Analyze the draft below, identify any necessary revisions, and generate revision history and comments."

[1268] "Analyze user sentiment and recommend the optimal contract review level. Please obtain user sentiment from the following message."

[1269] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1270] Step 1:

[1271] The user submits the draft agreement.

[1272] The user opens a messenger app, attaches a contract draft file, and sends it. The input is the contract draft file, and the output is sending the file to the server. Specifically, the user uploads "ContractDraft.docx" to the messenger app and presses the send button.

[1273] Step 2:

[1274] The server receives the draft.

[1275] The server receives the contract draft file sent from the messenger app. The input is the file sent by the user, and the output is a file saved in temporary storage on the server. Specifically, the server saves the received file in a specific directory.

[1276] Step 3:

[1277] The server parses the draft.

[1278] The server calls a generative AI model, such as Google Cloud Natural Language API or OpenAI's GPT-4, to analyze the received contract draft. The input is the saved contract draft text, and the output is the analysis result. Specifically, the server sends the draft text to the API and obtains the analysis result.

[1279] Step 4:

[1280] The server identifies the corrections.

[1281] Based on the analysis results, the server identifies the necessary revisions to the contract. The input is the analysis results, and the output is a list of revisions. Specifically, the server identifies revisions such as "the wording of Article 3 is ambiguous, so specific numerical values ​​need to be added."

[1282] Step 5:

[1283] The server generates revision history and comments.

[1284] The server generates a revision history and comments for the identified revisions. The input is a list of revisions, and the output is a revision history and comments. Specifically, the server generates a comment containing an explanation or instruction for the revisions.

[1285] Step 6:

[1286] The server provides the results to the user.

[1287] The server sends the generated revision history and comments to the user. The input is the revision history and comments, and the output is a notification to the user. Specifically, the server sends the revision history and comments to the user through a messenger app.

[1288] Step 7:

[1289] The user selects the review level.

[1290] The user opens an interface to select the review level of the contract. The input is the user's selection, and the output is the selected review level. In concrete terms, the user selects from three options: "hardcore negotiation," "normal," and "fastest closing priority."

[1291] Step 8:

[1292] The server analyzes the user's emotions.

[1293] The server calls the sentiment analysis API to analyze the user's sentiment. The input is the user's message and selections, and the output is the sentiment analysis result. Specifically, the server sends the user's message to the API and obtains the sentiment analysis result.

[1294] Step 9:

[1295] The server will recommend the optimal review level.

[1296] The server recommends the optimal review level for the user based on the sentiment analysis results. The input is the sentiment analysis results, and the output is the recommended review level. Specifically, if the server recommends "serious negotiation," it notifies the user.

[1297] Step 10:

[1298] The user confirms the recommended review level.

[1299] The user checks the recommendation from the server and adjusts the review level as needed. The input is the recommended review level, and the output is the final review level selection. As a specific operation, the user adjusts the review level according to the recommendation.

[1300] (Application example 2)

[1301] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1302] Conventional contract review systems require users to manually revise contract drafts, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to automatically adjust the optimal review level based on user sentiment, making it difficult to flexibly respond to user needs. This has led to issues with the inefficiency of contract creation and revision.

[1303] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1304] In this invention, the server includes means for connecting the AI ​​engine with the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for analyzing the contract draft provided by the user and identifying necessary revisions, means for generating a revision history and comments and providing them to the user, and means for analyzing the user's emotions and recommending a review level based on the emotions. This enables the user to review contracts efficiently and makes it possible to recommend an optimal review level according to the user's emotions.

[1305] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[1306] A "contract database" is a data storage system that stores contract templates and past contract data, allowing them to be searched and referenced as needed.

[1307] "User input information" refers to information such as text data, files, and emotional state that a user provides to the system.

[1308] "Draft Agreement" means a draft or initial version of the Agreement created by a User.

[1309] "Amendments" are the parts of the draft contract that require amendment.

[1310] "Amendment history" is a record of amendments made to a draft contract.

[1311] "Comments" are explanations or suggestions given regarding amendments to the draft contract.

[1312] An "emotion engine" is a system that analyzes a user's emotions and recommends appropriate actions based on the results.

[1313] "Review level" is an indicator of the rigor and detail of the contract review.

[1314] A system for carrying out the present invention is configured as follows: The server includes means for connecting an AI engine with a contract database, means for generating an optimal contract based on information input by a user, means for providing the generated contract to the user, means for analyzing the contract draft provided by the user and identifying necessary revisions, means for generating a revision history and comments and providing them to the user, and means for analyzing user sentiment and recommending a review level based on the sentiment.

[1315] Specifically, the server runs a Python program that uses the TextBlob library and a custom sentiment analysis engine to analyze user sentiment. Regular expressions are used to analyze the draft contract and identify necessary revisions. This allows users to efficiently review the contract and recommends the optimal review level based on the user's sentiment.

[1316] For example, when a user sends a draft contract to the server using a smartphone messenger app, the server analyzes the draft contract, generates corrections and comments, and analyzes the user's sentiment and recommends a review level based on the sentiment: "serious negotiation," "normal," or "fastest conclusion priority."

[1317] For example, if a user enters "I'm not satisfied with this contract," the emotion engine will recommend "serious negotiation." Also, if the contract draft does not include a "deadline," the engine will suggest "The deadline is not specified" as a correction.

[1318] An example of a prompt for the generative AI model is as follows:

[1319] Enter the text of the draft agreement.

[1320] Enter the user's feelings.

[1321] In this way, users can efficiently review contracts and receive recommendations for the optimal review level based on sentiment.

[1322] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1323] Step 1:

[1324] The user sends the contract draft to the server using a messenger app on their smartphone.

[1325] Input: Contract draft text file

[1326] Output: A draft contract is uploaded to the server

[1327] Specific operation: The user opens the messenger app, selects the contract draft file, and presses the send button. The file is then uploaded to the server.

[1328] Step 2:

[1329] The server receives the contract draft and begins text analysis.

[1330] Input: Uploaded draft agreement text file

[1331] Output: List of modifications and comments

[1332] What happens: The server reads the text of the draft contract and uses regular expressions to identify the necessary corrections. For example, if a "deadline" is missing, it adds "No deadline is specified" to the list of corrections.

[1333] Step 3:

[1334] The server analyzes the user's emotions.

[1335] Input: User sentiment text

[1336] Output: Sentiment analysis result (positive, negative, neutral)

[1337] Specific operation: The server uses the TextBlob library to analyze the user's sentiment text. For example, if the text is "I am dissatisfied with this contract," the sentiment analysis result will be "negative."

[1338] Step 4:

[1339] The server recommends a review level based on the sentiment analysis results.

[1340] Input: Sentiment analysis results

[1341] Output: Recommended review level (hard bargaining, normal, fastest conclusion priority)

[1342] Specific operation: The server determines the review level based on the sentiment analysis result. For example, if the sentiment analysis result is "negative," it recommends "serious negotiation."

[1343] Step 5:

[1344] The server provides the user with the corrections, comments, and recommended review level.

[1345] Input: List of corrections, comments, recommended review level

[1346] Output: Corrections, comments, and recommended review level provided to the user

[1347] What it does: The server sends a list of corrections, comments, and a recommended review level to the user, who can receive this information in their messenger app.

[1348] Through the above steps, the user can efficiently review the contract and receive recommendations for the optimal review level based on their sentiment.

[1349] Example 3

[1350] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1351] Conventional contract review systems were unable to provide optimal contract reviews because they struggled to respond flexibly to user emotions and business situations. Furthermore, because they conducted uniform reviews without considering user emotions, they were unable to fully address users' anxieties and concerns. This resulted in delays in the contract signing process and reduced business efficiency.

[1352] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1353] In this invention, the server includes means for a user to select a review level, means for a user to upload a contract draft, means for analyzing the user's emotions using an emotion analysis engine, means for adjusting the contract review based on the analyzed emotion information, means for reviewing the contents of the contract using an AI engine and identifying areas to be revised, and means for providing the review results to the user, thereby enabling an optimal contract review according to the user's emotions and business situation.

