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, making them unsuitable for real-time conversational interactions.
Innovation Solution
Implementing a univariate response approach with a machine learning model trained to generate probability error scores, using a microservice to collect variables, and incorporating error likelihood recommendations to reduce processing time and simplify result interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a multivariate machine learning model is used for error detection, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent segments the multivariate analysis into two distinct phases: (1) a rapid univariate screening phase that processes each variable independently to identify potential errors, and (2) a targeted multivariate analysis phase that applies complex models only to suspicious cases. This segmentation allows the system to achieve high error detection accuracy through multivariate modeling while minimizing overall processing time by limiting expensive computations to only necessary cases.
Solution Approach 2:
The patent implements partial multivariate action by applying the full multivariate machine learning model selectively rather than universally. The system performs complete multivariate analysis only on submissions flagged by the univariate screen, while applying simplified or no analysis to clearly valid submissions. This partial application of the complex model maintains high detection precision for problematic cases while reducing average processing time across all submissions.
2Measurement precision
If a multivariate approach is used, then error detection accuracy is improved, but result interpretation complexity increases
Solution Approach 1:
The patent segments the result delivery system into two pathways: (1) simple univariate results for clearly identified errors that require minimal interpretation, and (2) targeted multivariate analysis results only for complex borderline cases. This segmentation allows users to receive straightforward interpretations for most cases while still benefiting from sophisticated multivariate analysis when needed, without being overwhelmed by complexity in routine situations.
Solution Approach 2:
The patent introduces an intermediary layer between the multivariate model and the user that translates complex multivariate outputs into simplified interpretations. This intermediary component processes the sophisticated model outputs and presents them in user-friendly formats, maintaining the high detection accuracy of multivariate analysis while reducing the perceived complexity for end users through automated explanation and prioritization.
3Measurement precision
If numerous variables are collected for multivariate analysis, then error detection accuracy is improved, but user submission burden increases
Solution Approach 1:
The patent performs preliminary univariate analysis on variables immediately upon user submission, before triggering the full multivariate process. This preliminary action allows the system to quickly assess obvious errors using minimal variables, reducing the burden on users for straightforward cases. The system only requests additional variables or triggers comprehensive multivariate analysis when the preliminary screen indicates potential issues, thereby maintaining high detection accuracy while minimizing user effort for clear-cut submissions.
Solution Approach 2:
The patent implements a self-service variable collection approach where the system automatically gathers additional variables needed for multivariate analysis without requiring active user input. Once a submission triggers the multivariate pathway, the system autonomously retrieves supplementary data from available sources and processes it, eliminating the need for users to manually provide numerous variables and thereby maintaining ease of operation while enabling comprehensive analysis when necessary.
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.