Online Concierge Fulfillment Time Range Prediction
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Solution Overview
Problem
Conventional online concierge systems provide limited information to customers about potential order fulfillment times, leading to decreased customer selection of fulfillment options and inefficient allocation of pickers, as they only indicate the latest estimated time of fulfillment without accounting for earlier possibilities, thus affecting customer satisfaction and operational efficiency.
Innovation Solution
The online concierge system employs trained maximum and minimum time prediction models to estimate the range of fulfillment times based on order characteristics, providing customers with a more accurate and complete timeframe for order delivery, including both the latest and earliest possible times, and applies display rules to influence customer selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional online concierge systems provide only the latest estimated fulfillment time to customers, then the system minimizes the likelihood of orders being fulfilled later than the estimated time, but the system provides limited information to customers about potential order fulfillment times, decreasing customer selection of fulfillment options
Solution Approach 1:
The patent segments the single estimated fulfillment time into multiple time windows (first time window and second time window), where the first time window represents earlier fulfillment times and the second time window represents later fulfillment times. This segmentation provides customers with more comprehensive information about potential fulfillment times while maintaining reliability by ensuring orders are fulfilled within the appropriate time window.
Solution Approach 2:
The patent adds a temporal dimension to the fulfillment time estimation by introducing multiple time windows at different points in time. Instead of providing a single time estimate, the system now provides a multi-dimensional time perspective showing both earlier and later fulfillment possibilities, enabling customers to make more informed decisions.
2Device complexity
If conventional online concierge systems provide only the latest estimated fulfillment time, then the system simplifies the fulfillment estimation process, but the system decreases a likelihood of customer selecting fulfillment of an order by the estimated time, affecting operational efficiency
Solution Approach 1:
The patent segments the fulfillment estimation into multiple time windows that can be independently calculated and presented. This segmentation allows the system to maintain relatively simple individual time window calculations while providing comprehensive fulfillment information that improves customer selection and operational efficiency.
Solution Approach 2:
The patent performs preliminary calculations of multiple time windows before presenting options to customers. By pre-calculating both the first time window (earlier fulfillment) and second time window (later fulfillment), the system prepares comprehensive information in advance, enabling customers to make informed decisions that improve operational efficiency without adding significant complexity to the estimation process.
3Use of energy by moving object
If conventional online concierge systems identify only the latest estimated time by which the order is fulfilled, then the system reduces computing resource consumption, but the system provides incomplete information to customers, affecting customer satisfaction
Solution Approach 1:
The patent segments the fulfillment time information into distinct time windows (first time window for earlier fulfillment, second time window for later fulfillment). Each time window can be calculated using relatively simple methods, providing comprehensive information to customers while managing computing resource consumption through efficient segmentation of the calculation process.
Solution Approach 2:
The patent implements partial calculation of fulfillment times by focusing on key time windows that provide the most valuable information to customers. Rather than calculating every possible fulfillment scenario, the system calculates the most relevant time windows (earliest and latest fulfillment times), providing sufficient information to customers while optimizing computing resource usage.
Data Source
AI summary
An online concierge system delivers items from retailers to customers. The online concierge predicts a range of times during which an order may be fulfilled for presentation to a user. The online concierge system uses a trained maximum time prediction model to determine a maximum time for order fulfillment based on an order. A trained minimum time prediction model determines a minimum time for order fulfillment from the order and the maximum time. The minimum time may account for one or more rules (e.g., a percentage of orders fulfilled before the minimum time, a desired rate of selection of a range including the minimum time). A range bounded by the maximum time and the minimum time is transmitted to a customer to enable the customer to select a time interval for order fulfillment.