[1354] "Means for users to select review levels" refers to a function that provides an interface for users to select the rigor and speed of contract review.

[1355] "Means for users to upload draft contracts" refers to a function that allows users to send draft contracts they have created to the system.

[1356] An "emotion analysis engine" is a combination of software or hardware for analyzing a user's emotions and providing data based on those emotions.

[1357] The "means for adjusting the review of a contract based on analyzed emotional information" is a function for adjusting the method and content of reviewing a contract based on the emotional information of the user obtained from the emotion analysis engine.

[1358] An "AI engine" is a combination of software and hardware that uses artificial intelligence technology to analyze the contents of a contract and identify any necessary revisions.

[1359] "Means for providing review results to users" refers to a function that notifies users of the review results of the contract and provides a report including corrections and comments.

[1360] The present invention provides a system for enabling a user to review a contract efficiently and effectively. A specific embodiment of this system will be described below.

[1361] First, the user accesses the system using a terminal, and selects one of three options from the review level interface: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." This selection information is sent to the server.

[1362] Next, the user uploads the contract draft file to the server using a messenger app. For example, the user attaches a file called "ContractDraft.docx" and sends it. This file is saved on the server.

[1363] The server uses a sentiment analysis engine to analyze the user's emotions. The sentiment analysis engine can use IBM Watson or Microsoft Azure's sentiment analysis API. The sentiment analysis engine obtains emotional data from the user's messages and behavior, and identifies emotions such as "anxiety" or "relief."

[1364] The analyzed emotional information is returned to the server, which then adjusts the review of the contract based on this information. Specifically, the AI ​​engine carefully considers each clause of the contract based on the user's emotional information obtained from the emotion analysis engine. OpenAI's GPT-4 can be used as the AI ​​engine.

[1365] The AI ​​engine analyzes the contents of the contract and identifies the necessary revisions. For example, if the user is unsure, the AI ​​engine will examine each clause in the contract in more detail and identify the necessary revisions.

[1366] Finally, the server generates the review results and provides them to the user. Specifically, it generates a contract with the revisions highlighted and a report explaining the reasons for the revisions. For example, the server generates "Contract Draft_Revisions.docx" and "Revision Report.pdf" and sends them to the user.

[1367] Examples:

[1368] A user uses this system when signing a new business contract. The user selects the "Normal" review level and attaches "Contract Draft.docx" to a messenger app and sends it. The sentiment analysis engine determines that the user is feeling a little uneasy and passes that information on to the AI ​​engine. The AI ​​engine carefully reviews each clause of the contract and identifies any necessary revisions. The server generates "Contract Draft_Amendments.docx" and "Amendment Report.pdf" with the revisions highlighted, and sends them to the user.

[1369] Example prompt sentence:

[1370] "Please review the draft new business contract. The review level is 'Normal' and the user is feeling a bit uneasy. Please carefully review each clause in the contract and identify any necessary revisions."

[1371] In this way, the user can receive the most suitable contract review according to his / her business situation and feelings. The flow of the specification process in the third embodiment will be described with reference to FIG.

[1372] Step 1:

[1373] The user accesses the system using a terminal and selects a review level. The user selects one of three options: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." This selection information is sent to the server. The input is the user's review level selection, and the output is the review level information sent to the server.

[1374] Step 2:

[1375] A user uploads a contract draft file to the server using a messenger app. For example, the user sends a message with a file named "ContractDraft.docx" attached. This file is saved on the server. The input is the contract draft file uploaded by the user, and the output is the contract draft file saved on the server.

[1376] Step 3:

[1377] The server uses a sentiment analysis engine to analyze the user's emotions. A general sentiment analysis API can be used as the sentiment analysis engine. The server obtains emotional data from the user's messages and actions, and identifies emotions such as "anxiety" or "relief." The input is the user's message and behavior data, and the output is analyzed emotional information.

[1378] Step 4:

[1379] The server adjusts the contract review based on the analyzed emotional information. Specifically, the AI ​​engine carefully reviews each clause of the contract based on the user's emotional information obtained from the emotion analysis engine. A general generative AI model can be used as the AI ​​engine. The input is the analyzed emotional information and the contract draft file, and the output is a contract draft with identified revisions.

[1380] Step 5:

[1381] The server uses an AI engine to review the contents of the contract and identify any necessary revisions. For example, if the user is feeling uneasy, the AI ​​engine will examine each clause of the contract in more detail and identify any necessary revisions. The input is the draft contract file and the analyzed emotional information, and the output is a draft contract with identified revisions.

[1382] Step 6:

[1383] The server generates the review results and provides them to the user. Specifically, it generates a contract with the revisions highlighted and a report explaining the reasons for the revisions. For example, the server generates "Contract Draft_Revisions.docx" and "Revision Report.pdf" and sends them to the user. The input is the contract draft with the revisions identified, and the output is the revisions and report provided to the user.

[1384] (Application example 3)

[1385] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1386] Conventional contract generation systems have difficulty responding flexibly to user emotions and business situations, and have had issues in not being able to fully meet user needs when reviewing and generating contracts. Furthermore, when reviewing contracts, careful consideration that takes user emotions into account is sometimes required, and there has been no system that can do this automatically.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1388] In this invention, the server includes means for connecting the AI ​​engine and the contract database, means for generating an optimal contract based on information input by a user, means for providing the generated contract to the user, means including an emotion engine for analyzing the user's emotions, means for adjusting the review of the contract based on the emotions analyzed by the emotion engine, means for the user to select a review level, and means for reviewing the contract based on the selected review level. This enables flexible generation and review of contracts according to the user's emotions and business circumstances.

[1389] An "AI engine" is a system that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[1390] A "contract database" is a database that stores contract templates and past contract data, and can be searched and retrieved as needed.

[1391] "User Input" means data or instructions provided by a user to the system, including information required for contract generation or review.

[1392] The "emotion engine" is an engine that analyzes the user's emotions and adjusts the system's behavior based on the results.

[1393] "Review Level" refers to the multiple levels of caution or speed that users can select when reviewing a contract.

[1394] "Contract review" is the process of checking the contents of a contract and making any necessary corrections or comments.

[1395] "Revision history" is a record of changes made to a contract, showing who revised what part and how.

[1396] "Comments" are explanations or opinions added to a particular part of the contract and are used in the review process.

[1397] A system for carrying out the present invention has the following configuration: a server includes means for connecting an AI engine to a contract database, means for generating an optimal contract based on information input by a user, means for providing the generated contract to the user, means including an emotion engine for analyzing user emotions, means for adjusting review of the contract based on the emotions analyzed by the emotion engine, means for the user to select a review level, and means for reviewing the contract based on the selected review level.

[1398] Hardware and software used

[1399] Hardware: Smartphones, servers

[1400] Software: Python, sentiment analysis library (e.g., emotion_analysis), contract review library (e.g., contract_review)

[1401] Data processing and calculation

[1402] Emotion analysis

[1403] When a user uploads a contract draft using their smartphone, the emotion engine analyzes the user's emotions in real time. It uses an emotion analysis library (e.g., emotion_analysis) to obtain emotion data from the user's facial expressions and voice and generates analysis results.

[1404] Determining the review level

[1405] Based on the analyzed emotional data, the server determines the appropriate review level, for example, selecting "hard bargaining" if the user is feeling anxious or scared, or "normal" if the user is calm.

[1406] Contract review

[1407] Use the contract review library (e.g., contract_review) to review the contract based on the selected review level. During the review, revision history and comments are generated and provided to the user.

[1408] Specific examples

[1409] User A uploads a contract to use an electronic payment service. The emotion engine analyzes User A's emotions and detects that User A is feeling anxious. The server selects the "serious negotiation" review level and carefully reviews the contract. As a result of the review, a revision history and comments are generated and provided to User A.

