Method for configurable legal contract intelligent review and customized annotation based on stance recognition

Abstract

The application discloses a configurable legal contract intelligent review and customized annotation method based on stance recognition, relates to the technical field of legal contract intelligent review, and discloses the configurable legal contract intelligent review and customized annotation method based on stance recognition, which is characterized in that: the original contract document and the review mode instruction selected by the user are acquired, stance tendency analysis processing is performed on the contract semantic unit, segmented annotation suggestions adaptive to the user stance are generated, and a deliverable document version is generated, so that the problem of missing stance tendency recognition in the prior art is solved, the annotation content can be dynamically adjusted according to the user stance, personalized and strategized review suggestions are provided, and the practicability and adaptability of the review result are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent review technology for legal contracts, and in particular to a configurable intelligent review and customized annotation method for legal contracts based on position recognition. Background Technology

[0002] In legal contract review practice, automated systems have gradually integrated natural language processing and machine learning technologies. Through pre-trained language models, they perform deep semantic analysis of contract texts to classify clause types, quantify risk levels, and generate standardized annotation suggestions. These systems typically rely on large-scale contract corpora to train models, identifying common clause structures and matching them with preset risk rules to output generalized review opinions. However, existing technologies have a fundamental flaw: the systems lack the ability to automatically discern the bias of contract clauses, failing to differentiate the benefits of clauses to the parties involved. For example, clauses may clearly favor Party A's interests, Party B's interests, or maintain neutrality. Due to the lack of a bias identification mechanism, all users, regardless of their actual role (e.g., Party A's representative, Party B's representative, or neutral mediator), receive identical annotation suggestions, resulting in review results that fail to adapt to specific negotiation positions. In complex business scenarios, Party A may need to strengthen protective suggestions for its own rights, while Party B focuses on the fairness and risk avoidance of clauses. However, existing systems only provide undifferentiated general analysis and cannot dynamically adjust annotation content to reflect user positions. This "neutral" characteristic limits the practicality of automated review in contract negotiations involving multiple roles, making it difficult to meet the demand for personalized and strategic review recommendations in legal practice.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a configurable intelligent review and customized annotation method for legal contracts based on position recognition, which aims to improve the practicality and adaptability of the review results.

[0005] To achieve the above objectives, this application proposes a configurable intelligent review and customized annotation method for legal contracts based on position recognition. The method includes: Obtain the original contract document and the review mode instruction selected by the user, and perform format standardization conversion on the original contract document to obtain a standardized contract document; The standardized contract document is subjected to structural parsing processing to obtain a set of contract semantic units; the set of contract semantic units includes multiple semantic units. According to the review mode instructions, each semantic unit in the contract semantic unit set is subjected to position bias analysis to generate corresponding segmented annotation suggestions, and the segmented annotation suggestions of all semantic units are summarized into a segmented annotation suggestion set. Generate global annotation suggestions based on the segmented annotation suggestion set and the standardized contract document; Multiple deliverable document versions are generated based on the segmented annotation suggestion set, the global annotation suggestion, and the standardized contract document.

[0006] In one embodiment, the step of performing a stance bias analysis on each semantic unit in the contract semantic unit set according to the review mode instruction to generate corresponding segmented annotation suggestions includes: Semantic units are subjected to stance bias analysis to obtain clause stance identifiers; Perform clause type identification processing on semantic units to obtain clause type identifiers; A risk level assessment is performed based on the clause type identifier and the clause stance identifier to obtain a risk level identifier; The segmented annotation suggestions are generated based on the review mode instructions, the semantic units, the clause type identifier, the clause position identifier, and the risk level identifier.

[0007] In one embodiment, the step of performing stance analysis on semantic units to obtain clause stance identifiers includes: Extract the contract subject entities from the semantic units to obtain the set of contract subject entities; By analyzing the rights and obligations relationships within semantic units, we can obtain the identifiers of rights and responsibilities. The position inclination is determined based on the semantic unit, the set of contract subject entities, and the right and responsibility relationship identifier, and the position identifier of the clause is obtained.

[0008] In one embodiment, the step of generating the segmented annotation suggestion based on the review mode instruction, the semantic unit, the clause type identifier, the clause position identifier, and the risk level identifier includes: The annotation generation strategy is determined based on the review mode instructions; Based on the clause type identifier, the clause stance identifier, and the risk level identifier, a matching annotation template is retrieved from a preset annotation template library; The annotation template is adjusted according to the review mode instructions, the semantic unit, the clause type identifier, the clause position identifier, the risk level identifier, and the annotation generation strategy to obtain preliminary annotation content; The preliminary annotation content is optimized based on the review mode instructions and the annotation generation strategy to obtain the segmented annotation suggestions.

[0009] In one embodiment, the step of generating global annotation suggestions based on the segmented annotation suggestion set and the standardized contract document includes: Missing clauses are detected based on the standardized contract document and the set of segmented annotation suggestions. Missing clause identifiers are obtained, and supplementary clause suggestions are generated based on the missing clause identifiers. The standardized contract document is subjected to compliance verification of the contract subject information to obtain compliance issue identifiers, and supplementary compliance suggestions are generated based on the compliance issue identifiers. The standardized contract document is subjected to a legal provision citation accuracy check to obtain a provision timeliness identifier, and a provision update suggestion is generated based on the provision timeliness identifier; The aforementioned supplementary suggestions, compliance supplementary suggestions, and clause update suggestions are summarized into the global annotation suggestions.

[0010] In one embodiment, the step of detecting missing clauses based on the standardized contract document and the set of segmented annotation suggestions, and obtaining the missing clause identifier, includes: The contract type is identified based on the standardized contract document and the set of segmented annotation suggestions, and a contract type identifier is obtained. Based on the contract type identifier, the corresponding set of key clauses is retrieved from the preset key clause rule base; The standardized contract document is matched and compared with the set of key clauses to obtain the clause existence identifier; Based on the existence identifier of the aforementioned clauses, the missing key clauses are identified, and the missing clause identifier is obtained.

[0011] In one embodiment, the step of generating a clause supplement suggestion based on the missing clause identifier includes: The corresponding reference clause template is retrieved from the preset clause template library based on the missing clause identifier; The reference clause template is adjusted according to the review mode instructions, the missing clause identifier, the standardized contract document, and the reference clause template to obtain the position-based clause template and standardized contract document information; The context information is extracted and filled in based on the standardized contract document information and the position-based clause template to obtain the complete clause content; The proposed supplementary terms are generated based on the full terms.

[0012] In one embodiment, the step of generating multiple deliverable document versions based on the segmented annotation suggestion set, the global annotation suggestion, and the standardized contract document includes: Based on the segmented annotation suggestion set and the global annotation suggestion, annotation conflicts are coordinated to obtain a unified annotation set; A clean version of the document is obtained by synthesizing the unified annotation set and the standardized contract document; Based on the unified annotation set and the standardized contract document, a revised version is synthesized to obtain the annotated revised version document; Annotated preview version document is obtained by synthesizing the unified annotation set and the standardized contract document.

