Implicit Product Popularity Calculation in Physical Retail
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Solution Overview
Problem
Brick and mortar stores lack a system to gather and display consumer interaction data for product popularity, unlike online retail environments, which hinders their ability to dynamically reflect consumer preferences in real-time.
Innovation Solution
A computer-implemented method and system that tracks and aggregates consumer interactions in physical retail environments, calculating an implicit popularity number for products based on recorded actions and displaying it through specialized display devices using emojis or indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumer interaction data is collected and analyzed in real-time, then product popularity measurement is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary computing device that acts as a mediator between consumer monitoring devices and display devices. This intermediary collects raw consumer interaction data, processes it through algorithms to calculate implicit popularity numbers, and then transmits processed results to display devices. This mediator approach simplifies the overall system architecture by centralizing complex processing functions rather than requiring each component to be highly complex.
Solution Approach 2:
The patent replaces traditional manual or static popularity measurement methods with automated computational processing. Instead of manual surveys or static displays, the system uses computing devices to automatically collect, analyze, and update popularity data in real-time based on consumer interaction patterns, substituting mechanical/manual processes with electronic computation.
2Loss of information
If real-time consumer interaction tracking is implemented, then consumer preference visibility is improved, but data processing requirements increase
Solution Approach 1:
The patent extracts only the essential and relevant features from raw consumer interaction data to calculate implicit popularity numbers. Rather than processing all possible consumer behavior data, the system identifies and extracts key interaction patterns that directly indicate product popularity, such as viewing time, selection frequency, and purchase conversion. This selective extraction reduces data processing requirements while maintaining consumer preference visibility.
Solution Approach 2:
The patent implements partial processing by focusing on specific consumer interaction metrics that are most indicative of product popularity rather than analyzing every possible consumer behavior. The system processes a subset of interaction data that provides sufficient information for popularity measurement without the computational burden of complete data analysis.
3Measurement precision
If dynamic popularity displays are updated continuously, then consumer information accuracy is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic updates of popularity displays rather than continuous real-time updates. The computing device calculates implicit popularity numbers at defined intervals or when significant changes occur in consumer interaction patterns, rather than constantly updating displays. This periodic action maintains consumer information accuracy by providing timely updates while reducing energy consumption compared to continuous update mechanisms.
Data Source
AI summary
Systems and methods for calculating and displaying implicit popularity of products are disclosed. In embodiments, a computer-implemented method, comprises: determining, by a computing device, a set of consumer actions related to a product based on recorded consumer interaction data, wherein the set of consumer actions comprise a plurality of different physical actions of multiple consumers over a span of time; aggregating, by the computing device, the set of consumer actions by type of physical action over time to produce aggregated action event data; calculating, by the computing device, an implicit popularity number for the product based on the aggregated action event data; and sending, by the computing device, a display category to a product display device associated with the product based on the implicit popularity number, wherein the display category represents a measure of consumer popularity of the product.