Patents by Inventor Aaron WISE

Aaron WISE has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20230375697
    Abstract: A method includes: capturing, via a navigational sensor of a mobile automation apparatus, three-dimensional point cloud data depicting a portion of an aisle containing a support structure, the support structure having a forward plane facing into the aisle; generating, from the point cloud data, a two-dimensional projection in a facility coordinate system; retrieving, from a stored map, an expected location of the forward plane of the support structure in the facility coordinate system; selecting, from the projection, a subset of regions satisfying a positional criterion relative to the location of the forward plane; determining, based on the selected subset of regions from the projection, an actual location of the forward plane of the support structure in the facility coordinate system; and providing the actual location of the forward plane to a navigational controller of the mobile automation apparatus.
    Type: Application
    Filed: November 11, 2022
    Publication date: November 23, 2023
    Inventors: Aaron Hoy, Derek King, Steffen Fuchs, Vishnu Sudheer Menon, David Dymesich, Robert Isaiah Chatman III, Jenna Guergah, Brian Richard Cairi, Melonee Wise, Alison Cheng, Katrin Beauchaud
  • Publication number: 20230245724
    Abstract: Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.
    Type: Application
    Filed: December 3, 2018
    Publication date: August 3, 2023
    Applicant: ILLUMINA, INC.
    Inventors: Shile Zhang, Mengchi Wang, Aaron Wise, Han Kang, Vitor Ferreira Onuchic, Kristina Kruglyak
  • Publication number: 20230220589
    Abstract: The present disclosure relates to a binder that specifically binds to an N-terminally modified polypeptide through interaction with a modified N-terminal amino acid. Also provided herein is a method and related kits for treating a polypeptide using or comprising the binder and/or modified cleavase. In some embodiments, also provided herein is a method and related kits for transferring information using a plurality of enzymes, including for performing a ligation, extension, and cleavage reaction with nucleic acid molecules associated with the polypeptide for analysis.
    Type: Application
    Filed: September 29, 2021
    Publication date: July 13, 2023
    Applicant: Encodia, Inc.
    Inventors: Kevin L. GUNDERSON, Soumya GANGULY, Robert C. JAMES, Kenneth KUHN, Zachary MILES, Lei SHI, Stephen VERESPY, III, Aaron WISE, Zongxiang ZHOU
  • Publication number: 20220283175
    Abstract: The present disclosure relates to a metalloprotein binder that specifically binds to a N-terminally modified peptide. Also provided herein is a method and related kits for treating or analyzing a peptide using the metalloprotein binder and/or modified cleavase. In some embodiments, the method provided herein comprises binding metalloprotein binder-coding tag conjugates to a modified N-terminal amino acid residue of an immobilized peptide associated with a recording tag, transferring identifying information from the coding tag to the recording tag using a ligation or primer extension, and cleaving the modified N-terminal amino acid residue. The method and metalloprotein binders provided herein are useful for de novo peptide identification or sequencing.
    Type: Application
    Filed: April 22, 2022
    Publication date: September 8, 2022
    Applicant: Encodia, Inc.
    Inventors: Eric OKERBERG, Stephen VERESPY, III, Jason C. KLIMA, Soumya GANGULY, Zachary MILES, Jason Duarte JACINTHO, Aaron WISE
  • Publication number: 20210254170
    Abstract: A potential prognostic biomarker is based on a combination of a composite score generated by sequence analysis of global human endogenous retrovirus (hERV)/retro-transposon transactivation and a cell signature generated using deconvolution of immune cells within a tumor sample for predicting the efficacy of chemotherapeutic agents and immune checkpoint inhibitors. Correlation analysis of the composite score with cell signature within a tumor sample enables survival analysis in individuals receiving chemotherapeutic agents and immune checkpoint inhibitors.
    Type: Application
    Filed: February 10, 2021
    Publication date: August 19, 2021
    Inventors: Mahdi Golkaram, Shile Zhang, Li Liu, Aaron Wise, Joyee Yao, Shannon Kaplan, Alex So, Michael Salmans, Raakhee Vijayaraghavan
  • Publication number: 20200176083
    Abstract: Provided is a computer-implemented method, including inputting to a trained machine learning classifier genomic information of a non-training subject that includes features from a tumor sample, wherein the trained machine learning classifier trained on features of tumor samples obtained from training subjects and their a responsiveness to checkpoint inhibition treatment and the machine-learning classifier is trained to predict responsiveness to the treatment, and generating a checkpoint inhibition responsiveness classification predictive of the subject's responding to the checkpoint inhibition with the trained machine-learning classifier, and reporting the checkpoint inhibition responsiveness classification using a graphical user interface. Also provided are a computer system for performing the method and a machine learning classifier trained by the method.
    Type: Application
    Filed: December 3, 2018
    Publication date: June 4, 2020
    Applicant: ILLUMINA, INC.
    Inventors: Shile Zhang, Mengchi Wang, Aaron Wise, Han Kang, Vitor Ferreira Onuchic, Kristina Kruglyak
  • Publication number: 20190318806
    Abstract: We introduce a variant classifier that uses trained deep neural networks to predict whether a given variant is somatic or germline. Our model has two deep neural networks: a convolutional neural network (CNN) and a fully-connected neural network (FCNN), and two inputs: a DNA sequence with a variant and a set of metadata features correlated with the variant. The metadata features represent the variant's mutation characteristics, read mapping statistics, and occurrence frequency. The CNN processes the DNA sequence and produces an intermediate convolved feature. A feature sequence is derived by concatenating the metadata features with the intermediate convolved feature. The FCNN processes the feature sequence and produces probabilities for the variant being somatic, germline, or noise. A transfer learning strategy is used to train the model on two mutation datasets. Results establish advantages and superiority of our model over traditional classifiers.
    Type: Application
    Filed: April 12, 2019
    Publication date: October 17, 2019
    Applicant: Illumina, Inc.
    Inventors: Aaron WISE, Kristina M. KRUGLYAK