US 11,948,065 B1
Systems and methods for responding to predicted events in time-series data using synthetic profiles created by artificial intelligence models trained on non-homogeneous time-series data
Ernst Wilhelm Spannhake, II, Canal Winchester, OH (US); Thomas Francis Gianelle, Colleyville, TX (US); and Milan Shah, Plano, TX (US)
Assigned to Citigroup Technology, Inc., New York, NY (US)
Filed by Citigroup Technology, Inc., New York, NY (US)
Filed on Jun. 1, 2023, as Appl. No. 18/327,850.
Application 18/327,850 is a continuation of application No. 18/065,441, filed on Dec. 13, 2022, granted, now 11,704,540.
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/0464 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01)
CPC G06N 3/0464 (2023.01) [G06N 3/045 (2023.01); G06N 3/088 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for responding to predicted events in computer systems using artificial intelligence models, the system comprising:
one or more processors; and
a non-transitory machine readable medium comprising instructions that when executed by the one or more processors cause operations comprising:
generating a user profile for a user by:
receiving a first data set comprising a current state characteristic for a first system state; and
receiving a required future state characteristic for the first system state;
generating a synthetic profile for the user based on historical time-series data by:
receiving the historical time-series data;
training, using the historical time-series data, a second model using unsupervised learning, wherein the second model comprises a convolutional neural network; and
selecting, using the second model, a second data set from a plurality of available datasets based on similarities between state characteristics for the second data set and the current state characteristic and the required future state characteristic, wherein the second data set comprises second rate-of-change data over a second time period;
using the synthetic profile to generate a recommendation for the user by:
inputting the first data set into a first model to generate first rate-of-change data over a first time period for the first system state; and
generating for display, on a user interface, a recommendation based on the first rate-of-change data.