Abstract: Load balancing and distributing issue requests for resolution by assignable resources is described. A multimodal model determines attributes of an input issue request. A lower dimensional representation of the input issue request is generated based on the attributes; and compared to other lower dimensional representations of other issue requests to identify prior similar issue requests. The multi modal model outputs indications of similarity strength for the prior similar issue requests, and the resources assigned to resolve the prior similar issue requests. An expertise level for each assignable resource is determined based on the similarity strength and the previously assigned resources. A routing metric for each assignable resource is determined based on a combination of the expertise level and an availability of each assignable resource; and the input issue request is routed to an assignable resource in the assignable resources based on the routing metric.

Abstract: A disclosed method for incident dependency prediction includes identifying, for a newly-occurring network incident of interest, one or more similar historical incidents; obtaining causal dependency data for the one or more similar historical incidents, the causal dependency data identifying one or more historical responsible incidents responsible for causing the similar historical incidents; identifying candidate root cause incidents each occurring within a recent time interval and satisfying similarity criteria with a corresponding one of the one or more historical responsible incidents; and transmitting a prompt to an incident dependency prediction model, the prompt requesting prediction of a root cause incident responsible for causing the newly-occurring network incident of interest based on feature information for each of the candidate root cause incidents and for the incident of interest.

Abstract: A disclosed method for incident dependency prediction includes identifying, for a newly-occurring network incident of interest, one or more similar historical incidents; obtaining causal dependency data for the one or more similar historical incidents, the causal dependency data identifying one or more historical responsible incidents responsible for causing the similar historical incidents; identifying candidate root cause incidents each occurring within a recent time interval and satisfying similarity criteria with a corresponding one of the one or more historical responsible incidents; and transmitting a prompt to an incident dependency prediction model, the prompt requesting prediction of a root cause incident responsible for causing the newly-occurring network incident of interest based on feature information for each of the candidate root cause incidents and for the incident of interest.