Intel corporation (20240338563). METHODS AND APPARATUS FOR DATA-EFFICIENT CONTINUAL ADAPTATION TO POST-DEPLOYMENT NOVELTIES FOR AUTONOMOUS SYSTEMS simplified abstract
METHODS AND APPARATUS FOR DATA-EFFICIENT CONTINUAL ADAPTATION TO POST-DEPLOYMENT NOVELTIES FOR AUTONOMOUS SYSTEMS
Organization Name
Inventor(s)
Amanda Sofie Rios of Los Angeles CA (US)
Nilesh Ahuja of Cupertino CA (US)
Ibrahima Jacques Ndiour of Chandler AZ (US)
Ergin Utku Genc of Portland OR (US)
Omesh Tickoo of Portland OR (US)
METHODS AND APPARATUS FOR DATA-EFFICIENT CONTINUAL ADAPTATION TO POST-DEPLOYMENT NOVELTIES FOR AUTONOMOUS SYSTEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338563 titled 'METHODS AND APPARATUS FOR DATA-EFFICIENT CONTINUAL ADAPTATION TO POST-DEPLOYMENT NOVELTIES FOR AUTONOMOUS SYSTEMS
The abstract of the patent application describes an apparatus that utilizes interface circuitry, machine-readable instructions, and at least one processor circuit to extract neural network model features from deployment data, identify out-of-distribution data, generate scores associated with post-deployment data drift, and classify post-deployment data based on these scores.
- Interface circuitry, machine-readable instructions, and processor circuit work together to extract neural network model features from deployment data.
- The apparatus can identify out-of-distribution data based on the extracted neural network model features.
- It can generate one or more scores associated with post-deployment data drift by identifying samples with the out-of-distribution data.
- The apparatus is capable of classifying post-deployment data based on the generated scores.
Potential Applications: - Anomaly detection in machine learning models - Quality control in production environments - Monitoring data drift in real-time systems
Problems Solved: - Detecting out-of-distribution data in neural network models - Addressing post-deployment data drift issues - Improving the reliability and accuracy of machine learning systems
Benefits: - Enhanced model performance and accuracy - Early detection of data drift and anomalies - Improved decision-making based on classified data
Commercial Applications: Title: "Real-time Data Drift Detection Apparatus for Machine Learning Models" This technology can be used in industries such as finance, healthcare, and e-commerce to ensure the integrity and reliability of machine learning models in production environments. It can help companies maintain high-quality standards and make informed decisions based on accurate data analysis.
Questions about the technology: 1. How does the apparatus differentiate between in-distribution and out-of-distribution data? 2. What are the potential implications of misclassifying post-deployment data in machine learning models?
Original Abstract Submitted
an example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to extract neural network model features from deployment data, identify out-of-distribution data based on the neural network model features, identify samples with the out-of-distribution data to generate one or more scores associated with post-deployment data drift, and classify post-deployment data based on the one or more scores.
