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US Patent 10176217 Dynamically performing data processing in a data pipeline system

Patent 10176217 was granted and assigned to Palantir Technologies on January, 2019 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
Palantir Technologies
Palantir Technologies
1
Current Assignee
Palantir Technologies
Palantir Technologies
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
101762171
Patent Inventor Names
Gustav Brodman1
Yanran Lu1
Yi Xue1
Yifei Huang1
Hao Dang1
Stacey Milspaw1
Date of Patent
January 8, 2019
1
Patent Application Number
156985741
Date Filed
September 7, 2017
1
Patent Citations Received
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US Patent 12067466 Artificial intelligence and machine learning hyperscale infrastructure
2
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US Patent 11676072 Interface for incorporating user feedback into training of clustering model
3
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US Patent 11755559 Automatic entity control in a machine data driven service monitoring system
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US Patent 11768636 Generating a transformed dataset for use by a machine learning model in an artificial intelligence infrastructure
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US Patent 11803338 Executing a machine learning model in an artificial intelligence infrastructure
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US Patent 11403290 Managing an artificial intelligence infrastructure
1
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US Patent 11843528 Lower-tier application deployment for higher-tier system
4
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US Patent 11861423 Accelerating artificial intelligence (‘AI’) workflows
5
...
Patent Primary Examiner
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Amresh Singh
1
Patent abstract

Techniques for automatically scheduling builds of derived datasets in a distributed database system that supports pipelined data transformations are described herein. In an embodiment, a data processing method comprises, in association with a distributed database system that implements one or more data transformation pipelines, each of the data transformation pipelines comprising at least a first dataset, a first transformation, a second derived dataset and dataset dependency and timing metadata, detecting an arrival of a new raw dataset or new derived dataset; in response to the detecting, obtaining from the dataset dependency and timing metadata a dataset subset comprising those datasets that depend on at least the new raw dataset or new derived dataset; for each member dataset in the dataset subset, determining if the member dataset has a dependency on any other dataset that is not yet arrived, and in response to determining that the member dataset does not have a dependency on any other dataset that is not yet arrived: initiating a build of a portion of the data transformation pipeline comprising the member dataset and all other datasets on which the member dataset is dependent, without waiting for arrival of other datasets.

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