Jeb Rahn Linton from 11420 Hinson Mill Ln, Manassas, VA 20112, age 53, Phone: 703 331-0902

Us Patents

Cross-Protocol Federated Single Sign-On (F-Sso) For Cloud Enablement
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US Patent:

20120011578, Jan 12, 2012
Filed:

Jul 8, 2010
Appl. No.:

12/832307
Inventors:

Heather M. Hinton - Austin TX, US
Steven A. Bade - Apex TX, US
Jeb Linton - Manassas VA, US
Peter Rodriguez - Pleasanton CA, US
Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:

H04L 9/14
H04L 9/30
G06F 15/16
H04L 9/00
US Classification:

726 8, 380 44, 380 30, 709219
Abstract:

A method to enable access to resources hosted in a compute cloud begins upon receiving a registration request to initiate a user's registration to use resources hosted in the compute cloud. During a registration process initiated by receipt of the registration request, a federated single sign-on (F-SSO) request is received. The F-SSO request includes an assertion (e.g., an HTTP-based SAML assertion) having authentication data (e.g., an SSH public key, a CIFS username, etc.) for use to enable direct user access to a resource hosted in the compute cloud. Upon validation of the assertion, the authentication data is deployed within the cloud to enable direct user access to the compute cloud resource using the authentication data. In this manner, the cloud provider provides authentication, single sign-on and lifecycle management for the user, despite the “air gap” between the HTTP protocol used for F-SSO and the non-HTTP protocol used for the user's direct access to the cloud resource.

Virtual Machine Images Encryption Using Trusted Computing Group Sealing
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US Patent:

20130086383, Apr 4, 2013
Filed:

Oct 4, 2011
Appl. No.:

13/252713
Inventors:

Rajiv Augusto Santos Galvao de Andrade - Sao Paulo, BR
Steven A. Bade - Georgetown TX, US
Jeb R. Linton - Manassas VA, US
Dimitrios Pendarakis - Westport CT, US
George C. Wilson - Austin TX, US
Lee H. Wilson - Austin TX, US
Assignee:

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY
International Classification:

H04L 9/08
H04L 9/32
US Classification:

713171, 380277
Abstract:

A host machine provisions a virtual machine from a catalog of stock virtual machines. The host machine instantiates the virtual machine. The host machine configures the virtual machine, based on customer inputs, to form a customer's configured virtual machine. The host machine creates an image from the customer's configured virtual machine. The host machine unwraps a sealed customer's symmetric key to form a customer's symmetric key. The host machine encrypts the customer's configured virtual machine with the customer's symmetric key to form an encrypted configured virtual machine. The host machine stores the encrypted configured virtual machine to non-volatile storage.

System For Access To Direct Broadcast Satellite Services
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US Patent:

7472409, Dec 30, 2008
Filed:

Oct 4, 2000
Appl. No.:

09/679590
Inventors:

Jeb R. Linton - Manassas VA, US
Assignee:

Lockheed Martin Corporation - Bethesda MD
International Classification:

H04Q 7/36
H04Q 7/06
H01Q 3/00
US Classification:

725 76, 725 75, 725118, 342354, 342359, 342360, 343705, 343711, 343714, 343882
Abstract:

A system for receiving direct broadcast satellite signals in a mobile craft is disclosed. Generally, the system includes an orientation system for determining the first orientation of the mobile craft, a controller or processor for determining first position control data, and an electronically-pointable antenna adapted to receive first position control data from the controller, such that the antenna is pointable in accordance therewith, such that a first direct broadcast satellite signal is receivable from a first direct broadcast satellite, and a direct broadcast satellite receiver for processing a first radio frequency signal corresponding to the first direct broadcast satellite signal received by the electronically-pointable antenna.

Reducing Latency In Intelligent Rural Roadways
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US Patent:

20220180751, Jun 9, 2022
Filed:

Dec 3, 2020
Appl. No.:

17/110378
Inventors:

- Armonk NY, US
John Behnken - Hurley NY, US
Michael Amisano - East Northport NY, US
Jeb R. Linton - Manassas VA, US
David K. Wright - Monroe MI, US
Dennis Kramer - Siler City NC, US
International Classification:

G08G 1/00
G08G 1/0967
G08G 1/09
Abstract:

A method, a computer program product and a computer system update and share relevant event information among vehicles. The method includes acquiring event information by a device having a sensor. The method also includes classifying the event information as relevant to a vehicle. The method further includes the device transmitting the event information classified as relevant to a first intermediate storage device within a range of the first intermediate storage device. In addition, the method includes the first intermediate storage device transmitting the received event information to a node in a network. The network includes at least one other vehicle within a range of the first intermediate storage device and one or more other intermediate storage devices. Lastly, the method includes a vehicle receiving the event information classified as relevant and modifying the operation of the vehicle.

