Adaptive Map Display for Retail Pickers Using Batch Volume Scores
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Solution Overview
Problem
The existing online concierge systems do not effectively communicate batch availability at retail locations, leading to inefficiencies for pickers waiting around low-volume retail locations.
Innovation Solution
The concierge system identifies retail locations within a defined zone of a picker's client device, determines batch volumes, and generates a batch availability score using a machine learning model. This score is used to modify the map display, emphasizing retail locations with higher batch availability scores and allowing pickers to prioritize these locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the picker client application displays all retail locations on the map, then the picker can see all possible locations, but the picker cannot effectively identify which locations have high batch availability
Solution Approach 1:
The patent applies local quality by differentiating the visual representation of retail locations based on their batch availability characteristics. High-volume locations are displayed with distinctive visual markers (e.g., highlighted icons, color coding, or numerical indicators) while low-volume locations use standard markers. This allows the picker to quickly identify promising locations without the map becoming overly complex, as only specific locations receive enhanced visual treatment based on their local characteristics.
Solution Approach 2:
The patent utilizes color changes to encode batch availability information on the map. Retail locations with high batch volume are displayed using distinct colors or shading variations compared to low-volume locations. This visual encoding system allows pickers to rapidly assess which locations are worth visiting without requiring complex interfaces or additional data processing steps.
2Reliability
If the picker waits at low volume retail locations for batches, then the picker can fulfill orders from these locations, but the picker wastes time with low productivity
Solution Approach 1:
The system performs preliminary action by calculating and displaying batch volume metrics for all retail locations before the picker arrives or begins their route. This advance information allows the picker to plan their workflow optimally, prioritizing high-volume locations first and minimizing time spent at low-volume locations. The batch volume data is pre-computed based on historical order patterns, current demand, and location characteristics.
Solution Approach 2:
The patent implements feedback by continuously updating the map display with batch volume information as new orders are received and processed. As the concierge system receives new customer orders, it recalculates which retail locations are likely to generate batches and updates the visual indicators accordingly. This real-time feedback loop enables pickers to dynamically adjust their routes and priorities to maximize productivity.
3Ease of operation
If the system provides detailed batch volume data for each retail location, then the picker can make informed decisions, but the user interface becomes more complex
Solution Approach 1:
The patent applies segmentation by dividing the retail location information into hierarchical levels: the map view provides a high-level overview with visual indicators for high-volume locations, while detailed batch volume data and metrics are available only when the picker selects or hovers over specific locations. This segmented information architecture allows the interface to remain simple at the overview level while providing comprehensive data when needed, reducing cognitive load on the picker.
Data Source
AI summary
A concierge system identifies retail locations within a distance of a picker client device of a picker. This distance defines a zone and the system provides a map of the zone for display within a picker client application. For each retail location in the zone, the system determines a batch volume for the retail location and an average batch volume for the zone and generates a batch availability score using a model trained on batch volumes for the retail location and batch volume for the zone. The batch availability score can be a value reflecting batch availability or busyness of the retail location relative to other retail locations or can be a wait time prediction in minutes until the picker receives a batch at the retail location. The system modifies how the retail locations are displayed on the map to emphasize those with batch availability scores above a threshold value.