[1410] Prompt Sentence Examples

[1411] A user has uploaded a draft contract. Use the sentiment engine to analyze the user's sentiment and select the appropriate review level. If the user is feeling anxious or scared, select "Serious Negotiation" and carefully review the contract.

[1412] In this way, flexible contract generation and review can be achieved according to the user's emotions and business situation.

[1413] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1414] Step 1:

[1415] A user uploads a draft agreement using their smartphone.

[1416] Input: User selects contract draft file and presses upload button

[1417] Output: A draft contract file is sent to the server.

[1418] Specific operation: The user opens the application on their smartphone, selects the contract draft file, and presses the upload button, which sends the file to the server via the Internet.

[1419] Step 2:

[1420] The server uses an emotion engine to analyze the user's emotions.

[1421] Input: The uploaded contract draft file and the user's facial expression and voice data.

[1422] Output: User emotion data.

[1423] Specific operation: The server uses an emotion analysis library (e.g., emotion_analysis) to analyze the user's facial expressions and voice data and generate emotion data.

[1424] Step 3:

[1425] The server determines the review level based on the emotion data.

[1426] Input: User emotion data.

[1427] Output: Appropriate review level ("Serious Negotiation", "Normal", "Fastest Closure First").

[1428] Specific operation: The server analyzes the emotional data and selects "serious negotiation" if the user is feeling anxious or scared, "normal" if the user is calm, and "prioritize the fastest conclusion" if the user is in a hurry.

[1429] Step 4:

[1430] The server reviews the contract using a contract review library.

[1431] Input: The uploaded draft agreement file and the selected review level.

[1432] Output: The agreement with revision history and comments.

[1433] Specific Behavior: The server uses a contract review library (e.g., contract_review) to review the contract based on the selected review level and generate an amendment history and comments.

[1434] Step 5:

[1435] The server provides the review results to the user.

[1436] Input: The agreement with amendment history and comments.

[1437] Output: Review results sent to the user's smartphone.

[1438] Specific operation: The server sends the contract with the revision history and comments to the user's smartphone, allowing the user to check the review results.

[1439] In this way, flexible contracts can be generated and reviewed according to the user's emotions and business situations.

[1440] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1441] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1442] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1443] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1444] [Third embodiment]

[1445] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1446] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1447] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1448] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1449] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1450] The camera 42 includes an optical system including a lens, an aperture, and a shutter, and a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[1451] It is a small digital camera equipped with an imaging element such as a range sensor, and captures an image of the surroundings of the user 20 (for example, an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[1452] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1453] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1454] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1455] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1456] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1457] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1458] "Example 1"

[1459] One embodiment of the present invention is a system that connects an AI engine with a contract database. This system generates an optimal contract based on information input by the user. Specifically, when the user inputs "this type of business with this type of party" into a messenger app, the AI ​​engine analyzes this input information and selects the optimal contract template from the contract database. The system then generates a contract based on the selected template and provides it to the user.

[1460] "Example 2"

[1461] Furthermore, another embodiment of the present invention is a system that reviews contract drafts provided by users. Specifically, when a user attaches a contract draft file to a messenger app, the system analyzes the draft and identifies any necessary revisions. The system then generates a history of the revisions and comments and provides them to the user. This function allows users to efficiently create and revise contracts without having to review them themselves.

[1462] "Example 3"

[1463] Furthermore, as a further embodiment of the present invention, there is a function for selecting a review level. Specifically, the user can select a review level from three levels: "Serious Negotiation," "Normal," and "Fast Closure Priority." This function allows the user to select the optimal review level depending on the situation and needs of their business.

[1464] The processing flow of each embodiment will be described below.

[1465] "Example 1"

[1466] Step 1: The user enters information about their business and the person they are contracting with in the messenger app.

[1467] Step 2: The AI ​​engine analyzes this input information.

[1468] Step 3: The AI ​​engine selects the most suitable contract template from the contract database.

[1469] Step 4: Generate the contract based on the selected template.

[1470] Step 5: Provide the generated contract to the user.

[1471] "Example 2"

[1472] Step 1: The user attaches the draft contract file to a messenger app. Step 2: The system analyzes this draft.

[1473] Step 3: Identify the system's required modifications.

[1474] Step 4: Generate revision history and comments.

[1475] Step 5: Provide the generated revision history and comments to the user.

[1476] "Example 3"

[1477] Step 1: The user selects a review level from three levels: "Serious Negotiation," "Normal," and "Fastest Closure Priority."

[1478] Step 2: The system performs the review based on the selected review level.

[1479] Step 3: Provide the review results to the user.

[1480] Example 1

[1481] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1482] Conventional contract generation systems had the problem that it took a lot of time and effort for users to create a contract. In addition, specialized knowledge was required to check whether the content of the contract was appropriate, making it a difficult task for general users. Furthermore, the process of selecting and customizing a contract template was complicated and inefficient.

[1483] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1484] In this invention, the server includes means for a user to send input information using a communication application, means for the server to receive the user's input information and send it to an AI engine, means for the AI ​​engine to analyze the input information using a generative AI model, means for the AI ​​engine to select an optimal contract template from a contract database, means for the AI ​​engine to customize the selected template, and means for the server to provide the customized contract to the user. This enables users to easily generate optimal contracts and create appropriate contracts without specialized knowledge.

[1485] "User" means any person or entity that uses a communication application to generate a contract using the system.

[1486] "Communication application" refers to software that a user uses to send input information, and includes messenger applications, chat applications, and the like.

[1487] A "server" is a computer system that receives input information from users and sends it to the AI ​​engine.

[1488] An "AI engine" is a software component that uses a generative AI model to analyze user input information, select the most appropriate contract template from a contract database, and customize it.

[1489] A "generative AI model" is an artificial intelligence model used to analyze user input information, and uses natural language processing techniques.

[1490] The "contract database" is a database that stores contract templates corresponding to various business scenarios.

[1491] A "contract template" is a contract template that corresponds to a specific business scenario and is customized according to the user's requirements.

[1492] "Customization" is the process of modifying or adding to a selected contract template to reflect the user's specific requirements and conditions.

[1493] "Providing" means the act of sending the customized agreement to the user for review and modification.

[1494] The present invention provides a system for allowing a user to generate a contract using a communication application, the system including a server, an AI engine, a contract database, and a communication application.

[1495] The user uses a communication application to input the contents of the contract. For example, the user enters "I would like to enter into a software development contract with a certain company" and presses the send button. The communication application can be a messenger application or a chat application.

[1496] The server receives the input information sent by the user. The received information is sent to the AI ​​engine. The server converts the user's input information into an appropriate format and generates a prompt that the AI ​​engine can understand. For example, a prompt might be generated that reads, "The user has input that they would like to enter into a software development contract with a certain company. Please select and customize the most appropriate contract template."

[1497] The AI ​​engine uses a generative AI model (e.g., GPT-4) to analyze the user's input information. Natural language processing technology is used for the analysis to understand the user's intent and requirements. Based on the analysis results, the AI ​​engine selects the optimal contract template from a contract database, which stores templates corresponding to various business scenarios.

[1498] The selected template is then customized according to the user's specific requirements. For example, the name of the contracting party or specific contract terms may be added to the template. The customized contract is then provided to the user via the server. The user can then review the generated contract in their communication application and make any necessary modifications.

[1499] As a concrete example, consider the case where a user inputs "I would like to enter into a software development contract with a certain company" into a communication application. The server that receives this input information sends the information to an AI engine. The AI ​​engine analyzes the input information using a generative AI model (e.g., GPT-4) and selects a "software development contract" template from a contract database. The selected template is customized to suit the content of a contract with a certain company and provided to the user.

[1500] In this way, the system quickly generates contracts that meet the specific needs of the user.

[1501] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1502] Step 1:

[1503] A user sends input information using a communication application.

[1504] Specifically, the user opens a communication application (e.g., a messenger app), types in "I would like to enter into a software development contract with a certain company," and presses the send button.

