Ann E Skudlark

20100174736, Jul 8, 2010

Jan 6, 2009

12/349491

Colin R. GOODALL - Rumson NJ, US
Sylvia HALASZ - Fair Haven NJ, US
Wen-Ling HSU - Bridgewater NJ, US
Guy Joseph JACOBSON - Bridgewater NJ, US
Ritu KUMAR - Edison NJ, US
Bradley Keith MOHS - South Pasadena CA, US
Ann E. SKUDLARK - Westfield NJ, US

AT&T INTELLECTUAL PROPERTY I, L.P. - Reno NV
AT&T INTERACTIVE - Glendale CA

G06F 17/30

707769, 707E17014, 707E17044, 707E17017, 707E17032

Systems and methods are provided to evaluate a search engine using search results retrieved from a plurality of different search engines. For example, a controller can randomly distribute a set of pre-formulated queries to a plurality of computers to query the search engines at random times to obtain search results and combine the search results to evaluate the completeness of a query result provided by a particular search engine. The controller may simulate the random queries from users using a set of computers and may reduce the burden on the search engines by spreading out the query activities over a period of time, such as a month.

20100057560, Mar 4, 2010

Sep 4, 2008

12/204440

Ann Skudlark - Westfield NJ, US
Pradeep Bansal - Monmouth Junction NJ, US
Carroll W. Creswell - Basking Ridge NJ, US
Colin R. Goodall - Rumson NJ, US
Guy J. Jacobson - Bridgewater NJ, US
Andrea Skarra - Chatham NJ, US
Christopher Volinsky - Morristown NJ, US
James W. Watson - Mendham NJ, US

AT&T Labs, Inc. - Austin TX

G06Q 30/00

705 1449

Systems and techniques for predicting customer response to content and selecting content for delivery to particular customers in accordance with the predictions. As information is delivered to and received from a plurality of customers over multiple communication channels, data streams representing communications between providers and customers are analyzed and selected data extracted therefrom. Linkages are created between data collected from the different channels and data are anonymized. The data are analyzed to create a customer response predictor for each customer that models customer behavior and predicts customer response to advertisements. As content, such as advertisements, are to be delivered to a destination, information from a predictor created using data collected from a customer associated with the destination is used to select appropriate content.

20220277351, Sep 1, 2022

May 20, 2022

17/749347

- Atlanta GA, US
Appavu Siva Prakasam - New Providence NJ, US
Ann Eileen Skudlark - San Ramon CA, US
Siva Kolachina - Plano TX, US
Nisha Shahul Hameed - Bellevue WA, US
Prashanth Boddhireddy - Plano TX, US
Lien Tran - Seattle WA, US

AT&T Intellectual Property I, L.P. - Atlanta GA

G06Q 30/02
G06F 16/35
G06F 16/93
G06F 40/295
G06N 5/04
G06Q 10/10
G06N 20/00

Aspects of the subject disclosure may include, for example, determining classes from a corpus based on topic modeling, data clustering and unsupervised learning. Labels are determined for each of the classes and trained models are generated for each of the classes by assignment of a plurality of textual documents to labels based on a highest number of matches. A raw textual document can be tokenized and stop words removed. A corresponding one of the trained models can be selected according to a class that is applicable to subject matter of the raw textual document. The processed document can be assigned to a target label based on a highest number of matches of words. Other embodiments are disclosed.

20220264185, Aug 18, 2022

May 3, 2022

17/735462

- Atlanta GA, US
- New York NY, US
Sanjeev Misra - Somerset NJ, US
Deirdre Paul - Barrington RI, US
Appavu Siva Prakasam - New Providence NJ, US
Ann Eileen Skudlark - San Ramon CA, US
Christopher Volinsky - Morristown NJ, US

AT&T Intellectual Property I, L.P. - Atlanta GA
Warner Media, LLC - New York NY

H04N 21/475
H04L 61/5046
H04N 21/45
H04N 21/4627
H04N 21/442
H04N 21/643
H04L 61/5007
H04N 21/466

Aspects of the subject disclosure may include, for example, a system or method that collects or otherwise accesses information indicating connection patterns such as IP addresses being utilized by communication devices for communication services over a time period, where the communication devices use a same credential of a single account for accessing the communication services. Hubs can be identified according to groups of the communication devices that exhibit a particular sharing pattern such as having used the one or more common IP addresses. A prediction or estimation that the single account is engaging in sharing activity can be made based on an analysis of the hubs, such as based on a number of the hubs. Other embodiments are disclosed.

