Arnold Shoshana Greenland

20130346614, Dec 26, 2013

Jun 26, 2012

13/533164

Aaron K. Baughman - Silver Spring MD, US
Linda M. Boyer - Ossining NY, US
Christopher F. Codella - LaGrangeville NY, US
Richard L. Darden - Leesburg VA, US
William G. Dubyak - Severna Park MD, US
Arnold Greenland - Silver Spring MD, US

INTERNATIONAL BUSINESS MACHINES CORPORATION - Armonk NY

G06F 15/173

709226

A workload associated with a task is assessed with respect to each of a plurality of computing paradigms offered by a cloud computing environment. Adaptive learning is employed by maintaining a table of Q-values corresponding to the computing paradigms and the workload is distributed according to a ratio of Q-values. The Q-values may be adjusted responsive to a performance metric and/or a value, reward, and/or decay function. The workload is then assigned to available computing paradigms to be performed with improved utilization of resources.

20110305803, Dec 15, 2011

Jun 9, 2010

12/797346

Aaron K. Baughman - Silver Spring MD, US
Peter K. Malkin - Ardsley NY, US
Arnold Greenland - Silver Spring MD, US

International Business Machines Corporation - Armonk NY

G01N 33/02
G06G 7/48
C12M 1/34

426231, 4352872, 703 11

A system, method and program product for enhancing individualized and safe food production using an iterative co-evolutionary approach. A system is disclosed that includes a production system for generating a set of end products based on an inputted set of host chromosomes and a parasite chromosome (which may include machine/human contaminants); an evaluation system for evaluating each end product; an evolution system for generating a set of evolved host chromosomes and an updated parasite chromosome; and a mechanism for generating a further set of end products based on the evolved host chromosomes and the updated parasite chromosome.

20160078188, Mar 17, 2016

Nov 19, 2015

14/946471

- Armonk NY, US
Aaron K. Baughman - Silver Spring MD, US
Brenda L. Dietrich - Somers NY, US
Arnold Greenland - Silver Spring MD, US
Peter K. Malkin - Ardsley NY, US
Palani Sakthi - Palantine IL, US

G06F 19/00
G06N 7/00

Various embodiments provide systems, computer program products and computer implemented methods. In some embodiments, a system includes a method of providing a confidence-estimation-based inference, the method includes receiving a query concerning a patient from a user, accessing an electronic health record (EHR) for the patient, the EHR including a first component regarding the patient, querying the user, using a conversational interface, for a second component regarding the patient, receiving the second component regarding the patient in response to the query, calculating a first probability density function using the first component, and a second probability density function using the second component, combining the first and second probability density functions using a Gaussian mixture model, calculating at least one conditional probability table using the Gaussian mixture model and providing the confidence-estimation-based inference based on the at least one conditional probability table.

20150142701, May 21, 2015

Nov 20, 2013

14/085238

- Armonk NY, US
Aaron K. Baughman - Silver Spring MD, US
Brenda L. Dietrich - Somers NY, US
Arnold Greenland - Silver Spring MD, US
Peter K. Malkin - Ardsley NY, US
Palani Sakthi - Palantine IL, US

International Business Machines Corporation - Armonk NY

G06N 5/04
G06F 19/00

706 11

Various embodiments provide systems, computer program products and computer implemented methods. In some embodiments, a system includes a method of providing a confidence-estimation-based inference, the method includes receiving a query concerning a patient from a user, accessing an electronic health record (EHR) for the patient, the EHR including a first component regarding the patient, querying the user, using a conversational interface, for a second component regarding the patient, the second component being in a natural language information form, receiving the second component regarding the patient in response to the query, calculating a first probability density function using the first component, and a second probability density function using the second component, combining the first and second probability density functions using a Gaussian mixture model, calculating at least one conditional probability table using the Gaussian mixture model and providing the confidence-estimation-based inference based on the at least one conditional probability table.