[0013] In one embodiment, the step of resolving annotation conflicts and obtaining a unified annotation set based on the segmented annotation suggestion set and the global annotation suggestion set includes: Based on the segmented annotation suggestion set and the global annotation suggestion, different annotation suggestions for the same contract content are identified to obtain a set of conflicting annotation pairs. Priority rules are retrieved from a preset priority rule base based on the segmented annotation suggestion set, the global annotation suggestion, and the conflict annotation pair set; The unified annotation set is obtained by coordinating conflicting annotations based on the segmented annotation suggestion set, the global annotation suggestion, the conflicting annotation pair set, and the priority rule.

[0014] In one embodiment, the review mode instructions include a risk warning mode, a standard review mode, and an enhanced review mode; wherein, the risk warning mode corresponds to generating annotation suggestions for risk warning information; the standard review mode corresponds to generating annotation suggestions for risk warnings and standard modification suggestions; and the enhanced review mode corresponds to generating annotation suggestions that include risk warnings, standard modification suggestions, and optimization solutions.

[0015] The configurable legal contract intelligent review and customized annotation method proposed in this application, based on position recognition, obtains the original contract document and the review mode instruction selected by the user, performs position orientation analysis on the semantic units of the contract, generates segmented annotation suggestions adapted to the user's position, and generates a deliverable document version. This solves the problem of missing position orientation recognition in the prior art, and can dynamically adjust the annotation content according to the user's position, providing personalized and strategic review suggestions, and improving the practicality and adaptability of the review results. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating an embodiment of the configurable intelligent review and customized annotation method for legal contracts based on position recognition in this application. Figure 2 For this application Figure 1 Detailed flowchart of step S300; Figure 3 For this application Figure 2 A detailed flowchart of step S310; Figure 4 For this application Figure 2 A detailed flowchart of step S340; Figure 5 For this application Figure 1 Detailed flowchart of step S400; Figure 6 For this application Figure 5 A detailed flowchart of step S410 is provided in one embodiment. Figure 7 For this application Figure 5 A detailed flowchart of another embodiment of step S410 is provided; Figure 8 For this application Figure 1 A detailed flowchart of step S500; Figure 9 For this application Figure 8 A detailed flowchart of step S510.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] In existing technologies, automated legal contract review systems typically generate annotation suggestions based on generic templates when performing semantic encoding, type identification, and risk labeling of contract clauses. However, these systems cannot automatically identify the stance bias of contract clauses, resulting in a lack of personalized adjustments to the annotation suggestions to suit the user's position. This makes it difficult to provide customized review suggestions, thus limiting their application value in complex business negotiations and professional legal practice.

[0023] Based on this, the embodiments of this application provide a configurable intelligent review and customized annotation method for legal contracts based on position recognition, referring to... Figure 1 The configurable legal contract intelligent review and customized annotation method based on position recognition includes steps S100 to S500, wherein: Step S100: Obtain the original contract document and the review mode instruction selected by the user, and perform format standardization conversion on the original contract document to obtain a standardized contract document; Step S200: Perform structural parsing processing on the standardized contract document to obtain a set of contract semantic units; the set of contract semantic units includes multiple semantic units; Step S300: According to the review mode instruction, perform position bias analysis on each semantic unit in the contract semantic unit set to generate corresponding segmented annotation suggestions, and summarize the segmented annotation suggestions of all semantic units into a segmented annotation suggestion set. Step S400: Generate global annotation suggestions based on the segmented annotation suggestion set and the standardized contract document; Step S500: Generate multiple deliverable document versions based on the segmented annotation suggestion set, the global annotation suggestion, and the standardized contract document.

[0024] In this embodiment, the original contract document refers to the unprocessed legal contract document submitted by the user, whose format may include, but is not limited to, PDF, Word document, scanned copy, or plain text file. This document contains all the contract content to be reviewed. The review mode instruction refers to the review strategy selected by the user based on their review needs and positional preferences. This instruction guides the system to generate customized annotation suggestions for different positions in subsequent processing, such as selecting a risk warning mode, a standard review mode, or an enhanced review mode. The standardized contract document refers to a unified and standardized internal representation of the original contract document after format conversion processing. This document eliminates the format differences of the original document, facilitating subsequent structural parsing and semantic analysis.

[0025] In this embodiment, the contract semantic unit set refers to the collection of text fragments with independent semantics identified after structural parsing of a standardized contract document. Each semantic unit typically corresponds to a clause, paragraph, or sentence in the contract and serves as the basic unit for positional analysis and annotation suggestion generation. Segmented annotation suggestions refer to the specific annotation content generated for each semantic unit in the contract semantic unit set based on its positional analysis results. These suggestions aim to point out potential risks, areas requiring modification, or possible optimization solutions for that semantic unit. The segmented annotation suggestion set is a summary of segmented annotation suggestions for all semantic units. This set contains detailed review opinions on various parts of the contract document. Global annotation suggestions refer to supplementary annotations generated based on the segmented annotation suggestions, resulting from a macro-level review of the entire contract document. These suggestions may address overall issues such as the completeness and compliance of contract clauses and the timeliness of legal provisions. Deliverable document versions refer to the various forms of contract documents ultimately output by the system for user use. These versions may include clean versions, annotated revised versions, or annotated preview versions to meet user needs in different scenarios.

[0026] In this embodiment, the configurable legal contract intelligent review and customized annotation method based on position recognition first obtains the original contract document and the review mode instruction selected by the user, and then performs a format standardization conversion on the original contract document to obtain a standardized contract document. The original contract document can exist in various formats, such as PDF, Word document, or plain text file. To facilitate subsequent automated processing, these documents of different formats need to be uniformly converted into an internal standard format, such as XML or JSON structured data. This conversion process can be implemented by calling existing document parsing libraries or custom parsers. For example, for PDF documents, optical character recognition (OCR) technology can be used to extract its content into text, and then perform structured processing; for Word documents, its API interface can be directly used to extract text and structural information. The review mode instruction selected by the user, such as risk warning mode or standard review mode, will be received by the system and used as the basis for subsequent processing.

[0027] Furthermore, the standardized contract document undergoes structural parsing to obtain a set of contract semantic units. This set of semantic units comprises multiple semantic units. The structural parsing aims to decompose a continuous stream of text into logical units with independent meaning. For example, individual clauses, sub-clauses, or key statements in the contract can be identified through syntactic analysis, paragraph recognition, or rule-based pattern matching, and stored as independent semantic units. Each semantic unit is treated as an independent analysis object to facilitate subsequent refined processing.

[0028] Based on this, according to the review mode instruction, each semantic unit in the contract semantic unit set undergoes a stance bias analysis to generate corresponding segmented annotation suggestions. These suggestions are then aggregated into a segmented annotation suggestion set. Specifically, the stance bias analysis can be based on a pre-defined keyword list or a simple sentiment dictionary to perform text matching and scoring on each semantic unit, thereby roughly determining whether the semantic unit is favorable or unfavorable to a particular assumed party (e.g., Party A or Party B). For example, when the review mode instruction is in risk warning mode, the system will focus on identifying clauses that may pose potential risks to the user and generate corresponding suggestive annotations. These annotation suggestions can be predefined general text fragments directly associated with the identified stance bias. The segmented annotation suggestions generated from all semantic units are then collected and integrated to form a complete annotation suggestion set.

