Other

Genesys Telecommunications Laboratories, Inc.

Solaria Corporation

Conor McGann

Herbert Willi Artur Ristock

Bharath Aravamudhan

Felix Immanuel Wyss

Nikolay I Korolev

Yochai Konig

Petr Makagon

Vyacheslav I Zhakov

Gregory Duclos

Wenjin Gu

Ioana Grigoropol

Mariya Orshansky

Ankit Pat

Simon Doyle

Michael Orr

Canice Lambe

Damjan Pelemis

Patrick Breslin

Andriy Vladimirovich Ryabchun

Josef Eric Eisner

Gordon L Bell

Anand Pai Krishnanand Nitin

Francisco Gutierrez

Hardik Modi

Jeffery Karas

Graeme Provan

Bohdan Klepar

Jeffrey Eric Davitz

Stanislav Lypchanskyy

Stanislav Lypachanskyy

A method for selectively transcribing voice communications that includes: receiving keywords; receiving an audio stream of audio data of speech; searching the audio stream to detect keywords or keyword detections and recording parameter data for each that includes a location of the keyword within the audio stream; generating one or more cumulative datasets for one or more portions of the audio stream that each includes parameter data for the keyword detections occurring therein; for each of the one or more portions of the audio stream, calculating a transcription favorableness score via inputting the corresponding one of the one or more cumulative datasets into an algorithm; and determining whether to transcribe each of the one or more portions of the audio stream by comparing the corresponding transcription favorableness score against a predetermined threshold.

Systems and methods related to automated transcription of voice communications

A computer-implemented method for managing a dialog between a contact center system and a user thereof, comprising the steps of: hosting a dialog over a communication channel between an automated dialog engine of said contact center and said user thereof, said dialog comprising messages sent between said automated dialog engine and said user in both directions; said automated dialog engine receiving input messages from said user, and determining response messages in response to said inputs; detecting from said messages of said dialog a trigger event matching a rule; in response to detection of said trigger event: providing said agent station with a summary of said dialog; and providing control of said automated dialog engine to said agent station.

System and method for managing a dialog between a contact center system and a user thereof

A system and method for engaging in an automated dialog with a user. A processor retrieves a preset dialog flow that includes various blocks directing the dialog with the user. The processor provides a prompt to the user based on a current block of the dialog flow, receives an action from the user in response to the prompt, and retrieves a classification/decision tree corresponding to the dialog flow. The classification tree has a plurality of nodes mapped to the blocks of the dialog flow. Each of the nodes represents a user intent. The processor computes a probability for each of the nodes based on the action from the user. A particular one of the nodes is then selected based on the computed probabilities. A target block of the dialog flow is further identified based on the selected node, and a response is output in response to the identified target block.

System and method for extracting domain model for dynamic dialog control

A root behavior tree is stored with a flow control node and a plurality of child nodes. The flow control node may determine a sequence of execution of child nodes. A first-level behavior tree is stored and executed to accomplish a task with associated intent. The first-level tree comprises a child node of the root tree and nodes defining actions. A dialog between contact center and user is hosted over a communication channel and a processor receives inputs from the dialog and executes the root tree and the first-level tree as a child node of the root tree in response. Upon detecting a match between an input and a data pattern, the execution sequence within the root tree or a further tree is altered. Responsive to the altered sequence, an output is provided to cause an action.

A processor receives inputs from a dialog between an agent and a user performed over a communication channel A knowledge base is stored, comprising entries with a subset of said entries defined as higher priority. A match is detected between an input from said dialog and a plurality of said knowledge base entries. At least one of said plurality of entries is retrieved corresponding to said match. The at least one of said plurality of entries is pushed as an output to one or more of a device operated by said user and at least one device operated by one of said user and said agent. A further match is detected between an input from said dialog and a higher priority entry in said knowledge base and pushed to said at least one device while the dialog is ongoing.

