AI Engine for Real-Time Message Selection Using Log-Level Data
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Solution Overview
Problem
Existing automated methods for selecting and timing messages struggle to accurately identify consumer interest and preferences, especially in the presence of unanticipated external variables like news or weather events, and fail to effectively reach potential consumers who haven't expressed interest through traditional channels, while also lacking optimal message format and timing insights.
Innovation Solution
A computer-implemented method using an artificial intelligence engine that correlates log-level data from IoT, mobile, and PoS devices to select messages in real-time based on short-term and long-term hidden correlations, employing machine learning to predict consumer responses and adjust marketing campaigns dynamically, incorporating location and activity patterns to enhance message delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated methods focus on consumer interest monitoring through traditional channels, then interest information can be aggregated, but accuracy is rendered inaccurate by unanticipated external variables and potential consumers who haven't expressed preference
Solution Approach 1:
The patent replaces traditional mechanical interest tracking methods (cookies, search engine tracking) with an AI-based predictive model that processes log-level network data to identify consumer interest patterns, enabling more accurate and adaptable consumer profiling
Solution Approach 2:
The system changes the parameters for consumer interest measurement by using log-level data from multiple sources (mobile devices, IoT, PoS) and applying machine learning algorithms to detect hidden correlations, transforming how consumer behavior is measured and predicted
2Loss of information
If traditional interest information tracking methods are used, then consumer data can be collected, but there is lack of information about the most advantageous message format, medium, and time
Solution Approach 1:
The AI engine performs multiple functions simultaneously: it processes data from diverse sources (mobile devices, IoT, PoS), identifies consumer interest patterns, determines optimal message formats, selects delivery channels, and timing - consolidating what would otherwise require multiple separate systems into one unified platform
Solution Approach 2:
The machine learning model acts as an intermediary between raw log-level data and messaging decisions, processing and translating complex data patterns into actionable insights about consumer behavior and optimal message delivery strategies
3Productivity
If real-time message selection is implemented, then message timing can be optimized, but the system complexity increases with multiple data sources and processing requirements
Solution Approach 1:
The system segments the complex data processing task into distinct components: data collection from multiple sources, data processing and correlation analysis, consumer interest prediction, and message selection - making the overall complex system more manageable and maintainable
Data Source
AI summary
Methods and apparatus for improving automatic selection and timing of messages by a machine or system of machines include an inductive computational process driven by log-level network data from mobile devices and other network-connected devices, optionally in addition to traditional application-level data from cookies or the like. The methods and apparatus may be used, for example, to improve or optimize effectiveness of automatically-generated electronic communications with consumers and potential consumers for achieving a specified target.