[0029] In this embodiment, global annotation suggestions are generated based on the segmented annotation suggestion set and the standardized contract document. These global annotation suggestions provide supplementary review opinions for the entire contract document. For example, by examining the overall structure of the standardized contract document to identify obvious missing chapters or logical breaks, and combining this with common issues reflected in the segmented annotation suggestion set, suggestions regarding the contract's completeness or consistency can be generated. This process can be easily performed using manually preset rules; for example, if the contract lacks the title of a key chapter, corresponding supplementary suggestions are generated.

[0030] In this embodiment, multiple deliverable document versions are finally generated based on the segmented annotation suggestion set, the global annotation suggestion, and the standardized contract document. Specifically, the system can directly insert the segmented annotation suggestions and global annotation suggestions into the corresponding positions in the standardized contract document to generate a revised version containing all annotations. Alternatively, a "clean" version can be generated, which contains only the original contract text, but its content may have been slightly modified or supplemented based on the annotation suggestions. For example, annotation suggestions can be directly attached to the document as text boxes or annotation boxes, forming a preview version with annotations.

[0031] In this embodiment, by introducing user-selected review mode instructions and performing positional analysis on contract semantic units, customized segmented annotation suggestions can be generated based on the user's specific position, and combined with global annotations to form multiple deliverable document versions. This overcomes the limitation of traditional systems' lack of personalized annotation suggestions, enabling legal contract review to provide position-based and customized review suggestions, thus enhancing its application value in complex business negotiations and professional legal practice.

[0032] In one feasible implementation, refer to Figure 2 Step S300 includes steps S310 to S340, wherein: Step S310: Perform positional analysis on the semantic units to obtain the clause position identifier; Step S320: Perform clause type identification processing on the semantic unit to obtain the clause type identifier; Step S330: Perform risk level assessment based on the clause type identifier and the clause position identifier to obtain a risk level identifier; Step S340: Generate the segmented annotation suggestion based on the review mode instruction, the semantic unit, the clause type identifier, the clause position identifier, and the risk level identifier.

[0033] In this embodiment, the step of performing positional analysis on semantic units to obtain clause position identifiers aims to identify the contractual party favored by the semantic unit. By analyzing the linguistic features, keywords, and entity relationships involved in the semantic unit, the favored contractual party, such as Party A, Party B, or a neutral stance, is determined. This processing provides a fundamental basis for positional judgment in subsequent risk assessment and annotation generation.

[0034] In this embodiment, the step of performing clause type identification processing on semantic units to obtain clause type identifiers is used to determine the legal nature or functional classification of contract semantic units. For example, a semantic unit may be identified as a "breach of contract clause," "confidentiality clause," "payment clause," "jurisdiction clause," etc. This identification process can be implemented through a preset rule base, keyword matching, or a text classification model based on machine learning, thereby providing important contextual information for subsequent risk assessment and annotation generation.

[0035] Based on this, the risk level assessment process, which involves evaluating the clause type and clause stance identifiers to obtain the risk level identifier, aims to quantify the potential risk level inherent in the contract semantic unit. After obtaining the clause type and stance identifiers, the system can combine preset risk assessment rules or models to determine the risk level of the semantic unit, such as labeling it as "high risk," "medium risk," or "low risk." For example, a "breach of contract clause" that favors the other party might be assessed as high risk for the user. This risk level identifier provides guidance for the priority and level of detail in annotation suggestions.

[0036] In this embodiment, the final step of generating the segmented annotation suggestions based on the review mode instruction, the semantic unit, the clause type identifier, the clause stance identifier, and the risk level identifier involves comprehensively utilizing the aforementioned analysis results to generate specific and actionable annotation suggestions for each contract semantic unit. The system will customize the annotation content based on the user-selected review mode instruction, combined with the original content of the semantic unit, the identified clause type, clause stance, and risk level. This generation process can be based on a preset annotation template for filling and adjustment, ensuring that the annotation suggestions not only meet user needs but also are targeted and instructive.

[0037] In this embodiment, by introducing clause type identification and risk level assessment, this application can conduct in-depth analysis of contract semantic units from multiple dimensions. Clause stance identifiers reveal the bias of the clauses, clause type identifiers clarify the legal nature and function of the clauses, and risk level identifiers quantify the potential degree of risk. This multi-dimensional information, combined with review mode instructions, makes the generated segmented annotation suggestions no longer a single stance judgment, but a comprehensive and customized annotation that includes the nature of the clauses, the degree of risk, and specific modification suggestions. This improves the accuracy, practicality, and operability of the annotation suggestions, enabling users to understand the potential impact of contract clauses more comprehensively and efficiently, and to conduct targeted reviews and decisions according to their own needs.

[0038] In one feasible implementation, refer to Figure 3 Step S310 includes steps S311 to S313, wherein: Step S311: Extract the contract subject entity from the semantic unit to obtain the contract subject entity set; Step S312: Analyze the rights and obligations relationships in the semantic units to obtain the rights and responsibilities relationship identifier; Step S313: Determine the stance inclination based on the semantic unit, the set of contract subject entities, and the rights and responsibilities relationship identifier to obtain the clause stance identifier.

[0039] In this embodiment, contract subject entities are extracted from semantic units to obtain a set of contract subject entities. Contract subject entities refer to the parties with rights and obligations in a legal contract, such as role names like "Party A," "Party B," "Seller," "Buyer," "Principal," and "Agent," as well as specific company names and individual names. Extracting these entities is fundamental to understanding the stance of the terms. Implementation methods may include: utilizing a pre-trained Named Entity Recognition (NER) model, trained on a large amount of legal text, capable of recognizing various entities in the contract; or employing a rule-based method, identifying contract subject entities by matching a pre-defined contract role dictionary and company name database.

[0040] Based on this, the rights and obligations relationships within semantic units are analyzed to obtain the identifiers of these relationships. Rights and obligations relationships refer to the rights enjoyed and obligations borne by the parties described in the semantic unit. Examples include "Party A has the right to demand payment from Party B," "Party B shall bear liability for breach of contract," and "Party C may not transfer," etc. Analyzing these relationships helps reveal the substantive impact of the clauses. Implementation methods may include: employing dependency parsing techniques to identify the subject-verb-object structure and dependency relationships between verbs and nouns in the sentence, thereby determining the exerciser and recipient of the rights or obligations; or utilizing semantic role labeling (SRL) models to identify the argument structure of predicates in the sentence, thereby extracting right verbs (such as "enjoy," "demand") and obligation verbs (such as "bear," "pay") and their corresponding agents and recipients.

[0041] Further, the stance orientation is determined based on the semantic unit, the set of contractual entity subjects, and the rights and responsibilities relationship identifier to obtain the clause stance identifier. Stance orientation refers to the degree to which the semantic unit is beneficial or detrimental to a specific subject in the contract. The clause stance identifier is a quantification or classification of this orientation, such as "favorable to Party A," "favorable to Party B," "neutral," or "poses a risk to Party A." The determination of stance orientation can be implemented by constructing a rule-based reasoning engine, which judges based on the extracted contractual entity subjects, their roles in the clause, and the identified rights and obligations, combined with preset legal logic rules. For example, if "Party A" is the holder of rights and "Party B" is the bearer of obligations, the clause may be judged as "favorable to Party A." Alternatively, a machine learning classification model can be used, with the semantic unit text, the set of contractual entity subjects, and the rights and responsibilities relationship identifier as input features, to train the model to predict the stance orientation of the clause.

