Document Analysis Platform with Model Taxonomy
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Solution Overview
Problem
Quantifying attributes of document analysis in large corpuses is difficult, making it challenging to determine similarities, differences, and classify documents effectively.
Innovation Solution
A document analysis platform is developed, featuring a model building component to train classification models and a model library for taxonomy-based classification, utilizing user input to categorize documents as 'in class' or 'out of class', with features like keyword analysis, vector representation, and transfer learning for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional document analysis methods are used on large corpuses, then comprehensive analysis can be performed, but the process is time-consuming and lacks precision in quantifying attributes
Solution Approach 1:
The system performs preliminary actions by pre-processing documents to extract key attributes and store them in a structured format. This includes converting documents to vectors, extracting metadata, and organizing content before analysis, so that when analysis is needed, the work is already partially done, reducing both time and improving precision.
Solution Approach 2:
The patent replaces manual or traditional mechanical document analysis methods with automated computational systems. Machine learning models and algorithms automatically quantify document attributes, eliminating the need for time-consuming manual review while providing precise, consistent measurements of document characteristics.
2Productivity
If manual document classification is performed, then accuracy can be maintained, but productivity decreases significantly
Solution Approach 1:
The system enables self-service classification where documents automatically classify themselves through embedded metadata and structured attributes. The documents contain their own classification information in organized formats, allowing the system to autonomously categorize them without human intervention while maintaining accuracy through consistent application of classification rules.
Solution Approach 2:
The system implements feedback mechanisms where classification results are continuously evaluated and used to refine future classifications. The structured attributes and metadata provide feedback loops that allow the system to learn from previous classifications, improving both speed and accuracy over time through iterative optimization.
3Measurement precision
If detailed attribute analysis is performed on each document, then classification accuracy improves, but the complexity of processing increases
Solution Approach 1:
The system segments document analysis into distinct, manageable components: extracting specific attributes (author, date, keywords), converting to vectors, storing in structured formats, and analyzing separately. This segmentation allows each component to be processed independently with appropriate methods, reducing overall system complexity while maintaining comprehensive analysis capability.
Solution Approach 2:
The patent changes the parameters of document representation from unstructured text to structured vectors and metadata. By transforming documents into standardized numerical representations with defined attributes, the system simplifies processing while enabling precise measurement and comparison, reducing complexity without sacrificing analytical depth.
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.