Inference Engine for Label Coding Discrepancies
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Solution Overview
Problem
Conventional techniques for coding unstructured content with labels are limited as they ignore human behavior and variables like uncertainties, preferences, and biases, leading to subjective label definitions that can render machine learning models deficient.
Innovation Solution
A system and method that utilize machine learning to analyze contextual factors influencing label definitions, detect discrepancies in meanings, and infer strategic thinking of individuals generating labels, allowing for the detection and display of metadata associated with these discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If humans are used for coding unstructured content with labels, then the labeling process can be performed with current technology, but the label definitions become subjective and deficient due to human biases and uncertainties
Solution Approach 1:
The patent replaces human subjective labeling with an automated machine learning system that objectively analyzes unstructured content. The system uses computational models to process text, images, or audio and generate consistent labels based on predefined criteria, eliminating human biases and uncertainties while maintaining labeling feasibility.
Solution Approach 2:
The system enables self-service labeling where the machine learning model autonomously performs the coding task without requiring human intervention. The model analyzes content independently, applies labeling rules consistently, and generates labels automatically, transforming a previously manual process into an autonomous one.
2Productivity
If human individuals label unstructured content, then labeling can be completed, but discrepancies in meaning arise due to different interpretations and biases
Solution Approach 1:
The patent implements homogeneity by ensuring all labels are generated using the same objective criteria and machine learning model. This eliminates the variability inherent in human interpretations, where different individuals might label the same content differently based on their personal biases and perspectives.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor and adjust labeling decisions. By analyzing labeling patterns and discrepancies, the model refines its understanding and improves consistency over time, ensuring reliable and uniform label application across all content.
3Device complexity
If conventional labeling techniques are used, then the process is simple, but machine learning models trained on such data become deficient due to ignored human behavior variables
Solution Approach 1:
The patent introduces an intermediary layer that captures human behavior variables and contextual factors between the raw unstructured content and the final labels. This intermediary analysis layer processes additional metadata about labeling patterns, uncertainties, and contextual nuances, which are then integrated into the machine learning model training to improve performance.
Solution Approach 2:
The system adds another dimension to the labeling process by incorporating metadata about human behavior, uncertainties, and contextual factors alongside the primary labels. This dimensional expansion enriches the training data with additional layers of information that were previously ignored, enabling more robust and reliable machine learning models.
Data Source
AI summary
A method for inferring intent and discrepancies in a label coding scheme is described. The method includes compiling data indicating how one or more individuals labeled unstructured content according to the label coding scheme comprising a plurality of labels. The method also includes analyzing a context associated with a content labeled in a particular manner by the one or more individuals. The method further includes detecting discrepancies of meaning for a particular label used by the one or more individuals. The method also includes inferring a strategic thinking of the one or more individuals associated with the discrepancies of meaning detected for the particular label. The method further includes displaying recorded metadata associated with the strategic thinking and the discrepancies of meaning detected for the particular label between the one or more individuals regarding a coded dataset.