Data Aggregator Using Life Event Clustering for Transaction Vectors
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Solution Overview
Problem
Organizations face inefficiencies when clients generate large volumes of data, leading to slow data retrieval and resource-intensive processes for both the organization and the client, as existing systems often require transmitting and processing all client data in response to a request.
Innovation Solution
A data aggregator system that categorizes transactions based on life events using aggregation rules and transaction vectors, allowing for the retrieval and communication of only relevant data specific to the requested life event, thereby reducing data transmission and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all client data is retrieved and transmitted in response to a client request, then complete data accuracy is ensured, but network bandwidth consumption increases and processing time extends
Solution Approach 1:
The patent segments client data by organizing transactions into clusters based on life events (e.g., marriage, birth, death). Each cluster contains transactions related to a specific life event, allowing the system to retrieve only the relevant cluster instead of all client data. This segmentation enables selective data retrieval that maintains accuracy for requested events while reducing overall network bandwidth consumption.
Solution Approach 2:
The system retrieves only the necessary portion of data (partial action) by identifying and fetching only the cluster corresponding to the requested life event, rather than retrieving all client transactions. This partial retrieval approach reduces network bandwidth consumption while still providing complete and accurate data for the specific event being queried.
2Loss of information
If all client data is retrieved and processed, then complete information is provided to the client, but processor usage and memory requirements increase
Solution Approach 1:
By segmenting transactions into life-event-based clusters, the system can process only the relevant cluster data rather than all client transactions. This segmentation maintains information completeness for the requested event while significantly reducing processor usage and memory requirements by limiting the scope of data that needs to be loaded and processed.
Solution Approach 2:
The system extracts only the necessary data subset (the relevant life event cluster) from the complete client transaction database. This extraction approach ensures that all information needed for the specific event is retrieved while eliminating unnecessary data processing, thereby improving processing speed without sacrificing information completeness for the requested event.
3Measurement precision
If transaction data is organized by individual transactions, then detailed transaction information is maintained, but data retrieval efficiency decreases
Solution Approach 1:
The patent organizes transactions into segmented clusters based on life events, where each cluster contains all transactions related to a specific event. This segmentation maintains detailed transaction information within each cluster while enabling efficient retrieval by directly accessing only the relevant cluster identifier, thereby reducing data retrieval time without sacrificing transaction detail accuracy.
Data Source
AI summary
A data aggregator includes a memory, a network interface, a vectorization engine, and an aggregator. The memory stores a plurality of aggregation rules. The network interface retrieves a plurality of transactions that includes a first transaction and a second transaction. The vectorization engine determines a first transaction vector for the first transaction and a second transaction vector for the second transaction. The aggregator determines that the first transaction occurred during a first life event and that the second transaction occurred during a second life event. The aggregator also determines that the first life event and the second life event are the same and in response to that determination, assigns the first transaction and the second transaction to a cluster for the first life event. The aggregator further stores the first life event, the second life event, and an identifier for the cluster to the database.