AI Personalization System Semantic Clustering
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Solution Overview
Problem
Companies face high costs and inefficiencies in manually creating and managing personalized digital content across various channels, leading to slow innovation and limited personalization.
Innovation Solution
A system utilizing artificial intelligence (AI) and machine learning (ML) to automate the delivery of personalized digital content by processing assets into semantic features, clustering similar content, and using visitor data to optimize content selection and suggestion, enabling continuous optimization of digital channels based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processes are used to create and manage personalized digital content, then content can be customized for each customer, but costs increase and innovation slows
Solution Approach 1:
The system enables self-service personalization by automatically generating personalized content experiences without requiring manual intervention. The AI/ML system autonomously processes customer data, selects appropriate content assets, and assembles personalized experiences, eliminating the need for large digital marketing teams to manually create and manage personalized content for each customer.
Solution Approach 2:
The patent replaces manual mechanical processes with automated AI/ML systems. Instead of human marketers manually creating and managing personalized content, the system uses machine learning models to automatically process customer data, perform content selection, and generate personalized experiences, thereby reducing costs while maintaining personalization capabilities.
2Adaptability or versatility
If manual processes are used to manage digital content, then content can be customized, but the process becomes slow and innovation is limited
Solution Approach 1:
The system performs automated content selection and assembly without manual intervention, enabling rapid generation of personalized content experiences. The AI/ML system continuously processes customer data and updates content recommendations in real-time, accelerating innovation speed while maintaining content customization capabilities.
Solution Approach 2:
The system pre-processes and organizes content assets into semantic clusters beforehand, creating a structured content library that enables rapid content selection during runtime. This preliminary organization of content by semantic similarity allows the system to quickly assemble personalized experiences without time-consuming manual content curation.
3Productivity
If automated AI/ML systems are used to personalize digital channels, then productivity and speed improve, but system complexity increases
Solution Approach 1:
The patent segments the complex personalization task into distinct modular components: customer data processing, semantic feature extraction, content clustering, content selection, and experience assembly. Each module performs a specific function and can be independently developed and maintained, reducing overall system complexity while enabling high productivity through automated AI/ML processes.
Solution Approach 2:
The system introduces semantic feature vectors as an intermediary representation between raw customer data and final content selection. This intermediary layer simplifies the complexity by transforming diverse input data into a standardized semantic space, making the subsequent content selection process more manageable and efficient.
4Adaptability or versatility
If large digital marketing teams are used to create personalized content, then personalization quality can be maintained, but costs increase significantly
Solution Approach 1:
The system replaces human marketers with automated AI/ML processes that perform content selection and personalization tasks autonomously. The machine learning models continuously learn from customer interactions and automatically generate personalized content experiences, maintaining high personalization quality without requiring large teams of digital marketing professionals.
Solution Approach 2:
The system changes the operational parameters from manual human processes to automated computational processes. By transitioning from human creativity and judgment to AI/ML algorithms, the system maintains personalization quality while dramatically reducing resource consumption in terms of human labor and associated costs.
Data Source
AI summary
A method and system provide the ability to personalize a digital channel. Multiple content assets are obtained and include an image content asset. Each of the assets is associated with an associated set of semantic elements. The multiple content assets are clustered into content clusters based on a similarity of the semantic elements. A first content asset is selected. The clustering is used as a metric to estimate distances between the first content asset and remaining multiple content assets. The remaining multiple content assets are scored based on the distances. One of the remaining multiple content assets is selected based on the scoring and provided for a personalized component of the digital channel. In addition, a coverage map that includes both users and content may be generated based on the clusters and then utilized to select the content asset.