Patents by Inventor Eric Xing

Eric Xing 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: 11720068
    Abstract: The current disclosure is directed towards system and method for controlling industrial process. In one example, a method comprising deploying a forecast model for controlling an industrial process with training configurations that can be used as a single point of truth for guiding training and retraining versions of the forecast model using a model training algorithm without human input. The retraining and redeployment of the forecast model may be triggered when the performance of the forecast model degrades.
    Type: Grant
    Filed: January 6, 2020
    Date of Patent: August 8, 2023
    Assignee: OPRO.AI INC.
    Inventors: Hongbao Zhang, Roey Flor, Dian Li, Adam Schwab, Pengtao Xie, Eric Xing
  • Publication number: 20220261694
    Abstract: Methods and systems are presented for consuming different data sources, and deploying artificial intelligence and machine learning programs on different target devices or infrastructures. Many data types can be transformed into machine learning data shards (MLDS) while many machine learning programs written in various programming languages or frameworks are transformed to common operator representations. Operator representations are transformed into execution graphs (EG) for a chosen target device or infrastructure. The MLDS and EG are input to the targeted devices and infrastructures, which then execute the machine learning programs (now transformed to EGs) on the MLDS to produce trained models or predictions with trained models.
    Type: Application
    Filed: May 2, 2022
    Publication date: August 18, 2022
    Applicant: Petuum Inc.
    Inventors: Qirong Ho, Eric Xing
  • Patent number: 11348030
    Abstract: Methods and systems are presented for consuming different data sources, and deploying artificial intelligence and machine learning programs on different target devices or infrastructures. Many data types can be transformed into machine learning data shards (MLDS) while many machine learning programs written in various programming languages or frameworks are transformed to common operator representations. Operator representations are transformed into execution graphs (EG) for a chosen target device or infrastructure. The MLDS and EG are input to the targeted devices and infrastructures, which then execute the machine learning programs (now transformed to EGs) on the MLDS to produce trained models or predictions with trained models.
    Type: Grant
    Filed: December 20, 2017
    Date of Patent: May 31, 2022
    Assignee: Petuum Inc.
    Inventors: Qirong Ho, Eric Xing
  • Patent number: 11301438
    Abstract: Accordingly, a data engineering system for machine learning at scale is disclosed. In one embodiment, the data engineering system includes an ingest processing module having a schema update submodule and a feature statistics update submodule, wherein the schema update submodule is configured to discover new features and add them to a schema, and wherein the feature statistics update submodule collects statistics for each feature to be used in an online transformation, a record store to store data from a data source, and a transformation module, to receive a low dimensional data instance from the record store and to receive the schema and feature statistics from the ingest processing module, and to transform the low dimensional data instance into a high dimensional representation.
    Type: Grant
    Filed: September 2, 2020
    Date of Patent: April 12, 2022
    Assignee: Petuum Inc.
    Inventors: Wei Dai, Weiren Yu, Eric Xing
  • Patent number: 11282205
    Abstract: Organ segmentation in chest X-rays using convolutional neural networks is disclosed. One embodiment provides a method to train a convolutional segmentation network with chest X-ray images to generate pixel-level predictions of target classes. Another embodiment will also train a critic network with an input mask, wherein the input mask is one of a segmentation network mask and a ground truth annotation, and outputting a probability that the input mask is the ground truth annotation instead of the prediction by the segmentation network, and to provide the probability output by the critic network to the segmentation network to guide the segmentation network to generate masks more consistent with learned higher-order structures.
    Type: Grant
    Filed: April 1, 2020
    Date of Patent: March 22, 2022
    Assignee: PETUUM, INC.
    Inventors: Wei Dai, Xiaodan Liang, Hao Zhang, Eric Xing, Joseph Doyle
  • Patent number: 11252260
    Abstract: A computer in a distributed peer-to-peer system is disclosed. The distributed system includes a plurality of computers configured to run a distributed machine learning (ML) program represented as an expression of a target loss function with a model parameter matrix. The computer includes: a parser module configured to convert a loss function in the distributed program into an expression graph and then one or more multiplication trees; a parameter replica module in communication with the parser module, the parameter replica module configured to maintain the model parameter matrix of the ML program; a compressor module in communication with the parameter replica module, the compressor module configured to extract sufficient factors from the expression graph for updating the model matrix; and a communication module in communication with the compressor module, the communication module configured to send the sufficient factors for updating model matrix to other machines in the distributed system.
    Type: Grant
    Filed: December 20, 2017
    Date of Patent: February 15, 2022
    Assignee: PETUUM INC
    Inventors: Pengtao Xie, Qirong Ho, Eric Xing
  • Publication number: 20210343410
    Abstract: The present invention is a system and a method to classify clinical records into International Classification of Diseases (ICD) codes. The system includes a processor, and a memory communicatively coupled to the processor. The memory includes a generator (G), a feature extractor, a discriminator (D), a label encoder, and a keywords reconstructor. The generator (G) generates synthesized features corresponding to ICD code descriptions. The feature extractor extracts real latent features from clinical documents and generates real features by training a GANs. The generator (G) generates synthesized features after the GANs are trained and calibrate a binary code classifier with the real latent features generated by the feature extractor for a low-shot ICD code l. The feature extractor generates code-specific latent features conditioned on a textual description of each ICD code description by using a WGAN-GP.
