US 11,836,530 B2
Automatic suggestion of variation parameters and pre-packaged synthetic datasets
Kamran Zargahi, Bellevue, WA (US)
Assigned to MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed by MICROSOFT TECHNOLOGY LICENSING, LLC, Redmond, WA (US)
Filed on Nov. 30, 2018, as Appl. No. 16/206,695.
Claims priority of provisional application 62/732,949, filed on Sep. 18, 2018.
Prior Publication US 2020/0089999 A1, Mar. 19, 2020
Int. Cl. G06N 20/00 (2019.01); G06F 16/906 (2019.01); G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06F 9/50 (2006.01); G06F 16/907 (2019.01); G06F 9/48 (2006.01); G06F 9/54 (2006.01); H04L 67/63 (2022.01); G06F 18/24 (2023.01); G06V 10/774 (2022.01); G06V 10/96 (2022.01); G06V 20/64 (2022.01); G06F 16/9038 (2019.01); G06F 1/16 (2006.01); G06F 9/38 (2018.01); G06V 40/16 (2022.01)
CPC G06F 9/5044 (2013.01) [G06F 9/4881 (2013.01); G06F 9/5027 (2013.01); G06F 9/5055 (2013.01); G06F 9/542 (2013.01); G06F 16/906 (2019.01); G06F 16/907 (2019.01); G06F 16/9038 (2019.01); G06F 18/217 (2023.01); G06F 18/2148 (2023.01); G06F 18/2178 (2023.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01); G06V 10/774 (2022.01); G06V 10/96 (2022.01); G06V 20/647 (2022.01); H04L 67/63 (2022.05); G06F 9/3877 (2013.01); G06V 40/172 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer system comprising:
one or more hardware processors and memory configured to provide computer program instructions to the one or more hardware processors;
a frameset assembly engine configured to utilize the one or more hardware processors to:
receive, from a distributed Synthetic Data as a Service (SDaaS) interface, a selection of a machine learning scenario from a plurality of machine learning scenarios, wherein the SDaaS interface is associated with SDaaS distributed computing service operations that are based on a service-oriented architecture that supports SDaaS machine-learning training service operations while abstracting underlying SDaaS distributed computing service operations that managed via an SDaaS distributed computing service;
retrieve, from a seeding taxonomy that associates the plurality of machine learning scenarios with corresponding proper subsets of a plurality of variation parameters, a relevant proper subset of the plurality of variation parameters associated with the selected machine learning scenario by the seeding taxonomy,
wherein the seeding taxonomy comprises a plurality of previously identified mappings of machine learning scenarios to corresponding proper subsets of the plurality of variation parameters;
cause the SDaaS interface to prompt for a selected subset of the plurality of variation parameters in association with presenting a representation of the relevant proper subset of the plurality of variation parameters as suggested variation parameters for the selected machine learning scenario;
receive, from the SDaaS interface, input identifying the selected subset of the plurality of variation parameters; and
generate, using the selected subset of the plurality of variation parameters, a training dataset as a frameset package associated with training the machine learning model for the selected machine learning scenario,
wherein generating the frameset package is based on intrinsic-parameter variation and extrinsic-parameter variation of the selected subset of the plurality of variation parameters, wherein intrinsic-parameter variation and extrinsic-parameter variation provide programmable machine-learning data representations of synthetic data assets.