[1505] Input: Text information about the user's contract details

[1506] Output: Data sent from the communication application to the server

[1507] Step 2:

[1508] The server receives the user's input.

[1509] In a specific operation, the server receives a message sent from the communication application saying, "I would like to enter into a software development contract with a certain company."

[1510] Input: User input information sent from a communication application

[1511] Output: Received data in the server

[1512] Step 3:

[1513] The server sends the received information to the AI ​​engine.

[1514] Specifically, the server sends the received message to the AI ​​engine, with a prompt such as, "The user has entered that they would like to enter into a software development contract with a certain company. Please select and customize the most appropriate contract template."

[1515] Input: Data received within the server

[1516] Output: Prompt sentence to the AI ​​engine

[1517] Step 4:

[1518] The AI ​​engine analyzes the input information.

[1519] Specifically, the AI ​​engine uses a generative AI model (e.g., GPT-4) to analyze the message "I would like to enter into a software development contract with a certain company" and understand the user's intent.

[1520] Input: Prompt sentence to AI engine

[1521] Output: Analysis results (user intent and requirements)

[1522] Step 5:

[1523] The AI ​​engine selects the optimal template from the contract database.

[1524] Specifically, the AI ​​engine selects a "software development contract" template from the contract database based on the analysis results.

[1525] Input: Analysis results

[1526] Output: Selected Agreement Template

[1527] Step 6:

[1528] The AI ​​engine will customize the selected template.

[1529] Specifically, the AI ​​engine customizes a "Software Development Agreement" template by adding the name of a company and specific contract terms.

[1530] Input: Selected Agreement Template

[1531] Output: Customized contract

[1532] Step 7:

[1533] The server provides the customized contract to the user.

[1534] Specifically, the server sends the customized contract to the user, who can then review it in the communication application and make any necessary modifications.

[1535] Input: Customized Agreement

[1536] Output: Data provided to the user (contract)

[1537] (Application example 1)

[1538] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1539] In conventional contract generation systems, users had to take time to input business details and counterparty information, and the process of selecting the most suitable contract template was cumbersome. Furthermore, the content of the generated contract often did not fully meet the user's specific needs, resulting in issues such as time-consuming revisions and review of the contract.

[1540] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1541] In this invention, the server includes means for connecting the AI ​​engine to the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for the user to input business details and other party information, means for the AI ​​engine to analyze the input information and select a contract template, means for generating a contract based on the selected template, and means for displaying the generated contract. This enables the user to easily input business details and other party information and quickly generate an optimal contract.

[1542] An "AI engine" is software that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[1543] A "contract database" is a database system that stores various contract templates and past contract data and allows for searching and retrieval.

[1544] "Information input from the user" refers to data such as business details and other party information that the user provides to the system.

[1545] "Means for generating optimal contracts" refers to a function that selects an appropriate template from a contract database based on information entered by the user and creates a customized contract.

[1546] "Means for providing the generated contract to the user" refers to a function that displays the generated contract to the user or provides it in a downloadable format.

[1547] "Business content" refers to the specific content of the transactions or business conducted by the user.

[1548] "Information about the other party" refers to information such as the name and contact details of the other party with whom the user enters into a contract.

[1549] A "contract template" is a format in which the basic structure and content of a contract are predetermined.

[1550] The "means for generating a contract" is a function that creates a contract that reflects the information entered by the user based on the selected contract template.

[1551] "Means for displaying the contract" is a function that displays the generated contract on the user's device.

[1552] A system for implementing this invention includes means for connecting an AI engine to a contract database, means for generating an optimal contract based on information input by a user, means for providing the generated contract to the user, means for the user to input business details and information about the other party, means for the AI ​​engine to analyze the input information and select a contract template, means for generating a contract based on the selected template, and means for displaying the generated contract.

[1553] System program processing

[1554] Hardware and Software Configuration

[1555] Server: Hosts the AI ​​engine (OpenAI API) and contract database (SQLite).

[1556] User device: A device such as a smartphone or computer on which a user provides input information and checks the generated contract.

[1557] Data processing and calculation

[1558] 1. Getting user input:

[1559] Enter the business details and the other party's information from the user's device.

[1560] For example: "Enter your business: Food supply" or "Enter your contact details: Supplier"

[1561] 2. Template selection by AI engine:

[1562] The server uses the OpenAI API to analyze the user's input information and select the most appropriate contract template.

[1563] Example prompt: "Business: Food supply. Partner: Supplier. Choose the most appropriate contract template."

[1564] 3. Generate the contract:

[1565] The server retrieves the selected template from the contract database and embeds the user's input information to generate a customized contract.

[1566] Example: The template "Agreement regarding {business}\nWith: {partner}" is converted to "Agreement regarding the supply of food\nWith: Supplier".

[1567] 4. Providing the generated contract:

[1568] The server sends the generated contract to the user's terminal and displays it for the user to review.

[1569] Specific examples

[1570] If a user wants to generate a food supply contract using a smartphone, they can use the system by following the steps below.

[1571] 1. The user opens the application on their smartphone and enters "food supply" as the business type and "supply company" as the recipient information.

[1572] 2. The server uses the OpenAI API to analyze the input information and select the most appropriate contract template.

[1573] 3. The server generates a contract based on the selected template, reflecting the information entered by the user.

[1574] 4. The generated contract will be displayed on the user's smartphone, allowing the user to check the contents.

[1575] In this way, users can easily input business details and counterparty information and quickly generate the optimal contract.

[1576] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1577] Step 1:

[1578] The user opens the application on their smartphone and inputs the business details and the other party's information. The input information is "food supply" for the business details and "supply company" for the other party's information. This input information is sent to the server.

[1579] Step 2:

[1580] The server analyzes the information received from the user. Specifically, it processes the business details and the other party's information as text data and passes it to the AI ​​engine. The input data is formatted as a prompt: "Business details: Food supply\nOther party: Supplier\nPlease select the most appropriate contract template."

[1581] Step 3:

[1582] The server sends the prompt text to the AI ​​engine using the OpenAI API. The AI ​​engine analyzes the prompt text and selects the most appropriate contract template from the contract database. The ID of the selected template is returned to the server.

[1583] Step 4:

[1584] The server retrieves the selected template from the contract database. The template is a format that contains placeholders (e.g., "Contract for {business}\nTo: {partner}").

[1585] Step 5:

[1586] The server then fills the template with the user's input, replacing the placeholders "{business}" with "food supply" and "{partner}" with "supplier," generating a customized contract.

[1587] Step 6:

[1588] The server sends the generated contract to the user's smartphone, where the user can view the contract through an application on the smartphone.

[1589] Step 7:

[1590] The user can review the generated contract and make any necessary corrections or add comments. The corrected contract is then sent back to the server and saved as the final contract.

[1591] Example 2

[1592] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1593] Conventional contract drafting systems required users to review draft contracts and identify revisions, which was time-consuming and laborious. There was also a risk that revisions would not be made appropriately from a legal perspective. This could result in a decline in contract quality and the possibility of legal issues arising.

[1594] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for connecting the AI ​​engine and the document database, means for generating an optimal document based on information input by a user, means for providing the generated document to the user, means for the user to attach a document draft to a messenger app, means for the server to receive the attached file, means for the server to convert the file into text format, means for the server to analyze the document using the generated AI model, means for the server to identify corrections and generate a correction history and comments, and means for the server to provide the generated correction history and comments to the user. This allows the user to efficiently review contracts and create high-quality contracts with appropriate corrections from a legal perspective.

[1595] An "AI engine" is a system that uses artificial intelligence technology to analyze data and make predictions.

[1596] A "document database" is a database system that stores contracts and other document templates and allows them to be searched and retrieved.

[1597] "User input" refers to data or instructions that a user provides to the system, upon which the system operates.

[1598] An "optimal document" is a document that is generated based on user input information and is most suitable for the purpose.

[1599] A "messenger app" is application software that allows users to send and receive text messages and files.

