Delivery Schedule Tables for Fast E-Commerce Time Estimates
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Solution Overview
Problem
E-commerce systems face challenges in quickly and reliably incorporating delivery time information into web content due to delayed API responses from fulfillment networks and privacy concerns, leading to user frustration and data transmission issues with large volumes of delivery time data.
Innovation Solution
A data structure is generated by the fulfillment network that includes a product-to-schedule mapping and delivery schedule tables, which are stored in a computer's memory for quick access, allowing for efficient retrieval of delivery time information without requiring extensive local storage or continuous data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If delivery time information is computed in real-time by requesting an API from the fulfillment network, then the information is reliable and accurate, but the response time is delayed causing user frustration
Solution Approach 1:
The patent pre-computes delivery time information for multiple products and stores it in a data structure before user requests arrive. When a user requests web content, the system quickly retrieves pre-computed delivery time data instead of making real-time API calls to the fulfillment network, thus eliminating the delay while maintaining accuracy.
Solution Approach 2:
The patent creates a local copy of delivery time information from the fulfillment network's API responses and stores it in a data structure on the web server. This copy allows the system to serve delivery time data without repeatedly querying the fulfillment network, reducing response time while preserving data accuracy.
2Speed
If pre-computed delivery time information is stored locally for quick access, then the response time is reduced, but the data storage and transmission burden increases
Solution Approach 1:
The patent implements a selective caching strategy where only delivery time information for products that are likely to be requested is pre-computed and stored locally. The data structure organizes information by product categories and popular items, allowing the system to maintain a compact local cache that prioritizes frequently accessed data while minimizing storage requirements.
Solution Approach 2:
The patent computes and stores delivery time information for a subset of products rather than all products in the catalog. By focusing on products that are currently in stock, frequently viewed, or part of active promotions, the system achieves fast retrieval speeds for the most relevant data while avoiding the storage burden of maintaining complete product catalog information.
3Productivity
If delivery time information is pre-computed for all products, then quick access is enabled, but the computation and data transmission load on the fulfillment network increases
Solution Approach 1:
The patent triggers pre-computation of delivery time information only under specific conditions: when products are added to inventory, when shipping rates or delivery estimates change, or during low-traffic periods. This event-driven approach ensures that the fulfillment network performs computational work only when necessary, rather than continuously computing data for all products.
Solution Approach 2:
The patent implements periodic updates of delivery time information at scheduled intervals (e.g., every 15 minutes or hourly) rather than continuous computation. The system refreshes the local cache periodically and intelligently determines when to request updates from the fulfillment network based on whether significant changes have occurred, reducing unnecessary computational load on the fulfillment network.
Data Source
AI summary
In e-commerce, web content (such as a checkout page) may include delivery time information associated with a product. To obtain the delivery time information, the computer may send an application programming interface (API) request to the fulfillment network. However, a fulfillment network is often delayed in responding to an API request. Also, there may be data privacy concerns with this approach. Alternatively, precomputed delivery time information may be sent from the fulfillment network, but this may result in a large amount of data that must be stored. In some embodiments, a data structure may be generated that includes a product-to-schedule mapping and a set of delivery schedule tables. Each of the delivery schedule tables may be based on a respective different set of one or more categories to which multiple ones of a plurality of products map. Computer memory resources may be saved.