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US Patent 11989627 Automated machine learning pipeline generation

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Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
0
Patent Number
119896270
Patent Inventor Names
Christopher Zachariah Jost0
Jianbo Liu0
Nikolay Kolotey0
Harnish Botadra0
Jakub Zablocki0
Aditya Vinayak Bhise0
Prince Grover0
Tanay Bhargava0
...
Date of Patent
May 21, 2024
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Patent Application Number
169158710
Date Filed
June 29, 2020
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Patent Citations
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US Patent 8463811 Automated correlation discovery for semi-structured processes
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US Patent 9508347 Method and device for parallel processing in model training
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US Patent 9529837 Systems and methods involving a multi-pass algorithm for high cardinality data
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US Patent 9552408 Nearest neighbor clustering determination and estimation algorithm that hashes centroids into buckets and redistributes vectors between clusters
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US Patent 9563854 Distributed model training
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US Patent 9838410 Identity resolution in data intake stage of machine data processing platform
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US Patent 10148623 Apparatus and methods ensuring data privacy in a content distribution network
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US Patent 10193913 Joint anomaly detection across IOT devices
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...
Patent Primary Examiner
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Michael I Ezewoko
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CPC Code
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G06N 5/046
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G06N 20/00
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G06N 5/025
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G06N 5/04
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Patent abstract

Various embodiments of apparatuses and methods for an automated machine learning pipeline service and an automated machine learning pipeline generator are described. In some embodiments, the service receives a request from a user to generate a machine learning solution, as well as a dataset that comprises values with different user variable types, and mapping of the user variable types to pre-defined types. The generator can validate the dataset, enrich the values of the dataset using external data sources, transform values of the dataset based on the pre-defined types, train a machine learning model using the enriched and transformed values, and compose an executable package, comprising enrichment recipes, transformation recipes, and the trained machine learning model, that generates scores for other data when executed. The service can further test the executable package using testing data, and provide results of the test to the user.

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