Context-Switching Taxonomy for Mobile Ad Relevance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current channel-based taxonomies used for mobile advertising are ineffective in mobile environments due to fast context switching behavior, where users quickly move between different application contexts, making it difficult to build relevance and connections within a single context.
Innovation Solution
A context-switching taxonomy system that collects usage data to determine switching context channels, trends, speed, and time, generating a classification model to classify and target invitational content based on these data points, allowing for dynamic and relevant ad delivery even during context changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If channel-based taxonomy is used for mobile advertising, then ad delivery is simplified and inventory classification is easier, but ad relevance deteriorates due to fast context switching
Solution Approach 1:
The patent transitions from a static channel-based taxonomy to a dynamic context-switching taxonomy that adapts to user behavior in real-time. The system monitors context switching events and adjusts ad delivery based on the current context, making the taxonomy flexible and responsive to changing user states rather than relying on fixed channel classifications.
Solution Approach 2:
The system changes the parameters used for ad targeting from broad channel categories to specific context-switching metrics such as switching frequency, context duration, and transition patterns. This parameter transformation enables more precise ad relevance while maintaining systematic classification through the new taxonomy framework.
2Speed
If users quickly switch between application contexts, then user mobility and app exploration increase, but building ad relevance and connections within a single context becomes difficult
Solution Approach 1:
The system performs preliminary actions by pre-monitoring and recording context switching events as they occur, building a historical record of user context patterns. This preliminary data collection enables the system to predict future context states and prepare relevant ad content in advance, maintaining relevance despite rapid switching.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining an ongoing context monitoring and tracking process that continuously updates the user's context profile. This continuous action preserves context relevance information across multiple rapid switches, preventing loss of useful contextual data while users explore different applications.
3Adaptability or versatility
If desktop browser channel taxonomy is applied to mobile environments, then advertising infrastructure remains consistent, but effectiveness deteriorates due to different user behavior patterns
Solution Approach 1:
The patent segments the mobile advertising environment into distinct context types and switching patterns, creating a specialized taxonomy for mobile that differs from the unified desktop channel model. This segmentation allows the system to capture the unique characteristics of mobile user behavior while maintaining the benefits of systematic classification.
Solution Approach 2:
The context-switching taxonomy is designed to be universally applicable across different mobile devices, applications, and user scenarios. This multi-functional taxonomy can adapt to various mobile contexts (social media, productivity apps, entertainment, etc.) while maintaining a consistent framework, enabling broad effectiveness across diverse mobile advertising scenarios.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable storage media for mobile advertisement based on a context-switching context taxonomy. The system collects usage data associated with a device. Based on the usage data, the system determines a switching context channel and a switching context trend, wherein the switching context channel defines a user's movement through channel classifications, and wherein the switching context trend defines the user's activity over a period of time. Next, the system generates a classification model based on the switching context channel and the switching context trend. The system then books a campaign of invitational content based on the classification model.