SYSTEMS AND METHODS FOR PROCESS OPTIMIZATION USING ADVANCED COMPUTATIONAL MODELS FOR DATA ANALYSIS AND AUTOMATED PROCESSING

Smart overview of the Invention

The patent application outlines systems, methods, and computer program products designed for optimizing processes through advanced computational models. These systems collect and analyze metadata from nodes that process data, utilizing a deep learning neural network known as an instantaneous process identifier (IPI). The IPI is trained with this metadata to classify and determine the necessary resources for data processing. This approach aims to address inefficiencies in resource allocation associated with traditional data processing methods.

Metadata Collection and Analysis

Metadata is gathered from nodes, including real-time parameters such as creation time, modification time, and access time of the data. The IPI uses this data to classify and assess the resources required for processing. The classification procedure involves determining the optimal processing node based on its availability and capabilities. This ensures efficient resource allocation, which is instance-based and guided by the IPI's analysis.

Training the Instantaneous Process Identifier

The IPI is trained by comparing current metadata with historical data to predict resource needs. This training involves transforming input data into vector data using distributed hash technology. The system also implements response triggers that set objectives for processing tasks, refining these objectives through feedback loops to balance immediate and long-term benefits.

Dynamic Resource Allocation

Resource allocation is dynamically configured in real time, guided by accuracy tests that evaluate the effectiveness of the allocation based on metadata and historical data. This dynamic adjustment ensures that resources are utilized efficiently, adapting to the real-time demands of the data processing tasks.

Applications and Implications

The described system is applicable to any entity handling large data volumes, such as financial institutions or technology companies. Users, including IT specialists and operations analysts, interact with the system through user interfaces that facilitate command input and data output. This technology aims to enhance the efficiency and accuracy of data processing, addressing existing challenges in resource management.