Lei L Tang

20120221387, Aug 30, 2012

Feb 24, 2011

13/034342

Kun Liu - Sunnyvale CA, US
Abraham Bagherjeiran - Sunnyvale CA, US
Vijay K. Narayanan - Mountain View CA, US
Rajen Subba - Mountain View CA, US
Lei Tang - Santa Clara CA, US

Yahoo! Inc. - Sunnyvale CA

G06Q 30/00

705 1416

A system for incentivizing sharing advertisements (“ads”) and associated deals with others includes a processor programmed to transmit to a user, for display in an application window of a communication device of a user, an advertisement and an associated deal with an economic incentive for sharing the advertisement with first persons in a social network of the user. The system tracks and stores referral activity by the first persons in the social network of the user in relation to the advertisement, the referral activity including the first persons sharing the advertisement with second persons. The system tracks and stores conversion activity such as purchasing by the first persons in the social network of the user in relation to the deal and purchasing by second persons referred by the first persons. The system delivers the economic incentive to the user for sharing with the first persons; for the first and second persons who share the advertisement; and/or for the first and second persons who convert the deal.

20130211906, Aug 15, 2013

Feb 15, 2012

13/397417

Ashish Sumant - Mountain View CA, US
Lei Tang - Santa Clara CA, US
Kun Liu - Sunnyvale CA, US
Wanlin Pang - Sunnyvale CA, US

Yahoo! Inc. - Sunnyvale CA

G06Q 30/02

705 1441

The present invention provides techniques for use in measuring effects of social sharing, and social sharing user characteristics, on advertisement effectiveness. Measurement information can be used in many ways, such as in optimizing advertisement campaigns and advertisement targeting. Techniques are provided in which bucket testing experiments are utilized. Advertisement performance may be tracked, including downstream advertisement performance, which may follow social sharing, in measuring differences in advertisement performance between content sharing users and content non-sharing users. Techniques are provided in which user social graph information may be used in determining downstream advertisement performance, even without information regarding specific sharing instances.

20220180401, Jun 9, 2022

Nov 11, 2021

17/524526

- Chicago IL, US
Abhaya Parthy - Palo Alto CA, US
Lei Tang - Jersey City NJ, US
Sri Subramaniam - Cupertino CA, US

G06Q 30/02
G06F 16/248
G06F 16/2457

In general, embodiments of the present invention provide systems, methods and computer readable media for a universal relevance service framework for ranking and personalizing items.

20200082315, Mar 12, 2020

Sep 7, 2018

16/125551

- San Francisco CA, US
Michael Yoshizawa - San Francisco CA, US
Gaurav Gupta - Burlingame CA, US
Ehud Milo - San Mateo CA, US
Akshay Balwally - San Francisco CA, US
Cameron Philip Bruggeman - San Francisco CA, US
Benjamin Owen Dear - Oakland CA, US
Benedict Kuester - San Francisco CA, US
Lei Tang - Milpitas CA, US
Jia Yan - San Francisco CA, US

G06Q 10/06
G06N 99/00

This disclosure describes a transportation matching system that manages the allocation of transportation providers by training and utilizing multiple machine-learning models to identify, allocate, and serve specific transportation providers with customized opportunities to relocate the transportation providers between geocoded areas in a geocoded region. For instance, the transportation matching system trains and utilizes an incremental provider model, a provider allocation model, and personalized provider behavioral models as well as a customized provider interface generator to satisfy anticipated transportation requests and improve transportation matching within a geocoded region.

20180150783, May 31, 2018

Jan 29, 2018

15/882993

- Sunnyvale CA, US
LEI TANG - Sunnyvale CA, US
VENKAT RANGAN - Sunnyvale CA, US

G06Q 10/06
G06N 3/02
G06F 15/18
G06F 17/18

A request is received for determining a task completion rate of each of a first set of tasks associated with a set of task attributes. The first set of tasks are scheduled to be completed within a first timer period. An MAPE score is calculated or obtained for each of the completion rate predictive models, which is determined based on prior predictions performed in a second time period in the past. The duration of the second time period is a multiple of the first time period. One of the predictive models is selected based on the MAPE scores of the predictive models, where the selected model has the lowest MAPE score amongst the predictive models in the set. In another embodiment, a predictive model is selected further based on the volatility scores of the predictive models. A model with a combination of lowest MAPE score and volatility score is selected.