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Yi Gai

age ~42

from Sunnyvale, CA

Yi Gai Phones & Addresses

  • 1137 Rockefeller Dr, Sunnyvale, CA 94087
  • Mountain View, CA
  • Hillsboro, OR
  • Los Angeles, CA

Us Patents

  • Methods And Apparatus To Improve Feature Engineering Efficiency With Metadata Unit Operations

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  • US Patent:
    20200372151, Nov 26, 2020
  • Filed:
    Feb 28, 2020
  • Appl. No.:
    16/805159
  • Inventors:
    - Santa Clara CA, US
    Yi Gai - Hillsboro OR, US
  • International Classification:
    G06F 21/55
    G06F 21/52
  • Abstract:
    Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency. An example method disclosed herein includes retrieving a log file in a first file format, the log file containing feature occurrence data, generating a first unit operation based on the first file format to extract the feature occurrence data from the log file to a string, the first unit operation associated with a first metadata tag, generating second unit operations to identify respective features from the feature occurrence data, the second unit operations associated with respective second metadata tags, and generating a first sequence of the first metadata tag and the second metadata tags to create a first vector output file of the feature occurrence data
  • Malicious Object Detection In A Runtime Environment

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  • US Patent:
    20200175166, Jun 4, 2020
  • Filed:
    Feb 3, 2020
  • Appl. No.:
    16/780218
  • Inventors:
    - Santa Clara CA, US
    Xiaoning Li - Santa Clara CA, US
    Ravi L. Sahita - Beaverton OR, US
    Aravind Subramanian - San Jose CA, US
    Abhay S. Kanhere - Fremont CA, US
    Chih-Yuan Yang - Portland OR, US
    Yi Gai - Hillsboro OR, US
  • Assignee:
    Intel Corporation - Santa Clara CA
  • International Classification:
    G06F 21/56
    G06N 20/00
    H04L 29/06
  • Abstract:
    A malicious object detection system for use in managed runtime environments includes a check circuit to receive call information generated by an application, such as an Android application. A machine learning circuit coupled to the check circuit applies a machine learning model to assess the information and/or data included in the call and detect the presence of a malicious object, such as malware or a virus, in the application generating the call. The machine learning model may include a global machine learning model distributed across a number of devices, a local machine learning model based on use patterns of a particular device, or combinations thereof. A graphical user interface management circuit halts execution of applications containing malicious objects and generates a user perceptible output.
  • Random Access Procedure For Handover

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  • US Patent:
    20190029048, Jan 24, 2019
  • Filed:
    Jan 22, 2018
  • Appl. No.:
    15/877268
  • Inventors:
    - Santa Clara CA, US
    YI GAI - Mountain View CA, US
  • International Classification:
    H04W 74/08
    H04W 36/30
    H04W 36/00
  • Abstract:
    Technology for switching from a wireless local area network (WLAN) to a wireless wide area network (WWAN) is disclosed. A multi-radio access technology (multi-RAT) user equipment (UE) can receive WLAN-specific dedicated physical random access channel (PRACH) allocation information from an evolved node B (eNB) to enable the multi-RAT UE to perform an inter-RAT WLAN-to-WWAN handover. The multi-RAT UE can initiate the inter-RAT WLAN-to-WWAN handover at the multi-RAT UE by performing random access with the eNB using the WLAN-specific dedicated PRACH allocation information.
  • Malicious Object Detection In A Runtime Environment

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  • US Patent:
    20180189489, Jul 5, 2018
  • Filed:
    Dec 30, 2016
  • Appl. No.:
    15/395053
  • Inventors:
    Mingwei Zhang - Hillsboro OR, US
    Xiaoning Li - Santa Clara CA, US
    Ravi L. Sahita - Beaverton OR, US
    Aravind Subramanian - San Jose CA, US
    Abhay S. Kanhere - Fremont CA, US
    Chih-Yuan Yang - Portland OR, US
    Yi Gai - Hillsboro OR, US
  • International Classification:
    G06F 21/56
    H04L 29/06
    G06N 99/00
  • Abstract:
    A malicious object detection system for use in managed runtime environments includes a check circuit to receive call information generated by an application, such as an Android application. A machine learning circuit coupled to the check circuit applies a machine learning model to assess the information and/or data included in the call and detect the presence of a malicious object, such as malware or a virus, in the application generating the call. The machine learning model may include a global machine learning model distributed across a number of devices, a local machine learning model based on use patterns of a particular device, or combinations thereof. A graphical user interface management circuit halts execution of applications containing malicious objects and generates a user perceptible output.
  • Systems, Methods, And Devices To Support Intra-Application Flow Prioritization

