Patents by Inventor Nadav Rakocz

Nadav Rakocz 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: 20240169264
    Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for generating a prediction output comprising one or more actions by receiving data associated with encounters in a tuple form, tokenizing the encounters, training a causal transformer machine learning model configured to predict outcomes of actions by translating action tokens from the tokenized encounters into one or more embedding spaces, and training a causal transformer machine learning model to select the one or more actions based on embeddings from the one or more embedding spaces.
    Type: Application
    Filed: June 8, 2023
    Publication date: May 23, 2024
    Inventors: Dominik Roman Christian Dahlem, Vijay S. Nori, Eran Halperin, Nadav Rakocz
  • Publication number: 20240169267
    Abstract: Various embodiments of the present disclosure provide machine learning training techniques for training a model to improve upon traditional prediction models for various prediction domains. The techniques may include receiving training tuples for a training entity. A machine learning model may be used to generate a prediction output for the training entity based on the training tuples. A composite loss function may be used to generate a composite loss metric for the machine learning model that is based on (i) a first loss metric based on a comparison between the prediction output and a plurality of historical reward measures and (ii) a second loss metric based on a comparison between the prediction output and an imitation output corresponding to the prediction output. One or more model parameters of the first machine earning model may be modified based on the composite loss metric.
    Type: Application
    Filed: October 23, 2023
    Publication date: May 23, 2024
    Inventors: Nadav Rakocz, Dominik Roman Christian Dahlem, Eran Halperin
  • Publication number: 20230045859
    Abstract: Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by “tiling” each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.
    Type: Application
    Filed: January 25, 2021
    Publication date: February 16, 2023
    Applicants: The Regents of the University of California, Doheny Eye Institute
    Inventors: Eran Halperin, Nadav Rakocz, Jeffrey Chiang, Muneeswar Gupta, Srinivas Sadda
  • Publication number: 20220287648
    Abstract: Systems and methods for training a signal generation model and generating imputed physiological waveform signals in accordance with embodiments of the invention are illustrated. One embodiment includes a method for measuring physiological waveform signals. The method includes steps for receiving a set of one or more input physiological waveform signals, processing the set of input physiological waveform signals, generating an output physiological waveform signal using a signal generation model, and providing outputs based on the generated output signal.
    Type: Application
    Filed: August 19, 2020
    Publication date: September 15, 2022
    Applicant: The Regents of the University of California
    Inventors: Maxime Cannesson, Brian Hill, Eran Halperin, Ira Hofer, Nadav Rakocz