Mobile Ad Tracking via First-Time View Identification
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Solution Overview
Problem
There is no effective system for valuing advertisements delivered through digital content, as existing methods cannot accurately measure repeated views or shared views of digital advertisements across non-traditional delivery platforms.
Innovation Solution
A client-server system that tracks ad impressions on mobile devices, using identifiers for digital content and advertisements transmitted over wireless networks, updates impression counts, and includes information such as location and device type, enabling accurate valuation of advertisements through peer-to-peer file sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital content with advertisements is delivered through peer-to-peer file sharing, then the advertisement can reach multiple users through sharing, but it becomes impossible to accurately measure how many times the advertisement is viewed or by how many people
Solution Approach 1:
The system segments the advertisement delivery process into distinct tracking events. Each time digital content is shared or viewed, a separate identification and reporting event is generated. This segmentation allows the system to track individual viewing instances and shared transmissions separately, enabling accurate measurement of both first-time views and repeated views across multiple users and devices.
Solution Approach 2:
The system implements feedback loops where mobile devices report advertisement viewing and sharing events back to a central server. The server processes these reports, updates impression counts, and provides valuation information. This feedback mechanism ensures that each viewing and sharing event is captured and measured, resolving the measurement problem in peer-to-peer delivery.
2Productivity
If digital content is downloaded once and stored locally, then the advertisement can be viewed repeatedly without additional downloads, but existing systems cannot distinguish between first-time viewing and repeated viewing for valuation purposes
Solution Approach 1:
The system performs preliminary identification and tracking setup when digital content is initially downloaded or accessed. An identification module is activated at this preliminary stage to begin tracking viewing events. This preliminary action ensures that both first-time views and subsequent repeated views are captured from the moment the content becomes available on the device, enabling accurate differentiation for valuation.
Solution Approach 2:
The system implements continuous feedback reporting where each advertisement viewing event triggers a report to the server. The server distinguishes between first-time views and repeated views by tracking unique identification data for each viewing instance. This feedback mechanism provides the precision needed to differentiate viewing types for accurate advertisement valuation.
3Measurement precision
If a comprehensive tracking system is implemented to measure all advertisement views including shared views, then accurate valuation becomes possible, but system complexity and data transmission requirements increase
Solution Approach 1:
The system extracts only the essential identification and tracking data needed for measurement from the complex peer-to-peer file sharing process. Rather than attempting to monitor all aspects of file sharing, the system focuses on capturing key events (downloading, viewing, sharing) through lightweight identification modules. This extraction approach maintains measurement precision while minimizing system complexity.
Solution Approach 2:
The system introduces an intermediary server that centralizes the complex tracking and valuation computations. Mobile devices perform simple local tracking and data collection, then transmit this data to the intermediary server which handles the complex tasks of distinguishing first-time from repeated views, calculating valuation metrics, and managing impression counts. This intermediary approach distributes complexity away from individual devices while maintaining comprehensive measurement capability.
Data Source
AI summary
An apparatus comprising a server. The server can be configured to receive a request for content from a mobile computing device. The server can be configured to transmit the content to the mobile computing device in response to the request. The content can include an advertisement. The mobile computing device can be configured to store an indication of whether the content has been previously presented on the mobile computing device. The server can be configured to receive an identifier from the mobile computing device in response to a presentation of the content by the mobile computing device if the content is being presented for the first time on the mobile computing device. The identifier can indicate whether the content is presented on the mobile computing device for the first time. The server can be configured to monitor the exposure of the advertisement based on the identifier.