1. Improved Real-Time Performance: The GRU-driven framework achieves superior real-time performance for sequential tasks in distributed computer vision compared to traditional methods.

2. Enhanced Fault Tolerance: The incorporation of agreement mechanisms ensures system reliability and data consistency even in the presence of node or network failures.

3. Scalability: The framework scales efficiently across multiple nodes, maintaining high accuracy and low latency as the system size increases.

Description:Title:
GRU-Driven Deep Learning Framework for Distributed Computer Vision with Fault-Tolerant Agreement Mechanisms
Field of the Invention

[0001] The present invention is related to the distributed systems in computer science and machine learning field.

Background

[0002] As computer vision applications scale in complexity, distributed frameworks are required to process and analyze large volumes of visual data across multiple interconnected devices. This poses challenges like data synchronization, latency, and system failures.
[0003] Gated Recurrent Units (GRUs) are efficient neural network architectures for processing sequential data. They excel in applications requiring temporal understanding and offer computational efficiency compared to LSTMs, making them suitable for real-time distributed systems.
[0004] In distributed environments, ensuring reliability is critical. Fault-tolerant agreement mechanisms help maintain system consistency and performance despite node failures or communication issues.
[0005] Sequential vision tasks, such as video analysis and object tracking, require the system to understand temporal dependencies in data. Distributed systems add complexity due to asynchronous processing and potential inconsistencies across nodes, necessitating advanced models like GRUs and reliable synchronization mechanisms.
[0006] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0007] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0008] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0009] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[0010] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

Objects of the Invention

[0011] Develop a GRU-Driven Framework: Design a distributed computer vision framework utilizing GRUs to enhance the real-time processing and analysis of sequential visual data across multiple nodes.
[0012]. Incorporate Fault-Tolerant Mechanisms: Integrate robust agreement protocols to ensure reliability and resilience in distributed systems, even under adverse conditions like node or communication failures.

 
Drawings

Figure 1

Brief Description of the Drawing

[0013] The figure 1 represents working model in the present invention with its prototype.
 
Detailed Description:

[0014] In figure 1, showing the input parameter; which is to be processed by the system 100.’
[0015] Develop and optimize GRU-based models for sequential visual data processing, such as object tracking and video analysis.
[0016] Create a distributed system architecture with interconnected nodes for parallel processing of visual data.

[0017] Implement efficient data partitioning strategies to divide and allocate visual datasets across nodes dynamically.
[0018] Develop robust communication protocols to minimize latency and ensure smooth data transfer between nodes.
[0019] Implement consensus algorithms (e.g., Paxos or Raft) to ensure reliability and agreement on shared data across nodes.
[0020] Use model pruning and quantization techniques to optimize GRU performance for resource-constrained nodes.
[0021] Evaluate the framework under various scenarios, such as node failures and increased data loads, to test fault tolerance and scalability.
[0022] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbours, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[0023] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0024] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
, Claims:We Claim:

1. Improved Real-Time Performance: The GRU-driven framework achieves superior real-time performance for sequential tasks in distributed computer vision compared to traditional methods.
2. Enhanced Fault Tolerance: The incorporation of agreement mechanisms ensures system reliability and data consistency even in the presence of node or network failures.
3. Scalability: The framework scales efficiently across multiple nodes, maintaining high accuracy and low latency as the system size increases.