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US Patent 10025813 Distributed data transformation system

Patent 10025813 was granted and assigned to Sas (company) on July, 2018 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Applicant
Sas (company)
Sas (company)
Current Assignee
Sas (company)
Sas (company)
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10025813
Patent Inventor Names
Biruk Gebremariam25
Xiangxiang Meng25
Date of Patent
July 17, 2018
Patent Application Number
15876543
Date Filed
January 22, 2018
Patent Citations Received
‌
US Patent 12001931 Simultaneous hyper parameter and feature selection optimization using evolutionary boosting machines
1
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US Patent 11481117 Storage volume clustering based on workload fingerprints
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US Patent 11715038 System and method for data visualization using machine learning and automatic insight of facts associated with a set of data
4
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US Patent 11360772 Instruction sequence merging and splitting for optimized accelerator implementation
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US Patent 11449490 Idempotent transaction requests
7
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US Patent 11907882 Model validation of credit risk
8
‌
US Patent 11478927 Hybrid computing architectures with specialized processors to encode/decode latent representations for controlling dynamic mechanical systems
9
‌
US Patent 10311044 Distributed data variable analysis and hierarchical grouping system
...
Patent Primary Examiner
‌
James K. Trujillo
Patent abstract

A computing system transforms variable values in a dataset using a transformation flow definition applied in parallel. The transformation flow definition indicates flow variables and transformation phases to apply to the flow variables. A computation is defined for each variable and for each transformation phase. A phase internal parameter value is computed for each defined computation from observation vectors read from the dataset. A current variable, a first variable value, a first transformation phase, the phase internal parameter value, and a current transformation phase are selected based on an observation vector read from the dataset. A result value is computed by executing the transformation function with the phase internal parameter value and the first variable value. The computed result value is output to a transformed input dataset. The process is repeated for each variable, transformation phase, and observation vector.

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