User Identity Pattern Matching via Contextual Profile Segmentation
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Solution Overview
Problem
Identifying users in locations like stores or restaurants using biometric data from video cameras is inefficient due to the large number of social media profiles, making passive management of customer loyalty programs unfeasible.
Innovation Solution
The method involves building subsets of social profiles to match contextual and static social media data, applying local filtering to input images for improved selection, and matching multiple sub-optimal images to photos in social media profiles, along with creating shadow profiles for customers without social media profiles and using social media data at the point of sale.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If biometric data from video cameras is used to identify users, then user identification can be performed without active user participation, but the large number of social media profiles makes the process inefficient and unfeasible for passive loyalty program management
Solution Approach 1:
The patent segments the large set of social media profiles into subsets based on contextual data (location, time, device information). Instead of comparing biometric data against all profiles, the system divides the search space into manageable segments, significantly improving identification efficiency while maintaining passive operation
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing social media profiles with contextual metadata before identification occurs. User profiles are pre-segmented and indexed based on location, time, and device characteristics, so that when biometric data is captured, the comparison is already optimized and ready for rapid matching
2Reliability
If all social media profiles are compared for user identification, then comprehensive coverage is achieved, but the complexity and computational burden increase significantly
Solution Approach 1:
The patent applies local quality by making different parts of the profile matching system have different functions. Contextual data (location, time, device) serves as a filtering layer that qualifies which profiles should be compared. Not all profiles are treated equally - only those with matching contextual characteristics are subjected to biometric comparison, reducing complexity while maintaining accuracy
Data Source
AI summary
In an approach to establishing personal identity using user identity patterns, one or more processors receive a set of input images corresponding to a period of time, where each input image in the set of input images corresponds to a specific time within the period of time. One or more processors may also identify a first user in the set of input images and determining, a user identity pattern based on the set of input images, where the user identity pattern includes multiple instances of at least one physical characteristic of the first user over the period of time. One or more processors may further determine a user behavior based on the user identity pattern. One or more processors may additionally associate the set of input images and the user identity pattern with a first user profile for the first user.