Neural Network Language Model for Document Replication
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Document replication across a network is resource-intensive and inefficient, particularly due to excessive network traffic and bandwidth consumption, especially when using rule-based replication methods that are complex to manage and often lead to over-reaching, wasting resources.
Innovation Solution
A neural network language model is used to identify semantic relationships between file storage specifications during replication sessions, treating these specifications as 'words' to determine replication vectors based on proximity in time, allowing for the suggestion of additional replication requests and optimizing resource utilization by grouping related documents for simultaneous replication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If individual document replication is performed for each client, then each client receives accurate document updates, but network traffic and bandwidth consumption increase rapidly with large numbers of clients
Solution Approach 1:
The patent combines multiple individual document replication operations into a single bulk replication session. Instead of establishing separate network connections for each client-document pair, the system groups multiple replication requests into one session, transmitting multiple documents over a single network connection. This merging approach maintains replication accuracy while dramatically reducing network traffic and bandwidth consumption.
Solution Approach 2:
The patent creates a universal replication session that can handle multiple different document replication requests simultaneously. A single replication session serves multiple clients with different document replication needs, making the replication mechanism multi-functional. This allows the system to accommodate various replication requirements while using a unified communication channel, reducing overall network overhead.
2Ease of operation
If rule-based replication is used to automatically select documents for replication, then replication convenience is improved, but the complexity of managing rules increases and resources are wasted when rules over-reach
Solution Approach 1:
The patent enables the replication system to automatically determine which documents need replication by analyzing user actions and document relationships without requiring manual rule creation. The system self-adjusts replication selections based on observed patterns in user behavior and document usage, eliminating the need for complex manual rule management while maintaining automation benefits.
Solution Approach 2:
The patent implements a feedback mechanism where the system monitors user interactions with replicated documents and uses this information to refine future replication selections. By continuously learning from user behavior patterns, the system automatically optimizes which documents are replicated, reducing the need for manual rule configuration and preventing resource waste from inappropriate replication decisions.
3Measurement precision
If complex rules are created to handle specific replication situations, then replication precision is improved, but the difficulty of developing and maintaining rules increases
Solution Approach 1:
The patent replaces the manual mechanical process of creating and maintaining complex replication rules with an automated intelligent system. Instead of requiring users to manually develop, configure, and maintain intricate rule sets, the system uses algorithms to automatically analyze document relationships and user behavior, making precise replication selections without human intervention. This substitution eliminates the complexity of rule development while maintaining high precision.
Solution Approach 2:
The patent dynamically adjusts replication parameters based on observed user behavior and document characteristics rather than relying on fixed predetermined rules. The system modifies replication selections in real-time based on changing conditions, allowing it to achieve high precision without requiring complex static rule sets. This dynamic parameter adjustment replaces rigid rule-based logic with flexible adaptive decision-making.
Data Source
AI summary
Embodiments of the present invention are directed toward systems, methods, and computer storage media for using a neural network language model to identify semantic relationships between file storage specifications for replication requests. By treating file storage specifications (or at least a portion thereof) as “words” in the language model, replication vectors can be determined based on the file storage specifications. Instead of determining the relationship of the file storage specifications based on ordering within a document, the relationship can be based on proximity of the replication requests in a replication session. When a replication request is received from a user, the replication vectors can be used to determine a semantic similarity between the received replication request and one or more additional replication requests.