Keyword Assignment Model for Cross-Platform Intent Prediction
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Solution Overview
Problem
Current computing systems have limited capabilities to analyze user behavior across platforms and predict user engagement and behavior, and they struggle with managing privacy and adhering to governmental regulations while providing real-time user intent predictions.
Innovation Solution
A predictive intelligence model that ingests user online data to identify devices, assign keywords, and predict user behavior by continuously updating and optimizing user analysis data, using machine learning to analyze user interactions and deliver relevant web content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional analysis mechanisms are used within a single provider's platform, then user behavior analysis is simple to implement, but the analysis capability is limited to that platform's data only
Solution Approach 1:
The patent introduces a predictive intelligence model as an intermediary layer between user interactions and analysis systems. This model processes user behavior data from multiple platforms and generates predictions about user intent, enabling cross-platform analysis without requiring direct integration between platforms. The model acts as a mediator that translates diverse platform-specific data into unified predictive insights.
Solution Approach 2:
The predictive intelligence model is designed to handle multiple types of user interactions across different platforms (video streaming, search, e-commerce, social media) using a single unified system. The model can process various data formats and interaction types, making it universally applicable across different platform ecosystems while maintaining a single analysis architecture.
2Measurement precision
If extensive user data is collected for predictive modeling, then prediction accuracy improves, but privacy management and regulatory compliance become more difficult
Solution Approach 1:
The patent extracts only the essential behavioral patterns and intent signals from user data rather than collecting and processing all raw user data. The predictive intelligence model focuses on extracting meaningful predictive features (such as intent categories and engagement probabilities) while leaving sensitive personal information behind, thereby reducing privacy management overhead while maintaining prediction accuracy.
Solution Approach 2:
The system transforms raw user behavior data into standardized predictive parameters (intent categories, engagement probabilities, time-to-event predictions) that are platform-agnostic and privacy-friendly. By changing the data representation from detailed user profiles to aggregated behavioral patterns, the system achieves accurate predictions while simplifying compliance with privacy regulations.
3Productivity
If real-time user intent prediction is implemented, then user engagement improvement is achieved, but computational resources and processing time increase
Solution Approach 1:
The predictive intelligence model implements partial prediction by focusing only on the most relevant user intents and behaviors for each context rather than predicting all possible user actions. The system adjusts the level of prediction detail based on what is necessary for the current task, reducing computational overhead while maintaining engagement optimization effectiveness.
Solution Approach 2:
The system performs preliminary processing of user behavior data to create optimized feature representations and pre-computed user profiles that can be quickly queried in real-time. By preparing predictive models and user representations in advance, the system reduces the computational burden during real-time prediction, enabling fast intent classification with minimal energy consumption.
Data Source
AI summary
In various implementations, a keyword assignment and predictive intelligence model scrapes content of web pages and stack ranks them according to a coordinates-based embedding system. The model further analyzes weblog data associated with user identifiers and stack ranks those user identifiers according to keywords associated with the weblog data. In particular embodiments, machine learning is applied to account for the entire web journey associated with the user identifiers associated with users, predicting future page URLs as well as content, products, services, information, etc., that the users may seek.