Deep Learning Data Search System for Feature Extraction
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Solution Overview
Problem
Current data search methodologies are limited by their reliance on traditional methods and are ineffective in filtering through the vast volume of data to find relevant information, leading to a 'needle in the haystack' problem, especially as data generation increases.
Innovation Solution
A deep learning system that uses a machine learning algorithm to recognize feature data associated with predetermined classifiers, dynamically updates training sets and classifiers, and aggregates data from various sources like social media and image repositories to identify specific criteria, enabling advanced search and filtering capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search methodologies are used to search data sources, then users can find information using standard search functions, but the system cannot effectively filter through the vast volume of data to locate relevant information
Solution Approach 1:
The patent replaces traditional mechanical search methodologies with a machine learning-based recognition system. The system uses trained models to automatically recognize and extract specific features (named entities, objects, relationships) from data sources, substituting manual or algorithmic search processes with intelligent pattern recognition that can handle vast data volumes efficiently
Solution Approach 2:
The system implements self-service through automated feature extraction and classification. The machine learning models autonomously identify and extract relevant features from data sources without requiring manual intervention or traditional search queries, enabling the system to serve itself in filtering and organizing information from massive data volumes
2Reliability
If the system processes all data from multiple data sources, then comprehensive information is available, but the time and computational resources required increase significantly
Solution Approach 1:
The patent extracts only the relevant features needed for specific search criteria rather than processing entire data sets. The system identifies and extracts specific named entities, objects, and relationships that match query parameters, discarding irrelevant data and significantly reducing processing time while maintaining information completeness for the required search objectives
Solution Approach 2:
The system segments the data processing task into distinct feature extraction stages. Different machine learning models handle different types of features (text entities, image objects, relationship patterns) independently, allowing parallel processing and reducing overall computation time while ensuring comprehensive coverage of relevant information across multiple data sources
3Adaptability or versatility
If traditional classifiers are used to categorize data, then data organization is maintained, but the system cannot adapt to new types of content or evolving search criteria
Solution Approach 1:
The patent implements dynamic classifiers based on machine learning models that can adapt to new content types and search criteria. The system continuously learns from new data and updates its feature extraction capabilities, allowing classifiers to evolve with emerging content formats and search requirements without requiring manual reconfiguration or increasing system complexity
Data Source
AI summary
The present invention relates to a deep learning system suitable for searching data sources for specific content. In particular, the present invention relates to an unconventional machine-implemented process, leveraging a machine learning algorithm, to provide a technology that searches data sources and recognizes feature data associated with one or more predetermined classifiers.