Audience Matching Engine for Digital Content Delivery
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Solution Overview
Problem
Current digital content delivery systems lack the ability to adapt and select content in real-time based on audience interests and device capabilities, relying on traditional broadcast models that do not account for individual preferences or optimal playback devices and transmission paths.
Innovation Solution
A method and system that monitor audience-related data, device-related data, and content-related data to determine matches and provide selected content dynamically, using processing circuitry and analysis engines to recommend and deliver content that enhances the audience experience on appropriate devices via optimal transmission paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional broadcast models are used for content delivery, then content can be delivered to large audiences, but the system cannot adapt content to individual audience preferences or device capabilities
Solution Approach 1:
The patent segments the audience into different profiles based on detected data points (demographics, device type, location, behavior patterns), and segments content into different versions or recommendations. The system processes different data types separately (audience data, device data, content data) and combines them to deliver personalized content selections to each audience segment.
Solution Approach 2:
The system changes multiple parameters simultaneously - audience parameters (demographics, preferences), device parameters (type, capabilities), and content parameters (selection, timing, delivery method). By monitoring and analyzing changes in these parameters over time, the system dynamically adjusts content delivery to match current audience-state-device conditions.
2Measurement precision
If content is altered based on audience interest, then content relevance improves, but the content compilation process becomes more complex
Solution Approach 1:
The patent performs preliminary actions by pre-compiling content in multiple versions or formats, pre-analyzing audience data to create profiles, and pre-establishing matching criteria between content and audience characteristics. This preparation work is done before actual content delivery, so that when content needs to be selected or altered, the system can quickly match from pre-prepared options rather than creating content in real-time.
Solution Approach 2:
The system introduces intermediary components - data analysis engines that process raw audience data into meaningful profiles, matching algorithms that bridge content and audience requirements, and content management systems that mediate between available content and delivery requirements. These intermediaries simplify the complexity by breaking down the content alteration process into manageable, automated steps.
3Productivity
If real-time content selection and delivery is implemented, then audience experience is enhanced, but the processing and delivery infrastructure becomes more complex
Solution Approach 1:
The patent designs the system with universal components that perform multiple functions - the data analysis engine processes various types of audience data (demographics, behavior, device info), the content management system handles different content types and delivery methods, and the matching algorithms work across different content-audience-device combinations. This multi-functionality reduces overall infrastructure complexity by avoiding separate specialized systems for each function.
Solution Approach 2:
The system implements feedback loops where audience responses to delivered content are monitored and fed back into the audience profiles and matching criteria. This feedback mechanism allows the system to learn and improve over time, automatically adjusting to audience preferences without requiring manual reconfiguration of the infrastructure. The feedback-driven adaptation simplifies real-time decision-making by using learned patterns rather than complex real-time analysis.
Data Source
AI summary
Digital content is indexed and available to be provided to a target audience based upon possible interest. Components and/or sensors detect audience-related data indicative of the target audience, possible audience interest, audience behavior, and other audience-related factors. Components and/or sensors detect device-related data indicative of available devices to which available content may be sent, transmission paths for the content, and related factors. An analysis and matching engine matches the available content to the target audience and one or more of the devices and selects content, devices, and transmission paths for directing the selected content for an enhanced audience experience.