Distributed Data Orchestration With NLP for Low-Latency Edge Workflows

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Solution Overview

Problem

Conventional distributed network systems lack adaptability for real-time, heterogeneous data environments and struggle with efficient integration and processing of textual data across diverse modalities, leading to increased latency and suboptimal utilization of edge resources.

Innovation Solution

A system and method for real-time data orchestration and natural language processing that includes an orchestration engine to discover and unify data streams, perform adaptive metadata mapping, and determine contextual meaning and inter-node dependencies, enabling intelligent workflow orchestration across cloud, edge, and IoT environments.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional rule-based frameworks are used for data integration, then system stability is maintained, but adaptability to real-time heterogeneous data environments deteriorates

Engineering Contradiction:

Improveadaptability to heterogeneous data environmentsVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic orchestration that adapts to changing data environments in real-time. The system continuously discovers new data sources, adjusts metadata mappings dynamically, and reconfigures execution workflows based on contextual meaning and inter-node dependencies, transforming the static rule-based framework into a dynamic adaptive system

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary actions by pre-discovering data sources and establishing metadata schemas before actual data processing begins. The orchestration engine proactively identifies inter-node dependencies and contextual relationships in advance, enabling faster adaptation when heterogeneous data streams arrive

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If centralized NLP processing is implemented, then processing accuracy is improved, but network latency increases

Engineering Contradiction:

ImproveNLP processing accuracyVSAvoidnetwork latency

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the centralized NLP processing function into distributed processing units deployed across multiple nodes in the network. Each node can perform local NLP operations on its data streams independently, while the orchestration engine coordinates these distributed operations to achieve accurate contextual understanding without requiring all data to traverse the entire network

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The orchestration engine acts as an intermediary that coordinates between distributed processing nodes and the NLP engine. It manages the distribution of data processing tasks across nodes, aggregates results, and maintains contextual coherence, enabling accurate NLP processing while minimizing network latency through intelligent task routing

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If stream processing engines are integrated for distributed data processing, then processing speed is improved, but network latency increases

Engineering Contradiction:

Improvedata processing speedVSAvoidnetwork latency

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent enables local processing of data streams at distributed nodes rather than centralizing all processing. Each node processes its local data streams using the stream processing engine, performing metadata mapping and initial analysis locally, which maintains high processing speed while minimizing data transmission and reducing network latency

Inventive Principle:

Principle #3Local quality

4Productivity

If edge resources are utilized for data processing, then resource utilization is improved, but system complexity increases

Engineering Contradiction:

Improveedge resource utilizationVSAvoidorchestration complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The orchestration engine provides universal functionality that manages diverse edge resources through a unified interface. It handles discovery, metadata mapping, workflow orchestration, and NLP coordination across different types of edge devices and data sources, abstracting the complexity away from individual resource management while maximizing edge resource utilization

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260044370A1System and method for real-time data orchestration and natural language processing in a distributed network

Publication Date: 2026.02.12 KAZA PHANI ROHITHA

AI summary

A system and method for real-time data orchestration and natural language processing in a distributed network. The method includes discovering, by an orchestration engine, a plurality of data sources in the distributed network. The method includes receiving, at the orchestration engine, one or more data streams from the plurality data sources. The method includes generating a unified data representation from the received one or more data streams. The method includes determining contextual meaning and one or more inter-node dependencies. The method includes orchestrating execution workflows across the plurality of data sources. The method includes updating, the unified data representation and the execution workflows in response to changes in at least one of one or more network conditions, a computational load, or contextual semantics.