Palantir technologies inc. (20240354646). OPERATIONALISING FEEDBACK LOOPS simplified abstract

OPERATIONALISING FEEDBACK LOOPS

Organization Name

palantir technologies inc.

Inventor(s)

Alexander Edwards of London (GB)

Anirvan Mukherjee of Brooklyn NY (US)

David Kebudi of New York NY (US)

Megha Arora of London (GB)

Sriram Krishnan of Jersey City NJ (US)

Max-Philipp Schrader of Germering (DE)

Jessica Winssinger of New York NY (US)

Philipp Hoefer of Munich (DE)

OPERATIONALISING FEEDBACK LOOPS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240354646 titled 'OPERATIONALISING FEEDBACK LOOPS

The abstract describes a method of providing feedback to a machine learning model, allowing a user to observe the model's output, input feedback based on the output, and incorporate the feedback to improve the model's performance.

• User observes output of a trained machine learning model
• User provides feedback on model or training dataset level
• Feedback is incorporated to enhance the machine learning model
• Method is performed using one or more processors
• Computer-readable storage media contain executable instructions for the method
• Computer system includes processors and storage media for executing the method

Potential Applications: - Enhancing the accuracy and efficiency of machine learning models - Improving the performance of AI systems in various industries such as healthcare, finance, and e-commerce

Problems Solved: - Addressing the need for continuous improvement and optimization of machine learning models - Providing a mechanism for users to provide feedback and enhance the model's capabilities

Benefits: - Increased accuracy and reliability of machine learning predictions - Enhanced user experience through feedback incorporation - Continuous learning and adaptation of the model based on real-world feedback

Commercial Applications: - AI-driven customer service platforms - Predictive maintenance systems in manufacturing - Personalized recommendation engines in e-commerce

Questions about Machine Learning Model Feedback: 1. How does the incorporation of user feedback improve the performance of machine learning models? 2. What are the potential challenges in implementing user feedback mechanisms in machine learning systems?

Frequently Updated Research: - Stay informed about the latest advancements in machine learning model feedback mechanisms - Explore new techniques for incorporating user feedback to enhance AI systems

Original Abstract Submitted

disclosed herein is a method of providing feedback to a machine learning model. the method includes allowing a user to observe an output of a trained machine learning model; allowing the user to input feedback to the machine learning model based on the output, wherein the feedback is on at least one of a model level or on a training dataset level; and incorporating the feedback into the machine learning model to improve the machine learning model, wherein the method is performed using one or more processors. disclosed herein are one or more computer-readable storage media including computer executable instructions which when executed by the one or more processors cause the one or more processors to perform the method. disclosed herein is a computer system which includes one or more processors and one or more computer-readable storage media which include computer executable instructions which when executed by the one or more processors cause the one or more processors to perform the method.