System

Abstract

To provide a system.SOLUTION: A system includes: first means for accepting an input of trouble contents by a user; second means for acquiring basic contract terms according to the trouble contents from a database; third means for generating a first prompt text for outputting a proposal for negotiation on the basis of the trouble contents and the basic contract terms; fourth means for generating a proposal for the negotiation by inputting the generated first prompt text into a generative AI model; and fifth means for transmitting the generated proposal for negotiation to a terminal of 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] When resolving disputes in a transaction, the process of reading and understanding the contents of the master agreement, determining which clauses apply to the particular dispute, and negotiating with the business partner based on the clauses of the master agreement is time-consuming and could lead to contract clauses being overlooked. [Means for solving the problem]

[0005] This invention provides a means for reading and understanding the contents of a contract, a means for determining which clauses are applicable to the particular problem, and a means for negotiating with a business partner based on the clauses of the basic contract. This reduces the time required for negotiations, prevents oversight of contract clauses, and contributes to improving work efficiency and quality. [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 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 Embodiment 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] The system of the present invention uses natural language processing technology as a means of reading and understanding the contents of a contract. Specifically, it analyzes the text data of the contract and extracts the contents of each clause. This extraction is performed using a function that takes the text data of the contract as input and outputs the contents of each clause.

[0029] "Example 2"

[0030] In addition, the system has the function of analyzing the content of the problem and identifying the relevant contract clauses as a means of determining which clauses the problem matches. Specifically, the content of the problem is input as text data, and the relevant contract clauses are identified based on that content. This identification is done by calculating the similarity between the content of the problem and each clause in the contract, and identifying clauses whose similarity is above a certain threshold as the relevant clauses.

[0031] "Example 3"

[0032] Furthermore, it has a function to generate a proposal for negotiation using the specified clauses as a means of negotiating with a business partner based on the clauses of the master agreement. Specifically, it automatically generates a proposal for negotiation based on the specified clauses and the details of the problem. For example, this proposal is generated in a format such as "Based on clause X, we propose to take measure Y."

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

[0034] "Example 1"

[0035] Step 1: Enter the contract text data into the system.

[0036] Step 2: The system uses natural language processing technology to analyze the text data and extract the content of each clause.

[0037] Step 3: The content of each extracted clause is stored internally in the system for further processing.

[0038] "Example 2"

[0039] Step 1: Enter the details of the problem into the system as text data.

[0040] Step 2: The system analyzes the content of the problem and identifies the relevant clauses in the contract. This identification is done by calculating the similarity between the content of the problem and each clause in the contract, and identifying clauses with a similarity above a certain threshold as relevant clauses.

[0041] Step 3: The identified clauses are stored internally in the system for further processing.

[0042] "Example 3"

[0043] Step 1: The system automatically generates a proposal for negotiation based on the identified clauses and issues.

[0044] Step 2: The proposal is generated in the form of, for example, "Based on clause X, we propose to take action Y."

[0045] Step 3: The generated proposal is presented to the user and used in negotiations with trading partners.

[0046] Example 1

[0047] 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."

[0048] With conventional contract management systems, it was difficult to accurately read and understand the contents of a contract, and a particular challenge was quickly and accurately extracting the contents of each clause. Furthermore, performing detailed analysis based on the contents of a contract required specialized knowledge, placing a significant burden on users. Furthermore, there was a lack of means to obtain detailed analysis results using a generative AI model based on the contents of a contract.

[0049] 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.

[0050] In this invention, the server includes a means for reading and understanding the contents of the contract, a means for analyzing the text data of the contract and extracting the contents of each clause, a means for providing the extracted contents of each clause to the user, and a means for inputting a prompt sentence into the generative AI model and obtaining a detailed analysis result. This makes it possible to quickly and accurately read and understand the contents of the contract and extract the contents of each clause. Furthermore, the user can obtain a detailed analysis result by utilizing the generative AI model without needing specialized knowledge.

[0051] A "contract" is a written legal agreement that clarifies the rights and obligations of the parties.

[0052] "Text data" is data that represents character information in a digital format and is used to analyze the contents of a contract.

[0053] "Analysis" is the process of breaking down text data and understanding its structure and meaning.

[0054] A "clause" refers to an individual condition or provision set forth in a contract, and is an element that constitutes the specific content of the contract.

[0055] "Extraction" is the process of extracting specific information from text data, and is performed to identify the content of each clause in the contract.

[0056] "User" refers to a person or organization that uses the system to read, understand, and obtain analytical results from the contents of a contract.

[0057] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and generate detailed analysis results based on prompt sentences.

[0058] A "prompt" is an instruction entered into a generative AI model and is used to clarify the target and purpose of the analysis.

[0059] The present invention relates to a system for reading and understanding the contents of a contract and extracting the contents of each clause. A specific embodiment of this system will be described below.

[0060] System configuration

[0061] The system consists of a server, a terminal, and a user. The server receives the text data of the contract, analyzes it, and extracts the contents of each clause. The terminal provides an interface for the user to input the contract data and check the analysis results.

[0062] Hardware and software used

[0063] A server is a computer with a powerful processor and ample memory. The software that runs on the server includes:

[0064] Natural Language Processing Libraries: "spaCy" and "NLTK"

[0065] PDF text extraction library: "PyMuPDF"

[0066] Generative AI models: Advanced natural language generation models such as "GPT-3 (registered trademark)"

[0067] Data processing and calculation

[0068] The server processes the data as follows:

[0069] 1. Receiving text data: The user sends the contract text data to the server via their device. The text data is extracted from a PDF or Word file.

[0070] 2. Preprocessing of text data: The server cleans the received text data and performs tokenization, part-of-speech tagging, and dependency analysis.

[0071] 3. Clause Extraction: The server detects specific keywords and phrases and identifies clauses based on them.

[0072] 4. Output of extracted results: The server provides the content of each extracted clause to the user. If necessary, it inputs a prompt to the generative AI model to obtain detailed analysis results.

[0073] Specific examples

[0074] For example, suppose we have the following text data for a contract:

[0075] Article 1 (Purpose of the contract)

[0076] This Agreement sets out the basic conditions for Party A and Party B to conduct business together.

[0077] Article 2 (Payment Terms)

[0078] Party A shall pay Party B the remuneration at the end of each month.

[0079] With this text data as input, the server processes it as follows:

[0080] 1. Receiving text data: The user inputs the text of the contract. The server receives the input text data.

[0081] 2. Preprocessing of text data: The server removes unnecessary whitespace and special characters from the text data, and performs tokenization, part-of-speech tagging, and dependency analysis.

[0082] 3. Clause extraction: The server detects keywords such as "purpose of the contract" and "payment terms," ​​extracts the surrounding text of the detected keywords, and identifies them as clauses.

[0083] 4. Output of extraction results: The server formats the extraction results in HTML and displays them to the user. If the user requests further analysis, the server sends a prompt to the generative AI model to obtain additional analysis results.

[0084] Prompt Sentence Examples

[0085] Below are some example prompts to input to a generative AI model:

[0086] Analyze the text data of the contract and extract the contents of each clause.

[0087] Text data:

[0088] Article 1 (Purpose of the contract)

[0089] This Agreement sets out the basic conditions for Party A and Party B to conduct business together.

[0090] Article 2 (Payment Terms)

[0091] Party A shall pay Party B the remuneration at the end of each month.

[0092] In this way, the server extracts the content of each clause of the contract and provides it to the user, allowing the user to quickly and accurately understand the content of the contract without requiring specialized knowledge.

[0093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0094] Step 1:

[0095] Receiving text data

[0096] The server receives the text data of the contract from the user. The user uploads the contract file via their device or inputs the text directly. The server temporarily stores the received data.

[0097] Input: Contract text data (PDF, Word file, or manually entered text)

[0098] Output: Temporarily saved text data

[0099] Specific behavior:

[0100] The user uploads the PDF file of the contract using the device.

[0101] The server uses the "PyMuPDF" library to convert PDF files into text data.

[0102] Step 2:

[0103] Preprocessing text data

[0104] The server pre-processes the received text data, including text cleaning, tokenization, part-of-speech tagging, and dependency analysis, using the "spaCy" library.

[0105] Input: Temporarily saved text data

[0106] Output: Preprocessed text data (tokenized, part-of-speech tagged, dependency parsed)

[0107] Specific behavior:

[0108] The server removes unnecessary spaces and special characters from the text data.

[0109] The server divides (tokenizes) the text data into words.

[0110] The server tags each token with a part of speech tag.

[0111] The server performs dependency analysis to analyze the structure of the sentence.

[0112] Step 3:

[0113] Extracting clauses

[0114] The server extracts the content of each clause from the preprocessed text data and performs functions to detect specific keywords and phrases and identify clauses based on them.

[0115] Input: Preprocessed text data

[0116] Output: Content of each extracted clause

[0117] Specific behavior:

[0118] The server detects keywords such as "payment terms" and "purpose of the contract."

[0119] The server extracts the text surrounding the detected keywords and identifies them as clauses.

[0120] Step 4:

[0121] Output of extraction results

[0122] The server provides the user with the content of each extracted clause. The extracted results are displayed in a format that is easy for the user to review. If necessary, a prompt can be input to the generative AI model to obtain more detailed analysis results.

[0123] Input: The content of each extracted clause

[0124] Output: Extraction results provided to the user, and detailed analysis results by the generative AI model (if required)

[0125] Specific behavior:

[0126] The server formats the extracted results in HTML format and displays them to the user.

[0127] If the user requests a more detailed analysis, the server sends a prompt to the generative AI model to obtain additional analysis results.

[0128] In this way, the server extracts the content of each clause of the contract and provides it to the user, allowing the user to quickly and accurately understand the content of the contract without requiring specialized knowledge.

[0129] (Application example 1)

[0130] 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."

[0131] Conventional contract management systems have the problem that it is difficult to read and understand the contents of contracts, making it difficult to quickly identify clauses that pose particular risks. Furthermore, analyzing contract contents and conducting risk assessments requires a great deal of time and effort, creating a need for efficient contract management. Furthermore, when a problem occurs based on a contract clause, it is difficult to quickly determine which clause applies.

[0132] 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.

[0133] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses meet the conditions of the problem, means for negotiating with the business partner based on the clauses of the basic contract, means for analyzing the text data of the contract and extracting the contents of each clause, and means for evaluating the risks of the extracted clauses and notifying high-risk clauses. This makes it possible to efficiently analyze the contents of the contract and perform risk evaluation, and quickly identify and notify high-risk clauses. It also makes it possible to quickly determine the applicable clauses when a problem occurs and take appropriate action.

[0134] A "contract" is a legal document that documents a transaction or agreement.

[0135] "Reading" is the process of analyzing text data and understanding its content.

[0136] "Means of understanding" are methods and techniques for analyzing text data and grasping its meaning.

[0137] "Trouble" refers to problems or disputes that arise in connection with a contract.

[0138] A "clause" is a specific condition or provision set out in a contract.

[0139] "Means for determining whether conditions are met" refers to methods or techniques for determining which clause in the contract a particular situation or problem falls under.

[0140] A "basic agreement" is a contract that sets out the basic terms and regulations of a transaction.

[0141] A "business partner" is a party with whom you enter into a contract or a business partner.

[0142] "Means of negotiation" refers to the methods and techniques used to discuss contract terms and resolve disputes with business partners.

[0143] "Text data" is data stored as character information.

[0144] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[0145] "Extraction means" refers to methods or techniques for extracting specific information from data.

[0146] "Risk" refers to the potential problems or dangers associated with a contract.

[0147] "Evaluation tools" are methods or techniques for determining risk or value based on specific criteria.

[0148] "Means of notification" refers to methods or techniques for notifying users of specific information.

[0149] A system for carrying out the present invention includes a program for reading and understanding the contents of a contract and performing risk assessment. A specific embodiment of this system will be described below.

[0150] System Configuration

[0151] The system includes a server, a user terminal, and a natural language processing model. The server analyzes the text data of the contract, extracts the contents of each clause, and performs risk assessment. The user terminal provides an interface for taking a photo of the contract and uploading the PDF file.

[0152] Hardware and software used

[0153] Hardware: Smartphones, servers

[0154] Software: Python, spaCy, Transformers library

[0155] Data processing and calculation

[0156] 1. Entering the contract text data: The user takes a photo of the contract using their smartphone or uploads a PDF file.

[0157] 2. Analysis of text data: The server receives the uploaded text data and splits it into sentences using spaCy.

[0158] 3. Extracting clause content: The divided sentences are analyzed using a natural language processing model (Transformers library) to extract the content of each clause.

[0159] 4. Risk Assessment: For each extracted clause, a risk assessment is performed using a generative AI model. High-risk clauses are notified to the user.

[0160] Specific examples

[0161] For example, if a user enters the following text data for a contract:

[0162] This is a sample contract. The party agrees to the following terms and conditions...

[0163] The server analyzes this text data, extracts each clause, and then uses a generative AI model to generate prompts like the following to assess risk:

[0164] Evaluate the risk of the following contract clause: "The party agrees to the following terms and conditions..."

[0165] If the risk assessment identifies any high-risk clauses, the user will be notified, allowing them to quickly understand the risks in the contract and take appropriate action.

[0166] This system makes it possible to efficiently analyze the contents of contracts, perform risk assessments, and quickly identify and notify high-risk clauses.It also makes it possible to quickly determine the relevant clauses when a problem occurs and take appropriate action.

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] Users can either take a photo of the contract using their smartphone or upload a PDF file.

[0170] Input: Image or PDF file of the contract

[0171] Output: Text data of the contract

[0172] What happens: The user takes a photo of the contract using the smartphone's camera or uploads a PDF file using the file chooser.

[0173] Step 2:

[0174] The terminal sends an image or PDF file of the uploaded contract to the server.

[0175] Input: Image or PDF file of the contract

[0176] Output: Text data of the contract sent to the server

[0177] Specific operation: The device uses image recognition technology (OCR) to extract text data from images and PDF files and sends the text data to the server.

[0178] Step 3:

[0179] The server analyzes the received text data and divides it into sentences.

[0180] Input: Text data of the contract

[0181] Output: Text data divided into sentences

[0182] Specific operation: The server uses the spaCy library to divide the text data into sentences.

[0183] Step 4:

[0184] The server analyzes the divided sentences using a natural language processing model and extracts the content of each clause.

[0185] Input: Text data divided into sentences

[0186] Output: Content of each extracted clause

[0187] What happens: The server uses the Transformers library to parse each sentence and extract the clause content.

[0188] Step 5:

[0189] The server performs risk assessment for each extracted clause using a generative AI model.

[0190] Input: The content of each extracted clause

[0191] Output: Risk assessment results for each clause

[0192] Specific operation: The server uses a generative AI model to assess the risk of each clause and identify high-risk clauses.

[0193] Step 6:

[0194] The server notifies the user of high-risk provisions.

[0195] Input: Risk assessment results

[0196] Output: User notification

[0197] Specific operation: The server identifies high-risk clauses, generates a message to notify the user, and sends it to the terminal.

[0198] Step 7:

[0199] Users receive notifications and review high-risk clauses.

[0200] Input: Notification from the server

[0201] Output: Check for high risk clauses

[0202] Specific behavior: The user uses the notification feature on their smartphone to check for high-risk clauses and take necessary action.

[0203] Example 2

[0204] 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."

[0205] With conventional contract management systems, it was difficult to quickly and accurately identify which contract clauses applied when a problem occurred. Furthermore, there was a lack of means to understand the contents of the contract and identify the appropriate clauses based on the nature of the problem, which often delayed negotiations with business partners. For these reasons, there is a need for more efficient contract management and faster problem response.

[0206] 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.

[0207] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses match the conditions of the problem, means for negotiating with the business partner based on the clauses of the basic contract, means for inputting the content of the problem as text data and identifying the relevant clauses of the contract based on that content, and means for calculating the similarity between the content of the problem and each clause of the contract and identifying clauses whose similarity is above a certain threshold as the relevant clauses. This makes it possible to quickly and accurately identify the relevant contract clauses when a problem occurs and to efficiently negotiate with the business partner.

[0208] A "contract" is a document that describes the legal agreement between business partners.

[0209] "Trouble" refers to problems or disputes that arise during the course of contract performance.

[0210] "Text data" is data that represents character information in digital form.

[0211] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[0212] "Similarity" is an index that indicates the degree of commonality or agreement between two pieces of data.

[0213] A "threshold" is a reference value for satisfying a specific condition.

[0214] "Identification" refers to clearly identifying and selecting an object.

[0215] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate and analyze data.

[0216] A "prompt sentence" is an instruction or question that a user inputs into a system.

[0217] This invention provides a method for efficiently negotiating with business partners by quickly and accurately identifying the relevant contract clauses in a contract management system when a problem occurs. A specific embodiment of this system is described below.

[0218] Hardware and software used

[0219] Hardware: Servers, terminals

[0220] Software: Generative AI models, text analysis tools, similarity calculation algorithms

[0221] System Operation

[0222] 1. The user enters the details of the problem

[0223] The user accesses the system interface and enters the details of the problem in text format.

[0224] Example: A user types "Problems with handling of late deliveries."

[0225] 2. The device sends the entered text data to the server.

[0226] The terminal receives the user's input and sends the data to the server.

[0227] Example: A terminal sends text data to a server saying, "Problems with handling when delivery is delayed."

[0228] 3. The server analyzes the text data

[0229] The server analyzes the received text data using a text analysis tool.

[0230] Example: The server uses a text analysis tool to extract keywords such as "delivery," "delay," and "response."

[0231] 4. The server calculates the similarity with each clause of the contract.

[0232] The server uses the generated AI model to calculate the similarity between the analysis results and each clause in the contract.

[0233] Example: The server uses a generative AI model to calculate the similarity between the "provision regarding delivery delays" and the "provision regarding response methods."

[0234] 5. Identify the terms that the server applies to

[0235] The server uses a similarity calculation algorithm to identify clauses whose similarity is equal to or greater than a certain threshold as relevant clauses.

[0236] Example: The server uses a similarity calculation algorithm to identify "provisions regarding delivery delays" and "provisions regarding response methods" with a similarity of 0.80 or higher.

[0237] 6. The server returns the specified terms to the user

[0238] The server returns the identified terms to the user and notifies the user.

[0239] Example: The server notifies the user that "the relevant clauses are the clause regarding delivery delays and the clause regarding response methods."

[0240] Prompt Sentence Examples

[0241] Please enter the details of the problem: Problems with handling of delayed delivery

[0242] This system has the ability to analyze the details of the problem entered by the user and identify the relevant contract clauses. Specifically, it uses text analysis tools and generative AI models to calculate the similarity between the problem details and each clause in the contract, and identifies clauses with a similarity above a certain threshold. This makes it possible to quickly and accurately identify the relevant contract clauses when a problem occurs, enabling efficient negotiations with business partners.

[0243] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0244] Step 1:

[0245] The user inputs the details of the problem.

[0246] Input: The user accesses the system interface and enters the details of the problem in text format.

[0247] Specific behavior: The user enters "Problems regarding handling of delayed delivery."

[0248] Output: The entered text data is sent to the terminal.

[0249] Step 2:

[0250] The terminal transmits the input text data to the server.

[0251] Input: Text data entered by the user.

[0252] Specific operation: The device sends text data to the server stating "Problems regarding response in the event of delayed delivery."

[0253] Output: Text data is sent to the server.

[0254] Step 3:

[0255] The server parses the text data.

[0256] Input: Text data sent from the terminal.

[0257] Specific operation: The server uses a text analysis tool to extract keywords such as "delivery," "delay," and "response."

[0258] Output: The extracted keyword list.

[0259] Step 4:

[0260] The server calculates the similarity with each clause of the contract.

[0261] Input: The extracted keyword list.

[0262] Specific operation: The server uses the generative AI model to calculate the similarity between the analysis results and each clause in the contract.

[0263] Output: Similarity score for each clause.

[0264] Step 5:

[0265] The server identifies the applicable terms.

[0266] Input: Similarity score for each clause.

[0267] Specific operation: The server uses a similarity calculation algorithm to identify clauses with a similarity score of 0.80 or higher.

[0268] Output: A list of applicable contract clauses.

[0269] Step 6:

[0270] The server returns the specified terms to the user.

[0271] Input: A list of applicable contract clauses.

[0272] Specific operation: The server notifies the user that "the relevant clauses are the clause regarding delivery delays and the clause regarding response methods."

[0273] Output: The applicable contract terms communicated to the user.

[0274] (Application example 2)

[0275] 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."

[0276] When a problem occurs with a contract, it is difficult to quickly and accurately identify which clause applies. In particular, when the contract is complex or the problem covers a wide range of issues, it takes a great deal of time and effort to find the appropriate clause. To solve this problem, a system is needed that can analyze the content of the problem and automatically identify the relevant contract clause.

[0277] 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.

[0278] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clause's conditions are met in the problem, means for negotiating with the business partner based on the clauses of the basic contract, means for inputting the content of the problem as text data and identifying the relevant clauses of the contract based on that content, and means for calculating the similarity between the content of the problem and each clause of the contract and identifying clauses whose similarity is above a certain threshold as the relevant clauses. This makes it possible to quickly and accurately identify the relevant clauses and take appropriate action when a problem regarding the contract occurs.

[0279] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[0280] "Trouble" refers to problems or disputes that arise in connection with a contract.

[0281] "Text data" is data that represents character information in digital form.

[0282] "Similarity" is an index that indicates the degree of match between the contents of two text data.

[0283] A "threshold" refers to a reference value for satisfying a specific condition.

[0284] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[0285] "Identification" refers to clearly identifying and selecting an object.

[0286] A "system" is a set of devices and programs in which multiple elements function in conjunction with one another.

[0287] The system for carrying out the present invention has the function of reading and understanding the contents of a contract and identifying the relevant clause of the contract based on the content of the problem. A specific embodiment of this system will be described below.

[0288] System configuration

[0289] The system consists of three main components: a server, a terminal, and a user. The server analyzes the contents of the contract and performs the main process of identifying the relevant clause based on the nature of the problem. The terminal provides an interface for users to input the details of the problem. When a problem occurs with the contract, users can use this system to quickly identify the appropriate clause.

[0290] Hardware and software used

[0291] The server is a computer equipped with a high-performance processor and large memory capacity. It uses the natural language processing library, Spacy, and the machine learning library, sklearn, to analyze the text data of the contract and calculate the degree of similarity between the content of the dispute and each clause in the contract.

[0292] Data processing and calculation

[0293] The server first analyzes the text data of the contract and extracts the contents of each clause. Next, it receives the trouble description entered by the user as text data and preprocesses it. Preprocessing includes morphological analysis and stop word removal. The preprocessed text data is vectorized using TfidfVectorizer. Finally, it calculates the similarity between the trouble description and each clause of the contract using cosine_similarity and identifies the clause with the highest similarity.

[0294] Specific examples

[0295] For example, if a user enters a problem such as "What to do if a payment is late," the system analyzes this content and identifies the most relevant clause in the contract. Below is an example of a prompt sentence.