[1600] "Attachment" refers to a document file sent via a messenger app.

[1601] "Text format" is a format in which the contents of a document are expressed as character data.

[1602] A "generative AI model" is a model that uses artificial intelligence technology to analyze and generate documents.

[1603] "Correction" means a portion of a document that has been identified as requiring correction.

[1604] "Revision history" is data that records the revisions made to a document.

[1605] A "comment" is text that includes specific instructions or explanations for the correction.

[1606] MODE FOR CARRYING OUT THE INVENTION

[1607] The present invention provides a system for allowing a user to efficiently review a contract draft and identify parts that need to be revised. A specific embodiment of this system will be described below.

[1608] Hardware and software used

[1609] The system uses the following hardware and software:

[1610] Server: Receives data, analyzes it, runs generative AI models, and generates revision history and comments.

[1611] Messenger app: The application through which the user attaches and sends the contract draft (e.g., Slack, Microsoft Teams).

[1612] Generative AI model: An artificial intelligence model (e.g., GPT-4) that analyzes contracts and identifies areas for revision.

[1613] PDF parser library: A library for converting PDF files to text format (e.g. PyPDF2).

[1614] Word parser library: A library for converting Word files to text format (e.g. Python-docx).

[1615] System Operation

[1616] A user creates a draft contract and logs in to a messenger app. Next, the user selects the draft contract file, attaches it to the chat window of the messenger app, and sends it. The server receives the file sent from the messenger app and obtains the contents of the file.

[1617] If the received file is in a format such as PDF or Word, the server converts it to text format. Specifically, for PDF files, it uses a PDF parser library (e.g., PyPDF2), and for Word files, it uses the Python-docx library to extract the text.

[1618] Next, the server inputs the converted draft contract into a generative AI model (e.g., GPT-4). The server generates a prompt to instruct the model to analyze the contents of the contract. An example of a prompt is, "Please analyze the following draft contract and identify the necessary revisions. Please provide specific comments for the revisions."

[1619] The server receives the analysis results from the generative AI model and identifies areas to be revised in the contract. For example, it identifies areas to be revised such as "the definition of confidential information is vague." The server then generates a revision history and comments for the identified areas to be revised. The comments include specific instructions such as "The definition of confidential information needs to be clarified."

[1620] Finally, the server sends the generated revision history and comments to the chat window of the messenger app, where the user can review the received revision history and comments and make revisions to the contract.

[1621] Specific examples

[1622] As a concrete example, consider the case where a user wants to review a draft NDA (non-disclosure agreement). The user attaches the draft NDA to Slack and sends it to the server. The server receives this draft and analyzes it using GPT-4. As a result of the analysis, areas that need to be revised, such as "the definition of confidential information is vague," are identified. The server generates a comment for this revision, such as "The definition of confidential information needs to be clarified," and provides it to the user.

[1623] Example prompts to input to a generative AI model:

[1624] Please analyze the NDA draft below and identify any necessary changes. Please provide specific comments for any changes.

[1625] NDA Draft:

[1626] 1. This Agreement is entered into between the Discloser and the Recipient of Confidential Information.

[1627] 2. Confidential Information means all information disclosed by the Disclosing Party to the Receiving Party.

[1628] ...

[1629] "

[1630] In this way, users can efficiently create and modify contracts.

[1631] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1632] Step 1:

[1633] The user attaches the draft contract to a messenger app and sends it.

[1634] Specifically, a user creates a draft contract and logs into a messenger app (e.g., Slack, Microsoft Teams). Next, the user selects the draft contract file, attaches it to the chat window of the messenger app, and sends it. The input is the draft contract file, and the output is the file sent through the messenger app.

[1635] Step 2:

[1636] The server receives the attachment.

[1637] The server receives the file sent from the messenger app. Specifically, the server checks the metadata of the received file (file name, file size, sender information, etc.) and obtains the file contents. The input is the file sent from the messenger app, and the output is the file data stored on the server.

[1638] Step 3:

[1639] The server converts the file into a text format.

[1640] If the received file is in a format such as PDF or Word, the server converts it to text format. Specifically, for PDF files, a PDF parser library (e.g., PyPDF2) is used, and for Word files, the Python-docx library is used to extract the text. The input is a PDF or Word file, and the output is text data.

[1641] Step 4:

[1642] The server analyzes the document using the generative AI model.

[1643] The server inputs the draft contract converted into text format into a generative AI model (e.g., GPT-4). Specifically, the server generates a prompt and instructs the model to analyze the contents of the contract. An example of a prompt would be, "Analyze the following draft contract and identify any necessary revisions. Please provide specific comments for the revisions." The input is the text draft contract and the prompt, and the output is the analysis result from the generative AI model.

[1644] Step 5:

[1645] The server identifies the modifications and generates a modification history and comments.

[1646] The server receives the analysis results from the generative AI model and identifies the areas to be revised in the contract. Specific operations include identifying areas to be revised, such as "the definition of confidential information is unclear." The server then generates a revision history and comments for the identified areas to be revised. The comments include specific instructions such as "The definition of confidential information needs to be clarified." The input is the analysis results from the generative AI model, and the output is a revision history and comments.

[1647] Step 6:

[1648] Providing server-generated revision history and comments to users.

[1649] The server sends the generated revision history and comments to the chat window of the messenger app. Specifically, the server sends the revision history and comments in text format to the messenger app so that the user can view them. The input is the revision history and comments, and the output is the revision history and comments provided to the user through the messenger app.

[1650] In this way, users can efficiently create and modify contracts.

[1651] (Application example 2)

[1652] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1653] In conventional contract creation systems, even if users uploaded a draft contract, they lacked the functionality to automatically analyze the content, identify revisions, and generate comments. This meant that users had to manually review the contract. This resulted in the problem of requiring a great deal of time and effort to create and revise contracts.

[1654] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1655] In this invention, the server includes means for connecting the AI ​​engine with the contract database, means for generating an optimal contract based on information input by a user, means for analyzing a contract draft uploaded by a user, identifying corrections to be made, and generating comments, and means for providing the generated comments to the user. This enables users to automate contract reviews and efficiently create and revise contracts.

[1656] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze data and automatically perform specific tasks.

[1657] A "contract database" is a database system that stores contract templates and past contract data, and allows for searching and retrieval as needed.

[1658] "User input information" refers to information provided by the user to the system, including data and conditions necessary for creating or amending a contract.

[1659] The "means for generating optimal contracts" is a system that has the function of selecting an appropriate template from a contract database based on information input by the user, and filling in the necessary information to create a contract.

[1660] A "means for providing a generated contract to a user" is a system that has the ability to display, transmit, or provide a system-generated contract to a user in a downloadable format.

[1661] A "draft contract" is an incomplete contract created by a user, which is a document that requires final confirmation and revision.

[1662] The "means for identifying the areas to be corrected and generating comments" is a system that has the function of analyzing a contract draft, automatically detecting the areas that need to be corrected, and generating comments and advice regarding them.

[1663] "Means for providing generated comments to users" means a system that has the functionality to provide system-generated comments and advice on corrections to users in a format that can be displayed, sent, or downloaded.

[1664] The system for implementing this invention consists of a server that includes an AI engine, a contract database, and a user interface, and a terminal for user access. The specific operation and usage of the system are explained below.

[1665] System configuration

[1666] 1. AI Engine

[1667] The AI ​​engine installed on the server uses a generative AI model to analyze and generate contracts. Specifically, it analyzes the contract draft provided by the user, identifies areas to be revised, and generates comments.

[1668] 2. Contract Database

[1669] The contract database stores contract templates and past contract data. The AI ​​engine selects the optimal template from this database and generates a contract based on the information entered by the user.

[1670] 3. User Interface

[1671] The user interface provides a screen for users to upload contracts and review generated contracts and comments, and is implemented as a web browser or mobile application.

[1672] System Operation

[1673] 1. Upload the contract

[1674] Users use their devices to upload draft contracts to the system, which are then analyzed by the server's AI engine.