20210345001, Nov 4, 2021

Apr 30, 2020

16/863139

- Atlanta GA, US
Ann Eileen Skudlark - San Ramon CA, US
Christopher Volinsky - Morristown NJ, US
Appavu Siva Prakasam - New Providence NJ, US

AT&T Intellectual Property I, L.P. - Atlanta GA

H04N 21/466
G06Q 30/02
G06Q 30/06
G06Q 20/12
G06N 5/04
G06N 20/00
H04N 21/442
H04N 21/45
H04N 21/414

Aspects of the subject disclosure may include, for example, identifying content consumption data associated with media content consumption at a customer device, and generating a content selection recommendation for the customer device. Some embodiments can include determining a habit-based content selection vector for the customer device. Various embodiments can include determining the habit-based content selection vector based on a habit profile for the customer device. Some embodiments can include adjusting a content selection vector for the customer device based on the habit-based content selection vector for the customer device. Various embodiments can include generating the content selection recommendation for the customer device based on the adjusted content selection vector. Other embodiments are disclosed.

20170118618, Apr 27, 2017

Jan 6, 2017

15/399845

- Atlanta GA, US
Ann E. Skudlark - San Ramon CA, US
Nan Jiang - Madison NJ, US

AT&T Intellectual Property I, L.P. - Atlanta GA

H04W 4/26
H04L 12/58
H04W 4/00
H04W 4/14
H04L 29/12

A method and system for identifying short message service (SMS) spammers identifies senders as spammers by determining that the senders are sending SMS messages to devices included in a grey phone space. Devices in the grey phone space are identified using various methods including an analysis of call detail records. A grey ratio for a sender is determined as a ratio of messages sent to grey phone numbers by the sender to messages sent to all phone numbers by the sender. A sender is identified based on a difference between the grey ratio for the sender and a second grey ratio being above a threshold.

20170118632, Apr 27, 2017

Oct 21, 2015

14/919275

- Atlanta GA, US
Sastry Isukapalli - Green Brook NJ, US
Ann E. Skudlark - San Ramon CA, US
Christopher T. Volinsky - Morristown NJ, US
Dan Melamed - New York NY, US

H04W 8/28
H04W 8/26

A method and apparatus for assigning a mobile device that is unassigned to a user are disclosed. Mobile phone numbers are used as a surrogate for device. For example, the method implemented via a processor obtains the phone number that is unassigned from a list of phone numbers associated with a billing account number, assigns the phone number that is unassigned to the user, wherein the user is selected from among a plurality of users associated with the billing account number, determines a confidence level for the assigning of the phone number that is unassigned to the user, and performs an analysis to provide a service for the user to whom the phone number is assigned.

20160205528, Jul 14, 2016

Mar 24, 2016

15/079577

- Atlanta GA, US
Ann E. SKUDLARK - San Ramon CA, US
Nan JIANG - Madison NJ, US

H04W 4/26
H04L 12/58
H04W 4/00
H04W 4/14
H04L 29/12

A method and system for identifying short message service (SMS) spammers identifies senders as spammers by determining that the senders are sending SMS messages to devices included in a grey phone space. Devices in the grey phone space are identified using various methods including an analysis of call detail records. A grey ratio for a sender is determined as a ratio of messages sent to grey phone numbers by the sender to messages sent to all phone numbers by the sender. A sender is identified based on a difference between the grey ratio for the sender and a second grey ratio being above a threshold.