Configurable Pattern Matching Engine for Dynamic Content Classification
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Solution Overview
Problem
Conventional content rating engines employ static pattern matching mechanisms that fail to adapt to changes in applications or circumstances, limiting their effectiveness in dynamically filtering and classifying internet content.
Innovation Solution
A model-based content classification system with a user-configurable pattern matching engine that receives bounds from a user interface, searches for patterns in content strings, computes scores, and updates them using weights, generating a content classification model for subsequent content classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a conventional pattern matching engine with static rules is used, then the engine structure is simple and easy to implement, but the engine cannot adapt to changes in applications or circumstances
Solution Approach 1:
The patent implements a pattern matching engine where the rule set is no longer static but dynamic and configurable. Users can add, remove, and modify pattern matching rules at runtime through a user interface. The engine loads rules from configuration files or databases, allowing the rule set to adapt to changing application requirements without requiring engine redesign.
Solution Approach 2:
The patent allows modification of pattern matching parameters such as sensitivity thresholds, scoring weights, and pattern priorities. These parameters can be adjusted dynamically to optimize the engine's performance for different applications. The configuration system enables parameter changes without recompiling or reconfiguring the entire engine architecture.
2Adaptability or versatility
If static rules are used in the pattern matching engine, then the implementation is straightforward, but users cannot change or update rules to suit different applications
Solution Approach 1:
The patent implements a user-friendly configuration interface that allows end-users to independently add, remove, and modify pattern matching rules without requiring programmer intervention. The system provides pre-defined rule templates and automatic rule generation capabilities, enabling users to configure the engine for their specific needs through simple graphical operations rather than complex coding.
Solution Approach 2:
The patent creates a universal configuration system that handles multiple rule types and patterns through a single unified interface. The engine can process various kinds of content (text, images, videos) using the same configurable rule framework, making it adaptable to different applications without requiring separate configuration systems for each use case.
3Adaptability or versatility
If the pattern matching engine uses fixed patterns, then the processing speed is fast, but the engine cannot adapt to new content types or formats
Solution Approach 1:
The patent implements a two-phase processing approach: in the first phase, the engine performs rapid pattern matching using pre-compiled fixed patterns for common content types. In the second phase, when new content types are detected, the system automatically generates or loads appropriate patterns and reprocesses the content. This preliminary action with fixed patterns maintains speed for known content while enabling adaptation to new formats.
Solution Approach 2:
The patent creates a nested pattern matching structure where general-purpose patterns are layered with specific content-type patterns. The engine first applies broad patterns to all content, then applies more specific patterns based on detected content types. This nested approach allows the system to maintain fast processing through general patterns while adapting to specific content formats through specialized nested patterns.
Data Source
AI summary
Some embodiments of on-the-fly pattern recognition with configurable bounds have been presented. In one embodiment, a pattern matching engine is configured based on user input, which may include values of one or more user configurable bounds on searching. Then the configured pattern matching engine is used to search for a set of features in an incoming string. A set of scores is updated based on the presence of any of the features in the string while searching for the features. Each score may indicate a likelihood of the content of the string being in a category. The search is terminated if the end of the string is reached or if the user configurable bounds are met. After terminating the search, the scores are output.