Predictive API Polling for Package Tracking
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Solution Overview
Problem
Carrier systems face limitations in managing high API request rates for package tracking, leading to unnecessary API calls and potential delays in obtaining shipping status updates, especially when many packages need frequent status checks.
Innovation Solution
The system predicts the timing of the next shipping event for a package based on the most recent event, allowing API requests to be sent during a designated time window, with increased frequency closer to the predicted event time, thereby optimizing API call distribution and reducing unnecessary requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If API requests are sent frequently to check shipping status, then the timeliness of shipping status updates is improved, but the API request rate increases causing unnecessary API calls and potential delays
Solution Approach 1:
The system performs preliminary actions by predicting the timing of next shipping events based on historical data and current status. API requests are scheduled in advance around predicted event times rather than using continuous polling, which reduces unnecessary requests while maintaining timely updates.
Solution Approach 2:
The system implements periodic action by sending API requests at predicted intervals based on when shipping events are expected to occur, rather than using continuous or fixed-frequency polling. This adaptive periodic approach optimizes the balance between update timeliness and request rate management.
2Reliability
If continuous API polling is used to monitor package status, then shipping status updates are obtained reliably, but computational resources are wasted on unnecessary API calls
Solution Approach 1:
The system uses self-service by leveraging the carrier's existing tracking infrastructure and event notification capabilities. Instead of continuously polling, the system predicts when events will occur and queries only at those moments, allowing the carrier system to serve the tracking needs efficiently without wasting computational resources on redundant requests.
Solution Approach 2:
The system changes the parameter of API request timing from fixed intervals to predicted event-based intervals. By dynamically adjusting when requests are sent based on predicted shipping event timing, the system maintains reliable status updates while significantly reducing unnecessary API calls and associated computational resource consumption.
3Stability of the object's composition
If API rate limits are enforced to manage high request volumes, then system stability is improved, but delays occur in obtaining shipping status updates
Solution Approach 1:
The system performs preliminary prediction of shipping event timing to schedule API requests in advance. By knowing when events are likely to occur, the system can plan requests to fall within allowed rate limits while still capturing status changes timely, avoiding both rate limit violations and unnecessary delays.
Solution Approach 2:
The system implements dynamic request timing based on predicted shipping event patterns rather than static polling intervals. This dynamic approach allows the system to adapt request frequency to actual shipping activity, maintaining stability by respecting rate limits while minimizing delays by intensifying requests around predicted event times.
Data Source
AI summary
When a merchant ships a product to a buyer, the merchant may wish to push shipping status updates to the buyer. Moreover, the buyer may wish to receive a notification when a shipping event has occurred in order to remain informed regarding the shipping status of their package without actively checking the package's status with the carrier. In some embodiments, there is provided a computer-implemented system and method that obtains a tracking identifier for a package, transmits the tracking identifier to a carrier's computing interface (e.g. the carrier's API), receives back an indication of the most recent shipping event, predicts the time of a next shipping event at least based on the most recent shipping event, and retransmits the tracking identifier to the computing interface based on the predicted time of the next shipping event.