Avaya Inc.

Roman Nekrashevich

Tamar Barzuza

Ronen Bar-Yoav

Gal Netanel

Eyal Serero

Contact centers often seek to provide the most effective agents for a particular communication. A communication may comprise a customer node and an agent node. In addition to selecting the particular agent node based on a skill of the agent or other static information, dynamic information may be received and processed to make a routing decision. For example, one agent may be presently located in a noisy area and a communication may be impeded by routing a call to a node associated with an agent in a noisy environment and a different agent node selected. Other dynamic inputs may include determining an agent's proximity to a resource likely required in order to successfully satisfy a purpose of the communication. Additionally, self-learning may be utilized in order to predict when a particular environmental factor is about to change and make routing decisions accordingly.

Enhanced real-time routing

A first request to establish a communication session with a contact center is received from a user communication device. For example, a request to establish a voice communication session is received at the contact center. A machine learning model uses prior communication sessions with the contact center to identify a first set of one or more sensors that are external to the user communication device. External sensor information is received (e.g., during the request to establish the communication session) by the contact center. The external sensor information is from the first set of one or more sensors that are external to the user communication device. The communication session in the contact center is routed based on the received external sensor information. For example, instead of the communication session being routed to an Interactive Voice Response (IVR) system, the communication session is routed to a high priority contact center queue.

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Rishon Lezion, Israel

Roman Nekrashevich