[0296] Example prompt sentence:

[0297] Problem: "What to do if payment is late"

[0298] Terms of the Agreement:

[0299] 1. Purpose of the Agreement

[0300] 2. Term of the Agreement

[0301] 3. Payment Terms

[0302] 4. Conditions for termination

[0303] When the user enters this prompt, the system identifies the clause in the contract that is most relevant to the problem and presents it to the user, allowing the user to take appropriate action quickly and accurately.

[0304] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0305] Step 1:

[0306] The user uses the terminal to input the details of the problem.

[0307] Input: Details of the problem (e.g., "What to do if payment is late")

[0308] Output: The details of the problem are sent to the server as text data.

[0309] Step 2:

[0310] The server reads the text data of the contract and extracts the contents of each clause.

[0311] Input: Text data of the contract

[0312] Output: A list of the contents of each clause (e.g., "Purpose of the contract," "Term of the contract," "Payment terms," ​​"Conditions for termination of the contract")

[0313] Step 3:

[0314] The server preprocesses the content of the problem. Specifically, it performs morphological analysis and removes stop words.

[0315] Input: Text data of the problem

[0316] Output: Preprocessed trouble details (e.g., "Payment delay response")

[0317] Step 4:

[0318] The server preprocesses the content of each clause in the contract by performing morphological analysis and removing stop words.

[0319] Input: Text data for each clause of the contract

[0320] Output: Content of each preprocessed clause (e.g., "Contract Purpose", "Contract Term", "Payment Terms", "Terms of Termination")

[0321] Step 5:

[0322] The server vectorizes the preprocessed trouble details and the contents of each clause of the contract using TfidfVectorizer (registered trademark).

[0323] Input: Preprocessed content of the problem and content of each clause

[0324] Output: Vectorized content of the problem and content of each clause

[0325] Step 6:

[0326] The server uses cosine_similarity (registered trademark) to calculate the similarity between the content of the problem and each clause of the contract.

[0327] Input: Vectorized content of the problem and content of each clause

[0328] Output: A list of similarity scores (e.g. "0.85", "0.60", "0.90", "0.70")

[0329] Step 7:

[0330] The server identifies the clause with the highest similarity score.

[0331] Input: A list of similarity scores

[0332] Output: The clause with the highest similarity (e.g., "Payment Terms")

[0333] Step 8:

[0334] The server presents the identified terms to the user.

[0335] Input: Most similar clause

[0336] Output: Identified terms (e.g., "Payment Terms") displayed on the user's device

[0337] Example 3

[0338] 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."

[0339] Negotiations with business partners based on traditional contracts required a great deal of time and effort to understand the contents of the contract, identify clauses that match the content of the problem, and generate appropriate proposals. It was also difficult to accurately link the contract clauses with the content of the problem, which could reduce the efficiency of negotiations. Furthermore, proposals were generated manually, which could result in a lack of consistency and objectivity.

[0340] 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.

[0341] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses the problem meets, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the content of the problem using natural language processing technology, means for generating a prompt sentence based on the analysis results and the acquired clauses, means for sending the prompt sentence to a generation AI model to generate a proposal for negotiation, and means for sending the generated proposal to the user's terminal. This makes it possible to quickly and accurately link the content of the contract with the content of the problem, and automatically generate an efficient and consistent proposal.

[0342] "Means for reading and understanding the contents of a contract" refers to a means for analyzing the text data of a contract and extracting the contents of each clause.

[0343] "Means of determining which clause's conditions are met in the particular problem" refers to a means of analyzing the content of the problem and identifying the relevant clause of the contract.

[0344] The "means for negotiating with a business partner based on the provisions of the master agreement" is a means for generating a proposal for negotiating with a business partner based on the specified provisions.

[0345] "Means for analyzing the content of the trouble using natural language processing technology" refers to means for analyzing the content of the trouble entered by the user using natural language processing technology and extracting important keywords and phrases.

[0346] The "means for generating a prompt sentence based on the analysis results and the acquired clauses" refers to a means for generating a prompt sentence to be sent to the generative AI model based on the content of the analyzed problem and the acquired contract clauses.

[0347] The "means for sending a prompt sentence to a generative AI model and generating a proposal for negotiation" is a means for sending a prompt sentence to a generative AI model and generating a proposal for negotiation in response thereto.

[0348] "Means for sending generated proposals to a user's terminal" refers to means for sending proposals received from a generative AI model to a user's terminal so that the user can confirm them.

[0349] The present invention is a system that reads and understands the contents of a contract and generates an appropriate proposal based on the details of the problem. A specific embodiment of this system will be described below.

[0350] Hardware and software used

[0351] The server uses a relational database management system (RDBMS) to read and understand the contents of the contract. Specifically, a database management system such as MySQL (registered trademark) is used. To analyze the details of the problem, it uses NLTK or spaCy, which are natural language processing libraries for Python (registered trademark). As a generative AI model, for example, GPT-4 (registered trademark) from OpenAI (registered trademark) is used.

[0352] Data processing and calculation

[0353] The server first retrieves the terms of the master contract from a database. Next, it receives the details of the problem entered by the user and analyzes them using natural language processing technology. Based on the analyzed details of the problem and the retrieved terms, the server generates a prompt to send to the generative AI model. The generative AI model then generates a proposal for negotiation based on this prompt. The generated proposal is sent to the user's device, where the user can review it and make any necessary corrections or additions.

[0354] Specific examples

[0355] For example, if a user types "There is a problem with the delivery being delayed," the server will do the following:

[0356] 1. The server retrieves the "Delivery Terms" from the MySQL database.

[0357] 2. The server uses Python's NLTK library to extract the keyword "Delivery Delay".

[0358] 3. The server generates a prompt message saying, "In accordance with clause X of the master agreement, a problem has occurred regarding a delay in delivery. Please suggest what measures should be taken to address this problem."

[0359] 4. The server sends a prompt to the generative AI model (GPT-4).

[0360] 5. The generative AI model generates the following recommendation: "In accordance with clause X, we propose an additional extension of delivery time as a measure to address the late delivery date. Please also consider requesting a waiver of the penalty associated with the delay."

[0361] 6. The server sends the generated proposal to the user's device.

[0362] 7. The user reviews the proposal and makes corrections, such as "I propose an additional delivery extension, but do not request a waiver of the penalty for the delay."

[0363] In this way, it is possible to quickly and accurately link the contents of the contract with the contents of the problem, and automatically generate an efficient and consistent proposal. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0364] Step 1:

[0365] The user inputs the details of the problem.

[0366] The user inputs the details of the problem through the system interface. For example, they might input "A problem has occurred regarding a delivery delay." The input data is then sent to the server.

[0367] Step 2:

[0368] The server retrieves the terms of the master contract from the database.

[0369] The server uses a relational database management system (RDBMS) such as MySQL to retrieve the terms of the master contract from the database. For example, it retrieves the "terms regarding delivery time." The input is the details of the problem entered by the user, and the output is the retrieved terms of the contract.

[0370] Step 3:

[0371] The server analyzes the details of the problem using natural language processing (NLP) technology.

[0372] The server uses Python's NLTK and spaCy libraries to analyze the content of the problem entered by the user. For example, it extracts the keyword "delivery delay." The input is the content of the problem entered by the user, and the output is the analyzed keywords and phrases.

[0373] Step 4:

[0374] The server generates a prompt based on the analysis results and the acquired clauses.

[0375] The server generates a prompt to send to the generative AI model based on the analysis results and the acquired clauses. For example, it generates a prompt such as, "In accordance with clause X of the master contract, a problem has occurred regarding a delivery delay. Please suggest what measures should be taken to address this problem." The input is the analysis results and the acquired clauses, and the output is the generated prompt.

[0376] Step 5:

[0377] The server sends a prompt to the generative AI model.

[0378] The server sends a prompt to a generative AI model (e.g., OpenAI's GPT-4). The generative AI model generates a proposal based on this prompt. The input is the generated prompt, and the output is the proposal generated by the generative AI model.

[0379] Step 6:

[0380] A generative AI model generates proposals for negotiation.

[0381] The generative AI model generates a proposal for negotiation based on a prompt. For example, it generates a proposal such as, "In accordance with clause X, we propose an additional extension of delivery time as a measure to address the delivery delay. Please also consider requesting a waiver of the penalty associated with the delay." The input is the prompt, and the output is the generated proposal.

[0382] Step 7:

[0383] The server sends the generated proposal to the user's terminal.

[0384] The server sends the proposal received from the generative AI model to the user's device, where the user can review it. The input is the generated proposal, and the output is the proposal sent to the user's device.

[0385] Step 8:

[0386] The user reviews the proposal and makes corrections or additions as necessary.

[0387] The user can check the proposal on the terminal and make corrections or additions as necessary. For example, a correction can be made such as "I propose an additional delivery extension, but I do not request an exemption from the penalty for the delay." The input is the proposal sent from the server, and the output is the proposal corrected or added by the user.

[0388] (Application example 3)

[0389] 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."

[0390] Conventional contract negotiation systems lack the functionality to automatically generate proposals for negotiations based on the contract clauses and the details of the problem, which reduces the efficiency of negotiations. Also, even if users input the contract clauses and the details of the problem, it is difficult to properly analyze them and generate specific proposals.

[0391] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clause's conditions are met in the relevant trouble, means for negotiating with the trading partner based on the clauses of the master contract, means for automatically generating a proposal for negotiation using a generative AI model, means for sending the contract clauses and the content of the trouble input by the user as prompt text to the generative AI model, and means for displaying the generated proposal. This makes it possible to automatically generate a specific and appropriate proposal based on the contract clauses and the content of the trouble, thereby improving the efficiency of negotiations.

[0392] A "contract" is a legally binding document exchanged between business partners, which contains the terms of the transaction, obligations, rights, etc.

[0393] "Clause" refers to the individual provisions and conditions set out in a contract, and is an element that constitutes the specific content of the contract.

[0394] "Trouble" refers to problems or disputes that arise during the course of contract performance, including acts that violate or fail to perform the terms of the contract.

[0395] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to generate text data, specifically a natural language processing model such as GPT-3.

[0396] A "prompt" is text data input to a generative AI model that contains instructions or questions that enable the model to generate appropriate output.

[0397] A "proposal" refers to a solution or response presented in contract negotiations, including specific actions or measures.

[0398] "Analysis" refers to the process of analyzing text data and information in detail to understand its meaning and structure.

[0399] "Display" refers to the act of visually presenting generated suggestions or information to a user, including outputting it as text or graphics on a screen.

[0400] The system for implementing this invention reads and understands the contents of a contract, identifies appropriate clauses based on the content of the problem, and automatically generates a proposal for negotiation using a generative AI model. Specific embodiments of this system are described below.

[0401] First, the server receives the text data of the contract and analyzes its contents. Natural language processing technology is used to extract the content of each clause, which is then stored in a database.

[0402] Next, the user inputs the details of the problem. The server analyzes the details of the problem and identifies the relevant clauses in the contract. This analysis is done using text mining technology.

[0403] The server then generates a proposal for negotiation using a generative AI model (e.g., OpenAI's GPT-3) based on the identified terms and the details of the problem. The generated proposal is obtained by sending the information entered by the user as a prompt to the generative AI model.

[0404] The generated proposal is sent from the server to the user's device and displayed on the screen, allowing the user to negotiate with a business partner based on a specific and appropriate proposal.

[0405] As a concrete example, the following prompt sentence is input into the generative AI model:

[0406] Example prompt sentence:

[0407] Contract clause: Article 10.2: Liability for service interruptions

[0408] Problem: Customer seeks compensation after service was interrupted for three days.

[0409] Generate a proposal.

[0410] Based on this prompt, the generative AI model generates the following suggestions:

[0411] An example of a generated suggestion:

[0412] Pursuant to clause 10.2, we offer to provide you with a 10% discount off your next bill as compensation for the interruption of our services.

[0413] In this way, it is possible to automatically generate specific and appropriate proposals based on the terms of the contract and the details of the problem, thereby improving the efficiency of negotiations.

[0414] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0415] Step 1:

[0416] The user inputs the text data of the contract using a terminal. The input text data is sent to the server. The server receives this text data and analyzes the contents of the contract using natural language processing technology. Specifically, it extracts each clause from the text data and stores it in a database. The input is the text data of the contract, and the output is the analyzed data of each clause.

[0417] Step 2:

[0418] The user uses a terminal to input the details of the problem. The input details of the problem are sent to the server. The server receives the details of the problem and analyzes them using text mining technology. Specifically, it analyzes the details of the problem and identifies the relevant clauses of the contract. The input is the details of the problem, and the output is the relevant clauses of the contract.

[0419] Step 3:

[0420] The server uses a generative AI model to generate a proposal for negotiation based on the identified contract clauses and the details of the problem. Specifically, the information entered by the user is sent to the generative AI model as a prompt, which generates a proposal. The input is the identified contract clauses and the details of the problem, and the output is the generated proposal.

[0421] Step 4:

[0422] The server sends the generated proposal to the user's device. The user's device receives the proposal and displays it on the screen. This allows the user to negotiate with a trading partner based on a specific and appropriate proposal. The input is the generated proposal, and the output is the proposal displayed on the screen.

[0423] 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.

[0424] "Example 1"

[0425] One embodiment of the present invention provides a system incorporating an emotion engine. This system includes a means for reading and understanding the contents of a contract, a means for determining which clauses are met in the current dispute, a means for negotiating with a business partner based on the clauses of the master contract, and an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's voice data to determine the user's emotional state. For example, if the user is feeling angry or frustrated, the emotion engine captures that information and feeds it back to the system. This allows the system to generate negotiation proposals taking the user's emotional state into consideration.

[0426] "Example 2"

[0427] As a specific example, consider a case where a user is dissatisfied with a part of a contract. The user communicates this dissatisfaction to the system. At this time, the user's voice data is analyzed by the emotion engine to determine the user's emotional state. The emotion engine determines that the user is feeling angry or dissatisfied and feeds this information back to the system. Based on this information, the system generates a proposal for negotiation that takes into account the user's emotional state. This proposal might be generated in the form of, for example, "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we propose that you take action Y."

[0428] "Example 3"

[0429] The emotion engine can also grasp the user's emotional state in real time. For example, if a user is using the system while negotiating with a business partner, the emotion engine analyzes the user's voice data in real time to determine the user's emotional state. This information is fed back to the system, which then generates negotiation proposals in real time that take the user's emotional state into account. This allows the user to obtain the optimal negotiation strategy for the situation at hand.

[0430] The processing flow of each embodiment will be described below.

[0431] "Example 1"

[0432] Step 1: The user enters the contract details into the system.

[0433] Step 2: The system reads and understands the contents of the contract.

[0434] Step 3: The user enters the details of the problem into the system.

[0435] Step 4: The system determines which clause conditions are met for the problem.

[0436] Step 5: The user inputs their emotional state into the system as voice data.

[0437] Step 6: The emotion engine analyzes the user's voice data and determines the user's emotional state.

[0438] Step 7: The system generates a proposal for negotiation after taking into account the user's emotional state.

[0439] "Example 2"

[0440] Step 1: The user is dissatisfied with some aspect of the contract and tells the system about it.

[0441] Step 2: The emotion engine analyzes the user's voice data and determines that the user's emotional state is dissatisfied.

[0442] Step 3: The system uses that information to generate proposals for negotiation that take into account the user's emotional state.

[0443] "Example 3"

[0444] Step 1: The user uses the system during negotiations with a business partner.

[0445] Step 2: The emotion engine analyzes the user's voice data in real time to determine their emotional state.

[0446] Step 3: The system generates proposals for negotiation that take into account the user's emotional state in real time.

[0447] Example 1

[0448] 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."

[0449] Conventional contract management systems could read and understand the contents of a contract, but they were unable to generate negotiation proposals that took the user's emotional state into account. This made it difficult to negotiate appropriately based on the user's emotions, resulting in a decrease in the efficiency of dispute resolution.

[0450] 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.

[0451] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses meet the conditions of the relevant trouble, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the user's voice data and determining the user's emotional state, means for generating a negotiation proposal based on the analyzed clauses of the contract and the user's emotional state, and means for displaying the generated negotiation proposal to the user. This makes it possible to generate an appropriate negotiation proposal that takes the user's emotional state into consideration.

[0452] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[0453] "Text data" is data that represents character information in digital form.

[0454] "Natural language processing" is a technology that uses computers to analyze and understand human language.

[0455] "Clause" means any individual condition or provision set out in a contract.

[0456] "Audio data" refers to data in which audio is recorded in digital format.

[0457] An "emotion engine" is software that analyzes voice and text data to determine emotional states.

[0458] A "generative AI model" is a model that uses artificial intelligence to generate new data and suggestions.

[0459] A "negotiation proposal" is a specific proposal to smoothly advance negotiations with a business partner.

[0460] "User" means an individual or corporation that uses the system.

[0461] A "server" is a computer system that processes and stores data over a network.

[0462] A "terminal" is a device such as a computer or smartphone that is directly operated by a user.

[0463] The present invention relates to a system that reads and understands the contents of a contract and generates a negotiation proposal that takes into account the emotional state of the user. This system operates in cooperation with a server, a terminal, and a user.

[0464] First, the user uploads the text data of the contract to their device. The device then sends the uploaded text data to the server. The server then uses natural language processing technology to analyze the contents of the contract and extract each clause. This analysis is performed using Python natural language processing libraries such as "spaCy" and "NLTK." For example, a function is executed that takes the text data of the contract as input and outputs the content of each clause.

[0465] Next, the user expresses their emotions through speech into the device. The device collects the user's voice data and sends it to the server. The server then uses an emotion engine to analyze the voice data and determine the user's emotional state. This analysis uses the voice recognition library "Google (registered trademark) Cloud Speech-to-Text" and the emotion analysis library "IBM Watson (registered trademark) Tone Analyzer." For example, if the user is feeling angry or frustrated, the emotion engine captures that information and feeds it back into the system.

[0466] The server then generates a negotiation proposal based on the analyzed contract clauses and the user's emotional state using a generative AI model, such as OpenAI GPT-3 or BERT (registered trademark). For example, if the user is feeling angry, the system will take that emotion into account when generating a negotiation proposal.

[0467] Finally, the server sends the generated negotiation proposal to the terminal, which displays it to the user, who can then review the proposal and make any necessary modifications or approvals.

[0468] As a concrete example, a negotiation proposal can be generated by inputting the following prompt sentence into a generative AI model:

[0469] "Generate a proposal to negotiate with a trading partner based on the terms of the contract. The user is currently angry. The terms of the contract are as follows: 'Article 1: Delivery time shall be within 30 days. Article 2: Quality guarantee shall be one year.'"

[0470] Using this prompt, the generative AI model can generate negotiation proposals that take into account the user's emotional state.

[0471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0472] Step 1:

[0473] The user uploads the text data of the contract to the terminal, which then transmits the uploaded text data to the server.

[0474] Input: Contract text file

[0475] Output: Text data sent to the server

[0476] Specific operation: The user clicks the "Select File" button and selects the contract text file. The device sends the selected file to the server.

[0477] Step 2:

[0478] The server analyzes the received text data of the contract using a natural language processing library (e.g., spaCy, NLTK), extracts each clause of the contract, and stores it in a database.

[0479] Input: Text data of the contract

[0480] Output: Data for each extracted clause

[0481] Specific operation: The server tokenizes the text data and identifies each clause. The server stores the identified clauses in a database.

[0482] Step 3:

[0483] The user expresses their emotions through speech into the device, which then collects the user's speech data and sends it to the server.

[0484] Input: User's voice data

[0485] Output: Audio data sent to the server

[0486] Specific operation: The user speaks into the device, which collects the voice data and sends it to the server.

[0487] Step 4:

[0488] The server analyzes the received voice data using an emotion engine (e.g., Google Cloud Speech-to-Text, IBM Watson Tone Analyzer), determines the user's emotional state, and stores that information in a database.

[0489] Input: Audio data

[0490] Output: User's emotional state

[0491] Specific operation: The server converts the voice data into text. The server analyzes the text data with an emotion engine to determine the emotional state. The server stores the emotional state in a database.

[0492] Step 5:

[0493] The server generates a negotiation proposal using a generative AI model (e.g., OpenAI GPT-3, BERT) based on the parsed contract clauses and the user's emotional state.

[0494] Input: Parsed contract clauses, user emotional state

[0495] Output: Generated negotiation proposal

[0496] Specific operation: The server passes the contract terms and emotional state as input to the generative AI model. The server then receives the generated negotiation proposal and sends it to the terminal.

[0497] Step 6:

[0498] The terminal displays the negotiation proposal received from the server to the user, who then checks the displayed proposal and makes any necessary corrections or approvals.

[0499] Input: Generated negotiation proposal

[0500] Output: The negotiation proposal displayed to the user

[0501] Specific operation: The terminal displays the negotiation proposal to the user, who then reviews the proposal and, if necessary, modifies or approves it.

[0502] (Application example 1)

[0503] 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."

[0504] Conventional contract management systems can read and understand the contents of a contract, but they cannot generate negotiation proposals that take the user's emotional state into account. As a result, if the user is dissatisfied or angry about the contract contents, they cannot respond appropriately, which can make negotiations difficult. Furthermore, proposals that ignore the user's emotional state can reduce the efficiency of negotiations.

[0505] 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.

[0506] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses the problem corresponds to, means for negotiating with the business partner based on the clauses of the master contract, means for recognizing the user's emotional state, and means for generating a proposal for negotiation based on the recognized emotional state. This makes it possible to accurately understand the contents of the contract and generate an appropriate negotiation proposal that takes the user's emotional state into consideration.

[0507] A "contract" is a written legal agreement that clarifies the rights and obligations of the parties.

[0508] "Means of reading and understanding" refers to the techniques and methods for analyzing the text data of a contract and understanding its contents.

[0509] "Means of determination" are techniques or methods for selecting appropriate provisions or measures based on specific conditions or circumstances.

[0510] "Means of negotiation" refers to the techniques and methods used to negotiate with business partners based on the terms of the contract.

[0511] "Means for recognizing emotional state" refers to techniques or methods for analyzing a user's voice data or other input data to determine the user's emotions.

[0512] A "means for generating proposals" is a technique or method for creating specific proposals for negotiation based on the perceived emotional state.

[0513] A system for implementing the present invention reads and understands the contents of a contract, recognizes the emotional state of a user, and generates a negotiation proposal. A specific embodiment of the system is described below.

[0514] Hardware and software used

[0515] Hardware: Smartphone, microphone

[0516] Software: Python, spaCy, Transformers, SpeechRecognition

[0517] Data processing and calculation

[0518] Contract Analysis

[0519] The server receives the text data of the contract and analyzes it using natural language processing technology. Specifically, it uses spaCy to extract each clause of the contract and understand its content. This process clarifies the important clauses of the contract.

[0520] emotion recognition

[0521] It uses a microphone to collect user-provided voice data and converts the speech to text using SpeechRecognition. It then uses a Transformers sentiment analysis model to recognize the user's emotional state. This process can determine whether the user is angry or frustrated.

[0522] Proposal Generation

[0523] The server generates negotiation suggestions based on the analyzed contract clauses and the user's perceived emotional state. For example, if the user is dissatisfied, the server may provide a suggestion to draw attention to the contract's termination clause. This suggestion helps the user take appropriate action.

