BitSet Subject Matching System for Stimulus Filtering
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Solution Overview
Problem
Existing systems are inefficient in determining matching subjects for features provided in a stimulus, as they rely on brute-force methods with a complexity of O(Subject Count*Feature Count), leading to slow and unsatisfactory results.
Innovation Solution
The method converts input attributes and features into BitSets and performs logical operations to quickly identify matching subjects by pre-processing criteria into inclusion, exclusion, and complementary BitSets, optimizing the processing of stimuli through logical AND and XOR operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute-force method is used to examine each subject against stimulus, then matching accuracy is maintained, but processing speed and efficiency deteriorate
Solution Approach 1:
The patent segments the subject features into distinct BitSets, where each BitSet represents a specific feature dimension (e.g., age, location, interests). This segmentation allows the system to process and compare features independently through bitwise operations, maintaining matching accuracy while dramatically improving processing speed by avoiding exhaustive pairwise comparisons.
Solution Approach 2:
The patent transforms the matching problem from traditional iterative comparison into bitwise mathematical operations. By representing subjects and stimulus features as BitSets and using bitwise AND, OR, and XOR operations, the system changes the computational parameter from O(Subject Count*Feature Count) complexity to O(Subject Count/64) complexity, achieving both accuracy and efficiency.
2Reliability
If brute-force method is used to examine each subject against stimulus, then complete subject evaluation is achieved, but time consumption increases
Solution Approach 1:
The patent performs preliminary action by pre-processing subject data into BitSets and organizing them by feature dimensions before receiving the stimulus. This pre-organization allows the system to quickly evaluate all subjects against the stimulus using efficient bitwise operations, ensuring complete evaluation without excessive time consumption.
Solution Approach 2:
The patent replaces the mechanical iterative comparison system with a mathematical bitwise operation system. Instead of sequentially comparing each subject feature with each stimulus feature, the system uses bitwise AND, OR, and XOR operations on pre-processed BitSets, substituting slow mechanical iteration with fast parallel mathematical computation.
3Adaptability or versatility
If traditional filtering methods are used, then subject matching is performed, but system efficiency and scalability deteriorate
Solution Approach 1:
The patent creates a universal BitSet-based matching framework that can handle multiple feature types (demographics, interests, behaviors) and multiple matching scenarios (exact match, partial match, exclusion) through the same bitwise operation mechanism. This universal approach maintains high adaptability while reducing system complexity by eliminating the need for separate processing logic for different feature types.
Data Source
AI summary
A method and system provide the ability to determine matching subjects for features provided in a stimulus. Subject information is pre-processed by receiving input (a feature type definition and a subject set) and building BitSets across all subjects in the subject set. The stimulus is received and includes stimulus features. For each feature type in the stimulus features, all feature type BitSets from the subject set that match are assembled. An evaluation BitSet is built by combining the assembled feature type BitSets. Matching subjects that match the stimulus are determined based on the evaluation BitSet.