Jason T Cramer

US Patent:

20230024574, Jan 26, 2023

Filed:

Nov 17, 2020

Appl. No.:

17/779538

Inventors:

- Emeryville CA, US
Carmen Yaiza Rancel Gil - Barcelona, ES
Anjana Gopakumar - Oakland CA, US
Jason Timothy Cramer - Brooklyn NY, US

International Classification:

G06F 16/635
G06F 16/638
G06Q 30/06
G06F 16/22
G06F 16/68

Abstract:

Methods and apparatus are disclosed to generate a recommendation, including an attribute vector aggregator to form a resultant attribute vector based on an input set of attribute vectors, the set of attribute vectors containing at least one of a media attribute vector, an attendee attribute vector, an artist attribute vector, an event attribute vector, or a venue attribute vector, and a recommendation generator, the recommendation generator including: a vector comparator to perform a comparison between an input attribute vector and other attribute vectors and a recommendation compiler to create one or more recommendations of at least one of media, an artist, an event, or a venue based on the comparison.

US Patent:

20210157835, May 27, 2021

Filed:

Nov 25, 2019

Appl. No.:

16/695169

Inventors:

- Emeryville CA, US
Carmen Yaiza Rancel Gil - Barcelona, ES
Anjana Gopakumar - Oakland CA, US
Jason Timothy Cramer - Brooklyn NY, US

International Classification:

G06F 16/635
G06F 16/638
G06F 16/68
G06F 16/22
G06Q 30/06

Abstract:

Methods and apparatus are disclosed to generate a recommendation, including an attribute vector aggregator to form a resultant attribute vector based on an input set of attribute vectors, the set of attribute vectors containing at least one of a media attribute vector, an attendee attribute vector, an artist attribute vector, an event attribute vector, or a venue attribute vector, and a recommendation generator, the recommendation generator including: a vector comparator to perform a comparison between an input attribute vector and other attribute vectors and a recommendation compiler to create one or more recommendations of at least one of media, an artist, an event, or a venue based on the comparison.

US Patent:

20210149939, May 20, 2021

Filed:

Jan 25, 2021

Appl. No.:

17/157796

Inventors:

- Emeryville CA, US
Jason Cramer - Berkeley CA, US
Phillip Popp - Oakland CA, US
Cameron Aubrey Summers - Oakland CA, US

International Classification:

G06F 16/35
G06N 3/04
G06F 16/41
G06F 16/61

Abstract:

A neural network-based classifier system can receive a query including a media signal and, in response, provide an indication that a particular received query corresponds to a known media type or media class. The neural network-based classifier system can select and apply various models to facilitate media classification. In an example embodiment, classifying a media query includes accessing digital media data and a context parameter from a first device. A model for use with the network-based classifier system can be selected based on the context parameter. In an example embodiment, the network-based classifier system provides a media type probability index for the digital media data using the selected model and spectral features corresponding to the digital media data. In an example embodiment, the digital media data includes an audio or video signal sample.

US Patent:

20170193362, Jul 6, 2017

Filed:

Jun 17, 2016

Appl. No.:

15/185616

Inventors:

- Emeryville CA, US
Jason Cramer - Berkeley CA, US
Phillip Popp - Oakland CA, US
Cameron Aubrey Summers - Oakland CA, US

International Classification:

G06N 3/08
G06F 17/30

Abstract:

A neural network-based classifier system can receive a query including a media signal and, in response, provide an indication that a particular received query corresponds to a known media type or media class. The neural network-based classifier system can select and apply various models to facilitate media classification. In an example embodiment, classifying a media query includes accessing digital media data and a context parameter from a first device. A model for use with the network-based classifier system can be selected based on the context parameter. In an example embodiment, the network-based classifier system provides a media type probability index for the digital media data using the selected model and spectral features corresponding to the digital media data. In an example embodiment, the digital media data includes an audio or video signal sample.