[0524] Specific examples

[0525] Contents of the contract

[0526] Suppose the contract contains a clause that reads, "This contract may be terminated by either party with a 30-day notice."

[0527] Audio data

[0528] The voice data "user_voice.wav" in which the user expresses dissatisfaction is used.

[0529] Prompt Sentence Examples

[0530] Analyze the contents of the contract, recognize the user's emotional state, and provide appropriate advice. The content of the contract is "This contract may be terminated by either party with a 30-day notice." The voice data is "user_voice.wav".

[0531] In this way, the system can accurately understand the contents of the contract and generate appropriate negotiation proposals that take into account the user's emotional state, allowing the user to appropriately deal with dissatisfaction or anger about the contract contents and conduct efficient negotiations.

[0532] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0533] Step 1:

[0534] The server receives the text data of the contract. It receives the text data of the contract as input and analyzes it using natural language processing technology. Specifically, it uses spaCy to extract each clause of the contract and understand the content of each clause. As output, it obtains a list of the extracted clauses.

[0535] Step 2:

[0536] The user provides voice data through a microphone. The system receives the user's voice data as input and converts the voice to text using SpeechRecognition. Specifically, it performs a process to convert the voice data into text data. The converted text data is obtained as output.

[0537] Step 3:

[0538] The server receives the converted text data and uses the Transformers sentiment analysis model to recognize the user's emotional state. It receives the text data as input and performs sentiment analysis. Specifically, it inputs the text data into the sentiment analysis model to determine the user's emotional state. It obtains the recognized emotional state as output.

[0539] Step 4:

[0540] The server generates a proposal for negotiation based on the analyzed contract clauses and the recognized emotional state of the user. As input, it receives the list of extracted clauses and the recognized emotional state and generates a proposal. Specifically, it generates a warning or advice for a specific clause in the contract depending on the user's emotional state. As output, it obtains the generated proposal.

[0541] Step 5:

[0542] The device presents the generated suggestions to the user. As input, the device receives the generated suggestions and presents them to the user visually or audibly. Specifically, the device displays the suggestions on a screen or reads them out loud. As output, the user can confirm the suggestions.

[0543] In this way, the system can accurately understand the contents of the contract, generate appropriate negotiation proposals that take into account the user's emotional state, and provide them to the user.

[0544] Example 2

[0545] 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."

[0546] With conventional contract management systems, it was difficult to quickly and accurately identify which clauses were applicable when a dispute arose. Furthermore, they lacked the functionality to generate negotiation proposals that took the user's emotional state into account, making it impossible to properly resolve the user's dissatisfaction. This could delay the resolution of contract disputes and worsen relationships with business partners.

[0547] 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.

[0548] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses match the conditions of the relevant trouble, means for negotiating with the business partner based on the clauses of the master contract, means for the user to input the details of the trouble, means for analyzing the user's emotional state using an emotion engine, means for identifying the relevant contract clauses using a similarity calculation algorithm, means for generating a proposal using a generative AI model, and means for presenting the generated proposal to the user. This makes it possible to quickly and accurately identify the relevant contract clauses based on the details of the trouble and generate a negotiation proposal that takes the user's emotional state into consideration.

[0549] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[0550] "Trouble" refers to issues or disputes related to the contract.

[0551] "User" refers to an individual or corporation that uses the system to check the contents of the contract and attempt to resolve any issues.

[0552] A "server" is a computer that is the core of a system and is a device that processes and stores data.

[0553] An "emotion engine" is software for analyzing a user's emotional state from input data.

[0554] The "similarity calculation algorithm" is a mathematical method for calculating the similarity between the content of the problem and each clause of the contract.

[0555] A "generative AI model" is an artificial intelligence model that generates suggestions based on a user's emotional state and specified clauses.

[0556] A "proposal" is a document or message that outlines specific actions or changes to be taken to resolve the problem.

[0557] "Text data" is data that includes text information and is used to describe contracts and trouble details.

[0558] "Negotiation" is the act of discussing terms and conditions with a business partner and aiming for an agreement.

[0559] This invention is a system that reads the contents of a contract, identifies the relevant clause when a problem occurs, and generates a negotiation proposal that takes into account the emotional state of the user. A specific embodiment of this system will be described below.

[0560] Hardware and software used

[0561] Hardware: Servers, terminals

[0562] Software: Emotion engine, similarity calculation algorithm, generative AI model

[0563] System Overview

[0564] This system allows users to input the details of the problem, identifies the relevant clauses in the contract based on that information, and generates proposals that take into account the user's emotional state.

[0565] Details of data processing and calculation

[0566] 1. User enters the problem:

[0567] The user uses a terminal to input the details of the problem into the system as text data. For example, the user might input, "I think the payment terms in this contract are unfair."

[0568] 2. Analysis by emotion engine:

[0569] The server launches an emotion engine to analyze the text data entered by the user. The emotion engine uses natural language processing technology to determine the user's emotional state. For example, it may extract the keyword "unfair" from the user's text and determine that the user is dissatisfied.

[0570] 3. Clause identification using similarity calculation algorithm:

[0571] The server uses a similarity calculation algorithm to calculate the degree of similarity between the content of the problem and each clause in the contract. Clauses with a similarity above a certain threshold are identified as relevant clauses. For example, if the problem is related to "payment terms," ​​clauses related to payment will be identified.

[0572] 4. Proposal generation:

[0573] The server launches a generative AI model to generate a proposal based on the user's emotional state and the identified clauses. The generative AI model creates a proposal for negotiation that takes the user's emotions into account. For example, a proposal might be generated such as, "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we propose taking measures to extend the payment deadline."

[0574] 5. Presenting the proposal:

[0575] The server sends the generated proposal to the user's device and presents it to the user, who can then check the proposal content through the device.

[0576] Specific examples

[0577] User input: The user types "I believe the payment terms in this contract are unfair" into the input field on the terminal and clicks the submit button.

[0578] Analysis result of emotion engine: The server starts the emotion engine and analyzes the user's text data. The emotion engine detects the keyword "unfair" and determines that the user is dissatisfied.

[0579] Similarity calculation: The server calculates the similarity between each clause of the contract and the user's problem. The clause regarding payment terms is identified as having a high similarity.

[0580] Proposal Generation: The server launches the generative AI model to generate a proposal based on the user's emotional state and the identified clauses. The generated proposal might be something like, "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we suggest taking measures to extend the payment deadline."

[0581] Proposal presentation: The server sends the generated proposal to the user's device, and the user confirms the proposal on the device screen.

[0582] Prompt Sentence Examples

[0583] Prompt: "If a user is dissatisfied with the payment terms in a contract, identify which clauses apply and generate suggestions that take the user's emotional state into account."

[0584] In this way, the system analyzes the user's problem, identifies the relevant contract clauses, generates suggestions that take into account the user's emotional state, and presents them to the user.

[0585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0586] Step 1:

[0587] The user inputs the details of the problem.

[0588] The user uses a terminal to input the details of the problem into the system as text data. For example, the user might type, "I think the payment terms in this contract are unfair," and click the send button. The input data is sent to the server.

[0589] Step 2:

[0590] The server uses an emotion engine to analyze the user's emotional state.

[0591] The server passes the received text data to the emotion engine for analysis. The emotion engine uses natural language processing technology to extract the user's emotional state from the text data. For example, it detects the keyword "unfair" and determines that the user is dissatisfied. The analysis results in the user's emotional state being output.

[0592] Step 3:

[0593] The server uses a similarity calculation algorithm to identify the relevant clauses of the contract.

[0594] The server uses a similarity calculation algorithm to calculate the similarity between the content of the problem and each clause in the contract. The content of the problem and the text data of the contract are used as input data. The similarity calculation algorithm calculates the similarity with each clause and identifies clauses whose similarity is above a certain threshold. The relevant clauses are obtained as output data.

[0595] Step 4:

[0596] The server generates suggestions using a generative AI model.

[0597] The server generates a proposal using a generative AI model based on the user's emotional state and the identified clauses. The user's emotional state and the identified clauses are used as input data. The generative AI model uses this data to create a proposal for negotiation that takes the user's emotions into account. For example, a proposal such as "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we propose taking measures to extend the payment deadline" is generated. The generated proposal is obtained as output data.

[0598] Step 5:

[0599] The server presents the generated suggestions to the user.

[0600] The server sends the generated proposal to the user's terminal and presents it to the user. The user can check the proposal content through the terminal. As output data, the proposal is obtained in a format that the user can check.

[0601] (Application example 2)

[0602] 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."

[0603] When a dispute arises regarding a contract, it is difficult for the user to identify which clause is problematic and conduct appropriate negotiations. Furthermore, if negotiations are conducted without taking into account the user's emotional state, the negotiations may not proceed smoothly. This can delay the resolution of the contract dispute and hinder the progress of business.

[0604] 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.

[0605] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clause matches the conditions of the problem, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the emotional state of the user, and means for generating a proposal for negotiation that takes the emotional state of the user into consideration. As a result, when a problem occurs regarding the contract, it becomes possible to identify the applicable clause and conduct appropriate negotiations while taking the emotional state of the user into consideration.

[0606] A "contract" is a written document of a transaction or agreement, and is a legal document that clarifies the rights and obligations of the parties.

[0607] "Trouble" refers to problems or disputes related to a contract that arise due to differences of opinion or dissatisfaction between the parties.

[0608] A "clause" refers to an individual provision or condition within a contract, which defines specific rights, obligations, procedures, etc.

[0609] "Emotional state" refers to the user's psychological state, and includes emotions such as anger, frustration, and joy.

[0610] "Analysis" refers to the process of examining and understanding data and information, and extracting information for a specific purpose.

[0611] A "proposal" refers to a specific idea or method presented for solving a problem or making an improvement, and is used during negotiations and decision-making.

[0612] "Negotiation" refers to the process of discussion between the parties to adjust their views and conditions and reach an agreement.

[0613] A "system" refers to an entire mechanism that is composed of multiple elements and functions, and is designed to achieve a specific purpose.

[0614] A system for implementing this invention includes means for reading and understanding the contents of a contract, means for determining which clause's conditions are met in the particular dispute, means for negotiating with a business partner based on the clauses of the master contract, means for analyzing the user's emotional state, and means for generating proposals for negotiation that take the user's emotional state into consideration.

[0615] The server uses natural language processing (NLP) technology to analyze the text data of the contract and extract the content of each clause. Specifically, it uses the spacy library to analyze the content of the contract and extract the content of each clause.

[0616] Next, the server analyzes the content of the problem and calculates the similarity between the content of the problem and each clause of the contract in order to identify the relevant clause of the contract. This similarity calculation is performed using the similarity calculation function of Spacy.

[0617] Furthermore, the server uses an emotion analysis model to analyze the emotional state of the user. Specifically, the server uses a pipeline from the transformers library to analyze the emotional state of the user's trouble report.

[0618] To generate a proposal for negotiation that takes into account the emotional state of the user, the server generates a proposal based on the analysis result, which takes into account the emotional state of the user and provides a specific plan for appropriate negotiation.

[0619] For example, if a user reports, "I'm dissatisfied with clause X of this contract," the server analyzes the contents of the contract and identifies the relevant clause. Next, it analyzes the user's emotional state and determines that the user is dissatisfied. Finally, the server generates a proposal such as, "Since the user is dissatisfied with clause X, we believe there is room for improvement. Specifically, we suggest that you take action Y."

[0620] An example of a prompt is as follows:

[0621] "The user is frustrated by clause X in the contract. Perform sentiment analysis to identify the relevant clauses in the contract and generate suggestions that take into account the user's emotional state."

[0622] In this way, when a problem occurs with a contract, it becomes possible to identify the relevant clauses and conduct appropriate negotiations while taking into account the user's emotional state.

[0623] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0624] Step 1:

[0625] A user reports a problem with a contract.

[0626] Input: The user inputs the details of the problem by voice or text.

[0627] Specific operation: The user uses a smartphone or terminal to input the details of the problem, such as "I am dissatisfied with clause X of this contract."

[0628] Step 2:

[0629] The server receives the details of the problem and saves them as text data.

[0630] Input: Voice or text data of the problem sent by the user.

[0631] Output: Text data of the saved trouble details.

[0632] Specific operation: The server receives the data sent by the user, and if it is voice data, converts it into text data using voice recognition technology and stores it in a database.

[0633] Step 3:

[0634] The server analyzes the contents of the contract and extracts the contents of each clause.

[0635] Input: Text data of the contract.

[0636] Output: The content of each extracted clause.

[0637] Specific operation: The server uses the spacy library to parse the text data of the contract and extract the contents of each clause.

[0638] Step 4:

[0639] The server calculates the degree of similarity between the problem and each clause in the contract.

[0640] Input: Text data of the problem, contents of each clause of the contract.

[0641] Output: Contract clauses that are most similar to the problem.

[0642] Specific operation: The server uses Spacy's similarity calculation function to calculate the similarity between the problem content and each clause in the contract, and identifies the clause with the highest similarity.

[0643] Step 5:

[0644] The server analyzes the user's emotional state.

[0645] Input: Text data of the problem.

[0646] Output: The user's emotional state (e.g., anger, frustration, joy, etc.).

[0647] Specific operation: The server uses the pipeline of the transformers library to input the text data of the trouble content into the emotion analysis model and analyze the user's emotional state.

[0648] Step 6:

[0649] The server generates proposals for negotiation that take into account the user's emotional state.

[0650] Input: Text data of the problem, relevant clauses of the contract, and the user's emotional state.

[0651] Output: Proposal for negotiation.

[0652] Specific operation: Based on the analysis results, the server generates a proposal that takes into account the user's emotional state. For example, it generates a proposal such as, "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we suggest that you take action Y."

[0653] Step 7:

[0654] The server notifies the user of the generated proposal.

[0655] Input: The generated proposal.

[0656] Output: The suggestion notified to the user.

[0657] Specific operation: The server notifies the user of the generated proposal to their smartphone or device, allowing the user to check the proposal contents.

[0658] Example 3

[0659] 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."

[0660] Conventional contract-based negotiation systems have difficulty automatically generating negotiation proposals based on contract clauses, and are unable to make proposals that take into account the user's emotional state. This has resulted in problems that reduce the efficiency and effectiveness of negotiations. Furthermore, there is a need for systems that can grasp the stress and emotional changes that users experience during negotiations in real time and make proposals accordingly.

[0661] 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.

[0662] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses are met in the relevant trouble, means for negotiating with the business partner based on the clauses of the master contract, means for automatically generating a negotiation proposal based on the identified clauses and the details of the trouble, and means for grasping the user's emotional state in real time and generating a negotiation proposal based on that information. This makes it possible to automatically generate specific and appropriate negotiation proposals based on the clauses of the contract and to make proposals that take the user's emotional state into consideration.

[0663] "Means for reading and understanding the contents of a contract" refers to a means that has the function of analyzing the text data of a contract and extracting the contents of each clause.

[0664] "Means for determining which clause's conditions are met in the particular problem" refers to a means that has the function of analyzing the content of the problem and identifying the relevant clause of the contract.

[0665] A "means for negotiating with a business partner based on the provisions of the master agreement" is a means having a function for generating a proposal for negotiating with a business partner based on the specified provisions of the agreement.

[0666] The "means for automatically generating a proposal for negotiation based on the identified clause and the content of the problem" refers to a means that has the function of receiving the identified clause of a contract and the content of the problem as input data, and automatically generating a proposal for negotiation based thereon.

[0667] "Means for grasping the user's emotional state in real time and generating a negotiation proposal based on that information" refers to means that has the function of analyzing the user's voice data in real time, determining the emotional state at that time, and generating a negotiation proposal based on that information.

[0668] This invention is a system that reads and understands the contents of a contract, determines which clauses are met in the event of a problem, and negotiates with a business partner based on the clauses of the master contract. Furthermore, it automatically generates a proposal for negotiation based on the identified clauses and the content of the problem, grasps the user's emotional state in real time, and generates a negotiation proposal based on that information.

[0669] Hardware and software used

[0670] Hardware:

[0671] Server: Processes data and runs generative AI models.

[0672] Terminal: A device through which a user provides input and receives suggestions.

[0673] software:

[0674] Generative AI model: Generates negotiation proposals based on contract clauses and the nature of the dispute.

[0675] Emotion engine: Analyzes the user's voice data in real time to determine their emotional state.

[0676] Data processing and calculation

[0677] How to read and understand the contract:

[0678] The server analyzes the text data of the contract and extracts the contents of each clause. For example, it uses natural language processing technology to analyze the text of the contract and classify it by clause.

[0679] How to analyze the problem and identify the applicable contract clauses:

[0680] The server analyzes the details of the problem entered by the user and identifies the relevant clause in the contract. For example, if the problem is "delay in delivery," it identifies the clause in the contract that relates to delivery dates.

[0681] Means of automatically generating proposals for negotiation:

[0682] The server generates a negotiation proposal using a generative AI model based on the identified clauses and the details of the problem. For example, it generates a prompt statement such as, "In accordance with clause 12 of the master agreement, we propose a penalty for delivery delays," and inputs it into the generative AI model.

[0683] A means of understanding a user's emotional state in real time and generating negotiation proposals based on that information:

[0684] While a user is using the system during negotiations, the emotion engine collects and analyzes the user's voice data in real time. For example, if the user is feeling stressed, the emotion engine outputs that information as an analysis result. Based on this information, the server generates negotiation proposals that take the user's emotional state into account.

[0685] Specific examples

[0686] The user enters "Clause 12" and "Delivery Delay" into the terminal. The server receives this and generates a prompt saying, "In accordance with Clause 12 of the master contract, we propose a penalty for the delivery delay." Based on this prompt, the generative AI model generates a specific proposal: "In accordance with Clause 12, we propose to charge a late fee of 1,000 yen per day as a penalty for the delivery delay."

[0687] If the user feels stressed during negotiation, the emotion engine analyzes the user's voice data and provides feedback to the server. Based on this information, the server generates a gentle suggestion to the user, such as, "Based on Article 12, we propose a penalty for delaying the delivery date, but we suggest that you first consider rescheduling the delivery date." The flow of the identification process in Example 3 will be described with reference to FIG. 21.

[0688] Step 1:

[0689] The user enters the terms of the basic contract and the details of the problem.

[0690] Input: The user uses a terminal to input the specified clause of the master agreement (e.g., "Clause 12") and the details of the current problem (e.g., "Delay in delivery") into the system.

[0691] Output: The entered data is sent to the server.

[0692] Specific operation: The user enters "Clause 12" and "Delivery Delay" into the input form on the terminal and presses the send button.

[0693] Step 2:

[0694] The server receives the input data and generates a prompt for the generative AI model.

[0695] Input: The server receives the data "Article 12" and "Delivery Delay" sent by the user.

[0696] Output: A prompt sentence to be passed to the generative AI model (e.g., "In accordance with clause 12 of the master agreement, we propose a penalty for late delivery.") is generated.

[0697] Specific behavior: The server parses the received data and applies it to a template to generate a prompt.

[0698] Step 3:

[0699] The server uses the generative AI model to generate a negotiation proposal.

[0700] Input: The server inputs the generated prompt sentence into the generative AI model.

[0701] Output: The generative AI model outputs a specific negotiation proposal (e.g., "In accordance with Article 12, we propose to charge a late fee of 1,000 yen per day as a penalty for late delivery.").

[0702] Specific operation: The server inputs a prompt sentence into the generative AI model and receives the output from the model.

[0703] Step 4:

[0704] The emotion engine is used to analyze voice data in real time while the user is negotiating.

[0705] Input: Voice data uttered by the user during negotiation.

[0706] Output: Analysis result by the emotion engine (e.g., the user is stressed).

[0707] Specific operation: The emotion engine collects and analyzes the user's voice data in real time.

[0708] Step 5:

[0709] The server generates suggestions that take into account the user's emotional state based on the analysis results of the emotion engine.

[0710] Input: The emotion engine's analysis results (e.g., the user is feeling stressed) and a negotiation suggestion from the generative AI model.

[0711] Output: A negotiation proposal that takes into account the user's emotional state (e.g., "In accordance with clause 12, we propose a penalty for late delivery, but we suggest that you first consider rescheduling the delivery date.").

[0712] Specific operation: The server receives the analysis results of the emotion engine, modifies the suggestions from the generative AI model, and generates suggestions that are appropriate for the user's emotional state.

[0713] Step 6:

[0714] The server provides the generated proposals to the user.

[0715] Input: Negotiation proposals that take into account the user's emotional state.

[0716] Output: Negotiation proposal displayed on the user's terminal.

[0717] Specific operation: The server sends the generated proposal to the user's device, and the user confirms the proposal on the device.

[0718] (Application example 3)

[0719] 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."

[0720] Conventional contract-based negotiation systems have difficulty understanding contract clauses and generating appropriate proposals to resolve disputes. Furthermore, they are unable to make proposals that take into account the user's emotional state, reducing the effectiveness of negotiations. Furthermore, in brick-and-mortar stores, staff lack the means to receive appropriate proposals in real time when dealing with customers. To solve these problems, a system is needed that can automatically generate negotiation proposals based on contract clauses and that take into account the user's emotional state.

[0721] 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.

[0722] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses the problem meets, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the user's voice data in real time and determining their emotional state, means for generating a negotiation proposal taking into account the emotional state, means for generating the proposal using a generative AI model, and means for displaying the proposal on the smart glasses. This makes it possible to generate appropriate negotiation proposals based on the clauses of the contract in real time and make optimal proposals taking into account the user's emotional state.

[0723] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[0724] "Trouble" refers to problems or disputes that arise in contracts or transactions.

[0725] "Clause" means any individual condition or provision set out in the Agreement.

[0726] "Negotiation" is the act of discussing conditions and solutions with a business partner and aiming for an agreement.

[0727] "Voice data" refers to data that is a digital recording of a user's speech.

[0728] "Emotional state" refers to the user's psychological state or feelings.

[0729] A "generative AI model" is a model that uses artificial intelligence to automatically generate text and suggestions.

[0730] "Smart glasses" are glasses-type devices equipped with a display and sensors and capable of displaying information.

[0731] A "proposal" is a proposed solution or action for a particular problem or situation.

[0732] The system for implementing this invention is capable of reading and understanding the contents of a contract, generating appropriate suggestions for dealing with problems, and analyzing the user's emotional state in real time to provide optimal suggestions based on that analysis.

[0733] Hardware and software used

[0734] Hardware: Smart glasses, microphone

[0735] Software: Python, speech_recognition library, transformers library, emotion_recognition library

[0736] System configuration

[0737] 1. How to read and understand the contents of the contract:

[0738] The server analyzes the text data of the contract and extracts the contents of each clause, which allows the server to understand the content of the contract and identify clauses related to the problem.

[0739] 2. How to determine which clause applies to your dispute:

[0740] The server analyzes the details of the problem and identifies the relevant clauses in the contract, thereby enabling the server to find the appropriate clauses for the problem.

[0741] 3. How to negotiate with business partners based on the terms of the Master Agreement:

[0742] The server generates a proposal for negotiation based on the identified clauses, using a generative AI model to automatically generate a specific proposal.

