Roman Akulshin

US Patent:

20190012746, Jan 10, 2019

Filed:

Feb 1, 2018

Appl. No.:

15/886523

Inventors:

Dion Sullivan - Mill Valley CA, US
Joshua Heenan - Oakland CA, US
Roman Akulshin - Walnut Creek CA, US
Eric Crook - San Francisco CA, US

International Classification:

G06Q 50/00
G06N 99/00

Abstract:

A non-transitory computer readable storage media having computer-executable instructions, when executed by a processor, performs a method for evaluating a reach of a social media influencer. The methods provides for receiving a plurality of influencers at a server, wherein a data set is associated with each of the plurality of influencers; parsing the data set into quantitative data readable by a machine learning algorithm at the server; receiving, inputting, or both, a type of product or service at the server; classifying the type of product or service into at least one class of goods or services; training a node using the machine learning algorithm using the date set an input; and executing the machine learning algorithm to determine a score of each influencer for each class of goods or services. Systems for evaluating the reach of a social media influencer as it relates to advertisers and content is also disclosed herein.

US Patent:

20180189709, Jul 5, 2018

Filed:

Jan 22, 2018

Appl. No.:

15/877118

Inventors:

Dion Sullivan - Mill Valley CA, US
Joshua Heenan - Oakland CA, US
Roman Akulshin - Walnut Creek CA, US
Eric Crook - San Francisco CA, US

International Classification:

G06Q 10/06
G07C 13/00
G06Q 10/10
G06N 99/00
G06K 9/66
G06K 9/00
G06K 9/62

Abstract:

A method for analyzing media to evaluate talent of a subject portrayed in the media is provided. The steps describe receiving a plurality of the media at a server, the media being performance based media; transforming the media into at least a data set corresponding to at least a characteristic of the media at the server, wherein the server is in communication with a node; identifying features in the data set that correspond to a probability of success of the subject in the media; receiving subjective opinion polling data from a plurality of participants as to whether the subject in the media has talent; training the node via machine learning algorithm to identify, using the identified features in the data set and the subjective opinion polling data provided, whether the subject is likely to have talent; and executing the machine learning algorithm to output a score relating to the talent of the subject in the media. A system for discovering talent is also provided herein.