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

Abstract

The invention discloses a historical revision trace-based contract review logic automatic generation method and system, and the key points of the technical scheme are that the system comprises a contract version data extraction module; the difference comparison module is used for extracting a revised text block and a difference tuple; the semantic analysis module is used for performing semantic analysis on the difference tuple and generating difference elements; the review logic generation module is used for automatically generating review logic according to the difference elements and generating corresponding review points; an examination list management module; a similarity calculation module; a contract processing and checking module; according to the method, the automation of contract review is realized, the cost of manual intervention is reduced, the review efficiency is improved, and important improvement is made in the aspects of utilization of historical revision traces and knowledge management. The system can automatically extract historical review experience, generate standardized review logic, continuously update the review list, and promote intellectualization and systematization of the contract review process.

Description

Technical Field

[0001] The present invention relates to the technical field of contract management, and particularly relates to a method and system for automatically generating contract review logic based on historical revision traces. Background Art

[0002] With the complexity of enterprise operations, contract management plays a crucial role in the daily operations of a company. Especially in large and medium-sized enterprises, the signing, review, performance, and revision of contracts are complex and frequent, often involving the collaboration and repeated review of multiple departments. During the contract management process, the legal department is responsible for contract risk control, compliance inspection, and clause optimization, etc., to ensure the legality of the contract and the smooth progress of its execution. Currently, as an information management tool, intelligent contract management systems have been widely used in the storage, management, and review of enterprise contracts. These systems mainly provide functions such as document storage, version control, information extraction, performance management, archiving, and retrieval through automated tools.

[0003] The introduction of intelligent contract review systems has improved the efficiency of contract review and can automatically identify potential legal risks. However, it still faces some significant challenges in practical applications, specifically including the following aspects:

[0004] 1. Existing intelligent contract review systems mostly rely on the prior knowledge and experience of legal personnel. Since the identification of risk points and review logic rely on the manual sorting of 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 only limited to file archiving and cannot be effectively converted into reusable review logic and strategies. Legal personnel often conduct reviews based on personal experience, resulting in a lack of systematization and standardization in the review process, affecting the quality and consistency of the review.

[0006] 3. Due to the lack of an effective knowledge management mechanism, enterprises fail to systematically summarize experience and knowledge in contract review, resulting in repeated review processes and low efficiency. Especially for newly recruited legal personnel, without the support of a comprehensive knowledge base;

[0007] 4. Existing systems often have difficulty in refining and summarizing the legal intent and review logic behind contract revisions;

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

[0009] In view of the deficiencies of the prior art, the present invention provides a method and system for automatically generating contract review logic based on historical revision traces, which solves the problems raised in the background art.

[0010] The above technical objectives of the present invention are achieved through the following technical solutions:

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

[0012] A contract version data extraction module, which is used to extract all historical contract version data that has passed legal review from the enterprise contract management system, including the initial version and the final version;

[0013] A difference comparison module, which is used to compare the initial version and the final version of each contract using a text comparison algorithm, and extract the revised text blocks and difference tuples;

[0014] A semantic analysis module, which is used to perform semantic analysis on the difference tuples, infer the modification intentions of legal personnel during contract modification, and generate difference elements;

[0015] A review logic generation module, which is used to automatically generate review logic based on the difference elements and generate corresponding review points;

[0016] A review list management module, which is used to generate and manage review lists, record review logic, and perform updates and maintenance to ensure the timeliness and consistency of the review lists;

[0017] A similarity calculation module, which 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, update the existing review logic; otherwise, add the newly generated review logic to the review list;

[0018] A contract processing inspection module, which is used to check whether all contracts have completed the generation of review logic and the update of the review list. If not, automatically re-execute the review logic extraction step until all contracts are 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 extract the revised difference content.

[0020] Furthermore, the semantic analysis module performs in-depth semantic analysis on the difference tuples based on a large language model (such as GPT-4), infers the legal intentions behind the contract revisions, and generates review logic elements related to the modification intentions.

[0021] Furthermore, the review logic generation module generates review logic based on the difference elements and combines with contract terms to generate review points, ensuring that the generated review logic has legal compliance.

[0022] Furthermore, the review checklist management module supports dynamic updating of the review checklist and can automatically adjust the review points according to the new contract revision information to ensure the timeliness and applicability of the review checklist.

[0023] Furthermore, the similarity calculation module uses an embedding model-based semantic similarity measurement method to calculate the similarity between the generated review logic and the existing review logic.

[0024] Furthermore, the contract processing and inspection module is embedded with a recursive mechanism that can automatically identify and handle changes in the complexity and quantity of different contracts, ensuring that the review logic can be extracted and the review checklist can be updated for all contracts.

