Patents by Inventor Hector Yee

Hector Yee 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).

  • Patent number: 12191007
    Abstract: Example embodiments relate to a method for training a predictive model from data. The method includes defining a multitude of predicates as binary functions operating on time sequences of the features or logical operations on the time sequences of the features. The method also includes iteratively training a boosting model by generating a number of new random predicates, scoring all the new random predicates by weighted information gain with respect to a class label associated with a prediction of the boosting model, selecting a number of the new random predicates with the highest weighted information gain and adding them to the boosting model, computing weights for all the predicates in the boosting model, removing one or more of the selected new predicates with the highest information gain from the boosting model in response to input from an operator. The method may include repeating the prior steps a plurality of times.
    Type: Grant
    Filed: September 29, 2017
    Date of Patent: January 7, 2025
    Assignee: Google LLC
    Inventors: Kai Chen, Eyal Oren, Hector Yee, James Wilson, Alvin Rajkomar, Michaela Hardt
  • Publication number: 20240355116
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing aquatic life data, e.g., aquatic life images. One of the methods includes obtaining aquatic life data; identifying a first subset of the aquatic life data that is to be annotated by one or more data annotators to generate annotated aquatic life data and a second subset of the aquatic life data that is not to be annotated; providing the first subset of the aquatic life data to the one or more data annotators, the one or more data annotators processing the first subset of the aquatic life data to generate the annotated aquatic life data; providing the second subset of the aquatic life data to a data storage curator; and determining whether and, if so, which storage device in one or more storage devices to store the second subset of the aquatic life data.
    Type: Application
    Filed: April 18, 2023
    Publication date: October 24, 2024
    Inventors: Grace Calvert Young, Yangli Hector Yee, Julia Black Ling, Cory Drew Schillaci
  • Publication number: 20240354666
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.
    Type: Application
    Filed: September 11, 2023
    Publication date: October 24, 2024
    Inventors: Antoni Jordi Ballester, Bertrand Louis Rene Delorme, Alexandre Szenicer, Julia Black Ling, Bianca Bahman, Yangli Hector Yee
  • Publication number: 20240355115
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing aquatic life data, e.g., aquatic life image. One of the methods includes receiving aquatic life data comprising a plurality of aquatic life images from a user through a user interface; receiving, within the user interface, a first user request to use the aquatic life data to train a machine learning model; determining a data curator score for each aquatic life image; identifying, based on the data curator scores, a proper subset of the plurality of aquatic life images; providing the proper subset of the plurality of aquatic life images to one or more data annotators; receiving annotation data generated by the one or more data annotators; and providing the annotation data to a training system configured to train the machine learning model by using the annotation data.
    Type: Application
    Filed: April 18, 2023
    Publication date: October 24, 2024
    Inventors: Grace Calvert Young, Yangli Hector Yee, Julia Black Ling, Cory Drew Schillaci
  • Publication number: 20240202654
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.
    Type: Application
    Filed: September 5, 2023
    Publication date: June 20, 2024
    Inventors: Yangli Hector Yee, Julia Black Ling, Antoni Jordi Ballester, Bertrand Louis Rene Delorme, Alexandre Szenicer
  • Publication number: 20240202655
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining elements of a shipping network. One of the methods includes obtaining environmental input data, wherein the environmental input data includes weather forecast data; providing the environmental input data to a circulation model; and providing output environmental condition from the circulation model to a machine learning model trained to generate a route for a ship.
    Type: Application
    Filed: September 8, 2023
    Publication date: June 20, 2024
    Inventors: Antoni Jordi Ballester, Alexandre Szenicer, Bertrand Louis Rene Delorme, Yangli Hector Yee, Julia Black Ling
  • Patent number: 11935634
    Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.
    Type: Grant
    Filed: August 30, 2017
    Date of Patent: March 19, 2024
    Assignee: Google LLC
    Inventors: Alexander Mossin, Alvin Rajkomar, Eyal Oren, James Wilson, James Wexler, Patrik Sundberg, Andrew Dai, Yingwei Cui, Gregory Corrado, Hector Yee, Jacob Marcus, Jeffrey Dean, Benjamin Irvine, Kai Chen, Kun Zhang, Michaela Hardt, Xiaomi Sun, Nissan Hajaj, Peter Junteng Liu, Quoc Le, Xiaobing Liu, Yi Zhang
  • Publication number: 20240062539
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for aquatic biomass estimation. One of the methods includes obtaining an image of an aquatic environment including aquatic grass; providing the image to a network model trained to construct a point cloud indicating a portion of the image that represents the aquatic grass; generating a floor model indicating a floor of the aquatic environment where the aquatic grass grows; identifying, using (i) the floor model and (ii) the point cloud indicating the aquatic grass, (i) a first subset of points in the point cloud as indicating aquatic grass and (ii) a second subset of points in the point cloud as indicating the floor of the aquatic environment; and generating, using the first subset of points in the point cloud, an indication of biomass within the aquatic environment.
