User Intent Prediction via Dynamic Content Relevance Analysis
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Solution Overview
Problem
Existing technologies struggle to accurately measure user intent in online marketing, often relying on generic or static intent categories that fail to capture the nuances of user preferences and the specific set of interchangeable products that meet their needs.
Innovation Solution
A system and method that analyze user search terms, website content, and network traffic data to identify keywords and websites relevant to specific items, generating a user intent score based on the relevance of the content and user demographics, and modifying marketing interactions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generic or static intent categories are used to measure user intent, then the measurement process is simple and fast, but the accuracy and precision of user intent measurement deteriorates
Solution Approach 1:
The patent transforms static intent categories into dynamic, adaptive intent measurement. The system continuously learns from user interactions, search patterns, and behavior data to update and refine intent categories in real-time. This dynamic approach allows the system to adapt to changing user preferences and market conditions, significantly improving measurement precision without requiring complete system redesign.
Solution Approach 2:
The patent implements preliminary action by pre-processing and analyzing user behavior data, search queries, and interaction patterns before formal intent measurement occurs. The system establishes baseline user profiles, pre-identifies relevant products, and prepares intent categories in advance, which streamlines the actual measurement process and reduces computational complexity during real-time operations.
2Measurement precision
If detailed analysis of user search terms and website content is performed, then user intent measurement accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the complex analysis task into distinct modular components: search term analysis, website content processing, user behavior tracking, and intent score calculation. Each module processes specific aspects independently and passes results to the next stage. This segmentation enables parallel processing, reduces computational bottlenecks, and allows selective deep-dive analysis only where needed, maintaining high precision while reducing overall processing time.
Solution Approach 2:
The patent implements partial action by analyzing only the most relevant portions of user data rather than processing everything in detail. The system identifies key search terms, focuses on high-importance website content sections, and selectively processes user behaviors that strongly indicate intent. This approach achieves sufficient measurement accuracy without the computational overhead of exhaustive analysis of all available data.
3Loss of information
If the system tracks and analyzes multiple user behaviors and demographics, then the ability to define user intent improves, but data processing complexity and storage requirements increase
Solution Approach 1:
The patent applies the extraction principle by selectively pulling out and isolating the most critical user intent indicators from the vast amount of available data. The system extracts key search terms, identifies pivotal website content, and isolates significant user behaviors that directly indicate purchase intent. By extracting only the essential information needed for intent definition, the system maintains complete user intent information while minimizing data storage requirements and processing complexity.
4Productivity
If the system modifies marketing interactions based on user intent scores, then marketing efficiency improves, but the complexity of interaction management increases
Solution Approach 1:
The patent applies parameter changes by using user intent scores as a dynamic parameter that automatically adjusts marketing interaction strategies. Instead of complex manual decision-making, the system changes interaction parameters (such as message timing, channel selection, content type, and follow-up frequency) based on the calculated intent score. This automated parameter adjustment significantly improves marketing efficiency while keeping interaction management complexity manageable through rule-based or algorithmic decision frameworks.
Data Source
AI summary
Systems, methods, and computer-readable storage media for predicting user interest, and more specifically to defining user interest based on user expressions of intent. The system can receive a list of items, where each item in the list of items has a similar purpose and can be substituted with other items in the list of items, and item content elements associated with items in the list. The system can then identify websites with content that is relevant to the item content elements associated with the list based on relevancy, and identify at least one user that accessed one of the websites, resulting in at least one interested user. The system can then generate, for each user in the set of at least one interested users, a user intent and modify a previously planned interaction with the each user based on the user intent score.