Workflow Notification Timing Prediction System
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Solution Overview
Problem
Sequential computational actions in workflows often lead to backlogs and increased completion times due to dependencies between actions, with notifications being missed or ignored due to timing and location mismatches between client devices.
Innovation Solution
A system that predicts the likelihood of action completion based on features like location, action duration, and historical usage, using a machine learning regression algorithm to select optimal time windows for notification transmission, ensuring that notifications are sent when the recipient can act on them, and prefetching content to reduce bandwidth and improve workflow efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If notifications are sent immediately when an action is completed, then the system responds quickly to workflow changes, but notifications are often missed or ignored due to timing and location mismatches
Solution Approach 1:
The system performs preliminary actions by predicting when and where notifications should be delivered based on historical data and machine learning models. It proactively schedules notifications at optimal times and locations before the recipient is likely to be available, ensuring higher delivery reliability and reducing workflow completion time.
Solution Approach 2:
The system uses feedback from historical notification delivery data and action completion patterns to continuously improve its predictions. By analyzing past performance, the machine learning model refines its timing and location predictions, creating a feedback loop that increases notification reliability over time.
2Measurement precision
If the system tracks detailed location and timing data for each action, then notification delivery accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system manages complexity by dynamically adjusting the level of detail in location and timing parameters based on the specific action type, historical patterns, and computational resources. It changes parameters such as prediction granularity, data retention periods, and model complexity to balance precision requirements with system manageability.
Solution Approach 2:
The system segments the complex task of tracking location and timing data into manageable components: collecting raw data, processing it through machine learning models, generating predictions, and executing notifications. This segmentation allows each component to be optimized independently and reduces overall system complexity.
3Productivity
If the system prefetches content for all possible actions, then content availability is maximized, but bandwidth consumption and storage requirements increase
Solution Approach 1:
The system performs preliminary prefetching of content based on predicted action completion times and locations. Instead of prefetching everything, it proactively loads only the content that is most likely to be needed soon, optimizing the balance between content availability and bandwidth consumption.
Solution Approach 2:
The system applies local quality by tailoring content prefetching to specific locations, times, and action types. Different content is prefetched for different contexts (e.g., documents vs. media files), and the prefetching strategy adapts to local patterns in the data, reducing unnecessary bandwidth usage while maintaining productivity.
Data Source
AI summary
The present disclosure describes a system and method to reduce the overall time taken to complete distributed process workflows. Each workflow can include multiple actions that are completed by or at different client devices. The actions of a workflow can be dependent on prior actions in the workflow. For example, a second client device may not be able to complete a second action until a first client device completes a first action in the workflow. The system can predict time periods and the geolocations where client devices are most likely to complete an assigned action. Using the selected time periods and geolocations, the system can transmit notifications to the client devices when the action is most likely to be completed.