Abstract: A method and a system of classifying textual data blocks are claimed. The method includes receiving at least one textual data block in an original version, including a plurality of textual data elements; performing a preprocessing procedure on the at least one textual data block in the original version, wherein the preprocessing procedure includes replacing the textual data elements characterized by pertinence to at least one specific part-of-speech (POS) category with a respective POS token, thereby obtaining the at least one textual data block in a preprocessed version; inferring a pretrained ML-based model on the at least one textual data block in the preprocessed version, to classify the at least one textual data block by pertinence to the at least one class.

Abstract: A method according to an embodiment includes determining a group of subject matter experts in a back office with expertise to address a question from a user of a contact center system, determining an availability of one or more of the subject matter experts via a back-office platform executed by corresponding back-office devices of the one or more subject matter experts, assigning, via the back-office platform, a communication token to a selected subject matter expert determined to be available, wherein the communication token is associated with the question from the user, monitoring, via the back-office platform, for updates to the back-office platform associated with the communication token, receiving an update from the back-office platform associated with the communication token, and transmitting, via the contact center system, a message to the user device based on an update to the communication token.

Abstract: A method for creating a student model from a teacher model for knowledge distillation. The method may include: providing the teacher model trained on a first training dataset; generating candidate student models, wherein each of the candidate student models is a model having a unique permutation of layers derived by randomly selecting one or more layers of the plurality of layers of the teacher model for removing; generating a second training dataset; for each of the candidate student models: providing the second training dataset as inputs; recording outputs generated; and based on the recorded outputs, evaluating a performance according to a predetermined model evaluation criterion; determining which of the candidate student models performed best among the candidate student models based on the predetermined model evaluation criterion; identifying a preferred candidate student model.

Abstract: A method for agent interaction analysis using artificial intelligence according to an embodiment includes receiving a transcript for a real-time agent interaction between a contact center agent and client, processing the interaction using at least one artificial intelligence model to determine a call adherence score, an agent positivity score, a client sentiment, an agent sentiment, and a plurality of agent skill levels associated with respective agent skills, determining a combined agent performance score based on the call adherence score and the agent positivity score, transmitting the combined agent performance score to an agent device for display on a gamification dashboard, retrieving agent-specific content of the contact center agent in response to determining that the agent sentiment is negative, and transmitting the agent-specific content to the agent device for display in conjunction with the real-time agent interaction to improve the agent sentiment.

Abstract: A method for conducting an automated dialogue between an inbound automated voice resource (IAVR) and an outbound automated voice resource (OAVR) during a voice communication session. The IAVR receives an initiation of the session from the OAVR and transmits a speech communication to the OAVR during the session, wherein a digital watermark is embedded in the speech communication. The OAVR identifies the digital watermark in response to analyzing the speech communication and converts an OAVR communication language from speech to machine language in response to determining that the IAVR interprets machine language based on the digital watermark. The OAVR transmits a machine language communication to the IAVR, whereby the IAVR converts an IAVR communication language from speech to machine language in response to determining that the OAVR interprets machine language based on the machine language communication. The automated dialogue is completed between the IAVR and the OAVR using machine language.

Abstract: A method according to one embodiment includes transmitting, by an enterprise system, a data request for user data stored in a software wallet to a software wallet provider, transmitting, by the software wallet provider, an authorization request to an end user device of the user in association with the data request, creating, by the end user device, a transaction signed with a first private cryptographic key to generate a signed transaction, transmitting, by the end user device, the signed transaction to the software wallet provider, signing, by the software wallet provider, the signed transaction with a second private cryptographic key to generate a multi-signed transaction, transmitting, by the software wallet provider, the multi-signed transaction to the enterprise system, and validating, by the enterprise system, the multi-signed transaction using a public cryptographic key associated with the first private cryptographic key and the second private cryptographic key.

Abstract: A method for measuring experience quality. The method including: receiving key experience aspects (“KEAs”); administering pre-surveys to a representative subset of the experience audience, including an importance pre-survey requesting relative importance between the KEAs; and perception pre-survey requesting how closely perceptions align with each KEAs; calculating importance weights and perception weights based on the pre-surveys; performing a general survey process that includes surveying the experience audience to provide a quality rating indicating how well each of the KEAs was delivered based on their own experience; and calculating an Experience Index score as a measure of the quality of the experience that is an aggregation of the received quality ratings weighted by at least one of the importance weights or the perception weights.

Abstract: A method for adaptive predictive routing in a contact center system according to an embodiment includes identifying an interaction to be routed to a contact center agent, determining, for each agent cohort of a plurality of agent cohorts in sequential order and for a cohort time period associated with the respective agent cohort, whether a contact center agent within the respective cohort is available to be routed the interaction, wherein the plurality of agent cohorts is in sequential order based on descending agent performance scores for at least one key performance indicator, and routing the interaction to a first contact center agent determined to be available to be routed the interaction.

Abstract: A method for leveraging automated process discovery in a contact center system according to an embodiment includes determining, by a computing system and for each of a plurality of agent-client interactions, an context of an interaction between a corresponding contact center agent and a corresponding contact center client, capturing, by the computing system and for each of the plurality of agent-client interactions, actions taken by the corresponding contact center agent during the corresponding interaction, determining, by the computing system and for each of the plurality of agent-client interactions, a corresponding sequence of actions taken by the corresponding contact center agent based on the captured actions, and applying, by the computing system, sequence matching to generate a common sequence of actions based on the determined corresponding sequence of actions taken for the plurality of agent-client interactions.

