Patents by Inventor Erik Ordentlich

Erik Ordentlich 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: 20240394280
    Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.
    Type: Application
    Filed: August 5, 2024
    Publication date: November 28, 2024
    Inventors: Faizaan Charania, Erik Ordentlich
  • Patent number: 12061979
    Abstract: The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of vectors for the plurality of subwords of each of the queries are obtained. Via a neural network, a vector for each of the queries is derived based on a plurality of vectors for the plurality of subwords of the query. A query/ads model is obtained via optimization with respect to an objective function, based on vectors associated with the plurality of subwords of each of the queries and vectors for the queries obtained from the neural network.
    Type: Grant
    Filed: February 9, 2018
    Date of Patent: August 13, 2024
    Assignee: YAHOO AD TECH LLC
    Inventors: Erik Ordentlich, Milind Rao, Jun Shi, Andrew Feng
  • Patent number: 12061630
    Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.
    Type: Grant
    Filed: April 7, 2023
    Date of Patent: August 13, 2024
    Assignee: YAHOO ASSETS LLC
    Inventors: Faizaan Charania, Erik Ordentlich
  • Publication number: 20230244700
    Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.
    Type: Application
    Filed: April 7, 2023
    Publication date: August 3, 2023
    Inventors: Faizaan Charania, Erik Ordentlich
  • Patent number: 11651286
    Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.
    Type: Grant
    Filed: April 22, 2022
    Date of Patent: May 16, 2023
    Assignee: Verizon Patent and Licensing Inc.
    Inventors: Andrew Feng, Erik Ordentlich, Lee Yang, Peter Cnudde
  • Patent number: 11625420
    Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.
    Type: Grant
    Filed: June 10, 2021
    Date of Patent: April 11, 2023
    Assignee: YAHOO ASSETS LLC
    Inventors: Faizaan Charania, Erik Ordentlich
  • Publication number: 20220398416
    Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. A plurality of combined neighborhoods are received from a plurality of local join executors. Each combined neighborhood represents a neighborhood of a source data point and has one or more pairs of neighbors, each of which includes the source data point, a neighbor of the source point, and a distance in-between. A plurality of KNN lists corresponding to a plurality of source data points are obtained. Each KNN list includes K neighbors to a corresponding source data point, each of which is represented by an index of the neighbor and a distance between the source data point and the neighbor. The plurality of KNN lists are updated based on the plurality of combined neighborhoods to generate updated KNN lists.
    Type: Application
    Filed: June 10, 2021
    Publication date: December 15, 2022
    Inventors: Faizaan Charania, Erik Ordentlich
  • Publication number: 20220398261
    Abstract: The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. One or more KNN lists corresponding to one or more source data points are received. Each KNN list includes K neighbors of a source data point and each of the K neighbors is a data point represented by an index. Neighbor pairs and reverse neighbor pairs are generated based on the one or more KNN lists. The neighbor pairs and reverse neighbor pairs having the same source data point are grouped to generate a grouped pairs of neighbors for the source data point. A local join operation is performed based on grouped pairs of neighbors for each source data point to generate a combined neighborhood for the source data point, which is then sent to a KNN server, where combined neighborhoods generated by multiple local join executors are integrated to update a plurality of global KNN lists.
    Type: Application
    Filed: June 10, 2021
    Publication date: December 15, 2022
    Inventors: Faizaan Charania, Erik Ordentlich
  • Publication number: 20220245525
    Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.
    Type: Application
    Filed: April 22, 2022
    Publication date: August 4, 2022
    Inventors: Andrew Feng, Erik Ordentlich, Lee Yang, Peter Cnudde
  • Patent number: 11334819
    Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.
    Type: Grant
    Filed: August 28, 2020
    Date of Patent: May 17, 2022
    Assignee: Verizon Patent and Licensing Inc.
    Inventors: Andrew Feng, Erik Ordentlich, Lee Yang, Peter Cnudde
  • Patent number: 11244351
    Abstract: The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of input vectors for the plurality of subwords of each of the queries are obtained and an input vector for each of the queries is derived based on a plurality of input vectors of a plurality of subwords of the query. A query/ads model is optimized with respect to an objective function via training an input vector for each of the plurality of subwords associated with each of the queries, an input vector for each of the advertisements and links, and a matrix.
    Type: Grant
    Filed: February 9, 2018
    Date of Patent: February 8, 2022
    Assignee: VERIZON MEDIA INC.
    Inventors: Erik Ordentlich, Milind Rao, Jun Shi, Andrew Feng
  • Publication number: 20210049507
    Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.
    Type: Application
    Filed: August 28, 2020
    Publication date: February 18, 2021
    Inventors: Andrew Feng, Erik Ordentlich, Lee Yang, Peter Cnudde
  • Patent number: 10789545
    Abstract: The present teaching relates to estimating one or more parameters on a system including a plurality of nodes. In one example, the system comprises: one or more learner nodes, each of which is configured for generating information related to a group of words for estimating the one or more parameters associated with a machine learning model; and a plurality of server nodes, each of which is configured for obtaining a plurality of sub-vectors each of which is a portion of a vector that represents a word in the group of words, updating the sub-vectors based at least partially on the information to generate a plurality of updated sub-vectors, and estimating at least one of the one or more parameters associated with the machine learning model based on the plurality of updated sub-vectors.
