Vikram Te Saxena

7353216, Apr 1, 2008

May 2, 2005

11/120921

Mahesh Anantharaman Iyer - Burlingame CA, US
Vikram Saxena - Mountain View CA, US

Synopsys, Inc. - Mountain View CA

G06F 17/00
G06N 5/02

706 46, 706 15, 706 14

Techniques are presented for identifying blockable subsets. Blockable subsets can increase the efficiency by which solutions to a constraint set representation (CSR) can be found. Nodes of a blockable subset can be marked as “blocked” and learning or implication procedures, used as part of a CSR solving process, can be designed to skip nodes marked as blocked. The identification of a particular blockable subset is typically associated with certain conditions being true. If and when the conditions no longer hold, the nodes of the blockable subset need to be unblocked. One type of blockable subset can be identified during the operation of an implication engine (IE) by a technique called justified node blocking (JNB). Another type of blockable subset can be identified by a technique called pivot node learning (PNL). PNL can be applied in-between application of an IE and application of case-based learning.

6810482, Oct 26, 2004

Jan 26, 2001

09/771322

Vikram Saxena - Milpitas CA
Renu Mehra - Santa Clara CA

Synopsys, Inc. - Mountain View CA

G06F 132

713320

The present invention facilitates relatively accurate power consumption estimates performed at the register transfer level for scaleable circuits with similar architectural characteristics and features. A power evaluation process of the present invention includes a critical path delay based macro energy model creation process and a scaleable power consumption estimation process. In one embodiment of the present invention, the critical path delay based macro energy model creation process provides a base macro energy table and scaling functions (e. g. , a bit width scaling function and a normalizing period scaling function). The scaleable power consumption estimation process utilizes the base macro energy table and scaling functions to estimate power consumption of a circuit. The base energy macro table comprises energy values that are based upon a critical path delay period and correspond to normalized toggle rates. Different bit width circuit toggle rates are converted to normalized toggle rates based upon time periods derived from a normalizing period scaling function.

20210123748, Apr 29, 2021

Jan 5, 2021

17/141982

- San Francisco CA, US
Berk Gurakan - Palo Alto CA, US
Vikram Saxena - Cupertino CA, US
Haider Ali Razvi - Santa Clara CA, US

G01C 21/32
G01C 21/34
G01S 19/42

A network system evaluates candidate GPS routes with respect to map routes that are based on ground truth map data to identify defects in the ground truth map data. Defects in the ground truth map data may include inconsistencies between various attributes of the map data and the actual road network represented by the candidate GPS routes under evaluation. The network system corrects the identified feedback to ensure that the ground truth map data accurately reflects the attributes of the actual road network.

20210063175, Mar 4, 2021

Sep 23, 2020

16/948576

- San Francisco CA, US
Upamanyu Madhow - Santa Barbara CA, US
Vikram Saxena - Cupertino CA, US
Livia Zarnescu Yanez - Menlo Park CA, US
Chandan Prakash Sheth - Fremont CA, US
Sheng Yang - Fremont CA, US
Alvin AuYoung - San Jose CA, US

G01C 21/34
G01C 21/36
G01C 21/20
G06N 5/02
G06F 16/29

Systems and methods of using sensor data for coordinate prediction are disclosed herein. In some example embodiments, for a place, a computer system accesses corresponding service data comprising pick-up data and drop-off data for requests, and accesses corresponding sensor data indicating at least one path of mobile devices of the requesters of the requests, with the at least one path comprising at least one of a pick-up path ending at the pick-up location indicated by the pick-up data and a drop-off path beginning at the drop-off location indicated by the drop-off data. In some example embodiments, the computer system generates at least one predicted geographic location using the paths indicated by the sensor data, and stores the at least one predicted geographic location in a database in association with an identification of the place.

20190390965, Dec 26, 2019

Nov 8, 2018

16/184749

- San Francisco CA, US
Berk Gurakan - Mountain View CA, US
Vikram Saxena - Cupertino CA, US
Haider Ali Razvi - Santa Clara CA, US

G01C 21/32
G01S 19/42
G01C 21/34

A network system evaluates candidate GPS routes with respect to map routes that are based on ground truth map data to identify defects in the ground truth map data. Defects in the ground truth map data may include inconsistencies between various attributes of the map data and the actual road network represented by the candidate GPS routes under evaluation. The network system corrects the identified feedback to ensure that the ground truth map data accurately reflects the attributes of the actual road network.

20190390972, Dec 26, 2019

Jun 24, 2019

16/450560

- San Francisco CA, US
Berk Gurakan - Palo Alto CA, US
Vikram Saxena - Cupertino CA, US
Haider Ali Razvi - Santa Clara CA, US

G01C 21/36
G06F 16/29

A network system receives geographic information from a device. The geographic information is representative of a candidate GPS route used by a vehicle to complete a trip. The candidate GPS route is more efficient that a map route determined based on ground truth map data. The network system identifies an ordered sequence of one or more known road segments in the ground truth map data that the transportation vehicle uses to complete a trip represented by the candidate GPS route. To determine the ordered sequence of road segments, the network system may relax constraints on attributes of the map data to identify the ordered sequence of road segments.

20190370349, Dec 5, 2019

May 30, 2018

15/993328

- San Francisco CA, US
Vikram Saxena - Cupertino CA, US

G06F 17/30

Systems and methods for maintaining a current database of point of interest (POI) by automatically detecting a change associated with a POI based on cohort analysis are provided. A networked system accesses trip data associated with the POI. Using the analyzed trip data, the networked system generates a cohort associated with the POI. The networked system monitors for, and detects a change in, trip behavior in the cohort associated with the POI. In response to detecting the change in trip behavior, the networked system updates a database in a data store to indicate a status update.

20190180434, Jun 13, 2019

Jun 28, 2018

16/021317

- San Francisco CA, US
Minzhen Yi - Madison WI, US
Livia Zarnescu Yanez - Menlo Park CA, US
Sheng Yang - Fremont CA, US
Shivendra Pratap Singh - Redwood City CA, US
Alvin AuYoung - San Jose CA, US
Vikram Saxena - Cupertino CA, US

G06T 7/00
H04W 4/024
G06F 17/30
G06F 15/18

Systems and methods of deep learning coordinate prediction using satellite and service data are disclosed herein. In some example embodiments, for each one of a plurality of places, a computer system trains a deep learning model based on training data of the plurality of places. The deep leaning model is configured to generate a predicted geographical location of a place based on satellite image data and service data associated with the place. The training data for each place comprises satellite image data of the place, service data, and a ground truth geographical location of the place. The service data comprises at least one of pick-up data indicating a geographical location at which a provider started transporting a requester in servicing a request associated with the place or drop-off data indicating a geographical location at which the provider completed transporting the requester in servicing the request associated with the place.