530 results about "Contact center" patented technology

The present disclosure generally relates to systems, software, and computer-implemented methods for using generative artificial intelligence to improve user interactions. One example method includes identifying, in a contact center application of an agent, a start of an assisted leg of an interaction of a user with a system. A user interaction summary is retrieved that summarizes events that have previously occurred in the interaction. A generative large language model (LLM) artificial intelligence (AI) context prompt is extracted based on the user interaction summary. The context prompt is provided to a generative LLM AI engine to set a context for the generative LLM AI engine. Event information is received that includes a query for the generative LLM AI engine. The query is provided to and a query response is received from the generative LLM AI engine. The contact center application is updated in response to the query response.

The present disclosure generally relates to systems, software, and computer-implemented methods for using generative artificial intelligence to improve user interactions. One example method includes receiving a notification from a contact center application that user interaction events have been generated during an interaction session. Event descriptions for events generated in the session are located in a contact center application use case definition. Event descriptions are enhanced with event information for to generate contextualized event information. The contextualized event information to is added to a generative large language model artificial intelligence context that is provided to a generative large language model artificial intelligence engine. A query is provided to the generative large language model artificial intelligence engine. A query response is received from the generative large language model artificial intelligence engine and the query response is used in the interaction session.

The present disclosure generally relates to systems, software, and computer-implemented methods for using generative artificial intelligence to improve user interactions. One example method includes receiving a notification from a contact center application that user interaction events have been generated during an interaction session. Event descriptions for events generated in the session are located in a contact center application use case definition. Event descriptions are enhanced with event information for to generate contextualized event information. The contextualized event information to is added to a generative large language model artificial intelligence context that is provided to a generative large language model artificial intelligence engine. A query is provided to the generative large language model artificial intelligence engine. A query response is received from the generative large language model artificial intelligence engine and the query response is used in the interaction session.

Systems and methods for adherence and compliance of contact center agents, and more specifically to systems and methods of monitoring, identifying, and correcting deviations of contact center agents from ideal workflows and checklists. In one aspect, the system monitors agent-customer interactions and assesses adherence to compliance guidelines through analysis of agent and customer utterances, identifying non-compliant situations and suggesting actions for correction. Each contact center agent is provided compliance assistance through a visual progress indicator or a checklist feature. A supervisor system provides monitoring of a set of contact center agents and notice of non-compliant situations.

The present invention discloses a method and system for intelligent switching of communication protocols for an omni-channel contact center, the method comprising the steps of: evaluating the performance of each communication protocol according to user needs and the current system status, selecting the optimal communication protocol and opening the corresponding communication service for the user; continuously monitoring the communication quality under the selected current communication protocol, and triggering the reselection of the communication protocol if it is detected that the communication quality is lower than expected; re-evaluating the performance of each communication protocol under the current system state to select the target communication protocol, and switching the current communication service from the current communication protocol to the target communication protocol. The present invention realizes smooth switching between different communication protocols, can intelligently identify and automatically select the optimal communication method, while maintaining the continuity and quality of the call unaffected. In addition, the present invention also supports dynamic adjustment of the communication method according to network conditions, ensuring that users can obtain the best communication experience regardless of the network environment.

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.

The disclosure relates to systems, methods, and computer-readable media for an artificial intelligence (AI) conversational agent configured for enhanced user interaction in multi-channel contact centers. An example method includes receiving a query regarding a particular property of an asset during a real-time conversation, classifying an intent of the query into a predefined category, triggering, based on the classifying of the intent, a finetuned large language model for the predefined category to answer the query, generating, by the finetuned large language model, a response to the query based on at least one of an information data model and a knowledge graph data store, and outputting the response to the query.

A method in a contact center for generating insights from conversation data derived from interactions and storing the insights in an index. The method may include: determining an insight type; based on the insight type, determining inputs including a question prompt, answer prefix, and relevant portion of the conversation data; inputting the inputs into a LLM configured to receive the inputs and generate output text answering a question contained in the question prompt pursuant to an answer form suggested by the answer prefix given content contained in the relevant portion of the conversation data; generating the output text via operation of the LLM; transforming the output text of the first insight via a sentence transformer into vector embedding representative of a semantic meaning of the output text; and storing the computed vector embedding of the first insight in the index.

A server identifies, by parsing through code of an online page associated with a contact center system, information related to offerings associated with the online page. The server identifies a set of intents based on the information, at least a portion of the set of intents corresponding to at least one of headers or menu items of the online page. The server updates the set of intents based on supplemental information accessible via a hyperlink from the online page. The server transmits the updated set of intents to a first user device. The server receives an acceptance of a subset of the updated set of intents from the first user device. The server stores, in an intent data repository, the accepted subset of the updated set of intents.

A method of dynamically updating a frequently asked questions knowledge base for agent assist according to an embodiment includes receiving, by a computing system, a transcript of a contact center interaction between a contact center agent and a client, analyzing, by the computing system, the transcript to generate a plurality of question-answer pairs based on an artificial intelligence model, determining, by the computing system, whether each question-answer pair of the plurality of question-answer pairs is stored in a dynamic frequently asked questions knowledge base, determining, by the computing system, whether a question-answer pair of the plurality of question-answer pairs is frequently asked in response to determining that the question-answer pair is not stored in the dynamic frequently asked questions knowledge base, and updating, by the computing system, the dynamic frequently asked questions knowledge base to include the question-answer pair in response to determining that the question-answer pair is frequently asked.

