Digital Document Construction Using Hierarchical Term Taxonomy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for creating and editing electronic documents, particularly legally-binding contracts, are inefficient and require significant time and expertise, making them costly for users.
Innovation Solution
A computer-based system utilizing a machine learning framework that constructs digital documents through a hierarchical taxonomy of terms, allows user interaction, and includes a 'human-in-the-loop' feedback mechanism to improve predictions and efficiency, enabling auto-negotiation of contracts without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional expert systems are used to create and edit legal contracts, then document accuracy and legal validity are improved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables self-service document creation by allowing users to interact with an intelligent interface that automatically generates legally binding contracts based on user inputs. The machine learning model processes user responses and autonomously constructs contract documents, eliminating the need for manual intervention by legal experts while maintaining document accuracy and validity.
Solution Approach 2:
The patent replaces the mechanical process of manual contract drafting by experts with an automated machine learning system. The neural network model processes natural language inputs, understands legal contexts, and generates structured contract documents automatically, substituting human expertise with an intelligent automated system that operates continuously without time loss.
2Productivity
If machine learning automation is implemented for document construction, then productivity and efficiency are improved, but system complexity increases
Solution Approach 1:
The system introduces an intelligent intermediary layer between the user and the complex machine learning model. The user interacts with a simplified natural language interface rather than the underlying complex system. The machine learning model acts as an intermediary that processes user inputs, understands legal contexts, and generates documents automatically, hiding the system complexity from the user while maintaining high productivity.
Solution Approach 2:
The system changes the operational parameters from manual expert judgment to automated machine learning predictions. The machine learning model processes inputs in terms of legal concepts, relationships, and contextual parameters, transforming the document creation process from manual to automated while managing system complexity through optimized algorithmic processing.
3Reliability
If human expertise is required for document review and validation, then legal accuracy is improved, but operational time and cost increase
Solution Approach 1:
The system provides self-service legal accuracy by enabling users to directly interact with the machine learning model through natural language queries. Users can ask questions about contract terms, review generated documents, and receive explanations automatically, eliminating the need for human expert review while maintaining high legal accuracy through the intelligent processing capabilities of the machine learning system.
Solution Approach 2:
The system implements feedback mechanisms where users can query about generated contract terms, request explanations, and provide corrections. The machine learning model processes this feedback and adjusts its outputs accordingly, ensuring legal accuracy through continuous interaction and refinement while maintaining ease of operation through automated responses.
Data Source
AI summary
In some aspects described herein, a computer-based system that is capable of constructing digital documents is provided. In some implementations, a machine learning system is provided that learns certain terms within a document. The terms may be, for example, part of a document that forms a legally-binding contract between two entities. In one implementation of the machine learning system, the machine learning system interoperates within a user interface to show predictions of certain terms within the document to the user. Further, the machine learning system may capture user answers relating to certain terms and provide feedback into the system that learns during operation of the system, improving user interactions, accuracy and reducing the number of user interactions.