The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Mar. 05, 2024
Filed:
Sep. 13, 2020
Oracle International Corporation, Redwood Shores, CA (US);
Alberto Polleri, London, GB;
Sergio Aldea Lopez, London, GB;
Marc Michiel Bron, London, GB;
Dan David Golding, London, GB;
Alexander Ioannides, London, GB;
Maria del Rosario Mestre, London, GB;
Hugo Alexandre Pereira Monteiro, London, GB;
Oleg Gennadievich Shevelev, London, GB;
Larissa Cristina Dos Santos Romualdo Suzuki, Wokingham, GB;
Xiaoxue Zhao, London, GB;
Matthew Charles Rowe, Milton Keynes, GB;
Oracle International Corporation, Redwood Shores, CA;
Abstract
A server system can receive an input identifying a problem to generate a solution using a machine-learning application. The method selects a machine-learning model template from a plurality of templates based at least in part on the input. The method analyzes one or more formats of the customer data to generate a customer data schema based at least in part a data ontology that applies to the identified problem. The method determines whether the customer data schema is misaligned with one or more key features of the selected machine-learning model template. Based on this determination, the method analyzes the metadata for the selected machine-learning model template to determine what additional information is required to re-align the customer data with the data expectations. The method can include gathering the addition information required to re-align the customer data with the data expectations of the selected machine-learning model template.