[1675] 2. Contract analysis and comment generation

[1676] The AI ​​engine uses a generative AI model to analyze the contents of the draft contract, identify areas for revision, and generate appropriate comments and advice for the identified areas.

[1677] 3. Submitting Comments

[1678] The generated comments are provided to the user through the user interface, allowing the user to refer to them when revising the contract.

[1679] Hardware and software used

[1680] Hardware: Servers, user devices (PCs, smartphones, tablets, etc.)

[1681] Software: Python, docx library, openai library, web browser or mobile application

[1682] Specific examples

[1683] When a user uploads terms of use for an electronic payment service, the following comment is generated:

[1684] 1. The description of "Payment Method" in Article 3 is unclear. Please specify the specific payment method.

[1685] 2. The description regarding the "Cancellation Policy" in Article 5 is insufficient. Please add the cancellation conditions and procedures.

[1686] 3. The description of "Privacy Policy" in Article 7 is out of date. Please update it to the latest privacy policy.

[1687] Prompt Sentence Examples

[1688] Please analyze the draft contract below, identify any necessary revisions, and generate comments.

[1689] [Contents of the contract draft]

[1690] In this way, users simply upload contracts or terms of use, and the AI ​​engine automatically analyzes the content, identifies areas to be corrected, and generates comments, allowing them to efficiently create and modify contracts.

[1691] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1692] Step 1:

[1693] The user uploads the draft agreement using a device.

[1694] Input: The user selects the contract draft file from the terminal and presses the upload button.

[1695] Output: A draft contract file is sent to the server.

[1696] Specific behavior: The user selects a contract draft file through a web browser or mobile application interface and clicks the upload button, which sends the file to the server via an HTTP request.

[1697] Step 2:

[1698] The server receives the contract draft and passes it to the AI ​​engine.

[1699] Input: The uploaded draft agreement file.

[1700] Output: The contents of the draft contract are passed to the AI ​​engine.

[1701] Specific operation: The server temporarily stores the received contract draft file, reads its contents, and converts it into text format to be passed to the AI ​​engine.

[1702] Step 3:

[1703] The AI ​​engine analyzes the contract draft, identifies areas to be revised, and generates comments.

[1704] Input: The text content of the draft contract.

[1705] Output: Corrections and comments.

[1706] What it does: The AI ​​engine uses the generative AI model to analyze the content of the contract draft, generate prompts and send them to the OpenAI API, analyze the results returned by the API, and extract corrections and comments.

[1707] Step 4:

[1708] The server provides the generated comments to the user.

[1709] Input: Corrections and comments returned by the AI ​​engine.

[1710] Output: Corrections and comments displayed in the user interface.

[1711] Specific operation: The server sends the corrections and comments received from the AI ​​engine to the user interface, where the user can view the corrections and comments through a web browser or mobile application.

[1712] Step 5:

[1713] The user checks the corrections and comments and modifies the contract.

[1714] Input: Corrections and comments provided by the server.

[1715] Output: Amended draft contract.

[1716] Specific operation: The user can revise the contract draft based on the provided revisions and comments. Once the revisions are complete, they can be uploaded back to the system.

[1717] In this way, users can automate contract review and efficiently create and modify contracts.

[1718] Example 3

[1719] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1720] Conventional contract generation systems have made it difficult to flexibly review contracts according to the user's business situation and needs. Furthermore, they lacked the ability to select the appropriate review level for cases where detailed negotiations were required or where quick conclusion was required. This often resulted in users spending a great deal of time and effort reviewing contracts.

[1721] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1722] In this invention, the server includes a means for allowing a user to select a review level, a means for generating a prompt sentence based on the selected review level, and a means for inputting the generated prompt sentence into a generative AI model to generate a review. This allows a user to select an optimal review level according to their business situation and needs, and efficiently review a contract.

[1723] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze and generate data.

[1724] A "contract database" is a database system that stores contract templates and past contract data and allows for searching and referencing.

[1725] "User input" refers to data or instructions provided by the user to the system, such as the content of the contract or the selection of the review level.

[1726] "Review Level" refers to three options that users can choose from when reviewing a contract, which vary in level of detail and priority: "Serious Negotiation," "Normal," and "Fastest Conclusion Priority."

[1727] A "prompt sentence" is an instruction sentence input to the generative AI model, and is generated according to the review level.

[1728] A "generative AI model" is an artificial intelligence model that generates text based on a given prompt, such as GPT-4.

[1729] A "review" is a document generated by a generative AI model that includes an evaluation of the contract and suggested modifications.

[1730] "Means provided to users" refers to the functionality for displaying or sending generated contracts and reviews to the user's device.

[1731] This invention is a system that allows users to select a contract review level based on their business situation and needs, and efficiently reviews contracts using a generative AI model. Specific embodiments of this system are described below.

[1732] Hardware and software used

[1733] Hardware: Servers, user devices (PCs, smartphones, etc.)

[1734] Software: web applications, generative AI models (e.g., GPT-4)

[1735] System Overview

[1736] The user accesses the web application using a terminal and logs in. After logging in, the user selects one of three options on the review level selection screen: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." The selected review level information is sent from the terminal to the server.

[1737] The server generates a prompt to be input to the generative AI model based on the received review level. For example, if "serious negotiation" is selected, the prompt will be "Please generate a review for detailed contract negotiations." The server inputs the generated prompt into the generative AI model (e.g., GPT-4) to generate a review. The generated review is stored on the server and sent to the user's device. The user can check the review results on their device.

[1738] Specific examples

[1739] If the user selects "Serious Negotiation"

[1740] Example prompt: "Generate a review to facilitate detailed contract negotiations."

[1741] Review produced: "With regard to Article 3 of this Agreement, the following points need to be negotiated..."

[1742] If the user selects "Normal"

[1743] Example prompt: "Generate a standard contract review."

[1744] Produces the following review: "This agreement is general and I have no issues with it, but I need to clarify section 5..."

[1745] If the user selects "Fastest conclusion priority"

[1746] Example prompt: "Generate a quick review to close the deal faster."

[1747] Produces review: "This contract can be executed quickly. No issues..."

[1748] In this way, users can select the optimal review level according to their business situation and needs, and efficiently review contracts using the generative AI model. This system allows users to significantly reduce the time and effort required to review contracts. The flow of the specific processing in Example 3 is explained using Figure 15.

[1749] Step 1:

[1750] User selects review level

[1751] Input: A user accesses a web application using a terminal and logs in.

[1752] Specific operation: The user selects one of the three options on the review level selection screen: "Serious Negotiation," "Normal," or "Fastest Conclusion Priority." Clicks the review level radio button and presses the "Submit" button.

[1753] Output: The selected review level information is saved to the device.

[1754] Step 2:

[1755] The device sends the selected review level to the server.

[1756] Input: User-selected review level information

[1757] Specific behavior: The device sends a POST request to the server with review-level data in JSON format.

[1758] Output: The server receives the review level information.

[1759] Step 3:

[1760] The server generates a prompt according to the review level.

[1761] Input: Review level information received by the server

[1762] Specific operation: The server determines the review level using conditional branching and generates the corresponding prompt text as a string.

[1763] Example: If "Serious Negotiation" is selected, the prompt will read "Generate a review to conduct detailed contract negotiations."

[1764] Output: The generated prompt text is saved on the server.

[1765] Step 4:

[1766] The server inputs a prompt into the generative AI model and generates a review.

[1767] Input: Server generated prompt text

[1768] Specific operation: The server sends a prompt to the API endpoint of the generative AI model and receives the generated review.

[1769] Output: The generated reviews are saved on the server.

[1770] Step 5:

[1771] The server sends the generated reviews to the user's device.

[1772] Input: Server-generated review

[1773] Specific behavior: The server sends the generated review in JSON format to the user's device as an HTTP response.

[1774] Output: The user's device receives the review.

