Machine learning models may be used during a communications session to process natural language communications and perform actions relating to the communications session. For example, a machine learning model may be used to provide an automated response to a user, to suggest a completion of text being entered by a user, or to provide information about a relevant resource. Machine learning models may rely on machine learning model data that is updated during a communications session as communications are processed by the machine learning model. To improve the performance of a machine learning model when a user leaves a first communications session and enters a second communications session, the machine learning model data may be stored during a first communications session and then retrieved during the second communications session to initialize a machine learning model for the second communications session.

Embodiments of the present disclosure include computer-implemented systems and methods for routing incoming customer requests or inquiries in a customer service center. The system may include at least one processor configured to execute instructions to perform steps. The steps may include receiving a plurality of incoming customer calls and a plurality of incoming customer chat sessions. The steps may include blending the received plurality of customer calls and the received plurality of customer chat sessions into a total queue for presentation to at least one user device. The steps may additionally include determining a respective priority for each of the blended plurality of customer calls and the blended plurality of customer chat sessions and sending, based on the determined priority, to the at least one user device at least one of the blended plurality of customer calls or the blended plurality of customer chat sessions.

A method and system enables communications with a call center via SMS/MMS and provides an additional communications channel to communicate with the call center in addition to voice, Internet email/IM chat and fax. The call center obtains location information and images from a customer using SMS messages and web pages via a messaging hub.

A text representation is received from a virtual assistant application on a user device. The text representation may be generated via a speech-to-text engine of the virtual assistant application from audio speech spoken by a customer. Device diagnostic data of the user device is also received from the virtual assistant application. An identifier of the customer is placed in an assistance queue. At least information in the text representation and the device diagnostic data is analyzed to determine an issue associated with the user device and a solution for resolving the issue, so that the solution is applied. In response to the identifier of the customer reaching a front of the assistance queue, session state information that includes at least the text representation and a description of the issue is provided to a support application. A voice support session is initiated between the support application and the virtual assistant application.

The present invention is a system and method of routing incoming communications to a CSR and providing guidance to the CSR based on the incoming communication using feedback information such as sentiment feedback, survey feedback, and feedback from actions taken by CSRs based on skill level. A CEC system receives an incoming communication, analyzes the communication and creates metadata based off of the analysis. The metadata is used by the RAE routing module to route the communication to an appropriate party. The metadata is also used by the GAE guidance module to determine the guidance to provide to the CSR. The CSR then performs an action based on the guidance. The CEC system continues to monitor the interaction until the interaction is completed. The communication metadata, the communication, the guidance, and the CSRs action are all provided to a RAS rules analysis module wherein the RAS analyzes the data and automatedly updates the rules (RAR and GAR) according to the analysis.

Techniques for sharing control of assigning tasks between an external pairing system and a task assignment system with an internal pairing system are disclosed. In one embodiment, the techniques may be realized as a method for sharing control of assigning tasks between an external pairing system and a task assignment system with an internal pairing system comprising receiving, from the task assignment system, a plurality of task pairing requests and an agent pairing request, wherein each task request of the plurality of task pairing requests is assigned to one of a first pairing strategy and a second pairing strategy. The agent pairing request may indicate an agent that is available for pairing. The method may further comprise transmitting, to the task assignment system, a pairing recommendation being based in part on the plurality of task pairing requests, the first pairing strategy, the second pairing strategy, and the agent pairing request.

A system and method provide an integrated automation solution that links multiple systems and applications of a contact center operation and provides a unified support interface and unified knowledge base that delivers relevant data in real-time to assist contact center personnel during a customer interaction. Robotic Process Automation (RPA) is used for automating workflows and processes with robots (e.g., attended and/or unattended) that perform various tasks and activities for capturing information (data, documents, etc.) from multiple front-end and/or back-end systems and applications to provide the necessary data and information in real-time during a contact center session.

Various embodiments of the present disclosure performing conversation sentiment monitoring for a conversation data object. In various embodiments, a text block that can be resized is identified within a conversation data object and successive regularized sentiment profile generation iterations are performed until a regularized sentiment score of the block exceeds a regularized sentiment score threshold. A current regularized sentiment profile generation iteration involves determining a regularized sentiment score for the block based on an initial sentiment score, a subjectivity probability value, and, optionally, a stage-wise penalty factor. A determination is then made as to whether the score exceeds the threshold. If so, then a regularized sentiment profile of the conversation data object is updated based on the regularized sentiment score. If not, then the text block is resized and a subsequent regularized sentiment profile generation iteration is performed based on the resized block.

Methods and systems for facilitating communication sessions between multiple communication points. The method includes receiving a communication request from a first user device associated with a first user, through a first communication point. The first communication point may be enabled with a context-to-call (C2C) feature. The method further includes determining a first communication mode selected by the first user device through the first communication point. The method includes identifying a second communication mode selected by a second user device associated with a second user. The method further includes processing the communication request based, at least in part, on a matching between the first communication mode and the second communication mode. The method further includes transmitting the communication request from the first user device to the second user device. The method includes initiating a communication session based on the processing step.

Systems and methods for flexible and extensible contact center routing are disclosed. Incoming communications are received at a contact center from one or more customers. A pairing score is determined for each agent capable of servicing the incoming communications for each of the one or more customers based on customer attributes and/or agent attributes that are adjustable and extensible. An incoming communication is routed from a customer to an agent having a highest pairing score with the customer. The incoming communication can be routed on a first communication channel and then switched over to a second communication channel. The first communication channel and second communication channel can be different types of communication channels.