On-Device Content Personalization for Secure Real-Time Ranking
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Solution Overview
Problem
Conventional content personalization techniques rely on cloud-computing solutions that expose user data to network vulnerabilities and require network connectivity, leading to security concerns and latency issues.
Innovation Solution
A framework for on-device personalization that performs content ranking and modification locally on the user's device, using machine learning models to securely maintain user data and provide personalized experiences without network exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If cloud-computing solutions are used for content personalization, then content delivery can be performed remotely through network infrastructure, but user data is exposed to network vulnerabilities and security threats
Solution Approach 1:
The patent extracts the personalization computation from the cloud environment and places it locally on the user device. The framework separates data processing functions from network infrastructure, allowing content personalization to occur entirely on-device while minimizing network exposure of sensitive user data.
Solution Approach 2:
The patent introduces an on-device personalization framework as an intermediary layer between network content delivery and user data processing. This mediator enables cloud-to-device content transmission while preventing direct access to user data, thus maintaining security while preserving functionality.
2Device complexity
If cloud-based personalization is used, then centralized processing can be performed, but network latency and connectivity requirements slow down content delivery
Solution Approach 1:
The patent segments the personalization system into distributed on-device components rather than centralized cloud processing. Each user device independently performs personalization computations locally, eliminating the need for continuous network communication during content delivery and reducing latency.
Solution Approach 2:
The patent enables user devices to perform self-service personalization computations locally without requiring external cloud processing. The on-device framework autonomously ranks and personalizes content using local machine learning models, making the system independent of network response time.
3Measurement precision
If machine learning models are trained using big-data infrastructure, then accurate content prediction can be achieved, but large volumes of data and computing resources are required
Solution Approach 1:
The patent performs machine learning model training in advance during device setup or idle periods, preparing personalized models locally before they are needed for content delivery. This preliminary action eliminates the need for real-time cloud computing resources during actual content personalization.
Solution Approach 2:
The patent creates and stores local copies of machine learning models on user devices. These copied models enable accurate content prediction locally without requiring continuous access to the original training data or cloud-based model repositories, reducing ongoing data and computing requirements.
Data Source
AI summary
The disclosed systems and methods provide a novel framework that provides on-device functionality to user devices for localized content ranking, modification and rendering. The disclosed systems and methods provide functionality for on-device personalization in a real-time, secure and network anonymous manner. Rather than exposing a user's data to the network for content tailoring, the disclosed framework performs the ranking and content manipulation locally on the user's device. The disclosed framework enables locally (on-device) built, updated and hosted user profiles to be used to tailor received content for display on a user device. This ensures the integrity of the personalization while maintaining security for the user's personalized data and activities.