Confidence Threshold Determination for Machine-Learned Map Labeling
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Solution Overview
Problem
Existing map labeling systems face challenges in efficiently and accurately labeling map properties, requiring significant computing resources and manual effort, which is slow and expensive.
Innovation Solution
A method is introduced to establish a confidence threshold for a machine-learned map labeling system by training a model using training data, generating test predictions with confidence scores, determining the relationship between confidence scores and correctness, and setting a threshold to balance efficiency and accuracy, ensuring that map properties are labeled optimally without exceeding an error tolerance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to ensure accuracy, then labeling precision is improved, but productivity deteriorates
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw map data and final labeled output. The model processes data automatically while confidence scores serve as an intermediary metric to determine which predictions require human review, reducing manual labeling workload while maintaining accuracy through selective human verification of low-confidence predictions
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated machine learning system. The ML model substitutes human operators for routine labeling tasks, using algorithmic processing to predict map properties and confidence scores, thereby dramatically improving productivity while maintaining acceptable accuracy levels through automated decision-making
2Productivity
If automated machine learning labeling is used to improve productivity, then labeling speed is improved, but reliability deteriorates
Solution Approach 1:
The patent implements feedback through confidence scores that indicate the reliability of each prediction. Low-confidence predictions trigger human review or additional verification, creating a feedback loop where the system adapts its processing based on predicted reliability. This feedback mechanism ensures that potentially inaccurate predictions are caught and corrected, maintaining overall system reliability
Solution Approach 2:
The patent applies partial automation rather than complete automation - using the ML model for high-confidence predictions while reserving manual verification for low-confidence cases. This partial action approach ensures that only necessary human intervention occurs, maintaining productivity while safeguarding reliability through targeted human review of uncertain predictions
3Measurement precision
If high confidence threshold is set to ensure accuracy, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent makes the confidence threshold dynamic rather than static. Instead of applying a single fixed threshold to all predictions, the system adjusts thresholds based on context, data characteristics, and confidence score distributions. This dynamic approach allows the system to maintain high accuracy where needed while accepting lower thresholds for more certain predictions, thereby improving overall productivity without sacrificing precision
4Productivity
If low confidence threshold is set to improve productivity, then output volume is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments predictions into different confidence tiers or categories. Rather than treating all predictions uniformly, the system divides them into groups based on confidence levels, allowing different handling strategies for each segment. High-confidence predictions can be accepted automatically with lower thresholds, while low-confidence segments undergo additional review, thereby maintaining productivity while protecting precision through segmented processing
Data Source
AI summary
A map labeling system trains a machine-learned model using a set of training data and generates a set of test predictions for a set of test properties by applying the machine-learned model to a set of testing data. Each prediction in the set of test predictions comprises a confidence score representing the machine-learned model's confidence in the prediction. The map labeling system determines a correctness of each prediction in the set of predictions and determines a relationship between the confidence scores and the correctness of the test predictions. The map labeling system establishes a confidence threshold for the machine-learned model based on the determined relationship and labels a production property by applying the machine-learned model to production data.