154 results about "Contact center" patented technology

A method in a contact center for generating an action classifier model and use thereof in selectively initiating turn set queries of a knowledge base to assist agents in real time during ongoing conversations with customers. The method includes: generating an action classifier model; receiving classification data that classifies a first plurality of the customer actions found in training samples as belonging to a first action category for which a knowledge base search is deemed needed, and a second plurality of the customer actions as belonging to a second action category for which a knowledge base search is deemed not needed; and using the action classifier model and the received classification data to perform a query filtering routine for selectively initiating a turn set query for a present turn set occurring in an ongoing conversation between an agent and customer.

Systems and methods for orchestrating interaction with an artificial intelligence (AI) application in a contact center environment receive, via an AI agent, a voice message from a user; convert the message from voice to text; generate an initial computational inference process based on the text message; determine whether or not all information required to execute the initial computational inference process is available to the processor; when a determination is made that all information required is available: execute the initial computational inference process; generate a text reply based on the initial computational inference process; convert the text reply to a voice reply; and send the voice reply to the user via the AI agent; when a determination is made that information is unavailable: generate a text query requesting the information; convert the text query to a voice query; and send the voice query to the user via the AI agent.

An example operation may include one or more of implementing a trained artificial intelligence (AI) model using a neural network training capability with at least one of call log data of calls determined to have an activity risk, activity risk data, activity context, and model feedback data, capturing audio from an ongoing telephone call, converting the audio from the ongoing telephone call into text, obtaining context of an activity from a computing device, executing the trained AI model to generate an activity risk profile based on the text and the context of the ongoing telephone call, and executing an action during the ongoing telephone call based on the activity risk profile.

A system, device, and method to reconnect a terminated communication to the original queue and provide queue status over a different communication channel. The method includes receiving a first communication over a first communication channel and placing the first communication in a contact center queue. The method also includes providing a user with an option to terminate the first communication over the first communication channel and receive queue status information over a second communication channel, and in response to the user selecting the option: terminating the first communication while maintaining a queue status in the contact center queue for the first communication; providing the queue status information; in response to the queue status of the first communication being within a threshold, notifying the user to initiate a second communication over the first communication channel; and replacing the first communication in the contact center queue with the second communication.

A method for efficient identification of knowledge base data to support interactions in a contact center may include identifying a query associated with a contemporaneous interaction between a client and an agent of a contact center and performing a search of a knowledge base to determine a set of one or more relevant documents responsive to the identified query, including ranking a set of resultant documents from the knowledge base as a function of a vector-based similarity determination in which each resultant document and the identified query is represented with a corresponding embedding. Additionally, the method may include identifying, with a transformer-based artificial intelligence model, a subset of content within each relevant document as being most pertinent to the identified query and visually demarcating the identified subset of each relevant document from other content of the relevant document in a user interface as the interaction occurs.

A method for tracking and analyzing signals associated with metrics and providing actionable insights according to an embodiment includes receiving metric data, including workload metric data, for a contact center during a predefined time interval, analyzing the metric data to generate analysis data, wherein the metric data is analyzed separately for each agent planning group, processing the analysis data using at least one large language model to generate a set of insights associated with the metric data of the contact center and at least one possible action to improve a condition associated with the set of insights, displaying the metric data, the set of insights, and an indicator of the at least one possible action on a graphical user interface accessible to a user, and automatically executing a possible action in response to the user's selection of the possible action via the graphical user interface.

A system for carbon emission reduction 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 determine whether an application for providing one or more services in a contact center is hosted in an on-premises system or a cloud-based system. The instructions may also cause the system to determine, in response to a determination that the application is hosted in a cloud-based system and as a function of one or more cloud hosting implementation properties, a score indicative of a carbon emission efficiency and provide data indicative of a potential carbon emission reduction for the application as a function of the determined score and at least one modification to the cloud hosting implementation properties.

A contact center controller is provided to automatically manage a large volume of audio and / or video electronic communications in a contact center. The controller routes electronic communications to available agents, and monitors the interactions between the agents and users participating in the electronic communications. The controller determines a state associated with each agent based on the interactions the agent has with one or more users in the same electronic communication, and routes new electronic communications away from a first agent in response to determining a first state for the first agent that indicates a specific level of fatigue. The controller performs different actions for different agents and / or electronic communications based on different or changing levels of fatigue associated with the agents.

