US 12,141,690 B2
Interactive machine learning
Chantal Bisson-Krol, Kanata (CA); Zhen Lin, Kanata (CA); Ishan Amlekar, Ottawa (CA); Kevin Shen, Ottawa (CA); Seyednaser Nourashrafeddin, Ottawa (CA); and Sebastien Ouellet, Ottawa (CA)
Assigned to Kinaxis Inc., Ottawa (CA)
Filed by Kinaxis Inc., Ottawa (CA)
Filed on Nov. 28, 2019, as Appl. No. 16/699,010.
Application 16/699,010 is a continuation in part of application No. 16/697,620, filed on Nov. 27, 2019.
Claims priority of provisional application 62/915,076, filed on Oct. 15, 2019.
Prior Publication US 2021/0110299 A1, Apr. 15, 2021
Int. Cl. G06N 3/08 (2023.01); G06F 18/23213 (2023.01); G06N 3/10 (2006.01); G06N 20/00 (2019.01)
CPC G06N 3/08 (2013.01) [G06F 18/23213 (2023.01); G06N 3/105 (2013.01); G06N 20/00 (2019.01)] 21 Claims
OG exemplary drawing
 
1. A system comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the system to:
convert a plurality of descriptions of items into a plurality of word vectors, each word vector having a plurality of dimensions;
project, onto a two-dimensional plane of a user interface, the plurality of word vectors;
train a neural network on the plurality of word vectors and a plurality of sets of two-dimensional coordinates, each set of two-dimensional coordinates associated with a respective word vector;
predict a result based on the trained neural network;
output, to the user interface, a prediction comprising cluster groupings of the plurality of sets of two dimensional coordinates;
amend, via the user interface and in response to user input, the prediction, to provide an amended prediction, wherein the amending comprises moving on a screen displaying the user interface a subset of the plurality of sets of two-dimensional coordinates from one of the cluster grouping to another one of the cluster groupings or to a new cluster grouping;
retrain the trained neural network based on the amended prediction, thereby providing a retrained neural network; and
predict a new result based on the retrained neural network.