Probabilistic Device Linking via IP Log Scoring
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Solution Overview
Problem
Service providers struggle to accurately categorize user interests across multiple computing devices, leading to incomplete user profiles and reduced engagement, as profiles built from single device activity fail to account for diverse user behaviors across different devices.
Innovation Solution
Network-based probabilistic device linking techniques filter and score log records from service providers to identify connected devices through IP addresses, creating linked device clusters without requiring user identities, thus enabling comprehensive analysis of user interactions across various devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If service providers build user profiles based on monitored activity from a single computing device, then the profile construction process is simple and computationally efficient, but the accuracy and completeness of user interest categorization deteriorates
Solution Approach 1:
The patent segments the user activity data by dividing it into device-specific activity sets, where each set contains resources accessed by a particular computing device. This segmentation allows the system to analyze and compare activity patterns across multiple devices independently, then integrate them to form a comprehensive user profile that accurately reflects overall user interests rather than just single-device behavior
Solution Approach 2:
The patent introduces activity similarity scores as an intermediary mechanism to bridge device-specific activity data and user profile construction. These scores quantify the relationship between activities on different devices, enabling the system to probabilistically determine device linking without complex direct observation, thus improving profile accuracy while managing system complexity
2Loss of information
If service providers analyze activity data from multiple computing devices to build comprehensive user profiles, then user interest categorization accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by computing activity similarity scores only for comparable activity pairs across devices (e.g., matching video streaming on one device with video streaming on another), rather than performing exhaustive comparisons of all activity combinations. This selective approach captures sufficient user behavior information to build accurate profiles while significantly reducing computational overhead compared to complete data analysis
Solution Approach 2:
The patent transforms raw activity data into standardized parameters including activity types, time stamps, and similarity scores. By changing the representation of activity data into comparable parameters, the system enables efficient processing of multi-device information through standardized operations rather than handling raw, unstructured data, thus improving processing efficiency while maintaining information completeness
3Adaptability or versatility
If service providers implement device linking to identify connected devices, then the ability to engage users with relevant content across devices improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The patent implements feedback mechanisms where activity similarity scores are computed and used to determine device linking relationships. This feedback loop allows the system to continuously refine its understanding of device connections based on observed activity patterns, enabling adaptive cross-device engagement strategies. The feedback-based approach provides versatility in user engagement while managing processing complexity through iterative refinement rather than complex upfront analysis
Data Source
AI summary
Network-based probabilistic device linking techniques are described that link multiple devices associated with a common entity. In one example, log records are received from service providers including a device identifier and an IP address associated with a computing device that uses the service providers to access resources. The received log records are filtered and analyzed to identify connection frequencies between each device identifier and various IP addresses. Connection frequencies are scored and used to identify a subset of connections for computing linked devices belonging to a common entity, such as a single user, a household of users, users in a specific location, and so on. Linked devices are computed from the subset of selected connections and combined into linked device clusters. These linked device clusters can then be output so that market analysis can be performed on the linked device cluster rather than data pertaining to a single device.