Display Content Selection Using Proximity and User Activity
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Solution Overview
Problem
Display devices in establishments such as stores and banks often present static information, failing to account for the diverse needs of individual customers, and lack the ability to identify and personalize interactions with them.
Innovation Solution
Systems and methods that utilize communication devices within a certain radius to tailor information display based on user identity and activity, track location, and employ machine learning models to predict positive customer reactions to content, enabling personalized greetings and product offerings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If display devices present static information, then device complexity is reduced and ease of operation is improved, but adaptability to different customer needs deteriorates
Solution Approach 1:
A server acts as an intermediary between the display device and customer data sources. The server receives identification information from various sources (mobile devices, cameras, loyalty cards), processes this data, and sends appropriate personalized content to the display device. This mediator approach enables complex adaptive functionality without increasing the complexity of the display device itself.
Solution Approach 2:
The system segments functionality between multiple components: the display device presents content, the server processes logic, and external systems provide customer data. This segmentation allows each component to remain relatively simple while the overall system achieves high adaptability through coordinated interaction between segmented parts.
2Loss of information
If display devices use personalized content based on customer identification, then customer engagement is improved, but loss of information increases due to tracking requirements
Solution Approach 1:
The system uses feedback loops where customer responses to displayed content are tracked and used to refine future content delivery. Machine learning models analyze customer interactions and adjust content selection to maximize engagement while maintaining appropriate privacy boundaries through controlled information collection.
3Area of stationary object
If multiple display devices are used throughout an establishment, then coverage area is improved, but device complexity increases due to coordination requirements
Solution Approach 1:
The server provides universal coordination functionality that serves multiple display devices simultaneously. A single server instance can manage content delivery to numerous displays throughout the establishment, eliminating the need for complex peer-to-peer coordination between devices. Each display device maintains a simple, uniform interface while the server handles the complexity of multi-device coordination.
Data Source
AI summary
A method may include receiving, at an application server, a set of device characteristics of a mobile device including: a current location data of the mobile device; a mobile device identifier of the mobile device; and an indication of current user activity being performed on the mobile device; accessing a segmentation group identifier based on the mobile device identifier; determining that the mobile device is within a threshold range of a display device based on the current location data; and based on the determining: generating an input feature data set based on the segmentation group identifier and the indication of current user activity; executing a machine learning model using the input feature data set as input to the machine learning model; automatically selecting a content identifier from a set of content identifiers based on an output of the machine learning model; and transmitting the content identifier to the display device.