ML Performance Abstraction Using Consumer-Readable Indices
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Solution Overview
Problem
Existing systems face challenges in interpreting and communicating machine learning (ML) performance metrics effectively between network entities, as consumers often lack the knowledge to understand complex performance indicators like accuracy, trustworthiness, and resource consumption.
Innovation Solution
A system comprising devices and methods for transmitting and receiving abstraction requests and reports to translate ML performance metrics into understandable indices, using a performance abstraction model that maps metrics to a standardized scale, enabling effective communication between ML producers and consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ML performance metrics are reported in detailed technical formats (accuracy, trustworthiness, resource consumption), then measurement precision is improved, but ease of operation deteriorates because consumers cannot understand these complex indicators
Solution Approach 1:
The patent introduces an intermediary abstraction layer that translates complex ML performance metrics into simplified, consumer-friendly indices. The system maintains detailed metric measurements internally but presents standardized, easily interpretable performance indices to consumers, bridging the gap between technical precision and user comprehension.
Solution Approach 2:
The patent transforms performance parameters by converting detailed ML metrics (accuracy, trustworthiness, resource consumption) into standardized performance indices with defined ranges and thresholds. This parameter transformation maintains the informational value while improving interpretability for end consumers.
2Ease of operation
If standardized performance indices are provided for consumer understanding, then ease of operation is improved, but loss of information increases due to abstraction
Solution Approach 1:
The patent segments information delivery into two distinct layers: detailed technical metrics are maintained and reported for systems requiring comprehensive data, while simplified performance indices are provided for general consumer understanding. This segmentation allows different consumer types to receive appropriately detailed information without forcing a one-size-fits-all approach.
Solution Approach 2:
The system dynamically adapts the level of information detail provided to consumers based on their capabilities and requirements. The abstraction mechanism can adjust between providing full detailed metrics and simplified indices, making the information delivery flexible rather than static.
3Measurement precision
If detailed ML performance metrics are transmitted between network entities, then measurement precision is improved, but device complexity increases due to the need for interpretation and translation mechanisms
Solution Approach 1:
The patent creates a universal performance abstraction mechanism that can handle multiple types of ML performance metrics (accuracy, trustworthiness, resource consumption) through a single standardized framework. This multi-functional approach reduces overall system complexity compared to implementing separate handling mechanisms for each metric type.
Data Source
AI summary
of machine learning performance, including: a first device transmits, to a second device, a first abstraction request for at least one performance of a machine learning (ML) entity provided by the first device, the machine learning entity used for a third device. The first device receives, from the second device, a first abstraction report comprising at least one index of the at least one performance, the at least one index being understandable by the third device. In this way, artificial intelligence (AI) or machine learning performance metrics are qualified and abstracted into AI or ML consumer understandable indices.