ML-Based Digital Activity Accelerator Registry
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Solution Overview
Problem
Users face burdensome and error-prone tasks in identifying and executing activities associated with electronic messages across disparate computer-based applications, as existing systems lack automation and intelligence in linking digital artifacts to relevant tasks.
Innovation Solution
A machine learning-informed system constructs a digital activity-accelerator registry with composite activity sequences, identifies target digital artifacts, and executes relevant tasks through ensemble models, enabling automatic task execution by constructing search queries based on intent, domain, and sub-domain inferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification and execution of tasks associated with electronic messages is performed, then users can complete tasks across different applications, but the process becomes burdensome and error-prone
Solution Approach 1:
The system performs self-service by automatically analyzing electronic messages, identifying associated tasks, and executing them without requiring manual user intervention. The machine learning models autonomously classify messages, determine relevant tasks, and coordinate execution across multiple applications, eliminating the burdensome manual process while maintaining high accuracy through intelligent automation
Solution Approach 2:
The patent replaces the mechanical manual process of task identification and execution with an automated intelligent system. Machine learning models substitute for human cognitive processes in analyzing messages and determining tasks, while automation mechanisms replace manual coordination between applications, thereby reducing user burden and improving reliability through consistent automated execution
2Ease of operation
If automated task execution is implemented, then user burden is reduced, but system complexity increases
Solution Approach 1:
The system segments the complex automated task execution process into distinct functional modules: message analysis module, task identification module, and task execution module. Each module performs a specific function and can be independently managed, which reduces the perceived system complexity while maintaining comprehensive automation capabilities. The segmentation allows the system to handle complexity internally while presenting a simple interface to users
Solution Approach 2:
The patent implements a universal automated task execution system that can handle multiple types of electronic messages, identify various kinds of tasks, and execute them across different applications through a single integrated platform. This multi-functionality consolidates what would otherwise require multiple separate systems, reducing overall system complexity while providing comprehensive automated support for diverse task scenarios
3Measurement precision
If machine learning models are used to infer task intent and domain, then task identification accuracy improves, but computational resources increase
Solution Approach 1:
The system applies partial action by using machine learning models selectively - deploying full-model inference only when necessary for complex messages, while using simpler classification methods for routine messages. This approach maintains high task identification accuracy for challenging cases while reducing computational resource consumption for straightforward tasks, achieving a balance between precision and energy efficiency
Data Source
AI summary
A method for machine learning-informed surfacing and automated execution of digital activity-accelerating actions includes identifying a target digital artifact; and based on identifying the target digital artifact: searching a digital activity-accelerator registry based on the target digital artifact; and in accordance with a determination that the digital activity-accelerator registry includes a composite activity sequence corresponding to the target digital artifact, displaying, via a graphical user interface, one or more selectable representations of one or more tasks included in the composite activity sequence.