Computer-implemented systems and methods are disclosed for simulating contact-agent pairings and analyzing contact center performance based on the simulated contact-agent pairings. An exemplary method includes obtaining a first set of available contacts and available agents; determining a first contact-agent pairing based on the first set of available contacts and available agents and a first pairing strategy; and establishing a first connection between a first agent device and a first contact device for the first contact-agent pairing. The exemplary method further includes storing a first set of pairing data corresponding to the first contact-agent pairing; generating, by a simulation model implementing a simulated pairing algorithm, a second contact-agent pairing based on the first set of available contacts and available agents and a second pairing strategy; and storing a second pairing data corresponding to the second contact-agent pairing.

A method of leveraging machine learning to predict empathy for improved contact center interactions according to an embodiment includes receiving, by a computing system, at least one user message from a real-time contact center interaction with a user, generating, by an artificial intelligence system of the computing system, at least one empathy score based on the at least one message using the machine learning, wherein each of the at least one empathy score is indicative of a real-time empathy of the user, generating, by the artificial intelligence system of the computing system, an empathetic text response to the at least one user message based on the at least one empathy score, and responding to the at least one user message in the real-time contact center interaction based on the empathetic text response generated by the artificial intelligence system of the computing system.

A contact center request is received from a user device. Respective sets of characteristics associated with respective agent devices are determined. The contact center request is routed to an agent device of the respective agent devices based on the respective sets of characteristics. In an example, respective environment scores for the respective agent devices are obtained based on the respective sets of characteristics. The respective agent devices can be ranked based on the respective environment scores.

The present disclosure provides a method for pairing contacts with agents in a contact center system, comprising: determining a threshold statistic; determining an ordered set of contacts; determining a first set of statistics corresponding to the ordered set of contacts; determining a second set of statistics corresponding to the ordered set of contacts and based on the first set of statistics; determining a subset of the ordered set of contacts based on the second set of statistics and the threshold statistic; and pairing a selected contact from the subset of the ordered set of contacts with an idle agent based on a pairing strategy, wherein the second set of statistics is based on a cumulative summation of the first set of statistics.

Techniques described herein relate to a client application framework for a contact center environment. A role-specific thick client framework may include a web browser-based, desktop application having multiple processes that can be distributed across the computing infrastructure of the client device. A portion of the framework may include a container application image received from an internal server of the contact center and launched by the client device. The desktop application may collect data associated with use of the container application image and transmit the collected data to a contact center server. The contact center server may process the data, in some instances along with data from additional desktop applications on additional client devices, to implement one or more integrated models across the contact center environment.

A computerized-system for converting a time-off request of an agent to a shift-trade with another agent or to a self-swap schedule, in a contact center is provided here in. Upon receiving a time-off request from a source-agent, operating a Time-off Convertor (TC) module. The TC module includes (i) searching open trade requests in dates where the source-agent doesn't have a scheduled-shift, to yield a list-of-trading-agents; (ii) retrieving from the yielded list-of-trading-agents, compatible-agents who do not have a scheduled-shift in the specified date; (iii) presenting the compatible-agents, via a display unit; (iv) enabling the source-agent a selection of an agent from the presented compatible-agents and upon selection of the agent by the source-agent, sending an approval request to the agent; and (v) upon acceptance of the agent, sending to the source-agent and to the agent a trade-success-notification, and operating change of scheduled shifts in one or more WFM MSs.

A method in a contact center for generating an action classifier model and use thereof in selectively initiating turn set queries of a knowledge base to assist agents in real time during ongoing conversations with customers. The method includes: generating an action classifier model; receiving classification data that classifies a first plurality of the customer actions found in training samples as belonging to a first action category for which a knowledge base search is deemed needed, and a second plurality of the customer actions as belonging to a second action category for which a knowledge base search is deemed not needed; and using the action classifier model and the received classification data to perform a query filtering routine for selectively initiating a turn set query for a present turn set occurring in an ongoing conversation between an agent and customer.

A method for incentivizing contact center agents with scheduling perks that includes: receiving a workload forecast and staffing plan for a current shift; receiving a service level target; receiving types of scheduling perks; performing a selection routine for determining a select scheduling perk for offering to a select agent; and sending a scheduling perk offer via electronic communication to the select agent. The selection routine may include: selecting a proposed scheduling perks and a proposed agent; modifying the staffing plan to create a proposed staffing plan that reflects the proposed scheduling perk; predicting a proposed forecasted target adherence using the modified staffing plan; determining whether the proposed forecasted target adherence satisfies a threshold defined by an acceptable forecasted target adherence and, if so, deeming the proposed scheduling perk as select scheduling perk and proposed agent as select agent.

