Accenture Patent, ML Ensemble Streaming Data, Granted Apr 21
Summary
The USPTO granted Patent US12608658B2 to Accenture Global Solutions Limited on April 21, 2026, for dynamically updated ensemble-based machine learning classification for streaming data. The patent, filed March 31, 2022 (Application No. 17711017), contains 19 claims under CPC classification G06N 20/20. The invention covers methods where new ML classifiers replace older ones in an ensemble when labels are obtained for previously unlabeled streaming data, enabling adaptation to statistical distribution changes.
“Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support dynamically updated ensemble-based machine learning (ML) classification.”
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GovPing monitors USPTO Patent Grants - AI & Computing (G06N) for new telecom & technology regulatory changes. Every update since tracking began is archived, classified, and available as free RSS or email alerts — 32 changes logged to date.
What changed
The USPTO granted Patent US12608658B2 to Accenture Global Solutions Limited on April 21, 2026, covering dynamically updated ensemble-based machine learning classification systems and methods for streaming data. The patent discloses an ensemble approach where trained ML classifiers generate predictions on unlabeled data streams, and newly labeled data is used to train replacement classifiers that learn changes in statistical distribution across recent input data.
Technology companies and ML practitioners should note this patent covers specific methods for dynamically updating ensemble classifiers in streaming data environments. The 19 granted claims may be relevant for freedom-to-operate analyses in similar ML deployment scenarios. Licensing discussions may be appropriate for parties developing comparable streaming ML systems.
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Apr 23, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Dynamically updated ensemble-based machine learning for streaming data
Grant US12608658B2 Kind: B2 Apr 21, 2026
Assignee
Accenture Global Solutions Limited
Inventors
Sourav Ghosh, Paritosh Pramanik, Jyoti Singh, Theerthala Siva Rama Sarma, Nivetha Suruliraj
Abstract
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support dynamically updated ensemble-based machine learning (ML) classification. An ensemble of ML classifiers may be created from a plurality of trained ML classifiers. These initial ML classifiers may be trained using labeled data to generate predictions based on input data. When an unlabeled data stream is received, the unlabeled data stream may be provided as input to the ensemble to generate predictions. After obtaining labels for the received data, the labels and the unlabeled data stream may be used to train new ML classifiers. The new ML classifiers may replace older ML classifiers in the ensemble. In this manner, the ensemble of ML classifiers is used to perform predictions on high volume streaming data while being dynamically updated with ML classifiers that have learned changes in statistical distribution across more recent input data.
CPC Classifications
G06N 20/20
Filing Date
2022-03-31
Application No.
17711017
Claims
19
Mentioned entities
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