    Type: Application
    Filed: August 21, 2023
    Publication date: February 22, 2024
    Inventors: Yangli Hector Yee, Terry Allan Smith, Bianca Bahman
  • Publication number: 20240062514
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for Measuring and Quantifying Biodiversity in an Environment. One of the methods includes receiving a set of images representing the marine environment; identifying, by one or more processing devices within the set of images, objects representing marine life in the marine environment; classifying, by the one or more processing devices, the objects into multiple clusters based on feature vectors identified for each of the objects; and computing, by the one or more processing devices based on attributes associated with the multiple clusters, a metric indicative of the biodiversity in the marine environment.
    Type: Application
    Filed: August 18, 2023
    Publication date: February 22, 2024
    Inventors: Julia Black Ling, Cory Drew Schillaci, Yangli Hector Yee, Grace Calvert Young, Karan Jhavar
  • Publication number: 20230329196
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for distribution-based machine learning. In some implementations, a method for distribution-based machine learning includes obtaining fish images from a camera device; generating predicted values using a machine learning model and one or more of the fish images; comparing the predicted values to distribution data representing features of multiple fish; and updating one or more parameters of the machine learning model based on the comparison.
    Type: Application
    Filed: April 12, 2023
    Publication date: October 19, 2023
    Inventors: Pedro Montebello Milani, Jung Ook Hong, Rajesh Machhindranath Jadhav, Yangli Hector Yee, Cory Drew Schillaci, Julia Black Ling
  • Patent number: 11398299
    Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.
    Type: Grant
    Filed: August 30, 2017
    Date of Patent: July 26, 2022
    Assignee: Google LLC
    Inventors: Kai Chen, Patrik Sundberg, Alexander Mossin, Nissan Hajaj, Kurt Litsch, James Wexler, Yi Zhang, Kun Zhang, Jacob Marcus, Eyal Oren, Hector Yee, Jeffrey Dean, Michaela Hardt, Benjamin Irvine, James Wilson, Andrew Dai, Peter Liu, Xiaomi Sun, Quoc Le, Xiaobing Liu, Alvin Rajkomar, Gregory Corrado, Gerardo Flores, Yingwei Cui, Gavin Duggan
  • Publication number: 20210358579
    Abstract: A method is described for training a predictive model which increases the interpretability and trustworthiness of the model for end-users. The model is trained from data having multitude of features. Each feature is associated with a real value and a time component. Many predicates (atomic elements for training the model) are defined as binary functions operating on the features, and typically time sequences of the features or logical combinations thereof. The predicates can be limited to those functions which have human understandability or encode expert knowledge relative to a predication task of the model. We iteratively train a boosting model with input from an operator or human-in-the-loop. The human-in-the-loop is provided with tools to inspect the model as it is iteratively built and remove one or more of the predicates in the model, e.g. if it does not have indicia of trustworthiness, is not causally related to a prediction of the model, or is not understandable.
    Type: Application
    Filed: September 29, 2017
    Publication date: November 18, 2021
    Inventors: Kai Chen, Eyal Oren, Hector Yee, James Wilson, Alvin Rajkomar, Michaela Hardt
  • Publication number: 20200388358
    Abstract: A machine learning method is described for generating labels for members of a training set where the labels are not directly available in the training set data. In a first stage of the method an iterative process is used to gradually build up a list of features (“partition features” herein) which are conceptually related to the class label using a human-in-the loop (expert). In a second part of the process we generate labels for the members of the training set, build up a boosting model using the labeling to come up with additional partition features, score the labeling of the training set members from the boosting model, and then with the human-in-the-loop evaluate a labels assigned to a small subset of the members depending on their score. The labels assigned to some or all of those members in the subset may be flipped depending on the evaluation. The final outcome of the process is an interpretable model that explains how the labels were generated and a labeled set of training data.