[0025] The present invention also provides a method for automatically generating contract review logic based on historical revision traces, including the following steps:

[0026] Step S1: Initialize the review checklist. By creating an empty review checklist (L), it provides storage space for subsequent review logic generation and review point updates.

[0027] Step S2: Extract historical contract versions. Extract historical contract data from the contract management system, including the initial version and the final version. The initial version refers to the contract version before legal review, and the final version refers to the contract version that is finally finalized after multiple rounds of modification.

[0028] Step S3: Version comparison and difference extraction. Use a text comparison algorithm to compare each pair of contract initial versions and final versions item by item, and extract the text difference content. The text comparison algorithm includes, but is not limited to, comparison methods based on difference recognition technology. Organize the difference content 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.

[0029] Step S4: Difference analysis and intention inference. Use a large language model (such as GPT-4) to perform semantic analysis on each revised text comparison tuple (Oi, Fi), infer the logical intention contained by the legal personnel during the contract revision process, and generate a difference element (Ei) based on the inference. This difference element represents the review intention and review logic behind the revision.

[0030] Step S5: Review logic generation. Through comprehensive analysis of the triple (Oi, Fi, Ei) by a large language model, generate the corresponding review logic (Ri) and review point title (Ti), and determine whether the review logic meets the predetermined standards.

[0031] Step S6: Update the review checklist. Calculate the similarity between the generated review logic (Ri) and review point title (Ti) and the items in the existing review checklist (L). If the similarity is greater than the set threshold, update the review checklist; otherwise, directly add the new review logic to the review checklist (L).

[0032] Step S7: Check for review completion. Check the review completion status of all contracts. If there are unfinished contracts, automatically return to Step S3 to continue execution until the review and logic extraction work for all contracts are completed.

[0033] In summary, the present invention mainly has the following beneficial effects:

[0034] 1. By automatically extracting contract amendment differences and inferring the legal intentions behind the amendments, the present invention reduces the dependence on the prior knowledge and experience of legal personnel and realizes the automatic generation of review logic. This not only significantly improves the efficiency of contract review but also reduces the cost of manual review. Especially in the face of a large number of contracts, the system can quickly respond and complete the review tasks.

[0035] 2. By systematically recording the historical amendment process of contracts and review logic, the present invention can transform historical review experience into reusable review logic and review point lists, avoiding the one-sidedness and subjectivity problems of experience in traditional contract review. This method effectively improves the consistency and standardization of review quality, ensuring that each review can be optimized based on accumulated experience.

[0036] 3. The present invention establishes an intelligent review checklist management module that can dynamically update review logic and review points, forming an ever-accumulating review knowledge base. This not only helps legal personnel quickly obtain review key points in daily work but also provides systematic knowledge support for newly recruited legal personnel, reducing training costs and time.

[0037] 4. Through in-depth semantic analysis of contract amendment differences by large language models (such as GPT-4), the present invention can more accurately infer the legal intentions implied by legal personnel during the amendment process. The systematic review logic generation and similarity calculation mechanism with existing review logic ensure the accuracy and timeliness of legal review logic, greatly reducing errors and omissions in traditional manual reviews. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is the system flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the embodiments of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0040] The following embodiments are used to illustrate the present invention, but cannot 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. Any simple improvement to the method of the present invention under the premise of the concept of the present invention falls within the scope of protection required by the present invention.

[0041] Embodiment 1

[0042] Reference Figure 1 , a system for automatically generating contract review logic based on historical revision traces, the system comprising:

[0043] A contract version data extraction module, configured to extract all historical contract version data that has undergone legal review from an enterprise contract management system, including the initial version and the final version;

[0044] A difference comparison module, configured to use a text comparison algorithm to compare the initial version and the final version of each contract to extract the revised text blocks and difference tuples;

[0045] A semantic analysis module, configured to perform semantic analysis on the difference tuples, infer the modification intentions of legal personnel during contract modification, and generate difference elements;

[0046] A review logic generation module, configured to automatically generate review logic based on the difference elements and generate corresponding review points;

[0047] A review checklist management module, configured to generate and manage a review checklist, record the review logic, and perform updates and maintenance to ensure the timeliness and consistency of the review checklist;

[0048] A similarity calculation module, configured to calculate the similarity between the newly generated review logic and the review logic in the existing review checklist. When the similarity exceeds a preset threshold, update the existing review logic; otherwise, add the newly generated review logic to the review checklist;

[0049] A contract processing inspection module, configured to check whether all contracts have completed the generation of review logic and the update of the review checklist. If not, automatically re-execute the review logic extraction step until all contracts are processed.

[0050] Further, the difference comparison module uses a text comparison algorithm to compare each sentence of the initial version and the final version of the contract, and extracts the revised difference content.

[0051] Further, the semantic analysis module conducts in-depth semantic analysis on the difference tuples based on a large language model (such as GPT-4), infers the legal intent behind the contract revision, and generates review logic elements related to the modification intent.

