Ad Personalization via Proximate Device Detection
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Solution Overview
Problem
Current advertisement technologies lack the ability to effectively personalize ads based on the presence of nearby devices, leading to irrelevant advertising and inefficient resource allocation.
Innovation Solution
A method where a user device identifies proximate devices via local wireless networks and modifies advertisements accordingly, using device-specific data to create contextually relevant ads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advertisements are personalized based on proximate device information, then ad relevance and engagement improve, but device complexity and processing requirements increase
Solution Approach 1:
The system segments the advertising process into distinct modules: ad generation, proximate device identification, ad modification, and display. Each module handles a specific aspect of personalization, distributing processing complexity across separate functional components rather than concentrating all processing in one system.
Solution Approach 2:
The system performs preliminary identification of proximate devices and their characteristics before ad modification occurs. By gathering device information in advance and preparing personalized ad variants beforehand, the system reduces real-time processing complexity when actual ad delivery occurs.
2Adaptability or versatility
If ad modification is performed in real-time based on proximate devices, then ad relevance improves, but processing time and computational resources increase
Solution Approach 1:
The system pre-generates multiple ad variants with different personalization parameters before actual ad delivery. When a proximate device is identified, the system simply selects and modifies the appropriate pre-prepared variant rather than creating ads from scratch in real-time, significantly reducing processing time.
Solution Approach 2:
The system modifies ads by changing specific parameters (such as device type, proximity distance, or user preferences) of pre-generated ad templates rather than performing complete ad creation. This parameter-based modification approach maintains contextual relevance while minimizing computational overhead and processing time.
3Measurement precision
If proximate device identification is implemented using local wireless networks, then ad targeting precision improves, but system complexity and energy consumption increase
Solution Approach 1:
The system uses local wireless networks as an intermediary mechanism to identify proximate devices without requiring direct complex communication between the advertising system and each device. The wireless network infrastructure handles the complex detection and identification processes, reducing energy consumption on the advertising device while maintaining high proximity detection precision.
Data Source
AI summary
Techniques include generating an advertisement (ad) including text and/or image data using a user device (e.g., a mobile computing device). The techniques further include identifying one or more proximate devices (e.g., networked computing devices or appliances) located proximate to the user device using a local wireless network (e.g., Wi-Fi, Bluetooth, or NFC). The techniques include modifying (e.g., personalizing) the ad based on the identified proximate devices, based on one or more device types (e.g., categories) associated with the devices, and/or based on one or more states of the devices. In some examples, the techniques include transmitting an indication of the identified proximate devices, their types, and/or their states and an indication of the ad to an ad system, and receiving the modified ad from the system. The techniques also include displaying the modified ad to a user at the user device (e.g., within a software application executing on the device).