A method of integrating an on-premises contact center with a cloud-based contact center according to an embodiment includes communicating resource data stored by the on-premises contact center to the cloud-based contact center, and synchronizing the resource data between the on-premises contact center and the cloud-based contact center to maintain consistency between the resource data stored by the on-premises contact center and the resource data stored by the cloud-based contact center.

Systems and methods develop and apply one or more extractive summarization models for locating contact center conversation details in a transcript, extracting pertinent verbiage, and, in a transformation of the communication details, automatically generating summaries at one or more levels of abstraction, the summaries in full sentences, in a manner that a contact center agent understands. The models are trained using machine learning algorithms.

A method for selecting forecasting models for generating timeseries workload forecasts for a contact center covering varying timeseries granularities and operating horizons. The method includes selecting, via a first selection process, a first select forecasting model from first candidate forecasting models for forecasting a workload level in accordance with a lower-granularity timeseries. The first selection process may include the steps of: receiving a first timeseries dataset; defining different timeseries datasets within the first timeseries dataset, including a first shorterm dataset and first longterm dataset; testing, using the first longterm dataset, each first candidate forecasting model in accordance with a first longterm cross-validation process; testing, using the first shorterm dataset, each first candidate forecasting model in accordance with a first shorterm cross-validation process; and calculating, for each of the first candidate forecasting models, an combined accuracy score based on the testing.

A method of leveraging improved conversational bots in a contact center system according to an embodiment includes performing knowledge distillation through machine learning to teach a student artificial intelligence model based on a teacher artificial intelligence model and reduce a size of an initial multilingual vocabulary, wherein the student artificial intelligence model includes fewer machine learning embedding layers than the teacher artificial intelligence model, removing tokens from the initial multilingual vocabulary based on a grouping of languages with linguistic similarities to reduce the size of the initial multilingual vocabulary, parsing user text from a human user into one or more tokens, identifying token indexes associated with the respective one or more tokens in a reduced multilingual vocabulary, determining embedding values associated with the identified token indexes, and generating a multilingual embedding output for the user text indicative of user intent based on the embedding values using machine learning.

A computerized-method for providing personalized-content for an interaction of an agent with a customer during an outbound conversational-marketing-campaign of a tenant operated via a cloud-based contact-center-platform. The computerized-method includes: (i) receiving details of the customer and details of the outbound conversational-marketing-campaign of the tenant; (ii) creating a prompt-text based on the received details of the customer and the details of the outbound conversational-marketing-campaign; (iii) generating the personalized content for the interaction with the customer in text-format by executing an LLM AI engine with a trained model with the created prompt-text; (iv) storing a text-file with the generated personalized content in text-format in a contents-database; (v) initiating the interaction of the agent by dialing to the customer; (vi) retrieving the text-file for the interaction from the contents-database; and (vii) sending the text-file to a computerized-device of the agent to be presented via a display-unit that is associated to the computerized-device.

The present disclosure generally relates to systems, software, and computer-implemented methods for using generative artificial intelligence to improve user interactions. One example method includes receiving a notification from a contact center application that user interaction events have been generated during an interaction session. Event descriptions for events generated in the session are located in a contact center application use case definition. Event descriptions are enhanced with event information for to generate contextualized event information. The contextualized event information to is added to a generative large language model artificial intelligence context that is provided to a generative large language model artificial intelligence engine. A query is provided to the generative large language model artificial intelligence engine. A query response is received from the generative large language model artificial intelligence engine and the query response is used in the interaction session.

A system for supporting effective campaign management in a cloud-based contact center platform. The system includes a campaign analytics engine. The campaign analytics engine includes retrieving campaign offerings of a running campaign; retrieving time-stamped text transcripts of interactions of agents of the running campaign; parsing the retrieved time-stamped text transcripts of interactions to yield parsed transcripts and related customer sentiment; comparing the yielded parsed transactions with the retrieved campaign offerings to generate analytical data. The generated analytical data includes at least one of: (i) agent campaign delivery effectiveness score; and (ii) probability of conversion. When an agent has an agent campaign delivery effectiveness score below a first threshold excluding the agent from the running campaign and sending the campaign delivery effectiveness score of the agent along with agent details and a preconfigured training list to be assigned to the agent by a QM application.

An automatic online voice translation system and method, comprising a virtual translator, a SIP gateway, and a storage, wherein the SIP gateway receives a call from a caller, when the virtual translator is not activated, the SIP gateway establishes a communication between the caller and the agent by streaming in real-time caller's voice directly to the agent and agent's voice directly to the caller, and when the virtual translator is activated, the SIP gateway creates audio files based on the received voice streams of both the caller and the agent, and sends the created audio files to the virtual translator for translating the caller's voice into a language understood by the agent, before transmitting the voice to the agent. In another embodiment, a contact center platform is used for applying features such as call recording, Interactive Voice Response (IVR) service, and conferencing service to the call.