[0743] 4. A means of analyzing a user's voice data in real time to determine their emotional state:

[0744] The server analyzes the audio data acquired through the microphone to determine the user's emotional state. It uses the emotion_recognition library to analyze the emotional state in real time.

[0745] 5. Means of generating proposals for negotiations taking into account emotional states:

[0746] The server generates optimal negotiation proposals taking into account the user's emotional state, thereby enabling proposals to be made according to the user's psychological state.

[0747] 6. How to generate suggestions using generative AI models:

[0748] The server uses a generative AI model (e.g., GPT-3) to generate a negotiation proposal based on the terms of the contract, the nature of the problem, and the user's emotional state.

[0749] 7. Means for displaying suggestions on smart glasses:

[0750] The server then displays the generated suggestions on the smart glasses, allowing store staff to view the suggestions in real time and respond to the customer.

[0751] Specific examples

[0752] Scenario: While handling a customer complaint, a brick-and-mortar store associate receives a real-time offer based on the terms of a contract.

[0753] Example prompt: "Generate a proposal for handling returns based on clause X of the master agreement. The user's emotional state is anger."

[0754] In this way, it is possible to generate appropriate negotiation proposals based on the terms of the contract in real time and make optimal proposals that take into account the user's emotional state.

[0755] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0756] Step 1:

[0757] The server acquires and analyzes the text data of the contract. Specifically, it reads the contents of the contract and extracts the contents of each clause. The input is the text data of the contract, and the output is a list of extracted clauses. This allows the contents of the contract to be understood.

[0758] Step 2:

[0759] The server analyzes the details of the problem and identifies the relevant clauses of the contract. The input is the details of the problem, and the output is the relevant clauses of the contract. This allows the server to find the appropriate clauses for the problem.

[0760] Step 3:

[0761] The server generates a proposal for negotiation based on the identified clauses. A generative AI model is used to automatically generate a specific proposal. The input is the identified clauses and the details of the problem, and the output is the generated proposal. This allows for the generation of an appropriate negotiation proposal based on the clauses of the contract.

[0762] Step 4:

[0763] The server analyzes the audio data acquired through the microphone to determine the user's emotional state. It uses the emotion_recognition library to analyze the emotional state in real time. The input is audio data, and the output is the user's emotional state. This allows us to understand the user's psychological state.

[0764] Step 5:

[0765] The server generates optimal negotiation proposals taking into account the user's emotional state. A generative AI model is used to generate proposals that correspond to the emotional state. The input is the user's emotional state, the identified clauses, and the details of the problem, and the output is a proposal that takes the emotional state into account. This allows for optimal proposals to be made that correspond to the user's emotional state.

[0766] Step 6:

[0767] The server displays the generated proposal on the smart glasses. The input is the generated proposal, and the output is the proposal displayed on the smart glasses display. This allows store staff to check the proposal in real time and respond to the customer.

[0768] 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.

[0769] 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.

[0770] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0771] 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.

[0772] [Second embodiment]

[0773] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0774] 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.

[0775] 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).

[0776] 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.

[0777] 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.

[0778] 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).

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0785] "Example 1"

[0786] The system of the present invention uses natural language processing technology as a means of reading and understanding the contents of a contract. Specifically, it analyzes the text data of the contract and extracts the contents of each clause. This extraction is performed using a function that takes the text data of the contract as input and outputs the contents of each clause.

[0787] "Example 2"

[0788] In addition, the system has the function of analyzing the content of the problem and identifying the relevant contract clauses as a means of determining which clauses the problem matches. Specifically, the content of the problem is input as text data, and the relevant contract clauses are identified based on that content. This identification is done by calculating the similarity between the content of the problem and each clause in the contract, and identifying clauses whose similarity is above a certain threshold as the relevant clauses.

[0789] "Example 3"

[0790] Furthermore, it has a function to generate a proposal for negotiation using the specified clauses as a means of negotiating with a business partner based on the clauses of the master agreement. Specifically, it automatically generates a proposal for negotiation based on the specified clauses and the details of the problem. For example, this proposal is generated in a format such as "Based on clause X, we propose to take measure Y."

[0791] The processing flow of each embodiment will be described below.

[0792] "Example 1"

[0793] Step 1: Enter the contract text data into the system.

[0794] Step 2: The system uses natural language processing technology to analyze the text data and extract the content of each clause.

[0795] Step 3: The content of each extracted clause is stored internally in the system for further processing.

[0796] "Example 2"

[0797] Step 1: Enter the details of the problem into the system as text data.

[0798] Step 2: The system analyzes the content of the problem and identifies the relevant clauses in the contract. This identification is done by calculating the similarity between the content of the problem and each clause in the contract, and identifying clauses with a similarity above a certain threshold as relevant clauses.

[0799] Step 3: The identified clauses are stored internally in the system for further processing.

[0800] "Example 3"

[0801] Step 1: The system automatically generates a proposal for negotiation based on the identified clauses and issues.

[0802] Step 2: The proposal is generated in the form of, for example, "Based on clause X, we propose to take action Y."

[0803] Step 3: The generated proposal is presented to the user and used in negotiations with trading partners.

[0804] Example 1

[0805] 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."

[0806] With conventional contract management systems, it was difficult to accurately read and understand the contents of a contract, and a particular challenge was quickly and accurately extracting the contents of each clause. Furthermore, performing detailed analysis based on the contents of a contract required specialized knowledge, placing a significant burden on users. Furthermore, there was a lack of means to obtain detailed analysis results using a generative AI model based on the contents of a contract.

[0807] 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.

[0808] In this invention, the server includes a means for reading and understanding the contents of the contract, a means for analyzing the text data of the contract and extracting the contents of each clause, a means for providing the extracted contents of each clause to the user, and a means for inputting a prompt sentence into the generative AI model and obtaining a detailed analysis result. This makes it possible to quickly and accurately read and understand the contents of the contract and extract the contents of each clause. Furthermore, the user can obtain a detailed analysis result by utilizing the generative AI model without needing specialized knowledge.

[0809] A "contract" is a written legal agreement that clarifies the rights and obligations of the parties.

[0810] "Text data" is data that represents character information in a digital format and is used to analyze the contents of a contract.

[0811] "Analysis" is the process of breaking down text data and understanding its structure and meaning.

[0812] A "clause" refers to an individual condition or provision set forth in a contract, and is an element that constitutes the specific content of the contract.

[0813] "Extraction" is the process of extracting specific information from text data, and is performed to identify the content of each clause in the contract.

[0814] "User" refers to a person or organization that uses the system to read, understand, and obtain analytical results from the contents of a contract.

[0815] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and generate detailed analysis results based on prompt sentences.

[0816] A "prompt" is an instruction entered into a generative AI model and is used to clarify the target and purpose of the analysis.

[0817] The present invention relates to a system for reading and understanding the contents of a contract and extracting the contents of each clause. A specific embodiment of this system will be described below.

[0818] System configuration

[0819] The system consists of a server, a terminal, and a user. The server receives the text data of the contract, analyzes it, and extracts the contents of each clause. The terminal provides an interface for the user to input the contract data and check the analysis results.

[0820] Hardware and software used

[0821] A server is a computer with a powerful processor and ample memory. The software that runs on the server includes:

[0822] Natural Language Processing Libraries: "spaCy" and "NLTK"

[0823] PDF text extraction library: "PyMuPDF"

[0824] Generative AI models: Advanced natural language generation models such as GPT-3

[0825] Data processing and calculation

[0826] The server processes the data as follows:

[0827] 1. Receiving text data: The user sends the contract text data to the server via their device. The text data is extracted from a PDF or Word file.

[0828] 2. Preprocessing of text data: The server cleans the received text data and performs tokenization, part-of-speech tagging, and dependency analysis.

[0829] 3. Clause Extraction: The server detects specific keywords and phrases and identifies clauses based on them.

[0830] 4. Output of extracted results: The server provides the content of each extracted clause to the user. If necessary, it inputs a prompt to the generative AI model to obtain detailed analysis results.

[0831] Specific examples

[0832] For example, suppose we have the following text data for a contract:

[0833] Article 1 (Purpose of the contract)

[0834] This Agreement sets out the basic conditions for Party A and Party B to conduct business together.

[0835] Article 2 (Payment Terms)

[0836] Party A shall pay Party B the remuneration at the end of each month.

[0837] With this text data as input, the server processes it as follows:

[0838] 1. Receiving text data: The user inputs the text of the contract. The server receives the input text data.

[0839] 2. Preprocessing of text data: The server removes unnecessary whitespace and special characters from the text data, and performs tokenization, part-of-speech tagging, and dependency analysis.

[0840] 3. Clause extraction: The server detects keywords such as "purpose of the contract" and "payment terms," ​​extracts the surrounding text of the detected keywords, and identifies them as clauses.

[0841] 4. Output of extraction results: The server formats the extraction results in HTML and displays them to the user. If the user requests further analysis, the server sends a prompt to the generative AI model to obtain additional analysis results.

[0842] Prompt Sentence Examples

[0843] Below are some example prompts to input to a generative AI model:

[0844] Analyze the text data of the contract and extract the contents of each clause.

[0845] Text data:

[0846] Article 1 (Purpose of the contract)

[0847] This Agreement sets out the basic conditions for Party A and Party B to conduct business together.

[0848] Article 2 (Payment Terms)

[0849] Party A shall pay Party B the remuneration at the end of each month.

[0850] In this way, the server extracts the content of each clause of the contract and provides it to the user, allowing the user to quickly and accurately understand the content of the contract without requiring specialized knowledge.

[0851] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0852] Step 1:

[0853] Receiving text data

[0854] The server receives the text data of the contract from the user. The user uploads the contract file via their device or inputs the text directly. The server temporarily stores the received data.

[0855] Input: Contract text data (PDF, Word file, or manually entered text)

[0856] Output: Temporarily saved text data

[0857] Specific behavior:

[0858] The user uploads the PDF file of the contract using the device.

[0859] The server uses the "PyMuPDF" library to convert PDF files into text data.

[0860] Step 2:

[0861] Preprocessing text data

[0862] The server pre-processes the received text data, including text cleaning, tokenization, part-of-speech tagging, and dependency analysis, using the "spaCy" library.

[0863] Input: Temporarily saved text data

[0864] Output: Preprocessed text data (tokenized, part-of-speech tagged, dependency parsed)

[0865] Specific behavior:

[0866] The server removes unnecessary spaces and special characters from the text data.

[0867] The server divides (tokenizes) the text data into words.

[0868] The server tags each token with a part of speech tag.

[0869] The server performs dependency analysis to analyze the structure of the sentence.

[0870] Step 3:

[0871] Extracting clauses

[0872] The server extracts the content of each clause from the preprocessed text data and performs functions to detect specific keywords and phrases and identify clauses based on them.

[0873] Input: Preprocessed text data

[0874] Output: Content of each extracted clause

[0875] Specific behavior:

[0876] The server detects keywords such as "payment terms" and "purpose of the contract."

[0877] The server extracts the text surrounding the detected keywords and identifies them as clauses.

[0878] Step 4:

[0879] Output of extraction results

[0880] The server provides the user with the content of each extracted clause. The extracted results are displayed in a format that is easy for the user to review. If necessary, a prompt can be input to the generative AI model to obtain more detailed analysis results.

[0881] Input: The content of each extracted clause

[0882] Output: Extraction results provided to the user, and detailed analysis results by the generative AI model (if required)

[0883] Specific behavior:

[0884] The server formats the extracted results in HTML format and displays them to the user.

[0885] If the user requests a more detailed analysis, the server sends a prompt to the generative AI model to obtain additional analysis results.

[0886] In this way, the server extracts the content of each clause of the contract and provides it to the user, allowing the user to quickly and accurately understand the content of the contract without requiring specialized knowledge.

[0887] (Application example 1)

[0888] 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."

[0889] Conventional contract management systems have the problem that it is difficult to read and understand the contents of contracts, making it difficult to quickly identify clauses that pose particular risks. Furthermore, analyzing contract contents and conducting risk assessments requires a great deal of time and effort, creating a need for efficient contract management. Furthermore, when a problem occurs based on a contract clause, it is difficult to quickly determine which clause applies.

[0890] 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.

[0891] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses meet the conditions of the problem, means for negotiating with the business partner based on the clauses of the basic contract, means for analyzing the text data of the contract and extracting the contents of each clause, and means for evaluating the risks of the extracted clauses and notifying high-risk clauses. This makes it possible to efficiently analyze the contents of the contract and perform risk evaluation, and quickly identify and notify high-risk clauses. It also makes it possible to quickly determine the applicable clauses when a problem occurs and take appropriate action.

[0892] A "contract" is a legal document that documents a transaction or agreement.

[0893] "Reading" is the process of analyzing text data and understanding its content.

[0894] "Means of understanding" are methods and techniques for analyzing text data and grasping its meaning.

[0895] "Trouble" refers to problems or disputes that arise in connection with a contract.

[0896] A "clause" is a specific condition or provision set out in a contract.

[0897] "Means for determining whether conditions are met" refers to methods or techniques for determining which clause in the contract a particular situation or problem falls under.

[0898] A "basic agreement" is a contract that sets out the basic terms and regulations of a transaction.

[0899] A "business partner" is a party with whom you enter into a contract or a business partner.

[0900] "Means of negotiation" refers to the methods and techniques used to discuss contract terms and resolve disputes with business partners.

[0901] "Text data" is data stored as character information.

[0902] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[0903] "Extraction means" refers to methods or techniques for extracting specific information from data.

[0904] "Risk" refers to the potential problems or dangers associated with a contract.

[0905] "Evaluation tools" are methods or techniques for determining risk or value based on specific criteria.

[0906] "Means of notification" refers to methods or techniques for notifying users of specific information.

[0907] A system for carrying out the present invention includes a program for reading and understanding the contents of a contract and performing risk assessment. A specific embodiment of this system will be described below.

[0908] System Configuration

[0909] The system includes a server, a user terminal, and a natural language processing model. The server analyzes the text data of the contract, extracts the contents of each clause, and performs risk assessment. The user terminal provides an interface for taking a photo of the contract and uploading the PDF file.

[0910] Hardware and software used

[0911] Hardware: Smartphones, servers

[0912] Software: Python, spaCy, Transformers library

[0913] Data processing and calculation

[0914] 1. Entering the contract text data: The user takes a photo of the contract using their smartphone or uploads a PDF file.

[0915] 2. Analysis of text data: The server receives the uploaded text data and splits it into sentences using spaCy.

[0916] 3. Extracting clause content: The divided sentences are analyzed using a natural language processing model (Transformers library) to extract the content of each clause.

[0917] 4. Risk Assessment: For each extracted clause, a risk assessment is performed using a generative AI model. High-risk clauses are notified to the user.

[0918] Specific examples

[0919] For example, if a user enters the following text data for a contract:

[0920] This is a sample contract. The party agrees to the following terms and conditions...

[0921] The server analyzes this text data, extracts each clause, and then uses a generative AI model to generate prompts like the following to assess risk:

[0922] Evaluate the risk of the following contract clause: "The party agrees to the following terms and conditions..."

[0923] If the risk assessment identifies any high-risk clauses, the user will be notified, allowing them to quickly understand the risks in the contract and take appropriate action.

[0924] This system makes it possible to efficiently analyze the contents of contracts, perform risk assessments, and quickly identify and notify high-risk clauses.It also makes it possible to quickly determine the relevant clauses when a problem occurs and take appropriate action.

[0925] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0926] Step 1:

[0927] Users can either take a photo of the contract using their smartphone or upload a PDF file.

[0928] Input: Image or PDF file of the contract

[0929] Output: Text data of the contract

[0930] What happens: The user takes a photo of the contract using the smartphone's camera or uploads a PDF file using the file chooser.

[0931] Step 2:

[0932] The terminal sends an image or PDF file of the uploaded contract to the server.

[0933] Input: Image or PDF file of the contract

[0934] Output: Text data of the contract sent to the server

[0935] Specific operation: The device uses image recognition technology (OCR) to extract text data from images and PDF files and sends the text data to the server.

[0936] Step 3:

[0937] The server analyzes the received text data and divides it into sentences.

[0938] Input: Text data of the contract

[0939] Output: Text data divided into sentences

[0940] Specific operation: The server uses the spaCy library to divide the text data into sentences.

[0941] Step 4:

[0942] The server analyzes the divided sentences using a natural language processing model and extracts the content of each clause.

[0943] Input: Text data divided into sentences

[0944] Output: Content of each extracted clause

[0945] What happens: The server uses the Transformers library to parse each sentence and extract the clause content.

[0946] Step 5:

[0947] The server performs risk assessment for each extracted clause using a generative AI model.

[0948] Input: The content of each extracted clause

[0949] Output: Risk assessment results for each clause

[0950] Specific operation: The server uses a generative AI model to assess the risk of each clause and identify high-risk clauses.

[0951] Step 6:

[0952] The server notifies the user of high-risk provisions.

[0953] Input: Risk assessment results

[0954] Output: User notification

[0955] Specific operation: The server identifies high-risk clauses, generates a message to notify the user, and sends it to the terminal.

[0956] Step 7:

[0957] Users receive notifications and review high-risk clauses.

[0958] Input: Notification from the server

[0959] Output: Check for high risk clauses

[0960] Specific behavior: The user uses the notification feature on their smartphone to check for high-risk clauses and take necessary action.

[0961] Example 2

[0962] 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."

[0963] With conventional contract management systems, it was difficult to quickly and accurately identify which contract clauses applied when a problem occurred. Furthermore, there was a lack of means to understand the contents of the contract and identify the appropriate clauses based on the nature of the problem, which often delayed negotiations with business partners. For these reasons, there is a need for more efficient contract management and faster problem response.

[0964] 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.

[0965] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses match the conditions of the problem, means for negotiating with the business partner based on the clauses of the basic contract, means for inputting the content of the problem as text data and identifying the relevant clauses of the contract based on that content, and means for calculating the similarity between the content of the problem and each clause of the contract and identifying clauses whose similarity is above a certain threshold as the relevant clauses. This makes it possible to quickly and accurately identify the relevant contract clauses when a problem occurs and to efficiently negotiate with the business partner.

[0966] A "contract" is a document that describes the legal agreement between business partners.

[0967] "Trouble" refers to problems or disputes that arise during the course of contract performance.

[0968] "Text data" is data that represents character information in digital form.

[0969] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[0970] "Similarity" is an index that indicates the degree of commonality or agreement between two pieces of data.

[0971] A "threshold" is a reference value for satisfying a specific condition.

[0972] "Identification" refers to clearly identifying and selecting an object.

[0973] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate and analyze data.

[0974] A "prompt sentence" is an instruction or question that a user inputs into a system.

[0975] This invention provides a method for efficiently negotiating with business partners by quickly and accurately identifying the relevant contract clauses in a contract management system when a problem occurs. A specific embodiment of this system is described below.

[0976] Hardware and software used

[0977] Hardware: Servers, terminals

[0978] Software: Generative AI models, text analysis tools, similarity calculation algorithms

[0979] System Operation

[0980] 1. The user enters the details of the problem

[0981] The user accesses the system interface and enters the details of the problem in text format.

[0982] Example: A user types "Problems with handling of late deliveries."

[0983] 2. The device sends the entered text data to the server.

[0984] The terminal receives the user's input and sends the data to the server.

[0985] Example: A terminal sends text data to a server saying, "Problems with handling when delivery is delayed."

[0986] 3. The server analyzes the text data

[0987] The server analyzes the received text data using a text analysis tool.

[0988] Example: The server uses a text analysis tool to extract keywords such as "delivery," "delay," and "response."

[0989] 4. The server calculates the similarity with each clause of the contract.

[0990] The server uses the generated AI model to calculate the similarity between the analysis results and each clause in the contract.

[0991] Example: The server uses a generative AI model to calculate the similarity between the "provision regarding delivery delays" and the "provision regarding response methods."

[0992] 5. Identify the terms that the server applies to

[0993] The server uses a similarity calculation algorithm to identify clauses whose similarity is equal to or greater than a certain threshold as relevant clauses.

[0994] Example: The server uses a similarity calculation algorithm to identify "provisions regarding delivery delays" and "provisions regarding response methods" with a similarity of 0.80 or higher.

[0995] 6. The server returns the specified terms to the user

[0996] The server returns the identified terms to the user and notifies the user.

[0997] Example: The server notifies the user that "the relevant clauses are the clause regarding delivery delays and the clause regarding response methods."

[0998] Prompt Sentence Examples

[0999] Please enter the details of the problem: Problems with handling of delayed delivery

[1000] This system has the ability to analyze the details of the problem entered by the user and identify the relevant contract clauses. Specifically, it uses text analysis tools and generative AI models to calculate the similarity between the problem details and each clause in the contract, and identifies clauses with a similarity above a certain threshold. This makes it possible to quickly and accurately identify the relevant contract clauses when a problem occurs, enabling efficient negotiations with business partners.

[1001] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1002] Step 1:

[1003] The user inputs the details of the problem.

[1004] Input: The user accesses the system interface and enters the details of the problem in text format.

[1005] Specific behavior: The user enters "Problems regarding handling of delayed delivery."

[1006] Output: The entered text data is sent to the terminal.

[1007] Step 2:

[1008] The terminal transmits the input text data to the server.

[1009] Input: Text data entered by the user.

[1010] Specific operation: The device sends text data to the server stating "Problems regarding response in the event of delayed delivery."

[1011] Output: Text data is sent to the server.

[1012] Step 3:

[1013] The server parses the text data.

[1014] Input: Text data sent from the terminal.

[1015] Specific operation: The server uses a text analysis tool to extract keywords such as "delivery," "delay," and "response."

[1016] Output: The extracted keyword list.

[1017] Step 4:

[1018] The server calculates the similarity with each clause of the contract.

[1019] Input: The extracted keyword list.

[1020] Specific operation: The server uses the generative AI model to calculate the similarity between the analysis results and each clause in the contract.

[1021] Output: Similarity score for each clause.

[1022] Step 5:

[1023] The server identifies the applicable terms.

[1024] Input: Similarity score for each clause.

[1025] Specific operation: The server uses a similarity calculation algorithm to identify clauses with a similarity score of 0.80 or higher.

[1026] Output: A list of applicable contract clauses.

[1027] Step 6:

[1028] The server returns the specified terms to the user.

[1029] Input: A list of applicable contract clauses.

[1030] Specific operation: The server notifies the user that "the relevant clauses are the clause regarding delivery delays and the clause regarding response methods."

[1031] Output: The applicable contract terms communicated to the user.

[1032] (Application example 2)

[1033] 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."

[1034] When a problem occurs with a contract, it is difficult to quickly and accurately identify which clause applies. In particular, when the contract is complex or the problem covers a wide range of issues, it takes a great deal of time and effort to find the appropriate clause. To solve this problem, a system is needed that can analyze the content of the problem and automatically identify the relevant contract clause.

[1035] 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.

