Content-Modification System Channel Identification Prioritization
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Solution Overview
Problem
The existing content-modification systems face inefficiencies in identifying channels due to the time-consuming process of comparing numerous reference fingerprint data sets, leading to missed content-modification opportunities, especially when end-users frequently change channels.
Innovation Solution
The system identifies a group of content-presentation devices tuned to the same channel, categorizes them into sub-groups based on content-transmission delays, and designates a priority channel when a threshold number of devices change to a new channel, allowing for quicker channel identification by prioritizing matching against reference fingerprints from the priority channel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system compares numerous reference fingerprint data sets to identify channels, then channel identification accuracy is maintained, but the time required for identification increases and content-modification opportunities are missed
Solution Approach 1:
The system segments the reference fingerprint data sets into multiple groups organized in a hierarchical structure. Instead of comparing against all reference fingerprints simultaneously, the system divides them into subsets and performs comparisons in stages, first against a smaller subset and then against additional subsets if needed, thereby reducing the time for each comparison step while maintaining overall identification accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-organizing reference fingerprint data sets into hierarchical groups and establishing comparison protocols before actual channel identification occurs. This pre-structuring allows the system to quickly navigate to relevant reference groups during identification, avoiding the need to process all reference fingerprints from scratch each time a channel change is detected.
2Reliability
If the system processes all reference fingerprint data sets equally, then comprehensive channel identification is achieved, but computational resources are wasted on low-priority channels
Solution Approach 1:
The system applies local quality by assigning different processing priorities and resource allocations to different reference fingerprint groups based on their relevance. High-priority reference groups (those more likely to match the current channel) receive intensive processing first, while lower-priority groups receive reduced processing or are skipped if high-priority matches are found, thereby optimizing computational resource distribution across the identification process.
Solution Approach 2:
The system performs partial action by comparing reference fingerprints only up to the point where a match is found or a predetermined number of comparisons are completed. Instead of exhaustively processing all reference fingerprint data sets in every case, the system stops processing once sufficient confidence in channel identification is achieved, reducing unnecessary computational overhead while maintaining reliable identification.
3Measurement precision
If the system performs exhaustive channel identification processing, then all channels are accurately identified, but content-modification opportunities are missed due to processing delays
Solution Approach 1:
The system implements dynamics by adjusting the depth and scope of reference fingerprint comparison based on real-time conditions such as detected channel change patterns, confidence thresholds, and timing constraints. The processing intensity is dynamically modulated to balance identification accuracy with the need to capture content-modification opportunities, allowing the system to perform faster when time is critical and more thorough processing when conditions permit.
Solution Approach 2:
The system applies skipping by selectively bypassing certain reference fingerprint comparison steps when high confidence in channel identification can be achieved through faster methods. When the system detects that a match is highly probable based on initial comparisons or pattern recognition, it skips unnecessary additional comparison steps to rapidly confirm the channel identity and proceed with content-modification operations, thereby capturing time-sensitive opportunities.
Data Source
AI summary
In one aspect, a method includes identifying a group of content-presentation devices that are each tuned to a same first channel and identifying, from the group, (i) a first sub-group of content-presentation devices that each have a respective content-transmission delay that is lower than a threshold delay and (ii) a second sub-group of content-presentation devices that each have a respective content-transmission delay that is greater than or equal to the threshold delay. The method also includes determining that within a predefined time-period, at least a threshold number of content-presentation devices of the first sub-group have changed from the first channel to a same second channel. The method also includes in response to the determining, storing an indication that the second channel is a priority channel for use in performing channel identification when a content-presentation device of the second sub-group has changed channels from the first channel to a new channel.