    Type: Application
    Filed: May 2, 2020
    Publication date: November 4, 2021
    Applicant: Petuum Inc.
    Inventors: Shanghang Zhang, Najmeh Sadoughi, Pengtao Xie, Eric Xing
  • Patent number: 11119992
    Abstract: Accordingly, a data engineering system for machine learning at scale is disclosed. In one embodiment, the data engineering system includes an ingest processing module having a schema update submodule and a feature statistics update submodule, wherein the schema update submodule is configured to discover new features and add them to a schema, and wherein the feature statistics update submodule collects statistics for each feature to be used in an online transformation, a record store to store data from a data source, and a transformation module, to receive a low dimensional data instance from the record store and to receive the schema and feature statistics from the ingest processing module, and to transform the low dimensional data instance into a high dimensional representation.
    Type: Grant
    Filed: April 23, 2018
    Date of Patent: September 14, 2021
    Assignee: PETUUM, INC.
    Inventors: Wei Dai, Weiren Yu, Eric Xing
  • Patent number: 11106998
    Abstract: A computer in a distributed computing system is disclosed. The computer includes: a graphics processing unit (GPU) memory; a central processing unit (CPU) memory comprising a Key-Value Store (KVS) module; an execution engine module configured to run a deep learning (DL) program to create a plurality of operator graph layers in the graphics processing unit memory; a client library module configured to create a GPU-CPU synchronization (GCS) module for each of the plurality of operator graph layers; a coordination service module configured to compute network cost of a first and a second communication scheme and select, based on the network cost, one of the first and second communication scheme for transmitting data associated with one of the plurality of operator graph layers from a corresponding GCS module.
    Type: Grant
    Filed: November 16, 2017
    Date of Patent: August 31, 2021
    Assignee: PETUUM INC
    Inventors: Wei Dai, Hao Zhang, Eric Xing, Qirong Ho
  • Patent number: 11101029
    Abstract: A system for predicting medications to prescribe to a patient includes a text encoding module and a medication prediction module. The text encoding module is configured to obtain a clinical-information vector from clinical information of the patient. The medication prediction module configured to apply a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient. The machine-learned medication-prediction algorithm is designed with a diversity-promoting regularization model, and is configured to simultaneously consider correlations among different medications and dependencies between patient information and medications when selecting a subset of medications to prescribe to the patient.
    Type: Grant
    Filed: December 1, 2018
    Date of Patent: August 24, 2021
    Assignee: PETUUM INC.
    Inventors: Pengtao Xie, Eric Xing
  • Patent number: 11087864
    Abstract: A system for assigning concepts to a medical image includes a visual feature module and a tagging module. The visual feature module is configured to obtain an image feature vector from the medical image. The tagging module is configured to apply a machine-learned algorithm to the image feature vector to assign a set of concepts to the image. The system may also include a text report generator that is configured to generate a written report describing the medical image based on the set of concepts assigned to the medical image.
    Type: Grant
    Filed: December 1, 2018
    Date of Patent: August 10, 2021
    Assignee: PETUUM INC.
    Inventors: Pengtao Xie, Eric Xing
  • Publication number: 20210208545
    Abstract: The current disclosure is directed towards system and method for controlling industrial process. In one example, a method comprising deploying a forecast model for controlling an industrial process with training configurations that can be used as a single point of truth for guiding training and retraining versions of the forecast model using a model training algorithm without human input. The retraining and redeployment of the forecast model may be triggered when the performance of the forecast model degrades.
    Type: Application
    Filed: January 6, 2020
    Publication date: July 8, 2021
    Inventors: Hongbao Zhang, Roey Flor, Dian Li, Adam Schwab, Pengtao Xie, Eric Xing
  • Publication number: 20210192717
    Abstract: The current disclosure is directed towards providing systems and methods for identifying atheromatous plaques in optical coherence tomography (OCT) images. In one example, a method for a trained neural network may include acquiring an OCT image slice of an artery, identifying one or more image features of the OCT image slice with the trained neural network, and responsive to the one or more image features indicating a thin-cap fibroatheroma (TCFA), segmenting the OCT image slice into a plurality of regions with the trained neural network, the plurality of regions including a first region depicting the TCFA, and determining start and end coordinates for the TCFA based on the first region.
