Patents by Inventor Moises Goldszmidt

Moises Goldszmidt 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: 20210224687
    Abstract: Systems and methods are disclosed for triggering an update to a machine-learning model upon detecting that a distribution of particular (e.g., recently collected) input data set is sufficiently different from a distribution training input data set used to train the model. The distributions may be determined to be sufficiently different when a classifier can identify to which distribution individual data elements belong (e.g., to at least a predetermined degree). An update to the machine-learning model can include morphing weights used by the model and/or retraining the model.
    Type: Application
    Filed: May 15, 2020
    Publication date: July 22, 2021
    Applicant: Apple Inc.
    Inventors: Moises Goldszmidt, Anatoly D. Adamov, Juan C. Garcia, Julia R. Reisler, Timothy S. Paek, Vishwas Kulkarni, Yu-Chung Hsiao, Pavan Chitta
  • Patent number: 10729979
    Abstract: A system for automated tuning of a computer-implemented game is configured to enable definition of a performance metric indicative of player performance in a computer-implemented game that has tunable gameplay parameters. A performance target is defined that represents target values for the performance metric during progress in the game. The system executes a gameplay simulation using an automated player, and performs an iterative tuning operation based on results of the simulation. The tuning operation automatically determines a suggested value set for the tunable parameters.
    Type: Grant
    Filed: September 24, 2018
    Date of Patent: August 4, 2020
    Assignee: Zynga Inc.
    Inventors: Jason Bucher, Alexandros Ntoulas, Xinxian Huang, Brett Bauleke, Moises Goldszmidt, Samer Ead
  • Patent number: 10315116
    Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a Clustering Engine that determines that respective actions, performed in a first instance of a virtual environment by a first user during a first time range, correspond with a first latent state. The Clustering Engine determines that respective actions, performed in a second instance of the virtual environment by a second user during the first time range, correspond with a second latent state. The Clustering Engine triggers a first virtual environment feature based on a first latent state parameter space for the first user. The Clustering Engine triggers a second virtual environment feature based on a second latent state parameter space for the second user.
    Type: Grant
    Filed: October 7, 2016
    Date of Patent: June 11, 2019
    Assignee: Zynga Inc.
    Inventors: Alexandros Ntoulas, Moises Goldszmidt, Xuyang Tan, Yuanli Pei
  • Publication number: 20190022531
    Abstract: A system for automated tuning of a computer-implemented game is configured to enable definition of a performance metric indicative of player performance in a computer-implemented game that has tunable gameplay parameters. A performance target is defined that represents target values for the performance metric during progress in the game. The system executes a gameplay simulation using an automated player, and performs an iterative tuning operation based on results of the simulation. The tuning operation automatically determines a suggested value set for the tunable parameters.
    Type: Application
    Filed: September 24, 2018
    Publication date: January 24, 2019
    Inventors: Jason Bucher, Alexandros Ntoulas, Xinxian Huang, Brett Bauleke, Moises Goldszmidt, Samer Ead
  • Patent number: 10175054
    Abstract: A system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model including latent variables that are associated with the trip. The machine learning model may be trained from historical trip data that is based on location-based measurements reported from mobile devices. Once trained, the machine learning model may be utilized for predicting variability of travel time. A process may include receiving an origin, a destination, and a start time associated with a trip, obtaining candidate routes that run from the origin to the destination, and predicting, based at least in part on the machine learning model, a probability distribution of travel time for individual ones of the candidate routes. One or more routes may be recommended based on the predicted probability distribution, and a measure of travel time for the recommended route(s) may be provided.
    Type: Grant
    Filed: April 10, 2015
    Date of Patent: January 8, 2019
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Dawn Woodard, Eric J. Horvitz, Galina Nogin, Paul B. Koch, David Racz, Moises Goldszmidt
  • Patent number: 10115115
    Abstract: One or more all-distances sketches are generated for nodes in a graph. An all-distances sketch for a node includes a subset of the nodes of the graph, and a shortest distance between the node and each of the nodes in the subset of nodes. The generated all-distances sketches are used to estimate the closeness similarity of nodes. The estimated closeness similarity can be used for targeted advertising or for content item recommendation, for example.
