IP Address Attributes for Privacy-Aware New Mover Advertising
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Solution Overview
Problem
Existing methods for determining new movers and tailoring advertisements are inaccurate, invasive, and prone to fraud, as they rely on cookies and user verification which may not work for all requests and raise privacy concerns.
Innovation Solution
A system that associates IP addresses with physical locations and attributes using request data and secondary information to identify new movers, determine fraud scores, and tailor advertisements based on residential likelihood and temporal thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cookies and user verification information are used to ascertain user information for tailored advertisements, then advertisement personalization can be achieved, but privacy concerns arise and the method becomes invasive
Solution Approach 1:
The patent introduces IP address attributes as an intermediary to bridge the gap between user identification and privacy protection. Instead of directly using cookies or user verification information, the system uses IP address attributes (residential likelihood, temporal characteristics, discovery timestamp) as a mediator to infer user status without invasive data collection. This intermediary approach enables advertisement personalization while minimizing privacy intrusion.
2Measurement precision
If user provided verification information is used to determine new mover status, then accurate identification can be achieved, but the method becomes subject to fraud due to false information
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring IP address usage patterns, temporal characteristics, and behavioral data. Instead of relying on one-time user-provided verification, the system gathers ongoing feedback from multiple sources (request timestamps, browsing patterns, IP mobility) to continuously validate and update the new mover status determination, making fraud detection more reliable.
Solution Approach 2:
The patent applies preliminary action by establishing baseline IP address attributes and temporal patterns before fraud can occur. The system pre-establishes discovery timestamps, residential likelihood scores, and usage patterns that serve as reference points for future fraud detection, enabling the system to identify anomalous behavior before it can compromise the identification accuracy.
3Productivity
If cookies are used to track user information across requests, then user behavior can be analyzed for advertising, but many requests are not associated with cookies limiting effectiveness
Solution Approach 1:
The patent makes the IP address attribute system universal by designing it to work across all types of requests regardless of cookie presence. The IP address-based attribution system serves multiple functions: it tracks new movers, identifies advertising opportunities, and maintains continuity across sessions without requiring cookies. This multi-functional approach ensures 100% request coverage compared to cookie-based methods that miss many requests.
4Loss of information
If conventional techniques are used to ascertain visitor information, then additional user data can be obtained, but the determination that a visitor is a new mover cannot be accurately achieved
Solution Approach 1:
The patent fundamentally changes the parameters used for new mover determination from user-provided verification information to IP address-derived attributes. Specifically, it transforms static user-claimed data into dynamic, objectively measurable parameters including discovery timestamp, temporal patterns, residential likelihood scores, and behavioral characteristics. This parameter transformation enables accurate new mover identification by relying on objective evidence rather than subjective user claims.
Data Source
AI summary
Methods related to determining and utilizing one or more attributes to associate with an IP addresses. Attributes are determined based on request data provided with requests from an IP address and one or more available secondary information sources. Attributes may include physical locations and/or category designations for the IP address. One or more attributes may be assigned a likelihood value indicative of likelihood that the attribute is associated with the IP address. Some implementations are directed to utilizing the attributes and likelihood values to identify likely fraudulent information provided with requests. Some implementations are directed to utilizing the attributes and likelihood values to provide advertisements in response to requests from IP addresses.