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  • US Patent:
    20180152389, May 31, 2018
  • Filed:
    Jan 25, 2018
  • Appl. No.:
    15/880213
  • Inventors:
    - Santa Clara CA, US
    Yi Gai - Mountain View CA, US
  • Assignee:
    INTEL CORPORATION - SANTA CLARA CA
  • International Classification:
    H04L 12/805
    H04W 72/04
  • Abstract:
    Systems and methods to support intra-application flow prioritization are disclosed herein. User equipment (UE) may be configured to communicatively couple to an Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Node B (eNB). The eNB may transmit packets from the UE to an evolved packet core (EPC), which may transmit schedule packets to an application function (AF) via a network. The AF may provide classification information and prioritization information for a plurality of intra-application flows transmitted between the AF and the UE. The EPC may classify uplink and/or downlink traffic into the intra-application flows and mark and/or schedule the traffic based on the prioritization information. Absolute and/or modular length, payload values, and/or packet type may be used to classify the traffic into the intra-application flows.
  • Methods And Apparatus To Improve Feature Engineering Efficiency With Metadata Unit Operations

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  • US Patent:
    20180089424, Mar 29, 2018
  • Filed:
    Sep 29, 2016
  • Appl. No.:
    15/280044
  • Inventors:
    - Santa Clara CA, US
    Yi Gai - Hillsboro OR, US
  • International Classification:
    G06F 21/55
    G06F 21/53
    G06N 99/00
  • Abstract:
    Methods, apparatus, systems and articles of manufacture are disclosed to improve feature engineering efficiency. An example method disclosed herein includes retrieving a log file in a first file format, the log file containing feature occurrence data, generating a first unit operation based on the first file format to extract the feature occurrence data from the log file to a string, the first unit operation associated with a first metadata tag, generating second unit operations to identify respective features from the feature occurrence data, the second unit operations associated with respective second metadata tags, and generating a first sequence of the first metadata tag and the second metadata tags to create a first vector output file of the feature occurrence data
  • Machine Learning In Adversarial Environments

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  • US Patent:
    20180005136, Jan 4, 2018
  • Filed:
    Jul 1, 2016
  • Appl. No.:
    15/201224
  • Inventors:
    YI GAI - Hillsboro OR, US
    CHIH-YUAN YANG - Portland OR, US
    RAVI L. SAHITA - Beaverton OR, US
  • International Classification:
    G06N 99/00
    G06F 21/55
  • Abstract:
    An adversarial environment classifier training system includes feature extraction circuitry to identify a number of features associated with each sample included in an initial data set that includes a plurality of samples. The system further includes sample allocation circuitry to allocate at least a portion of the samples included in the initial data set to at least a training data set; machine-learning circuitry communicably coupled to the sample allocation circuitry, the machine-learning circuitry to: identify at least one set of compromiseable features for at least a portion of the initial data set; define a classifier loss function [l(x, y, w)] that includes: a feature vector (x) for each sample included in the initial data set; a label (y) for each sample included in the initial data set; and a weight vector (w) associated with the classifier; and determine the minmax of the classifier loss function (minmaxl(x, y, w)).
  • Methods And Apparatus To Provide Group-Based Row-Level Security For Big Data Platforms

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  • US Patent:
    20170344749, Nov 30, 2017
  • Filed:
    May 27, 2016
  • Appl. No.:
    15/166922
  • Inventors:
    - Santa Clara CA, US
    Yi Gai - Hillsboro OR, US
  • International Classification:
    G06F 21/62
    G06F 17/30
    H04L 29/06
  • Abstract:
    Methods, apparatus, systems and articles of manufacture are disclosed to facilitate electronic data security. An example apparatus includes a data storage including a memory adjusted to store data organized according to a data table including columns identifying a first data record and a first security tag associated with the first data record. In the example apparatus, retrieval of data from the data storage involves a bit operation comparing the first security tag with a first privilege tag. In the example apparatus, the data storage provides the first data record when the bit operation comparing the first security tag with the first privilege tag has a non-zero result, and the data storage does not provide the first data record when the bit operation comparing the first security tag with the first privilege tag has a zero result.

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