    Type: Application
    Filed: September 29, 2017
    Publication date: December 10, 2020
    Inventors: Kai CHEN, Kun ZHANG, Jacob MARCUS, Eyal OREN, Hector YEE, Michaela HARDT, James WILSON, Alvin RAJKOMAR, Jian LU
  • Patent number: 10664855
    Abstract: This disclosure includes methods for predicting demand based on the price of a time-expiring inventory. An online system provides a connection between a manager of a time-expiring inventory and a plurality of clients. The online system provides a listing for the manager's time-expiring inventory to clients on the online system. The manager specifies the price of the time-expiring inventory in the listing. A demand function predicts the demand for the time-expiring inventory based on features of the listing and the time-expiring inventory. The online system determines a likelihood of receiving a request for the time-expiring inventory from a client on the online system based on the predicted demand. The online system may use the determined likelihoods to provide to the manager information about how changes in the price of the listing are likely to affect the demand for the time-expiring inventory.
    Type: Grant
    Filed: November 25, 2015
    Date of Patent: May 26, 2020
    Assignee: Airbnb, Inc.
    Inventors: Bar Ifrach, David Michael Holtz, Yangli Hector Yee, Li Zhang
  • Patent number: 10621548
    Abstract: This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.
    Type: Grant
    Filed: November 19, 2019
    Date of Patent: April 14, 2020
    Assignee: Airbnb, Inc.
    Inventors: Spencer de Mars, Yangli Hector Yee, Peng Ye, Fenglin Liao, Li Zhang, Kim Pham, Julian Qian, Benjamin Yolken
  • Publication number: 20200090116
    Abstract: This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.
    Type: Application
    Filed: November 19, 2019
    Publication date: March 19, 2020
    Inventors: Spencer de Mars, Yangli Hector Yee, Peng Ye, Fenglin Liao, Li Zhang, Kim Pham, Julian Qian, Benjamin Yolken
  • Patent number: 10528909
    Abstract: This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.
    Type: Grant
    Filed: April 7, 2017
    Date of Patent: January 7, 2020
    Assignee: Airbnb, Inc.
    Inventors: Spencer de Mars, Yangli Hector Yee, Peng Ye, Fenglin Liao, Li Zhang, Kim Pham, Julian Qian, Benjamin Yolken
  • Publication number: 20190034591
    Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.
    Type: Application
    Filed: August 30, 2017
    Publication date: January 31, 2019
    Inventors: Alexander Mossin, Alvin Rajkomar, Eyal Oren, James Wilson, James Wexler, Patrik Sundberg, Andrew Dai, Yingwei Cui, Gregory Corrado, Hector Yee, Jacob Marcus, Jeffrey Dean, Benjamin Irvine, Kai Chen, Kun Zhang, Michaela Hardt, Xiaomi Sun, Nissan Hajaj, Peter Liu, Quoc Le, Xiaobing Liu, Yi Zhang
  • Publication number: 20190034589
    Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.
    Type: Application
    Filed: August 30, 2017
    Publication date: January 31, 2019
    Inventors: Kai Chen, Patrik Sundberg, Alexander Mossin, Nissan Hajaj, Kurt Litsch, James Wexler, Yi Zhang, Kun Zhang, Jacob Marcus, Eyal Oren, Hector Yee, Jeffrey Dean, Michaela Hardt, Benjamin Irvine, James Wilson, Andrew Dai, Peter Liu, Xiaomi Sun, Quoc Le, Xiaobing Liu, Alvin Rajkomar, Gregory Corrado, Gerardo Flores, Yingwei Cui, Gavin Duggan
  • Patent number: RE48322
    Abstract: Methods and systems for detection of a construction zone sign are described. A computing device, configured to control the vehicle, may be configured to receive, from an image-capture device coupled to the computing device, images of a vicinity of the road on which the vehicle is travelling. Also, the computing device may be configured to determine image portions in the images that may depict sides of the road at a predetermined height range. Further, the computing device may be configured to detect a construction zone sign in the image portions, and determine a type of the construction zone sign. Accordingly, the computing device may be configured to modify a control strategy associated with a driving behavior of the vehicle; and control the vehicle based on the modified control strategy.
    Type: Grant
    Filed: November 7, 2018
    Date of Patent: November 24, 2020
    Assignee: Waymo LLC
    Inventors: Nathaniel Fairfield, David I. Ferguson, Abhijit S. Ogale, Matthew Wang, Yangli Hector Yee