AI-Driven Document Processing Latency Optimization
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Solution Overview
Problem
Existing software services face challenges in providing real-time dynamic performance enhancement for document processing systems, particularly in managing latency and resource usage effectively.
Innovation Solution
A document processing system equipped with a machine learning model configured with nodes for evaluating compression, processor usage, storage usage, and memory latency, interactingively processing states for regression and gradient-boosted decisions to optimize resource allocation and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional document processing systems are used, then system simplicity is maintained, but real-time performance and latency reduction are insufficient
Solution Approach 1:
The system is segmented into multiple independent nodes (data process node, processor usage node, storage usage node, memory usage node) that can evaluate different aspects of system performance separately. This segmentation allows parallel processing and real-time evaluation without requiring complete system redesign, thus improving speed while managing complexity.
Solution Approach 2:
The patent implements dynamic adaptation by allowing nodes to communicate states and adjust their behavior based on real-time system conditions. The machine learning model dynamically processes states from multiple nodes and adjusts document service performance accordingly, enabling real-time optimization without static configuration limitations.
2Productivity
If resource usage is increased to improve processing capability, then productivity increases, but resource consumption and costs increase
Solution Approach 1:
The system implements feedback mechanisms where nodes evaluate their own resource usage states and communicate this information to the machine learning model. The model uses this feedback to optimize resource allocation, adjusting processing capabilities based on actual resource consumption patterns rather than static over-provisioning, thus improving productivity while controlling resource usage.
Solution Approach 2:
The patent changes operational parameters dynamically by allowing the machine learning model to adjust processing states based on real-time resource availability. Instead of fixed resource allocation, the system modifies processing parameters (such as compression algorithms, memory usage patterns) based on current resource states, optimizing the balance between productivity and resource consumption.
3Loss of time
If system memory latency is reduced through hardware improvements, then speed improves, but device complexity and cost increase
Solution Approach 1:
The patent introduces a machine learning model as an intermediary layer between the hardware and document services. This intermediary optimizes memory access patterns and data flow through intelligent decision-making based on system states, reducing effective latency without requiring hardware modifications. The ML model acts as a software-based mediator that achieves latency reduction through algorithmic optimization rather than hardware changes.
Data Source
AI summary
Apparatuses and methods relate generally to adaptation. In an apparatus, a document processing system is programmed with document services. A network interface receives user requests for the document services. A machine learning model has nodes and edges and allows the nodes to communicate with one another. A data process node evaluates compression and decompression responsive to first states from the nodes. A processor usage node evaluates processor unit usage of the processor units responsive to second states from the nodes. A storage usage node evaluates storage usage of the data storage responsive to third states from the nodes. A memory usage node evaluates latency response of the system memory responsive to fourth states from the nodes. The machine learning model combines regression models and a distributed gradient-boosting framework to interactively process the states to analyze user requested data to reduce the latency response of the document processing system.