Location Inference from Bid Requests via Movement Graphs
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Solution Overview
Problem
Real-time bid requests in mobile advertising often lack fine-grained location information due to privacy concerns and IP address translation, making it difficult to accurately target mobile devices.
Innovation Solution
A method and system that determine device locations by analyzing movement information between IP addresses using a predictive model, generating a movement graph, and assigning census block group information to infer latitude and longitude, thereby supplementing real-time bid requests with location data without revealing precise user or device identities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fine-grained location information (latitude and longitude) is collected and stored for real-time bid requests, then location-based targeting precision is improved, but user privacy is compromised and processing complexity increases
Solution Approach 1:
The patent applies local quality by differentiating between mobile and non-mobile IP addresses, applying different processing strategies to different types of location data. Mobile IP addresses undergo movement analysis and predictive modeling, while non-mobile addresses use direct geolocation lookup, optimizing both privacy protection and location accuracy for each category
Solution Approach 2:
The patent introduces census block groups as an intermediary layer between precise location data and advertising targeting. Instead of using exact latitude and longitude coordinates, the system maps locations to census block groups, which serve as privacy-preserving proxies that maintain sufficient granularity for effective ad targeting without exposing precise user locations
2Measurement precision
If precise location data is stored and processed, then targeting accuracy is improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent segments the location processing system into distinct modules: IP address classification (mobile vs. non-mobile), movement graph generation for mobile devices, predictive modeling components, and census block group mapping. This segmentation allows each component to handle specific tasks efficiently, reducing overall system complexity while maintaining high targeting accuracy
Solution Approach 2:
The patent performs preliminary actions by pre-processing IP addresses to classify them as mobile or non-mobile, and by pre-building movement graphs and predictive models for mobile devices. This preliminary processing reduces the computational burden during real-time bidding, as the heavy lifting of movement analysis and location prediction is completed in advance
3Measurement precision
If movement information is monitored and predictive models are applied, then location inference accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary action by pre-generating movement graphs from historical IP address data and pre-training predictive models before real-time bidding occurs. During actual bidding, the system only needs to query these pre-computed structures and apply the trained models, significantly reducing processing time while maintaining high location inference accuracy
Solution Approach 2:
The patent applies dynamics by using adaptive predictive models that learn from ongoing movement patterns in the network. The system continuously updates its understanding of device movements and adjusts predictions accordingly, improving accuracy over time while the pre-computed movement graphs provide a stable foundation for rapid querying
Data Source
AI summary
Methods, systems, and media for determining location information from real-time bid requests are provided. In some implementations, a method for determining device locations is provided, the method comprising: receiving a real-time data stream that includes location proxies corresponding to devices; generating a movement graph of interconnected nodes and edges, wherein each node represents one of the location proxies and wherein each edge represents movement information between the location proxies; calculating, for a node in the movement graph, location information by applying a predictive model having weights based on the movement information; and assigning, for the node in the movement graph, a census-based identifier to the calculated location information. The census-based identifier can be used, for example, to supplement or enrich bid requests within the real-time data stream.