Patents by Inventor Eran Halperin

Eran Halperin 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: 20240169185
    Abstract: Embodiments of the present disclosure provide for improved data processing using interconnected variational autoencoder models, which may be used for any of a myriad of purposes. Some embodiments specially train the interconnected variational autoencoder models by utilizing different training scenarios corresponding to presence and/or absence of particular data in a training data set. Particular encoder(s) and/or decoder(s) from the specially trained interconnected variational autoencoder models may then be utilized to improve accuracy of the desired data processing tasks, for example, to generate particular output data.
    Type: Application
    Filed: August 9, 2023
    Publication date: May 23, 2024
    Inventors: Sanjit S. BATRA, Robert E. TILLMAN, Brian Lawrence Hill, Eran HALPERIN, Josue Ramon NASSAR
  • 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: 20240170160
    Abstract: Embodiments provide for application of personalized or individualized sensor-based risk profiles for impacts of external events. An example method includes receiving sensor data from one or more sensors couplable with a subject body of a subject population comprising a plurality of subject bodies; receiving external factor data associated with the subject population; generating a population-level external event impact metric, where the population-level external event impact metric represents a predicted impact of one or more external events on a physiological or other metric of the subject population; generating a subject-level external impact metric, where the subject-level external event impact metric represents a predicted impact of the one or more external events on the physiological or other metric associated with the subject body; and initiating the performance of one or more prediction-based actions based on the subject-level external event impact metric.
    Type: Application
    Filed: June 20, 2023
    Publication date: May 23, 2024
    Inventors: Gregory D. Lyng, Brian Lawrence Hill, James Zou, Kimmo M. Karkkainen, Kailas Vodrahalli, Eran Halperin
  • 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: 20240153605
    Abstract: Methods, apparatuses, systems, computing devices, and/or the like are provided. An example method may generate a plurality of multidimensional patient-drug tensors based at least in part on a plurality of patient record data objects and a plurality of combined drug input vectors, generate an interaction-attentive prediction data object based at least in part on the plurality of multidimensional patient-drug tensors and at least one interaction-attentive machine learning model, generate an interaction-inattentive prediction data object based at least in part on the plurality of patient record data objects and an interaction-inattentive machine learning model, determine a drug combination indicator based at least in part on comparing the interaction-attentive prediction data object and the interaction-inattentive prediction data object, generate a predicted multi-drug contraindication data object based at least in part on the drug combination indicator, and perform one or more prediction-based actions.
    Type: Application
    Filed: November 3, 2022
    Publication date: May 9, 2024
    Inventors: Eran Halperin, Brian Hill, George Austin
  • Publication number: 20240095583
    Abstract: Various embodiments of the present disclosure disclose a machine learning training approach for intelligently training a plurality of machine learning models associated with a multitask environment. The techniques include jointly training the plurality of machine learning models based on task similarities by generating a similarity matrix corresponding to a plurality machine learning models, generating a sharing loss value for the at least two machine learning models, generating, using a loss function and a training dataset, a prediction loss value for a particular machine learning model of the at least two machine learning models, generating an aggregated loss value for the particular machine learning model based on the similarity matrix, the sharing loss value, and the prediction loss value, and updating the particular machine learning model based on the aggregated loss value for the particular machine learning model.
    Type: Application
    Filed: January 17, 2023
    Publication date: March 21, 2024
    Inventors: George AUSTIN, Eran HALPERIN, Fazlolah MOHAGHEGH, Aldo CORDOVA PALOMERA
  • Publication number: 20240095591
    Abstract: Embodiments of the disclosure provide for improved processing of data with different timescales, for example high-frequency data and low-frequency data. Embodiments specifically improve such processing of different timescale data processed by a machine learning model. Additionally or alternatively, some embodiments include improved processing of data with different timescales by selecting an optimal variant from a plurality of possible variants of a prediction model.
    Type: Application
    Filed: May 30, 2023
    Publication date: March 21, 2024
    Inventors: Gregory D. Lyng, Eran Halperin, Brian Lawrence Hill, Kimmo M. Karkkainen, Kailas Vodrahalli
  • Publication number: 20240070533
    Abstract: Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.
    Type: Application
    Filed: February 22, 2023
    Publication date: February 29, 2024
    Inventors: Dominik Roman Christian DAHLEM, Gregory D. LYNG, Christopher A. Hane, Eran HALPERIN
  • Publication number: 20240070534
    Abstract: Various embodiments of the present disclosure describe feature bias mitigation techniques for machine learning models. The techniques include generating or receiving a contextual bias correction function, a protected bias correction function, or an aggregate bias for a machine learning model. The aggregate bias correction function for the model may be based on the contextual or protected bias correction functions. At least one of the generated or received functions may be configured to generate an individualized threshold tailored to specific attributes of an input to the machine learning model. Each of the functions may generate a respective threshold based on one or more individual parameters of the input. An output from the machine learning model may be compared to the individualized threshold to generate a bias adjusted output that accounts for the individual parameters of the input.
