Machine-Learned Document Templates with Dynamic Structural Updates
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Solution Overview
Problem
Conventional systems lack the ability to leverage knowledge from prior documents to efficiently generate similar electronic documents, leading to resource-intensive and error-prone manual document creation, especially for legal documents with standard portions and structures.
Innovation Solution
A system utilizing machine learning models to analyze and extract structural arrangements from electronic documents, generating templates that can be dynamically updated based on user feedback, allowing for efficient and accurate creation of similar documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually create electronic documents from scratch using conventional systems, then documents can be generated, but the process is resource-intensive, time-consuming, and error-prone
Solution Approach 1:
The system performs preliminary action by automatically extracting and storing document structures, clauses, and portions from existing documents in a database before new documents need to be created. This pre-processing enables rapid document generation by reusing proven templates and content rather than creating everything from scratch each time.
Solution Approach 2:
The system implements copying by retrieving and reusing document structures, clauses, and portions from existing documents in the database. Instead of manually recreating standard legal provisions and document formats, the system automatically copies proven content from the database, significantly reducing creation time and improving consistency.
2Reliability
If users manually create electronic documents without template reuse, then documents can be generated, but accuracy decreases and errors increase
Solution Approach 1:
The system applies segmentation by breaking down documents into discrete reusable components such as structures, clauses, and portions, which are stored separately in the database. This modular approach allows individual components to be validated and refined independently, improving overall document accuracy while maintaining manageable system complexity through organized modularity.
Solution Approach 2:
The system implements feedback by continuously learning from user interactions with generated documents. When users modify, accept, or reject extracted clauses and structures, this feedback is used to refine the extraction algorithms and improve future document generation accuracy, creating a self-improving system that increases reliability over time.
3Productivity
If conventional systems are used for document generation, then documents can be created, but resource consumption is high and efficiency is low
Solution Approach 1:
The system performs preliminary action by pre-extracting and storing document structures, clauses, and portions from existing documents in a database during off-peak times or incrementally. This upfront processing reduces the computational burden during actual document generation, as the system only needs to retrieve and assemble pre-processed components rather than performing heavy analysis in real-time.
Solution Approach 2:
The system reduces resource consumption by copying pre-extracted document components from the database rather than re-analyzing and re-extracting them each time a new document is needed. This approach significantly lowers computational requirements during document generation while maintaining high productivity through rapid assembly of reused components.
4Loss of energy
If manual document creation is used, then documents can be generated, but standard portions and structures must be recreated repeatedly, wasting resources
Solution Approach 1:
The system implements universality by creating a flexible database structure and extraction framework that can handle multiple document types and formats. The same core infrastructure extracts and stores structures, clauses, and portions from various document sources, allowing the system to efficiently serve diverse document generation needs while reducing resource waste through reusable components.
Solution Approach 2:
The system applies dynamics by making the document generation process adaptive and flexible. The extraction algorithms dynamically adjust to different document types and structures, and the system can dynamically assemble different combinations of stored clauses and portions based on the specific document being created, maintaining versatility while avoiding repetition of standard content.
Data Source
AI summary
A method, a system, and a computer program product for generation of templates for electronic documents. A first template is generated based on a plurality of electronic documents. The first template defines a first structural arrangement of one or more portions extracted from the electronic documents and includes one or more portions. A machine learning model determines the first structural arrangement. An update to at least one portion is received. A second template is generated based on the first template and the received update. The second template defines a second structural arrangement of the portions as determined based on the first structural arrangement and the received update. An object model representative of at least one of: the first template, the second template, and a difference between the first and second templates is stored.