[1036] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clause's conditions are met in the problem, means for negotiating with the business partner based on the clauses of the basic contract, means for inputting the content of the problem as text data and identifying the relevant clauses of the contract based on that content, and means for calculating the similarity between the content of the problem and each clause of the contract and identifying clauses whose similarity is above a certain threshold as the relevant clauses. This makes it possible to quickly and accurately identify the relevant clauses and take appropriate action when a problem regarding the contract occurs.

[1037] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[1038] "Trouble" refers to problems or disputes that arise in connection with a contract.

[1039] "Text data" is data that represents character information in digital form.

[1040] "Similarity" is an index that indicates the degree of match between the contents of two text data.

[1041] A "threshold" refers to a reference value for satisfying a specific condition.

[1042] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[1043] "Identification" refers to clearly identifying and selecting an object.

[1044] A "system" is a set of devices and programs in which multiple elements function in conjunction with one another.

[1045] The system for carrying out the present invention has the function of reading and understanding the contents of a contract and identifying the relevant clause of the contract based on the content of the problem. A specific embodiment of this system will be described below.

[1046] System configuration

[1047] The system consists of three main components: a server, a terminal, and a user. The server analyzes the contents of the contract and performs the main process of identifying the relevant clause based on the nature of the problem. The terminal provides an interface for users to input the details of the problem. When a problem occurs with the contract, users can use this system to quickly identify the appropriate clause.

[1048] Hardware and software used

[1049] The server is a computer equipped with a high-performance processor and large memory capacity. It uses the natural language processing library, Spacy, and the machine learning library, sklearn, to analyze the text data of the contract and calculate the degree of similarity between the content of the dispute and each clause in the contract.

[1050] Data processing and calculation

[1051] The server first analyzes the text data of the contract and extracts the contents of each clause. Next, it receives the trouble description entered by the user as text data and preprocesses it. Preprocessing includes morphological analysis and stop word removal. The preprocessed text data is vectorized using TfidfVectorizer. Finally, it calculates the similarity between the trouble description and each clause of the contract using cosine_similarity and identifies the clause with the highest similarity.

[1052] Specific examples

[1053] For example, if a user enters a problem such as "What to do if a payment is late," the system analyzes this content and identifies the most relevant clause in the contract. Below is an example of a prompt sentence.

[1054] Example prompt sentence:

[1055] Problem: "What to do if payment is late"

[1056] Terms of the Agreement:

[1057] 1. Purpose of the Agreement

[1058] 2. Term of the Agreement

[1059] 3. Payment Terms

[1060] 4. Conditions for termination

[1061] When the user enters this prompt, the system identifies the clause in the contract that is most relevant to the problem and presents it to the user, allowing the user to take appropriate action quickly and accurately.

[1062] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1063] Step 1:

[1064] The user uses the terminal to input the details of the problem.

[1065] Input: Details of the problem (e.g., "What to do if payment is late")

[1066] Output: The details of the problem are sent to the server as text data.

[1067] Step 2:

[1068] The server reads the text data of the contract and extracts the contents of each clause.

[1069] Input: Text data of the contract

[1070] Output: A list of the contents of each clause (e.g., "Purpose of the contract," "Term of the contract," "Payment terms," ​​"Conditions for termination of the contract")

[1071] Step 3:

[1072] The server preprocesses the content of the problem. Specifically, it performs morphological analysis and removes stop words.

[1073] Input: Text data of the problem

[1074] Output: Preprocessed trouble details (e.g., "Payment delay response")

[1075] Step 4:

[1076] The server preprocesses the content of each clause in the contract by performing morphological analysis and removing stop words.

[1077] Input: Text data for each clause of the contract

[1078] Output: Content of each preprocessed clause (e.g., "Contract Purpose", "Contract Term", "Payment Terms", "Terms of Termination")

[1079] Step 5:

[1080] The server vectorizes the preprocessed problem details and the contents of each clause of the contract using TfidfVectorizer.

[1081] Input: Preprocessed content of the problem and content of each clause

[1082] Output: Vectorized content of the problem and content of each clause

[1083] Step 6:

[1084] The server uses cosine_similarity to calculate the similarity between the content of the problem and each clause in the contract.

[1085] Input: Vectorized content of the problem and content of each clause

[1086] Output: A list of similarity scores (e.g. "0.85", "0.60", "0.90", "0.70")

[1087] Step 7:

[1088] The server identifies the clause with the highest similarity score.

[1089] Input: A list of similarity scores

[1090] Output: The clause with the highest similarity (e.g., "Payment Terms")

[1091] Step 8:

[1092] The server presents the identified terms to the user.

[1093] Input: Most similar clause

[1094] Output: Identified terms (e.g., "Payment Terms") displayed on the user's device

[1095] Example 3

[1096] 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."

[1097] Negotiations with business partners based on traditional contracts required a great deal of time and effort to understand the contents of the contract, identify clauses that match the content of the problem, and generate appropriate proposals. It was also difficult to accurately link the contract clauses with the content of the problem, which could reduce the efficiency of negotiations. Furthermore, proposals were generated manually, which could result in a lack of consistency and objectivity.

[1098] 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.

[1099] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses the problem meets, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the content of the problem using natural language processing technology, means for generating a prompt sentence based on the analysis results and the acquired clauses, means for sending the prompt sentence to a generation AI model to generate a proposal for negotiation, and means for sending the generated proposal to the user's terminal. This makes it possible to quickly and accurately link the content of the contract with the content of the problem, and automatically generate an efficient and consistent proposal.

[1100] "Means for reading and understanding the contents of a contract" refers to a means for analyzing the text data of a contract and extracting the contents of each clause.

[1101] "Means of determining which clause's conditions are met in the particular problem" refers to a means of analyzing the content of the problem and identifying the relevant clause of the contract.

[1102] The "means for negotiating with a business partner based on the provisions of the master agreement" is a means for generating a proposal for negotiating with a business partner based on the specified provisions.

[1103] "Means for analyzing the content of the trouble using natural language processing technology" refers to means for analyzing the content of the trouble entered by the user using natural language processing technology and extracting important keywords and phrases.

[1104] The "means for generating a prompt sentence based on the analysis results and the acquired clauses" refers to a means for generating a prompt sentence to be sent to the generative AI model based on the content of the analyzed problem and the acquired contract clauses.

[1105] The "means for sending a prompt sentence to a generative AI model and generating a proposal for negotiation" is a means for sending a prompt sentence to a generative AI model and generating a proposal for negotiation in response thereto.

[1106] "Means for sending generated proposals to a user's terminal" refers to means for sending proposals received from a generative AI model to a user's terminal so that the user can confirm them.

[1107] The present invention is a system that reads and understands the contents of a contract and generates an appropriate proposal based on the details of the problem. A specific embodiment of this system will be described below.

[1108] Hardware and software used

[1109] The server uses a relational database management system (RDBMS) to read and understand the contents of the contract. Specifically, a database management system such as MySQL is used. To analyze the details of the problem, Python's natural language processing libraries NLTK and spaCy are used. OpenAI's GPT-4, for example, is used as the generative AI model.

[1110] Data processing and calculation

[1111] The server first retrieves the terms of the master contract from a database. Next, it receives the details of the problem entered by the user and analyzes them using natural language processing technology. Based on the analyzed details of the problem and the retrieved terms, the server generates a prompt to send to the generative AI model. The generative AI model then generates a proposal for negotiation based on this prompt. The generated proposal is sent to the user's device, where the user can review it and make any necessary corrections or additions.

[1112] Specific examples

[1113] For example, if a user types "There is a problem with the delivery being delayed," the server will do the following:

[1114] 1. The server retrieves the "Delivery Terms" from the MySQL database.

[1115] 2. The server uses Python's NLTK library to extract the keyword "Delivery Delay".

[1116] 3. The server generates a prompt message saying, "In accordance with clause X of the master agreement, a problem has occurred regarding a delay in delivery. Please suggest what measures should be taken to address this problem."

[1117] 4. The server sends a prompt to the generative AI model (GPT-4).

[1118] 5. The generative AI model generates the following recommendation: "In accordance with clause X, we propose an additional extension of delivery time as a measure to address the late delivery date. Please also consider requesting a waiver of the penalty associated with the delay."

[1119] 6. The server sends the generated proposal to the user's device.

[1120] 7. The user reviews the proposal and makes corrections, such as "I propose an additional delivery extension, but do not request a waiver of the penalty for the delay."

[1121] In this way, it is possible to quickly and accurately link the contents of the contract with the contents of the problem, and automatically generate an efficient and consistent proposal. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1122] Step 1:

[1123] The user inputs the details of the problem.

[1124] The user inputs the details of the problem through the system interface. For example, they might input "A problem has occurred regarding a delivery delay." The input data is then sent to the server.

[1125] Step 2:

[1126] The server retrieves the terms of the master contract from the database.

[1127] The server uses a relational database management system (RDBMS) such as MySQL to retrieve the terms of the master contract from the database. For example, it retrieves the "terms regarding delivery time." The input is the details of the problem entered by the user, and the output is the retrieved terms of the contract.

[1128] Step 3:

[1129] The server analyzes the details of the problem using natural language processing (NLP) technology.

[1130] The server uses Python's NLTK and spaCy libraries to analyze the content of the problem entered by the user. For example, it extracts the keyword "delivery delay." The input is the content of the problem entered by the user, and the output is the analyzed keywords and phrases.

[1131] Step 4:

[1132] The server generates a prompt based on the analysis results and the acquired clauses.

[1133] The server generates a prompt to send to the generative AI model based on the analysis results and the acquired clauses. For example, it generates a prompt such as, "In accordance with clause X of the master contract, a problem has occurred regarding a delivery delay. Please suggest what measures should be taken to address this problem." The input is the analysis results and the acquired clauses, and the output is the generated prompt.

[1134] Step 5:

[1135] The server sends a prompt to the generative AI model.

[1136] The server sends a prompt to a generative AI model (e.g., OpenAI's GPT-4). The generative AI model generates a proposal based on this prompt. The input is the generated prompt, and the output is the proposal generated by the generative AI model.

[1137] Step 6:

[1138] A generative AI model generates proposals for negotiation.

[1139] The generative AI model generates a proposal for negotiation based on a prompt. For example, it generates a proposal such as, "In accordance with clause X, we propose an additional extension of delivery time as a measure to address the delivery delay. Please also consider requesting a waiver of the penalty associated with the delay." The input is the prompt, and the output is the generated proposal.

[1140] Step 7:

[1141] The server sends the generated proposal to the user's terminal.

[1142] The server sends the proposal received from the generative AI model to the user's device, where the user can review it. The input is the generated proposal, and the output is the proposal sent to the user's device.

[1143] Step 8:

[1144] The user reviews the proposal and makes corrections or additions as necessary.

[1145] The user can check the proposal on the terminal and make corrections or additions as necessary. For example, a correction can be made such as "I propose an additional delivery extension, but I do not request an exemption from the penalty for the delay." The input is the proposal sent from the server, and the output is the proposal corrected or added by the user.

[1146] (Application example 3)

[1147] 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."

[1148] Conventional contract negotiation systems lack the functionality to automatically generate proposals for negotiations based on the contract clauses and the details of the problem, which reduces the efficiency of negotiations. Also, even if users input the contract clauses and the details of the problem, it is difficult to properly analyze them and generate specific proposals.

[1149] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clause's conditions are met in the relevant trouble, means for negotiating with the trading partner based on the clauses of the master contract, means for automatically generating a proposal for negotiation using a generative AI model, means for sending the contract clauses and the content of the trouble input by the user as prompt text to the generative AI model, and means for displaying the generated proposal. This makes it possible to automatically generate a specific and appropriate proposal based on the contract clauses and the content of the trouble, thereby improving the efficiency of negotiations.

[1150] A "contract" is a legally binding document exchanged between business partners, which contains the terms of the transaction, obligations, rights, etc.

[1151] "Clause" refers to the individual provisions and conditions set out in a contract, and is an element that constitutes the specific content of the contract.

[1152] "Trouble" refers to problems or disputes that arise during the course of contract performance, including acts that violate or fail to perform the terms of the contract.

[1153] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to generate text data, specifically a natural language processing model such as GPT-3.

[1154] A "prompt" is text data input to a generative AI model that contains instructions or questions that enable the model to generate appropriate output.

[1155] A "proposal" refers to a solution or response presented in contract negotiations, including specific actions or measures.

[1156] "Analysis" refers to the process of analyzing text data and information in detail to understand its meaning and structure.

[1157] "Display" refers to the act of visually presenting generated suggestions or information to a user, including outputting it as text or graphics on a screen.

[1158] The system for implementing this invention reads and understands the contents of a contract, identifies appropriate clauses based on the content of the problem, and automatically generates a proposal for negotiation using a generative AI model. Specific embodiments of this system are described below.

[1159] First, the server receives the text data of the contract and analyzes its contents. Natural language processing technology is used to extract the content of each clause, which is then stored in a database.

[1160] Next, the user inputs the details of the problem. The server analyzes the details of the problem and identifies the relevant clauses in the contract. This analysis is done using text mining technology.

[1161] The server then generates a proposal for negotiation using a generative AI model (e.g., OpenAI's GPT-3) based on the identified terms and the details of the problem. The generated proposal is obtained by sending the information entered by the user as a prompt to the generative AI model.

[1162] The generated proposal is sent from the server to the user's device and displayed on the screen, allowing the user to negotiate with a business partner based on a specific and appropriate proposal.

[1163] As a concrete example, the following prompt sentence is input into the generative AI model:

[1164] Example prompt sentence:

[1165] Contract clause: Article 10.2: Liability for service interruptions

[1166] Problem: Customer seeks compensation after service was interrupted for three days.

[1167] Generate a proposal.

[1168] Based on this prompt, the generative AI model generates the following suggestions:

[1169] An example of a generated suggestion:

[1170] Pursuant to clause 10.2, we offer to provide you with a 10% discount off your next bill as compensation for the interruption of our services.

[1171] In this way, it is possible to automatically generate specific and appropriate proposals based on the terms of the contract and the details of the problem, thereby improving the efficiency of negotiations.

[1172] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1173] Step 1:

[1174] The user inputs the text data of the contract using a terminal. The input text data is sent to the server. The server receives this text data and analyzes the contents of the contract using natural language processing technology. Specifically, it extracts each clause from the text data and stores it in a database. The input is the text data of the contract, and the output is the analyzed data of each clause.

[1175] Step 2:

[1176] The user uses a terminal to input the details of the problem. The input details of the problem are sent to the server. The server receives the details of the problem and analyzes them using text mining technology. Specifically, it analyzes the details of the problem and identifies the relevant clauses of the contract. The input is the details of the problem, and the output is the relevant clauses of the contract.

[1177] Step 3:

[1178] The server uses a generative AI model to generate a proposal for negotiation based on the identified contract clauses and the details of the problem. Specifically, the information entered by the user is sent to the generative AI model as a prompt, which generates a proposal. The input is the identified contract clauses and the details of the problem, and the output is the generated proposal.

[1179] Step 4:

[1180] The server sends the generated proposal to the user's device. The user's device receives the proposal and displays it on the screen. This allows the user to negotiate with a trading partner based on a specific and appropriate proposal. The input is the generated proposal, and the output is the proposal displayed on the screen.

[1181] 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.

[1182] "Example 1"

[1183] One embodiment of the present invention provides a system incorporating an emotion engine. This system includes a means for reading and understanding the contents of a contract, a means for determining which clauses are met in the current dispute, a means for negotiating with a business partner based on the clauses of the master contract, and an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's voice data to determine the user's emotional state. For example, if the user is feeling angry or frustrated, the emotion engine captures that information and feeds it back to the system. This allows the system to generate negotiation proposals taking the user's emotional state into consideration.

[1184] "Example 2"

[1185] As a specific example, consider a case where a user is dissatisfied with a part of a contract. The user communicates this dissatisfaction to the system. At this time, the user's voice data is analyzed by the emotion engine to determine the user's emotional state. The emotion engine determines that the user is feeling angry or dissatisfied and feeds this information back to the system. Based on this information, the system generates a proposal for negotiation that takes into account the user's emotional state. This proposal might be generated in the form of, for example, "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we propose that you take action Y."

[1186] "Example 3"

[1187] The emotion engine can also grasp the user's emotional state in real time. For example, if a user is using the system while negotiating with a business partner, the emotion engine analyzes the user's voice data in real time to determine the user's emotional state. This information is fed back to the system, which then generates negotiation proposals in real time that take the user's emotional state into account. This allows the user to obtain the optimal negotiation strategy for the situation at hand.

[1188] The processing flow of each embodiment will be described below.

[1189] "Example 1"

[1190] Step 1: The user enters the contract details into the system.

[1191] Step 2: The system reads and understands the contents of the contract.

[1192] Step 3: The user enters the details of the problem into the system.

[1193] Step 4: The system determines which clause conditions are met for the problem.

[1194] Step 5: The user inputs their emotional state into the system as voice data.

[1195] Step 6: The emotion engine analyzes the user's voice data and determines the user's emotional state.

[1196] Step 7: The system generates a proposal for negotiation after taking into account the user's emotional state.

[1197] "Example 2"

[1198] Step 1: The user is dissatisfied with some aspect of the contract and tells the system about it.

[1199] Step 2: The emotion engine analyzes the user's voice data and determines that the user's emotional state is dissatisfied.

[1200] Step 3: The system uses that information to generate proposals for negotiation that take into account the user's emotional state.

[1201] "Example 3"

[1202] Step 1: The user uses the system during negotiations with a business partner.

[1203] Step 2: The emotion engine analyzes the user's voice data in real time to determine their emotional state.

[1204] Step 3: The system generates proposals for negotiation that take into account the user's emotional state in real time.

[1205] Example 1

[1206] 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."

[1207] Conventional contract management systems could read and understand the contents of a contract, but they were unable to generate negotiation proposals that took the user's emotional state into account. This made it difficult to negotiate appropriately based on the user's emotions, resulting in a decrease in the efficiency of dispute resolution.

[1208] 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.

[1209] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses meet the conditions of the relevant trouble, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the user's voice data and determining the user's emotional state, means for generating a negotiation proposal based on the analyzed clauses of the contract and the user's emotional state, and means for displaying the generated negotiation proposal to the user. This makes it possible to generate an appropriate negotiation proposal that takes the user's emotional state into consideration.

[1210] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[1211] "Text data" is data that represents character information in digital form.

[1212] "Natural language processing" is a technology that uses computers to analyze and understand human language.

[1213] "Clause" means any individual condition or provision set out in a contract.

[1214] "Audio data" refers to data in which audio is recorded in digital format.

[1215] An "emotion engine" is software that analyzes voice and text data to determine emotional states.

[1216] A "generative AI model" is a model that uses artificial intelligence to generate new data and suggestions.

[1217] A "negotiation proposal" is a specific proposal to smoothly advance negotiations with a business partner.

[1218] "User" means an individual or corporation that uses the system.

[1219] A "server" is a computer system that processes and stores data over a network.

[1220] A "terminal" is a device such as a computer or smartphone that is directly operated by a user.

[1221] The present invention relates to a system that reads and understands the contents of a contract and generates a negotiation proposal that takes into account the emotional state of the user. This system operates in cooperation with a server, a terminal, and a user.

[1222] First, the user uploads the text data of the contract to their device. The device then sends the uploaded text data to the server. The server then uses natural language processing technology to analyze the contents of the contract and extract each clause. This analysis is performed using Python natural language processing libraries such as "spaCy" and "NLTK." For example, a function is executed that takes the text data of the contract as input and outputs the content of each clause.

[1223] Next, the user expresses their emotions through speech into the device. The device collects the user's voice data and sends it to the server. The server then uses an emotion engine to analyze the voice data and determine the user's emotional state. This analysis uses the voice recognition library "Google Cloud Speech-to-Text" and the emotion analysis library "IBM Watson Tone Analyzer." For example, if the user is feeling angry or frustrated, the emotion engine captures that information and feeds it back into the system.

[1224] The server then generates a negotiation proposal based on the parsed contract clauses and the user's emotional state using a generative AI model, such as OpenAI GPT-3 or BERT. For example, if the user is feeling angry, the system will take that emotion into account when generating a negotiation proposal.

[1225] Finally, the server sends the generated negotiation proposal to the terminal, which displays it to the user, who can then review the proposal and make any necessary modifications or approvals.

[1226] As a concrete example, a negotiation proposal can be generated by inputting the following prompt sentence into a generative AI model:

[1227] "Generate a proposal to negotiate with a trading partner based on the terms of the contract. The user is currently angry. The terms of the contract are as follows: 'Article 1: Delivery time shall be within 30 days. Article 2: Quality guarantee shall be one year.'"

[1228] Using this prompt, the generative AI model can generate negotiation proposals that take into account the user's emotional state.

[1229] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1230] Step 1:

[1231] The user uploads the text data of the contract to the terminal, which then transmits the uploaded text data to the server.

[1232] Input: Contract text file

[1233] Output: Text data sent to the server

[1234] Specific operation: The user clicks the "Select File" button and selects the contract text file. The device sends the selected file to the server.

[1235] Step 2:

[1236] The server analyzes the received text data of the contract using a natural language processing library (e.g., spaCy, NLTK), extracts each clause of the contract, and stores it in a database.

[1237] Input: Text data of the contract

[1238] Output: Data for each extracted clause

[1239] Specific operation: The server tokenizes the text data and identifies each clause. The server stores the identified clauses in a database.

[1240] Step 3:

[1241] The user expresses their emotions through speech into the device, which then collects the user's speech data and sends it to the server.

[1242] Input: User's voice data

[1243] Output: Audio data sent to the server

[1244] Specific operation: The user speaks into the device, which collects the voice data and sends it to the server.

[1245] Step 4:

[1246] The server analyzes the received voice data using an emotion engine (e.g., Google Cloud Speech-to-Text, IBM Watson Tone Analyzer), determines the user's emotional state, and stores that information in a database.

[1247] Input: Audio data

[1248] Output: User's emotional state

[1249] Specific operation: The server converts the voice data into text. The server analyzes the text data with an emotion engine to determine the emotional state. The server stores the emotional state in a database.

[1250] Step 5:

[1251] The server generates a negotiation proposal using a generative AI model (e.g., OpenAI GPT-3, BERT) based on the parsed contract clauses and the user's emotional state.

[1252] Input: Parsed contract clauses, user emotional state

[1253] Output: Generated negotiation proposal

[1254] Specific operation: The server passes the contract terms and emotional state as input to the generative AI model. The server then receives the generated negotiation proposal and sends it to the terminal.

[1255] Step 6:

[1256] The terminal displays the negotiation proposal received from the server to the user, who then checks the displayed proposal and makes any necessary corrections or approvals.

[1257] Input: Generated negotiation proposal

[1258] Output: The negotiation proposal displayed to the user

[1259] Specific operation: The terminal displays the negotiation proposal to the user, who then reviews the proposal and, if necessary, modifies or approves it.

[1260] (Application example 1)

[1261] 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."

[1262] Conventional contract management systems can read and understand the contents of a contract, but they cannot generate negotiation proposals that take the user's emotional state into account. As a result, if the user is dissatisfied or angry about the contract contents, they cannot respond appropriately, which can make negotiations difficult. Furthermore, proposals that ignore the user's emotional state can reduce the efficiency of negotiations.