[0042] In this embodiment, by employing the aforementioned technical solution, this application overcomes the challenges faced by traditional methods in analyzing the stance of legal contracts, achieving accurate identification of the stance inclination of semantic units. Specifically, firstly, by extracting the contractual entities, the core stakeholders involved in the clauses are identified; secondly, a thorough analysis of the rights and obligations reveals the specific direction and extent of the clauses' impact on these entities. Based on this, by comprehensively considering the textual content of the semantic units, the set of contractual entities, and the identifiers of rights and responsibilities, the stance inclination of the clauses can be determined more comprehensively and accurately, generating reliable clause stance identifiers. This meticulous analytical method makes subsequent risk level assessments and segmented annotation suggestions more targeted and professional, improving the accuracy and practicality of intelligent legal contract review, and providing users with more insightful customized annotations.

[0043] In one feasible implementation, refer to Figure 4 Step S340 includes steps S341 to S344, wherein: Step S341: Determine the annotation generation strategy according to the review mode instruction; Step S342: Retrieve a matching annotation template from a preset annotation template library based on the clause type identifier, the clause position identifier, and the risk level identifier; Step S343: Adjust the annotation template according to the review mode instruction, the semantic unit, the clause type identifier, the clause position identifier, the risk level identifier, and the annotation generation strategy to obtain preliminary annotation content; Step S344: Optimize the preliminary annotation content according to the review mode instruction and the annotation generation strategy to obtain the segmented annotation suggestion.

[0044] In this embodiment, the review mode instructions (e.g., risk warning mode, standard review mode, or enhanced review mode) reflect the user's expectations and focus regarding the contract review results. The annotation generation strategy is a series of rules defining the depth, breadth, detail, and presentation of the annotation content based on these instructions. For example, when the review mode instruction is "risk warning mode," the annotation generation strategy might be configured to generate only brief tips targeting high-risk points; while when the review mode instruction is "enhanced review mode," the strategy might require generating comprehensive annotations including detailed risk analysis, modification suggestions, and optimization solutions. This strategy can be further refined to dimensions such as the annotation's language style, information density, and the level of detail in citing legal provisions to ensure that the annotation content highly matches the user's needs.

[0045] In this embodiment, matching annotation templates are retrieved from a pre-built annotation template library based on the clause type identifier, clause stance identifier, and risk level identifier. The annotation template library is a pre-built knowledge base that stores annotation templates designed for different clause types, clause stances, and risk levels. Each template is accompanied by metadata tags, such as "Clause Type: Breach of Contract Liability," "Clause Stance: Unfavorable to Our Side," and "Risk Level: High." After receiving the clause type identifier, clause stance identifier, and risk level identifier of a semantic unit, the system performs a multi-dimensional matching query to quickly locate the annotation base content that best matches the characteristics of the current semantic unit. For example, for a semantic unit identified as a "Breach of Contract Liability" clause, whose stance is determined to be "unfavorable to our side" and has a "high" risk level, the system will retrieve templates that suggest modifying the breach of contract liability clause to reduce our risk.

[0046] In this embodiment, the annotation template is adjusted based on the review mode instructions, semantic units, clause type identifiers, clause stance identifiers, risk level identifiers, and annotation generation strategies to obtain preliminary annotation content. The retrieved annotation templates are typically general and require personalized adjustments based on specific semantic unit content and the user-selected review mode instructions to generate preliminary, more targeted annotations. The adjustment process may involve filling, replacing, adding, or deleting template content. For example, the template may contain placeholders such as "[Specific Breach of Contract]" or "[Suggested Amendments]," which the system will automatically extract and fill in based on the text content of the semantic units. Simultaneously, depending on the annotation generation strategy (e.g., requiring more detailed legal basis in the enhanced review mode), the system may automatically import relevant legal provisions or cases from an external knowledge base. This step ensures that the preliminary annotation content is not only based on the template but also incorporates the contextual information of the semantic units and the specific requirements of the review mode.

[0047] In this embodiment, the initial annotation content is finally optimized according to the review mode instructions and annotation generation strategy to obtain segmented annotation suggestions. After generation, the initial annotation content may still need further refinement and optimization to ensure its final accuracy, readability, consistency, and complete compliance with user expectations under a specific review mode. The optimization process may include grammar checking, semantic proofreading, removal of redundant information, and simplification of expression. For example, in risk warning mode, overly detailed modification suggestions in the initial annotations may be simplified into concise and clear risk point prompts. In enhanced review mode, legal basis, case analysis, or alternative solutions may be further supplemented to provide more comprehensive information. Furthermore, optimization may also involve formatting the annotations to make them easier to read and understand. This step is crucial in ensuring that the final generated segmented annotation suggestions are not only accurate in content but also highly matched in form and depth to the review mode instructions.

[0048] In this embodiment, through the above technical solution, this application can flexibly determine the annotation generation strategy according to the user's selected review mode instruction, and, in conjunction with the clause type identifier, clause position identifier, and risk level identifier of the semantic unit, retrieve and adjust the matching annotation template from the preset annotation template library. This step-by-step adjustment and optimization mechanism ensures that the final generated segmented annotation suggestions are not only accurate and targeted, but also deeply meet the personalized needs of users for the depth and form of annotation information under different review modes. For example, in the risk warning mode, the annotation will focus on concise and clear risk point prompts; in the enhanced review mode, it can provide comprehensive annotations including detailed risk analysis, modification suggestions, and optimization solutions. This improves the flexibility, practicality, and user satisfaction of intelligent legal contract review, effectively solving the problem of the lack of targeting and flexibility in annotation content.

[0049] In one feasible implementation, refer to Figure 5 Step S400 includes steps S410 to S440, wherein: Step S410: Detect missing clauses based on the standardized contract document and the set of segmented annotation suggestions, obtain missing clause identifiers, and generate clause supplement suggestions based on the missing clause identifiers; Step S420: Perform compliance verification on the standardized contract document for the contract subject information to obtain compliance issue identifiers, and generate supplementary compliance suggestions based on the compliance issue identifiers; Step S430: Perform a legal provision citation accuracy check on the standardized contract document to obtain a provision timeliness identifier, and generate a provision update suggestion based on the provision timeliness identifier; Step S440: The supplementary suggestions for the clauses, the supplementary suggestions for compliance, and the suggestions for updating the clauses are summarized into the global annotation suggestions.

[0050] In this embodiment, the steps of detecting missing clauses based on a standardized contract document and a set of segmented annotation suggestions, obtaining missing clause identifiers, and generating supplementary clause suggestions based on these identifiers aim to identify potentially omitted key content in the contract, ensuring its completeness and rigor. Implementation may include: the system intelligently inferring the contract type or main purpose based on the text content and structural features of the standardized contract document, as well as the contract theme and scope revealed by the generated set of segmented annotation suggestions. Based on this, the system can retrieve a list of clauses related to the contract type and generally considered necessary or key from a pre-set knowledge base or rule set. Subsequently, through deep semantic analysis and pattern matching of the standardized contract document, the system compares the actual contract content with this list of key clauses to identify any missing, incomplete, or ambiguous key clauses. Once such omissions or incompleteness are detected, the system generates corresponding missing clause identifiers, providing a basis for subsequent supplementary clause suggestions. Based on these missing clause identifiers, the system can retrieve the corresponding reference clause templates from the preset clause template library, and adjust the reference clause templates according to the review mode instructions, missing clause identifiers, standardized contract documents, and other information to form a position-based clause template. It can also extract the context by combining the information from the standardized contract documents to fill in the complete clause content and finally generate specific clause supplement suggestions.

