Document Classification via Feature Vector Comparison
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Solution Overview
Problem
Current search engines face challenges in extracting structured data from unstructured documents like PDFs and images, making it difficult to determine semantic information such as section titles, dish names, and prices without annotations, which is essential for semantic search techniques.
Innovation Solution
A system and method that involves identifying training tokens from content sources, generating features for these tokens, and training classifiers to classify input documents by extracting sequences of text, converting multimedia representations into text using OCR, and comparing feature vectors to signature vectors to determine document types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search algorithms are used, then search results can be provided, but semantic understanding and contextual meaning are lost
Solution Approach 1:
The patent extracts structured data elements (section titles, dish names, prices) from unstructured document content using machine learning classifiers. The system identifies and separates semantic information from the raw document text, enabling search engines to understand and process the meaning behind the content rather than just matching keywords.
Solution Approach 2:
The patent introduces an intermediary processing layer between the unstructured document and the search query. This layer includes feature extraction modules and classifiers that transform the raw document into structured representations, allowing semantic search algorithms to operate on processed information rather than raw text.
2Measurement precision
If manual annotation is used to classify documents, then classification accuracy is improved, but processing time and labor requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-training classifiers on a body of known-good data before processing new documents. This pre-processing step creates the knowledge base needed for rapid classification, eliminating the need for manual annotation during the actual classification process and significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The system enables self-service classification by using automatically trained classifiers to process new documents without human intervention. The classifiers learn from the training data and independently perform classification tasks, replacing manual annotation processes and scaling efficiently to large volumes of documents.
3Adaptability or versatility
If structured data extraction is implemented, then semantic search capability is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal classification system that can handle multiple document types and extraction tasks using a single framework. The same trained classifiers can process different kinds of unstructured documents (menus, invoices, forms) and extract various types of information, reducing the need for separate specialized systems for each document type.
4Productivity
If unstructured documents are processed without annotations, then processing speed is improved, but extraction accuracy deteriorates
Solution Approach 1:
The patent replaces manual mechanical annotation processes with automated machine learning classifiers. The system uses trained neural networks to automatically identify and classify semantic elements in unstructured documents, achieving both high processing speed and accurate extraction without requiring human annotators to review each document.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables effective classification and management of unstructured data, such as restaurant menus, by automatically extracting and updating menu content, improving search relevance and reducing manual effort in maintaining consistent pricing and offerings across different platforms.
Implementation Method 1
converting multimedia representations into text using OCR
Data Source
AI summary
A system and method for document classification are presented. An input document is received (e.g., by at least one server communicatively coupled to a network). A plurality of features are identified in the input document. The plurality of features include sequences of text extracted from the input document. A feature vector of the input document is generated based upon the sequences of text, and the feature vector of the input document is compared to each of a plurality of signature vectors to determine a primary type of the input document. The primary type of the input document is stored into a storage system in communication with the at least one server.