Hua Y Li

8090648, Jan 3, 2012

Mar 4, 2009

12/398141

Scott M. Zoldi - San Diego CA, US
Hua Li - San Diego CA, US
Xinwei Xue - San Diego CA, US

Fair Isaac Corporation - Minneapolis MN

G06Q 40/00

705 38

A computerized method for detecting fraud includes obtaining frequency information on entities in transaction data for at least one individual account, converting frequency information to a frequency variable, and predicting whether an activity is fraudulent in response to the frequency variable. In some embodiments, the frequency variable is used with at least one other variable to predict fraudulent activity.

20120101937, Apr 26, 2012

Dec 29, 2011

13/340469

Scott M. Zoldi - San Diego CA, US
Hua Li - San Diego CA, US
Xinwei Xue - San Diego CA, US

G06Q 40/00

705 38

A computerized method for detecting fraud includes obtaining frequency information on entities in transaction data for at least one individual account, converting frequency information to a frequency variable, and predicting whether an activity is fraudulent in response to the frequency variable. In some embodiments, the frequency variable is used with at least one other variable to predict fraudulent activity.

20210241120, Aug 5, 2021

Jan 28, 2021

17/161525

- Costa Mesa CA, US
Mason L. Carpenter - Richmond VA, US
Yi He - San Diego CA, US
Hua Li - San Diego CA, US
Zhixuan Wang - San Diego CA, US
Christer DiChiara - Carlsbad CA, US
Sophie Liu - San Diego CA, US
Eric Haller - Encinitas CA, US
Shanji Xiong - San Diego CA, US
Honghao Shan - San Diego CA, US
Liang Lin - San Diego CA, US
Brian Duke - Poway CA, US
Chi Zhang - San Diego CA, US
Doris Wang - San Diego CA, US
Seth Kressin - Carlsbad CA, US

G06N 3/08
G06N 3/04

Systems and methods are provided for implementing machine learning techniques to distinguish a real identity, such as a set of identity information representing a real person, from a synthetic identity that may include portions of real identity information. Attributes regarding a target identity derived from a variety of retrieved data records may be provided as input to multiple machine learning models that generate scores associated with the potential of the target identity being synthetic. The scores may be combined and compared to a threshold to generate a determination of whether the target identity is a synthetic identity.

20200359228, Nov 12, 2020

May 9, 2019

16/408070

Alexander Sirotkin - Tel-Aviv, IL
Alexei Davydov - Nizhny Novgorod, RU
Andrey Chervyakov - Nizhny Novgorod, RU
Anthony Lee - San Diego CA, US
Avik Sengupta - San Jose CA, US
Bharat Shrestha - Hillsboro OR, US
Candy Yiu - Portland OR, US
Chang Hong Shan - Portland OR, US
Dae Won Lee - Portland OR, US
Dawei Ying - Hillsboro OR, US
Debdeep Chatterjee - San Jose CA, US
Farid Adrangi - Lake Oswego OR, US
Gang Xiong - Beaverton OR, US
Geng Wu - Portland OR, US
Gregory Morozov - Nizhny Novgorod, RU
Hassan Ghozlan - Hillsboro OR, US
Hong He - Beijing, CN
Honglei Miao - Nuernberg, DE
Hua Li - Arlington VA, US
Jaemin Han - Hillsboro OR, US
Jeongho Jeon - San Jose CA, US
Jie Cui - Santa Clara CA, US
Jing Zhu - Portland OR, US
Joey Chou - Scottsdale AZ, US
Joonbeom Kim - Carrollton TX, US
Joonyoung Cho - Santa Clara CA, US
Lu Lu - Hillsboro OR, US
May Wu - Shanghai, CN
Naveen Palle - San Diego CA, US
Puneet Jain - Hillsboro OR, US
Qian Li - Beaverton OR, US
Richard C. Burbidge - Shrivenham, GB
Robert Zaus - Munchen, DE
Rui Huang - Beijing, CN
Satish Chandra Jha - Hillsboro OR, US
Seau S. Lim - Swindon, GB
Sergey Panteleev - Nizhny Novgorod, RU
Song Noh - Hillsboro OR, US
Yaser M. Fouad - Hillsboro OR, US
Yi Guo - Shanghai, CN
Yizhi Yao - Chandler AZ, US
Yongjun Kwak - Portland OR, US
Youn Hyoung Heo - Seoul, KR
Yuhan Zhou - Santa Clara CA, US
Yujian Zhang - Beijing, CN
Yushu Zhang - Beijing, CN

H04W 16/14
H04L 5/00
H04W 28/20

Embodiments of apparatus and methods for signaling for resource allocation and scheduling in 5G-NR integrated access and backhaul are generally described herein. In some embodiments, User Equipment configured for reporting a channel quality indicator (CQI) index in a channel state information (CSI) reference resource assumes a physical resource block (PRB) bundling size of two PRBs to derive the CQI index.

