Client-Side Offer Scoring for Real-Time Personalized Content
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Solution Overview
Problem
Existing marketing and advertising systems require significant computer infrastructure, which can be costly and inefficient for targeted, real-time content delivery to consumers.
Innovation Solution
Systems and methods for dynamically delivering content, such as advertisements, by generating a recommendation matrix based on consumer profiles and event data, using a processor to calculate offer scores, and updating the matrix in real-time to provide personalized content on client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If businesses use traditional marketing infrastructure (servers, memory devices, routers) to support targeted advertising, then they can deliver personalized content to consumers, but the cost and complexity of the infrastructure outweigh the benefits
Solution Approach 1:
The client device performs advertising selection and delivery functions locally using a recommendation matrix and feature data, eliminating the need for complex centralized infrastructure. The device serves itself by calculating offer scores and selecting advertisements without requiring extensive server resources.
Solution Approach 2:
The patent extracts the core advertising selection logic from centralized servers and places it on the client device. By taking out the essential functions (recommendation matrix calculation, feature data processing, offer score computation) and implementing them locally, the system reduces infrastructure complexity while maintaining targeted advertising capabilities.
2Productivity
If businesses deploy extensive computer infrastructure for real-time advertising operations, then they can process consumer data and deliver personalized ads, but the cost becomes prohibitively high
Solution Approach 1:
The client device autonomously performs real-time advertising selection by processing feature data and calculating offer scores using the recommendation matrix. This self-service approach enables real-time content delivery without requiring extensive server resources, reducing the quantity of infrastructure resources needed.
Solution Approach 2:
The system implements only the essential functions needed for real-time advertising delivery on the client device (feature data processing, recommendation matrix calculation, offer score computation). By performing partial action locally rather than duplicating full advertising operations across extensive infrastructure, the system achieves real-time productivity with minimal resources.
3Reliability
If centralized systems process all advertising decisions, then consistent branding can be maintained, but response time to consumer behavior changes is delayed
Solution Approach 1:
The recommendation matrix is pre-calculated and stored on the client device based on consumer profiles and offer data. This preliminary action enables the device to immediately process feature data and select advertisements in real-time without waiting for centralized processing, reducing response time while maintaining brand consistency through pre-approved offer selections.
Solution Approach 2:
The advertising system is segmented into centralized components (recommendation matrix generation, offer data management) and decentralized components (feature data collection, offer score calculation, ad selection). This segmentation allows brand consistency to be maintained centrally while enabling fast local response to consumer behavior changes.
Data Source
AI summary
Systems and methods are disclosed for dynamically providing content to a user. The disclosed embodiments include generating a recommendation matrix based on event data received from a client. In certain embodiments, the client may be configured to generate event data associated with the client or a user associated with the client. The disclosed embodiments may also include generating an offer score matrix based on the recommendation matrix and the event data. In certain aspects, the offer score matrix may include score values associated with a set of offers to be provided to the user. The disclosed embodiments may identify a first offer from the set of offers based on the score values for the offers and provide an identification of the first offer. The disclosed embodiments may also receive content associated with the first offer for display on the client.