Sumit Chopra, Age 481433 Valley Ave, Pleasanton, CA 94566

8400809, Mar 19, 2013

Mar 24, 2011

13/071303

Aidan Shori - Plano TX, US
Sumit Chopra - Richardson TX, US

Micron Technology, Inc. - Boise ID

G11C 5/06

365 63, 36518917, 36518905, 36523003

A memory array contains a plurality of banks coupled to each other by a plurality of data lines. Each of the data lines is divided into a plurality of segments within the array. Respective bidirectional buffers couple read data from one of the segments to another in a first direction, and to couple write data from one of the segments to another in a second direction that is opposite the first direction. The data lines may be local data read/write lines that couple different banks of memory cells to each other and to respective data terminals, digit lines that couple memory cells in a respective column to respective sense amplifiers, word lines that activate memory cells in a respective row, or some other signal line within the array. The memory array also includes precharge circuits for precharging the segments of respective data lines to a precharge voltage.

2010017, Jul 15, 2010

Jan 14, 2009

12/353661

Aidan Shori - Plano TX, US
Sumit Chopra - Richardson TX, US

MICRON TECHNOLOGY, INC. - Boise ID

G11C 7/00
G11C 8/00
H03L 5/00

36518905, 365189011, 36523003, 327108, 365203

A memory array contains a plurality of banks coupled to each other by a plurality of data lines. Each of the data lines is divided into a plurality of segments within the array. Respective bidirectional buffers couple read data from one of the segments to another in a first direction, and to couple write data from one of the segments to another in a second direction that is opposite the first direction. The data lines may be local data read/write lines that couple different banks of memory cells to each other and to respective data terminals, digit lines that couple memory cells in a respective column to respective sense amplifiers, word lines that activate memory cells in a respective row, or some other signal line within the array. The memory array also includes precharge circuits for precharging the segments of respective data lines to a precharge voltage.

2013021, Aug 22, 2013

Mar 18, 2013

13/846452

Micron Technology, Inc. - , US
Sumit Chopra - Allen TX, US

Micron Technology, Inc. - Boise ID

G11C 7/10

365203

A memory array contains a plurality of banks coupled to each other by a plurality of data lines. Each of the data lines is divided into a plurality of segments within the array. Respective bidirectional buffers couple read data from one of the segments to another in a first direction, and to couple write data from one of the segments to another in a second direction that is opposite the first direction. The data lines may be local data read/write lines that couple different banks of memory cells to each other and to respective data terminals, digit lines that couple memory cells in a respective column to respective sense amplifiers, word lines that activate memory cells in a respective row, or some other signal line within the array. The memory array also includes precharge circuits for precharging the segments of respective data lines to a precharge voltage.

2019034, Nov 7, 2019

Jul 15, 2019

16/512128

- Menlo Park CA, US
Keith Adams - Palo Alto CA, US
Sumit Chopra - Jersey City NJ, US

G06N 20/00
G06Q 50/00

In one embodiment, a method includes retrieving a first vector representation of a first entity, with which a user has interacted, and a second vector representation of a second entity, with which the user has not interacted. The first and second vector representations are determined using an initial deep-learning model. A first similarity score is computed between a vector representation of the user and the first vector representation, and a second similarity score is computed between the vector representation of the user and the second vector representation. The second vector representation is updated if the second similarity score is greater than the first similarity score using the initial deep-learning model. An updated deep-learning model is generated based on the initial deep-learning model and on the updated second vector representation.

2019033, Oct 31, 2019

Jul 8, 2019

16/505521

- Menlo Park CA, US
Keith Adams - Palo Alto CA, US
Sumit Chopra - Jersey City NJ, US

G06F 16/33
G06N 3/04

In one embodiment, a method includes receiving, from a client system, a text input comprising one or more n-grams, determining, using a deep-learning model, a vector representation of the text input based on the one or more n-grams, determining an embedding of the vector representation of the text input in a d-dimensional embedding space, identifying one or more labels based on, for each of the one or more labels, a respective similarity of an embedding of a vector representation of the label in the embedding space to the embedding of the vector representation of the text input, and sending, to the client system in response to the received text input, instructions for presenting a user interface comprising one or more of the identified labels as suggested labels.

2018033, Nov 22, 2018

Jul 31, 2018

16/051281

- Boise ID, US
Sumit Chopra - Fremont CA, US

MICRON TECHNOLOGY, INC. - Boise ID

G11C 7/12
G11C 11/4097
G11C 11/4096
G11C 11/4094
G11C 8/10
G11C 7/10
G11C 8/06
G11C 7/22
G11C 8/08

A memory array contains a plurality of banks coupled to each other by a plurality of data lines. Each of the data lines is divided into a plurality of segments within the array. Respective bidirectional buffers couple read data from one of the segments to another in a first direction, and to couple write data from one of the segments to another in a second direction that is opposite the first direction. The data lines may be local data read/write lines that couple different banks of memory cells to each other and to respective data terminals, digit lines that couple memory cells in a respective column to respective sense amplifiers, word lines that activate memory cells in a respective row, or some other signal line within the array. The memory array also includes precharge circuits for precharging the segments of respective data lines to a precharge voltage.

2018011, Apr 26, 2018

Dec 20, 2017

15/848891

- Menlo Park CA, US
Lubomir Bourdev - Mountain View CA, US
Robert D. Fergus - New York NY, US
Sumit Chopra - Jersey City NJ, US

G06K 9/00

Systems, methods, and non-transitory computer-readable media can acquire video content for which video feature descriptors are to be determined. The video content can be processed based at least in part on a convolutional neural network including a set of two-dimensional convolutional layers and a set of three-dimensional convolutional layers. One or more outputs can be generated from the convolutional neural network. A plurality of video feature descriptors for the video content can be determined based at least in part on the one or more outputs from the convolutional neural network.

2017019, Jul 6, 2017

Dec 30, 2015

14/984956

- Menlo Park CA, US
Keith Adams - Palo Alto CA, US
Sumit Chopra - Jersey City NJ, US

G06N 99/00
G06Q 50/00

In one embodiment, a method includes accessing a first set of entities, with which a user has interacted, and a second set of entities in a social-networking system. A first set of vector representations of the first set of entities are determined using a deep-learning model. A target entity is selected from the first set of entities, and the vector representation of the target entity is removed from the first set. The remaining vector representations in the first set are combined to determine a vector representation of the user. A second set of vector representations of the second set of entities are determined using the deep-learning model. Similarity scores are computed between the user and each of the target entity and the entities in the second set of entities. Vector representations of entities in the second set of entities are updated based on the similarity scores using the deep-learning model.