    Type: Grant
    Filed: September 16, 2014
    Date of Patent: October 30, 2018
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Renato F. Werneck, Moises Goldszmidt, Andrew V. Goldberg, Edith Cohen, Daniel Delling, Fabian Fuchs
  • Patent number: 10105603
    Abstract: A system for automated tuning of a computer-implemented game is configured to enable definition of a performance metric indicative of player performance in a computer-implemented game that has tunable gameplay parameters. A performance target is defined that represents target values for the performance metric during progress in the game. The system executes a gameplay simulation using an automated player, and performs an iterative tuning operation based on results of the simulation. The tuning operation automatically determines a suggested value set for the tunable parameters.
    Type: Grant
    Filed: November 14, 2016
    Date of Patent: October 23, 2018
    Assignee: Zynga Inc.
    Inventors: Jason Bucher, Alexandros Ntoulas, Xinxian Huang, Brett Bauleke, Moises Goldszmidt, Samer Ead
  • Publication number: 20170136362
    Abstract: A system for automated tuning of a computer-implemented game is configured to enable definition of a performance metric indicative of player performance in a computer-implemented game that has tunable gameplay parameters. A performance target is defined that represents target values for the performance metric during progress in the game. The system executes a gameplay simulation using an automated player, and performs an iterative tuning operation based on results of the simulation. The tuning operation automatically determines a suggested value set for the tunable parameters.
    Type: Application
    Filed: November 14, 2016
    Publication date: May 18, 2017
    Inventors: Jason Bucher, Alexandros Ntoulas, Xinxian Huang, Brett Bauleke, Moises Goldszmidt, Samer Ead
  • Publication number: 20170100674
    Abstract: A system, a machine-readable storage medium storing instructions, and a computer-implemented method are described herein for a Clustering Engine that determines that respective actions, performed in a first instance of a virtual environment by a first user during a first time range, correspond with a first latent state. The Clustering Engine determines that respective actions, performed in a second instance of the virtual environment by a second user during the first time range, correspond with a second latent state. The Clustering Engine triggers a first virtual environment feature based on a first latent state parameter space for the first user. The Clustering Engine triggers a second virtual environment feature based on a second latent state parameter space for the second user.
    Type: Application
    Filed: October 7, 2016
    Publication date: April 13, 2017
    Inventors: Alexandros Ntoulas, Moises Goldszmidt, Xuyang Tan, Yuanli Pei
  • Patent number: 9612128
    Abstract: One or more techniques and/or systems are provided for controlling a travel route planning module associated with a user device. Travel related data, for a user and regarding previously traveled routes by the user, may be indicative of user travel preferences and/or behaviors. The travel related data is evaluated against computed routes derived from different weighting values applied to travel metrics (e.g., a cost associated with a U-turn, a highway, an industrial zone, etc.). For example, weighting values may be iteratively adjusted to generate a plurality of modified computed routes that may be evaluated to identify a target computed route having a similarity to a previously traveled route of the user above a threshold. User preference weighted travel metrics, generated based upon weighted travel metrics of the target computed route, are used to control a travel route planning module to generate a customized travel route for the user.
    Type: Grant
    Filed: April 29, 2015
    Date of Patent: April 4, 2017
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Daniel Delling, Moises Goldszmidt, Andrew V. Goldberg, John Krumm, Renato Fonseca Furquim Werneck
  • Publication number: 20160320200
    Abstract: One or more techniques and/or systems are provided for controlling a travel route planning module associated with a user device. Travel related data, for a user and regarding previously traveled routes by the user, may be indicative of user travel preferences and/or behaviors. The travel related data is evaluated against computed routes derived from different weighting values applied to travel metrics (e.g., a cost associated with a U-turn, a highway, an industrial zone, etc.). For example, weighting values may be iteratively adjusted to generate a plurality of modified computed routes that may be evaluated to identify a target computed route having a similarity to a previously traveled route of the user above a threshold. User preference weighted travel metrics, generated based upon weighted travel metrics of the target computed route, are used to control a travel route planning module to generate a customized travel route for the user.
