Cross-Stream Event Correlation for Real-Time Distributed File Updates

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Solution Overview

Problem

Traditional computing architectures are not well-suited for updating distributed data files in real-time due to scalability limitations, particularly in single-threaded server architectures that process API calls sequentially, hindering efficient engagement with customers in dynamic computing environments.

Innovation Solution

A cross-stream data processor that facilitates real-time or near-real-time updates of distributed data files by correlating event data across multiple data streams, using a multi-stream event correlator, diffusivity index controller, and data compatibility controller to identify and integrate compatible data files.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If traditional single-threaded server architectures are used to process API calls, then system stability is maintained, but processing speed and scalability deteriorate due to sequential processing

Engineering Contradiction:

ImproveAPI call processing speedVSAvoidserver architecture complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The patent segments the monolithic server architecture into multiple microservices that can process API calls independently and concurrently. Each microservice handles specific data processing tasks, enabling parallel execution and improving overall processing speed while maintaining system stability through modular design.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces a new architectural dimension by implementing an event-driven architecture with message queues and asynchronous processing layers. This adds temporal and spatial dimensions to the processing model, allowing requests to be queued, processed in batches, and handled by multiple workers simultaneously, thereby overcoming the sequential processing limitation.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Productivity

If real-time data integration across multiple streams is implemented, then customer engagement optimization is improved, but computational resource requirements and system complexity increase

Engineering Contradiction:

Improvedata update efficiencyVSAvoiddata processing system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements preliminary action by pre-processing and filtering data streams before they reach the main integration engine. Data validation, normalization, and initial correlation are performed at the source or in intermediate buffering layers, reducing the computational burden on the real-time integration system and enabling faster processing with reduced complexity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces intermediary components such as event brokers, message queues, and data normalization layers that mediate between multiple data streams and the integration engine. These intermediaries standardize data formats, filter irrelevant events, and manage backpressure, thereby simplifying the core integration logic and improving overall system efficiency.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If distributed data files are updated in real-time across multiple data streams, then data relevance and customer engagement are improved, but system scalability is hindered by traditional architectures

Engineering Contradiction:

Improvedata consistencyVSAvoidsystem scalability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent implements a universal data integration platform that can handle multiple data stream types, formats, and sources through a common architecture. The system uses standardized interfaces, configurable data models, and polymorphic processing logic that can adapt to different data sources and update patterns, thereby achieving both data consistency and system scalability across diverse distributed environments.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent introduces dynamic characteristics by implementing configurable data flow patterns, adaptive query generation, and flexible update propagation mechanisms. The system can dynamically adjust processing priorities, select relevant data streams based on current context, and propagate updates only to affected data files, thereby maintaining consistency while scaling efficiently across distributed systems.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12572506B2Aggregating data to form generalized profiles based on archived event data and compatible distributed data files with which to integrate data across multiple data streams

Publication Date: 2026.03.10 SIGHTLY ENTERPRISES INC

AI summary

Various embodiments relate generally to data science and data analysis, computer software and systems, to provide a platform to facilitate updating compatible distributed data files, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate correlation of event data via analysis of electronic messages, including executable instructions and content, etc., via a cross-stream data processor application configured to, for example, update or modify one or more compatible distributed data files automatically. In some examples, a method may include activating APIs to receive via a message throughput data pipe different data streams, extracting features from data using the APIs, identifying event-related data across data sources, correlating the event-related data to form data representing an even, classifying event-related data into a state classification, determining compatible data at data sources, identifying compatible data, and transmitting integration data to integrate with a data source.