Notification Engine for Reducing IT Resource Consumption
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Solution Overview
Problem
Existing electronic transaction-based technologies and platforms inefficiently utilize computing and network resources, leading to excessive server-client requests, processing delays, and increased bandwidth usage due to complex transaction rules and events in electronic tax benefit accounts.
Innovation Solution
A system that determines resource consumption trends and configures a notification engine to generate proactive communications based on event correlation coefficients, shifting transactions from resource-intensive to resource-efficient, thereby optimizing resource allocation and reducing processing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex transaction rules and events are implemented in electronic tax benefit accounts, then functionality and versatility are improved, but resource consumption and processing overhead increase
Solution Approach 1:
The patent creates simplified copies of transaction processing logic in the form of pre-configured event templates and correlation coefficient rules. Instead of processing each complex transaction individually through multiple server requests, the system uses template-based copying where common transaction patterns are predefined, allowing rapid matching and processing without repeated full validation cycles.
Solution Approach 2:
The system performs preliminary configuration of event templates, correlation thresholds, and notification rules before transactions occur. Event templates are pre-defined with expected patterns, and correlation coefficients are pre-calculated based on historical data, enabling fast matching during transaction processing without real-time complex analysis.
2Reliability
If multiple server-client requests are made to process transactions, then transaction accuracy and rule application are improved, but processing time and bandwidth usage increase
Solution Approach 1:
The patent merges multiple separate server-client request functions into a single consolidated event processing flow. By combining event detection, correlation analysis, template matching, and notification generation into one integrated process, the system maintains comprehensive rule validation while eliminating redundant communication cycles between client and server.
Solution Approach 2:
The system introduces an intermediary event correlation layer that sits between transaction initiation and full server processing. This intermediary layer performs initial filtering and matching using pre-configured templates and correlation coefficients, allowing only truly exceptional transactions to require full server-client interaction, thereby reducing overall processing time while maintaining accuracy.
3Reliability
If resource-intensive transaction processing is used, then comprehensive rule validation and event monitoring are achieved, but system performance and efficiency decrease
Solution Approach 1:
The patent applies local quality by using different processing intensities for different transaction types. Common, predictable transactions are processed using lightweight template matching with low correlation thresholds, while unusual or high-value transactions trigger more intensive validation. This localized approach ensures comprehensive rule validation where needed while maintaining high performance for routine operations.
Solution Approach 2:
The system dynamically changes processing parameters based on transaction characteristics. Correlation coefficients and threshold values are adjusted according to event types, account priorities, and historical patterns. This allows the system to optimize the balance between validation thoroughness and processing speed by modifying parameters like correlation thresholds, notification priorities, and processing queues based on real-time conditions.
Data Source
AI summary
The present disclosure is directed to reducing resource consumption via information technology infrastructure. A server of the present disclosure receives one or more data packets including data indicating a healthcare transaction event. The server selects healthcare trend model trained by the server using previously received data packets. The server determines a correlation coefficient between each of a plurality of healthcare related recommendations and the selected healthcare trend model. The server retrieves a notification template from a notification data structure that maps to the highest ranking healthcare related recommendation. The server generates a request to deliver the notification corresponding to the highest ranking healthcare related recommendation at a destination address of a computing device of a participant.