Store Traffic Indicator Using Sensor Fusion
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Solution Overview
Problem
Current methods for determining in-store traffic levels are often inaccurate and labor-intensive, relying on manual counts or single data sources that can fail to account for variations in customer behavior and are not resilient when one data source becomes unavailable.
Innovation Solution
A system that uses a combination of historical data, camera inputs, transaction data, and employee-collected data to generate and update a store traffic model, providing frequent and accurate traffic condition estimation by correlating various data sources and adapting to changes in available data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual counts of guests are performed to determine store traffic levels, then the data can be collected, but the process becomes labor intensive and may not be accurate
Solution Approach 1:
The patent replaces manual mechanical counting methods with automated electronic systems including cameras, sensors, and computing devices that automatically detect and count guests using image recognition and data processing algorithms
Solution Approach 2:
The system enables self-service guest counting where the store infrastructure (cameras, sensors, POS systems) automatically tracks and reports traffic levels without requiring employee intervention or manual counting processes
2Reliability
If single data sources are used to estimate traffic levels, then the system is simpler, but the accuracy decreases and the system is not resilient when the data source becomes unavailable
Solution Approach 1:
The patent combines multiple data sources including camera feeds, sensor data, POS transaction data, and historical traffic patterns into a unified traffic estimation system that cross-validates information and maintains operation when individual sources become unavailable
Solution Approach 2:
The system dynamically adjusts the weighting and importance of different data sources based on their availability and reliability, changing parameters in real-time to optimize traffic level estimation using the most current and trustworthy data sources
3Ease of operation
If frequent traffic updates are provided to guests, then guests can make informed decisions, but the data processing and communication infrastructure becomes more complex
Solution Approach 1:
The patent introduces intermediary communication devices such as mobile apps, web portals, and in-store displays that act as mediators between the complex backend traffic monitoring system and guests, presenting simplified traffic information in an easily consumable format
Solution Approach 2:
The system implements feedback loops where traffic data is continuously collected, analyzed, and immediately communicated back to guests through various channels, enabling real-time decision-making while the backend systems handle the complexity of data processing automatically
Data Source
AI summary
In one implementation, a method for determining accurate store traffic levels includes receiving, at a computer system, historical store data for a physical store that was generated by internal data sources located within the physical store; generating, by the computer system, a historical store model that correlates values from store data with guest traffic levels within the physical store; receiving, by the computer system, current store data from one or more of the internal data sources; determining, by the computer system, current guest traffic level in the physical store based on the current store data and the historical store model; and outputting, by the computer system, the current guest traffic level in the physical store.