[0051] In this embodiment, the steps of verifying the compliance of contract subject information in standardized contract documents, obtaining compliance issue identifiers, and generating supplementary compliance suggestions based on these identifiers are used to ensure that the information of all parties involved in the contract complies with laws, regulations, and industry standards. The implementation may include: the system extracting information on all contract subjects (such as company name, legal representative, registered capital, unified social credit code, etc.) from the standardized contract document. Then, it cross-references this information with authoritative external databases (such as business registration information databases, lists of dishonest judgment debtors, etc.) or verifies it according to preset compliance rules (such as name completeness, information consistency, etc.). If inconsistencies, incompleteness, falsification, or involvement of negative credit records are found in the subject information, a compliance issue identifier is generated. Based on these identifiers, the system generates specific supplementary compliance suggestions, such as recommending verification of information, supplementation of missing information, or warning of potential risks.

[0052] In this embodiment, the steps of checking the accuracy of legal citations in standardized contract documents, obtaining clause timeliness identifiers, and generating clause update suggestions based on these identifiers aim to verify whether the legal and regulatory provisions cited in the contract are accurate, valid, and up-to-date. Implementation may include: the system identifying the names and clause numbers of all explicitly cited laws and regulations in the standardized contract document. Subsequently, by accessing a legal and regulatory database, the system compares the current status of these cited clauses, including whether they have been repealed, revised, replaced, or whether new judicial interpretations exist. If a cited clause is found to be expired, modified, or has a more accurate latest version, the system generates a clause timeliness identifier. Based on these identifiers, the system provides clause update suggestions, such as recommending replacement with the latest clause, deletion of repealed clauses, or supplementation with relevant judicial interpretations.

[0053] In this embodiment, the step of summarizing clause supplement suggestions, compliance supplement suggestions, and clause update suggestions into a global annotation suggestion involves integrating the above-mentioned inspection results and suggestions to form a comprehensive annotation set for the entire contract. This can be achieved by the system uniformly collecting, organizing, and formatting clause supplement suggestions corresponding to detected missing clauses, compliance supplement suggestions corresponding to issues discovered during entity information compliance checks, and clause update suggestions corresponding to issues discovered during legal citation checks. These suggestions can be categorized and ordered according to their importance, type, or position within the contract, ultimately forming a structured and easily understandable global annotation suggestion, providing users with review opinions at the overall contract level.

[0054] In this embodiment, by detecting missing clauses, key content that may be omitted in the contract can be effectively identified, ensuring the contract's completeness and rigor. By verifying the compliance of the contract's subject information, potential legal risks can be promptly identified and highlighted, guaranteeing the contract's legal validity. By checking the accuracy and timeliness of legal citations, the contract content can be ensured to be consistent with the latest laws and regulations, avoiding the risk of contract invalidation due to legal updates. These global reviews and suggestions, complementing the segmented annotation suggestions, together construct a more comprehensive and in-depth intelligent contract review system. This allows the final generated global annotation suggestions to provide comprehensive risk warnings and improvement solutions from both macro and micro dimensions, improving the quality and efficiency of contract review and providing users with more valuable decision support.

[0055] In one feasible implementation, refer to Figure 6 Step S410 includes steps S411A to S414A, wherein: Step S411A: Identify the contract type based on the standardized contract document and the segmented annotation suggestion set to obtain the contract type identifier; Step S412A: Retrieve the corresponding set of key clauses from the preset key clause rule base according to the contract type identifier; Step S413A: Match and compare the standardized contract document with the set of key clauses to obtain the clause existence identifier; Step S414A: Identify the missing key clauses based on the existence identifier of the clauses, and obtain the missing clause identifier.

[0056] In this embodiment, the step of identifying the contract type and obtaining a contract type identifier aims to determine the specific contract category to which the contract belongs, such as a sales contract, lease contract, service contract, or employment contract, based on the contract's textual content and structural features. Implementation methods may include, but are not limited to: using Natural Language Processing (NLP) technology to analyze keywords, phrases, and sentence structures in the contract document, and combining this with machine learning models (such as text classification models) for training and prediction; or using a pre-defined rule base to match characteristic words in the contract title, introduction, or specific clauses to identify the contract type. The resulting contract type identifier provides a clear classification of the contract's nature, laying the foundation for subsequent accurate review.

[0057] In this embodiment, the step of retrieving the corresponding set of key clauses from a preset key clause rule base refers to the key clause rule base being a knowledge base that stores information on various contract types and their corresponding core clauses. For example, for a "sales contract," its key clauses may include the subject matter, price, delivery method, and liability for breach of contract. This step, based on the contract type identifier obtained in the previous step, queries and extracts a list of indispensable clauses highly relevant to the current contract type from this rule base. The retrieval method can be based on database queries, index lookups, or rule matching, etc.

[0058] In this embodiment, the step of matching and comparing the standardized contract document with the set of key clauses to obtain the clause existence identifier aims to compare the actual clauses in the standardized contract document with the set of key clauses retrieved from the key clause rule base one by one. Matching and comparison can be achieved through various techniques, such as: semantic similarity calculation to determine whether there are expressions in the contract that are semantically similar to the key clauses; keyword or phrase matching to find whether the core words in the key clauses appear in the contract; or using deep learning models to perform structured analysis of the contract text and identify paragraphs corresponding to the function or content of the key clauses. The matching results will generate clause existence identifiers, clearly indicating whether each key clause exists or is missing in the standardized contract document.

[0059] In this embodiment, the step of identifying missing key clauses based on their existence identifiers and obtaining missing clause identifiers involves filtering out all key clauses marked as "missing" after obtaining the existence identifier for each key clause. These missing key clauses constitute a missing clause identifier, which is a list or set that clearly indicates which important clauses are missing in the current contract. For example, if a sales contract lacks a "liability for breach of contract" clause, then "liability for breach of contract" will be identified as a missing key clause.

[0060] In this embodiment, through the above technical solution, when detecting missing clauses, this application first identifies the contract type of the standardized contract document. This allows for the precise retrieval of the necessary set of key clauses for that specific contract type from a pre-defined key clause rule base. Based on this, the standardized contract document is matched and compared with these specific types of key clauses, accurately identifying the truly missing key clauses and generating corresponding missing clause identifiers. This customized detection method based on contract type avoids the inaccuracies or irrelevance issues that may arise from general detection, improving the accuracy and efficiency of missing clause detection. Consequently, the subsequently generated clause supplement suggestions will be more targeted and practical, making the global annotation suggestions more accurate and effective, greatly enhancing the professionalism and user experience of intelligent legal contract review.

