ML-Based Data Item Scoring for Consistent Quality Evaluation
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Solution Overview
Problem
Manual quality assessment of AI output is prone to inconsistency, human error, and requires significant time and resources, lacking standardization and efficiency in evaluating large volumes of data.
Innovation Solution
A system and method using a machine learning model to automatically evaluate data items by generating answers to questions, calculating scores, and transmitting results over a network, with the ability to produce justifications and explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual quality assessment is performed, then human intuition and creativity can be applied to complex tasks, but consistency and objectivity are compromised due to subjective interpretations and fatigue
Solution Approach 1:
The system enables automated self-assessment of data items through machine learning models that independently evaluate quality metrics without requiring continuous human intervention. The ML model processes data items autonomously, applying consistent evaluation criteria while freeing human resources from repetitive manual review tasks.
2Productivity
If manual reviews are conducted, then detailed human analysis can be performed, but time consumption and resource investment increase significantly
Solution Approach 1:
The patent replaces the mechanical human review process with an automated machine learning-based evaluation system. The ML model processes data items at machine speed, eliminating the time constraints of manual review while maintaining comprehensive quality assessment through automated analysis of multiple metrics.
3Productivity
If automated evaluation is implemented, then processing speed and consistency improve, but the ability to handle complex nuanced cases may be reduced
Solution Approach 1:
The machine learning model is designed with multi-functionality to handle both routine evaluation tasks and complex nuanced cases. The system can process large volumes of standard data items while also adapting to evaluate complex scenarios by leveraging trained patterns and anomaly detection capabilities, making it versatile across different evaluation contexts.
4Manufacturing precision
If manual quality control is performed, then human judgment can be applied, but standardization across assessments is difficult to achieve
Solution Approach 1:
The system standardizes quality assessment by transforming subjective human judgment into objective measurable parameters. The ML model evaluates data items based on defined quality metrics and thresholds, converting nuanced human assessment criteria into quantifiable parameters that ensure consistent, standardized evaluation across all data items regardless of who or what performs the assessment.
Data Source
AI summary
A system and method are provided for evaluating data items using a machine learning model, including: producing, by a machine learning model, answers to questions applied to an input data item, where the questions and data item are input to the machine learning model; calculating a score for the input data item based on the produced answers; and transmitting one or more output data items to a remote computer over a communication network based on the calculated score. Some nonlimiting embodiments of the invention may relate to analyzing text data such as interaction transcripts in a contact center environment. In some embodiments questions may be included in a form and/or evaluation plan, and questions may include critical questions which are to be answered in the data item is to be assigned a non-zero scores. Questions may be organized in levels, and scores may be calculated based on the levels.