In-Video Object Highlighting for Contextual Ad Engagement
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Solution Overview
Problem
Existing in-video advertising methods are often ineffective as consumers tend to ignore scheduled advertisements during media consumption, and products of interest within the primary content go unnoticed, requiring manual search which wastes time and resources.
Innovation Solution
A system that identifies objects of interest within primary content using object recognition, highlights them, and provides associated supplemental content, allowing users to interact with these objects seamlessly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If scheduled advertisement-breaks are used to present advertising content, then advertising content can be delivered to consumers, but consumers use these breaks to step away from programming and advertisements go unwatched, reducing effectiveness
Solution Approach 1:
The system performs preliminary actions by identifying objects of interest within the primary content before the advertisement is delivered. It analyzes the video content, detects products or items that characters interact with, and prepares targeted advertising content in advance. This allows the advertisement to be seamlessly integrated and immediately relevant when presented, ensuring consumer engagement without requiring them to step away from the programming.
Solution Approach 2:
The system introduces an intermediary mechanism - an object recognition and analysis system - that bridges the primary content and advertising content. This intermediary analyzes the video content, identifies relevant objects, and matches them with appropriate advertisements. This creates a seamless connection between what the consumer is watching and the advertising content, making the transition natural and maintaining engagement.
2Ease of operation
If consumers manually search for advertising content matching products in primary program, then they can find relevant advertisements, but this wastes time, computing resources, and bandwidth due to lack of available information
Solution Approach 1:
The system implements self-service by automatically performing the task of matching products in the primary content with relevant advertising content. The object recognition system autonomously analyzes the video, identifies items of interest, and retrieves corresponding advertisements without requiring consumer intervention. This eliminates the manual search process entirely, saving time and resources while providing relevant advertisements seamlessly.
Solution Approach 2:
The system performs preliminary actions by pre-identifying objects within the primary content and pre-matching them with relevant advertising content. This preparation happens in the background before the consumer needs to view advertisements, so when ads are presented, they are already optimized for relevance. This eliminates the need for consumers to spend time searching, as the system has already done the matching work in advance.
3Productivity
If all identified objects are highlighted in primary content, then all potential advertising opportunities are presented, but this overwhelms the user and distracts from enjoying the primary content
Solution Approach 1:
The system applies local quality by selectively highlighting only certain objects within the primary content rather than all objects. It uses object recognition to identify items and applies highlighting only to those with available advertising content or highest relevance scores. This localized approach ensures that advertising opportunities are presented without overwhelming the user, as only specific, relevant objects are marked for attention.
Solution Approach 2:
The system changes parameters by adjusting the highlighting intensity, color, and visibility based on the importance and relevance of each identified object. It dynamically modifies these parameters to ensure that highlighted objects attract attention without creating visual clutter or distracting from the primary content. This parameter adjustment allows the system to present advertising opportunities effectively while maintaining user experience quality.
Data Source
AI summary
Systems and methods are described herein for highlighting objects with a primary content that are likely to be of interest to a user viewing the primary content. More particularly, when the system receives a segment of primary content to be displayed on a user equipment device for consumption, the system analyzes the received segment to identify an object within the received segment. The system then checks a database storing supplemental content to determine whether supplemental content associated with the identified object is available. When supplemental content associated with the identified object is available within the database, the system modifies the received segment of the primary content to highlight the identified object and displays the modified segment of the primary content on the user equipment device for consumption.