Avaya Inc.

Avaya Management L.p.

Avaya Technology LLC

Wen-Hua Ju

George William Erhart

Reinhard P Klemm

Patrick Tendick

Robert C Steiner

Lorraine Denby

James M Landwehr

Atsushi Hirano

Michael Sisselman

David Skiba

Anjur Sundaresan Krishnakumar

P Krishnan

Colin Lingwood Mallows

Fei Chen

Shmuel Shaffer

Andrew Derek Flockhart

John A Young

Larry John Roybal

Sheldon Joseph Davis

Rafal Sitkowski

Sami Qutub

Peter Matteo

Didina Burok

Leta G Herman

Gail Levenelm

David Madigan

Holger Vatter

Oliver Huber

Tamas Smahajcsik-Szabo

Embodiments of the disclosure provide a method of processing messages received in an asynchronous communication system. In some embodiments, the method includes analyzing interactions on a digital communication channel, determining that the interactions have paused for an amount of time, analyzing content of the interactions to determine an estimate of the amount of time, updating a state of the agent to release the agent for the estimated amount of time, and setting a timer that will automatically change the state of the agent back to an occupied state for the interactions at a future time that aligns with an expiration of the timer.

Enhanced digital messaging

Systems and methods include generating an urgency score associated with data associated with a communication session based on processing the data and using the urgency score to modify a behavior of a bot or a call center agent and optimize a contact center. Systems and methods include modifying behavior of a bot or an agent during the communication session in response to the data. Systems and methods include modifying dialog of a bot, or a script of an agent, and modifying a control flow for the communication session. Optimizing a contact center includes adjusting a workload of an agent associated with the communication session.

Methods and systems of providing response based on communication session participant request urgency

Embodiments of the disclosure provide a method of processing messages received in an asynchronous communication system. In some embodiments, the method includes determining that a conversation is established with a customer communication device using an asynchronous communication channel, analyzing one or more messages exchanged over the asynchronous communication channel to determine a likelihood of communication disengagement, comparing the likelihood of communication disengagement with a predetermined disengagement threshold, and based on the comparison of the likelihood of communication disengagement with the predetermined threshold, updating a graphical user interface of an agent communication device being used to engage in the conversation.

Embodiments of the disclosure provide a method of processing messages received in an asynchronous communication system. In some embodiments, the method includes receiving a message from a customer communication device, determining that a conversation is already established in association with the customer communication device, including the message among a plurality of messages that are already assigned to the conversation, analyzing the message to determine a topic classification and a topic confidence score for the message, and based on the analysis of the message, determining whether the topic classification determined for the message corresponds to a continuation of a topic classification for the plurality of messages or whether the topic classification determined for the message corresponds to a different topic classification than the topic classification for the plurality of messages.

Systems are provided to manage pauses, including on-hold, conditions in communications, such as text-based communications. Users may be provided with messages at a pace that, among other things, allows a particular cadence of messages to be maintained, even if a reply has been created. Accordingly, resources of a contact center may be better allocated to other matters requiring attention without causing undue customer dissatisfaction or the burden of handling additional messages. While voice-based 'hold' conditions may be managed with messages or music on hold, text-based communications may be managed with social messaging content and/or comfort messages.

Social messaging hold and wait treatment systems

A mechanism for adaptive modification of an attribute tree in a graph based contact center is described along with various methods and mechanisms for administering the same. Adaptive modification methods are disclosed that allow a graph database to automatically remove and create categories as well as block removal of categories with active relationships. Staff assignment, administrator productivity, and customer service are improved with the assessment, merging, and removal of atrophied categories and the operationally desirable expansion and/or addition of categories.

Mechanism for adaptive modification of an attribute tree in graph based contact centers

In order to provide better service with self-service applications, metrics for a communication session between a user and a self-service application are identified. Based on the metrics, a pattern is determined that will likely predict an initial outcome of the communication session with the self-service application. In response to determining that the pattern will likely predict the initial outcome of the communication session with the self-service application, the way the user is managed in the communication session is changed. For example, if a particular pattern of IVR responses is detected that will likely lead to a caller abandoning a voice call, the voice call can be automatically transferred from the IVR system to a contact center agent. This provides for increased customer satisfaction and better utilization of contact center resources.

Prediction of contact center interactions

A graph database is described for use in connection with a contact center. The graph database includes a plurality of nodes and relationships that describe the operations, entities, personnel, and attributes in the contact center. Also described is the operation of a work assignment engine that leverages the graph database to make intelligent and flexible work assignment decisions.

Mechanism for work assignment in a graph-based contact center

Text-based automated customer service agents provide an attractive contact center interaction for may customer-agent communications. However, the 'say anything' nature of text may result in additional problems not encountered in prompted interactions. A single misunderstanding or error may cause an automated communication to fail, however, by providing micro-tasks to a human agent, an automated communication may be maintained longer with benefit of a human agent's input. A lack of progress towards completion of a work item may cause the communication to be routed to a human agent and/or a different channel (e.g., voice, email, etc.). Accordingly, a micro-task or an opt-out operation may be provided to maintain the communication with an automated agent for as long as possible but yet provide improved customer service when necessary.

System initiated dialog adjustment

To provide better metrics for a contact center, the system determines when a contact center agent has been assigned to a plurality of agent skills in a skill group. A skill group is a set of skills for which the contact center seeks to calculate overall metrics across those skills. Information is received that the contact center agent is in a first state for the plurality of agent skills. For example, the contact center agent is available to support the different products. A status is calculated for the contact center agent in the skill group. The status for the contact center agent in the skill group is calculated based a minimum or maximum of the first state of the contact center agent in the skill group. The status of the first agent in the skill group is sent to a contact center administrator to better manage the contact center.

Management of contact center group metrics

Contact centers comprise agents, human and automated, interacting with customers over a network to accomplish an objective for the interaction. While a blunt, factual exchange may appear to be the most expeditious means to accomplish the objective, often this is not the case. Humans often respond better when the interaction comprises a mix of progress (e.g., portions of the interaction directed towards the objective) and banter (e.g., portions of the interaction not directed to the progress). Determining the mix may be provided by analyzing historic interactions and their success. A current interaction may be analyzed and when the mix is outside an acceptable range and an automated agent may be configured to alter the mix accordingly or, when the agent is a human, signal the agent to make the alteration. Success may be continually monitored and the target mix adjusted based on subsequent interactions.

On-topic monitor

A predictive model for abandoned calls provided by a contact center communication system for routing optimization is described along with various methods and mechanisms for administering the same. Based on multiple attributes, the system can calculate the probabilities, risks, and costs associated with abandonment and wait, taking into account the patience of a caller, region where the caller lives, agents available to handle the call, length of time that the caller has already been waiting, potential cost of having the caller and call type abandon, and potential total cost of having the caller wait to be served.

Predictive model for abandoned calls

A graph database is described for use in connection with a contact center. The graph database includes a plurality of nodes and relationships that describe the operations, entities, personnel, and attributes in the contact center. Also included in the graph database is an attribute tree that enables a work assignment engine in the contact center to make proximity-based work assignment decisions.

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Monmouth Junction, NJ, United States of America

Wen-Hua Ju