Joseph Davy resumes & CV records
Resumes
Joseph Davy
Location:
United States
Joseph Davy
Location:
United States
Joseph P Davy, 368230 Northrop Pl SW, Seattle, WA 98136
Joseph Davy Phones & Addresses
8230 Northrop Pl SW, Seattle, WA 98136
Bainbridge Island, WA
Durham, NC
Chapel Hill, NC
Angier, NC
Mentions for Joseph P Davy
Publications & IP owners
Wikipedia
Joseph Burtt Davy
Joseph Burtt Davy (7 March 1870 Findern, Derbyshire - 20 August 1940 Birmingham, was a Quaker botanist and agrostologist. He was the first curator of the Forest ...
Us Patents
Products Or Services Demand Analytics Systems And Related Methods And Electronic Exchanges
US Patent:
2013034, Dec 26, 2013
Filed:
Jun 6, 2013
Appl. No.:
13/911770
Inventors:
Joseph P. Davy - Durham NC, US
Daniel Friel - Charlotte NC, US
Philip DeSimone - Chapel Hill NC, US
Daniel Friel - Charlotte NC, US
Philip DeSimone - Chapel Hill NC, US
International Classification:
G06Q 30/06
US Classification:
705 731, 705 2661
Abstract:
Products or services demand analytics systems are provided. The systems may include a processor configured to determine whether to make an offer from a buyer to purchase a product or service at a buyer-determined price available to a seller of the product or service, based on a demand analytics preference of the seller for the product or service. Moreover, the processor may be configured to adjust whether the offer is made available to the seller and/or how the offer is communicated to the seller in response to an adjustment by the seller of the demand analytics preference for the product or service. Related methods and electronic exchanges are also described.
Methods And Electronic Exchanges For Facilitating A Buyer-Driven Transaction
US Patent:
2013034, Dec 26, 2013
Filed:
Jun 6, 2013
Appl. No.:
13/911671
Inventors:
Joseph P. Davy - Durham NC, US
Daniel Friel - Charlotte NC, US
Philip DeSimone - Chapel Hill NC, US
Daniel Friel - Charlotte NC, US
Philip DeSimone - Chapel Hill NC, US
International Classification:
G06Q 30/06
US Classification:
705 2661
Abstract:
Methods and electronic exchanges that facilitate buyer-driven transactions are provided. The methods may include receiving at an electronic exchange an offer from a buyer to purchase a product or service at a buyer-determined price. The methods may also include searching seller inventory data or seller capacity data from at least one database to match the offer with at least one seller inventory or seller schedule that includes the product or service. The methods may further include receiving at the electronic exchange an acceptance of the offer from an individual seller whose inventory or schedule is included among the at least one seller inventory or seller schedule. Related electronic exchanges are also described.
Real-Time Analytics For Large Data Sets
US Patent:
2013019, Jul 25, 2013
Filed:
Jul 18, 2012
Appl. No.:
13/552435
Inventors:
Leonard Jerome Buck - Chapel Hill NC, US
Charles Boyd Burnette - Wake Forest NC, US
Joseph Patrick Davy - Durham NC, US
Alexey Alexeyevich Melnichenko - Raleigh NC, US
Charles Boyd Burnette - Wake Forest NC, US
Joseph Patrick Davy - Durham NC, US
Alexey Alexeyevich Melnichenko - Raleigh NC, US
Assignee:
EvoApp, Inc. - Durham NC
International Classification:
G06F 15/173
US Classification:
709223
Abstract:
A cloud computing system is described herein that enables fast processing of queries over massive amounts of stored data. The system is characterized by the ability to scan tens of billions of data items and to perform aggregate calculations like counts, sums, and averages in real-time (less than three seconds). Ad hoc queries are supported including grouping, sorting, and filtering without the need to predefine queries by providing highly efficient loading and processing of data items across an arbitrarily large number of processors. The system does not require any fixed schema, thus the system supports any type of data. Calculations made to satisfy a query may be distributed across a large number of processors to parallelize the work. In addition, an optimal blob size for storing multiple serialized data items is determined, and existing blobs that are too large or too small are proactively redistributed or coalesced to increase performance.
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