Contextual Data Stream Metadata Encapsulation via Probability Vectors
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Solution Overview
Problem
Existing systems for receiving sensor data from computing devices lack efficient methods to convey metadata, often transmitting large narrative descriptions instead of concise probability vectors, which can lead to data inefficiencies and increased processing loads.
Innovation Solution
The system defines metadata as a probability vector, combining sensor data with context-specific probability values to create a contextual data stream, allowing for efficient transmission and processing by converting narrative descriptions into numerical probability ranges, such as 0 to 100 percentages, indicating the likelihood of specific operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If narrative descriptions are used to transmit metadata, then comprehensive context information is provided, but data transmission size increases and processing load increases
Solution Approach 1:
The patent transforms metadata from narrative descriptions to structured parameter representations (probability vectors with numerical values ranging from 0 to 1). This parameter transformation maintains the semantic meaning of context information while significantly reducing data size and enabling efficient computational processing.
Solution Approach 2:
The patent creates a simplified numerical copy (probability vector) that represents the essential meaning of complex narrative metadata. Instead of transmitting full narrative descriptions, the system transmits condensed numerical representations that can be efficiently processed while preserving the core contextual information.
2Loss of information
If narrative descriptions are used to transmit metadata, then comprehensive context information is provided, but processing efficiency decreases
Solution Approach 1:
The patent converts narrative metadata into structured numerical parameters (probability vectors), enabling efficient computational operations. This transformation allows processors to quickly compare, analyze, and utilize context information without the overhead of parsing and interpreting natural language descriptions.
3Quantity of substance
If probability vectors are used to represent metadata, then data transmission size is reduced, but narrative context descriptions are lost
Solution Approach 1:
The patent creates a numerical copy (probability vector) that captures the essential meaning of narrative context. The probability values preserve the semantic information about operating conditions, sensor reliability, and environmental context in a compact format that maintains information integrity while reducing size.
4Productivity
If probability vectors are used to represent metadata, then processing efficiency is enhanced, but detailed narrative metadata is eliminated
Solution Approach 1:
The patent transforms detailed narrative metadata into structured numerical parameters that are optimized for processing. The probability vectors maintain the essential informational content while being computationally efficient to handle, enabling faster data analysis and decision-making processes.
Data Source
AI summary
A method for encapsulating metadata in a contextual data stream is described. The method may include receiving sensor data from a sensor. The method may include receiving metadata associated with the sensor data, wherein the metadata is defined as a probability vector based on a context set associated with the sensor data. The method may also include combining the sensor data and the metadata into a contextual data stream.