Zero-Touch Processing Quotient for Automation Monitoring
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Solution Overview
Problem
Current operational processes automation lacks effective tools for monitoring progress, making it difficult for organizations to reduce human intervention and achieve zero-touch potential, as they lack clear assessment and evaluation of automation technologies.
Innovation Solution
A system and method for evaluating zero-touch potential in event-to-entry processes using process-mining, machine learning models, and predictive accounting factors to determine input-related and rule-related zero-touch quotients, and generate a zero-touch processing quotient, which quantifies incremental zero-touch potential and attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If zero-touch processing is implemented in operational processes, then human intervention is reduced, but the ability to monitor and evaluate automation progress is insufficient
Solution Approach 1:
The patent replaces manual assessment methods with machine learning models and automated evaluation systems. The system uses AI algorithms to automatically analyze process data, calculate zero-touch quotients, and generate assessments without human intervention, thereby enabling precise monitoring of automation progress while maintaining high extent of automation.
Solution Approach 2:
The patent introduces an intermediary evaluation system that acts as a mediator between the automated processes and human decision-makers. This system includes components like the zero-touch quotient calculator and predictive accounting factor model, which translate complex automation data into actionable insights, enabling both high automation and effective monitoring.
2Adaptability or versatility
If comprehensive evaluation tools are developed to assess zero-touch potential, then automation strategy can be improved, but the complexity of the evaluation system increases
Solution Approach 1:
The patent divides the evaluation system into distinct modular components: process mining module, zero-touch quotient calculator, predictive accounting factor model, and recommendation engine. Each module performs a specific function and can be independently configured or adjusted, enabling comprehensive evaluation while maintaining manageable system complexity through clear separation of concerns.
Solution Approach 2:
The patent designs the evaluation system to handle multiple types of processes and evaluation criteria through a unified framework. The machine learning models can be trained on diverse data and applied to different operational processes, making the system versatile without requiring separate complex tools for each scenario.
3Measurement precision
If multiple evaluation metrics (input-related zero-touch quotient, rule-related zero-touch quotient, ZTP PAF) are calculated, then assessment accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary process mining and data preparation to extract relevant features before the main evaluation. By pre-processing the data and identifying key patterns upfront, the system reduces the computational burden during the actual calculation of multiple metrics, enabling accurate assessment without excessive processing power requirements during execution.
Solution Approach 2:
The patent dynamically adjusts evaluation parameters and model complexity based on the specific process being analyzed. The system can select appropriate metric combinations and model depths tailored to each scenario, maintaining high assessment accuracy while optimizing computational resource usage by avoiding unnecessary calculations for simpler cases.
Data Source
AI summary
Zero-touch monitoring devices and systems are disclosed that are configured with hardware to perform a process-mining on an event-to-entry process associated with an organization to collect data information associated with the process, determine an input-related zero-touch quotient for the event-to-entry process based on the data information, determine a rule-related zero-touch quotient for the event-to-entry process based on the data information, determine a zero-touch potential predictive accounting factor (ZTP PAF) value for the event-to-entry process, and generate a zero-touch processing quotient for the event-to-entry process based on the input-related zero-touch quotient, the rule-related zero-touch quotient, and the ZTP PAF value. The ZTP PAF value quantifies an incremental zero-touch potential for the event-to-entry process and corresponding attributes.