A user of a user device initiates a contact center engagement with a first contact center. The first contact center processes a prompt received, by a first contact center software service associated with a first entity. Determine, by the first contact center software service and based on stored data associated with the prompt or an account of the contact center user device, that the prompt relates to a second contact center software service associated with a second entity. Train, using online learning techniques, an artificial intelligence agent of the first contact center software service based on prior responses received from the second contact center software service during a merged contact session. The artificial intelligence agent is capable of providing a resolution to a user of the contact center user device without contacting the second contact center software service after a response is provided to questions in the merged contact session.

This disclosure relates to method and system for determination of personality traits of agents in a contact center. The method includes retrieving textual data corresponding to a conversation between a first agent and a first customer. The method further includes generating a natural language justification corresponding to a set of personality traits of the first agent based on the textual data through a first Machine Learning (ML) model. The natural language justification may include one or more sentences. The one or more sentences may include a mapping of the textual data with the set of personality traits and a qualitative label associated with each of the set of personality traits. The method further includes determining a value corresponding to each of the set of personality traits of the first agent through the first ML model based on the natural language justification and the associated qualitative label.

A method for verifying anonymization by a first user that the subject text does not contain personal identifiable information (PII). The method includes: receiving the subject text; receiving a non-PII wordlist listing non-PII words; comparing each word appearing in the subject text to the non-PII words to determine matches therebetween so that, via the comparison, the words of the subject text are classified as being either first text, which includes the words in the subject text found to match one of the non-PII words, and second text, which includes the words in the subject text found not to match any of the non-PII words; and generating a first user interface that displays the subject text such that a visual format of the first text differs from a visual format of the second text in accordance with a visual format alteration.

A computerized-method for dynamically prioritizing social-media interactions in real-time, based on social-media posts in feeds, in a contact-center. The computerized-method includes: (i) monitoring by processors the social-media posts in the feeds of one or more social-media platforms which are integrated to the contact center. The social-media posts have been published during a preconfigured period, for each social-media post of a customer in each feed in the feeds: a. calculating a quality-score by operating an Artificial intelligence (AI) driven Content Quality Analysis (ACQA) module; and b. calculating a social-impact score based on the calculated quality score and one or more parameters; (ii) automatically prioritizing the social-media posts based on the calculated social-impact score to yield a priorities queue of social-media interactions. Each social-media post represents a social-media interaction, and (iii) automatically routing social-media interactions by a routine-engine to an available agent based on the yielded priorities queue of social-media interactions.

A system for dynamic content generation for contact center interactions using artificial intelligence according to an embodiment includes determining, by a computing system and using an artificial intelligence model, an intent of an interaction between a contact center agent and a client based on a live transcript, identifying, by the computing system in real time, at least one image relevant to the determined intent in response to determining the intent of the live transcript, displaying, by the computing system in real time, the at least one image for the contact center agent on a dynamic interaction widget application executed by an agent device of the contact center agent, and sending, by the computing system, a selected image of the at least one image displayed on the dynamic interaction widget application to the client in response to selection of the selected image by the contact center agent.

The invention belongs to the technical field of robot assembly, and relates to a multi-sensor information hierarchical modulation and fusion method, system and device for robot assembly and a medium. The method comprises the following steps: firstly, performing feature extraction on preprocessed multi-sensor data, and then fusing preprocessed ontology information and force sense information and feature vectors of contact area, contact center position, contact direction and assembly gap area after feature extraction to form a state space input vector S; training the state space input vector S, then outputting an original action instruction vector, and finally modulating and fusing the original action instruction vector, the tactile capture state feature vector after feature extraction and the visual relative pose feature vector to generate an assembly action instruction A; according to the method, the problems that a multi-sensor fusion mode is single in hierarchy, reinforcement learning action lacks safety constraint and vision and learning decision collaboration is insufficient are solved, and the safety, the alignment precision and the assembly efficiency of the robot in complex and precise assembly are remarkably improved.

