Daniel Lidral-Porter

Title: Daniel Lidral-Porter: Inventor in Machine Learning Data Processing.

Introduction

Daniel Lidral-Porter is an inventor based in Seattle, Washington. His patent work concerns machine learning methods for assessing uncertainty in labeled data and deciding when that data may need to be relabeled.

Latest Patents

The provided information lists one patent associated with Lidral-Porter: “Processing Labeled Data in a Machine Learning Operation.” The invention describes systems, methods, and software for evaluating whether labeled data should be relabeled. It processes each data item with multiple machine learning models, compares their classification results, and calculates a label uncertainty score using an average entropy score and an adjustment score. The resulting scores help determine which items may warrant relabeling. Lidral-Porter is credited with 1 patent.

Career Highlights

Lidral-Porter is associated with Cylance Inc. The patent reflects work on improving the evaluation and quality of labeled data used in machine learning operations.

Collaborations

The provided information identifies Tian Chen and John Brock as some of Lidral-Porter’s coworkers at Cylance Inc.

Conclusion

Daniel Lidral-Porter’s listed invention focuses on using multiple machine learning models to identify uncertainty in labeled data and guide potential relabeling.