Abstract: The disclosure generally pertains to systems and methods for contact center channel merging. In an example method, a first inbound communication associated with a vehicle event may be received from a first vehicle component of a vehicle via a first communication medium, the first inbound communication including a first identifier. A second inbound communication associated with the vehicle event may be received from a second vehicle component of the vehicle via a second communication medium, the second inbound communication including a second identifier. It may be determined that a first time associated with the first inbound communication and a second time associated with the second inbound communication are within a predetermined threshold, and that the first identifier and the second identifier are both associated with the vehicle. At least one notification may be sent based in part on an aggregation of the first inbound communication and the second inbound communication.

Abstract: The disclosure generally pertains to systems and methods for contact center channel merging. In an example method, a first inbound communication associated with a vehicle event may be received from a first vehicle component of a vehicle via a first communication medium, the first inbound communication including a first identifier. A second inbound communication associated with the vehicle event may be received from a second vehicle component of the vehicle via a second communication medium, the second inbound communication including a second identifier. It may be determined that a first time associated with the first inbound communication and a second time associated with the second inbound communication are within a predetermined threshold, and that the first identifier and the second identifier are both associated with the vehicle. At least one notification may be sent based in part on an aggregation of the first inbound communication and the second inbound communication.

Abstract: The present subject matter relates to techniques of printing contents within a webdocument that are relevant for printing. In one example, the web document including a plurality of content may be received and thereafter, each content in the web document may be classified as one of relevant or non-relevant for printing. In one example, the classification may be done by analyzing metadata associated with the contents using machine learning techniques. Further, the contents classified as relevant may be sent for printing.