Archetype-Based Data Optimization for Document Processing
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Solution Overview
Problem
Existing systems struggle to efficiently identify and implement optimizations in large numbers of similar documents or data sets, such as tax returns, due to the high variability and customization of each instance.
Innovation Solution
The system generates archetypes of documents or data sets using machine learning algorithms, filtering, and clustering, allowing for the automatic identification and implementation of potential improvements in similar documents or data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are applied to identify optimizations in large numbers of similar documents, then the accuracy of optimization identification improves, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the large dataset into smaller batches or chunks that can be processed independently and in parallel. This allows the machine learning algorithms to work on manageable portions of data simultaneously, reducing overall processing time while maintaining accuracy through consistent application across segments.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the data, pre-training models on representative samples, or creating feature extractions in advance. This prepares the data and models beforehand so that when actual optimization identification is needed, the process can proceed faster without sacrificing accuracy.
2Measurement precision
If machine learning algorithms are applied to identify optimizations in large numbers of similar documents, then the accuracy of optimization identification improves, but the computational resources and processing complexity increase significantly
Solution Approach 1:
The patent introduces intermediary components such as feature extraction layers, dimensionality reduction techniques, or intermediate representation models that simplify the data before it reaches the main machine learning algorithms. This reduces computational complexity while preserving the essential information needed for accurate optimization identification.
Solution Approach 2:
The patent uses copying by creating simplified representations or proxies of the original complex data structures. These copies can be processed more efficiently by machine learning algorithms, reducing computational resource requirements while maintaining the fidelity needed for accurate optimization detection.
3Adaptability or versatility
If each document is customized and highly variable, then the adaptability and relevance of each document improves, but the ability to identify patterns and implement optimizations across multiple documents decreases
Solution Approach 1:
The patent applies local quality by identifying and preserving the specific customized features of each individual document while simultaneously identifying common patterns across the dataset. This allows the system to respect document-specific variations (local quality) while still enabling cross-document optimization through pattern recognition in shared features.
Solution Approach 2:
The patent creates universal optimization rules or templates that can be applied across multiple document types and variations. By abstracting common optimization patterns into universal templates, the system can efficiently implement optimizations across highly variable documents without needing to customize the optimization process for each individual document.
Data Source
AI summary
A processor may filter data to generate a subset of the data less than an entire set of the data. The subset may include at least one string and at least one numeric value. The processor may match the at least one string and the at least one numeric value to one of a plurality of archetypes by applying a clustering algorithm. Each archetype may include a subset of archetype data less than an entire set of archetype data. The processor may compare the entire set of data to the entire set of archetype data to identify at least one difference between the entire set of data and the entire set of archetype data. The processor may apply at least one optimization to address the at least one difference.