Document Containers With Machine-Learned Candidate Matching
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Solution Overview
Problem
Existing document management systems struggle to accurately identify and suggest related documents for electronic documents, leading to inefficiencies in document generation and increased potential for errors.
Innovation Solution
A document management platform utilizes a machine learning model to determine attributes of input documents, assign similarity scores to candidate documents, and generate graphical user interfaces to suggest related documents, allowing users to select and automatically fill common fields in electronic documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is used to identify candidate documents, then the accuracy of document suggestion is improved, but the device complexity increases
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the document management system and the document suggestion output. The model processes input documents, determines attributes, and generates similarity scores for candidate documents, thereby improving suggestion accuracy while adding computational complexity to the system architecture.
2Ease of operation
If more candidate documents are included in the container, then the user experience is improved, but the time required for document processing increases
Solution Approach 1:
The system includes more candidate documents in the container than strictly necessary, using excessive action to ensure comprehensive document suggestions. This approach improves user experience by providing more relevant documents for selection, even though it increases processing time and computational resources required.
3Ease of operation
If manual selection of related documents is required, then the ease of operation is improved, but the productivity decreases
Solution Approach 1:
The machine learning model performs preliminary action by automatically determining attributes of input documents and generating similarity scores for candidate documents before user selection. This pre-processing reduces the manual effort required from users while maintaining their control over the final document selection, thereby improving productivity without sacrificing ease of operation.
Data Source
AI summary
Techniques are described for a system document management comprising one or more processors having access to a memory. The system is configured to determine an attribute for an input document for execution by a signer. The system is also configured to generate a similarity score for each of a plurality of candidate documents using a machine learning model, wherein using the machine learning model comprises providing the attribute as an input to the machine learning model. The system is also configured to generate data for a graphical user interface comprising an indication of at least a subset of the candidate documents based on the similarity scores generated for each of the plurality of candidate documents. The system is configured to output, for display, the data to a user device.