Cognitive Elevator Advertisements: Real-Time Passenger Personalization
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Solution Overview
Problem
Elevator advertising systems lack personalization and context-sensitivity, leading to mis-targeted and static advertisements that fail to engage passengers effectively, resulting in potential revenue loss for advertisers.
Innovation Solution
A cognitive elevator advertisements system that collects real-time data to identify passengers, determines their preferences based on past purchase histories, and displays targeted advertisements on digital screens within elevators, while also analyzing viewer feedback to optimize ad placement and improve ad effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static advertisement rolling systems are used in elevators, then device complexity is reduced and ease of operation is improved, but advertisement relevance to passengers deteriorates and revenue generation capability worsens
Solution Approach 1:
The advertisement system transitions from static pre-programmed content to dynamic real-time personalization. Sensors continuously detect passenger characteristics (age, gender, attire, carry-on items) and the system dynamically selects and displays relevant advertisements, making the advertising content adaptive to changing conditions rather than fixed in advance.
Solution Approach 2:
The system performs self-analysis of sensor data to automatically identify passenger profiles and select appropriate advertisements without human intervention. The cognitive processing occurs autonomously within the system, eliminating the need for manual configuration while achieving personalized advertising.
2Measurement precision
If real-time sensor data collection and analysis systems are implemented, then advertisement targeting precision is improved, but device complexity and data processing requirements worsen
Solution Approach 1:
The complex data processing task is segmented into distinct functional modules: sensor data acquisition, passenger characteristic extraction, profile classification, and advertisement selection. Each module handles a specific aspect of the processing pipeline, making the overall complex system manageable through functional decomposition.
Solution Approach 2:
The system introduces intermediate processing layers between raw sensor data and final advertisement selection. Cognitive algorithms serve as intermediaries that translate complex sensor inputs into simplified passenger profiles, which then map to predefined advertisement categories, reducing the direct complexity burden.
3Loss of information
If cognitive algorithms analyze passenger characteristics and purchase histories, then advertisement relevance and engagement are improved, but loss of information privacy and security risks worsen
Solution Approach 1:
The system extracts only the essential characteristics needed for advertising purposes from passenger data (age range, gender, attire type, carry-on items) while leaving out sensitive personal information. This selective extraction maintains advertising effectiveness while minimizing privacy exposure.
Solution Approach 2:
The system transforms detailed personal information into aggregated demographic parameters and categorical descriptors. Instead of processing individual identifiable data points, the cognitive algorithms work with parameterized passenger profiles that preserve statistical utility for advertising while reducing identifiability and privacy risk.
Data Source
AI summary
A method, computer system, and computer program product for cognitive elevator advertisements are provided. The embodiment may include identifying one or more passengers utilizing real-time sensor data. The embodiment may also include determining a preference value of each identified passenger for a plurality of product categories based on a plurality of data related to past purchase histories or purchasing patterns received from a plurality of databases simultaneously or almost simultaneously. The embodiment may further include computing corrected passenger preference values for the plurality of product categories based on unprejudiced preference values of the passengers multiplied by the preference values assigned to each product category. The embodiment may also include determining one or more targeted advertisements for one or more targeted passengers based on each computed passenger preference values. The embodiment may further include displaying one or more advertisements on one or more display screens within an elevator based on the one or more targeted advertisements.