View source for 18744278. METHODS AND APPARATUS FOR DATA-EFFICIENT CONTINUAL ADAPTATION TO POST-DEPLOYMENT NOVELTIES FOR AUTONOMOUS SYSTEMS simplified abstract (Intel Corporation)

=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 18744278 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 generates one or more scores associated with post-deployment data drift by identifying samples with the out-of-distribution data.
* Post-deployment data is classified based on the generated scores.

Potential Applications:
- This technology can be used in various industries such as finance, healthcare, and manufacturing to monitor data drift post-deployment.
- It can help in ensuring the accuracy and reliability of machine learning models in real-world applications.

Problems Solved:
- Addresses the challenge of detecting data drift in neural network models after deployment.
- Helps in maintaining the performance and effectiveness of machine learning systems over time.

Benefits:
- Improves the overall performance and reliability of neural network models.
- Enables proactive monitoring and management of data drift issues in deployed machine learning systems.

Commercial Applications:
Title: "Advanced Data Drift Detection Technology for Machine Learning Systems"
This technology can be commercially used in industries where accurate and reliable machine learning models are crucial, such as financial institutions, healthcare organizations, and manufacturing companies. It can help in ensuring the continued effectiveness of machine learning systems post-deployment, leading to improved decision-making and operational efficiency.

Questions about Data Drift Detection Technology:
1. How does this technology contribute to the long-term success of machine learning systems?
- This technology helps in proactively identifying and addressing data drift issues in neural network models, ensuring their continued accuracy and reliability over time.

2. What are the potential implications of not detecting data drift in machine learning systems?
- Failure to detect data drift can lead to inaccurate predictions and decisions, impacting the overall performance and effectiveness of machine learning applications.


==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]]