A method and system for automatically generating contract review logic based on historical revision traces

Abstract

This invention discloses a method and system for automatically generating contract review logic based on historical revision traces. The key technical points are: the system includes: a contract version data extraction module; a difference comparison module for extracting revised text blocks and difference tuples; a semantic analysis module for performing semantic analysis on the difference tuples and generating difference elements; a review logic generation module for automatically generating review logic based on the difference elements and generating corresponding review points; a review checklist management module; a similarity calculation module; and a contract processing inspection module. This invention automates contract review, reduces the cost of manual intervention, improves review efficiency, and makes significant improvements in utilizing historical revision traces and knowledge management. The system can automatically extract experience from historical reviews, generate standardized review logic, and continuously update the review checklist, promoting the intelligence and systematization of the contract review process.

Description

Technical Field

[0001] This invention relates to the field of contract management technology, specifically to a method and system for automatically generating contract review logic based on historical revision traces. Background Technology

[0002] As business operations become increasingly complex, contract management plays a crucial role in daily operations, especially in large and medium-sized enterprises. The signing, review, performance, and revision of contracts are complex and frequent, often involving collaboration and repeated reviews across multiple departments. In the contract management process, the legal department is responsible for contract risk control, compliance checks, and clause optimization to ensure the legality of contracts and their smooth execution. Currently, intelligent contract management systems, as an information management tool, are widely used in the storage, management, and review of enterprise contracts. These systems mainly provide functions such as document storage, version control, information retrieval, performance management, archiving, and retrieval through automated tools.

[0003] While the introduction of intelligent contract review systems has improved the efficiency of contract review and enabled the automated identification of potential legal risks, it still faces some significant challenges in practical application, including the following aspects:

[0004] 1. Existing intelligent contract review systems rely heavily on the prior knowledge and experience of legal personnel. Since the identification of risk points and the review logic depend on the manual sorting by professional legal personnel, the review efficiency is low and the labor cost is high.

[0005] 2. In many cases, the revision records of historical contracts are limited to document archiving and cannot be effectively transformed into reusable review logic and strategies. Legal personnel often review these records based on their personal experience, resulting in a lack of systematization and standardization in the review process, which affects the quality and consistency of the review.

[0006] 3. Due to the lack of an effective knowledge management mechanism, companies have failed to systematically summarize their experience and knowledge during contract review, resulting in repetitive and inefficient review processes, especially for newly hired legal personnel who lack comprehensive knowledge base support.

[0007] 4. Existing systems often struggle to extract and summarize the legal intent and review logic behind contract revisions;

[0008] Therefore, existing intelligent contract review systems have not yet been able to fully realize their potential in improving review efficiency, reducing legal costs, and optimizing contract management processes. To address these issues, we propose a method and system for automatically generating contract review logic based on historical revision traces. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a method and system for automatically generating contract review logic based on historical revision traces, thus resolving the problems mentioned in the background section.

[0010] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0011] A system for automatically generating contract review logic based on historical revision traces, the system comprising:

[0012] The contract version data extraction module is used to extract all historical contract version data that have been reviewed by the legal department from the enterprise contract management system, including initial versions and final versions.

[0013] The difference comparison module is used to compare the initial version and the final version of each contract using text comparison algorithms, and extract the revised text blocks and difference tuples.

[0014] The semantic analysis module is used to perform semantic analysis on the difference tuples, infer the legal personnel's modification intentions in the contract modification, and generate difference elements;

[0015] The review logic generation module is used to automatically generate review logic based on the differences in factors and generate corresponding review points. It also combines contract terms to generate corresponding review points to ensure that the generated review logic is legally compliant.

[0016] The review checklist management module is used to generate and manage review checklists, record review logic, and update and maintain them to ensure the timeliness and consistency of the review checklists.

[0017] The similarity calculation module is used to calculate the similarity between the newly generated review logic and the review logic in the existing review list. When the similarity exceeds a preset threshold, the existing review logic is updated; otherwise, the newly generated review logic is added to the review list.

[0018] The contract processing check module is used to check whether all contracts have completed the review logic generation and review checklist update. If not, the review logic extraction will be automatically re-executed until all contracts have been processed.

[0019] Furthermore, the difference comparison module uses a text comparison algorithm to compare the initial version and the final version of the contract sentence by sentence, and extracts the revised differences.

[0020] Furthermore, the semantic analysis module performs deep semantic analysis on the difference tuples based on a large language model to infer the legal intent behind the contract revision and generate review logic elements related to that legal intent.

[0021] Furthermore, the similarity calculation module uses an embedding model measurement method based on semantic similarity to calculate the similarity between the generated review logic and the existing review logic.

