Dynamically selecting artificial intelligence models and hardware environments to execute tasks
US12386667B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 3, 2024 |
| Grant date | Aug 12, 2025 |
| Priority date | — |
| Expiry date | Jun 3, 2044 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L41/22
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to systems, non-transitory computer-readable media, and methods for selecting machine-learning models and hardware environments for executing a task. In particular, in one or more embodiments, the disclosed systems select a designated machine-learning model for executing a task based on workload features of the task and task routing metrics for a plurality of machine-learning models. In addition, in one or more embodiments, the disclosed systems select a designated hardware environment for executing the task based on workload features for the task and task routing metrics for a plurality of hardware environments. In some embodiments, the disclosed systems select a fallback machine-learning model and a fallback hardware environment for executing the task if the designated machine-learning model or designated hardware environment are unavailable. Moreover, in one or more embodiments, the disclosed systems can pause and initiate tasks based on bandwidth availability.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.