[1263] 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.

[1264] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses the problem corresponds to, means for negotiating with the business partner based on the clauses of the master contract, means for recognizing the user's emotional state, and means for generating a proposal for negotiation based on the recognized emotional state. This makes it possible to accurately understand the contents of the contract and generate an appropriate negotiation proposal that takes the user's emotional state into consideration.

[1265] A "contract" is a written legal agreement that clarifies the rights and obligations of the parties.

[1266] "Means of reading and understanding" refers to the techniques and methods for analyzing the text data of a contract and understanding its contents.

[1267] "Means of determination" are techniques or methods for selecting appropriate provisions or measures based on specific conditions or circumstances.

[1268] "Means of negotiation" refers to the techniques and methods used to negotiate with business partners based on the terms of the contract.

[1269] "Means for recognizing emotional state" refers to techniques or methods for analyzing a user's voice data or other input data to determine the user's emotions.

[1270] A "means for generating proposals" is a technique or method for creating specific proposals for negotiation based on the perceived emotional state.

[1271] A system for implementing the present invention reads and understands the contents of a contract, recognizes the emotional state of a user, and generates a negotiation proposal. A specific embodiment of the system is described below.

[1272] Hardware and software used

[1273] Hardware: Smartphone, microphone

[1274] Software: Python, spaCy, Transformers, SpeechRecognition

[1275] Data processing and calculation

[1276] Contract Analysis

[1277] The server receives the text data of the contract and analyzes it using natural language processing technology. Specifically, it uses spaCy to extract each clause of the contract and understand its content. This process clarifies the important clauses of the contract.

[1278] emotion recognition

[1279] It uses a microphone to collect user-provided voice data and converts the speech to text using SpeechRecognition. It then uses a Transformers sentiment analysis model to recognize the user's emotional state. This process can determine whether the user is angry or frustrated.

[1280] Proposal Generation

[1281] The server generates negotiation suggestions based on the analyzed contract clauses and the user's perceived emotional state. For example, if the user is dissatisfied, the server may provide a suggestion to draw attention to the contract's termination clause. This suggestion helps the user take appropriate action.

[1282] Specific examples

[1283] Contents of the contract

[1284] Suppose the contract contains a clause that reads, "This contract may be terminated by either party with a 30-day notice."

[1285] Audio data

[1286] The voice data "user_voice.wav" in which the user expresses dissatisfaction is used.

[1287] Prompt Sentence Examples

[1288] Analyze the contents of the contract, recognize the user's emotional state, and provide appropriate advice. The content of the contract is "This contract may be terminated by either party with a 30-day notice." The voice data is "user_voice.wav".

[1289] In this way, the system can accurately understand the contents of the contract and generate appropriate negotiation proposals that take into account the user's emotional state, allowing the user to appropriately deal with dissatisfaction or anger about the contract contents and conduct efficient negotiations.

[1290] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1291] Step 1:

[1292] The server receives the text data of the contract. It receives the text data of the contract as input and analyzes it using natural language processing technology. Specifically, it uses spaCy to extract each clause of the contract and understand the content of each clause. As output, it obtains a list of the extracted clauses.

[1293] Step 2:

[1294] The user provides voice data through a microphone. The system receives the user's voice data as input and converts the voice to text using SpeechRecognition. Specifically, it performs a process to convert the voice data into text data. The converted text data is obtained as output.

[1295] Step 3:

[1296] The server receives the converted text data and uses the Transformers sentiment analysis model to recognize the user's emotional state. It receives the text data as input and performs sentiment analysis. Specifically, it inputs the text data into the sentiment analysis model to determine the user's emotional state. It obtains the recognized emotional state as output.

[1297] Step 4:

[1298] The server generates a proposal for negotiation based on the analyzed contract clauses and the recognized emotional state of the user. As input, it receives the list of extracted clauses and the recognized emotional state and generates a proposal. Specifically, it generates a warning or advice for a specific clause in the contract depending on the user's emotional state. As output, it obtains the generated proposal.

[1299] Step 5:

[1300] The device presents the generated suggestions to the user. As input, the device receives the generated suggestions and presents them to the user visually or audibly. Specifically, the device displays the suggestions on a screen or reads them out loud. As output, the user can confirm the suggestions.

[1301] In this way, the system can accurately understand the contents of the contract, generate appropriate negotiation proposals that take into account the user's emotional state, and provide them to the user.

[1302] Example 2

[1303] 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."

[1304] With conventional contract management systems, it was difficult to quickly and accurately identify which clauses were applicable when a dispute arose. Furthermore, they lacked the functionality to generate negotiation proposals that took the user's emotional state into account, making it impossible to properly resolve the user's dissatisfaction. This could delay the resolution of contract disputes and worsen relationships with business partners.

[1305] 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.

[1306] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses match the conditions of the relevant trouble, means for negotiating with the business partner based on the clauses of the master contract, means for the user to input the details of the trouble, means for analyzing the user's emotional state using an emotion engine, means for identifying the relevant contract clauses using a similarity calculation algorithm, means for generating a proposal using a generative AI model, and means for presenting the generated proposal to the user. This makes it possible to quickly and accurately identify the relevant contract clauses based on the details of the trouble and generate a negotiation proposal that takes the user's emotional state into consideration.

[1307] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[1308] "Trouble" refers to issues or disputes related to the contract.

[1309] "User" refers to an individual or corporation that uses the system to check the contents of the contract and attempt to resolve any issues.

[1310] A "server" is a computer that is the core of a system and is a device that processes and stores data.

[1311] An "emotion engine" is software for analyzing a user's emotional state from input data.

[1312] The "similarity calculation algorithm" is a mathematical method for calculating the similarity between the content of the problem and each clause of the contract.

[1313] A "generative AI model" is an artificial intelligence model that generates suggestions based on a user's emotional state and specified clauses.

[1314] A "proposal" is a document or message that outlines specific actions or changes to be taken to resolve the problem.

[1315] "Text data" is data that includes text information and is used to describe contracts and trouble details.

[1316] "Negotiation" is the act of discussing terms and conditions with a business partner and aiming for an agreement.

[1317] This invention is a system that reads the contents of a contract, identifies the relevant clause when a problem occurs, and generates a negotiation proposal that takes into account the emotional state of the user. A specific embodiment of this system will be described below.

[1318] Hardware and software used

[1319] Hardware: Servers, terminals

[1320] Software: Emotion engine, similarity calculation algorithm, generative AI model

[1321] System Overview

[1322] This system allows users to input the details of the problem, identifies the relevant clauses in the contract based on that information, and generates proposals that take into account the user's emotional state.

[1323] Details of data processing and calculation

[1324] 1. User enters the problem:

[1325] The user uses a terminal to input the details of the problem into the system as text data. For example, the user might input, "I think the payment terms in this contract are unfair."

[1326] 2. Analysis by emotion engine:

[1327] The server launches an emotion engine to analyze the text data entered by the user. The emotion engine uses natural language processing technology to determine the user's emotional state. For example, it may extract the keyword "unfair" from the user's text and determine that the user is dissatisfied.

[1328] 3. Clause identification using similarity calculation algorithm:

[1329] The server uses a similarity calculation algorithm to calculate the degree of similarity between the content of the problem and each clause in the contract. Clauses with a similarity above a certain threshold are identified as relevant clauses. For example, if the problem is related to "payment terms," ​​clauses related to payment will be identified.

[1330] 4. Proposal generation:

[1331] The server launches a generative AI model to generate a proposal based on the user's emotional state and the identified clauses. The generative AI model creates a proposal for negotiation that takes the user's emotions into account. For example, a proposal might be generated such as, "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we propose taking measures to extend the payment deadline."

[1332] 5. Presenting the proposal:

[1333] The server sends the generated proposal to the user's device and presents it to the user, who can then check the proposal content through the device.

[1334] Specific examples

[1335] User input: The user types "I believe the payment terms in this contract are unfair" into the input field on the terminal and clicks the submit button.

[1336] Analysis result of emotion engine: The server starts the emotion engine and analyzes the user's text data. The emotion engine detects the keyword "unfair" and determines that the user is dissatisfied.

[1337] Similarity calculation: The server calculates the similarity between each clause of the contract and the user's problem. The clause regarding payment terms is identified as having a high similarity.

[1338] Proposal Generation: The server launches the generative AI model to generate a proposal based on the user's emotional state and the identified clauses. The generated proposal might be something like, "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we suggest taking measures to extend the payment deadline."

[1339] Proposal presentation: The server sends the generated proposal to the user's device, and the user confirms the proposal on the device screen.

[1340] Prompt Sentence Examples

[1341] Prompt: "If a user is dissatisfied with the payment terms in a contract, identify which clauses apply and generate suggestions that take the user's emotional state into account."

[1342] In this way, the system analyzes the user's problem, identifies the relevant contract clauses, generates suggestions that take into account the user's emotional state, and presents them to the user.

[1343] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1344] Step 1:

[1345] The user inputs the details of the problem.

[1346] The user uses a terminal to input the details of the problem into the system as text data. For example, the user might type, "I think the payment terms in this contract are unfair," and click the send button. The input data is sent to the server.

[1347] Step 2:

[1348] The server uses an emotion engine to analyze the user's emotional state.

[1349] The server passes the received text data to the emotion engine for analysis. The emotion engine uses natural language processing technology to extract the user's emotional state from the text data. For example, it detects the keyword "unfair" and determines that the user is dissatisfied. The analysis results in the user's emotional state being output.

[1350] Step 3:

[1351] The server uses a similarity calculation algorithm to identify the relevant clauses of the contract.

[1352] The server uses a similarity calculation algorithm to calculate the similarity between the content of the problem and each clause in the contract. The content of the problem and the text data of the contract are used as input data. The similarity calculation algorithm calculates the similarity with each clause and identifies clauses whose similarity is above a certain threshold. The relevant clauses are obtained as output data.

[1353] Step 4:

[1354] The server generates suggestions using a generative AI model.

[1355] The server generates a proposal using a generative AI model based on the user's emotional state and the identified clauses. The user's emotional state and the identified clauses are used as input data. The generative AI model uses this data to create a proposal for negotiation that takes the user's emotions into account. For example, a proposal such as "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we propose taking measures to extend the payment deadline" is generated. The generated proposal is obtained as output data.

[1356] Step 5:

[1357] The server presents the generated suggestions to the user.

[1358] The server sends the generated proposal to the user's terminal and presents it to the user. The user can check the proposal content through the terminal. As output data, the proposal is obtained in a format that the user can check.

[1359] (Application example 2)

[1360] 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."

[1361] When a dispute arises regarding a contract, it is difficult for the user to identify which clause is problematic and conduct appropriate negotiations. Furthermore, if negotiations are conducted without taking into account the user's emotional state, the negotiations may not proceed smoothly. This can delay the resolution of the contract dispute and hinder the progress of business.

[1362] 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.

[1363] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clause matches the conditions of the problem, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the emotional state of the user, and means for generating a proposal for negotiation that takes the emotional state of the user into consideration. As a result, when a problem occurs regarding the contract, it becomes possible to identify the applicable clause and conduct appropriate negotiations while taking the emotional state of the user into consideration.

[1364] A "contract" is a written document of a transaction or agreement, and is a legal document that clarifies the rights and obligations of the parties.

[1365] "Trouble" refers to problems or disputes related to a contract that arise due to differences of opinion or dissatisfaction between the parties.

[1366] A "clause" refers to an individual provision or condition within a contract, which defines specific rights, obligations, procedures, etc.

[1367] "Emotional state" refers to the user's psychological state, and includes emotions such as anger, frustration, and joy.

[1368] "Analysis" refers to the process of examining and understanding data and information, and extracting information for a specific purpose.

[1369] A "proposal" refers to a specific idea or method presented for solving a problem or making an improvement, and is used during negotiations and decision-making.

[1370] "Negotiation" refers to the process of discussion between the parties to adjust their views and conditions and reach an agreement.

[1371] A "system" refers to an entire mechanism that is composed of multiple elements and functions, and is designed to achieve a specific purpose.

[1372] A system for implementing this invention includes means for reading and understanding the contents of a contract, means for determining which clause's conditions are met in the particular dispute, means for negotiating with a business partner based on the clauses of the master contract, means for analyzing the user's emotional state, and means for generating proposals for negotiation that take the user's emotional state into consideration.

[1373] The server uses natural language processing (NLP) technology to analyze the text data of the contract and extract the content of each clause. Specifically, it uses the spacy library to analyze the content of the contract and extract the content of each clause.

[1374] Next, the server analyzes the content of the problem and calculates the similarity between the content of the problem and each clause of the contract in order to identify the relevant clause of the contract. This similarity calculation is performed using the similarity calculation function of Spacy.

[1375] Furthermore, the server uses an emotion analysis model to analyze the emotional state of the user. Specifically, the server uses a pipeline from the transformers library to analyze the emotional state of the user's trouble report.

[1376] To generate a proposal for negotiation that takes into account the emotional state of the user, the server generates a proposal based on the analysis result, which takes into account the emotional state of the user and provides a specific plan for appropriate negotiation.

[1377] For example, if a user reports, "I'm dissatisfied with clause X of this contract," the server analyzes the contents of the contract and identifies the relevant clause. Next, it analyzes the user's emotional state and determines that the user is dissatisfied. Finally, the server generates a proposal such as, "Since the user is dissatisfied with clause X, we believe there is room for improvement. Specifically, we suggest that you take action Y."

[1378] An example of a prompt is as follows:

[1379] "The user is frustrated by clause X in the contract. Perform sentiment analysis to identify the relevant clauses in the contract and generate suggestions that take into account the user's emotional state."

[1380] In this way, when a problem occurs with a contract, it becomes possible to identify the relevant clauses and conduct appropriate negotiations while taking into account the user's emotional state.

[1381] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1382] Step 1:

[1383] A user reports a problem with a contract.

[1384] Input: The user inputs the details of the problem by voice or text.

[1385] Specific operation: The user uses a smartphone or terminal to input the details of the problem, such as "I am dissatisfied with clause X of this contract."

[1386] Step 2:

[1387] The server receives the details of the problem and saves them as text data.

[1388] Input: Voice or text data of the problem sent by the user.

[1389] Output: Text data of the saved trouble details.

[1390] Specific operation: The server receives the data sent by the user, and if it is voice data, converts it into text data using voice recognition technology and stores it in a database.

[1391] Step 3:

[1392] The server analyzes the contents of the contract and extracts the contents of each clause.

[1393] Input: Text data of the contract.

[1394] Output: The content of each extracted clause.

[1395] Specific operation: The server uses the spacy library to parse the text data of the contract and extract the contents of each clause.

[1396] Step 4:

[1397] The server calculates the degree of similarity between the problem and each clause in the contract.

[1398] Input: Text data of the problem, contents of each clause of the contract.

[1399] Output: Contract clauses that are most similar to the problem.

[1400] Specific operation: The server uses Spacy's similarity calculation function to calculate the similarity between the problem content and each clause in the contract, and identifies the clause with the highest similarity.

[1401] Step 5:

[1402] The server analyzes the user's emotional state.

[1403] Input: Text data of the problem.

[1404] Output: The user's emotional state (e.g., anger, frustration, joy, etc.).

[1405] Specific operation: The server uses the pipeline of the transformers library to input the text data of the trouble content into the emotion analysis model and analyze the user's emotional state.

[1406] Step 6:

[1407] The server generates proposals for negotiation that take into account the user's emotional state.

[1408] Input: Text data of the problem, relevant clauses of the contract, and the user's emotional state.

[1409] Output: Proposal for negotiation.

[1410] Specific operation: Based on the analysis results, the server generates a proposal that takes into account the user's emotional state. For example, it generates a proposal such as, "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we suggest that you take action Y."

[1411] Step 7:

[1412] The server notifies the user of the generated proposal.

[1413] Input: The generated proposal.

[1414] Output: The suggestion notified to the user.

[1415] Specific operation: The server notifies the user of the generated proposal to their smartphone or device, allowing the user to check the proposal contents.

[1416] Example 3

[1417] 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."

[1418] Conventional contract-based negotiation systems have difficulty automatically generating negotiation proposals based on contract clauses, and are unable to make proposals that take into account the user's emotional state. This has resulted in problems that reduce the efficiency and effectiveness of negotiations. Furthermore, there is a need for systems that can grasp the stress and emotional changes that users experience during negotiations in real time and make proposals accordingly.

[1419] 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.

[1420] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses are met in the relevant trouble, means for negotiating with the business partner based on the clauses of the master contract, means for automatically generating a negotiation proposal based on the identified clauses and the details of the trouble, and means for grasping the user's emotional state in real time and generating a negotiation proposal based on that information. This makes it possible to automatically generate specific and appropriate negotiation proposals based on the clauses of the contract and to make proposals that take the user's emotional state into consideration.

[1421] "Means for reading and understanding the contents of a contract" refers to a means that has the function of analyzing the text data of a contract and extracting the contents of each clause.

[1422] "Means for determining which clause's conditions are met in the particular problem" refers to a means that has the function of analyzing the content of the problem and identifying the relevant clause of the contract.

[1423] A "means for negotiating with a business partner based on the provisions of the master agreement" is a means having a function for generating a proposal for negotiating with a business partner based on the specified provisions of the agreement.

[1424] The "means for automatically generating a proposal for negotiation based on the identified clause and the content of the problem" refers to a means that has the function of receiving the identified clause of a contract and the content of the problem as input data, and automatically generating a proposal for negotiation based thereon.

[1425] "Means for grasping the user's emotional state in real time and generating a negotiation proposal based on that information" refers to means that has the function of analyzing the user's voice data in real time, determining the emotional state at that time, and generating a negotiation proposal based on that information.

[1426] This invention is a system that reads and understands the contents of a contract, determines which clauses are met in the event of a problem, and negotiates with a business partner based on the clauses of the master contract. Furthermore, it automatically generates a proposal for negotiation based on the identified clauses and the content of the problem, grasps the user's emotional state in real time, and generates a negotiation proposal based on that information.

[1427] Hardware and software used

[1428] Hardware:

[1429] Server: Processes data and runs generative AI models.

[1430] Terminal: A device through which a user provides input and receives suggestions.

[1431] software:

[1432] Generative AI model: Generates negotiation proposals based on contract clauses and the nature of the dispute.

[1433] Emotion engine: Analyzes the user's voice data in real time to determine their emotional state.

[1434] Data processing and calculation

[1435] How to read and understand the contract:

[1436] The server analyzes the text data of the contract and extracts the contents of each clause. For example, it uses natural language processing technology to analyze the text of the contract and classify it by clause.

[1437] How to analyze the problem and identify the applicable contract clauses:

[1438] The server analyzes the details of the problem entered by the user and identifies the relevant clause in the contract. For example, if the problem is "delay in delivery," it identifies the clause in the contract that relates to delivery dates.

[1439] Means of automatically generating proposals for negotiation:

[1440] The server generates a negotiation proposal using a generative AI model based on the identified clauses and the details of the problem. For example, it generates a prompt statement such as, "In accordance with clause 12 of the master agreement, we propose a penalty for delivery delays," and inputs it into the generative AI model.

[1441] A means of understanding a user's emotional state in real time and generating negotiation proposals based on that information:

[1442] While a user is using the system during negotiations, the emotion engine collects and analyzes the user's voice data in real time. For example, if the user is feeling stressed, the emotion engine outputs that information as an analysis result. Based on this information, the server generates negotiation proposals that take the user's emotional state into account.

[1443] Specific examples

[1444] The user enters "Clause 12" and "Delivery Delay" into the terminal. The server receives this and generates a prompt saying, "In accordance with Clause 12 of the master contract, we propose a penalty for the delivery delay." Based on this prompt, the generative AI model generates a specific proposal: "In accordance with Clause 12, we propose to charge a late fee of 1,000 yen per day as a penalty for the delivery delay."

[1445] If the user feels stressed during negotiation, the emotion engine analyzes the user's voice data and provides feedback to the server. Based on this information, the server generates a gentle suggestion to the user, such as, "Based on Article 12, we propose a penalty for delaying the delivery date, but we suggest that you first consider rescheduling the delivery date." The flow of the identification process in Example 3 will be described with reference to FIG. 21.

[1446] Step 1:

[1447] The user enters the terms of the basic contract and the details of the problem.

[1448] Input: The user uses a terminal to input the specified clause of the master agreement (e.g., "Clause 12") and the details of the current problem (e.g., "Delay in delivery") into the system.

[1449] Output: The entered data is sent to the server.

[1450] Specific operation: The user enters "Clause 12" and "Delivery Delay" into the input form on the terminal and presses the send button.

[1451] Step 2:

[1452] The server receives the input data and generates a prompt for the generative AI model.

[1453] Input: The server receives the data "Article 12" and "Delivery Delay" sent by the user.

[1454] Output: A prompt sentence to be passed to the generative AI model (e.g., "In accordance with clause 12 of the master agreement, we propose a penalty for late delivery.") is generated.

[1455] Specific behavior: The server parses the received data and applies it to a template to generate a prompt.

[1456] Step 3:

[1457] The server uses the generative AI model to generate a negotiation proposal.

[1458] Input: The server inputs the generated prompt sentence into the generative AI model.

[1459] Output: The generative AI model outputs a specific negotiation proposal (e.g., "In accordance with Article 12, we propose to charge a late fee of 1,000 yen per day as a penalty for late delivery.").

[1460] Specific operation: The server inputs a prompt sentence into the generative AI model and receives the output from the model.

[1461] Step 4:

[1462] The emotion engine is used to analyze voice data in real time while the user is negotiating.

[1463] Input: Voice data uttered by the user during negotiation.

[1464] Output: Analysis result by the emotion engine (e.g., the user is stressed).

[1465] Specific operation: The emotion engine collects and analyzes the user's voice data in real time.

[1466] Step 5:

[1467] The server generates suggestions that take into account the user's emotional state based on the analysis results of the emotion engine.

[1468] Input: The emotion engine's analysis results (e.g., the user is feeling stressed) and a negotiation suggestion from the generative AI model.

[1469] Output: A negotiation proposal that takes into account the user's emotional state (e.g., "In accordance with clause 12, we propose a penalty for late delivery, but we suggest that you first consider rescheduling the delivery date.").

[1470] Specific operation: The server receives the analysis results of the emotion engine, modifies the suggestions from the generative AI model, and generates suggestions that are appropriate for the user's emotional state.

[1471] Step 6:

[1472] The server provides the generated proposals to the user.

[1473] Input: Negotiation proposals that take into account the user's emotional state.

[1474] Output: Negotiation proposal displayed on the user's terminal.

[1475] Specific operation: The server sends the generated proposal to the user's device, and the user confirms the proposal on the device.

[1476] (Application example 3)

[1477] 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."

[1478] Conventional contract-based negotiation systems have difficulty understanding contract clauses and generating appropriate proposals to resolve disputes. Furthermore, they are unable to make proposals that take into account the user's emotional state, reducing the effectiveness of negotiations. Furthermore, in brick-and-mortar stores, staff lack the means to receive appropriate proposals in real time when dealing with customers. To solve these problems, a system is needed that can automatically generate negotiation proposals based on contract clauses and that take into account the user's emotional state.

