Autonomous Supply Chain Data Hub and Platform
AssigneeBlue Yonder Group, Inc.
InventorRubesh Mehta
Life of this patent
Estimating the term…
Abstract
A system and method of autonomous data hub processing that uses semantic metadata, machine learning models, and a permissioned blockchain to autonomously standardize, identify and correct errors in supply chain data is disclosed. Embodiments input supply chain data stored in a supply chain database, train with the machine learning model trainer, one or more machine learning models to identify one or more data errors in the supply chain data, clean the one or more identified data errors from the supply chain data, and store cleaned supply chain data. Embodiments also update one or more machine learning models to identify one or more data errors in cleaned supply chain data, and join and aggregate one or more sets of cleaned supply chain data.
Claims20
A system for a machine learning data pipeline, comprising:
- a computer comprising a processor and memory, the computer configured to:
- transfer one or more initial data sets to input data;
- clean the one or more initial data sets and store the one or more data sets as valid and quality base data;
- join the valid and quality base data and store the joined valid and quality base data as a joined data set;
- clean the joined data set and store the cleaned joined data set as a valid and quality joined data set;
- aggregate the valid and quality joined data set and store the aggregated valid and quality joined data set as an aggregated data set;
- and
- clean the aggregated data set and store the clean aggregated data set as a valid and quality aggregate data set.
A computer implemented method for a machine learning data pipeline, comprising:
- transferring, by a computer comprising a processor and memory, one or more initial data sets to input data;
- cleaning, by the computer, the one or more initial data sets and store the one or more data sets as valid and quality base data;
- joining, by the computer, the valid and quality base data and storing, by the computer, the joined valid and quality base data as a joined data set;
- cleaning, by the computer, the joined data set and storing, by the computer, the cleaned joined data set as a valid and quality joined data set;
- aggregating, by the computer, the valid and quality joined data set and storing, by the computer, the aggregated valid and quality joined data set as an aggregated data set;
- and
- cleaning, by the computer, the aggregated data set and storing, by the computer, the clean aggregated data set as a valid and quality aggregate data set.
A non-transitory computer-readable medium embodied with computer program instructions for a machine learning data pipeline, the computer program instructions when executed by one or more processors:
- transfers one or more initial data sets to input data;
- cleans the one or more initial data sets and stores the one or more data sets as valid and quality base data;
- joins the valid and quality base data and stores the joined valid and quality base data as a joined data set;
- cleans the joined data set and stores the cleaned joined data set as a valid and quality joined data set;
- aggregates the valid and quality joined data set and stores the aggregated valid and quality joined data set as an aggregated data set;
- and
- cleans the aggregated data set and stores the clean aggregated data set as a valid and quality aggregate data set.
Dependencies are inferred from the claim text.