[0061] In one feasible implementation, refer to Figure 7 Step S410 further includes steps S411B to S414B, wherein: Step S411A: Retrieve the corresponding reference clause template from the preset clause template library according to the missing clause identifier; Step S412A: Adjust the reference clause template according to the review mode instruction, the missing clause identifier, the standardized contract document, and the reference clause template to obtain the position-based clause template and standardized contract document information; Step S413A: Extract context information and fill it in according to the standardized contract document information and the position-based clause template to obtain the complete clause content; Step S414A: Generate the supplementary proposal for the terms based on the complete terms content.

[0062] In this embodiment, the system retrieves the corresponding reference clause template from a pre-set clause template library based on the missing clause identifier. The missing clause identifier clearly indicates the specific type or content of the missing clause in the contract; for example, it may indicate the absence of a "confidentiality clause," a "breach of contract clause," or a "dispute resolution clause." The pre-set clause template library is a knowledge base containing a large number of standard, general, or industry-specific legal clauses. These templates have been reviewed and categorized by legal professionals and can be indexed according to clause type, legal field, common scenarios, etc. The system uses the missing clause identifier as a query condition to perform a matching search in this clause template library to obtain the basic reference clause template most relevant to the missing clause type. For example, if the missing clause identifier indicates a missing confidentiality clause, the system will retrieve multiple different versions of confidentiality clause templates for reference.

[0063] In this embodiment, the system adjusts the reference clause template based on the review mode instructions, the missing clause identifier, the standardized contract document, and the reference clause template to obtain a position-based clause template and standardized contract document information. This adjustment process is crucial to ensuring that the generated suggestions are targeted and meet user needs. The review mode instructions (e.g., risk warning mode, standard review mode, or enhanced review mode) guide the direction and extent of the adjustment. For example, in risk warning mode, the template may be adjusted to emphasize risk avoidance; in enhanced review mode, more clauses favorable to the user or stricter constraints may be added. The adjustment can also incorporate the existing content of the standardized contract document, such as analyzing existing subject information, contract amount, performance period, and governing law to ensure that the adjusted template is consistent with the overall context of the contract. Through this step, the reference clause template is refined and customized, forming a position-based clause template that conforms to the user's review stance and is coordinated with the overall content of the contract. Simultaneously, key information required for subsequent filling is extracted from the standardized contract document to form standardized contract document information.

[0064] In this embodiment, the system extracts and fills in contextual information based on the standardized contract document information and the position-based clause template to obtain complete clause content. Specifically, the position-based clause template typically contains placeholders or variables, such as "[Party A's Name]", "[Party B's Name]", "[Contract Amount]", "[Place of Performance]", etc. Using the previously extracted standardized contract document information, the system accurately identifies the corresponding entities or values ​​from the standardized contract document through technologies such as Named Entity Recognition (NER) and information extraction, and automatically fills them into the corresponding placeholders in the position-based clause template. For example, if the standardized contract document information identifies Party A as "XX Company" and Party B as "YY Company", the system will accurately fill this information into the template, thereby transforming the abstract template into concrete, executable clause text.

[0065] In this embodiment, the system finally generates supplementary clause suggestions based on the complete clause content. The generated complete clause content is presented to the user in a clear and easy-to-understand manner as supplementary suggestions to the contract. These suggestions can be displayed in various forms, such as being directly inserted into the suggested section of the contract, or provided as annotations with explanations of their source, purpose, and relevance to the existing contract content, facilitating user review and adoption.

[0066] In this embodiment, through the above-described technical solution, this application overcomes the limitations of merely identifying missing clauses without providing high-quality supplementary content that aligns with the specific contract context and user review model. Specifically, by intelligently retrieving reference clause templates based on missing clause identifiers, and combining review model instructions, standardized contract documents, and missing clause identifiers to finely adjust the templates, a position-based clause template is generated, ensuring that the supplementary suggestions are highly consistent with user needs. Furthermore, by extracting contextual information from standardized contract documents and filling it into the position-based clause template, the generated clause content is not only complete but also seamlessly integrated with existing contract content, greatly improving the relevance, accuracy, and practicality of the supplementary clause suggestions. This not only improves the efficiency of contract review but also reduces the workload of manual modification and verification, ensuring the integrity and compliance of the contract.

[0067] In one feasible implementation, refer to Figure 8 Step S500 includes steps S510 to S540, wherein: Step S510: Based on the segmented annotation suggestion set and the global annotation suggestion, coordinate annotation conflicts to obtain a unified annotation set; Step S520: Synthesize a clean version based on the unified annotation set and the standardized contract document to obtain a clean version document; Step S530: Based on the unified annotation set and the standardized contract document, a revised version is synthesized to obtain the annotated revised version document; Step S540: Synthesize a preview version based on the unified annotation set and the standardized contract document to obtain an annotated preview version document.

[0068] In this embodiment, before generating a deliverable document version, it is first necessary to coordinate annotation conflicts based on the segmented annotation suggestion set and the global annotation suggestion to obtain a unified annotation set. This step aims to resolve potential contradictions or duplications between segmented annotation suggestions (which typically provide detailed modifications or risk warnings for local semantic units of the contract) and global annotation suggestions (which may involve macro-level suggestions such as the overall structure of the contract, missing clauses, or compliance). When the two offer different or even contradictory suggestions on the same contract content, the system will coordinate. The coordination process may include identifying conflict points, assessing the severity of the conflict, and making a ruling based on preset priority rules (e.g., global suggestions take precedence over local suggestions, or suggestions with specific risk levels take precedence). In addition, a user interaction mechanism can be introduced, allowing users to manually select or adjust conflicts, ultimately forming a logically consistent unified annotation set without internal contradictions, providing a reliable basis for subsequent document generation.

[0069] In this embodiment, after obtaining the unified annotation set, the system synthesizes a clean version based on the unified annotation set and the standardized contract document, resulting in a clean version document. The clean version document refers to the final contract text generated after adopting all modification suggestions from the unified annotation set, which does not contain any annotation traces or revision marks. The purpose of this step is to provide users with a directly usable, revised final draft of the contract. Specifically, the system performs actual text operations on the standardized contract document according to the modifications indicated in the unified annotation set (such as text replacement, deletion, and addition), thereby generating a contract document that is updated in content but maintains a clean format. This document is typically used for final signing or external submission.

[0070] In this embodiment, the system will also synthesize a revised version based on the unified annotation set and the standardized contract document, resulting in an annotated revised version document. The revised version document refers to a document that clearly displays all modification suggestions from the unified annotation set using visible revision markers (such as annotation boxes, strikethroughs, underlines, and color highlighting) on ​​top of the standardized contract document. This step aims to help users intuitively understand the entire change history and specific modifications to the contract text. For example, newly added text can be displayed with a green underline, deleted text with a red strikethrough, and annotation content can be presented in the form of sidebar annotation boxes. This version facilitates users to review each line, compare the differences before and after the modification, and conduct further discussion or confirmation.

[0071] In addition, the system synthesizes a preview version based on a unified set of annotations and standardized contract documents, resulting in an annotated preview document. This preview document aims to provide a highly summarized or specific perspective on the annotations. It may not include all detailed revisions, but rather presents key annotation information in a more concise and understandable way. For example, the preview version could be a summary report listing all high-risk clauses and their corresponding annotation suggestions; or it could be a document with interactive annotations, allowing users to click on specific areas to view relevant annotation details without having to view all annotations directly in the document. The purpose of this step is to provide customized information presentations for users with different needs (such as management who only need a quick understanding of risk points, or legal personnel who need to conduct preliminary reviews), improving the efficiency and relevance of information acquisition.