[0052] Further, the review logic generation module generates review logic based on the difference elements and combines it with the contract terms to generate review points, ensuring that the generated review logic is legally compliant.

[0053] Further, the review checklist management module supports dynamic updating of the review checklist, and can automatically adjust the review points according to the new contract revision information to ensure the timeliness and applicability of the review checklist.

[0054] Further, the similarity calculation module uses an embedding model (embedding) measurement method based on semantic similarity to calculate the similarity between the generated review logic and the existing review logic.

[0055] Further, the contract processing and inspection module incorporates a recursive mechanism that can automatically identify and handle changes in the complexity and quantity of different contracts, ensuring that the review logic can be extracted and the review checklist can be updated for all contracts.

[0056] The present invention also provides an automatic generation method for contract review logic based on historical revision traces, including the following steps:

[0057] Step S1: Initialize the review checklist. By creating an empty review checklist (L), it provides storage space for subsequent review logic generation and review point updates.

[0058] Step S2: Extract historical contract versions. Extract historical contract data from the contract management system, including the initial version and the final version. The initial version refers to the contract version before legal review, and the final version refers to the contract version that is finally finalized after multiple rounds of modification.

[0059] Step S3: Version comparison and difference extraction. Use a text comparison algorithm to compare each pair of the initial version and the final version of the contract item by item, and extract the text difference content. The text comparison algorithm includes, but is not limited to, comparison methods based on difference recognition technology. Organize the difference content into revised text comparison tuples (Oi, Fi), where Oi is the text content in the initial version and Fi is the text content in the final version.

[0060] Step S4: Difference Analysis and Intention Inference. Use a large language model (such as GPT-4) to perform semantic analysis on each revised text comparison tuple (Oi, Fi), infer the logical intention implied by the legal personnel during the contract revision process, and generate difference elements (Ei) based on the inference. These difference elements represent the review intention and review logic behind the revision;

[0061] Step S5: Review Logic Generation. Through comprehensive analysis of the triple (Oi, Fi, Ei) by the large language model, generate the corresponding review logic (Ri) and review point title (Ti), and determine whether the review logic meets the predetermined standards;

[0062] Step S6: Update the Review Checklist. Calculate the similarity between the generated review logic (Ri) and review point title (Ti) and the items in the existing review checklist (L). If the similarity is greater than the set threshold, update the review checklist; otherwise, directly add the new review logic to the review checklist (L);

[0063] Step S7: Check for Review Completion. Check the review completion status of all contracts. If there are unfinished contracts, automatically return to Step S3 to continue execution until the review and logic extraction work for all contracts is completed.

[0064] Embodiment 2

[0065] This embodiment provides an actual application process of a contract review logic automatic generation system based on historical revision traces, specifically showing how to use the system and method of the present invention to automatically generate contract review logic and update review points for a certain enterprise's contracts.

[0066] The implementation process is as follows:

[0067] 1. Contract Version Data Extraction:

[0068] All contract versions that have passed legal review are stored in the company's contract management system, including the initial version and the final version after legal review and modification. First, the contract version data extraction module in the system automatically extracts these historical contract version data from the contract management database;

[0069] For example, the company extracts the initial version (before signing) and the final version (the version after multiple rounds of modification) of a certain supply contract.

[0070] 2. Difference Comparison and Revision Extraction:

[0071] The extracted contract versions are handed over to the difference comparison module, which identifies all text differences by comparing the initial version and the final version of the contract sentence by sentence through a text comparison algorithm;

[0072] The comparison results may include: the amount of a certain clause is modified, some clauses are deleted or added, and the legal terms expressed in some clauses have changed;

[0073] 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 finalized version.

[0074] 3. Semantic analysis and inference of review intent:

[0075] Next, the semantic analysis module conducts in-depth semantic analysis on each revised text comparison tuple (Oi, Fi). For example, in the revision of the "Payment Method" clause, the initial version stipulates that "the payment will be made within 30 days", and the finalized version is changed to "the payment will be made within 45 days";

[0076] The system uses a large language model (such as GPT-4) to infer the legal intent behind the modification, identifies that the modification is to relax the payment deadline, thereby reducing the financial pressure on the enterprise, and speculates the review element: the review risk of the change in the payment deadline.

[0077] 4. Generation of review logic and creation of review points:

[0078] Based on the above difference elements, the review logic generation module automatically generates review logic. For example, for the modification of the payment deadline, a review point of "review whether the payment deadline conforms to the financial status of the contract signatories, industry standards, and relevant laws and regulations" is generated;

[0079] 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 modified liquidated damages clause conforms to the principle of fairness", etc.

