/Leveraging Multiple Disparate Machine Learning Model Data Outputs To Generate Recommendations For The Next Best Action
Abstract

A system monitors signals in a service context specific channel (SCS) among multiple SCS channels and sends an advisory message when discharges are disproportionate with respect to a peer-representative index (PRI). The system monitors signals in multiple bidirectional SCS channels between multiple system devices and at least one user device, each SCS channel conveying signals to and from a respective system device of the multiple system devices. The system identifies discharges to a third party from an account associated with the user, determines the discharges are disproportionate relative to the PRI, and generates an advisory message regarding disproportionate discharges for at least one SCS channel. The advisory message is sent via the at least one SCS channel to at least one of the user device and the system device of the at least one SCS channel. Advisory messaging may be sent directly to client device, or indirectly via system agents.

Full Text

What is claimed is:

A system monitors signals in a service context specific channel (SCS) among multiple SCS channels and sends an advisory message when discharges are disproportionate with respect to a peer-representative index (PRI). The system monitors signals in multiple bidirectional SCS channels between multiple system devices and at least one user device, each SCS channel conveying signals to and from a respective system device of the multiple system devices. The system identifies discharges to a third party from an account associated with the user, determines the discharges are disproportionate relative to the PRI, and generates an advisory message regarding disproportionate discharges for at least one SCS channel. The advisory message is sent via the at least one SCS channel to at least one of the user device and the system device of the at least one SCS channel. Advisory messaging may be sent directly to client device, or indirectly via system agents.
Timeline
Filed
03/19/2026
Published
07/23/2026
Granted
Not Available
IPC Codes(3)
G06N 3/006:based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
G06N 3/044:Recurrent networks, e.g. Hopfield networks
G06N 3/08:Learning methods