Temporal Availability Model for Online Concierge Inventory
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Solution Overview
Problem
Conventional online concierge systems fail to accurately account for the availability of items at different warehouse locations and times, leading to discrepancies between the items displayed to users and those actually available for order fulfillment.
Innovation Solution
An online concierge system uses a temporal availability model that leverages information about items, warehouses, and times to predict availability by mapping availability data to a multi-dimensional feature space, determining distances, selecting samples, and calculating weights to provide users with predicted likelihoods of item availability at different times and locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If the online concierge system selects a warehouse location based on physical distance to the order location, then the distance traveled by the shopper is reduced, but the accuracy of item availability prediction deteriorates because the system does not account for availability changes at different times
Solution Approach 1:
The patent adds a temporal dimension to the warehouse location selection process. Instead of only considering spatial distance, the system now evaluates availability predictions across multiple time points (current time and future times) to select warehouse locations that maintain item availability throughout the order fulfillment process
Solution Approach 2:
The system performs preliminary availability predictions at multiple future time points before the shopper actually visits the warehouse. This allows the system to anticipate availability changes and select warehouse locations where items are likely to remain available, preventing the need to send shoppers to locations where items may become unavailable
2Ease of operation
If the system displays items available at a warehouse without considering time variations, then the simplicity of the ordering process is maintained, but the reliability of the displayed inventory information deteriorates due to availability fluctuations over time
Solution Approach 1:
The system performs periodic availability predictions at multiple time intervals (current time, future time points) and uses this temporal pattern information to provide more reliable inventory displays. The availability is reassessed at different time periods to account for fluctuations while maintaining a user-friendly interface
Solution Approach 2:
The system incorporates feedback from availability predictions at multiple time points to adjust the displayed inventory information. By analyzing availability patterns across time, the system can provide more accurate reliability indicators to users while maintaining the simplicity of the ordering process
3Device complexity
If the system uses a single time point for availability prediction, then the computational complexity is reduced, but the adaptability to availability changes at different times deteriorates
Solution Approach 1:
The patent transforms the static single-time-point prediction model into a dynamic multi-time-point prediction system. The model adapts to availability changes by incorporating temporal variations and selecting warehouse locations based on predicted availability across different time periods, making the system responsive to dynamic inventory conditions
Data Source
AI summary
An online concierge system allows users to order items from a warehouse, which may have multiple warehouse locations. The online concierge system provides a user interface to users for ordering the items, with the user interface providing an indication of whether an item is predicted to be available at the warehouse at different times. To predict availability of an item model at different times, the online concierge system selects data from historical information about availability of items at one or more warehouses based on temporal, geospatial, and socioeconomic information about observations of historical availability of items at warehouses. The online concierge system accounts for distances between observations and a time and geographic location in a feature space to select observations for predicting item availability at the time and the geographic location.