View source for 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==

[[:Category:intel corporation|intel corporation]]


[[Category:intel corporation]]

==Inventor(s)==

[[:Category:Amanda Sofie Rios of Los Angeles CA (US)|Amanda Sofie Rios of Los Angeles CA (US)]][[Category:Amanda Sofie Rios of Los Angeles CA (US)]]

[[:Category:Nilesh Ahuja of Cupertino CA (US)|Nilesh Ahuja of Cupertino CA (US)]][[Category:Nilesh Ahuja of Cupertino CA (US)]]

[[:Category:Ibrahima Jacques Ndiour of Chandler AZ (US)|Ibrahima Jacques Ndiour of Chandler AZ (US)]][[Category:Ibrahima Jacques Ndiour of Chandler AZ (US)]]

[[:Category:Ergin Utku Genc of Portland OR (US)|Ergin Utku Genc of Portland OR (US)]][[Category:Ergin Utku Genc of Portland OR (US)]]

[[:Category:Omesh Tickoo of Portland OR (US)|Omesh Tickoo of Portland OR (US)]][[Category: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.



[[Category:G06N3/08]]
[[Category:CPC_G06N3/08]]