Arjun A Gupta

US Patent:

20230110027, Apr 13, 2023

Filed:

Sep 29, 2021

Appl. No.:

17/449306

Inventors:

- Santa Clara CA, US
Arjun Gupta - Stanford CA, US
George Tang - Alexandria VA, US
Hae-Jong Seo - Campbell CA, US

International Classification:

B60W 30/095
B60W 30/09
B60W 40/02
G06N 3/08
G06K 9/62

Abstract:

In various examples, systems and methods are disclosed that use one or more machine learning models (MLMs) - such as deep neural networks (DNNs) - to compute outputs indicative of an estimated visibility distance corresponding to sensor data generated using one or more sensors of an autonomous or semi-autonomous machine. Once the visibility distance is computed using the one or more MLMs, a determination of the usability of the sensor data for one or more downstream tasks of the machine may be evaluated. As such, where an estimated visibility distance is low, the corresponding sensor data may be relied upon for less tasks than when the visibility distance is high.

US Patent:

20230101307, Mar 30, 2023

Filed:

Jul 7, 2022

Appl. No.:

17/860008

Inventors:

Martin Gammon - Mill Valley CA, US
Arjun Gupta - Tiburon CA, US

International Classification:

G06Q 30/08

Abstract:

A process for determining open interest in an artwork and creating an emerging demand graph or chart is described. This is done by displaying information on the artwork for the public to view. An absentee bidding period is set wherein the online platform accepts absentee bids for the artwork. Certain absentee bids are used to calculate an open interest index in the artwork which is used to adjust the emerging demand graph. This open interest index can be used to measure interest in the artwork before the actual auction of the artwork begins. The actual auction is a modified Dutch auction that uses data from the absentee bidding period, such as the highest absentee bid.