Feature Adoption Prediction Using ML-Driven Analytics Queries
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Solution Overview
Problem
Existing systems require manual effort by data analysts to define new features and prepare reports, which is time-consuming, and lack automated solutions for predicting feature adoption, especially for rapidly changing or unknown features.
Innovation Solution
An automated analytical tool using trained machine learning models to enable on-the-fly feature definition and prediction of adoption likelihood, based on large datasets of hardware and software solutions, allowing users to define new features and generate analytics automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data analysts manually define new features and prepare reports, then analysis accuracy can be maintained, but time consumption and workload increase significantly
Solution Approach 1:
The system enables self-service by allowing users to define new features through natural language queries without requiring manual feature engineering. The machine learning model automatically processes these queries, retrieves relevant data, generates analytics, and creates visualizations autonomously, eliminating the need for data analysts to manually prepare reports while maintaining analysis accuracy.
Solution Approach 2:
The patent replaces the mechanical manual process of feature definition and report preparation with an automated machine learning system. The ML model substitutes for human analysts in processing queries, collecting data sets, running analyses, and generating visualizations, thereby significantly reducing time consumption while preserving measurement precision through automated intelligent processing.
2Loss of time
If automated feature definition is implemented, then time consumption is reduced, but system complexity increases
Solution Approach 1:
The system achieves universality by implementing a multi-functional machine learning model that can handle various tasks including feature definition, data collection, analytics generation, and visualization creation. This single unified system replaces multiple separate manual processes, reducing overall system complexity despite the advanced capabilities required for automated feature definition and analysis.
3Measurement precision
If manual data collection and analysis is performed, then data quality can be controlled, but productivity decreases
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously learns from user interactions and data patterns to improve its automated feature definition and analysis capabilities. This feedback loop enables the system to maintain high data quality through intelligent processing while significantly improving productivity by automatically collecting and analyzing data without manual intervention.
Data Source
AI summary
In one aspect, a method of identifying network features includes receiving a first-time definition of a feature, the feature representing a user query for analytics associated with the feature based on data collected on a plurality of devices in one or more networks, generating the analytics associated with the feature, determining, using a trained machine learning model, a likelihood of adoption of at least the feature by one or more users of the plurality of devices, wherein the trained machine learning model receives as input the first-time definition and provides, as output, the likelihood of adoption of at least the feature, and configuring a user interface on a terminal to provide a visualization of at least one of the likelihood of adoption of at least the feature and the analytics associated with the feature.