    Type: Application
    Filed: April 29, 2015
    Publication date: November 3, 2016
    Inventors: Daniel Delling, Moises Goldszmidt, Andrew V. Goldberg, John Krumm, Renato Fonseca Furquim Werneck
  • Patent number: 9439053
    Abstract: A graph of a social network is received. The graph may include a node for each user account and an edge between nodes that represent social networking relationships such as messages between the user accounts or a friend relationship. The graph is transformed into a transformed graph where nodes have direct edges depending on a local test among its neighbors in the original graph. Small subsets of the transformed graph are categorized. The categories are used to identify subgraphs in the transformed graph. Each subgraph is grown by adding an edge from the transformed graph to the subgraph depending on local tests among nodes associated with the edge that have at least one edge that is already in the subgraph. The categorized subgraphs are used to provide targeted advertising, suggest new connections, identify different personalities and interests of users, or to provide other services.
    Type: Grant
    Filed: January 30, 2013
    Date of Patent: September 6, 2016
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Ittai Abraham, Joseph K. Bradley, Shiri Chechik, Moises Goldszmidt, Aleksandrs Slivkins, David Kempe
  • Publication number: 20160202074
    Abstract: A system for predicting variability of travel time for a trip at a particular time may utilize a machine learning model including latent variables that are associated with the trip. The machine learning model may be trained from historical trip data that is based on location-based measurements reported from mobile devices. Once trained, the machine learning model may be utilized for predicting variability of travel time. A process may include receiving an origin, a destination, and a start time associated with a trip, obtaining candidate routes that run from the origin to the destination, and predicting, based at least in part on the machine learning model, a probability distribution of travel time for individual ones of the candidate routes. One or more routes may be recommended based on the predicted probability distribution, and a measure of travel time for the recommended route(s) may be provided.
    Type: Application
    Filed: April 10, 2015
    Publication date: July 14, 2016
    Inventors: Dawn Woodard, Eric J. Horvitz, Galina Nogin, Paul B. Koch, David Racz, Moises Goldszmidt
  • Publication number: 20160078148
    Abstract: One or more all-distances sketches are generated for nodes in a graph. An all-distances sketch for a node includes a subset of the nodes of the graph, and a shortest distance between the node and each of the nodes in the subset of nodes. The generated all-distances sketches are used to estimate the closeness similarity of nodes. The estimated closeness similarity can be used for targeted advertising or for content item recommendation, for example.
    Type: Application
    Filed: September 16, 2014
    Publication date: March 17, 2016
    Inventors: Renato F. Werneck, Moises Goldszmidt, Andrew V. Goldberg, Edith Cohen, Daniel Delling, Fabian Fuchs
  • Patent number: 8904209
    Abstract: Power consumption of computing devices are monitored with performance counters and used to generate a power model for each computing device. The power models are used to estimate the power consumption of each computing device based on the performance counters. Each computing device is assigned a power cap, and a software-based power control at each computing device monitors the performance counters, estimates the power consumption using the performance counters and the model, and compares the estimated power consumption with the power cap. Depending on whether the estimated power consumption violates the power cap, the power control may transition the computing device to a lower power state to prevent a violation of the power cap or a higher power state if the computing device is below the power cap.
    Type: Grant
    Filed: November 14, 2011
    Date of Patent: December 2, 2014
    Assignee: Microsoft Corporation
    Inventors: John D. Davis, Moises Goldszmidt, Suzanne M. Rivoire
  • Publication number: 20140214936
    Abstract: A graph of a social network is received. The graph may include a node for each user account and an edge between nodes that represent social networking relationships such as messages between the user accounts or a friend relationship. The graph is transformed into a transformed graph where nodes have direct edges depending on a local test among its neighbors in the original graph. Small subsets of the transformed graph are categorized. The categories are used to identify subgraphs in the transformed graph. Each subgraph is grown by adding an edge from the transformed graph to the subgraph depending on local tests among nodes associated with the edge that have at least one edge that is already in the subgraph. The categorized subgraphs are used to provide targeted advertising, suggest new connections, identify different personalities and interests of users, or to provide other services.