[0072] In this embodiment, through the above technical solution, this application can effectively integrate the segmented annotation suggestion set and the global annotation suggestion, resolving potential conflicts between the two, thereby generating a unified annotation set with clear logic and consistent content. Based on this, it further intelligently synthesizes a clean version document, an annotated revised version document, and an annotated preview version document according to different user needs. The clean version document provides users with a final contract text that can be used directly, avoiding the tedious manual editing; the annotated revised version document clearly displays all modification traces, greatly facilitating users' review and confirmation of the modified content; and the annotated preview version document provides flexible and diverse annotation display methods, meeting the personalized needs of different users for information granularity and presentation format. Overall, this solution improves the efficiency and user experience of intelligent legal contract review, ensures the accuracy, usability, and traceability of the final delivered document, and allows users to choose the most suitable document version for subsequent processing according to their own needs, thereby optimizing the entire contract review and management process.

[0073] In one feasible implementation, refer to Figure 9 Step S510 includes steps S511 to S513, wherein: Step S511: Identify different annotation suggestions for the same contract content based on the segmented annotation suggestion set and the global annotation suggestion to obtain a set of conflicting annotation pairs; Step S512: Retrieve priority rules from a preset priority rule base according to the segmented annotation suggestion set, the global annotation suggestion, and the conflict annotation pair set; Step S513: Based on the segmented annotation suggestion set, the global annotation suggestion, the conflicting annotation pair set, and the priority rule, coordinate conflicting annotations to obtain the unified annotation set.

[0074] In this embodiment, different annotation suggestions for the same contract content are identified based on the segmented annotation suggestion set and the global annotation suggestion, thereby obtaining a set of conflicting annotation pairs. This step aims to accurately identify situations where there are differences or contradictions between segmented annotation suggestions and global annotation suggestions at the same location in the contract text or for the same semantic unit. For example, a segmented annotation might suggest modifying the wording of a clause, while a global annotation might point out a compliance risk and suggest its deletion. The identification process can be achieved by comparing the contract content identifiers (such as paragraph numbers, sentence numbers, keyword positions, etc.) pointed to by the annotations. Once multiple different or contradictory annotations for the same content are found, they are paired and included in the set of conflicting annotation pairs.

[0075] In this embodiment, priority rules are retrieved from a preset priority rule base based on the segmented annotation suggestion set, the global annotation suggestion, and the conflicting annotation pair set. The priority rule base is a pre-configured knowledge base containing strategies for resolving different types of annotation conflicts. These rules can be set based on multiple dimensions; for example, global compliance risk warnings have higher priority than local wording optimization suggestions; annotations involving mandatory legal provisions have higher priority than suggestive annotations; or, based on user preferences set in the review mode instructions, specific types of annotations are assigned higher priority. The system intelligently matches and retrieves the most applicable priority rule based on the specific type and context information of the conflicting annotation pair.

[0076] In this embodiment, conflicting annotations are coordinated according to the segmented annotation suggestion set, the global annotation suggestion, the conflicting annotation pair set, and the priority rules to finally obtain the unified annotation set. After obtaining the applicable priority rules, the system will process each conflict in the conflicting annotation pair set. The coordination process may include: fully adopting the higher-priority annotation and discarding the lower-priority annotation; integrating the information of the two annotations to form a more comprehensive and complete annotation; or, in some cases, marking the conflicting annotations and prompting the user to make a manual decision. Through this coordination mechanism, all potential annotation conflicts will be effectively resolved, ensuring that the final output annotation set is logically consistent and without contradictions.

[0077] In this embodiment, through the above technical solution, the method can effectively identify and resolve potential contradictions between the segmented annotation suggestion set and the global annotation suggestion. By using preset priority rules, the system can intelligently select, merge, or adjust conflicting annotations, thereby ensuring that the final generated unified annotation set has a high degree of consistency and accuracy. This improves the reliability and usability of the intelligent review results of legal contracts, avoids misunderstandings or decision-making errors caused by annotation conflicts, and enables users to obtain clear and unambiguous contract review reports, thereby improving review efficiency and the professionalism of contract management.

[0078] In one feasible implementation, the review mode instructions include a risk warning mode, a standard review mode, and an enhanced review mode; wherein, the risk warning mode corresponds to generating annotation suggestions for risk warning information; the standard review mode corresponds to generating annotation suggestions for risk warnings and standard modification suggestions; and the enhanced review mode corresponds to generating annotation suggestions that include risk warnings, standard modification suggestions, and optimization solutions.

[0079] In this embodiment, the review mode instruction is an input parameter provided by the user to the system when initiating a contract review. It instructs the system on the depth and focus of the review and the generation of annotations. Essentially, it is a configuration parameter that allows the user to customize the review behavior according to their actual needs. When the user selects the risk warning mode, the system will prioritize identifying and highlighting potential risks in the contract and clearly inform the user in the form of annotations. These annotations are typically concise and directly point out the risks, such as "This clause may lead to unlimited joint and several liability on our part" or "The stipulated liquidated damages are too high and there is a risk of adjustment by the court." The system focuses on risk identification and warnings rather than providing specific modification solutions, making it suitable for scenarios where users need a quick overview of contract risks.

[0080] In this embodiment, when a user selects the standard review mode, the system not only identifies risks and generates risk warning information, but also provides targeted standard modification suggestions. This means that the annotation content will include two parts: first, a description of the risk, and second, a recommended modification plan for the risk or non-compliance. For example, the annotation might display as "This clause's definition of force majeure is too broad; it is recommended to modify it to: 'Force majeure refers to objective circumstances that are unforeseeable, unavoidable, and insurmountable at the time of signing the contract.'" This mode is suitable for scenarios where users need specific modification guidance to ensure the compliance and reasonableness of the contract.

[0081] In this embodiment, when the user selects the enhanced review mode, the system will provide the most comprehensive and in-depth annotation suggestions. It not only covers risk warnings and standard modification suggestions, but also adds optimization solutions for contract terms. These optimization solutions aim to enhance the commercial value of the contract, reduce potential disputes, or secure a more favorable position for the user. For example, in addition to pointing out risks and providing standard modifications, the annotations may also suggest, "Considering our advantageous position in the market, we recommend adding an exclusivity clause to restrict the other party's cooperation with competitors, further consolidating market share." This mode is suitable for scenarios where users need to conduct in-depth negotiations, pursue the maximization of contract benefits, or engage in strategic contract planning.

[0082] In this embodiment, through the above technical solution, this application enhances the flexibility and user customization capabilities of the intelligent legal contract review system. Users can flexibly choose different review modes according to their review objectives and required annotation depth. For example, when a user only needs to quickly identify contract risks, they can choose the risk warning mode, allowing the system to focus on generating risk warning information and avoid redundant modification suggestions, thereby improving review efficiency. When a user needs specific modification guidance, the standard review mode can provide risk warnings and standard modification suggestions to ensure the compliance and reasonableness of the contract. When a user pursues the maximization of contract benefits or conducts strategic contract planning, the enhanced review mode can provide comprehensive annotations including risk warnings, standard modification suggestions, and optimization solutions, helping users discover potential business opportunities and optimize contract terms. This hierarchical review mode configuration enables the system to accurately respond to the personalized needs of different users, avoiding the problem of overly general and insufficiently targeted annotation suggestions, thereby improving the practicality of the annotations.

