Document Analysis Platform Vector Classification
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Solution Overview
Problem
Determining the relevance and classification of documents within large corpora, such as patents and patent applications, is challenging due to the difficulty in quantifying attributes and identifying similarities and differences effectively.
Innovation Solution
A document analysis platform is developed, featuring a model building component and a model library, which trains classification models to determine whether documents are in or out of class based on user input, utilizing techniques like term frequency-inverse document frequency, vectorization, and transfer learning, and organizes models in a taxonomy for efficient retrieval and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classification models are trained on large corpora of documents, then classification accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-computing document vectors and organizing them in a vector space before classification is needed. Documents are pre-processed and stored with their vector representations, so when classification is required, the system can quickly compare new documents against the pre-organized corpus without performing full training or computation at query time.
Solution Approach 2:
The system creates vector representations (copies) of documents that capture their semantic meaning. Instead of working with the full text of documents during classification, the system uses these compact vector copies that can be efficiently compared and processed, reducing computational overhead while maintaining classification accuracy.
2Measurement precision
If more documents are analyzed and classified, then relevance identification is improved, but processing complexity increases
Solution Approach 1:
The system replaces traditional text-based mechanical comparison methods with vector space operations. Instead of manually or algorithmically comparing document texts word-by-word, the system transforms documents into vector representations and uses mathematical operations in vector space to determine similarity and relevance, significantly simplifying the processing complexity.
Solution Approach 2:
The system changes the parameter space from raw text to vector representations. By transforming documents into vectors with specific dimensions that capture semantic features, the system enables efficient computation of similarity metrics and classification operations that would be computationally intensive to perform on raw text data.
3Measurement precision
If user input is required for model training, then model accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically organizing documents into vector space and creating classifications without requiring extensive manual user input. The vector space model enables the system to autonomously process and classify documents based on their semantic content, reducing the need for users to manually label or categorize large numbers of documents while maintaining high accuracy.
Data Source
AI summary
Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.