Generalized Vocabulary Tokens for Document Processing
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Solution Overview
Problem
Current machine learning systems face challenges in efficiently training and tuning models for document processing applications, particularly in terms of compute resources and accuracy, especially when dealing with varying content types like text, images, and videos, due to the complexity of processing and the need for intensive computations.
Innovation Solution
The development of generalized vocabulary tokens that can be used to construct vocabularies from a training corpus, allowing for the creation of feature vectors with low processing overhead, which can span different content types and enable efficient training and tuning of machine learning models without requiring compute-intensive image and video processing tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If compute-intensive image and video processing tasks are used to process diverse content types, then model accuracy is improved, but processing time and compute resources increase
Solution Approach 1:
The patent creates a simplified copy of image and video data by converting them into vocabulary tokens that represent visual and video content. Instead of processing actual images and videos through computationally intensive deep learning models, the system uses tokenized representations that capture essential content information in a much lighter format, enabling fast processing while maintaining accuracy
Solution Approach 2:
The patent changes the representation parameters of content by transforming images and videos into discrete vocabulary tokens. This parameter transformation converts continuous pixel data into discrete categorical representations, significantly reducing computational complexity while preserving the essential information needed for document processing tasks
2Measurement precision
If compute-intensive image and video processing tasks are used to process diverse content types, then model accuracy is improved, but compute resources increase
Solution Approach 1:
The patent creates a simplified copy of image and video data by converting them into vocabulary tokens that represent visual and video content. Instead of processing actual images and videos through computationally intensive deep learning models, the system uses tokenized representations that capture essential content information in a much lighter format, enabling fast processing while maintaining accuracy
Solution Approach 2:
The patent replaces complex mechanical processing systems (deep learning models for image and video analysis) with simpler token-based processing mechanisms. The system substitutes heavy computational operations with lightweight token matching and counting operations, dramatically reducing compute resource requirements while maintaining processing effectiveness
3Adaptability or versatility
If a large vocabulary is used to handle diverse content types, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal vocabulary that serves multiple functions across different content types. The same vocabulary structure handles text, images, and videos by assigning tokens to represent each content type. This multi-functional vocabulary reduces the need for separate processing systems for each content type, simplifying the overall device architecture while maintaining high adaptability
Solution Approach 2:
The patent merges the processing of different content types into a unified framework. By combining text, images, and videos into a single vocabulary-based representation system, the patent eliminates the need for separate processing pipelines for each content type, reducing device complexity while maintaining the ability to handle diverse content
Data Source
AI summary
Techniques are described herein for training and evaluating machine learning (ML) models for document processing computing applications using generalized vocabulary tokens. In some embodiments, an ML system determines a set of tokens for non-textual content in a plurality of documents. The ML system generates a fixed-length vocabulary that includes the set of tokens for the non-textual content. The ML system further generates for each respective document in a training dataset of documents, a respective feature vector based at least in part on which tokens in the fixed-length vocabulary occur in the respective document. The ML system trains a ML model based at least in part on the respective feature vector for each respective document in the training dataset.