[0083] In the embodiments of this application, the configurable legal contract intelligent review and customized annotation method based on position recognition obtains the original contract document and the review mode instruction selected by the user, performs position orientation analysis on the semantic units of the contract, generates segmented annotation suggestions adapted to the user's position, and generates a deliverable document version. This solves the problem of missing position orientation recognition in the prior art, can dynamically adjust the annotation content according to the user's position, provides personalized and strategic review suggestions, and improves the practicality and adaptability of the review results.

[0084] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0085] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A configurable intelligent review and customized annotation method for legal contracts based on position recognition, characterized in that, The method includes: Obtain the original contract document and the review mode instruction selected by the user, and perform format standardization conversion on the original contract document to obtain a standardized contract document; The standardized contract document is subjected to structural parsing processing to obtain a set of contract semantic units; the set of contract semantic units includes multiple semantic units. According to the review mode instructions, each semantic unit in the contract semantic unit set is subjected to position bias analysis to generate corresponding segmented annotation suggestions, and the segmented annotation suggestions of all semantic units are summarized into a segmented annotation suggestion set. Generate global annotation suggestions based on the segmented annotation suggestion set and the standardized contract document; Multiple deliverable document versions are generated based on the segmented annotation suggestion set, the global annotation suggestion, and the standardized contract document.

2. The configurable legal contract intelligent review and customized annotation method based on position recognition as described in claim 1, characterized in that, The steps of performing stance bias analysis on each semantic unit in the contract semantic unit set according to the review mode instructions to generate corresponding segmented annotation suggestions include: Semantic units are subjected to stance bias analysis to obtain clause stance identifiers; Perform clause type identification processing on semantic units to obtain clause type identifiers; A risk level assessment is performed based on the clause type identifier and the clause stance identifier to obtain a risk level identifier; The segmented annotation suggestions are generated based on the review mode instructions, the semantic units, the clause type identifier, the clause position identifier, and the risk level identifier.

3. The configurable legal contract intelligent review and customized annotation method based on position recognition as described in claim 2, characterized in that, The steps for performing stance analysis on semantic units to obtain clause stance identifiers include: Extract the contract subject entities from the semantic units to obtain the set of contract subject entities; By analyzing the rights and obligations relationships within semantic units, we can obtain the identifiers of rights and responsibilities. The position inclination is determined based on the semantic unit, the set of contract subject entities, and the right and responsibility relationship identifier, and the position identifier of the clause is obtained.

4. The configurable legal contract intelligent review and customized annotation method based on position recognition as described in claim 2, characterized in that, The steps for generating the segmented annotation suggestions based on the review mode instructions, the semantic units, the clause type identifier, the clause position identifier, and the risk level identifier include: The annotation generation strategy is determined based on the review mode instructions; Based on the clause type identifier, the clause stance identifier, and the risk level identifier, a matching annotation template is retrieved from a preset annotation template library; The annotation template is adjusted according to the review mode instructions, the semantic unit, the clause type identifier, the clause position identifier, the risk level identifier, and the annotation generation strategy to obtain preliminary annotation content; The preliminary annotation content is optimized based on the review mode instructions and the annotation generation strategy to obtain the segmented annotation suggestions.

5. The configurable legal contract intelligent review and customized annotation method based on position recognition as described in claim 1, characterized in that, The steps for generating global annotation suggestions based on the segmented annotation suggestion set and the standardized contract document include: Missing clauses are detected based on the standardized contract document and the set of segmented annotation suggestions. Missing clause identifiers are obtained, and supplementary clause suggestions are generated based on the missing clause identifiers. The standardized contract document is subjected to compliance verification of the contract subject information to obtain compliance issue identifiers, and supplementary compliance suggestions are generated based on the compliance issue identifiers. The standardized contract document is subjected to a legal provision citation accuracy check to obtain a provision timeliness identifier, and a provision update suggestion is generated based on the provision timeliness identifier; The aforementioned supplementary suggestions, compliance supplementary suggestions, and clause update suggestions are summarized into the global annotation suggestions.

6. The configurable legal contract intelligent review and customized annotation method based on position recognition as described in claim 5, characterized in that, The step of detecting missing clauses and obtaining missing clause identifiers based on the standardized contract document and the set of segmented annotation suggestions includes: The contract type is identified based on the standardized contract document and the set of segmented annotation suggestions, and a contract type identifier is obtained. Based on the contract type identifier, the corresponding set of key clauses is retrieved from the preset key clause rule base; The standardized contract document is matched and compared with the set of key clauses to obtain the clause existence identifier; Based on the existence identifier of the aforementioned clauses, the missing key clauses are identified, and the missing clause identifier is obtained.

7. The configurable legal contract intelligent review and customized annotation method based on position recognition as described in claim 5, characterized in that, The steps for generating supplementary clause suggestions based on the missing clause identifier include: The corresponding reference clause template is retrieved from the preset clause template library based on the missing clause identifier; The reference clause template is adjusted according to the review mode instructions, the missing clause identifier, the standardized contract document, and the reference clause template to obtain the position-based clause template and standardized contract document information; The context information is extracted and filled in based on the standardized contract document information and the position-based clause template to obtain the complete clause content; The proposed supplementary terms are generated based on the full terms.

8. The configurable legal contract intelligent review and customized annotation method based on position recognition as described in claim 1, characterized in that, The steps of generating multiple deliverable document versions based on the segmented annotation suggestion set, the global annotation suggestion, and the standardized contract document include: Based on the segmented annotation suggestion set and the global annotation suggestion, annotation conflicts are coordinated to obtain a unified annotation set; A clean version of the document is obtained by synthesizing the unified annotation set and the standardized contract document; Based on the unified annotation set and the standardized contract document, a revised version is synthesized to obtain the annotated revised version document; Annotated preview version document is obtained by synthesizing the unified annotation set and the standardized contract document.

9. The configurable legal contract intelligent review and customized annotation method based on position recognition as described in claim 1, characterized in that, The steps for resolving annotation conflicts and obtaining a unified annotation set based on the segmented annotation suggestion set and the global annotation suggestion include: Based on the segmented annotation suggestion set and the global annotation suggestion, different annotation suggestions for the same contract content are identified to obtain a set of conflicting annotation pairs. Priority rules are retrieved from a preset priority rule base based on the segmented annotation suggestion set, the global annotation suggestion, and the conflict annotation pair set; The unified annotation set is obtained by coordinating conflicting annotations based on the segmented annotation suggestion set, the global annotation suggestion, the conflicting annotation pair set, and the priority rule.

10. The configurable legal contract intelligent review and customized annotation method based on position recognition as described in claim 1, characterized in that, The review mode instructions include a risk warning mode, a standard review mode, and an enhanced review mode; wherein, the risk warning mode generates annotation suggestions for risk warning information; the standard review mode generates annotation suggestions for risk warnings and standard modification suggestions; and the enhanced review mode generates annotation suggestions that include risk warnings, standard modification suggestions, and optimization solutions.

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