Document Version Mapping Using Neural Feature Comparison
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Solution Overview
Problem
Users face difficulty in locating document elements in different versions of product documentation due to changes such as splitting or merging of topics, making it hard to find relevant information.
Innovation Solution
A machine learning-based approach using a neural network to analyze documents by representing text as a matrix, similar to an image, to identify common elements across different document versions, with user feedback for model improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If document versions are revised by splitting or merging topics, then the documentation adapts to product changes, but users cannot easily locate contents in other versions
Solution Approach 1:
The patent creates a visual copy of the document structure as a navigable map that replicates the hierarchical organization of topics across different document versions. This visual map allows users to see and navigate to corresponding contents in different versions without manually searching through revised documentation structures.
Solution Approach 2:
The patent introduces a visual navigation map as an intermediary between the user and the revised document versions. This intermediary layer provides a consistent visual interface that maps to different document structures, allowing users to locate contents across versions without being affected by the underlying structural changes.
2Measurement precision
If machine learning models process document versions to identify shared elements, then accuracy in identifying common elements improves, but computational complexity increases
Solution Approach 1:
The patent segments the document processing task into distinct stages: text extraction, matrix population, machine learning processing, and result visualization. This segmentation allows each component to be optimized independently and processed efficiently, reducing overall computational complexity while maintaining identification accuracy.
Solution Approach 2:
The patent replaces traditional text-based comparison methods with machine learning-based image processing of populated matrices. This substitution enables more accurate identification of shared document elements by leveraging the pattern recognition capabilities of trained models, achieving higher precision despite increased computational requirements.
Data Source
AI summary
A present invention embodiment analyzes documents. A first document is received comprising a plurality of sentences that each include one or more words. A matrix is populated with the plurality of sentences, wherein each of the one or more words of each sentence in the matrix is encoded as a numerical value. The matrix is processed using a machine learning model to generate a first feature map. The first feature map is compared to a second feature map of a corresponding second document to identify a shared document element between the first document and the second document based on a common feature in the first feature map and the second feature map. The shared document element is indicated via a user interface.