Kim Ngoc Pham

20200090116, Mar 19, 2020

Nov 19, 2019

16/688569

- San Francisco CA, US
Yangli Hector Yee - San Francisco CA, US
Peng Ye - Foster City CA, US
Fenglin Liao - San Francisco CA, US
Li Zhang - Palo Alto CA, US
Kim Pham - San Francisco CA, US
Julian Qian - San Francisco CA, US
Benjamin Yolken - San Francisco CA, US

G06Q 10/08
G06Q 10/06
G06N 5/04
G06Q 30/02
G06N 20/00

This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.

20170308846, Oct 26, 2017

Apr 7, 2017

15/482453

- San Francisco CA, US
Yangli Hector Yee - San Francisco CA, US
Peng Ye - Foster City CA, US
Fenglin Liao - San Francisco CA, US
Li Zhang - Palo Alto CA, US
Kim Pham - San Francisco CA, US
Julian Qian - San Francisco CA, US
Benjamin Yolken - San Francisco CA, US

G06Q 10/08
G06N 5/04
G06N 99/00
G06Q 10/06

This disclosure includes systems for regression-tree-modified feature vector machine learning models for utilization prediction in time-expiring inventory. An online computing system receives a feature vector for a listing and inputs the feature vector and modified feature vectors into a demand function to generate demand estimates. The system inputs the demand estimates into a likelihood model to generate a set of request likelihoods, each request likelihood representing a likelihood that the time-expiring inventory will receive a transaction request at each of a set of test price and test times to expiration. The system further trains a regression tree model based on a set of training data comprising each of the request likelihoods from the set and the test price and test time period to expiration used to generate the demand estimate that was used to generate the request likelihood.

20170031914, Feb 2, 2017

Jul 31, 2015

14/815655

- San Francisco CA, US
Kim Pham - San Francisco CA, US
Maxim Charkov - San Francisco CA, US

G06F 17/30

A computer implemented system and method for selecting and notifying operators of the option to enable a record activation feature for a short interval of time for the records they offer in a selected geographic area. Enabling record activation for a record indicates that the record may be booked by a user without first requesting the operator to manually approve the transaction request and waiting for the operator's approval of the request. Before selecting and notifying operators, a demand for database requests is predicted. Operators that are most likely to offer their record for record activation are identified. A quality score is determined for each identified record based on the likelihood that the record will get booked once the operator has programmatically enabled record activation. The records needed to fulfil the demand for database requests are selected based on their quality score and the operators of the selected records are notified of the option to enable record activation.