A method of integrating a locally deployed contact center with a cloud-based contact center according to an embodiment includes communicating resource data stored by the locally deployed contact center to the cloud-based contact center; and synchronizing the resource data between the local deployment contact center and the cloud-based contact center to maintain consistency between the resource data stored by the local deployment contact center and the resource data stored by the cloud-based contact center.

Systems and methods are provided for monitoring and managing call center operations within a virtual reality environment. A processing system generates a virtual call center comprising virtual workstations associated with respective agents. Each workstation includes a monitor displaying call information, a call health panel presenting real-time call status, and an agent health panel presenting agent condition or performance data. The system receives call audio and related data, performs transcription and signal analysis, and applies machine learning models to determine call sentiment, agent status, and recommended actions. Based on the analysis, the system dynamically updates the virtual reality environment, including visual indicators, supervisor alerts, and interactive panels. Supervisors may navigate the environment, monitor multiple agents, and interact with calls using virtual tools such as notes, scoring, or intervention modes. The disclosed embodiments improve real-time awareness, training, and decision-making in contact center environments.

A server of a contact center generates an engagement recording and engagement metadata associated with a contact center engagement. The engagement metadata identifies one or more user devices and one or more business servers. The server stores the engagement recording and the engagement metadata in a block of a private blockchain. The block includes a cryptographic hash of a previous block in the private blockchain, the engagement recording, and the engagement metadata. The server provides access to the block to the one or more user devices and the one or more business servers.

The invention relates to the technical field of intelligent contact center outbound voice processing, and discloses a low-delay streaming ICC outbound large model voice-to-text method, which comprises the following steps: capturing a bidirectional RTP voice stream through a FreeSWITCH media server, marking according to a Call-ID, analyzing SDP media parameters by a Java control center, associating the SDP media parameters with the Call-ID, and pushing the SDP media parameters to an ASR service; after the ASR service decodes the audio and preprocesses the audio, the audio is submitted to the FUNASR engine in a streaming mode to obtain a transcription text; and executing semantic correction through a large language model, outputting a structured text, asynchronously distributing the structured text to real-time quality inspection and intelligent auxiliary service through a message queue, and finally pushing a result to a seat workbench in real time. According to the method, voice stream-signaling collaboration, low-delay transcription and semantic precision optimization are realized, real-time quality inspection and intelligent assistance of outbound calling are supported, and the service quality and conversion efficiency are improved.

A computerized-method for matching customer queries across diverse digital channels with a response in a multichannel contact center. The computerized-method includes: (i) continuously receiving queries from customers via digital communication channels; in each preconfigured time-window: (ii) converting each customer query in the received queries into a high-dimensional vector representation and scoring the customer query to yield a similarity score by operating a semantic embedder module; (iii) clustering each customer query based on the high-dimensional vector representation and the similarity score by operating a AIQA module to yield clusters of customer queries with semantic similarity; (iv) matching each cluster of customer queries with the response based on a preconfigured threshold by operating a CVSE; (v) automatically generating a notification for each customer query based on the matched response by operating a notification module; and (vi) automatically sending the notification to each customer that is associated with the customer query.

Provided is a method for pairing contacts with agents in a contact center system, including: determining a threshold statistic; determining an ordered set of contacts; determining a set of first statistics that correspond to the ordered set of contacts; determining a set of second statistics that correspond to the ordered set of contacts and is based on the set of first statistic, in which the set of second statistics is based on a cumulative summation of the set of first statistics; determining a subset of the set of contacts based on the ordered set of second statistics and the threshold statistic; and pairing a selected contact from the subset of the ordered set of contacts with an available agent based on a pairing strategy.

This disclosure provides methods, systems, and artifacts for benchmarking at least two pairing strategies in a contact center system. The method includes: assigning sequence numbers to a series of events; initiating a first pairing strategy based on the sequence numbers; terminating the first pairing strategy based on the sequence numbers; initiating a second pairing strategy based on the sequence numbers; terminating the second pairing strategy based on the sequence numbers; assigning a first set of values ​​to contacts assigned by the first pairing strategy; assigning a second set of values ​​to contacts assigned by the second pairing strategy; and determining a metric comparing the first and second pairing strategies based on the first and second sets of values, wherein at least one contact paired with the first pairing strategy and at least one contact paired with the second pairing strategy are paired with the same agent. The technology of this disclosure overcomes the drawbacks and / or defects of time-based benchmarking.