Authorizing Uses Of Goods Or Services Using Bonding Agreement
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US Patent:

20220182371, Jun 9, 2022
Filed:

Dec 7, 2020
Appl. No.:

17/113280
Inventors:

- Armonk NY, US
Scott D. FREI - Rochester MN, US
Chad ALBERTSON - Rochester MN, US
Jeb R. LINTON - Manassas VA, US
International Classification:

H04L 29/06
G06Q 20/38
G06Q 20/40
Abstract:

Aspects described herein include a computer-implemented method (and related system and computer program product) comprising receiving, from a bonding service, an authorization request for a predefined authorized use of a good or service by a user. The authorization request indicates that the user meets one or more predefined criteria for the predefined authorized use. The method further comprises determining one or more penalty conditions of a bonding agreement for the predefined authorized use by the user, and receiving, from the bonding service, a confirmation that the user agrees to meet the one or more penalty conditions of the bonding agreement. The method further comprises receiving, from an owner of the good or service, an authorization of the authorization request, and transmitting, responsive to authorization of the authorization request, a token to the bonding service that enables the user to access the predefined authorized use of the good or service.

Training An Agent-Based Healthcare Assistant Model
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US Patent:

20210397427, Dec 23, 2021
Filed:

Jun 17, 2020
Appl. No.:

16/903851
Inventors:

- Armonk NY, US
Jeb R. LINTON - Manassas VA, US
Khoa Dang HYUNH - Round Rock TX, US
Newton E. BOSWORTH - Round Rock TX, US
Jonathan SAMN - Austin TX, US
International Classification:

G06F 8/65
G16H 10/60
G16H 50/20
G16H 40/67
G06N 20/20
G06N 5/04
H04L 29/06
A61B 5/0205
A61B 5/145
A61B 5/00
A61B 5/0402
Abstract:

Systems and methods for training an agent-based assistant model are provided. In embodiments, a method includes: obtaining biometric data of a user from a software application utilizing an assistant model that determines functions of the software application; filtering the biometric data based on predetermined categories, thereby extracting select biometric data; training a first version of the assistant model based on the select biometric data, thereby generating an updated assistant model; generating a summary of changes including changes to the first version of the assistant model that occurred during the training; and sending the summary of changes to a remote federated learning server, wherein the federated learning server trains a general version of the assistant model based on the summary of changes and other summary of changes received from computing devices of other users, thereby generating an updated general version of the assistant model.

Audio-Spectral-Masking-Deep-Neural-Network Crowd Search
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US Patent:

20210295828, Sep 23, 2021
Filed:

Mar 19, 2020
Appl. No.:

16/823725
Inventors:

- ARMONK NY, US
Poojitha Bikki - Austin TX, US
Jeb R. Linton - Manassas VA, US
Minsik Lee - Fort Lee NJ, US
International Classification:

G10L 15/16
G10L 15/30
G10L 15/32
G06N 3/04
Abstract:

A system includes a memory having instructions therein and at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to communicate, into a user device, a deep neural network comprising a predictive audio spectral mask. The at least one processor is also configured to execute the instructions to: generate data corresponding to ambient sound via a multi-microphone device; separate amplitude data and/or phase data from the data via the deep neural network comprising the predictive audio spectral mask; and determine, via the user device and based on the amplitude data and/or phase data, a location of origin of target speech relative to the user device. The at least one processor is configured to execute the instructions to display, via the user device, the location of origin of the target speech relative to the user device.

Blockchain-Enabled Decentralized Ecosystem For Secure Training Of Deep Neural Networks Using Trusted Execution Environments
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US Patent:

20210271963, Sep 2, 2021
Filed:

Feb 28, 2020
Appl. No.:

16/804076
Inventors:

- Armonk NY, US
John Behnken - Hurley NY, US
Jeb R. Linton - Manassas VA, US
John Melchionne - Kingston NY, US
David K. Wright - Monroe MI, US
Dennis Kramer - Siler City NC, US
International Classification:

G06N 3/08
G06F 21/57
H04L 9/06
Abstract:

Training a deep neural network model using a trusted execution environment is provided. A selection of two or more encrypted files owned by different entities within a plurality of encrypted files containing sensitive datasets is made by a user of a client device. The two or more encrypted files owned by the different entities are decrypted within the trusted execution environment to form decrypted sensitive datasets owned by the different entities. The decrypted sensitive datasets owned by the different entities are combined within the trusted execution environment to form combined sensitive data owned by the different entities. The deep neural network model is generated within the trusted execution environment based on the combined sensitive data owned by the different entities. The deep neural network model is trained within the trusted execution environment using the combined sensitive data owned by the different entities.