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.

In one example embodiment, one or more machine learning models of at least one processor determine an agent of a communication center to receive a communication from a user according to one or more objectives that optimize a corresponding metric from interaction of the user and the agent. Features for training the one or more machine learning models are dynamically selected based on relevance of the features to attaining the one or more objectives. The at least one processor routes the communication to the agent of the communication center.

Techniques described herein relate to message distribution in a contact center. Messages generated by an administrator and / or by a trained model processing data from customer interactions with the contact center are stored in a message queue. A desktop application running on a representative device and hosted by the contact center queries the message queue for messages intended for a representative using the representative device. Messages intended for the representative are automatically displayed on the representative device, e.g., on the desktop.

A contact center system. The contact center system comprises a node comprising a plurality of modules (e.g., microservices), each module comprising a shared memory module (e.g., shared memory library), wherein the shared memory module is configured to: obtain a shared memory key; obtain a shared memory segment identifier (smhid) using the shared memory key, the shared memory segment identifier identifying a shared memory segment; use the shared memory segment identifier to attach to the shared memory segment, wherein, for each of a plurality of memory block sizes, the shared memory segment stores information pertaining to a recycle list, and the plurality of memory block sizes comprises a first memory block size and a second memory block size that is greater than the first memory block size.

Techniques described herein relate to representative monitoring in a contact center environment. Data associated with a customer interaction, including content of the interaction and video of a representative display during processing associated with the interaction, are presented to an administrator in real time or near real time for monitoring during an interaction. In some examples, a contact center server may receive an event notification when a live contact begins, and may generate data files associated with the live contact to allow the administrator to monitor the contact. In some instances, the contact center server may continue to generate data after conclusion of the live contact, to allow the administrator to monitor and guide post-contact processing performed by the representative. The contact center server also may allow administrators to select live contacts for monitoring and / or receive alerts when pre-selected representatives commence live contacts that can be monitored.

Systems adapted to measure impact of supervisor actions and methods, and non-transitory computer readable media, include identifying an interaction where a contact center supervisor performed a supervisor action, where the supervisor supervised a contact center agent; identifying a supervisor intervention point in the interaction; determining an impact score for each of a plurality of behavioral factors; aggregating the impact scores for the plurality of behavioral factors and determining an average of the impact scores to provide an overall impact score for the supervisor action; and performing an action automatically based on the overall impact score to improve contact center performance.

Techniques for action recommendation based on conversational log for real time assistance are described. Pre-generated intent clusters can be used to identify a relevant intent of a user in a given conversation between the user and a contact center agent. Based on the identified intent, certain recommended actions can be performed on the computing device of the contact center agent to facilitate the conversation between the user and the contact center agent. Feedback relating to the conversation and / or the recommended actions can be recorded and used to update the pre-generated intent clusters to improve the quality and relevance of the actions recommended for future conversations.

A method for mining conversation flows according to an embodiment includes receiving a plurality of transcripts of conversations between contact center agents and users, generating a summary of each transcript of the plurality of transcripts by extracting, for each transcript, one or more intents associated with the respective transcript and one or more slot entries associated with the respective transcript, clustering the plurality of transcripts into a plurality of intent categories based on the respective summary of each transcript, wherein each intent category includes intents that are similar to one another, and analyzing each transcript within a selected intent category of to generate a guided flow for the selected intent category, wherein the guided flow defines a set of actions to be taken by a human / virtual contact center agent to resolve an intent associated with the selected intent category.

Systems adapted to auto-approve pending requests of contact center agents based in a dynamic environment and methods, and non-transitory computer readable media, include monitoring operational parameters affecting staffing needs and automatic approval criteria; establishing that a new staffing situation is present based on the monitored operational parameters, automatic approval criteria, or both; determining that the new staffing situation meets a current staffing threshold; identifying a pending schedule change request submitted by a contact center agent; comparing the identified pending schedule change request with the automatic approval criteria; updating a schedule of the contact center agent based on the approved identified pending schedule change request or approved generated recommendation; and sending a schedule notification to the contact center agent.

Apparatuses and methods concerning providing a data-communications virtual assistant are disclosed. As an example, one apparatus includes a data-communications server. The data-communications server is configured to process user-data-communications between a client station and another station participating in data-communications via the data-communications services where the client station is associated with one client entity. The server is also configured to identify a context for each respective user-data-communication between the client station and the participating station, where the context data corresponds to at least one communications-specific characteristic associated with the user-data-communications, and to retrieve structured and unstructured data relating to previous data-communications provided by the data-communications server. The server is also configured to provide the integrated contact center to particular end users based on the identified context, and including data generated from the retrieved structured and unstructured data.

Computer-implemented methods intelligently determine a stress level of an agent at a contact center. Such computer-implemented methods include identifying one or more escalation factors that are indicative of an escalation of stress. The computer-implemented methods include aggregating the one or more escalation factors that have been identified into a total stress level. The computer-implemented methods include presenting one or more stress reduction suggestions for reducing the total stress level.