Sensor Data Filtering for ML Prompt-Based Robotic Alerts
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Solution Overview
Problem
Existing robotic systems face challenges in efficiently and dynamically performing actions based on sensor data, particularly in large environments with numerous entities, obstacles, and structures, leading to inefficiencies and potential errors in action definition and execution.
Innovation Solution
A system and method for dynamically generating prompts for machine learning models using sensor data filtering and input parameters, enabling dynamic action performance by decoupling actions from missions and utilizing a computing system to implement machine learning models for anomaly detection and alert generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is processed without filtering for machine learning model prompts, then all available data is utilized, but computational resource intensity and processing time increase significantly
Solution Approach 1:
The patent segments sensor data processing by filtering data based on relevance to specific actions. The system divides the processing pipeline into filtering operations that select only pertinent sensor data portions, reducing the volume of data fed to machine learning models while maintaining action accuracy.
Solution Approach 2:
The patent applies local quality by tailoring the filtering process to specific action contexts. Different filter parameters are applied depending on the action being performed, ensuring that only locally relevant sensor data (specific to each action type) is processed, thereby reducing overall computational load while preserving necessary precision.
2Reliability
If sensor data is filtered based on multiple parameters, then data relevance to actions is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic filtering where filter parameters are adjusted based on the current action context and robot state. The filtering system adapts its parameters dynamically rather than using fixed filters, allowing the system to maintain high reliability for different actions without requiring a complex static filtering architecture for every possible scenario.
Solution Approach 2:
The patent creates a universal filtering framework that handles multiple action types through a single adaptable system. The same filtering infrastructure serves diverse action contexts by adjusting parameters, eliminating the need for separate dedicated filtering systems for each action type and thereby reducing overall system complexity.
3Adaptability or versatility
If actions are decoupled from missions and dynamically generated, then system adaptability improves, but control and coordination difficulty increases
Solution Approach 1:
The patent prepares for dynamic action generation by pre-defining action templates and filter parameter configurations. This preliminary preparation allows the system to quickly assemble appropriate actions from pre-configured components rather than creating everything from scratch, reducing the complexity of real-time control while maintaining high adaptability.
Solution Approach 2:
The patent introduces an intermediary layer between missions and execution that handles the coordination of dynamically generated actions. This intermediary management system abstracts the complexity of action coordination, allowing high-level mission planning while delegating detailed action coordination to a specialized intermediate layer.
Data Source
AI summary
Systems and methods are described for provision of alerts. A system can obtain sensor data associated with an environment. The system can obtain an input indicating one or more filter parameters, one or more requests, and/or one or more alert parameters. For example, the input may indicate a request, a region of the environment, a region of sensor data, etc. The system can filter the sensor data based on the one or more filter parameters to obtain a filtered portion of the sensor data. The system may generate a prompt for a machine learning model that includes the filtered portion of the sensor data, the one or more requests, and the one or more alert parameters. The system can provide the prompt to a computing system. The system can obtain a output from the computing system and can provide an alert based on the output.