[0080] 5. Review checklist management and update:

[0081] The generated review logic and review points are added to the review checklist management module, which will compare the new review points with the review points in the existing review checklist. If duplicate or similar review points are found, it will decide whether to update the existing review points or add new review points according to the results of the similarity calculation module;

[0082] Suppose that in multiple contracts, the liquidated damages clauses of multiple contracts have similar revisions, and the system will automatically update the review logic of these clauses.

[0083] 6. Dynamic update of the review checklist and completion check:

[0084] During the entire review process, the review checklist management module dynamically updates the review checklist to ensure its real-time nature at all times. Reviewers can adjust the review points in the review checklist according to the new revision information and contract revision history at any time;

[0085] The contract processing 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 re-execute step 3 for difference extraction and review point generation until the review work for all contracts is completed.

[0086] In summary, the present invention realizes the automation of contract review, reduces the cost of manual intervention, improves the review efficiency, and makes important improvements in the utilization of historical revision traces and knowledge management. The system can automatically extract the experience of historical reviews, generate standardized review logic, and continuously update the review checklist, promoting the intelligence and systematization of the contract review process.

[0087] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that unless otherwise defined, the technical terms or scientific terms used in the present invention should be the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The words such as "including" or "comprising" used in the present invention mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents.

[0088] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for automatically generating a contract review logic based on historical revision traces, characterized in that The system includes: A contract version data extraction module, which is used to extract all historical contract version data that has passed legal review from the enterprise contract management system, including the initial version and the final version; A difference comparison module, which is used to compare the initial version and the final version of each contract using a text comparison algorithm, and extract the revised text blocks and difference tuples; A semantic analysis module, which is used to perform semantic analysis on the difference tuples, infer the modification intentions of legal personnel during contract modification, and generate difference elements; A review logic generation module, which is used to automatically generate review logic based on the difference elements and generate corresponding review points; A review checklist management module, which is used to generate and manage review checklists, record review logic, and perform updates and maintenance to ensure the timeliness and consistency of review checklists; A similarity calculation module, which is used to calculate the similarity between the newly generated review logic and the review logic in the existing review checklist. When the similarity exceeds a preset threshold, update the existing review logic. Otherwise, add the newly generated review logic to the review checklist; A contract processing inspection module, which is used to check whether all contracts have completed the generation of review logic and the update of review checklists. If not, automatically re-execute the review logic extraction step until all contracts are processed.

2. The contract review logic automatic generation 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 revised difference content.

3. The contract review logic automatic generation system according to claim 1, characterized in that The semantic analysis module performs in-depth semantic analysis on the difference tuples based on a large language model (such as GPT-4), infers the legal intentions behind the contract revisions, and generates review logic elements related to the modification intentions.

4. The contract review logic automatic generation system according to claim 1, wherein The review logic generation module generates review logic based on the difference elements and generates review points in combination with contract terms to ensure that the generated review logic is legally compliant.

5. The contract review logic automatic generation system according to claim 1, characterized in that The review checklist management module supports dynamic updates of review checklists and can automatically adjust review points according to new contract revision information to ensure the timeliness and applicability of review checklists.

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

7. The contract review logic automatic generation system according to claim 1, characterized in that The contract processing inspection module is embedded with a recursive mechanism, which can automatically identify and handle changes in the complexity and quantity of different contracts, ensuring that all contracts can complete the extraction of review logic and the update of review checklists.

8. A method for automatically generating a contract review logic based on historical revision traces, characterized in that, Applied to the contract review logic automatic generation system as described in claims 1-7, it includes the following steps: Step S1: Initialize the review checklist by creating an empty review checklist (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 the initial version and the final version. The initial version refers to the contract version before legal review, and the final version refers to the contract version that is finally finalized after multiple rounds of modification; Step S3: Version comparison and difference extraction. Use a text comparison algorithm to compare each pair of the initial and final contract versions line by line, and extract the text differences. The text comparison algorithm includes, but is not limited to, comparison methods based on difference recognition technology. Organize the difference content 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 intention inference. Use a large language model (such as GPT-4) to perform semantic analysis on each revised text comparison tuple (Oi, Fi), infer the logical intention implied by the legal staff during the contract revision process, and generate a difference element (Ei) based on the inference. This difference element represents the review intention and review logic behind the revision; Step S5: Review logic generation. Through comprehensive analysis of the triple (Oi, Fi, Ei) by a large language model, generate the corresponding review logic (Ri) and review point title (Ti), and determine whether the review logic meets the predetermined standards; Step S6: Update the review checklist. Calculate the similarity between the generated review logic (Ri) and review point title (Ti) and the items in the existing review checklist (L). If the similarity is greater than the set threshold, update the review checklist; otherwise, directly add the new review logic to the review checklist (L); Step S7: Review completion check. Check the review completion status of all contracts. If there are unfinished contracts, automatically return to Step S3 to continue execution until the review and logic extraction work for all contracts are completed.

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