Document Analysis Platform Using Classification Model Taxonomy
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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-class 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
1Productivity
If manual analysis methods are used to determine document relevance and classification, then measurement precision can be maintained, but productivity decreases significantly when dealing with large corpora
Solution Approach 1:
The patent introduces classification models as intermediary components that automatically analyze document attributes and determine relevance. These models serve as mediators between the large corpus of documents and the user, performing the complex classification task that would be impractical to do manually at scale, while maintaining consistent and objective classification standards across all documents.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated computational systems. Classification models use algorithmic processing to evaluate document attributes, substitute human analysts in the classification process, and enable handling of large document volumes that would be impossible to process manually while maintaining or improving classification consistency.
2Productivity
If automated classification models are trained on large datasets, then productivity increases, but device complexity increases due to model training and management requirements
Solution Approach 1:
The patent segments the classification system into distinct components: a model building component for training classification models, a model library component for storing and managing trained models, and an application component for using models to classify documents. This segmentation allows each component to be developed, maintained, and scaled independently, reducing overall system complexity.
Solution Approach 2:
The patent creates a universal model library that stores trained classification models that can be reused across multiple document classification tasks. Once a model is trained, it serves multiple purposes and can be applied to different document sets, reducing the need to train separate models for each task and simplifying the overall system architecture.
3Measurement precision
If comprehensive document attributes are analyzed for accurate classification, then measurement precision improves, but loss of time increases due to the complexity of quantifying multiple attributes
Solution Approach 1:
The patent performs preliminary actions by pre-training classification models on comprehensive document attributes before actual classification tasks. The models learn to efficiently evaluate multiple attributes simultaneously during training, so that during deployment, they can rapidly classify new documents by applying the learned patterns without manually computing each attribute from scratch.
Solution Approach 2:
The patent replaces manual attribute quantification with automated model-based analysis. The classification models automatically compute and evaluate multiple document attributes in parallel, substituting the time-consuming manual process of analyzing each attribute separately with a single integrated computational operation that maintains high precision.
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.