20200226284, Jul 16, 2020

Jan 9, 2020

16/738942

- Costa Mesa CA, US
Kevin Chen - San Diego CA, US
Hua Li - San Diego CA, US
Juncheng Liu - San Diego CA, US
Zhixuan Wang - San Diego CA, US
Shumin Guo - Irvine CA, US
Eric Haller - Encinitas CA, US

G06F 21/62
G06Q 50/26
G06Q 40/02
G06F 21/60
G06F 16/9535

Systems and methods for data aggregation and processing are provided in manner that is decentralized and preserves privacy. A data aggregation and computation system may include an interface, a controller, and one or more clusters of computation nodes. The interface may receive an inquiry from a requesting entity for computing information regarding an individual based on pieces of information held by a plurality of entities. The controller may communicate an identifier for the individual to a processor system associated with each of the entities based on the inquiry. The clusters of computation nodes may each receive encrypted data fragments from each of the processor systems, the data fragments comprising unrecognizable fragments that no individual processor system can re-assemble to recover the information, perform secure, multi-party computations based on the data fragments, and generate a result based on the secure, multi-party computations for the individual.

20190334600, Oct 31, 2019

Nov 13, 2018

16/189157

Alexander Sirotkin - Tel-Aviv, IL
Alexei Davydov - Nizhny Novgorod, RU
Andrey Chervyakov - Nizhny Novgorod, RU
Anthony Lee - San Diego CA, US
Avik Sengupta - San Jose CA, US
Bharat Shrestha - Hillsboro OR, US
Candy Yiu - Portland OR, US
Chang Hong Shan - Portland OR, US
Dae Won Lee - Portland OR, US
Dawei Ying - Hillsboro OR, US
Debdeep Chatterjee - San Jose CA, US
Farid Adrangi - Lake Oswego OR, US
Gang Xiong - Beaverton OR, US
Geng Wu - Portland OR, US
Gregory Morozov - Nizhny Novgorod, RU
Hassan Ghozlan - Hillsboro OR, US
Hong He - Beijing, CN
Honglei Miao - Nuernberg, DE
Hua Li - Arlington VA, US
Jaemin Han - Hillsboro OR, US
Jeongho Jeon - San Jose CA, US
Jie Cui - Santa Clara CA, US
Jing Zhu - Portland OR, US
Joey Chou - Scottsdale AZ, US
Joonbeom Kim - Carrollton TX, US
Joonyoung Cho - Santa Clara CA, US
Lu Lu - Hillsboro OR, US
May Wu - Shanghai, CN
Naveen Palle - San Diego CA, US
Puneet Jain - Hillsboro OR, US
Qian Li - Beaverton OR, US
Richard C. Burbidge - Shrivenham, GB
Robert Zaus - Munchen, DE
Rui Huang - Beijing, CN
Satish Chandra Jha - Hillsboro OR, US
Seau S. Lim - Swindon, GB
Sergey Panteleev - Nizhny Novgorod, RU
Song Noh - Hillsboro OR, US
Yaser M. Fouad - Hillsboro OR, US
Yi Guo - Santa Clara CA, US
Yizhi Yao - Chandler AZ, US
Yongjun Kwak - Portland OR, US
Youn Hyoung Heo - San Jose CA, US
Yuhan Zhou - Santa Clara CA, US
Yujian Zhang - Beijing, CN
Yushu Zhang - Beijing, CN

H04B 7/06
H04L 1/00
H04W 76/27
H04W 72/12

Embodiments of apparatus and methods for signaling for resource allocation and scheduling in 5G-NR integrated access and backhaul are generally described herein. In some embodiments, User Equipment configured for reporting a channel quality indicator (CQI) index in a channel state information (CSI) reference resource assumes a physical resource block (PRB) bundling size of two PRBs to derive the CQI index.

20160171380, Jun 16, 2016

Dec 10, 2014

14/566545

- San Jose CA, US
Hua Li - San Diego CA, US
Scott Michael Zoldi - San Diego CA, US

G06N 7/00
G06N 99/00

A predictive estimator, trained on a data corpus, is used to generate a probability estimate based a sequence of data related to an entity. The predictive estimator computes an instantaneous surprise score which is a quantification of a short-term deviation of a datum from the probability estimate. To compute the instantaneous surprise score, the predictive estimator is initialized with default values of the predictive estimator. Then, for each of data input of the datum to the predictive estimator, the instantaneous surprise score is calculated, corresponding to the deviation of the data input from the probability estimate. This generates an estimate of the probability of observing the datum given past data on the entity and the predictive estimator. The predictive estimator is updated with the datum and the time step advanced.

20160014147, Jan 14, 2016

Sep 21, 2015

14/860454

- Roseville MN, US
Jehangir Athwal - San Diego CA, US
Hua Li - San Diego CA, US
Matthew Bochner Kennel - San Diego CA, US
Xinwai Xue - San Diego CA, US

FAIR ISAAC CORPORATION - Roseville MN

H04L 29/06

A system and method of detecting command and control behavior of malware on a client computer is disclosed. One or more DNS messages are monitored from one or more client computers to a DNS server to determine a risk that one or more client computers is communicating with a botnet. Real-time entity profiles are generated for at least one of each of the one or more client computers, DNS domain query names, resolved IP addresses of query domain names, client computer-query domain name pairs, pairs of query domain name and corresponding resolved IP address, or query domain name-IP address cliques based on each of the one or more DNS messages. Using the real-time entity profiles, a risk that any of the one or more client computers is infected by malware that utilizes DNS messages for command and control or illegitimate data transmission purposes is determined. One or more scores are generated representing probabilities that one or more client computers is infected by malware.