345 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.

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.

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 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.

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.

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.

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.

A method, system, and article of manufacture for modifying a queue assignment for a contact center are provided. At least one computing device determines, by solving for constraints, at least one staffing change for at least one of the first queue or the second queue, using as inputs (a) first time-based service metrics, (b) second time-based service metrics, (c) data describing eligible staffing changes, and (d) at least one predetermined constraint. The at least one staffing change is a modification to the queue assignment of a subset of the computing device instances that is predicted to improve net staffing relating to service targets selected from (a) an average speed of answer (ASA) specifying an average time for one of the computing device instances to initiate handling of a particular task after the particular task has been assigned to the relevant queue and (b) a service level specifying a percentage of tasks for which handling is initiated within a specified time frame after the tasks are assigned to the queue. The at least one staffing change can be implemented by modifying the queue assignment of the subset of the computing device instances.

A system and method for outbound call campaign management model in a contact center using a generative artificial intelligence which processes prompts such as “Schedule a follow-up call with client X,” understanding intent and context. Leveraging pre-trained language understanding, it interfaces with campaign components, scheduling calls or updating client information. The model dynamically adapts to varying scenarios, enhancing efficiency and personalization. The system employs secure interfaces, integrates with contact center tools, and undergoes continuous improvement through periodic retraining, ensuring optimal performance and compliance with evolving campaign requirements. Overall, it streamlines outbound call operations, automating actions and facilitating smoother interactions for a more productive and customer-centric contact center environment.

A method of leveraging artificial intelligence for post-call actions in contact center systems 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 in real time, analyzing, by the computing system, the transcript of the interaction to identify one or more action items to be performed with respect to the contact center interaction based on an artificial intelligence model, determining, by the computing system and for each action item of the identified one or more action items, at least one predefined action associated with the respective action item, receiving, by the computing system, an indication that the contact center agent has selected a particular predefined action associated with a particular action item, and performing, by the computing system, the selected predefined action.

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.

A method for efficiently encoding a trie data structure for transmission according to an embodiment includes receiving an application programming interface (API) request pertaining to the trie data structure that is indicative of flows of customer interactions with automated agents of a contact center, obtaining the trie data structure in which each of multiple nodes has an associated prefix key that defines a path from a root to the corresponding node, and encoding the nodes in a transmission format having a dictionary data structure. The nodes in the transmission format do not have the prefix key that defines the path from the root to the corresponding node. The method also includes transmitting a response to the API request based on the trie data structure encoded in the transmission format.

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 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.

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.