String Transformation Model Using Pruning and Scoring
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Solution Overview
Problem
Existing string transformation techniques often compromise on accuracy or efficiency, failing to provide both high accuracy and fast results, which is crucial for applications requiring large-scale string transformations.
Innovation Solution
A probabilistic approach is developed using training data to create a string transformation model that assigns ranking scores to transformation rules, allowing for the generation of candidate string transformations while employing a rule index and pruning strategies to enhance efficiency, including the use of an Aho-Corasick tree and dynamic programming for top k pruning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is employed to generate an accurate transformation model, then accuracy is improved, but efficiency deteriorates
Solution Approach 1:
The patent segments the transformation process into distinct phases: candidate generation using efficient data structures, scoring using the machine learning model, and pruning to eliminate low-scoring candidates. This segmentation allows the ML model to be applied selectively rather than to all possible transformations, improving efficiency while maintaining accuracy.
Solution Approach 2:
The patent applies partial action by using the machine learning model only for scoring a subset of candidate transformations rather than evaluating all possible transformations. The pruning step eliminates excessive candidates early, so the computationally intensive ML model processes only necessary candidates, resolving the efficiency-accuracy tradeoff.
2Productivity
If efficient data structures with a fixed similarity model are used, then efficiency is improved, but accuracy deteriorates
Solution Approach 1:
The patent merges efficient data structures (for fast candidate generation) with a machine learning-based similarity model (for accurate scoring). The combination allows the system to leverage the speed of efficient data structures while achieving high accuracy through the adaptive ML model, rather than being limited to a fixed similarity model.
Solution Approach 2:
The patent introduces dynamics by replacing the fixed similarity model with a machine learning model that can adapt and learn from data. The ML model dynamically adjusts similarity assessments based on patterns learned during training, enabling accurate transformations while maintaining efficiency through the structured candidate generation and pruning process.
3Measurement precision
If all candidate string transformations are generated and evaluated, then accuracy is improved, but efficiency deteriorates
Solution Approach 1:
The patent performs preliminary action by generating and scoring candidate transformations in a structured sequence, using the pruning step to eliminate low-scoring candidates early in the process. This preliminary filtering prevents unnecessary computation on candidates unlikely to be optimal, reducing computation time while ensuring the best candidates are still evaluated for accuracy.
Solution Approach 2:
The patent extracts and evaluates only the most promising candidate transformations using the pruning mechanism, rather than processing all possible candidates. By taking out and focusing computational resources on high-priority candidates identified through initial scoring, the system achieves accurate results without the time cost of exhaustive evaluation.
Data Source
AI summary
Candidate string transformations may be generated from applying applicable transformation rules from a rules index. Candidate string transformations may be eliminated based on a score associated with the candidate string transformation. The score may be based on probabilistic values associated with the applied applicable transformation rules.