Steven V Carr

US Patent:

7313575, Dec 25, 2007

Filed:

Jun 14, 2004

Appl. No.:

10/868434

Inventors:

Steven R. Carr - San Jose CA, US
Venkatakrishnan Muthuswamy - Cupertino CA, US
Tudor Har - Santa Clara CA, US

Assignee:

Hewlett-Packard Development Company, L.P. - Houston TX

International Classification:

G06F 17/00
G06F 7/00
G06F 15/16

US Classification:

7071041, 707101, 707102, 709201

Abstract:

A data services handler comprises an interface between a data store and applications that supply and consume data, and a real time information director (RTID) that transforms data under direction of polymorphic metadata that defines a security model and data integrity rules for application to the data.

US Patent:

20030220860, Nov 27, 2003

Filed:

Apr 24, 2003

Appl. No.:

10/423678

Inventors:

Michael Heytens - Austin TX, US
Steven Carr - San Jose CA, US
Gregory Battas - Indianapolis IN, US
Philip Bosinoff - Ashland MA, US

Assignee:

Hewlett-Packard Development Company,L.P. - Houston TX

International Classification:

G06F017/00
G06N005/02
G06F017/60
G06F015/18
G06F017/30
G06F007/00

US Classification:

705/035000, 706/012000, 707/003000, 706/047000, 705/007000

Abstract:

Knowledge discovery through analytic learning cycles is founded on a coherent, real-time view of data from across an enterprise, the data having been captured and aggregated and is available in real-time at a central repository. Knowledge discovery is an iterative process where each cycle of analytic learning employs data mining. Thus, an analytic learning cycle includes defining a problem, exploring the data at the central repository in relation to the problem, preparing a modeling data set from the explored data, building a model from the modeling data set, assessing the model, deploying the model back to the central repository, and applying the model to a set of inputs associated with the problem. Application of the model produces results and, in turn, creates historic data that is saved at the central repository. Subsequent iterations of the analytic learning cycle use the historic data, as well as current data accumulated in the central repository, thereby creating up-to-date knowledge for evaluating and refreshing the model.

US Patent:

20030220901, Nov 27, 2003

Filed:

Mar 27, 2003

Appl. No.:

10/402283

Inventors:

Steven Carr - San Jose CA, US
Harry Gentilozzi - Marietta GA, US
Tudor Har - Santa Clara CA, US
Venkatakrishnan Muthuswamy - Cupertino CA, US

Assignee:

Hewlett-Packard Development Company

International Classification:

G06F007/00

US Classification:

707/001000

Abstract:

An interaction manager (IM) is provided which is designed for gathering information associated with customer interactions, loading customer-related data at the beginning of each session, enriching the customer interactions with offers from a rules service, and performing data caching for more efficient customer interaction data retrieval and processing, including caching a session context with the gathered information and customer-related data after each interaction and restoring the session context at the beginning of each interaction. Offers to the customers are based on a comprehensive real-time view of the customer-related data and are augmented by rules and/or data mining, wherein the rules include enterprise rules and/or policies. The data caching includes saving data in denormalized form. The denormalized form is fashioned by taking the data in the normalized form and caching it lined up flatly and serially, end-to-end, in a long record so that it can be quickly retrieved in subsequent interactions and forwarded to the rules service. The long record with the data in denormalized form is cached along with a session identification key for easy association of the data in the denormalized form with a particular session.

US Patent:

20030229884, Dec 11, 2003

Filed:

Mar 27, 2003

Appl. No.:

10/402482

Inventors:

Steven Carr - San Jose CA, US
Harry Gentilozzi - Marietta GA, US
Tudor Har - Santa Clara CA, US
Venkatakrishnan Muthuswamy - Cupertino CA, US

Assignee:

Hewlett-Packard Development Company - Houston TX

International Classification:

G06F009/44
G06F009/00

US Classification:

717/101000, 709/106000

Abstract:

A template for deployment of enterprise applications one example of which is an interaction manager. For deployment of the interaction manager, the template is configured with a class framework for building source code of the interaction manager and a test driver. The template is further configured with wizards. An application wizard provides for building a base class portion of the class framework. Interaction and attribute wizards provide for extending or modifying the class framework once the base class portion is built so as to provide for enterprise-specific deployment of the interaction manager along with the test driver. In addition, test driver script is provided through which the test driver is to be used for validating and measuring the performance of the interaction manager once their source code is converted to executable code.

US Patent:

20050125280, Jun 9, 2005

Filed:

Dec 5, 2003

Appl. No.:

10/728708

Inventors:

Steven Carr - San Jose CA, US
Venkatakrishnan Muthuswamy - Cupertino CA, US
Tudor Har - Santa Clara CA, US
Philip Bosinoff - Ashland MA, US

Assignee:

Hewlett-Packard Development Company, L.P. - Houston TX

International Classification:

G06F017/60

US Classification:

705010000

Abstract:

An information handling method comprises a recommender with an aggregation engine capable of computing aggregates in from in-memory data that is dynamically cached during a transaction with an entity.

US Patent:

20050125401, Jun 9, 2005

Filed:

Dec 5, 2003

Appl. No.:

10/728686

Inventors:

Steven Carr - San Jose CA, US
Venkatakrishnan Muthuswamy - Cupertino CA, US
Tudor Har - Santa Clara CA, US
Philip Bosinoff - Ashland MA, US

Assignee:

Hewlett-Packard Development Company, L. P. - Houston TX

International Classification:

G06F017/30

US Classification:

707005000

Abstract:

An information handling method includes constructing aggregates for a case set, computing aggregates from detailed in-memory data that is dynamically cached, and iteratively mining data during an entity transaction to define new aggregates and refine existing aggregates based on entity responses. The aggregates are defined and refined using a wizard.

US Patent:

20210406915, Dec 30, 2021

Filed:

Sep 8, 2021

Appl. No.:

17/468918

Inventors:

- Atlanta GA, US
Ellis L. Wong - Lexington MA, US
Steven R. Carr - San Jose CA, US

International Classification:

G06Q 30/00

Abstract:

In one example, a data package created by a particular wireless mobile device, and comprising metrics collected by the wireless mobile device, is retrieved. The metrics concern performance of the wireless mobile device in a wireless communication network, and are transformed into measures that enable derivation of a key performance indicator concerning the performance of the wireless mobile device. An enriched measure is generated by processing the measures based on correlations of wireless mobile device events in a time sequence, and the enriched measure is transformed into a KPI that is specific to the performance of the wireless mobile device. The KPI of the wireless mobile device is compared to a KPI of the population of wireless mobile devices, and results of the comparison of KPIs serve as a basis to resolve a problem regarding operation of the wireless mobile device.

US Patent:

20190026748, Jan 24, 2019

Filed:

Sep 26, 2018

Appl. No.:

16/143333

Inventors:

- Sunnyvale CA, US
Ellis L. Wong - Lexington MA, US
Steven R. Carr - San Jose CA, US

International Classification:

G06Q 30/00

Abstract:

In one example, a data package created by a particular wireless mobile device, and comprising metrics collected by the wireless mobile device, is retrieved. The metrics concern performance of the wireless mobile device in a wireless communication network, and are transformed into measures that enable derivation of a key performance indicator concerning the performance of the wireless mobile device. An enriched measure is generated by processing the measures based on correlations of wireless mobile device events in a time sequence, and the enriched measure is transformed into a KPI that is specific to the performance of the wireless mobile device. The KPI of the wireless mobile device is compared to a KPI of the population of wireless mobile devices, and results of the comparison of KPIs serve as a basis to resolve a problem regarding operation of the wireless mobile device.