A method for engaging in an automated dialog with a user that includes: retrieving a preset dialog flow that includes various blocks directing the dialog with the user; providing a prompt to the user based on a current block of the dialog flow; receiving an action from the user in response to the prompt; and retrieving a classification tree corresponding to the dialog flow. The classification tree has a plurality of nodes mapped to the blocks of the dialog flow representing user intents. The processor computes a probability for each of the nodes based on the action from the user. A particular one of the nodes is then selected based on the computed probabilities. A target block of the dialog flow is identified based on the selected node, and a response is output in response to the identified target block.

System and method for dynamic dialog control for contact center system

System and method for dynamic dialog control for contact center systems

A system includes a router to route an interaction by a customer to a contact center. A journey patience value is determined for the customer, where the journey patience value is based on a current journey stage of the customer. An action by the contact center is determined based on the determined journey patience value for the customer. The action is performed by the contact center based on the determined action.

System and method for predictive routing based on a customer journey patience

A system and method for making engagement offers. A processor monitors a navigation path of a user navigating a website. The processor makes an offer for engagement with the user on a first web page reached by the user upon following the navigation path. The processor monitors a reaction of the user to the offer for engagement, and updates tag data associated with a second web page encountered by the user while traversing the navigation path. The tag data is updated based on the reaction of the user to the offer for engagement.

System and method for making engagement offers based on observed navigation path

A system and method for proactively making knowledge offers. A processor is configured to gather information on interactions by a user with resources provided by an enterprise having a customer contact center. The processor anticipates need of the user based on the gathered information, and generates a query based on the anticipated need. Prior to the user expressly requesting knowledge relating to a particular topic, the processor proactively identifies and suggests the knowledge to the user based on the generated query. The processor receives feedback relating to the suggested knowledge and outputs based on the feedback, a relevance score for the suggested knowledge.

Proactive knowledge offering system and method

A system and method for routing interactions to contact center agents. The system is adapted to concurrently identify a plurality of interactions waiting to be routed, and identify a plurality of candidate agents viable for handling the plurality of interactions. The system is also adapted to calculate a predicted wait time associated with each of the candidate agents. For each agent of the plurality of candidate agents, the system is adapted to estimate an expected value to be obtained by routing each of the plurality of the interaction to the agent. The expected value is a function of the predicted wait time. The system is further adapted to assign each of the plurality of the interactions to one of the plurality of candidate agents based on the estimated reward, and signal a routing device for routing each of the plurality of the interactions to the agent assigned to the interaction.

Optimized routing of interactions to contact center agents based on forecast agent availability and customer patience

A system and method for muting interactions to contact center agents. The system is adapted to identify a network of agents and customers. Each node of the network represents either an agent or a customer, and a connection is made between the node representing the agent and the node representing the customer, in response to identifying a fit between the customer and the agent. The system is adapted to identify a current interaction to be routed, identify a first group of agents based on one or more constraints for generating one or more candidate agents, and determine the fit between a current customer and each of the candidate agents based on the network of agents and customers. The system is further adapted to select a particular agent of the candidate agents with a best fit, and transmit a signal for routing the current interaction to the particular agent.

System and method for generating a network of contact center agents and customers for optimized routing of interactions

A system that is adapted to route interactions to contact center agents. More specifically, the system is adapted to identify an interaction to be routed, and identify a group of agents based on one or more constraints for generating one or more candidate agents. The system is also adapted to gather context data surrounding the candidate agents. For each agent of the candidate agents, the system is adapted to estimate an expected value to be obtained by routing the interaction to the agent. The system is further adapted to select a particular agent of the candidate agents based on the estimates, and signal a routing device for routing the interaction to the particular agent.

Optimized routing of interactions to contact center agents based on machine learning

Intentions of a user as expressed in an on-line conversation or other user generated content (UGC) are identified and subsequently acted upon, for example by bringing relevant or related content to the attention of the user and/or making others aware of the conversation. The intention signals derived from the UGC may be used to fashion or update a user profile or inform a broader view of the individual associated with the UGC.

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San Carlos, CA, United States of America

Conor McGann