Affinity Profiling for Real-Time Content Audience Prediction
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Solution Overview
Problem
Existing TV content distribution systems face challenges in accurately determining user preferences and behaviors due to the diversity of content sources and platforms, leading to inefficiencies in targeting and advertising, particularly in addressable advertising, where manual analytical methods are slow, expensive, and uncertain, and the capability for automated generation of powerful audience attributes is underserved.
Innovation Solution
A computer-implemented method using affinity profiles to determine audience categories based on user content consumption, employing machine learning models to generate affinity profiles from first-party data, independent of demographic data, and providing content recommendations in real-time or near real-time to millions of users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analytical methods are used to determine user affinity attributes, then accuracy of user profiling can be improved, but processing time and cost increase significantly
Solution Approach 1:
The patent replaces manual analytical methods with automated machine learning models and algorithms that process user data and generate affinity attributes automatically. This substitution of mechanical/manual analysis with automated computational systems resolves the contradiction by maintaining high accuracy through sophisticated algorithms while dramatically reducing processing time and operational costs.
Solution Approach 2:
The system transforms user data into affinity attributes by changing parameters through machine learning models. The models process raw user behavior data and convert it into meaningful affinity scores and categories, enabling automated decision-making while maintaining precision through configurable thresholds and weighting parameters that can be optimized for different use cases.
2Productivity
If automated machine learning models are used to generate affinity profiles, then processing speed and efficiency are improved, but system complexity increases
Solution Approach 1:
The patent segments the complex affinity profiling system into distinct modular components: data collection modules, machine learning model modules, affinity attribute generation modules, and application modules. Each component performs a specific function and can be independently developed, tested, and maintained. This segmentation reduces system complexity by breaking down the monolithic automated system into manageable, interchangeable units while maintaining high processing speed through parallel operation of these modules.
Solution Approach 2:
The machine learning models are designed with universal applicability across different content types, user demographics, and business use cases. The same core affinity profiling infrastructure serves multiple functions including content recommendation, targeted advertising, user experience optimization, and business intelligence, thereby justifying the system complexity through multi-functional utility and reducing the need for separate systems for each application.
3Measurement precision
If first-party data is used instead of demographic data for affinity profiling, then accuracy of targeting is improved, but data privacy requirements and processing challenges increase
Solution Approach 1:
The patent introduces affinity attributes as an intermediary layer between raw first-party user data and business applications. Instead of directly using sensitive user data for targeting decisions, the system processes user behavior data through machine learning models to generate abstracted affinity scores and categories. This intermediary representation maintains targeting accuracy while reducing data privacy risks and simplifying compliance with privacy regulations, as the affinity attributes contain no personally identifiable information.
Data Source
AI summary
A computer-implemented method of using affinity profiles to determine at least one audience category that is expected to consume a selected content item type, the method comprises:obtaining affinity profiles for a plurality of users of a content distribution system, wherein each affinity profile comprises at least one selected affinity category or affinity category score, and the affinity categories are selected from a stored set of affinity categories, each representing a user's affinity for a respective subject area;monitoring consumption of selected items of content of the selected type to determine which of the users consumed the items of content of the selected type;determine an audience that is expected to consume the items of content of the selected type based on the affinity profiles of the users that were determined to have consumed the selected items of content.