Document Analysis Architecture for Classification Model Training
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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
1Measurement precision
If classification models are trained on large document corpora to improve classification accuracy, then measurement precision improves, but device complexity and training time increase
Solution Approach 1:
The patent segments the classification model training process into distinct components: a model building component that trains classification models on document corpora, and a model library component that stores and manages multiple trained models. This segmentation allows the system to handle large corpora systematically while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent implements preliminary action by pre-training classification models on large document corpora before actual classification tasks. The model building component trains models in advance and stores them in the model library, so that when classification is needed, pre-trained models are readily available. This eliminates the need for real-time training during classification operations.
2Reliability
If more documents are analyzed to improve classification reliability, then reliability improves, but loss of time increases
Solution Approach 1:
The system performs preliminary action by pre-training classification models on large document corpora in advance. The model building component analyzes extensive documents during the training phase and stores the learned patterns in the model library. During actual classification, these pre-trained models can quickly and reliably classify new documents without requiring re-analysis of the entire training corpus.
Solution Approach 2:
The patent uses copying by creating multiple trained classification models and storing them in the model library. Instead of re-training models for each classification task, the system copies and applies pre-trained models to new documents. This allows reliable classification based on extensive training data while maintaining fast processing speed.
3Adaptability or versatility
If classification models are trained from scratch for each document set, then adaptability improves, but loss of time and computational resources increase
Solution Approach 1:
The patent implements universality by creating a model library that stores multiple trained classification models that can be applied to different document sets and classification tasks. The system provides a unified interface for both training new models and applying existing models, making the classification system versatile and adaptable to various needs without requiring separate training processes for each task.
Solution Approach 2:
The system performs preliminary action by pre-training and storing multiple classification models in the model library for different document types and classification purposes. When a new classification task arises, the system can select from pre-trained models rather than training from scratch, significantly reducing training time while maintaining adaptability through the diverse model collection.
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.