Patrick J Driscoll, 6638 Standish Rd, Marshfield, MA 02050

2022019, Jun 23, 2022

Dec 23, 2020

17/133222

- Redmond WA, US
Patrick Ryan Driscoll - Oakland CA, US
Michael Mario Jennings - San Francisco CA, US
Robert Perrin Reeves - Castro Valley CA, US
Bo Yang - Brighton MA, US

G06N 3/08
G06F 16/23

In an example embodiment, a machine-learned model is trained to specifically identify anomaly points in time series data. The model is capable of being applied in parallel to many different time series simultaneously, allowing for a scalable solution for large scale online networks. The model classifies each data point in a specified time window and outputs rich contextual information for downstream applications, such as ranking and display of the anomalous data points.

2022019, Jun 23, 2022

Dec 23, 2020

17/133259

- Redmond WA, US
Robert Perrin REEVES - Castro Valley CA, US
Bo YANG - Brighton MA, US
Wan Qi GAO - San Francisco CA, US
William TANG - Saratoga CA, US
Patrick Ryan DRISCOLL - Oakland CA, US
Shan ZHOU - San Jose CA, US
Taylor Shelby BURFIELD - Greenbrae CA, US
Adriana Dominique MEZA - Austin TX, US

G06N 3/08
G06F 16/23

In an example embodiment, a machine-learned model is trained to rank anomaly points in time series data. The model is capable of being applied in parallel to many different time series simultaneously, allowing for a scalable solution for large scale online networks. The model outputs a ranking score for an input anomaly and allows for ranking of anomalies not just in the same time series but anomalies across multiple time series as well. This ranking can then be used to determine how best to present the ranked anomalies to users in a graphical user interface.

2020017, Jun 4, 2020

Nov 29, 2018

16/204921

Beibei Wang - Santa Clara CA, US
Patrick Driscoll - Oakland CA, US
Divyakumar Menghani - Sunnyvale CA, US
Luxin Kang - Clyde Hill WA, US

G06F 16/22
G06F 16/26

Disclosed are systems, methods, and non-transitory computer-readable media for generating a diversity index. A diversity index system determines distribution values indicating a distribution of a set of users among demographic groups defined by two or more diversity dimensions. The diversity index system generates a distribution vector based on the distribution values. The diversity index system determines, based on the distribution vector and a similarity matrix, a set of diversity index values forming a diversity index vector. The similarity index includes a set of similarity scores for the demographic groups. The diversity index system determines a diversity index score for the set of users based on the diversity index vector and the distribution vector. The diversity index score indicates a level of diversity amongst the users from the set of users.