Distributed Intelligence Agent Parameterized Configuration
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Solution Overview
Problem
Existing IoT endpoints struggle to efficiently generate multiple results from a single snapshot of real-time data using different configurations of parameters, which can lead to resource inefficiencies in network communication devices.
Innovation Solution
An agent on an IoT endpoint processes both a baseline and a modified configuration of parameters to generate multiple results from a single snapshot of real-time data, allowing for different outcomes based on the same input data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an agent generates multiple results by processing the same snapshot of real-time data with different parameter configurations, then resource utilization efficiency improves, but computational complexity increases
Solution Approach 1:
The patent implements a single agent that performs multiple functions by processing the same snapshot of real-time data with different parameter configurations. This multi-functional approach allows the agent to generate multiple results (e.g., different statistics, alerts, or predictions) from one data processing operation, thereby improving resource utilization efficiency without requiring separate agents for each function.
Solution Approach 2:
The patent utilizes parameter changes as the core mechanism to generate diverse results from the same input data. By varying parameters such as time windows, threshold values, or algorithmic constants in the processing function, the agent produces different outputs (statistics, alerts, predictions) without reprocessing the raw data, thus reducing computational complexity while maintaining productivity.
2Adaptability or versatility
If multiple agents are deployed to handle different parameter configurations, then result diversity increases, but device resource consumption increases
Solution Approach 1:
The patent merges the functionality of multiple potential agents into a single agent that handles all parameter configurations. This consolidation reduces device resource consumption by eliminating redundant agent instances while maintaining result diversity through the use of multiple parameter sets within the unified agent architecture.
Solution Approach 2:
The single agent is designed with universal functionality to handle various parameter configurations and generate diverse results. This multi-functional design achieves the same adaptability as multiple specialized agents would provide, but with reduced resource consumption since only one agent instance needs to be maintained and executed.
3Measurement precision
If real-time data is reprocessed for each parameter configuration, then result accuracy improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by loading and preparing multiple parameter configurations in advance before actual data processing occurs. This pre-loading of parameters allows the agent to quickly switch between different configurations during execution without the overhead of loading parameters at runtime, thus maintaining result accuracy while reducing processing time.
Solution Approach 2:
The patent efficiently handles parameter changes by implementing a mechanism that allows rapid switching between different parameter configurations during the processing of a single data snapshot. This approach enables the system to generate multiple accurate results (different statistics, alerts, predictions) from the same data without reprocessing, significantly reducing processing time while preserving result accuracy.
Data Source
AI summary
An agent, of a distributed intelligence application, generates feature data for a feature. A baseline configuration of parameters is processed, associated with the distributed intelligence application, to determine a first set of parameters. The baseline configuration of parameters and a modified configuration of parameters are processed to determine a second set of parameters. The modified configuration is associated with the distributed intelligence application and indicates a difference from the baseline configuration. A snapshot of dynamic data is processed according to an algorithm using the first set of parameters to determine a first result and using the second set of parameters to determine a second result. The first result and the second result are each provided with a respective indication of the configuration used to generate the result.