Shopper Location Tracking for Automated Gift Recommendations
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Solution Overview
Problem
Current methods for creating wish lists are manual and may not accurately reflect a shopper's interests, leading to missed gift opportunities and inefficiencies in gift giving, especially for busy individuals or those who do not easily know the preferences of co-workers or hard-to-shop-for recipients.
Innovation Solution
A computer-implemented method that tracks a shopper's movement and dwell times within retail environments to infer interests and recommend gift items, using thresholds for dwell time, visit counts, and combined impressions to automatically suggest items for wish lists or gift guides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual wish list creation is used, then users can control their wish lists, but the accuracy of reflecting shopper interests is poor
Solution Approach 1:
The system enables automatic wish list generation by tracking shopper behavior data (dwell time, visit frequency, proximity to merchandise) and autonomously creating gift recommendations without requiring manual user input. This resolves the contradiction by allowing the system to self-generate accurate wish lists based on observed shopping patterns.
Solution Approach 2:
The system continuously monitors shopper behavior and uses this feedback to refine and update gift recommendations in real-time. By analyzing dwell time, visit counts, and merchandise proximity data, the system adjusts recommendations to better reflect current shopper interests, improving accuracy while maintaining ease of operation.
2Measurement precision
If automated tracking is implemented, then gift recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The system uses a mobile device tracker that serves multiple functions: it tracks shopper location, measures dwell time, counts visits to different areas, and collects proximity data to merchandise. By making the tracking system multi-functional, the patent reduces the need for separate specialized devices, thereby managing complexity while improving recommendation accuracy through comprehensive data collection.
Solution Approach 2:
The patent introduces a gift recommendation system as an intermediary layer that processes raw tracking data and translates it into actionable gift suggestions. This intermediary component simplifies the overall system architecture by decoupling the data collection mechanism from the recommendation logic, making the system more manageable while maintaining high accuracy.
3Speed
If real-time tracking is performed, then gift ideas are identified promptly, but data processing requirements increase
Solution Approach 1:
The system applies threshold-based filtering to track only the most significant shopping behaviors (e.g., dwell time exceeding a certain threshold, repeated visits to the same category). By focusing on partial aspects of shopper behavior that are most indicative of gift interests, the system achieves real-time recommendation capability while reducing the overall data processing load and energy consumption.
Data Source
AI summary
Triangulated movement and non-movement of a mobile device of a shopper are tracked among different locations within at least one retail environment to identify a shopper merchandise proximity history of the shopper within the at least one retail environment. The tracking uses a coordinated combination of mobile device global positioning system (GPS) information with detected triangulated proximity of the mobile device relative to a plurality of different retail environment low-energy communication devices. Responsive to identifying within the shopper merchandise proximity history an item of merchandise that meets at least one shopper merchandise threshold interest criterion, an electronic message that includes a gift recommendation of the item of merchandise is provided to at least one other user associated with and located remotely from the shopper.