[0022] Furthermore, the contract processing and inspection module incorporates a recursive mechanism to automatically identify and process changes in the complexity and number of contracts, ensuring that all contracts can complete the extraction of review logic and the updating of the review list.

[0023] This invention also provides a method for automatically generating contract review logic based on historical revision traces, comprising the following steps:

[0024] Step S1: Initialize the review list by creating an empty review list L to provide storage space for subsequent review logic generation and review point updates;

[0025] Step S2: Extract historical contract versions. Extract historical contract data from the contract management system, including initial versions and final versions. The initial version refers to the contract version before legal review, and the final version refers to the final draft of the contract after multiple rounds of revisions.

[0026] Step S3: Version comparison and difference extraction. Use a text comparison algorithm to compare each pair of initial and final versions of the contract line by line, extract the text differences, and organize the differences into a revised text comparison tuple (Oi, Fi), where Oi is the text content in the initial version and Fi is the text content in the final version.

[0027] Step S4: Difference Analysis and Intent Inference. A large language model is used to perform semantic analysis on the comparison tuples (Oi,Fi) of each revised text to infer the logical intent of the legal personnel in the process of contract revision. Based on the inference, a difference element Ei is generated, which represents the review intent and review logic behind the revision.

[0028] Step S5: Review logic generation. The triples (Oi, Fi, Ei) are comprehensively analyzed using a large language model to generate the corresponding review logic Ri and review point titles Ti, and to determine whether the review logic meets the predetermined standards.

[0029] Step S6: Update the review list. Calculate the similarity between the generated review logic Ri and review point title Ti and the items in the existing review list L. If the similarity is greater than the set threshold, update the review list; otherwise, add the new review logic directly to the review list L.

[0030] Step S7: Review completion check. Check the review completion status of all contracts. If there are any incomplete contracts, automatically return to step S3 to continue execution until the review and logical extraction of all contracts are completed.

[0031] In summary, the present invention has the following main beneficial effects:

[0032] 1. This invention reduces reliance on the prior knowledge and experience of legal personnel by automatically extracting differences in contract revisions and inferring the legal intent behind the revisions. It achieves automated generation of review logic, which not only significantly improves the efficiency of contract review but also reduces the cost of manual review. Especially when faced with a large number of contracts, the system can quickly respond to and complete the review task.

[0033] 2. By systematically recording the historical revision process and review logic of contracts, this invention can transform historical review experience into reusable review logic and review checklists, avoiding the one-sidedness and subjectivity of experience in traditional contract review. This approach effectively improves the consistency and standardization of review quality, ensuring that each review can be optimized based on accumulated experience.

[0034] 3. This invention establishes an intelligent review checklist management module that can dynamically update review logic and review points, forming a continuously accumulating review knowledge base. This not only helps legal personnel quickly acquire review points in their daily work, but also provides systematic knowledge support for newly hired legal personnel, reducing training costs and time.

[0035] 4. This invention uses a large language model (such as GPT-4) to perform in-depth semantic analysis on contract revision differences, which can more accurately infer the legal intent of legal personnel during the revision process. The systematic generation of review logic and the similarity calculation mechanism with existing review logic ensure the accuracy and timeliness of legal review logic, and greatly reduce errors and omissions in traditional manual review. Attached Figure Description

[0036] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] The following embodiments are used to illustrate the present invention, but should not be used to limit the scope of protection of the present invention. The conditions in the embodiments can be further adjusted according to specific conditions, and simple improvements to the method of the present invention under the premise of the concept of the present invention are all within the scope of protection claimed by the present invention.

[0039] Example 1

[0040] refer to Figure 1 A system for automatically generating contract review logic based on historical revision traces, the system comprising:

[0041] The contract version data extraction module is used to extract all historical contract version data that have been reviewed by the legal department from the enterprise contract management system, including initial versions and final versions.

[0042] The difference comparison module is used to compare the initial version and the final version of each contract using text comparison algorithms, and extract the revised text blocks and difference tuples.

[0043] The semantic analysis module is used to perform semantic analysis on the difference tuples, infer the legal personnel's modification intentions in the contract modification, and generate difference elements;

[0044] The review logic generation module is used to automatically generate review logic based on the differences in factors and generate corresponding review points. It also combines contract terms to generate corresponding review points to ensure that the generated review logic is legally compliant.

[0045] The review checklist management module is used to generate and manage review checklists, record review logic, and update and maintain them to ensure the timeliness and consistency of the review checklists.

[0046] The similarity calculation module is used to calculate the similarity between the newly generated review logic and the review logic in the existing review list. When the similarity exceeds a preset threshold, the existing review logic is updated; otherwise, the newly generated review logic is added to the review list.

[0047] The contract processing check module is used to check whether all contracts have completed the review logic generation and review checklist update. If not, the review logic extraction will be automatically re-executed until all contracts have been processed.