[1479] 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.

[1480] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses the problem meets, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the user's voice data in real time and determining their emotional state, means for generating a negotiation proposal taking into account the emotional state, means for generating the proposal using a generative AI model, and means for displaying the proposal on the smart glasses. This makes it possible to generate appropriate negotiation proposals based on the clauses of the contract in real time and make optimal proposals taking into account the user's emotional state.

[1481] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[1482] "Trouble" refers to problems or disputes that arise in contracts or transactions.

[1483] "Clause" means any individual condition or provision set out in the Agreement.

[1484] "Negotiation" is the act of discussing conditions and solutions with a business partner and aiming for an agreement.

[1485] "Voice data" refers to data that is a digital recording of a user's speech.

[1486] "Emotional state" refers to the user's psychological state or feelings.

[1487] A "generative AI model" is a model that uses artificial intelligence to automatically generate text and suggestions.

[1488] "Smart glasses" are glasses-type devices equipped with a display and sensors and capable of displaying information.

[1489] A "proposal" is a proposed solution or action for a particular problem or situation.

[1490] The system for implementing this invention is capable of reading and understanding the contents of a contract, generating appropriate suggestions for dealing with problems, and analyzing the user's emotional state in real time to provide optimal suggestions based on that analysis.

[1491] Hardware and software used

[1492] Hardware: Smart glasses, microphone

[1493] Software: Python, speech_recognition library, transformers library, emotion_recognition library

[1494] System configuration

[1495] 1. How to read and understand the contents of the contract:

[1496] The server analyzes the text data of the contract and extracts the contents of each clause, which allows the server to understand the content of the contract and identify clauses related to the problem.

[1497] 2. How to determine which clause applies to your dispute:

[1498] The server analyzes the details of the problem and identifies the relevant clauses in the contract, thereby enabling the server to find the appropriate clauses for the problem.

[1499] 3. How to negotiate with business partners based on the terms of the Master Agreement:

[1500] The server generates a proposal for negotiation based on the identified clauses, using a generative AI model to automatically generate a specific proposal.

[1501] 4. A means of analyzing a user's voice data in real time to determine their emotional state:

[1502] The server analyzes the audio data acquired through the microphone to determine the user's emotional state. It uses the emotion_recognition library to analyze the emotional state in real time.

[1503] 5. Means of generating proposals for negotiations taking into account emotional states:

[1504] The server generates optimal negotiation proposals taking into account the user's emotional state, thereby enabling proposals to be made according to the user's psychological state.

[1505] 6. How to generate suggestions using generative AI models:

[1506] The server uses a generative AI model (e.g., GPT-3) to generate a negotiation proposal based on the terms of the contract, the nature of the problem, and the user's emotional state.

[1507] 7. Means for displaying suggestions on smart glasses:

[1508] The server then displays the generated suggestions on the smart glasses, allowing store staff to view the suggestions in real time and respond to the customer.

[1509] Specific examples

[1510] Scenario: While handling a customer complaint, a brick-and-mortar store associate receives a real-time offer based on the terms of a contract.

[1511] Example prompt: "Generate a proposal for handling returns based on clause X of the master agreement. The user's emotional state is anger."

[1512] In this way, it is possible to generate appropriate negotiation proposals based on the terms of the contract in real time and make optimal proposals that take into account the user's emotional state.

[1513] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1514] Step 1:

[1515] The server acquires and analyzes the text data of the contract. Specifically, it reads the contents of the contract and extracts the contents of each clause. The input is the text data of the contract, and the output is a list of extracted clauses. This allows the contents of the contract to be understood.

[1516] Step 2:

[1517] The server analyzes the details of the problem and identifies the relevant clauses of the contract. The input is the details of the problem, and the output is the relevant clauses of the contract. This allows the server to find the appropriate clauses for the problem.

[1518] Step 3:

[1519] The server generates a proposal for negotiation based on the identified clauses. A generative AI model is used to automatically generate a specific proposal. The input is the identified clauses and the details of the problem, and the output is the generated proposal. This allows for the generation of an appropriate negotiation proposal based on the clauses of the contract.

[1520] Step 4:

[1521] The server analyzes the audio data acquired through the microphone to determine the user's emotional state. It uses the emotion_recognition library to analyze the emotional state in real time. The input is audio data, and the output is the user's emotional state. This allows us to understand the user's psychological state.

[1522] Step 5:

[1523] The server generates optimal negotiation proposals taking into account the user's emotional state. A generative AI model is used to generate proposals that correspond to the emotional state. The input is the user's emotional state, the identified clauses, and the details of the problem, and the output is a proposal that takes the emotional state into account. This allows for optimal proposals to be made that correspond to the user's emotional state.

[1524] Step 6:

[1525] The server displays the generated proposal on the smart glasses. The input is the generated proposal, and the output is the proposal displayed on the smart glasses display. This allows store staff to check the proposal in real time and respond to the customer.

[1526] 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.

[1527] 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.

[1528] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1529] 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.

[1530] [Third embodiment]

[1531] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1532] 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.

[1533] 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).

[1534] 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.

[1535] 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.

[1536] 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).

[1537] 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.

[1538] 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.

[1539] 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.

[1540] 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.

[1541] 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.

[1542] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1543] "Example 1"

[1544] The system of the present invention uses natural language processing technology as a means of reading and understanding the contents of a contract. Specifically, it analyzes the text data of the contract and extracts the contents of each clause. This extraction is performed using a function that takes the text data of the contract as input and outputs the contents of each clause.

[1545] "Example 2"

[1546] In addition, the system has the function of analyzing the content of the problem and identifying the relevant contract clauses as a means of determining which clauses the problem matches. Specifically, the content of the problem is input as text data, and the relevant contract clauses are identified based on that content. This identification is done by calculating the similarity between the content of the problem and each clause in the contract, and identifying clauses whose similarity is above a certain threshold as the relevant clauses.

[1547] "Example 3"

[1548] Furthermore, it has a function to generate a proposal for negotiation using the specified clauses as a means of negotiating with a business partner based on the clauses of the master agreement. Specifically, it automatically generates a proposal for negotiation based on the specified clauses and the details of the problem. For example, this proposal is generated in a format such as "Based on clause X, we propose to take measure Y."

[1549] The processing flow of each embodiment will be described below.

[1550] "Example 1"

[1551] Step 1: Enter the contract text data into the system.

[1552] Step 2: The system uses natural language processing technology to analyze the text data and extract the content of each clause.

[1553] Step 3: The content of each extracted clause is stored internally in the system for further processing.

[1554] "Example 2"

[1555] Step 1: Enter the details of the problem into the system as text data.

[1556] Step 2: The system analyzes the content of the problem and identifies the relevant clauses in the contract. This identification is done by calculating the similarity between the content of the problem and each clause in the contract, and identifying clauses with a similarity above a certain threshold as relevant clauses.

[1557] Step 3: The identified clauses are stored internally in the system for further processing.

[1558] "Example 3"

[1559] Step 1: The system automatically generates a proposal for negotiation based on the identified clauses and issues.

[1560] Step 2: The proposal is generated in the form of, for example, "Based on clause X, we propose to take action Y."

[1561] Step 3: The generated proposal is presented to the user and used in negotiations with trading partners.

[1562] Example 1

[1563] 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."

[1564] With conventional contract management systems, it was difficult to accurately read and understand the contents of a contract, and a particular challenge was quickly and accurately extracting the contents of each clause. Furthermore, performing detailed analysis based on the contents of a contract required specialized knowledge, placing a significant burden on users. Furthermore, there was a lack of means to obtain detailed analysis results using a generative AI model based on the contents of a contract.

[1565] 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.

[1566] In this invention, the server includes a means for reading and understanding the contents of the contract, a means for analyzing the text data of the contract and extracting the contents of each clause, a means for providing the extracted contents of each clause to the user, and a means for inputting a prompt sentence into the generative AI model and obtaining a detailed analysis result. This makes it possible to quickly and accurately read and understand the contents of the contract and extract the contents of each clause. Furthermore, the user can obtain a detailed analysis result by utilizing the generative AI model without needing specialized knowledge.

[1567] A "contract" is a written legal agreement that clarifies the rights and obligations of the parties.

[1568] "Text data" is data that represents character information in a digital format and is used to analyze the contents of a contract.

[1569] "Analysis" is the process of breaking down text data and understanding its structure and meaning.

[1570] A "clause" refers to an individual condition or provision set forth in a contract, and is an element that constitutes the specific content of the contract.

[1571] "Extraction" is the process of extracting specific information from text data, and is performed to identify the content of each clause in the contract.

[1572] "User" refers to a person or organization that uses the system to read, understand, and obtain analytical results from the contents of a contract.

[1573] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and generate detailed analysis results based on prompt sentences.

[1574] A "prompt" is an instruction entered into a generative AI model and is used to clarify the target and purpose of the analysis.

[1575] The present invention relates to a system for reading and understanding the contents of a contract and extracting the contents of each clause. A specific embodiment of this system will be described below.

[1576] System configuration

[1577] The system consists of a server, a terminal, and a user. The server receives the text data of the contract, analyzes it, and extracts the contents of each clause. The terminal provides an interface for the user to input the contract data and check the analysis results.

[1578] Hardware and software used

[1579] A server is a computer with a powerful processor and ample memory. The software that runs on the server includes:

[1580] Natural Language Processing Libraries: "spaCy" and "NLTK"

[1581] PDF text extraction library: "PyMuPDF"

[1582] Generative AI models: Advanced natural language generation models such as GPT-3

[1583] Data processing and calculation

[1584] The server processes the data as follows:

[1585] 1. Receiving text data: The user sends the contract text data to the server via their device. The text data is extracted from a PDF or Word file.

[1586] 2. Preprocessing of text data: The server cleans the received text data and performs tokenization, part-of-speech tagging, and dependency analysis.

[1587] 3. Clause Extraction: The server detects specific keywords and phrases and identifies clauses based on them.

[1588] 4. Output of extracted results: The server provides the content of each extracted clause to the user. If necessary, it inputs a prompt to the generative AI model to obtain detailed analysis results.

[1589] Specific examples

[1590] For example, suppose we have the following text data for a contract:

[1591] Article 1 (Purpose of the contract)

[1592] This Agreement sets out the basic conditions for Party A and Party B to conduct business together.

[1593] Article 2 (Payment Terms)

[1594] Party A shall pay Party B the remuneration at the end of each month.

[1595] With this text data as input, the server processes it as follows:

[1596] 1. Receiving text data: The user inputs the text of the contract. The server receives the input text data.

[1597] 2. Preprocessing of text data: The server removes unnecessary whitespace and special characters from the text data, and performs tokenization, part-of-speech tagging, and dependency analysis.

[1598] 3. Clause extraction: The server detects keywords such as "purpose of the contract" and "payment terms," ​​extracts the surrounding text of the detected keywords, and identifies them as clauses.

[1599] 4. Output of extraction results: The server formats the extraction results in HTML and displays them to the user. If the user requests further analysis, the server sends a prompt to the generative AI model to obtain additional analysis results.

[1600] Prompt Sentence Examples

[1601] Below are some example prompts to input to a generative AI model:

[1602] Analyze the text data of the contract and extract the contents of each clause.

[1603] Text data:

[1604] Article 1 (Purpose of the contract)

[1605] This Agreement sets out the basic conditions for Party A and Party B to conduct business together.

[1606] Article 2 (Payment Terms)

[1607] Party A shall pay Party B the remuneration at the end of each month.

[1608] In this way, the server extracts the content of each clause of the contract and provides it to the user, allowing the user to quickly and accurately understand the content of the contract without requiring specialized knowledge.

[1609] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1610] Step 1:

[1611] Receiving text data

[1612] The server receives the text data of the contract from the user. The user uploads the contract file via their device or inputs the text directly. The server temporarily stores the received data.

[1613] Input: Contract text data (PDF, Word file, or manually entered text)

[1614] Output: Temporarily saved text data

[1615] Specific behavior:

[1616] The user uploads the PDF file of the contract using the device.

[1617] The server uses the "PyMuPDF" library to convert PDF files into text data.

[1618] Step 2:

[1619] Preprocessing text data

[1620] The server pre-processes the received text data, including text cleaning, tokenization, part-of-speech tagging, and dependency analysis, using the "spaCy" library.

[1621] Input: Temporarily saved text data

[1622] Output: Preprocessed text data (tokenized, part-of-speech tagged, dependency parsed)

[1623] Specific behavior:

[1624] The server removes unnecessary spaces and special characters from the text data.

[1625] The server divides (tokenizes) the text data into words.

[1626] The server tags each token with a part of speech tag.

[1627] The server performs dependency analysis to analyze the structure of the sentence.

[1628] Step 3:

[1629] Extracting clauses

[1630] The server extracts the content of each clause from the preprocessed text data and performs functions to detect specific keywords and phrases and identify clauses based on them.

[1631] Input: Preprocessed text data

[1632] Output: Content of each extracted clause

[1633] Specific behavior:

[1634] The server detects keywords such as "payment terms" and "purpose of the contract."

[1635] The server extracts the text surrounding the detected keywords and identifies them as clauses.

[1636] Step 4:

[1637] Output of extraction results

[1638] The server provides the user with the content of each extracted clause. The extracted results are displayed in a format that is easy for the user to review. If necessary, a prompt can be input to the generative AI model to obtain more detailed analysis results.

[1639] Input: The content of each extracted clause

[1640] Output: Extraction results provided to the user, and detailed analysis results by the generative AI model (if required)

[1641] Specific behavior:

[1642] The server formats the extracted results in HTML format and displays them to the user.

[1643] If the user requests a more detailed analysis, the server sends a prompt to the generative AI model to obtain additional analysis results.

[1644] In this way, the server extracts the content of each clause of the contract and provides it to the user, allowing the user to quickly and accurately understand the content of the contract without requiring specialized knowledge.

[1645] (Application example 1)

[1646] 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."

[1647] Conventional contract management systems have the problem that it is difficult to read and understand the contents of contracts, making it difficult to quickly identify clauses that pose particular risks. Furthermore, analyzing contract contents and conducting risk assessments requires a great deal of time and effort, creating a need for efficient contract management. Furthermore, when a problem occurs based on a contract clause, it is difficult to quickly determine which clause applies.

[1648] 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.

[1649] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses meet the conditions of the problem, means for negotiating with the business partner based on the clauses of the basic contract, means for analyzing the text data of the contract and extracting the contents of each clause, and means for evaluating the risks of the extracted clauses and notifying high-risk clauses. This makes it possible to efficiently analyze the contents of the contract and perform risk evaluation, and quickly identify and notify high-risk clauses. It also makes it possible to quickly determine the applicable clauses when a problem occurs and take appropriate action.

[1650] A "contract" is a legal document that documents a transaction or agreement.

[1651] "Reading" is the process of analyzing text data and understanding its content.

[1652] "Means of understanding" are methods and techniques for analyzing text data and grasping its meaning.

[1653] "Trouble" refers to problems or disputes that arise in connection with a contract.

[1654] A "clause" is a specific condition or provision set out in a contract.

[1655] "Means for determining whether conditions are met" refers to methods or techniques for determining which clause in the contract a particular situation or problem falls under.

[1656] A "basic agreement" is a contract that sets out the basic terms and regulations of a transaction.

[1657] A "business partner" is a party with whom you enter into a contract or a business partner.

[1658] "Means of negotiation" refers to the methods and techniques used to discuss contract terms and resolve disputes with business partners.

[1659] "Text data" is data stored as character information.

[1660] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[1661] "Extraction means" refers to methods or techniques for extracting specific information from data.

[1662] "Risk" refers to the potential problems or dangers associated with a contract.

[1663] "Evaluation tools" are methods or techniques for determining risk or value based on specific criteria.

[1664] "Means of notification" refers to methods or techniques for notifying users of specific information.

[1665] A system for carrying out the present invention includes a program for reading and understanding the contents of a contract and performing risk assessment. A specific embodiment of this system will be described below.

[1666] System Configuration

[1667] The system includes a server, a user terminal, and a natural language processing model. The server analyzes the text data of the contract, extracts the contents of each clause, and performs risk assessment. The user terminal provides an interface for taking a photo of the contract and uploading the PDF file.

[1668] Hardware and software used

[1669] Hardware: Smartphones, servers

[1670] Software: Python, spaCy, Transformers library

[1671] Data processing and calculation

[1672] 1. Entering the contract text data: The user takes a photo of the contract using their smartphone or uploads a PDF file.

[1673] 2. Analysis of text data: The server receives the uploaded text data and splits it into sentences using spaCy.

[1674] 3. Extracting clause content: The divided sentences are analyzed using a natural language processing model (Transformers library) to extract the content of each clause.

[1675] 4. Risk Assessment: For each extracted clause, a risk assessment is performed using a generative AI model. High-risk clauses are notified to the user.

[1676] Specific examples

[1677] For example, if a user enters the following text data for a contract:

[1678] This is a sample contract. The party agrees to the following terms and conditions...

[1679] The server analyzes this text data, extracts each clause, and then uses a generative AI model to generate prompts like the following to assess risk:

[1680] Evaluate the risk of the following contract clause: "The party agrees to the following terms and conditions..."

[1681] If the risk assessment identifies any high-risk clauses, the user will be notified, allowing them to quickly understand the risks in the contract and take appropriate action.

[1682] This system makes it possible to efficiently analyze the contents of contracts, perform risk assessments, and quickly identify and notify high-risk clauses.It also makes it possible to quickly determine the relevant clauses when a problem occurs and take appropriate action.

[1683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1684] Step 1:

[1685] Users can either take a photo of the contract using their smartphone or upload a PDF file.

[1686] Input: Image or PDF file of the contract

[1687] Output: Text data of the contract

[1688] What happens: The user takes a photo of the contract using the smartphone's camera or uploads a PDF file using the file chooser.

[1689] Step 2:

[1690] The terminal sends an image or PDF file of the uploaded contract to the server.

[1691] Input: Image or PDF file of the contract

[1692] Output: Text data of the contract sent to the server

[1693] Specific operation: The device uses image recognition technology (OCR) to extract text data from images and PDF files and sends the text data to the server.

[1694] Step 3:

[1695] The server analyzes the received text data and divides it into sentences.

[1696] Input: Text data of the contract

[1697] Output: Text data divided into sentences

[1698] Specific operation: The server uses the spaCy library to divide the text data into sentences.

[1699] Step 4:

[1700] The server analyzes the divided sentences using a natural language processing model and extracts the content of each clause.

[1701] Input: Text data divided into sentences

[1702] Output: Content of each extracted clause

[1703] What happens: The server uses the Transformers library to parse each sentence and extract the clause content.

[1704] Step 5:

[1705] The server performs risk assessment for each extracted clause using a generative AI model.

[1706] Input: The content of each extracted clause

[1707] Output: Risk assessment results for each clause

[1708] Specific operation: The server uses a generative AI model to assess the risk of each clause and identify high-risk clauses.

[1709] Step 6:

[1710] The server notifies the user of high-risk provisions.

[1711] Input: Risk assessment results

[1712] Output: User notification

[1713] Specific operation: The server identifies high-risk clauses, generates a message to notify the user, and sends it to the terminal.

[1714] Step 7:

[1715] Users receive notifications and review high-risk clauses.

[1716] Input: Notification from the server

[1717] Output: Check for high risk clauses

[1718] Specific behavior: The user uses the notification feature on their smartphone to check for high-risk clauses and take necessary action.

[1719] Example 2

[1720] 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."

[1721] With conventional contract management systems, it was difficult to quickly and accurately identify which contract clauses applied when a problem occurred. Furthermore, there was a lack of means to understand the contents of the contract and identify the appropriate clauses based on the nature of the problem, which often delayed negotiations with business partners. For these reasons, there is a need for more efficient contract management and faster problem response.

[1722] 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.

[1723] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses match the conditions of the problem, means for negotiating with the business partner based on the clauses of the basic contract, means for inputting the content of the problem as text data and identifying the relevant clauses of the contract based on that content, and means for calculating the similarity between the content of the problem and each clause of the contract and identifying clauses whose similarity is above a certain threshold as the relevant clauses. This makes it possible to quickly and accurately identify the relevant contract clauses when a problem occurs and to efficiently negotiate with the business partner.

[1724] A "contract" is a document that describes the legal agreement between business partners.

[1725] "Trouble" refers to problems or disputes that arise during the course of contract performance.

[1726] "Text data" is data that represents character information in digital form.

[1727] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[1728] "Similarity" is an index that indicates the degree of commonality or agreement between two pieces of data.

[1729] A "threshold" is a reference value for satisfying a specific condition.

[1730] "Identification" refers to clearly identifying and selecting an object.

[1731] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate and analyze data.

[1732] A "prompt sentence" is an instruction or question that a user inputs into a system.

[1733] This invention provides a method for efficiently negotiating with business partners by quickly and accurately identifying the relevant contract clauses in a contract management system when a problem occurs. A specific embodiment of this system is described below.

[1734] Hardware and software used

[1735] Hardware: Servers, terminals

[1736] Software: Generative AI models, text analysis tools, similarity calculation algorithms

[1737] System Operation

[1738] 1. The user enters the details of the problem

[1739] The user accesses the system interface and enters the details of the problem in text format.

[1740] Example: A user types "Problems with handling of late deliveries."

[1741] 2. The device sends the entered text data to the server.

[1742] The terminal receives the user's input and sends the data to the server.

[1743] Example: A terminal sends text data to a server saying, "Problems with handling when delivery is delayed."

[1744] 3. The server analyzes the text data

[1745] The server analyzes the received text data using a text analysis tool.

[1746] Example: The server uses a text analysis tool to extract keywords such as "delivery," "delay," and "response."

[1747] 4. The server calculates the similarity with each clause of the contract.

[1748] The server uses the generated AI model to calculate the similarity between the analysis results and each clause in the contract.

[1749] Example: The server uses a generative AI model to calculate the similarity between the "provision regarding delivery delays" and the "provision regarding response methods."

[1750] 5. Identify the terms that the server applies to

[1751] The server uses a similarity calculation algorithm to identify clauses whose similarity is equal to or greater than a certain threshold as relevant clauses.

[1752] Example: The server uses a similarity calculation algorithm to identify "provisions regarding delivery delays" and "provisions regarding response methods" with a similarity of 0.80 or higher.

[1753] 6. The server returns the specified terms to the user

[1754] The server returns the identified terms to the user and notifies the user.

[1755] Example: The server notifies the user that "the relevant clauses are the clause regarding delivery delays and the clause regarding response methods."

[1756] Prompt Sentence Examples

[1757] Please enter the details of the problem: Problems with handling of delayed delivery

[1758] This system has the ability to analyze the details of the problem entered by the user and identify the relevant contract clauses. Specifically, it uses text analysis tools and generative AI models to calculate the similarity between the problem details and each clause in the contract, and identifies clauses with a similarity above a certain threshold. This makes it possible to quickly and accurately identify the relevant contract clauses when a problem occurs, enabling efficient negotiations with business partners.

[1759] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1760] Step 1:

[1761] The user inputs the details of the problem.

