Graph Traversal for Signal Attribute Detection in AI Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
There is a need for digital signal processors to perform AI communication generation in complex computing networks to determine whether an input signal has desired signal attributes.
Innovation Solution
An apparatus is provided for AI communication generation by traversing routes of a graph in a complex computing network. The apparatus includes a signal communication interface, a signal sensor, a memory, and a signal processor that determines signal attributes and generates communications to computing devices associated with input signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI communication generation is performed by traversing graph routes in complex computing networks, then the ability to determine desired signal attributes is improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex signal attribute determination process into modular graph traversal operations. The computing network is represented as a graph with nodes and edges, where each node represents a signal attribute and edges represent relationships between attributes. This segmentation allows the system to traverse and analyze signal attributes in a structured, manageable way, improving determination accuracy while organizing complexity into discrete, traversable units.
Solution Approach 2:
The patent introduces an intermediary graph structure that mediates between the input signal and the determination of desired signal attributes. The graph acts as an intermediate representation layer that captures relationships between signal attributes, allowing the system to infer unknown attributes through graph traversal and route analysis without directly complex processing of the original signal.
2Extent of automation
If graph traversal is used to determine signal attributes, then the intelligence of communication generation is improved, but the time required for processing increases
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing signal attributes into a graph structure before actual determination is needed. The graph is constructed with nodes representing signal attributes and edges representing relationships, allowing the system to perform efficient traversals during runtime. This preliminary organization enables faster intelligent communication generation by avoiding ad-hoc analysis during the determination phase.
Solution Approach 2:
The patent replaces traditional mechanical signal processing methods with an information-based graph traversal approach. Instead of direct signal manipulation and analysis, the system uses graph route traversal to infer signal attributes, substituting physical/mechanical processing with computational graph operations that are more efficient and scalable for intelligent communication generation.
3Loss of information
If multiple signal attributes are determined through graph routes, then the completeness of signal analysis is improved, but the quantity of computations increases
Solution Approach 1:
The patent extracts only the necessary signal attributes and relationships into the graph structure, focusing computation on relevant attributes rather than processing all possible signal characteristics. By taking out and isolating the specific signal attributes that are relevant to communication generation, the system achieves complete analysis of necessary attributes while reducing overall computation volume by excluding irrelevant information.
Solution Approach 2:
The patent creates a universal graph structure that can determine multiple signal attributes through a single traversal process. The graph is designed to capture relationships between various signal attributes (such as amplitude, frequency, phase, and other characteristics), allowing the system to infer multiple attributes simultaneously through route traversal, thereby achieving complete signal analysis without proportionally increasing computation for each individual attribute.
Data Source
AI summary
This disclosure is directed to communication generation by traversing routes of a graph in a complex computing network. The communication generation is used for determining whether certain input data has certain desired data attributes.