    Type: Application
    Filed: December 18, 2019
    Publication date: June 24, 2021
    Inventors: Najmeh Sadoughi, Suhaila Mumtaj Shakiah, Pengtao Xie, Eric Xing
  • Publication number: 20210026818
    Abstract: Accordingly, a data engineering system for machine learning at scale is disclosed. In one embodiment, the data engineering system includes an ingest processing module having a schema update submodule and a feature statistics update submodule, wherein the schema update submodule is configured to discover new features and add them to a schema, and wherein the feature statistics update submodule collects statistics for each feature to be used in an online transformation, a record store to store data from a data source, and a transformation module, to receive a low dimensional data instance from the record store and to receive the schema and feature statistics from the ingest processing module, and to transform the low dimensional data instance into a high dimensional representation.
    Type: Application
    Filed: September 2, 2020
    Publication date: January 28, 2021
    Inventors: Wei Dai, Weiren Yu, Eric Xing
  • Publication number: 20210019866
    Abstract: A system for manipulating images according to styles chosen by a user includes a feed-forward image manipulation model for everyday use and an optimization image manipulation model for more professional use. The optimization image manipulation model optimizes directly over output image pixels to minimize both the content loss and style loss. Users can select their own content and style images, and can choose between using the feed-forward image manipulation model and optimization image manipulation model.
    Type: Application
    Filed: October 6, 2020
    Publication date: January 21, 2021
    Inventors: Zhiting Hu, Eric Xing, Chenyu Wang
  • Patent number: 10832387
    Abstract: A system for manipulating images according to styles chosen by a user includes a feed-forward image manipulation model for everyday use and an optimization image manipulation model for more professional use. The optimization image manipulation model optimizes directly over output image pixels to minimize both the content loss and style loss. Users can select their own content and style images, and can choose between using the feed-forward image manipulation model and optimization image manipulation model.
    Type: Grant
    Filed: April 5, 2018
    Date of Patent: November 10, 2020
    Assignee: PETUUM INC.
    Inventors: Zhiting Hu, Eric Xing, Chenyu Wang
  • Publication number: 20200293721
    Abstract: A constituent-centric neural architecture for reading comprehension is disclosed. One embodiment provides a method that performs reading comprehension comprising encoding individual constituents from a text passage using a chain of trees long short-term encoding, encodes question related to the text passage using a tree long short-term memory encoding, generates a question-aware representation for each constituent in the passage using a tree-guided attention mechanism, generates a plurality of candidate answers from the question-aware representation using hierarchical relations among constituents, and predicts an answer to the question in relation to the text passage using a feed-forward network. Other embodiments are disclosed herein.
    Type: Application
    Filed: May 28, 2020
    Publication date: September 17, 2020
    Inventors: Pengtao Xie, Eric Xing
  • Publication number: 20200234448
    Abstract: Organ segmentation in chest X-rays using convolutional neural networks is disclosed. One embodiment provides a method to train a convolutional segmentation network with chest X-ray images to generate pixel-level predictions of target classes. Another embodiment will also train a critic network with an input mask, wherein the input mask is one of a segmentation network mask and a ground truth annotation, and outputting a probability that the input mask is the ground truth annotation instead of the prediction by the segmentation network, and to provide the probability output by the critic network to the segmentation network to guide the segmentation network to generate masks more consistent with learned higher-order structures.
    Type: Application
    Filed: April 1, 2020
    Publication date: July 23, 2020
    Inventors: Wei Dai, Xiaodan Liang, Hao Zhang, Eric Xing, Joseph Doyle
  • Patent number: 10706234
    Abstract: A constituent-centric neural architecture for reading comprehension is disclosed. One embodiment provides a method that performs reading comprehension comprising encoding individual constituents from a text passage using a chain of trees long short-term encoding, encodes question related to the text passage using a tree long short-term memory encoding, generates a question-aware representation for each constituent in the passage using a tree-guided attention mechanism, generates a plurality of candidate answers from the question-aware representation using hierarchical relations among constituents, and predicts an answer to the question in relation to the text passage using a feed-forward network. Other embodiments are disclosed herein.
    Type: Grant
    Filed: April 9, 2018
    Date of Patent: July 7, 2020
    Assignee: Petuum Inc.
    Inventors: Pengtao Xie, Eric Xing
  • Patent number: 10699412
    Abstract: Organ segmentation in chest X-rays using convolutional neural networks is disclosed. One embodiment provides a method to train a convolutional segmentation network with chest X-ray images to generate pixel-level predictions of target classes. Another embodiment will also train a critic network with an input mask, wherein the input mask is one of a segmentation network mask and a ground truth annotation, and outputting a probability that the input mask is the ground truth annotation instead of the prediction by the segmentation network, and to provide the probability output by the critic network to the segmentation network to guide the segmentation network to generate masks more consistent with learned higher-order structures.
    Type: Grant
    Filed: March 20, 2018
    Date of Patent: June 30, 2020
    Assignee: PETUUM INC.
    Inventors: Wei Dai, Xiaodan Liang, Hao Zhang, Eric Xing, Joseph Doyle