Fei Li Xiao

8438080, May 7, 2013

May 28, 2010

12/790551

Fei Xiao - Mountain View CA, US
Cristos Goodrow - Mountain View CA, US

Google Inc. - Mountain View CA

G06Q 30/00
G06F 17/30

705 271, 705 261

A learning module of an information retrieval system is configured to automatically learn distinctive characteristics used by different web sites when presenting data variables of interest. The learned information can then be used to identify data variables of interest on arbitrary web pages of the web sites. In one embodiment, the learning process is guided by feeds provided by the web sites that list values for data variables of interest, and by web pages also provided by the web sites. The values of the feeds enable the learning module to identify candidate portions of the web pages that may represent a data variable of interest. Weights are computed for different values of various properties of the candidate portions, aggregated over all the analyzed pages, and used to identify one of the candidate portions as being the best candidates.

20200216027, Jul 9, 2020

Jan 4, 2019

16/240535

- Santa Clara CA, US
Enrique Israel Hernandez - San Jose CA, US
Xiao Liu - Menlo Park CA, US
Divyansh Pal - Milpitas CA, US
Baharak Soltanian - Mountain View CA, US
Jian Wang - Milpitas CA, US
Fei Xiao - San Jose CA, US
Fangming Ye - San Jose CA, US

B60R 25/32
B60W 40/09
H04L 12/40
H04L 29/06

Embodiments and examples are disclosed for intelligent detection of vehicle intrusion using command pattern modeling and analysis. For one example, a vehicle control unit (VCU) may monitor, using a model of a user's expected driving behavior, the user's current driving behavior and detect anomalous driving behavior based on the model. The VCU may determine that the anomalous driving behavior does not correspond to one or more commands on a network bus of the vehicle and analyze, using a command pattern model, a pattern among the one or more commands on the network bus. The VCU may then compare, using the command pattern model, the pattern among the one or more commands to a historical command pattern to determine if an intrusion (e.g., by a hacker) is taking place.

20200216080, Jul 9, 2020

Jan 4, 2019

16/240567

- Santa Clara CA, US
Divyansh Pal - Milpitas CA, US
Enrique Israel Hernandez - San Jose CA, US
Fangming Ye - San Jose CA, US
Fei Xiao - San Jose CA, US
Jian Wang - Milpitas CA, US
Xiao Liu - Menlo Park CA, US
Yan Deng - San Jose CA, US

B60W 40/09
G06K 9/00
G06N 20/00
H04L 29/08
G07C 5/00

Embodiments and examples are disclosed for intelligent vehicle diagnostics using driving behavior modeling and monitoring. For one example, a data processing system for a vehicle includes a plurality of sensors and a vehicle control unit (VCU). The VCU may sample the output from each of the plurality of sensors and assemble a dataset which may be transmitted to a cloud computing center. The cloud computing center may apply statistical machine learning algorithms to the dataset and training data to develop a model of a user's expected driving behavior. The cloud computing center may transmit the model to the vehicle, wherein the VCU may utilize the model to monitor the user's driving behavior. In response to detecting driving behavior that is anomalous to the expected driving behavior, the VCU may diagnose the cause of the anomalous behavior and take one or more preventative actions based on the diagnosis.