    Type: Grant
    Filed: April 14, 2016
    Date of Patent: September 29, 2020
    Assignee: Oath Inc.
    Inventors: Andrew Feng, Erik Ordentlich, Lee Yang, Peter Cnudde
  • Publication number: 20190251595
    Abstract: The present teaching relates to identifying content that matches a query. Train data include queries, advertisement, and hyperlinks associated with query sessions. A plurality of subwords for each of the queries in the training data are identified. A query/ads model is then trained by optimizing vectors associated the plurality of subwords for each of the queries, advertisements, and hyperlinks in the training data with respect to an objective function. At least one vector associated with each of the queries is derived based on the plurality of subword vectors in the query/ads model that represent the plurality of subwords of the query.
    Type: Application
    Filed: February 9, 2018
    Publication date: August 15, 2019
    Inventors: Erik Ordentlich, Andrew Feng, Milind Rao, Jun Shi
  • Publication number: 20190251428
    Abstract: The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of vectors for the plurality of subwords of each of the queries are obtained. Via a neural network, a vector for each of the queries is derived based on a plurality of vectors for the plurality of subwords of the query. A query/ads model is obtained via optimization with respect to an objective function, based on vectors associated with the plurality of subwords of each of the queries and vectors for the queries obtained from the neural network.
    Type: Application
    Filed: February 9, 2018
    Publication date: August 15, 2019
    Inventors: Erik Ordentlich, Milind Rao, Jun Shi, Andrew Feng
  • Publication number: 20190251594
    Abstract: The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of input vectors for the plurality of subwords of each of the queries are obtained and an input vector for each of the queries is derived based on a plurality of input vectors of a plurality of subwords of the query. A query/ads model is optimized with respect to an objective function via training an input vector for each of the plurality of subwords associated with each of the queries, an input vector for each of the advertisements and links, and a matrix.
    Type: Application
    Filed: February 9, 2018
    Publication date: August 15, 2019
    Inventors: Erik Ordentlich, Milind Rao, Jun Shi, Andrew Feng
  • Patent number: 10320420
    Abstract: Bit-flip coding uses a bit-flip encoder to flip bits in a redundancy-intersecting vector of a binary array having n rows and n columns until Hamming weights of the binary array are within a predetermined range ? of n divided by two. Information bits of an input data word to the bit-flip coding apparatus are stored in locations within the binary array that are not occupied by n redundancy bits of a redundancy vector.
    Type: Grant
    Filed: January 24, 2014
    Date of Patent: June 11, 2019
    Assignee: Hewlett-Packard Enterprise Development LP
    Inventors: Erik Ordentlich, Ron M. Roth
  • Patent number: 10175906
    Abstract: In an example, in a method for encoding data within a crossbar memory array containing cells, bits of input data may be received. The received bits of data may be mapped to the cells in a row of the memory array, in which the cells are to be assigned to one of a low resistance state and a high resistance state. A subset of the mapped bits in the row may be grouped into a word pattern. The word pattern may be arranged such that more low resistance states are mapped to cells that are located closer to a voltage source of the row of the memory array than to cells that are located farther away from the voltage source.
    Type: Grant
    Filed: July 31, 2014
    Date of Patent: January 8, 2019
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Naveen Muralimanohar, Erik Ordentlich, Cong Xu
  • Patent number: 10146619
    Abstract: According to an example, a method for assigning redundancy in encoding data onto crossbar memory arrays is provided wherein each of said crossbar memory arrays include cells. The data may be allocated to a subset of the cells in multiple crossbar memory arrays. The redundancy for the data may then be assigned based on coordinates of the subset of cells within the multiple crossbar memory arrays onto which the data is allocated.
    Type: Grant
    Filed: July 31, 2014
    Date of Patent: December 4, 2018
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Naveen Muralimanohar, Erik Ordentlich, Amit S. Sharma
  • Patent number: 10102205
    Abstract: In one implementation, a data storage system includes a memory array having memory devices in a crossbar configuration, and a memory controller for controlling data storage in the memory array. The memory controller includes an encoder to generate a 2-dimensional encoded bit pattern that encodes an input data. Each run-length of 0's and each run-length of 1's in each row or each column of the encoded bit pattern are at least of a predefined lower limit. The predefined lower limit is at least two. The memory controller includes a write controller to write the encoded bit pattern into the memory devices of the memory array, such that a number of consecutive memory devices in each row or each column of the memory array having a same state is based on the encoded bit pattern.
    Type: Grant
    Filed: January 30, 2014
    Date of Patent: October 16, 2018
    Assignee: Hewlett Packard Enterprise Development LP
    Inventors: Erik Ordentlich, Ron M. Roth