Online Concierge Preference Mapping via Warehouse Floorplan
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current online concierge systems rely solely on digital customer preferences and history, failing to effectively incorporate physical store patterns and real-time location data to suggest items during online ordering sessions.
Innovation Solution
An online concierge system that maps customer location data to a warehouse floorplan layout, determining preferences based on visited locations and time spent, and uses an item graph to rank and suggest items, integrating physical shopping behavior into the recommendation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If online concierge systems rely solely on digital customer preferences and history, then the system complexity remains low, but the relevance and personalization of item suggestions deteriorate
Solution Approach 1:
The patent merges digital customer preferences with physical store behavior patterns by integrating location data from mobile devices with online ordering history. The system combines these diverse data sources through a unified preference determination module that processes both digital and physical shopping signals to generate comprehensive customer profiles, thereby improving suggestion accuracy while managing complexity through integrated architecture.
Solution Approach 2:
The patent introduces an intermediary preference determination module that acts as a mediator between raw location data and final item suggestions. This intermediary component processes physical store patterns, translates them into preference signals, and integrates them with digital history, thereby bridging the gap between complex raw data and actionable recommendations without exposing the full complexity to the user interface.
2Adaptability or versatility
If the system incorporates physical store patterns and location data, then item suggestion relevance improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the preference determination process into distinct functional modules: location data acquisition, physical pattern analysis, digital history processing, and integrated preference generation. Each module handles specific aspects of data processing independently, allowing the system to incorporate complex physical store patterns while managing overall complexity through modular architecture and specialized processing pipelines.
3Measurement precision
If the system tracks customer location data and maps it to warehouse floorplan, then customer preference accuracy improves, but the measurement and processing difficulty increases
Solution Approach 1:
The patent creates a digital copy of the warehouse floorplan layout and maps customer location data onto this virtual representation. Instead of directly processing complex physical spatial relationships, the system uses a simplified digital twin of the warehouse environment where location coordinates can be easily tracked, analyzed, and correlated with item positions, thereby reducing the difficulty of measuring and processing physical store patterns.
Data Source
AI summary
An online concierge system determines customer preferences based on physical store patterns of the customer and provides search results based on the customer preferences during an online customer ordering session. The online concierge system may obtain customer location data while the customer is shopping in a physical warehouse. The online concierge system maps the customer location data to a warehouse floorplan layout. Based on the locations visited and the time spend at each location in the warehouse, the online concierge system determines that the customer is interested in certain types of items. The online concierge system may use the customer preferences to suggest items during online ordering sessions.