[0048] Furthermore, the difference comparison module uses a text comparison algorithm to compare the initial version and the final version of the contract sentence by sentence, and extracts the revised differences.

[0049] Furthermore, the semantic analysis module performs deep semantic analysis on the difference tuples based on a large language model to infer the legal intent behind the contract revision and generate review logic elements related to that legal intent.

[0050] Furthermore, the similarity calculation module uses an embedding model measurement method based on semantic similarity to calculate the similarity between the generated review logic and the existing review logic.

[0051] Furthermore, the contract processing and inspection module incorporates a recursive mechanism to automatically identify and process changes in the complexity and number of contracts, ensuring that all contracts can complete the extraction of review logic and the updating of the review list.

[0052] This invention also provides a method for automatically generating contract review logic based on historical revision traces, comprising the following steps:

[0053] Step S1: Initialize the review list by creating an empty review list L to provide storage space for subsequent review logic generation and review point updates;

[0054] Step S2: Extract historical contract versions. Extract historical contract data from the contract management system, including initial versions and final versions. The initial version refers to the contract version before legal review, and the final version refers to the final draft of the contract after multiple rounds of revisions.

[0055] Step S3: Version comparison and difference extraction. Use a text comparison algorithm to compare each pair of initial and final versions of the contract line by line, extract the text differences, and organize the differences into a revised text comparison tuple (Oi, Fi), where Oi is the text content in the initial version and Fi is the text content in the final version.

[0056] Step S4: Difference Analysis and Intent Inference. A large language model is used to perform semantic analysis on the comparison tuples (Oi,Fi) of each revised text to infer the logical intent of the legal personnel in the process of contract revision. Based on the inference, a difference element Ei is generated, which represents the review intent and review logic behind the revision.

[0057] Step S5: Review logic generation. The triples (Oi, Fi, Ei) are comprehensively analyzed using a large language model to generate the corresponding review logic Ri and review point titles Ti, and to determine whether the review logic meets the predetermined standards.

[0058] Step S6: Update the review list. Calculate the similarity between the generated review logic Ri and review point title Ti and the items in the existing review list L. If the similarity is greater than the set threshold, update the review list; otherwise, add the new review logic directly to the review list L.

[0059] Step S7: Review completion check. Check the review completion status of all contracts. If there are any incomplete contracts, automatically return to step S3 to continue execution until the review and logical extraction of all contracts are completed.

[0060] Example 2

[0061] This embodiment provides a practical application of a contract review logic automatic generation system based on historical revision traces, specifically demonstrating how to use the system and method of this invention to automatically generate review logic and update review points for a company's contracts.

[0062] The implementation process is as follows:

[0063] 1. Contract version data extraction:

[0064] The company's contract management system stores all contract versions that have been reviewed by the legal department, including the initial version and the final version that has been reviewed and revised by the legal department. First, the contract version data extraction module in the system automatically extracts these historical contract version data from the contract management database.

[0065] For example, the company retrieved the initial version (before signing) and the final version (after multiple revisions) of a supply contract.

[0066] 2. Difference comparison and revision extraction:

[0067] The extracted contract version is handed over to the difference comparison module, which uses a text comparison algorithm to compare the initial version and the final version of the contract sentence by sentence to identify all text differences;

[0068] The comparison results may include: the amount of a certain clause has been modified, some clauses have been deleted or added, and the legal terminology of some clauses has changed;

[0069] The system generates a set of revised text comparison tuples (Oi, Fi), where Oi represents the text content in the initial version and Fi represents the modified text content in the final version.

[0070] 3. Semantic analysis and inference of censorship intent:

[0071] Next, the semantic analysis module performs deep semantic analysis on each revised text comparison tuple (Oi,Fi). For example, in the revision of the "Payment Method" clause, the initial version stipulated that "the payment will be made within 30 days", while the final version changed it to "the payment will be made within 45 days".

[0072] The system uses large language models (such as GPT-4) to infer the legal intent behind the modification, identify that the modification is to relax the payment period, thereby reducing the financial pressure on the company, and infer the review element: the review risk of changes in the payment period.

[0073] 4. Review logic generation and review point creation:

[0074] Based on the above differences, the review logic generation module automatically generates review logic. For example, for the modification of the payment period, a review point is generated to "review whether the payment period complies with the financial status of the contract signatory, industry standards and relevant legal provisions".

[0075] Through this module, the system also generates similar review logic and corresponding review points for other revised clauses of the contract, such as "whether the revised liquidated damages clause complies with the principle of fairness".

[0076] 5. Review list management and updates:

[0077] The generated review logic and review points are added to the review list management module. This module compares the new review points with the review points in the existing review list. If duplicate or similar review points are found, the module will decide whether to update the existing review points or add new review points based on the results of the similarity calculation module.