    Type: Application
    Filed: February 22, 2023
    Publication date: February 29, 2024
    Inventors: Dominik Roman Christian DAHLEM, Gregory D. LYNG, Christopher A. HANE, Eran HALPERIN
  • Publication number: 20240062864
    Abstract: Various embodiments of the present disclosure disclose machine-learning based data augmentation and prediction techniques for generating predictive classifications based on temporal data. A machine-learning based model is provided that can receive an input data object associated with a plurality of predictive temporal parameters; determine augmented temporal data objects based on the predictive temporal parameters; generate predictive data representations for the input data object based on the predictive temporal parameters and the augmented temporal data objects; generate a multi-channel predictive data representation based on the predictive data representations for the input data object; and generate a predictive classification for the input data object based on the multi-channel predictive data representation.
    Type: Application
    Filed: November 22, 2022
    Publication date: February 22, 2024
    Inventors: Eran HALPERIN, Gregory L. LYNG, Brian L. HILL
  • Publication number: 20230334887
    Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing document classification system outputs, wherein classification routine iterations are performed using masked document data objects comprising one or more masked text blocks. Text block importance score for text blocks are generated and compared to generate predictive data output comprising text blocks determined to be the most influential in classifying the document data objects with respect to one or more classification labels.
    Type: Application
    Filed: October 14, 2022
    Publication date: October 19, 2023
    Inventors: Joel Stremmel, Eran Halperin, Brian Hill
  • 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
  • Patent number: 9092391
    Abstract: The present invention provides methods of determining a Genetic Composite Index score by assessing the association between an individual's genotype and at least one disease or condition. The assessment comprises comparing an individual's genomic profile with a database of medically relevant genetic variations that have been established to associate with at least one disease or condition.
    Type: Grant
    Filed: November 30, 2007
    Date of Patent: July 28, 2015
    Assignee: Navigenics, Inc.
    Inventors: Dietrich A. Stephan, Melissa Floren Filippone, Jennifer Wessel, Michele Cargill, Eran Halperin
  • Publication number: 20130013217
    Abstract: The present disclosure provides methods and systems for assessing an individual's genotype correlations to a phenotype by analyzing the individual's genomic profile and using ancestral data to determine the correlations between genotypes and phenotypes.
    Type: Application
    Filed: September 12, 2012
    Publication date: January 10, 2013
    Applicant: NAVIGENICS, INC.
    Inventors: Dietrich A. STEPHAN, Jennifer Wessel, Michele Cargill, Eran Halperin
  • Publication number: 20100293130
    Abstract: The present invention provides methods of determining a Genetic Composite Index score by assessing the association between an individual's genotype and at least one disease or condition. The assessment comprises comparing an individual's genomic profile with a database of medically relevant genetic variations that have been established to associate with at least one disease or condition.
    Type: Application
    Filed: November 30, 2007
    Publication date: November 18, 2010
    Inventors: Dietrich A. Stephan, Melissa Floren Filippone, Jennifer Wessel, Michele Cargill, Eran Halperin
  • Publication number: 20100070455
    Abstract: The present disclosure provides methods and systems for incorporating multiple environmental and genetic risk factors into an individual's genomic profile. Methods include assessing the association between an individual's genotype and at least one disease or condition by incorporating multiple genetic risk factors, environmental risk factors, or a combination of both.
    Type: Application
    Filed: September 11, 2009
    Publication date: March 18, 2010
    Applicant: Navigenics, Inc.
    Inventors: Eran Halperin, Jennifer Wessel, Michele Cargill, Dietrich A. Stephan
  • Publication number: 20090099789
    Abstract: The present disclosure provides methods and systems for assessing an individual's genotype correlations to a phenotype by analyzing the individual's genomic profile and using ancestral data to determine the correlations between genotypes and phenotypes.
    Type: Application
    Filed: September 26, 2008
    Publication date: April 16, 2009
    Inventors: Dietrich A. STEPHAN, Jennifer Wessel, Michele Cargill, Eran Halperin
  • Publication number: 20050079504
    Abstract: A method of comparing nucleic acid sequences being ESTs included in a first database of sequences and nucleic acid sequences included in a second database of sequences to form groups of sequences from the two databases that all relate to the same gene. For each one or more n-groups of sequences of one of the two databases, associating therewith lists of nucleic acid sequences, each from one of said two databases, each sequence on the list containing the n-groups, and matching sequences on the lists to generate said group.
    Type: Application
    Filed: August 18, 2003
    Publication date: April 14, 2005
    Inventors: Mor Amitai, Raveh Gill-More, Eran Halperin, Avner Magen, Sarah Pollock
  • Patent number: 6625545
    Abstract: A method of obtaining an mRNA sequence having alternative spliced variants from a database of ESTs, comprising: providing a raw database comprising a plurality of ESTs; and assembling ones of said ESTs into mRNA sequences, wherein said assembling includes identifying alternative spliced regions.
    Type: Grant
    Filed: August 13, 1998
    Date of Patent: September 23, 2003
    Assignee: Compugen Ltd.
    Inventors: Mor Amitai, Raveh Avraham Gill-More, Eran Halperin, Avner Magen, Sarah Rachel Pollock