ML-Based Condition Rendering for Automation Interface
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Rules-based conditions in automatic computer actions require extensive user input, leading to prolonged device interaction and resource usage, and often result in under-triggering or over-triggering, causing inefficiencies and unnecessary resource utilization.
Innovation Solution
Implementing machine learning (ML) based conditions that are determined by analyzing predicted outputs from ML models, allowing for reduced user input and optimized rendering of conditions in an automation interface, thereby mitigating under-triggering and over-triggering and improving resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rules-based conditions are used for automatic computer actions, then automation is achieved, but extensive user input is required leading to prolonged device interaction and resource usage
Solution Approach 1:
The system pre-generates a set of candidate conditions using machine learning models before the user needs to define automation rules. These pre-generated conditions are based on analysis of user behavior patterns and data characteristics, allowing the user to select from ready-made options rather than creating conditions from scratch, thereby reducing interaction time while maintaining automation capability
Solution Approach 2:
The system automatically generates candidate conditions through machine learning models that analyze user behavior data and patterns. This self-service mechanism creates condition candidates without requiring user input, allowing the system to prepare automation options proactively. The user then only needs to review and select appropriate conditions, significantly reducing the time and resources required for rule definition
2Adaptability or versatility
If rules-based conditions are manually defined, then specific automation rules can be created, but the conditions are often too narrowly or broadly defined leading to under-triggering or over-triggering
Solution Approach 1:
The system uses machine learning models to analyze user behavior patterns and feedback from actual data interactions to generate optimized candidate conditions. These models learn from user corrections and adjustments, continuously improving the accuracy of generated conditions. The feedback loop ensures that conditions are neither too narrow nor too broad, achieving reliable triggering while maintaining user customizability through selection and modification of candidates
Solution Approach 2:
The machine learning models dynamically adjust condition parameters based on analyzed data patterns and user behavior. Instead of fixed manually-defined thresholds, the system optimizes condition parameters such as sensitivity levels, time windows, and data patterns to achieve accurate triggering. This parameter optimization maintains adaptability while improving reliability by preventing under-triggering and over-triggering
3Reliability
If rules-based conditions are redefined to mitigate under-triggering or over-triggering, then accuracy may improve, but client device components remain active and in higher power state for prolonged duration
Solution Approach 1:
The system performs condition generation and optimization in advance using machine learning models, creating a ready-set of accurate condition candidates before user interaction is needed. This preliminary analysis achieves high triggering accuracy without requiring prolonged user device activation, as the heavy computational work is done proactively and the user only needs to review and select from pre-generated options
Solution Approach 2:
The machine learning system automatically performs the iterative process of condition optimization and accuracy improvement without requiring repeated user intervention. The model self-adjusts parameters and generates refined condition candidates based on analyzed patterns, achieving high reliability while minimizing the time user device components need to remain active. User involvement is reduced to selective approval rather than iterative redefinition
Data Source
AI summary
Implementations are directed to automatically performing one or more computer actions responsive to satisfaction of one or more machine learning (ML)-based conditions. Some implementations are directed to determining which ML-based condition(s) to render in an automation interface and/or how to render the machine-learning based conditions in the automation interface. Those implementations can result in a reduced quantity of user inputs (or even no user inputs) being needed to define action condition(s) for computer action(s). Those implementations can additionally or alternatively result in a shortened duration of interaction in defining the action condition(s), which can reduce the duration that component(s) of a client device, being used to interact with the interface, are active and/or are active at a higher-powered state.