Smart Cart Location Data for Personalized In-Store Screen Content
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Solution Overview
Problem
Conventional systems fail to leverage sensor data from multiple devices within an environment to dynamically select and personalize content for users moving around, as they primarily focus on single-user interactions with digital devices, lacking the ability to identify and adapt to changing user views.
Innovation Solution
A system that personalizes content on display screens using location data from smart shopping carts, combining context and personalization scores to select content items based on user demographics and preferences within a threshold area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems use digital interactions from single devices to select content, then the system complexity remains low, but the ability to identify users in dynamic environments deteriorates
Solution Approach 1:
The patent combines location data from multiple smart shopping carts with content selection systems to create a unified approach for identifying users in real-time. By merging data from multiple devices (shopping carts, display screens, sensors) the system achieves accurate user identification without requiring complex tracking infrastructure, as each component performs a simple function that collectively solves the problem.
Solution Approach 2:
The patent introduces an intermediary system that processes location data from smart shopping carts and translates it into user identification information for content selection. This intermediary layer simplifies the overall system by creating a clear data flow: shopping carts capture location → intermediary processes and identifies users → content selection system delivers personalized content, avoiding direct complex interactions between all components.
2Adaptability or versatility
If the system captures location data from multiple smart shopping carts to identify users, then the content personalization accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent segments the data processing into distinct functional components: location data collection from shopping carts, user identification processing, content selection, and display. Each segment handles a specific task independently, reducing the complexity burden on any single component while maintaining overall system adaptability and personalization capability.
Solution Approach 2:
The patent utilizes changes in location parameters (coordinates, proximity to display screens) as dynamic inputs for content selection. By monitoring parameter changes in shopping cart locations and translating them into user presence detection, the system achieves high adaptability without complex processing, as the parameter changes themselves carry the identification information.
3Productivity
If display screens present content dynamically based on user location, then the user engagement improves, but the measurement and detection difficulty increases
Solution Approach 1:
The patent replaces complex visual or mechanical detection methods with a simpler data-driven approach using location information from shopping carts. Instead of attempting to detect users through complex sensor arrays or visual recognition, the system uses the already-available location data from shopping carts to infer user presence and deliver content efficiently, substituting a simpler data processing mechanism for a more complex detection system.
Data Source
AI summary
A system stores content items at a content data store, each content item corresponding to an item within an environment. The system accesses location data captured by a plurality of location sensors in the environment, each coupled to a smart cart system located within the environment. The location data indicates a current location of a corresponding smart cart system. The system computes a number of smart cart systems within a threshold area around a display screen within the environment based on the location data. The system computes a presentation score for each of the content items by combining a context relevance score and a personal relevance score weighted based on a personalization weighting. The system selects a subset of the content items for display based on the presentation scores and causes the display screen to present the subset of content items.