Unified User Intent Prediction Across Physical and Online Channels
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Solution Overview
Problem
Existing methods for tracking user actions are insulated and limited, failing to accurately predict user intent and preferences across different environments, leading to inefficient information presentation and resource allocation.
Innovation Solution
A system that aggregates user action data from multiple physical and online establishments using software and hardware infrastructure, allowing for the quantification of user intent and entity affinity, enabling proactive aid and resource optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user actions are tracked in insulated ways (separate loyalty programs, independent tracking systems), then each system can be implemented independently with simpler infrastructure, but the tracking accuracy and ability to predict user intent deteriorates due to information silos
Solution Approach 1:
The patent merges user action data from multiple independent physical establishments and online environments into a unified tracking system. This consolidation breaks down information silos and enables comprehensive user behavior analysis, directly improving user intent prediction accuracy while managing integration complexity through standardized data processing protocols
Solution Approach 2:
The tracking system is designed with universal functionality to handle diverse user actions across different physical establishments and online platforms. By creating a multi-functional system that can process various types of user interactions (browsing, purchasing, in-store behavior) through a common framework, the patent achieves accurate cross-environment user intent prediction without requiring separate specialized systems for each channel
2Measurement precision
If user action data is collected from multiple physical establishments and online environments, then the basis for determining user intent and entity affinity improves, but the complexity of data aggregation and processing increases
Solution Approach 1:
The patent introduces an intermediary data processing layer that sits between multiple data sources (physical establishments, online platforms) and the user intent analysis system. This intermediary layer standardizes and aggregates raw user action data from diverse sources into a unified format, improving measurement precision while shielding the core analysis system from the complexity of multi-source data integration
Solution Approach 2:
The data aggregation process is segmented into distinct functional modules: data collection from physical establishments, data collection from online environments, data filtering, data normalization, and user intent calculation. This segmentation allows each module to handle specific aspects of data processing independently, reducing overall system complexity while maintaining high measurement precision through specialized processing at each stage
3Productivity
If proactive user aid is presented based on accurate user intent prediction, then user efficiency and resource allocation improve, but the system requires more sophisticated tracking and analysis infrastructure
Solution Approach 1:
The system performs preliminary analysis of user actions and predicts user intent before the user actually performs the intended action. By calculating user intent indicators and entity affinity metrics in advance based on aggregated user behavior data, the system can proactively present relevant aid (such as targeted offers, product recommendations, or information) at the optimal moment, improving user efficiency without requiring complex real-time reaction systems
Solution Approach 2:
The patent implements a feedback mechanism where user responses to presented aid are tracked and fed back into the user intent prediction model. This feedback loop continuously refines the accuracy of user intent predictions by learning from actual user responses, enabling increasingly precise proactive aid presentation while managing system complexity through iterative improvement rather than requiring perfect initial system design
Data Source
AI summary
User action data, quantifying the actions of the user in a physical establishment, can be obtained through various forms of tracking and monitoring that can be implemented by software or hardware infrastructure supported by physical establishment, and agreed to by the user. Such user action data is obtained from multiple physical establishments, which, in combination with tracking of user actions in an online environment, provides a more accurate basis on which to determine a user's intent to act upon an item, a user's affinity for items associated with a specific entity, or combinations thereof. User intent can be quantified in terms of predetermined levels of intent. User content and user entity affinity provide a more accurate basis on which to proactively offer user aid to facilitate the user's acquiring of items, or otherwise make more efficient the user's activities with respect to such items.