[1762] Input: The user accesses the system interface and enters the details of the problem in text format.

[1763] Specific behavior: The user enters "Problems regarding handling of delayed delivery."

[1764] Output: The entered text data is sent to the terminal.

[1765] Step 2:

[1766] The terminal transmits the input text data to the server.

[1767] Input: Text data entered by the user.

[1768] Specific operation: The device sends text data to the server stating "Problems regarding response in the event of delayed delivery."

[1769] Output: Text data is sent to the server.

[1770] Step 3:

[1771] The server parses the text data.

[1772] Input: Text data sent from the terminal.

[1773] Specific operation: The server uses a text analysis tool to extract keywords such as "delivery," "delay," and "response."

[1774] Output: The extracted keyword list.

[1775] Step 4:

[1776] The server calculates the similarity with each clause of the contract.

[1777] Input: The extracted keyword list.

[1778] Specific operation: The server uses the generative AI model to calculate the similarity between the analysis results and each clause in the contract.

[1779] Output: Similarity score for each clause.

[1780] Step 5:

[1781] The server identifies the applicable terms.

[1782] Input: Similarity score for each clause.

[1783] Specific operation: The server uses a similarity calculation algorithm to identify clauses with a similarity score of 0.80 or higher.

[1784] Output: A list of applicable contract clauses.

[1785] Step 6:

[1786] The server returns the specified terms to the user.

[1787] Input: A list of applicable contract clauses.

[1788] Specific operation: The server notifies the user that "the relevant clauses are the clause regarding delivery delays and the clause regarding response methods."

[1789] Output: The applicable contract terms communicated to the user.

[1790] (Application example 2)

[1791] 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."

[1792] When a problem occurs with a contract, it is difficult to quickly and accurately identify which clause applies. In particular, when the contract is complex or the problem covers a wide range of issues, it takes a great deal of time and effort to find the appropriate clause. To solve this problem, a system is needed that can analyze the content of the problem and automatically identify the relevant contract clause.

[1793] 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.

[1794] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clause's conditions are met in the problem, means for negotiating with the business partner based on the clauses of the basic contract, means for inputting the content of the problem as text data and identifying the relevant clauses of the contract based on that content, and means for calculating the similarity between the content of the problem and each clause of the contract and identifying clauses whose similarity is above a certain threshold as the relevant clauses. This makes it possible to quickly and accurately identify the relevant clauses and take appropriate action when a problem regarding the contract occurs.

[1795] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[1796] "Trouble" refers to problems or disputes that arise in connection with a contract.

[1797] "Text data" is data that represents character information in digital form.

[1798] "Similarity" is an index that indicates the degree of match between the contents of two text data.

[1799] A "threshold" refers to a reference value for satisfying a specific condition.

[1800] "Analysis" is the process of examining data in detail to understand its structure and meaning.

[1801] "Identification" refers to clearly identifying and selecting an object.

[1802] A "system" is a set of devices and programs in which multiple elements function in conjunction with one another.

[1803] The system for carrying out the present invention has the function of reading and understanding the contents of a contract and identifying the relevant clause of the contract based on the content of the problem. A specific embodiment of this system will be described below.

[1804] System configuration

[1805] The system consists of three main components: a server, a terminal, and a user. The server analyzes the contents of the contract and performs the main process of identifying the relevant clause based on the nature of the problem. The terminal provides an interface for users to input the details of the problem. When a problem occurs with the contract, users can use this system to quickly identify the appropriate clause.

[1806] Hardware and software used

[1807] The server is a computer equipped with a high-performance processor and large memory capacity. It uses the natural language processing library, Spacy, and the machine learning library, sklearn, to analyze the text data of the contract and calculate the degree of similarity between the content of the dispute and each clause in the contract.

[1808] Data processing and calculation

[1809] The server first analyzes the text data of the contract and extracts the contents of each clause. Next, it receives the trouble description entered by the user as text data and preprocesses it. Preprocessing includes morphological analysis and stop word removal. The preprocessed text data is vectorized using TfidfVectorizer. Finally, it calculates the similarity between the trouble description and each clause of the contract using cosine_similarity and identifies the clause with the highest similarity.

[1810] Specific examples

[1811] For example, if a user enters a problem such as "What to do if a payment is late," the system analyzes this content and identifies the most relevant clause in the contract. Below is an example of a prompt sentence.

[1812] Example prompt sentence:

[1813] Problem: "What to do if payment is late"

[1814] Terms of the Agreement:

[1815] 1. Purpose of the Agreement

[1816] 2. Term of the Agreement

[1817] 3. Payment Terms

[1818] 4. Conditions for termination

[1819] When the user enters this prompt, the system identifies the clause in the contract that is most relevant to the problem and presents it to the user, allowing the user to take appropriate action quickly and accurately.

[1820] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1821] Step 1:

[1822] The user uses the terminal to input the details of the problem.

[1823] Input: Details of the problem (e.g., "What to do if payment is late")

[1824] Output: The details of the problem are sent to the server as text data.

[1825] Step 2:

[1826] The server reads the text data of the contract and extracts the contents of each clause.

[1827] Input: Text data of the contract

[1828] Output: A list of the contents of each clause (e.g., "Purpose of the contract," "Term of the contract," "Payment terms," ​​"Conditions for termination of the contract")

[1829] Step 3:

[1830] The server preprocesses the content of the problem. Specifically, it performs morphological analysis and removes stop words.

[1831] Input: Text data of the problem

[1832] Output: Preprocessed trouble details (e.g., "Payment delay response")

[1833] Step 4:

[1834] The server preprocesses the content of each clause in the contract by performing morphological analysis and removing stop words.

[1835] Input: Text data for each clause of the contract

[1836] Output: Content of each preprocessed clause (e.g., "Contract Purpose", "Contract Term", "Payment Terms", "Terms of Termination")

[1837] Step 5:

[1838] The server vectorizes the preprocessed problem details and the contents of each clause of the contract using TfidfVectorizer.

[1839] Input: Preprocessed content of the problem and content of each clause

[1840] Output: Vectorized content of the problem and content of each clause

[1841] Step 6:

[1842] The server uses cosine_similarity to calculate the similarity between the content of the problem and each clause in the contract.

[1843] Input: Vectorized content of the problem and content of each clause

[1844] Output: A list of similarity scores (e.g. "0.85", "0.60", "0.90", "0.70")

[1845] Step 7:

[1846] The server identifies the clause with the highest similarity score.

[1847] Input: A list of similarity scores

[1848] Output: The clause with the highest similarity (e.g., "Payment Terms")

[1849] Step 8:

[1850] The server presents the identified terms to the user.

[1851] Input: Most similar clause

[1852] Output: Identified terms (e.g., "Payment Terms") displayed on the user's device

[1853] Example 3

[1854] 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."

[1855] Negotiations with business partners based on traditional contracts required a great deal of time and effort to understand the contents of the contract, identify clauses that match the content of the problem, and generate appropriate proposals. It was also difficult to accurately link the contract clauses with the content of the problem, which could reduce the efficiency of negotiations. Furthermore, proposals were generated manually, which could result in a lack of consistency and objectivity.

[1856] 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.

[1857] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses the problem meets, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the content of the problem using natural language processing technology, means for generating a prompt sentence based on the analysis results and the acquired clauses, means for sending the prompt sentence to a generation AI model to generate a proposal for negotiation, and means for sending the generated proposal to the user's terminal. This makes it possible to quickly and accurately link the content of the contract with the content of the problem, and automatically generate an efficient and consistent proposal.

[1858] "Means for reading and understanding the contents of a contract" refers to a means for analyzing the text data of a contract and extracting the contents of each clause.

[1859] "Means of determining which clause's conditions are met in the particular problem" refers to a means of analyzing the content of the problem and identifying the relevant clause of the contract.

[1860] The "means for negotiating with a business partner based on the provisions of the master agreement" is a means for generating a proposal for negotiating with a business partner based on the specified provisions.

[1861] "Means for analyzing the content of the trouble using natural language processing technology" refers to means for analyzing the content of the trouble entered by the user using natural language processing technology and extracting important keywords and phrases.

[1862] The "means for generating a prompt sentence based on the analysis results and the acquired clauses" refers to a means for generating a prompt sentence to be sent to the generative AI model based on the content of the analyzed problem and the acquired contract clauses.

[1863] The "means for sending a prompt sentence to a generative AI model and generating a proposal for negotiation" is a means for sending a prompt sentence to a generative AI model and generating a proposal for negotiation in response thereto.

[1864] "Means for sending generated proposals to a user's terminal" refers to means for sending proposals received from a generative AI model to a user's terminal so that the user can confirm them.

[1865] The present invention is a system that reads and understands the contents of a contract and generates an appropriate proposal based on the details of the problem. A specific embodiment of this system will be described below.

[1866] Hardware and software used

[1867] The server uses a relational database management system (RDBMS) to read and understand the contents of the contract. Specifically, a database management system such as MySQL is used. To analyze the details of the problem, Python's natural language processing libraries NLTK and spaCy are used. OpenAI's GPT-4, for example, is used as the generative AI model.

[1868] Data processing and calculation

[1869] The server first retrieves the terms of the master contract from a database. Next, it receives the details of the problem entered by the user and analyzes them using natural language processing technology. Based on the analyzed details of the problem and the retrieved terms, the server generates a prompt to send to the generative AI model. The generative AI model then generates a proposal for negotiation based on this prompt. The generated proposal is sent to the user's device, where the user can review it and make any necessary corrections or additions.

[1870] Specific examples

[1871] For example, if a user types "There is a problem with the delivery being delayed," the server will do the following:

[1872] 1. The server retrieves the "Delivery Terms" from the MySQL database.

[1873] 2. The server uses Python's NLTK library to extract the keyword "Delivery Delay".

[1874] 3. The server generates a prompt message saying, "In accordance with clause X of the master agreement, a problem has occurred regarding a delay in delivery. Please suggest what measures should be taken to address this problem."

[1875] 4. The server sends a prompt to the generative AI model (GPT-4).

[1876] 5. The generative AI model generates the following recommendation: "In accordance with clause X, we propose an additional extension of delivery time as a measure to address the late delivery date. Please also consider requesting a waiver of the penalty associated with the delay."

[1877] 6. The server sends the generated proposal to the user's device.

[1878] 7. The user reviews the proposal and makes corrections, such as "I propose an additional delivery extension, but do not request a waiver of the penalty for the delay."

[1879] In this way, it is possible to quickly and accurately link the contents of the contract with the contents of the problem, and automatically generate an efficient and consistent proposal. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1880] Step 1:

[1881] The user inputs the details of the problem.

[1882] The user inputs the details of the problem through the system interface. For example, they might input "A problem has occurred regarding a delivery delay." The input data is then sent to the server.

[1883] Step 2:

[1884] The server retrieves the terms of the master contract from the database.

[1885] The server uses a relational database management system (RDBMS) such as MySQL to retrieve the terms of the master contract from the database. For example, it retrieves the "terms regarding delivery time." The input is the details of the problem entered by the user, and the output is the retrieved terms of the contract.

[1886] Step 3:

[1887] The server analyzes the details of the problem using natural language processing (NLP) technology.

[1888] The server uses Python's NLTK and spaCy libraries to analyze the content of the problem entered by the user. For example, it extracts the keyword "delivery delay." The input is the content of the problem entered by the user, and the output is the analyzed keywords and phrases.

[1889] Step 4:

[1890] The server generates a prompt based on the analysis results and the acquired clauses.

[1891] The server generates a prompt to send to the generative AI model based on the analysis results and the acquired clauses. For example, it generates a prompt such as, "In accordance with clause X of the master contract, a problem has occurred regarding a delivery delay. Please suggest what measures should be taken to address this problem." The input is the analysis results and the acquired clauses, and the output is the generated prompt.

[1892] Step 5:

[1893] The server sends a prompt to the generative AI model.

[1894] The server sends a prompt to a generative AI model (e.g., OpenAI's GPT-4). The generative AI model generates a proposal based on this prompt. The input is the generated prompt, and the output is the proposal generated by the generative AI model.

[1895] Step 6:

[1896] A generative AI model generates proposals for negotiation.

[1897] The generative AI model generates a proposal for negotiation based on a prompt. For example, it generates a proposal such as, "In accordance with clause X, we propose an additional extension of delivery time as a measure to address the delivery delay. Please also consider requesting a waiver of the penalty associated with the delay." The input is the prompt, and the output is the generated proposal.

[1898] Step 7:

[1899] The server sends the generated proposal to the user's terminal.

[1900] The server sends the proposal received from the generative AI model to the user's device, where the user can review it. The input is the generated proposal, and the output is the proposal sent to the user's device.

[1901] Step 8:

[1902] The user reviews the proposal and makes corrections or additions as necessary.

[1903] The user can check the proposal on the terminal and make corrections or additions as necessary. For example, a correction can be made such as "I propose an additional delivery extension, but I do not request an exemption from the penalty for the delay." The input is the proposal sent from the server, and the output is the proposal corrected or added by the user.

[1904] (Application example 3)

[1905] 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."

[1906] Conventional contract negotiation systems lack the functionality to automatically generate proposals for negotiations based on the contract clauses and the details of the problem, which reduces the efficiency of negotiations. Also, even if users input the contract clauses and the details of the problem, it is difficult to properly analyze them and generate specific proposals.

[1907] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clause's conditions are met in the relevant trouble, means for negotiating with the trading partner based on the clauses of the master contract, means for automatically generating a proposal for negotiation using a generative AI model, means for sending the contract clauses and the content of the trouble input by the user as prompt text to the generative AI model, and means for displaying the generated proposal. This makes it possible to automatically generate a specific and appropriate proposal based on the contract clauses and the content of the trouble, thereby improving the efficiency of negotiations.

[1908] A "contract" is a legally binding document exchanged between business partners, which contains the terms of the transaction, obligations, rights, etc.

[1909] "Clause" refers to the individual provisions and conditions set out in a contract, and is an element that constitutes the specific content of the contract.

[1910] "Trouble" refers to problems or disputes that arise during the course of contract performance, including acts that violate or fail to perform the terms of the contract.

[1911] A "generative AI model" refers to an algorithm or system that uses artificial intelligence technology to generate text data, specifically a natural language processing model such as GPT-3.

[1912] A "prompt" is text data input to a generative AI model that contains instructions or questions that enable the model to generate appropriate output.

[1913] A "proposal" refers to a solution or response presented in contract negotiations, including specific actions or measures.

[1914] "Analysis" refers to the process of analyzing text data and information in detail to understand its meaning and structure.

[1915] "Display" refers to the act of visually presenting generated suggestions or information to a user, including outputting it as text or graphics on a screen.

[1916] The system for implementing this invention reads and understands the contents of a contract, identifies appropriate clauses based on the content of the problem, and automatically generates a proposal for negotiation using a generative AI model. Specific embodiments of this system are described below.

[1917] First, the server receives the text data of the contract and analyzes its contents. Natural language processing technology is used to extract the content of each clause, which is then stored in a database.

[1918] Next, the user inputs the details of the problem. The server analyzes the details of the problem and identifies the relevant clauses in the contract. This analysis is done using text mining technology.

[1919] The server then generates a proposal for negotiation using a generative AI model (e.g., OpenAI's GPT-3) based on the identified terms and the details of the problem. The generated proposal is obtained by sending the information entered by the user as a prompt to the generative AI model.

[1920] The generated proposal is sent from the server to the user's device and displayed on the screen, allowing the user to negotiate with a business partner based on a specific and appropriate proposal.

[1921] As a concrete example, the following prompt sentence is input into the generative AI model:

[1922] Example prompt sentence:

[1923] Contract clause: Article 10.2: Liability for service interruptions

[1924] Problem: Customer seeks compensation after service was interrupted for three days.

[1925] Generate a proposal.

[1926] Based on this prompt, the generative AI model generates the following suggestions:

[1927] An example of a generated suggestion:

[1928] Pursuant to clause 10.2, we offer to provide you with a 10% discount off your next bill as compensation for the interruption of our services.

[1929] In this way, it is possible to automatically generate specific and appropriate proposals based on the terms of the contract and the details of the problem, thereby improving the efficiency of negotiations.

[1930] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1931] Step 1:

[1932] The user inputs the text data of the contract using a terminal. The input text data is sent to the server. The server receives this text data and analyzes the contents of the contract using natural language processing technology. Specifically, it extracts each clause from the text data and stores it in a database. The input is the text data of the contract, and the output is the analyzed data of each clause.

[1933] Step 2:

[1934] The user uses a terminal to input the details of the problem. The input details of the problem are sent to the server. The server receives the details of the problem and analyzes them using text mining technology. Specifically, it analyzes the details of the problem and identifies the relevant clauses of the contract. The input is the details of the problem, and the output is the relevant clauses of the contract.

[1935] Step 3:

[1936] The server uses a generative AI model to generate a proposal for negotiation based on the identified contract clauses and the details of the problem. Specifically, the information entered by the user is sent to the generative AI model as a prompt, which generates a proposal. The input is the identified contract clauses and the details of the problem, and the output is the generated proposal.

[1937] Step 4:

[1938] The server sends the generated proposal to the user's device. The user's device receives the proposal and displays it on the screen. This allows the user to negotiate with a trading partner based on a specific and appropriate proposal. The input is the generated proposal, and the output is the proposal displayed on the screen.

[1939] 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.

[1940] "Example 1"

[1941] One embodiment of the present invention provides a system incorporating an emotion engine. This system includes a means for reading and understanding the contents of a contract, a means for determining which clauses are met in the current dispute, a means for negotiating with a business partner based on the clauses of the master contract, and an emotion engine for recognizing the user's emotions. The emotion engine analyzes the user's voice data to determine the user's emotional state. For example, if the user is feeling angry or frustrated, the emotion engine captures that information and feeds it back to the system. This allows the system to generate negotiation proposals taking the user's emotional state into consideration.

[1942] "Example 2"

[1943] As a specific example, consider a case where a user is dissatisfied with a part of a contract. The user communicates this dissatisfaction to the system. At this time, the user's voice data is analyzed by the emotion engine to determine the user's emotional state. The emotion engine determines that the user is feeling angry or dissatisfied and feeds this information back to the system. Based on this information, the system generates a proposal for negotiation that takes into account the user's emotional state. This proposal might be generated in the form of, for example, "The user is dissatisfied with clause X, so we believe there is room for improvement. Specifically, we propose that you take action Y."

[1944] "Example 3"

[1945] The emotion engine can also grasp the user's emotional state in real time. For example, if a user is using the system while negotiating with a business partner, the emotion engine analyzes the user's voice data in real time to determine the user's emotional state. This information is fed back to the system, which then generates negotiation proposals in real time that take the user's emotional state into account. This allows the user to obtain the optimal negotiation strategy for the situation at hand.

[1946] The processing flow of each embodiment will be described below.

[1947] "Example 1"

[1948] Step 1: The user enters the contract details into the system.

[1949] Step 2: The system reads and understands the contents of the contract.

[1950] Step 3: The user enters the details of the problem into the system.

[1951] Step 4: The system determines which clause conditions are met for the problem.

[1952] Step 5: The user inputs their emotional state into the system as voice data.

[1953] Step 6: The emotion engine analyzes the user's voice data and determines the user's emotional state.

[1954] Step 7: The system generates a proposal for negotiation after taking into account the user's emotional state.

[1955] "Example 2"

[1956] Step 1: The user is dissatisfied with some aspect of the contract and tells the system about it.

[1957] Step 2: The emotion engine analyzes the user's voice data and determines that the user's emotional state is dissatisfied.

[1958] Step 3: The system uses that information to generate proposals for negotiation that take into account the user's emotional state.

[1959] "Example 3"

[1960] Step 1: The user uses the system during negotiations with a business partner.

[1961] Step 2: The emotion engine analyzes the user's voice data in real time to determine their emotional state.

[1962] Step 3: The system generates proposals for negotiation that take into account the user's emotional state in real time.

[1963] Example 1

[1964] 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."

[1965] Conventional contract management systems could read and understand the contents of a contract, but they were unable to generate negotiation proposals that took the user's emotional state into account. This made it difficult to negotiate appropriately based on the user's emotions, resulting in a decrease in the efficiency of dispute resolution.

[1966] 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.

[1967] In this invention, the server includes means for reading and understanding the contents of the contract, means for determining which clauses meet the conditions of the relevant trouble, means for negotiating with the business partner based on the clauses of the master contract, means for analyzing the user's voice data and determining the user's emotional state, means for generating a negotiation proposal based on the analyzed clauses of the contract and the user's emotional state, and means for displaying the generated negotiation proposal to the user. This makes it possible to generate an appropriate negotiation proposal that takes the user's emotional state into consideration.

[1968] A "contract" is a document that sets out the terms and conditions of a transaction or agreement.

[1969] "Text data" is data that represents character information in digital form.

[1970] "Natural language processing" is a technology that uses computers to analyze and understand human language.

[1971] "Clause" means any individual condition or provision set out in a contract.

[1972] "Audio data" refers to data in which audio is recorded in digital format.

[1973] An "emotion engine" is software that analyzes voice and text data to determine emotional states.

[1974] A "generative AI model" is a model that uses artificial intelligence to generate new data and suggestions.

[1975] A "negotiation proposal" is a specific proposal to smoothly advance negotiations with a business partner.

[1976] "User" means an individual or corporation that uses the system.

[1977] A "server" is a computer system that processes and stores data over a network.

[1978] A "terminal" is a device such as a computer or smartphone that is directly operated by a user.

[1979] The present invention relates to a system that reads and understands the contents of a contract and generates a negotiation proposal that takes into account the emotional state of the user. This system operates in cooperation with a server, a terminal, and a user.

[1980] First, the user uploads the text data of the contract to their device. The device then sends the uploaded text data to the server. The server then uses natural language processing technology to analyze the contents of the contract and extract each clause. This...

Claims

1. A first means for accepting an input of a content of a trouble by a user; A second means for acquiring, from a database, a clause of the basic contract corresponding to the content of the trouble; a third means for generating a first prompt sentence for outputting a proposal for negotiation based on the content of the trouble and the clauses of the basic contract; a fourth means for inputting the generated first prompt sentence into a generative AI model to generate a proposal for the negotiation; a fifth means for transmitting the generated proposal for negotiation to a terminal of the user; A system including:

2. a sixth means for determining an emotional state of the user from the voice of the user using an emotion engine; the third means generates a second prompt sentence for outputting a proposal for the negotiation taking into consideration the emotional state of the user, based on the content of the trouble, the clauses of the basic contract, and the emotional state of the user; The fourth means inputs the generated second prompt sentence into a generative AI model to generate a proposal for the negotiation taking into account the emotional state of the user. The system of claim 1 .

3. The sixth means uses the emotion engine to determine an emotional state of the user during the negotiation from a voice of the user during the negotiation with a counterparty, the third means generates, based on an emotional state of the user during the negotiation, a third prompt sentence for outputting a proposal for the negotiation taking into consideration the emotional state; The fourth means inputs the generated third prompt sentence into a generative AI model to generate a proposal for the negotiation taking into account the emotional state of the user during the negotiation. The system of claim 2.

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