Real-Time Error Resolution Using Univariate ML Responses
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Solution Overview
Problem
Multivariate machine learning models require additional processing time, produce complex results that need interpretation, and necessitate the collection of numerous variables, which disrupt real-time conversational interactions between users.
Innovation Solution
The system generates error likelihood recommendations using a univariate response, reduces processing time by limiting variables, and uses a microservice to collect data, ensuring minimal disruption during interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multivariate machine learning model is used to detect errors, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the multivariate analysis into two phases: an offline training phase where the model learns from multiple variables, and an online inference phase where pre-computed features are used. This segmentation allows the system to maintain high accuracy while reducing real-time processing time by avoiding complex multivariate computations during user interactions.
Solution Approach 2:
The system performs preliminary actions by pre-computing and storing feature representations during an offline training phase. These pre-computed features are then reused during real-time error detection, eliminating the need to recalculate complex multivariate relationships during user interactions and thus reducing processing time while maintaining accuracy.
2Measurement precision
If a multivariate machine learning model is used to detect errors, then detection accuracy is improved, but result interpretability worsens
Solution Approach 1:
The patent extracts the essential error detection signal from the complex multivariate model and presents it as a simple binary classification result or a concise probability score to users. By separating the complex internal computations from the user-facing output, the system maintains high detection accuracy while providing easily interpretable results that users can understand without pausing the conversation.
3Measurement precision
If a multivariate machine learning model is used to detect errors, then detection accuracy is improved, but the number of variables required increases
Solution Approach 1:
The system uses partial action by selecting and using only the most relevant pre-computed features for error detection, rather than processing all possible variables. This approach maintains high detection accuracy by focusing on critical features while reducing the total number of variables that need to be collected and processed during user interactions.
Data Source
AI summary
Methods and systems are for generating real-time resolutions of errors arising from user submissions, computer processing tasks, etc. For example, the methods and systems described herein recite improvements for detecting errors in one or more user submissions and providing resolutions in real-time. To provide these improvements, the methods and systems use a machine learning model that is trained to return probability error scores based on a plurality of variables. By using the multivariate approach, the methods and systems may produce a highly accurate detection.