Patent AU-2022215198-A1 (AU)
Title: Utilizing an artificial intelligence model determined for a target domain based on a dataset associated with a source domain
Inventor: LECUE FREDDY
IPC: G06N7/08
Filed: 2022-08-10
Abstract: A device, including one or more memories, and one or more processors, 
communicatively coupled to the one or more memories, that receive source data, target data, 
external data, and a target task associated with the source data and the target data, the 
external data including expert data, and at least one or more of weather data, calendar data, 
or social media data, and the external data received from one or more resource devices, 
generate features of the source data and the target data by analyzing the source data and 
the target data to determine correspondences among concepts and possible conflicts, 
generate, based on the generated features, differentiators between the source data and the 
target data, the differentiators including misalignment data indicating at least one of 
granularity data misalignment, type data misalignment, or coverage data misalignment, and 
wherein generating the features of and the differentiators between the source data and the 
target data includes utilizing a Bayesian or multivariate Gaussian mixture model, identify, 
utilizing one of a data mapping technique or a nonlinear data mapping technique, which 
utilizes neural networks, a set of mappings between the source data and the target data 
based on the differentiators between the source data and the target data, each mapping 
dependent upon a level of misalignment between the source data and the target data, the 
set of mappings used to embed or transfer data from the source data to the target data, 
determine, utilizing at least one of a hierarchical clustering technique, a centroid-based 
clustering technique, or a distribution-based clustering technique, different clusters of the 
source data based on the external data, the features of the source data and the target data, 
the differentiators between the source data and the target data, and the sets of mappings, 
the sets of mappings being utilized to force alignment of the source data and the target data 
based on the misalignment data included in the differentiators, generate, based on the 
external data, a set of artificial intelligence models as candidates to perform the target task, 
generate a performance measure for the set of artificial intelligence models based on the 
features of the source data and the target data, the differentiators between the source data 
and the target data, the sets of mappings, and the different clusters of the source data, 
generating the performance measure utilizing at least one of a Euclidean loss function to 
determine the performance measure, a quadratic loss function to determine the performance 
measure, or a 0-1 loss function to determine the performance measure, refine the set of
mappings based on the different clusters of the source data and based on the performance 
measure to generate enhanced target data, identify, utilizing a stochastic gradient descent 
(SGD) technique, an artificial intelligence model, from the set of artificial intelligence models, 
to perform the target task based on the different clusters of the source data and based on 
the performance measure, the enhanced target data used to train the identified artificial 
intelligence model, receive a request to perform the target task, and utilize the trained 
identified artificial intelligence model to perform the target task based on the request, and 
transmit information associated with the target task to a user device.