Michael B Jennings

US Patent:

20120054546, Mar 1, 2012

Filed:

Aug 30, 2010

Appl. No.:

12/871798

Inventors:

Theocharis Kampouris - Neapoli, GR
Michael Jennings - Menlo Park CA, US
Yunrui Li - Fremont CA, US
Vinay H. Srihari - San Mateo CA, US
Mahesh Baburao Girkar - Cupertino CA, US

Assignee:

ORACLE INTERNATIONAL CORPORATION - Redwood Shores CA

International Classification:

G06F 11/07
G06F 17/30

US Classification:

714 16, 707687, 707E17007, 714E11024

Abstract:

A method and apparatus for detecting split brain in a distributed system is provided. After determining that a rogue instance is no longer an active member of the cluster, a recovery instance detects activity associated with a redo log that is updated by the rogue instance to store log records that describe changes made by the rogue instance to data associated with the cluster.

US Patent:

20220198263, Jun 23, 2022

Filed:

Dec 23, 2020

Appl. No.:

17/133222

Inventors:

- 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

International Classification:

G06N 3/08
G06F 16/23

Abstract:

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.

US Patent:

20220180181, Jun 9, 2022

Filed:

Dec 9, 2020

Appl. No.:

17/116184

Inventors:

- Redmond WA, US
Chaofan HUANG - Atlanta GA, US
Songtao GUO - Cupertino CA, US
Robert Perrin REEVES - Castro Valley CA, US
Wan Qi GAO - San Francisco CA, US
Patrick Ryan DRISCOLL - Oakland CA, US
Kristina Caroline RYAN - San Francisco CA, US
Michael Mario JENNINGS - San Francisco CA, US
Jeremy LWANGA - San Francisco CA, US
Manzarul Azad KAZI - Walnut Creek CA, US

International Classification:

G06N 3/08
G06K 9/62

Abstract:

In an example embodiment, a model is trained to specifically identify reversal points in data and then to rank these reversal points in order of importance. A reversal point shall be defined as a point in which a particular metric, specifically a first order derivative, crosses over from positive to negative or vice-versa. Users are more likely to be interested in abnormal and significant changes in data, and thus the machine-learned model is trained to evaluate a reversal point based on two dimensions: abnormality and significance.

US Patent:

20210065124, Mar 4, 2021

Filed:

Aug 29, 2019

Appl. No.:

16/555552

Inventors:

- Redmond WA, US
Michael Jennings - San Francisco CA, US

International Classification:

G06Q 10/10
G06F 16/28
G06F 16/2455
G06F 16/248
G06F 16/9035
G06F 16/23

Abstract:

An online system server determines changes in employment stages for the employees of an organization, where the organization may have multiple employees. The online system server may store employee record in an employment history database, and implement an employment history database schema facilitate the determination of the employment movement. The employment history database schema may define that the rows of an employee history database table includes pointers, where the pointers indicate a prior employer or a subsequent employer. Using formulated queries with the employment history database schema, the online system server allows an organization to more accurately log the changes in employment stages for the employees of the organization.

US Patent:

20200401582, Dec 24, 2020

Filed:

Jun 24, 2019

Appl. No.:

16/450851

Inventors:

- Redmond WA, US
Michael Mario Jennings - San Francisco CA, US

International Classification:

G06F 16/242
G06F 16/23
G06F 16/2457
G06F 16/28
G06F 16/2458

Abstract:

In an embodiment, the disclosed technologies include a method for generating a typeahead suggestion list for an input field of a search interface, including receiving, as digital input, a query string that has been extracted from the input field and context data that comprises one or more of: a search term that has been extracted from another input field of the search interface or a search criterion that has been extracted from a member profile that is associated with the query string via an online connection network; executing, on digital data extracted from the online connection network, one or more machine-readable queries that comprise one or more of the query string and the context data, to produce a set of candidate entities; outputting at least part of the set of candidate entities as a suggestion list that the search interface may display in association with the input field to facilitate query formulation via the input field.

US Patent:

20200175453, Jun 4, 2020

Filed:

Nov 30, 2018

Appl. No.:

16/206704

Inventors:

- Redmond WA, US
Blake Henderson - San Francisco CA, US
Michael Booz - San Francisco CA, US
Juanyan Li - Milpitas CA, US
Joseph Kelly - San Francisco CA, US
Michael Jennings - San Francisco CA, US

International Classification:

G06Q 10/06
G06Q 10/10

Abstract:

Described herein are techniques for generating for each company in a set of companies a rating score that represents the desirability of employment with the company. The rating score for each company is derived by analyzing the member profiles of members of an online system to identify employee transitions between companies over certain time periods. At the beginning of a first time period, each company is assigned a baseline score. Each transition represents an employee departure (loss), for one company, and an employee hire (win), for the other company. With each transition by an employee to/from a company, the rating score of the impacted companies are adjusted—increased for a win, decreased for a loss—in some proportion that is relative to the current rating scores of the impacted companies. Accordingly, company A hires an employee from Company B, the rating scores of companies A and. B will impact the level of the adjustment that is made to their respective rating scores as a result of the transition.

US Patent:

20200104799, Apr 2, 2020

Filed:

Sep 28, 2018

Appl. No.:

16/146669

Inventors:

- Redmond WA, US
Hitesh Kumar - San Francisco CA, US
Michael M. Jennings - San Francisco CA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G06Q 10/10
G06F 17/27
G06F 17/30
G06K 9/62
G06F 15/18

Abstract:

The disclosed embodiments provide a system for identifying fake positions in member profiles. During operation, the system determines features and labels for real positions and fake positions listed in member profiles with an online network, wherein the features indicate inclusion of one or more attributes in the member profiles. Next, the system inputs the features and the labels as training data for a machine learning model. The system then applies the machine learning model to additional features for additional members with the online network to produce scores representing likelihoods that positions listed in additional member profiles for the additional members are fake. Finally, the system stores predictions represented by the scores in association with the positions.

US Patent:

20200005204, Jan 2, 2020

Filed:

Jun 29, 2018

Appl. No.:

16/023895

Inventors:

- Redmond WA, US
Michael Jennings - San Francisco CA, US
Xin Xia - Pacifica CA, US
David Golland - Oakland CA, US
Daniel Francis - Belmont CA, US
Ted Tomlinson - Oakland CA, US

International Classification:

G06Q 10/06
G06F 15/18
G06F 17/30
G06Q 50/00

Abstract:

Methods, systems, and computer programs are presented for determining the employment type of online-service members and the generation of employment reports. One method includes training a machine learning program (MLP) for categorizing employment type, for title and company, as field or full-time-corporate. The full-time-corporate category is for full-time corporate employees. For each employee title in a first company, the method includes accessing data for members of an online service having the title and employed by the first company, and determining, by the trained MLP, the employment type for the title and the first company based on the accessed data. Further, the method includes operations for providing a user interface for generating an employment report for the first company, the user interface including one or more options for filtering data based on the employment type, and for presenting the employment report on the user interface.