[1775] Step 6:

[1776] The user checks the review results

[1777] Input: Review sent to user's device

[1778] Specific behavior: The user scrolls through the review results displayed on the device screen, reads them, and downloads or prints them as needed.

[1779] Output: User checks the review results and decides on the next action.

[1780] (Application example 3)

[1781] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1782] Conventional contract generation systems have difficulty in providing flexible reviews that are tailored to the user's business situation and needs, and have had problems in that they were unable to meet user requirements, particularly in terms of review quality and speed. Furthermore, the system was unable to understand how detailed the user wanted the review to be and provide appropriate guidelines, which sometimes reduced the efficiency and effectiveness of the review.

[1783] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1784] In this invention, the server includes means for connecting the AI ​​engine with the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for the user to select a review level from three levels: "serious negotiation," "normal," and "fastest conclusion priority," and means for providing review guidelines according to the selected review level. This enables flexible and efficient reviews according to the user's business situation and needs.

[1785] An "AI engine" is a software or hardware system that uses artificial intelligence technology to analyze data and derive optimal results.

[1786] A "contract database" is a database system that stores contract templates and past contract data, and can be searched and retrieved as needed.

[1787] "User Input" means information provided by a user to the system, including data necessary for contract generation and review.

[1788] "Means for generating optimal contracts" refers to a function that selects an appropriate template from a contract database based on information input by the user, and creates a contract by filling in the necessary information.

[1789] The "means for providing the generated contract to the user" is a function for displaying or sending the generated contract to the user.

[1790] The "means of selecting review level" is a function that allows users to select the level of detail and speed of the review from three levels: "serious negotiation," "normal," and "priority for fastest conclusion."

[1791] "Means for providing review guidelines" is a function that presents users with appropriate review methods and points to note depending on the review level selected.

[1792] The system for implementing this invention is composed of a server including an AI engine, a contract database, and a user interface, and a terminal for user access. A specific embodiment of the system is described below.

[1793] System configuration

[1794] The server has a means for connecting the AI ​​engine with the contract database. The AI ​​engine analyzes the information input by the user and selects the optimal contract template from the contract database. The server also has a means for generating the optimal contract based on the selected template. The generated contract is provided to the user.

[1795] Furthermore, the server has a means for the user to select a review level from three levels: "serious negotiation," "normal," and "fastest conclusion priority." It also includes a means for providing review guidelines according to the selected review level.

[1796] Program processing

[1797] The server is implemented using programming languages ​​such as Python and Java. The AI ​​engine uses a generative AI model that uses natural language processing (NLP) technology. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used. The contract database is built using SQL or NoSQL databases.

[1798] When a user accesses the system from their device and requests the generation of a contract, the server receives the user's input information, which is then analyzed by the AI ​​engine. Based on the analysis results, the optimal template is selected from the contract database and the contract is generated. The generated contract is then provided to the user's device.

[1799] When a user selects a review level, the server generates review guidelines according to the selected level and provides them to the user. For example, if "serious negotiation" is selected, detailed review guidelines will be displayed. Specifically, a specific review such as "This product is of very high quality, with particularly good battery life. However, I'm concerned about the high price" is expected.

[1800] Prompt Sentence Examples

[1801] "Please select your review level. Choose from hard negotiation, normal, or fastest closing priority."

[1802] In this way, flexible and efficient reviews can be performed according to the user's business situation and needs.

[1803] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1804] Step 1:

[1805] The user accesses the system from a terminal and requests the generation of a contract.

[1806] Input: User's contract generation request information (contract content, conditions, etc.)

[1807] Output: Contract generation request to the server

[1808] Specific operations: The user enters the information required to generate a contract through the terminal interface and presses the send button.

[1809] Step 2:

[1810] The server receives the user's input information and sends it to the AI ​​engine.

[1811] Input: User's contract generation request information

[1812] Output: Analysis request to the AI ​​engine

[1813] Specific operation: The server analyzes the received user input information, converts it into an appropriate format, and sends it to the AI ​​engine.

[1814] Step 3:

[1815] The AI ​​engine analyzes the information entered by the user and selects the most appropriate contract template from the contract database.

[1816] Input: User's contract generation request information

[1817] Output: Optimal contract template

[1818] Specific operation: The AI ​​engine uses natural language processing technology to analyze the information entered by the user and select the most appropriate template from the contract database.

[1819] Step 4:

[1820] The server generates an optimal contract based on the selected contract template.

[1821] Input: Optimal contract template, user contract generation request information

[1822] Output: Generated contract

[1823] Specific operation: The server embeds the user's input information into the template and generates a complete contract.

[1824] Step 5:

[1825] The server provides the generated contract to the user.

[1826] Input: Generated Agreement

[1827] Output: The contract displayed on the user's device

[1828] Specific operation: The server sends the generated contract to the user's terminal and displays it on the user interface.

[1829] Step 6:

[1830] The user selects the review level.

[1831] Input: User review level selection information ("Serious Negotiation", "Normal", "Fastest Conclusion Priority")

[1832] Output: Review level selection information to the server

[1833] Specific operation: The user selects the review level through the terminal interface and presses the send button.

[1834] Step 7:

[1835] The server generates review guidelines according to the selected review level and provides them to the user.

[1836] Input: User review level selection information

[1837] Output: Review guidelines according to review level

[1838] Specific operation: The server generates appropriate review guidelines based on the selected review level and sends them to the user's device.

[1839] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1840] "Example 1"

[1841] In one embodiment of the present invention, the emotion engine recognizes a user's emotions and adjusts the generation and review of contracts based on those emotions. Specifically, when a user enters information about the business and the contracting party in a messaging app, the emotion engine analyzes the user's emotions. For example, if the user is feeling angry or anxious, the emotion engine conveys that information to the AI ​​engine, which then generates and reviews contracts more carefully.

[1842] "Example 2"

[1843] In another embodiment of the present invention, the emotion engine automatically adjusts the review level based on the user's emotions. Specifically, when the user selects a review level from three levels—"hard bargaining," "normal," and "fastest closing priority"—the emotion engine analyzes the user's emotions and recommends the optimal review level based on those emotions. For example, if the user is feeling strong dissatisfaction or anger, the emotion engine recommends "hard bargaining."

[1844] "Example 3"

[1845] In another embodiment of the present invention, the emotion engine analyzes a user's emotions and adjusts the generation and review of the contract based on the emotions. Specifically, when a user attaches a draft contract file to a messenger app, the emotion engine analyzes the user's emotions and identifies necessary revisions based on the emotions. For example, if the user is feeling anxious or scared, the emotion engine conveys that information to the AI ​​engine, which then reviews the contract more carefully.

[1846] The processing flow of each embodiment will be described below.

[1847] "Example 1"

[1848] Step 1: The user enters information about their business and the person they are contracting with in the messenger app.

[1849] Step 2: The emotion engine analyzes the user's emotions.

[1850] Step 3: The emotion engine communicates this information to the AI ​​engine.

[1851] Step 4: The AI ​​engine generates and reviews contracts more carefully.

[1852] "Example 2"

[1853] Step 1: The user selects a review level from three levels: "Serious Negotiation," "Normal," and "Fastest Closure Priority."

[1854] Step 2: The emotion engine analyzes the user's emotions.

[1855] Step 3: The sentiment engine recommends the optimal review level based on the sentiment.

[1856] "Example 3"

[1857] Step 1: The user attaches the draft contract file to the messenger app. Step 2: The emotion engine analyzes the user's emotions.

[1858] Step 3: The emotion engine identifies the necessary modifications based on the emotion.

[1859] Step 4: The emotion engine communicates this information to the AI ​​engine.

[1860] Step 5: The AI ​​engine reviews the contract more carefully.

[1861] Example 1

[1862] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1863] Conventional contract generation systems have the problem that it is difficult to generate optimal contracts based on user input information, and the generation and review of contracts do not take user sentiment into consideration, resulting in low user satisfaction. Furthermore, the contract template selection and generation process is not automated, which creates issues of inefficiency and time.

