Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process incoming call-center calls based on inferred sentiments. An incoming call is routed to a call agent based on an inferred topic, classified based on one or more inferred sentiments of a current caller's speech, determining, based on the call classification, that a complaint has been articulated and initiating an automated assistance by searching for one or more similar callers to the current caller. Based on finding a successful call outcome associated with one or more similar callers, the system suggests one or more phrases to the call agent for use in a dialog with the current caller to improve the one or more inferred sentiments.

A method and a system for reporting metrics relating to customer calls for fungible agents that are utilizing multiple servicing applications in a contact center environment are provided. The method includes: receiving a service request call from a customer; determining applications that are usable for responding to the service request call from among a predetermined plurality of applications; monitoring the service request call in order to obtain information relating to call timing and events occurring during the call; when the service request call is completed, determining call-specific metrics such as hold time, number of transfers, and or idle time based on the information obtained during the monitoring; and reporting the metrics to a repository. Additional metrics that are specific to an agent assigned to handle the call may also be determined.

A system and method for intelligently routing calls between customers and agents. The system and method use knowledge graphs to generate route recommendations for a route selection system. The system uses dynamically selected objective functions to generate the route recommendations. The objective functions may be selected according to the intent of the call. The system and method can also be used to reroute ongoing calls when the intent of the call changes.

A computing system facilities live and robo-advising sessions, and transitions therebetween based on detection of triggers. The computing system may extract voice inputs of a user from ambient sounds captured using a sound sensor. Based on an analysis of the voice inputs and detection of corresponding triggers, the computing system may initiate a robo-advising session, or a live communication session between a user computing device and an advisor computing device. Detection of another trigger during the robo-advising session or the live communication session may lead to a transition to a live communication session or a robo-advising session, respectively.

A method and an apparatus for predicting satisfaction of a customer pursuant to a call between the customer and an agent, in which the method comprises receiving a transcribed text of the call, dividing the transcribed text into a plurality of phases of a conversation, extracting at least one call feature for each of the plurality of phases, receiving call metadata, extracting metadata features from the call metadata, combining the call features and the metadata features, and generating an output, using a trained machine learning (ML) model, based on the combined features, indicating whether the customer is satisfied or not. The ML model is trained to generate an output indicating whether the customer is satisfied or not, based on an input of the combined features.

Certain aspects of the disclosure are directed to monitoring user-data communications corresponding to a user-generated message. According to a specific example, user-data communications, which are addressed to a client among a plurality of remotely-situated client entities, are directed to a message recording system. Each of the plurality of remotely-situated client entities are respectively configured and arranged to interface with a data communications server providing data communications services on a subscription basis. During recording of a message associated with the user-data communications and on the message recording system, speech characteristic parameters of the message may be analyzed, and a sentiment score and a criticality score for the message, may be determined. During the recording of the message, the user-data communications may be routed based on the determined sentiment score and criticality score.

A system for data recording across a network includes a session border controller connecting incoming data from the network to an endpoint recorder. A load balancer is connected to the network between the session border controller and the endpoint and receives the incoming data from the session border controller, wherein the load balancer comprises computer memory and a processor configured to parse the incoming data into video data and audio data according to identification protocols accessible by the processor from the computer memory. A recording apparatus includes recording memory that receives the incoming data from the load balancer, stores a duplicate version of the incoming data in the recording memory, and connects the incoming data to the endpoint.

Techniques for workforce management in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for workforce management in a contact center system comprising generating historical workforce data regarding an agent workforce capacity of the contact center system, and initiating an increase or decrease to an agent workforce of the contact center system based at least in part on the historical workforce data to increase an amount of choice among available agents or waiting contacts.

A system and method for managing a customer's experience with a contact center that takes available data about the customer, the agents of the contact center, and the organization represented by the contact center, for identifying opportunities for additional conversations/interactions with the customer and engaging in those additional conversations/interactions at a time and with a resource predicted to maximize outcomes for the organization. A processor is configured to identify express and/or implied intents for an interaction between the customer and the contact center. A business goal related to the express and/or implied intents is also identified for determining a current performance of the contact center and for identifying any performance gaps. Contact center targets are identified based on their performance in handling the express and/or implied intents, and the identified performance gaps. An available one of the identified targets is then selected for routing the interaction to the target.

A speech recognition server has a speech recognition engine, and a mode control table to hold a speech recognition mode for each call. The speech recognition engine has a mode management unit to designate a speech recognition mode for a decoder, and an output analysis unit to analyze recognition result data speech-to-text converted by speech recognition. The output analysis unit designates the speech recognition mode for the mode management unit in accordance with result of analysis of the recognition result data speech-to-text converted by the speech recognition. The mode management unit designates the speech recognition mode for the decoder in accordance with the designation with the output analysis unit. Upon speech recognition of call data, it is possible to suppress hardware resource consumption while improve users' satisfaction.