Yifan Hu75 Bellvale Rd, Mountain Lakes, NJ 07046

2010014, Jun 17, 2010

Dec 11, 2008

12/332477

Emden R. Gansner - Morristown NJ, US
Yifan Hu - Mountain Lakes NJ, US

G06T 11/20

345440

Methods, systems, and products are disclosed for graphing data. A layout is retrieved that comprises locations for vertices. A proximity graph is generated using triangulation. Nodal overlaps are removed.

2011013, Jun 9, 2011

Dec 8, 2009

12/633233

Yifan Hu - Mountain Lakes NJ, US
Emden Gansner - Morristown NJ, US
Stephen Kobourov - Tucson AZ, US
Christopher T. Volinsky - Morristown NJ, US

AT&T Intellectual Property I, L.P. - Reno NV

G06T 11/20

345440

Relational data can be visualized in a map representation. A plurality of vertices and edges are determined based on a set of data identifying objects (i.e., vertices) and relationships (i.e., edges). The vertices and edges are embedded in a planar graph and a Voronoi diagram is generated based at least the plurality of vertices within a set of bounding-points located at least a distance away from the vertices of the map. A bounding-box is associated with each of the vertices, and a set of points generated along each bounding-box. The Voronoi diagram is generated based on the vertices and the set of points of each bounding-box. Each resulting Voronoi cell associated with a common vertex is merged to form a plurality of common cells (i.e., map). The size of each bounding-box can correlate to the importance of its associated vertex, and the points along the bounding-box can be randomly perturbed.

2011015, Jun 23, 2011

Dec 21, 2009

12/643237

Yehuda Koren - Elizabeth NJ, US
Yifan Hu - Mountain Lakes NJ, US
Christopher T. Volinsky - Morristown NJ, US

AT&T INTELLECTUAL PROPERTY I, L.P. - Reno NV

G06F 17/30

707776, 707E17039

Systems and methods to provide a recommendation engine that uses implicit feedback observations are provided. A particular method includes receiving accessing data comprising a plurality of implicit feedback observations for a plurality of users. The plurality of users includes a first user that requested a recommendation. Each implicit feedback observation is associated with a particular user and a particular item of a plurality of items. The method includes determining a plurality of preference ratings and a plurality of confidence ratings for each user of the plurality of users for each item based on the plurality of implicit feedback observations. The method includes generating a recommendation list of one or more of the plurality of items for the first user based on the plurality of preference ratings and the plurality of confidence ratings.

2010013, Jun 3, 2010

Dec 2, 2008

12/326185

Yifan Hu - Mountain Lakes NJ, US
Yehuda Koren - Zichron Yaakov, IL

G06T 11/20

345440

Methods, systems, and products are disclosed for graphing data. A layout is retrieved that comprises locations for vertices. A proximity location is generated for each vertex. Each vertex's location from the layout is merged with each vertex's proximity location. A cost function associated with the layout is minimized.

2022035, Nov 10, 2022

May 7, 2021

17/314137

- New York NY, US
Changwei Hu - New Providence NJ, US
Kevin Yen - Jersey City NJ, US
Manisha Verma - Secaucus NJ, US
Yifan Hu - Mountain Lakes NJ, US
Maxim Ivanovich Sviridenko - New York NY, US
Avinash Chukka - Milpitas CA, US
Max Edward Beech - Surrey, GB
Chao-Hung Wang - Taipei City, TW
Hua-Ying Tsai - Hsinchu City, TW
Kamil Michal Zasadzinski - Taipei City, TW
Wei Yu Lin - Taipei City, TW
Yu Tian - Secaucus NJ, US

G06F 16/35
G06K 9/62
G06N 20/00
G06F 16/335

One or more computing devices, systems, and/or methods are provided. In an example, a first performance metric score may be determined based upon first content item text. A plurality of similarity scores associated with a plurality of sets of content item text may be determined. One or more sets of content item text may be selected from among the plurality of sets of content item text based upon the plurality of similarity scores and a plurality of performance metric scores associated with the plurality of sets of content item text. The plurality of performance metric scores may comprise one or more performance metric scores associated with the one or more sets of content item text. The one or more performance metric scores may be higher than the first performance metric score. One or more representations of the one or more sets of content item text may be displayed.

2022029, Sep 15, 2022

Mar 12, 2021

17/199884

- New York NY, US
Yifan HU - Mountain Lakes NJ, US
Francis HSU - Santa Clara CA, US
Lachlan MAXWELL - Ashburn VA, US
Saurabh TEWARI - Sunnyvale CA, US
Durga Sankari Sundara MANICKAM - Sunnyvale CA, US

H04L 29/06
H04L 29/08
G06N 20/00
G06N 5/04

The disclosed systems and methods provide a novel framework that leverages collected logged-in daily active users (LIDAU) data to drive network traffic to network resources, as well as engage these users to continue or remain engaged with the resources through personalization and customization according to their behaviors and patterns. LIDAU data, which is based on a raw data feed of information from network resources and an identified dataset determined from the raw data feed, is used as a basis to increase LIDAU for a specific user or a set of other users. The determined understanding of LIDAU, and its impact on users as well as the network resources the users are interacting with, enables the framework to personalize the resources that are anticipated as being visited by the users in advance of the users visiting them.

2022023, Jul 28, 2022

Apr 12, 2022

17/718664

- Atlanta GA, US
Yifan Hu - Mountain Lakes NJ, US
Robert Bell - Murray Hill NJ, US
Jeffrey Pang - Berkeley CA, US
Jeffrey Erman - Morristown NJ, US

AT&T Intellectual Property I, L.P. - Atlanta GA

H04L 65/60
H04N 21/454
H04N 21/433
H04N 21/81
H04N 21/466
H04L 67/306
H04L 67/02
H04W 4/70
G06F 16/9032
H04L 67/50
H04N 21/414
H04L 65/1016
G06Q 30/06
G06Q 50/00
H04L 65/1069
H04L 65/612
H04L 65/80
H04L 67/568
G06F 16/957
H04L 67/30

A method that incorporates teachings of the subject disclosure may include, for example transmitting, via a connection to a network having an available digital bandwidth, a first pre-fetch media content item of media content items included in the a media recommendation transmitted to a communication device, the first pre-fetch media content item having a first rank. Responsive to a determination of a change in the available digital bandwidth, the transmitting of the first pre-fetch media content item is stopped and a second pre-fetch media content item of media content items included in the media recommendation is transmitted via the connection to the network. The second pre-fetch media content item has a second rank. Other embodiments are disclosed.

2022019, Jun 23, 2022

Dec 17, 2020

17/125660

- BASKING RIDGE NJ, US
Yifan Hu - Mountain Lakes NJ, US
Changwei Hu - New Providence NJ, US
Keqian Li - New York NY, US
Kevin Yen - Jersey City NJ, US

G06F 40/166
G06F 40/284
G06K 9/62
G06N 20/00

The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including a plurality of text strings, a plurality of manipulated text strings are generated for each of the plurality of training text strings by first applying a manipulation to each of at least one original token in the text string to generate a manipulated token, where the original token has a ground truth token label and then determining, with respect to each manipulated token, a ground truth action which, when applied to the manipulated token, yields the original token with the ground truth token label. Training data are generated with a plurality of training data packs, each of which corresponds to one of the plurality of text strings in the input data and includes a manipulated text string with at least one manipulated token, at least one ground truth token label, and at least one ground truth action which, when applied to the at least one manipulated token produces the at least one ground truth token label. The training data are for training text moderation models that facilitate text moderation.