Abstract: A method for configurable caching, deduplication, and rate limit handling of user interface data requests according to an embodiment includes receiving a data request initiated by a user interface element of a user interface, determining whether a deduplication cache includes a deduplication entry associated with the data request, determining whether a main cache includes a cache entry associated with the data request in response to determining that the deduplication cache does not include the deduplication entry, determining whether the data request is associated with a rate-limited group of application programming interface endpoints in response to determining that the main cache does not include the cache entry or determining that the cache entry has expired, and making the data request to a backend system after a predefined server retry period associated with the rate-limited group has elapsed in response to determining that the data request is associated with the rate-limited group.

Abstract: A method of leveraging a global confidence classifier for information retrieval in contact centers according to an embodiment includes receiving, by a computing system, a user query from a contact center client communication with a knowledge-only bot of the computing system, performing, by the computing system, feature extraction on the user query by converting query words of the user query into a numerical vector representation of the user query, identifying, by the computing system, a subset of documents most likely to be responsive to the user query, and re-ranking, by the computing system, the subset of documents most likely to be responsive to the user query based on a global confidence classifier model.

Abstract: A method for automated deployment of application servers in a contact center system according to one embodiment includes generating a deployment package for the application servers in the contact center system based on deployment sizing information and environment information associated with the contact center system preparing a temporary deployment server of the contact center system for deployment of software to the application servers, and installing the software to the application servers using the temporary deployment server.

Abstract: A method for optimizing a workflow of deferrable work interactions in a contact center that includes: providing a NLP models and a priority model; using text derived from received deferrable working interactions as inputs to the NLP models to generate the NLP scores; using the LP scores as inputs to the priority model to generate the priority score; identifying candidate agents of the agents for handling the deferrable work interactions; receiving an inbound work forecast; receiving agent work schedule data; using an optimization process to generate an optimized workflow for the deferrable work interactions, where the optimized workflow includes assignments in which an agent from the candidate agents is selected to handle the deferrable work interaction and a target timeframe for handling is scheduled; and routing the deferrable work interactions in accordance with the assignments of the optimized workflow.

Abstract: A method of asynchronously restoring an incomplete co-browse session according to an embodiment includes initiating an interaction between a user and a chat bot, determining whether data indicative of an incomplete co-browse session is stored in association with a user in response to initiating the interaction between the user and the chat bot, retrieving an intent configuration file associated with the incomplete co-browse session, wherein the intent configuration file defines a plurality of actions to be executed by the chat bot to resolve a user intent of the interaction between the user and the chat bot, and performing, via the chat bot and during a resumed co-browse session with the user, one or more actions of the plurality of actions defined by the intent configuration file that were not previously performed during the incomplete co-browse session.

Abstract: A system for real-time agent assist in a crisis scenario according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to receive an information request from a contact center client, evaluate the information request using natural language understanding to determine a knowledge fragment associated with the information request, analyze the knowledge fragment based on a crisis knowledge base of the system with respect to at least one of a confidence in the knowledge fragment, a veracity of the knowledge fragment, a frequency of the knowledge fragment, or a proximity of the knowledge fragment to the crisis scenario, and generate a response to the contact center client in response to analysis of the knowledge fragment based on the crisis knowledge base.

Abstract: A method for data bidding in an enterprise system comprising a first enterprise and a second enterprise using a permissioned blockchain infrastructure according to one embodiment includes receiving, by an end user device, a request for data from an end user; generating, by the end user device, an announcement including the request for data to the enterprise system, wherein the announcement initiates the data bidding by the enterprise system; receiving, by the end user device, at least one bid including requested data of at least one of the first enterprise and the second enterprise in response to the announcement; analyzing, by the end user device, the at least one bid from the at least one of the first enterprise and the second enterprise; selecting, by the end user device, the at least one of the first enterprise and the second enterprise; displaying, by the end user device, the at least one of the first enterprise and the second enterprise to the end user; receiving, by the end user device, an indication t

Abstract: An on-premises system for pre-processing data for forecasting according to an embodiment includes at least one processor and at least one memory having a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the on-premises system to receive a request to forecast contact center data using a cloud system, determine a first number of interactions per unit of time for a source interval, determine a second number of units of time in a destination interval, and determine a third number of interactions in the destination interval based on the first number of interactions per unit of time for the source interval and the second number of units of time in the destination interval.

Abstract: A system for utilizing conversational artificial intelligence (AI) to train contact center agents according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored therein that, in response to execution by the at least one processor, causes the system to place a virtual call from an automated training system to an agent device of an agent, connect the virtual call to a chatbot in response to establishing a communication connection with the agent device, transmit one or more statements from the chatbot, receive, from the agent device, one or more agent responses of the agent corresponding to the one or more statements, and analyze the one or more agent responses to determine one or more training characteristics associated with AI-based contact center training of the agent.

Abstract: A method of dynamic prioritization of collaboration between human and virtual agents according to an embodiment includes generating a priority score for a conversation between a human user and a chat bot, wherein the priority score is based on at least a sentiment of the human user and a temporal aspect of the conversation, providing the priority score to a human agent via an interface for monitoring the priority score, transferring control of the conversation from the chat bot to the human agent based on an interaction of the human agent with the interface, and yielding control of the conversation from the human agent back to the chat bot in response to another interaction of the human agent with the interface.

Abstract: A method of routing interactions to contact center agents according to an embodiment includes identifying an interaction to be routed to a contact center agent, identifying a group of contact center agents as candidates for routing of the interaction, retrieving agent performance data for each candidate agent of the group of contact center agents identified as candidates for routing of the interaction, determining a predicted score for a key performance indicator for each candidate agent based on the agent performance data, determining an occupancy rate of each candidate agent based on the agent performance data, generating a ranking of the candidate agents for routing prioritization based on the predicted score for the key performance indicator for each candidate agent and the occupancy rate of each candidate agent, and signaling a routing device to route the interaction to a selected candidate agent based on the ranking of the candidate agents.