Time-Recurrent Pattern Identification in Resource Management
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Solution Overview
Problem
Conventional systems are inadequate in identifying and extracting recurring patterns from disparate digital records due to metadata discrepancies and differing formats, leading to inefficiencies in resource management operations across various corporate institutions.
Innovation Solution
A distributed computing platform that receives digital records from disparate systems, transforms them into visualizations, and applies anchor tags using DBSCAN and Hamming Distances to identify time-recurrent clusters, facilitating resource management operations by decoupling indexing from tagging constraints and enhancing performance through hierarchical filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems process digital records from disparate sources, then data can be collected and stored, but metadata discrepancies and differing formats obscure patterns and reduce identification accuracy
Solution Approach 1:
The patent introduces an intermediary processing layer that standardizes metadata from disparate sources before pattern identification. This intermediary layer transforms varying formats into a common structure, enabling accurate pattern detection without directly comparing heterogeneous raw data, thus resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The system changes the parameter representation of digital records by transforming metadata into a standardized format with consistent data types and structures. This parameter transformation enables reliable pattern matching across different sources while managing complexity through systematic standardization rather than ad-hoc handling.
2Reliability
If the system analyzes enormous and disparate data streams comprehensively, then patterns can be detected, but processing time and computational resources increase
Solution Approach 1:
The patent segments the analysis process into distinct phases: data ingestion, standardization, pattern identification, and validation. This segmentation allows the system to process enormous data streams comprehensively while managing time consumption through structured, modular processing that can be optimized at each stage independently.
Solution Approach 2:
The system performs preliminary standardization and preprocessing of digital records before pattern identification. By preparing data in advance with consistent metadata structures, the system ensures reliable pattern detection without requiring excessive processing time during the actual analysis phase.
3Adaptability or versatility
If the system accommodates different standards across corporate checking account types, then versatility is improved, but system complexity and difficulty of operation increase
Solution Approach 1:
The patent implements a universal metadata standard that can accommodate multiple corporate checking account types and formats. This universal structure enables the system to handle diverse standards through a single unified interface, improving versatility while maintaining ease of operation by eliminating the need for separate processing paths for different account types.
Data Source
AI summary
A system and method are described that receive digital records received from disparate computer systems wherein the records are heterogeneous in format and thus noisy. The systems utilize mapping to higher-dimensional vector spaces, clustering, reduction, and autocorrelation to identify and extract groups of related resource management operations of a time-recurrent nature from the noise of the system inputs.