Smart Cart Sensor Correlation for Display-Driven User Action Detection
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Solution Overview
Problem
Conventional systems struggle to establish cross-device causality correlations, particularly in environments where user interactions with physical content are indirect, as they lack the capability to correlate user actions with dynamically changing display screens in physical environments.
Innovation Solution
A system that contextualizes sensor data from smart shopping carts to identify user interactions with displayed content by leveraging location and sensor data to determine proximity and timing of actions, enabling the presentation of relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems use data from a single device to establish causality, then device independence is maintained, but cross-device event correlation capability is lost
Solution Approach 1:
The patent introduces a remote system as an intermediary that receives data from multiple devices (shopping carts, display screens, mobile devices) and processes it to establish cross-device causality correlations. This intermediary layer consolidates data from diverse sources and applies processing logic to determine event relationships across devices without requiring direct complex interconnections between all devices.
2Adaptability or versatility
If display screens dynamically change content based on remote instructions, then content adaptability is improved, but direct user interaction detection capability is lost
Solution Approach 1:
The patent implements a feedback mechanism where the remote system receives data from display screens about content presented and user proximity, then uses this information to determine user actions and adjust content delivery. The system detects user interactions indirectly through proximity data and action detection algorithms, creating a feedback loop that adapts content based on inferred user behavior without requiring direct touch or input from the display screen itself.
3Loss of information
If systems correlate events from user-associated devices with environmental devices, then comprehensive event monitoring is achieved, but data unification and causality establishment difficulty increases
Solution Approach 1:
The patent segments the data processing into distinct functional components: shopping carts provide location and action data, display screens provide content and proximity data, and the remote system performs unification and causality analysis. This segmentation allows each component to handle specific data types independently while the remote system consolidates them using standardized processing logic, reducing overall system complexity.
4Adaptability or versatility
If display screens do not have user interaction detection capabilities, then device simplicity is maintained, but content-user action correlation capability is lost
Solution Approach 1:
The patent makes the remote system universal by having it handle multiple functions: receiving data from various device types (shopping carts, display screens, mobile devices), detecting user actions, determining causality relationships, and controlling content delivery. This universal processor eliminates the need for each individual device to have complex interaction detection capabilities while still achieving comprehensive content-user action correlation.
Data Source
AI summary
A system causes a display screen to present a set of content for a first time period. The system accesses a first set of location data captured by location sensors coupled to shopping carts. The first set of location data indicates a location of each of a plurality of users of the shopping carts]. The system identifies a set of users within a distal proximity of the display screen during the first time period. The system accesses sensor data captured by sensors of the shopping carts and detects an action performed by a first user in relation to a first item. The system identifies a timestamp for the action. In response to the timestamp being within a threshold amount of time after the first time period, the system stores proximity data indicative of an interaction with the set of content in association with the first user.