Viewer Identification Model for Multi-User Locations
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Solution Overview
Problem
Determining the identity of content viewers in multi-user locations is challenging due to accuracy and validity issues, making it difficult to provide user-specific secondary content in real-time.
Innovation Solution
A machine learning model is trained using demographic attributes from single-user locations and content metadata to determine which user is consuming a content item at a multi-user location, using network data and validation data to adjust probabilities and retrain the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional techniques are used to determine identity of content viewers, then the process is simple when only one user resides at the user location, but accuracy and validity deteriorate when multiple people reside at the user location
Solution Approach 1:
The patent segments the viewer identification process into multiple independent analysis components: device identifiers, biometric data, viewing behavior patterns, and demographic information. Each segment analyzes a specific aspect of viewer identity, and their combined results resolve the contradiction by maintaining simplicity through modular processing while improving accuracy through multi-factor verification.
Solution Approach 2:
The system dynamically changes multiple parameters including biometric thresholds, device recognition criteria, and behavior pattern weights based on the number of users detected at the location. When multiple users are present, the system adjusts parameter sensitivity and combines multiple data sources, transforming a simple single-parameter check into a multi-parameter analysis that maintains operational simplicity while achieving high accuracy.
2Measurement precision
If machine learning techniques are implemented to determine which user is consuming content at multi-user locations, then accuracy of viewer identification improves, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with extensive demographic and viewing behavior data before actual viewer identification. This pre-processing creates ready-to-use classification models that can quickly and accurately identify viewers without requiring complex real-time computations, thus improving accuracy while managing system complexity through advance preparation.
Solution Approach 2:
The patent introduces intermediary components including probability engines that translate complex machine learning outputs into actionable viewer identification results, and validation modules that verify predictions against multiple data sources. These intermediaries simplify the interface between complex AI algorithms and practical application, improving accuracy through layered verification while presenting a simpler operational interface.
3Adaptability or versatility
If user-specific secondary content is to be provided in real-time for a given user at a multi-user location, then personalization quality improves, but computational requirements and time consumption increase
Solution Approach 1:
The system performs preliminary content preparation by pre-processing and categorizing secondary content based on user demographics and viewing preferences before actual content delivery. This advance preparation creates ready-to-deliver personalized content packages that can be quickly deployed in real-time without requiring intensive computation during content delivery, thus improving personalization quality while minimizing time loss.
Solution Approach 2:
The patent implements feedback loops where viewer responses to personalized content are continuously monitored and used to refine future content personalization. This real-time feedback mechanism improves adaptability by learning from actual viewer behavior while optimizing delivery time through iterative refinement rather than exhaustive real-time analysis, balancing personalization quality with time efficiency.
Data Source
AI summary
Methods, systems, and apparatuses for determining viewership of a content item are described herein. Machine learning techniques may be used to determine which user(s) among a user group at a multi-user location is consuming a content item. A machine learning model may be trained using demographic attributes and content attributes associated with a plurality of single-user locations. A probability engine may train a machine learning model using the demographic attributes and content attributes and one or more machine learning algorithms. The trained machine learning model may be used to determine which user(s) among at least two users is consuming a content item at a multi-user location at which multiple people reside.