Abstract: Systems and methods provide customers with a need-based warranty using a deep learning neural network. After categorizing, a customer need is mapped to a warranty type based on the SLA needs. Warranties may then be suggested based on customer need. In another embodiment, systems and methods detect an optimal warranty based on part failure and performance of a server. A mean time to resolve or replace can be minimized in future failure instances by comparing the derived replacement time with available warranty offerings to identify potential deviations and thereby recommend an optimal warranty from the available offerings. In a further embodiment, systems and methods identify and offer additional service contracts for vender services. A warranty proposer looks for warranty types that are emitted by a warranty-types analyzer and by a technical-support analyzer. The overlapping warranty offers are provided to customers.

Abstract: Applications related to non-invasive magnetic resonance guided focused ultrasound (MRgFUS) in a patient's vasculature are described. For example, the applications can include an ablation procedure, an occlusion procedure, a cauterization procedure, and the like. Accordingly, one aspect of the present disclosure is directed to a method for performing an MRgFUS application that includes selecting a target area within a patient's vasculature, configuring multifocal acoustic waves, and applying the multifocal acoustic waves to the target area to heat sequential locations in the target area simultaneously to facilitate the MRgFUS application.