[1864] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1865] In this invention, the server includes means for receiving input information from a user, means including an artificial intelligence engine for analyzing the received input information, means including an emotion analysis engine for analyzing the user's emotions, means for selecting an optimal contract template from a contract database based on the analysis results, means for generating a contract based on the selected template, and means for providing the generated contract to the user. This makes it possible to automatically generate an optimal contract that takes into account the user's input information and emotions.

[1866] "Means for receiving input information from users" refers to a function that allows the server to receive information entered by users through a messenger app or other interface.

[1867] "Means including an artificial intelligence engine" refers to a function that uses artificial intelligence technology to analyze received user input information and extract information such as the type of business and the characteristics of the contracting party.

[1868] The "means including an emotion analysis engine" is a function for analyzing emotions from information input by a user and extracting the emotion information.

[1869] The "means for selecting the most appropriate contract template from the contract database" is a function for selecting the most appropriate template from the contract database based on the analysis results of the artificial intelligence engine and the sentiment analysis engine.

[1870] The "means for generating a contract based on a selected template" is a function for generating a specific contract by reflecting the information input by the user in the selected contract template.

[1871] "Means for providing the generated contract to the user" refers to a function for sending the generated contract to the user so that the user can review, modify, and approve it.

[1872] MODE FOR CARRYING OUT THE INVENTION

[1873] This invention is a system that generates an optimal contract based on information input by a user and provides it to the user. This system is composed of a server that includes an artificial intelligence engine, an emotion analysis engine, and a contract database, and a terminal used by the user.

[1874] Hardware and software used

[1875] The server uses the following hardware and software:

[1876] Hardware: High-performance server, network interface

[1877] Software: Artificial intelligence engine (e.g., OpenAI GPT-4), sentiment analysis engine (e.g., IBM Watson Tone Analyzer), contract database, messenger app API

[1878] Users use common devices such as smartphones and PCs, which have messenger apps installed.

[1879] Program processing

[1880] The server receives information entered by the user in the messenger app. For example, the user enters, "I want to launch a new marketing campaign with a partner company." This information is then sent to the server.

[1881] The server sends the received input information to the AI ​​engine, which analyzes the input information and extracts information such as the type of business and characteristics of the contracting party. At the same time, the emotion analysis engine analyzes the emotions from the user's input information and extracts that emotion information.

[1882] Based on the analysis results, the server selects the most appropriate contract template from the contract database. For example, it may select a template suitable for a marketing campaign. Based on the selected template, the AI ​​engine generates a specific contract. In this process, it also takes into account the analysis results of the sentiment analysis engine. If the user is unsure, it will adjust the content to be more detailed and careful.

[1883] The generated contract is then sent from the server to the user's messaging app, where the user can review the contract and make any necessary amendments or approvals.

[1884] Examples of concrete examples and prompts

[1885] For example, consider a case where a user types into a messaging app, "I'd like to launch a new marketing campaign with a partner company." If the sentiment analysis engine detects uncertainty in the user's input, the artificial intelligence engine will take that information into account and generate a more detailed and careful contract.

[1886] Example prompt sentence:

[1887] "We would like to launch a new marketing campaign with a partner company. Please draw up a contract."

[1888] When this prompt sentence is input into the generative AI model, the artificial intelligence engine selects the most appropriate template from the contract database and generates the contract taking into account the analysis results of the sentiment analysis engine.

[1889] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1890] Step 1:

[1891] The user enters information in the messenger app.

[1892] Users open a messenger app on their smartphone or PC and type in, "I want to start a new marketing campaign with a partner company." This input information is sent to the server.

[1893] Step 2:

[1894] The server receives the input information and sends it to the artificial intelligence engine.

[1895] The server receives the information sent from the messenger app and sends an HTTP request to the API endpoint of the AI ​​engine. The input information includes the text data entered by the user. The output is the analysis result returned by the AI ​​engine.

[1896] Step 3:

[1897] An artificial intelligence engine analyzes the input information.

[1898] The AI ​​engine uses natural language processing technology to analyze the received information and extract keywords such as "marketing campaign" and "partner company." Input includes user text data. Output includes the analyzed keywords and business type.

[1899] Step 4:

[1900] The sentiment analysis engine analyzes the user's emotions.

[1901] The emotion analysis engine analyzes emotions from user input sentences and identifies emotions such as "anxiety" or "anger." For example, it can read the user's positive intentions from the expression "I want to do it." The input includes the user's text data. The output includes analyzed emotional information.

[1902] Step 5:

[1903] The server selects the most suitable template from the contract database.

[1904] The server selects a "marketing campaign contract template" from the contract database based on the analysis results from the artificial intelligence engine and the sentiment analysis engine. The input includes the analysis results. The output includes the selected template.

[1905] Step 6:

[1906] An artificial intelligence engine generates contracts based on templates.

[1907] The AI ​​engine generates a contract based on the selected template and reflects the user's input information. It also takes into account the results of the sentiment analysis engine and adjusts the content to be more detailed and cautious if the user feels uneasy. The inputs include the template and the user's input information. The output includes the generated contract.

[1908] Step 7:

[1909] The server provides the generated contract to the user.

[1910] The server sends the generated contract to the user's messenger app, where the user can review the contract and make any necessary amendments or approvals. Inputs include the generated contract. Outputs include the contract provided to the user.

[1911] (Application example 1)

[1912] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1913] While conventional contract generation systems can generate contracts based on user input, they have problems such as being unable to adjust the contract to take user emotions into account or display the contract in real time. Furthermore, they do not support contract generation using voice input, which reduces user convenience.

[1914] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1915] In this invention, the server includes means for connecting the AI ​​engine and the contract database, means for generating an optimal contract based on information input by the user, means for providing the generated contract to the user, means for acquiring voice input, means for analyzing emotions, means for adjusting the content of the contract based on the analyzed emotions, and means for displaying the generated contract on a display device in real time. This makes it possible to generate and adjust a contract taking the user's emotions into consideration, supports contract generation using voice input, and enables contract display in real time.

[1916] An "AI engine" is an artificial intelligence system that analyzes information input by users and selects the most appropriate contract template from a contract database.

[1917] A "contract database" is a database that stores various contract templates and provides them as needed.

[1918] "Information input by the user" refers to information about the business content and the contracting party that the user provides to generate the contract.

[1919] The "means for generating the optimal contract" is the process of selecting an appropriate template from a contract database based on information input by the user, and filling in the necessary information to create a contract.

[1920] A "means for providing the generated agreement to the user" is a method for displaying or transmitting the generated agreement to the user.

[1921] "Means for acquiring voice input" refers to technology for recognizing the user's voice and acquiring it as text data.

[1922] "Means for analyzing emotions" refers to technology for reading and analyzing emotions from the user's voice and facial expressions.

[1923] The "means for adjusting the content of the contract based on the analyzed emotions" is a process for appropriately changing the content and wording of the contract depending on the emotional state of the user.

[1924] The "means for displaying in real time on a display device" is a technique for displaying the generated contract in real time on the user's display device.

[1925] The system for implementing this invention includes an AI engine, a contract database, a voice input acquisition means, an emotion analysis means, a contract content adjustment means, and a real-time display means. Specific embodiments of each means are described below.

[1926] System configuration

[1927] 1. AI Engine

[1928] The server analyzes the information input by the use...

Claims

1. A means for a user to send input information using a communication application; A server receives the user's input information and transmits it to an AI engine; means for the AI ​​engine to analyze the input information using a generative AI model; A means for the AI ​​engine to select an optimal contract template from a contract database based on the analysis result by the analyzing means; A means for the AI ​​engine to customize the selected contract template; means for the server to provide the customized contract template to the user; A system including:

2. The system of claim 1 , wherein the AI ​​engine analyzes input information from the user and selects the optimal contract template from the contract database based on the input information.

3. The system according to claim 1 , further comprising a means for reviewing the contract draft provided by the user and outputting a revision history and comments.

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