    Type: Application
    Filed: January 30, 2013
    Publication date: July 31, 2014
    Applicant: Microsoft Corporation
    Inventors: Ittai Abraham, Joseph K. Bradley, Shiri Chechik, Moises Goldszmidt, Aleksandrs Slivkins, David Kempe
  • Patent number: 8504874
    Abstract: In a distributed system a plurality of devices (including computing units, storage and communication units) are monitored by an automated repair service that uses sensors and performs one or more repair actions on computing devices that are found to fail according to repair policies. The repair actions include automated repair actions and non-automated repair actions. The health of the computing devices is recorded in the form of states along with the repair actions that were performed on the computing devices and the times at which the repair actions were performed, and events generated by both sensors and the devices themselves. After some period of the time, the history of states of each device, the events, and the repair actions performed on the computing devices are analyzed to determine the effectiveness of the repair actions.
    Type: Grant
    Filed: September 21, 2010
    Date of Patent: August 6, 2013
    Assignee: Microsoft Corporation
    Inventors: Moises Goldszmidt, Mihai Budiu, Yue Zhang, Michael Pechuk
  • Publication number: 20130124885
    Abstract: Power consumption of computing devices are monitored with performance counters and used to generate a power model for each computing device. The power models are used to estimate the power consumption of each computing device based on the performance counters. Each computing device is assigned a power cap, and a software-based power control at each computing device monitors the performance counters, estimates the power consumption using the performance counters and the model, and compares the estimated power consumption with the power cap. Depending on whether the estimated power consumption violates the power cap, the power control may transition the computing device to a lower power state to prevent a violation of the power cap or a higher power state if the computing device is below the power cap.
    Type: Application
    Filed: November 14, 2011
    Publication date: May 16, 2013
    Applicant: Microsoft Corporation
    Inventors: John D. Davis, Moises Goldszmidt, Suzanne M. Rivoire
  • Patent number: 8380960
    Abstract: In a distributed storage system such as those in a data center or web based service, user characteristics and characteristics of the hardware such as storage size and storage throughput impact the capacity and performance of the system. In such systems, an allocation is a mapping from the user to the physical storage devices where data/information pertaining to the user will be stored. Policies regarding quality of service and reliability including replication of user data/information may be provided by the entity managing the system. A policy may define an objective function which quantifies the value of a given allocation. Maximizing the value of the allocation will optimize the objective function. This optimization may include the dynamics in terms of changes in patterns of user characteristics and the cost of moving data/information between the physical devices to satisfy a particular allocation.
    Type: Grant
    Filed: November 4, 2008
    Date of Patent: February 19, 2013
    Assignee: Microsoft Corporation
    Inventors: Hongzhong Jia, Moises Goldszmidt
  • Publication number: 20120072769
    Abstract: In a distributed system a plurality of devices (including computing units, storage and communication units) are monitored by an automated repair service that uses sensors and performs one or more repair actions on computing devices that are found to fail according to repair policies. The repair actions include automated repair actions and non-automated repair actions. The health of the computing devices is recorded in the form of states along with the repair actions that were performed on the computing devices and the times at which the repair actions were performed, and events generated by both sensors and the devices themselves. After some period of the time, the history of states of each device, the events, and the repair actions performed on the computing devices are analyzed to determine the effectiveness of the repair actions.
    Type: Application
    Filed: September 21, 2010
    Publication date: March 22, 2012
    Applicant: Microsoft Corporation
    Inventors: Moises Goldszmidt, Mihai Budiu, Yue Zhang, Michael Pechuk