Jeffrey B Nichols

US Patent:

6450922, Sep 17, 2002

Filed:

Oct 29, 1998

Appl. No.:

09/182977

Inventors:

Scott J. Henderson - Los Angeles CA
Tyler C. Marthaler - New York NY
Jeffrey Q. Nichols - New York NY

Assignee:

Graber Products, Inc. - Madison WI

International Classification:

A63B 2100

US Classification:

482 8, 482 4, 482 9, 482901

Abstract:

A physical activity monitor, during a first period, records data characterizing a physical activity by a user, and an exercise device uses the recorded data of the physical activity monitor to simulate the physical activity for the user during a second period.

US Patent:

20130055268, Feb 28, 2013

Filed:

Aug 31, 2011

Appl. No.:

13/222083

Inventors:

Saleema A. Amershi - Seattle WA, US
Tessa A. Lau - Mountain View CA, US
Jalal U. Mahmud - San Jose CA, US
Jeffrey W. Nichols - San Jose CA, US

Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

International Classification:

G06F 9/46

US Classification:

718101

Abstract:

Embodiments of the invention relate to generating automated web task procedures from an analysis of web history logs. One aspect of the invention concerns a method that comprises identifying sequences of related web actions from a web log, grouping each set of similar web actions into an action class, and mapping the sequences of related web actions into sequences of action classes. The method further clusters each group of similar sequences of action classes into a cluster, wherein relationships among the action classes in the cluster are represented by a state machine, and generates automated web task procedures from the state machine.

US Patent:

20160012552, Jan 14, 2016

Filed:

Mar 4, 2015

Appl. No.:

14/638752

Inventors:

- Armonk NY, US
Nimrod Megiddo - Palo Alto CA, US
Jeffrey W. Nichols - San Jose CA, US
Michelle X. Zhou - Saratoga CA, US

International Classification:

G06Q 50/00
G06F 17/30

Abstract:

Embodiments relate to optimizing user response rate in social media. One embodiment includes generating a model for modeling a likelihood of social media user response from data collected from social media based on social media user filtering criteria and historical information. Modeling the likelihood includes estimating ability of social media users to perform a particular task based on social media user information including tracking of the social media users, evaluating previous task responses and personality features. Social media users are selected based on estimated ability to perform the particular task for maximizing a response rate.