Cross-Stream Event Processing for Real-Time Distributed Data Updates

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Traditional computing architectures are not well-suited for updating distributed data files in real-time, especially in dynamic environments, and fail to optimize engagement with users across various communication channels and data networks, lacking scalability and relevance to user classifications.

Innovation Solution

A cross-stream data processor that analyzes event data across multiple data streams to identify compatible distributed data files, facilitating real-time updates and integration of executable instructions and content using a publish-subscribe messaging service, machine learning algorithms, and natural language processing to determine compatibility and diffusivity of events.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If traditional server architectures are used to update distributed data files, then code deployment and maintenance is simplified, but scalability and real-time performance are hindered

Engineering Contradiction:

Improvecode deployment and maintenanceVSAvoidreal-time data update capability

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The patent replaces traditional mechanical server-based code deployment with an automated system that uses machine learning algorithms to generate, validate, and deploy persona classification data and integration data to distributed data sources. This substitution eliminates the need for manual code deployment while enabling real-time updates through automated pipelines that process events and immediately propagate changes across the distributed system.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If single threaded server architectures are used, then system simplicity is maintained, but API processing speed and scalability are reduced

Engineering Contradiction:

Improveserver architecture simplicityVSAvoidAPI processing speed

Core Design Contradiction:

Device complexityVSSpeed

Solution Approach 1:

The patent segments the server architecture into multiple independent threads that process API calls concurrently. Each thread handles specific tasks such as event processing, persona classification, integration data generation, and data source updates independently. This segmentation enables parallel processing of API requests, significantly improving processing speed while maintaining manageable system complexity through modular thread design.

Inventive Principle:

Principle #1Segmentation

3Stability of the object's composition

If traditional computing architectures are used, then infrastructure stability is maintained, but real-time engagement optimization with users is insufficient

Engineering Contradiction:

Improveinfrastructure stabilityVSAvoidreal-time engagement optimization

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The patent introduces dynamic elements to the stable infrastructure by implementing real-time event-driven processing. The system dynamically generates persona classification data based on user events, automatically updates integration data in distributed data sources, and adapts content delivery in real-time. This dynamic capability allows the system to optimize user engagement by responding to current events while the underlying infrastructure remains stable through standardized data formats and automated pipelines.

Inventive Principle:

Principle #15Dynamics

4Productivity

If automated persona classification and integration data generation is implemented, then real-time data updating is enabled, but system complexity increases

Engineering Contradiction:

Improvereal-time data update capabilityVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements self-service automation where the system automatically generates persona classification data from user events, validates integration data compatibility, and deploys updates to distributed data sources without human intervention. The machine learning algorithms automatically learn from event patterns and refine classifications over time. This self-service capability enables real-time data updating while managing complexity through automated decision-making processes that eliminate manual configuration and reduce operational overhead.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12619646B2Automating generation of persona classification data to customize integration data into compatible distributed data sources at various networked computing devices

Publication Date: 2026.05.05 SIGHTLY ENTERPRISES INC

AI summary

Various embodiments relate generally to data science and data analysis, computer software and systems, and control 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. Further, a computing platform is configured to receive inputs as natural language to facilitate automatic generation and integration to form a modified distributed file responsive to events, or moments, among other things including data relevant to an entity, which may provide a good or service.