Omni-channel Event Correlation for Resource Availability Prediction
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Solution Overview
Problem
Enterprises face challenges in deriving a timely and comprehensive state of their devices and business operations due to uncorrelated transaction data across different channels, devices, and systems, which hinders proactive and remedial actions.
Innovation Solution
An Omni-channel management platform that collects and correlates events and data from touchpoint devices and systems, using channel agents to assign identifiers, normalize data, identify patterns, and apply machine-learning algorithms to predict outcomes and automate remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple channels and devices, then the quantity of information increases, but the data becomes uncorrelated and harder to analyze
Solution Approach 1:
The patent merges data from multiple independent channels (retail, restaurant, travel, banking) into a unified analytics platform. The event correlator combines events and data from different touchpoint devices and systems, normalizing them into a common format to enable cross-channel correlation and comprehensive business intelligence.
Solution Approach 2:
The event correlator serves as an intermediary component that receives data from various channel agents, normalizes the data formats, correlates events across channels, and generates unified analytics. This mediator enables correlation between previously siloed data sources without requiring changes to the source systems.
2Productivity
If traditional data collection methods are used, then implementation is simple, but proactive and remedial actions cannot be taken timely
Solution Approach 1:
The system performs preliminary analysis by continuously correlating events and identifying patterns that indicate future issues. Machine learning models predict resource availability and potential failures before they occur, enabling enterprises to take preventive actions in advance rather than reacting to problems after they happen.
Solution Approach 2:
The event correlator establishes feedback loops by continuously monitoring correlated events across channels and providing real-time insights about business operations. This feedback enables dynamic decision-making and timely adjustments to maintain optimal performance and prevent issues.
3Device complexity
If data is not correlated across channels and devices, then system complexity is low, but anomalies and localized trends cannot be identified
Solution Approach 1:
The system segments data collection by implementing channel-specific agents for different business channels (retail, restaurant, travel, banking), each handling their own touchpoint devices. The event correlator then segments and normalizes events from these agents, enabling manageable processing while maintaining the ability to detect cross-channel anomalies through structured correlation.
Data Source
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AI summary
Events associated with touchpoint devices of a plurality of systems are collected in real time by channel agents over a plurality of communication channels. The events are aggregated and normalized across the channels, touchpoint devices, and systems and processed for correlations to expected outcomes based on known previous outcomes. The expected outcomes are communicated in real time to the systems for remediation actions, preventive actions, and planning actions.