AI Contract Clause Analysis for Edit Approval Workflow
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Solution Overview
Problem
The manual process of editing legal documents is slow and prone to human error, and requires multiple parties to review and approve edits, leading to inefficiencies in the workflow.
Innovation Solution
Utilizing machine learning models, specifically natural language processing models like generative and extractive language models, to analyze contractual terms and clauses, recommending edits and changes to the workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review and approval processes are used for document editing, then human oversight and control are maintained, but the workflow becomes slow and inefficient
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the manual review process and the document editing workflow. The AI assistant automatically analyzes edits, identifies required approvers, and manages the approval workflow, thereby maintaining the reliability of human oversight while dramatically improving workflow efficiency by eliminating manual coordination overhead
Solution Approach 2:
The system enables self-service by allowing the AI assistant to autonomously perform tasks such as identifying the correct parties for approval, routing edits to appropriate reviewers, and tracking approval status. This reduces the burden on human users who would otherwise need to manually manage each step of the review process
2Reliability
If multiple parties manually review and approve edits, then error detection and quality control are improved, but the process becomes time-consuming and complex
Solution Approach 1:
The AI assistant performs preliminary actions by automatically analyzing edits before they are submitted for review. It pre-identifies which parties need to approve or reject specific edits based on the document content and edit type, preparing the workflow in advance so that human reviewers can immediately focus on their evaluation without needing to manually determine approval routing
Solution Approach 2:
The system implements feedback mechanisms where the AI assistant continuously monitors the review process, tracks approval status, and provides real-time updates to users. This feedback loop ensures that multiple parties can review edits efficiently while maintaining clear visibility into the approval workflow status, reducing unnecessary delays
3Adaptability or versatility
If manual processes are used for managing document edits, then flexibility and human judgment are preserved, but human error and inefficiency increase
Solution Approach 1:
The AI assistant serves as an intermediary that handles routine and error-prone tasks such as identifying the correct parties for approval, determining the appropriate review workflow, and tracking document status. This allows human users to focus on exercises of human judgment and adaptability while the AI intermediary ensures consistent, accurate execution of procedural tasks, thereby improving overall reliability without sacrificing flexibility
Data Source
AI summary
Aspects discussed herein may relate to using machine learning models as part of methods and techniques for ingesting, creating, storing, editing, and managing a document. The document may be a legal contract that includes one or more clauses. Among other things, one or more machine learning models may be configured to recognize clauses and/or classifications, or types, of clauses. For example, the one or more generative language models may be used to generate one or more recommended edits to a clause, generate one or more suggested clauses that are missing from the contract, and/or generate one or more suggested locations where a clause may be inserted into or moved within the contract.