Dynamic Digital Display Ad Selection for Broadcast Visibility
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Solution Overview
Problem
Existing digital display technologies during sporting events fail to optimize the selection and timing of advertisements for viewers watching broadcasts, leading to ineffective targeted advertising.
Innovation Solution
A method utilizing a cognitive analysis engine to predict future on-field actions and audience sentiment, dynamically selecting and displaying advertisements on digital displays based on predicted camera angles and audience mood, ensuring optimal visibility and sentiment alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If advertisements are displayed on fixed video displays in the venue, then spectators at the venue can easily see the advertisements, but the target audience watching via electronic broadcast cannot be effectively targeted
Solution Approach 1:
The system dynamically selects and switches between multiple advertisements displayed on different digital displays based on real-time analysis of camera feeds and predicted future actions. Instead of static fixed-position advertising, the system adapts the advertisement content and display location dynamically to match what will be captured by broadcast cameras, thereby serving both venue spectators and broadcast audience effectively
Solution Approach 2:
The cognitive analysis engine predicts future on-field actions and camera movements in advance to determine which digital displays will be captured by broadcast cameras. This preliminary prediction allows the system to pre-select appropriate advertisements before the actual broadcast capture occurs, ensuring that the broadcast audience sees relevant ads while spectators continue to see advertisements on venue displays
2Device complexity
If advertisements are statically displayed on digital displays, then the display system is simple to operate, but the advertising effectiveness for different audience segments cannot be optimized
Solution Approach 1:
The system employs automated cognitive analysis engines and machine learning algorithms that independently analyze camera feeds, predict future actions, determine audience sentiment, and select optimal advertisements without human intervention. This self-service approach handles the complexity of real-time decision-making automatically, maintaining operational simplicity while achieving high advertising effectiveness through intelligent automation
Solution Approach 2:
The system continuously monitors actual camera captures and broadcast content to validate its predictions and adjust its advertisement selection strategy. By incorporating feedback from the actual broadcast output and audience response data, the system refines its predictive models and optimization algorithms, improving advertising effectiveness while managing system complexity through iterative learning
Data Source
AI summary
An approach is provided for optimizing a digital display. Classifications of advertisements are determined. A location of a future action in an event is predicted. A sentiment of an audience is determined. The audience includes spectators at the venue, viewers who are viewing the event via devices receiving a live broadcast of the event, or a combination of the spectators and the viewers. Based on the location of the future action, a digital display is selected from digital displays in the venue and content on the digital display is predicted to be captured as an image by a camera and broadcast to devices of the viewers. Based on the classifications, the sentiment, the location of the future action, and the content being predicted to be captured and broadcast, an advertisement is selected. The selected advertisement is sent to the selected digital display.