US 11,811,925 B2
Techniques for the safe serialization of the prediction pipeline
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); and Matthew Charles Rowe, Milton Keynes (GB)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Sep. 12, 2020, as Appl. No. 17/019,256.
Claims priority of provisional application 62/900,537, filed on Sep. 14, 2019.
Prior Publication US 2021/0083855 A1, Mar. 18, 2021
Int. Cl. H04L 9/08 (2006.01); G06N 20/20 (2019.01); G06F 16/36 (2019.01); G06N 20/00 (2019.01); G06F 16/901 (2019.01); G06F 11/34 (2006.01); G06F 16/907 (2019.01); G06F 16/9035 (2019.01); G06F 8/75 (2018.01); G06F 8/77 (2018.01); G06N 5/025 (2023.01); G06F 16/28 (2019.01); G06F 16/21 (2019.01); G06F 16/2457 (2019.01); H04L 9/32 (2006.01); G06F 16/23 (2019.01); G06F 11/30 (2006.01); G06F 18/10 (2023.01); G06F 18/213 (2023.01); G06F 18/2115 (2023.01); G06F 18/214 (2023.01); G06N 5/01 (2023.01)
CPC H04L 9/0894 (2013.01) [G06F 8/75 (2013.01); G06F 8/77 (2013.01); G06F 11/3003 (2013.01); G06F 11/3409 (2013.01); G06F 11/3433 (2013.01); G06F 11/3452 (2013.01); G06F 11/3466 (2013.01); G06F 16/211 (2019.01); G06F 16/2365 (2019.01); G06F 16/24573 (2019.01); G06F 16/24578 (2019.01); G06F 16/285 (2019.01); G06F 16/367 (2019.01); G06F 16/907 (2019.01); G06F 16/9024 (2019.01); G06F 16/9035 (2019.01); G06F 18/10 (2023.01); G06F 18/213 (2023.01); G06F 18/2115 (2023.01); G06F 18/2155 (2023.01); G06N 5/01 (2023.01); G06N 5/025 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); H04L 9/088 (2013.01); H04L 9/3236 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method performed by a server system for receiving tenant data in which a machine-learning model is associated therewith, the method comprising:
authorizing a tenant system to communicate with the server system;
loading the machine-learning model associated with the tenant system;
receiving data from the tenant system, wherein the data is to configure the machine-learning model, wherein the data comprises one or more library components including at least one of a pipeline, a microservice routine, a software module, or an infrastructure model, and wherein the one or more library components are encrypted with key;
authenticating the data in accordance with the key;
discarding the data if the authentication fails; and
configuring the machine-learning model with the data if the authentication succeeds.