[0078] If similar revisions are made to the penalty clauses in multiple contracts, the system will automatically update the review logic for these clauses.

[0079] 6. Review the dynamic updating of the checklist and complete the inspection:

[0080] Throughout the review process, the review checklist management module will dynamically update the review checklist to ensure that it remains up-to-date. Reviewers can adjust the review points in the review checklist at any time based on new revision information and contract revision history.

[0081] The contract processing and inspection module checks whether all contracts have completed the review logic generation and review checklist update. If some contracts are not completed, the system will automatically return to step 3 to extract differences and generate review points until the review of all contracts is completed.

[0082] In summary, this invention automates contract review, reduces the cost of manual intervention, improves review efficiency, and makes significant improvements in utilizing historical revision traces and knowledge management. The system can automatically extract experience from past reviews, generate standardized review logic, and continuously update the review checklist, promoting the intelligent and systematic nature of the contract review process.

[0083] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that, unless otherwise defined, the technical or scientific terms used in this invention should be understood in the ordinary sense by those skilled in the art to which this invention pertains, and the terms "comprising" or "including" or similar terms used in this invention mean that the element or object preceding the word covers the element or object listed after the word and its equivalents.

[0084] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for automatically generating contract review logic based on historical revision traces, characterized in that, The system includes: The contract version data extraction module is used to extract all historical contract version data that have been reviewed by the legal department from the enterprise contract management system, including initial versions and final versions. The difference comparison module is used to compare the initial version and the final version of each contract using text comparison algorithms, and extract the revised text blocks and difference tuples. The semantic analysis module is used to perform semantic analysis on the difference tuples, infer the legal personnel's modification intentions in the contract modification, and generate difference elements; The review logic generation module is used to automatically generate review logic based on the differences in factors and generate corresponding review points in conjunction with contract terms to ensure that the generated review logic is legally compliant. The review checklist management module is used to generate and manage review checklists, record review logic, and update and maintain them to ensure the timeliness and consistency of the review checklists. The similarity calculation module is used to calculate the similarity between the newly generated review logic and the review logic in the existing review list. When the similarity exceeds a preset threshold, the existing review logic is updated; otherwise, the newly generated review logic is added to the review list. The contract processing check module is used to check whether all contracts have completed the review logic generation and review checklist update. If not, the review logic extraction will be automatically re-executed until all contracts have been processed.

2. The system according to claim 1, characterized in that, The difference comparison module uses a text comparison algorithm to compare the initial version and the final version of the contract sentence by sentence, and extracts the differences in the revisions.

3. The system according to claim 1, characterized in that, The semantic analysis module performs deep semantic analysis on the difference tuples based on a large language model, infers the legal intent behind the contract revision, and generates review logic elements related to that legal intent.

4. The system according to claim 1, characterized in that, The similarity calculation module uses an embedding model measurement method based on semantic similarity to calculate the similarity between the generated review logic and the existing review logic.

5. The system according to claim 1, characterized in that, The contract processing and inspection module incorporates a recursive mechanism to ensure that all contracts can complete the extraction of review logic and the updating of the review checklist.

6. A method for automatically generating contract review logic based on historical revision traces, characterized in that, Applied to the system as described in any one of claims 1-5, comprising the following steps: Step S1: Initialize the review list by creating an empty review list L to provide storage space for subsequent review logic generation and review point updates; Step S2: Extract historical contract versions. Extract historical contract data from the contract management system, including initial versions and final versions. The initial version refers to the contract version before legal review, and the final version refers to the final draft of the contract after multiple rounds of revisions. Step S3: Version comparison and difference extraction. Use a text comparison algorithm to compare each pair of initial and final versions of the contract line by line, extract the text differences, and organize the differences into a revised text comparison tuple (Oi, Fi), where Oi is the text content in the initial version and Fi is the text content in the final version. Step S4: Difference Analysis and Intent Inference. A large language model is used to perform semantic analysis on the comparison tuples (Oi,Fi) of each revised text to infer the logical intent of the legal personnel in the process of contract revision. Based on the inference, a difference element Ei is generated, which represents the review intent and review logic behind the revision. Step S5: Review logic generation. The triples (Oi, Fi, Ei) are comprehensively analyzed using a large language model to generate the corresponding review logic Ri and review point titles Ti, and to determine whether the review logic meets the predetermined standards. Step S6: Update the review list. Calculate the similarity between the generated review logic Ri and review point title Ti and the items in the existing review list L. If the similarity is greater than the set threshold, update the review list; otherwise, add the new review logic directly to the review list L. Step S7: Review completion check. Check the review completion status of all contracts. If there are any incomplete contracts, automatically return to step S3 to continue execution until the review and logical extraction of all contracts are completed.

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