Kore.ai, Inc.

Prasanna Kumar Arikala Gunalan

Rajkumar Koneru

Rajavardhan Nalluri

Girish Ahankari

Thirupathi Bandam

Santhosh Kumar Myadam

Venkata Praveen Kumar Suvanam

Timothy Loewenstein, Ii

Vishnu Vardhan Sai Lanka

Jayesh Arunkumar Jain

Hari Krishna Poludasu

Sri Vishnu Sankar Srinivasan

Sudhamsh Reddy Annam

Gopinath Polavarapu

Methods, non-transitory computer readable media, and a data server that assist with enabling conversational access to tabular data includes determining in response to a user input, tabular data comprising a header row with header data in each column and one or more table data rows with row data in each column. A first prompt is provided to a large language model to generate a dummy table comprising the header row and a dummy row with dummy row data in each column and a dummy table is received. An alias table comprising the header row and an alias row with alias row data in each column is generated. A second prompt is provided to the large language model to generate a dummy row text representation of the dummy row data, wherein the dummy row text representation includes the alias row data inserted as placeholders of the dummy row data and the dummy row text representation is received. A row text representation is generated for each of the one or more table data rows by replacing the alias row data in the dummy row text representation with row data of corresponding ones of the one or more table data rows.

Systems and methods for enabling conversational access to tabular data

A virtual assistant server determines a subset of test data corresponding to changes between a first version of training data of a virtual assistant and a second version of the training data of the virtual assistant. Subsequently, the virtual assistant server creates a test suite with the subset of test data and runs the test suite on a second language model of the virtual assistant created using the second version of the training data. Based on the running the test suite, the virtual assistant server generates one or more executable corrective actions to be implemented at the user device and provides the one or more executable corrective actions to the user device to implement to train the virtual assistant.

Systems and methods for training a virtual assistant

A virtual assistant server receives from a network communication device, message data provided by a user operating a first two-way radio via a first channel. Subsequently, the virtual assistant server generates response data to the message data and determines a response channel based on the message data. The virtual assistant server establishes a communication session with a network communication device based on the determined response channel and transmits the response data to the network communication device via the established communication session.

Systems and methods for using natural language processing in two way radio communications

The present disclosure relates to the development and design of bot interfaces and, more particularly, to one or more components, systems and methods of an intelligent development and design platform configured to assist users in the design, development and deployment of bot applications.

Systems and methods of implementing platforms for bot interfaces within an intelligent development platform

A contact center server provides to an agent device one or more automated response recommendations determined by an executable virtual assistant platform to correspond to customer message data received from a customer device as part of conversation data. Further, the contact center server receives a selection of one of the automated response recommendations from the agent device and identifies agent response data to the customer message data transmitted from the agent device to the customer device. Further, the contact center server, using one or more classification models, determines when there is a change between the selected response recommendation and the identified agent response data. Further, the contact center server associates one or more tags to the identified agent response data in the conversation data when the determination indicates the change. Subsequently, the contact center server updates training of the executable virtual assistant platform based on the conversation data with the associated one or more tags.

Systems and methods for training a virtual assistant platform configured to assist human agents at a contact center

A contact center server receives an utterance from a customer device as part of a conversation. The contact center server identifies utterance parameters of the utterance, generates a first response to the utterance based on the utterance parameters, and outputs the utterance parameters and the first response to an agent device. The contact center server receives agent-identified-information corresponding to the utterance or agent-modified-information corresponding to the utterance parameters from the agent device. Subsequently, the contact center server generates a second response to the utterance based on the agent-identified-information or the agent-modified-information and outputs the second response to the agent device.

Systems and methods for handling customer conversations at a contact center

A contact center server receives an input from a customer device as part of a conversation. The contact center server (CCS) identifies at least one of: one or more intents, one or more entities, or one or more entity values from the input. The CCS detects one or more escalation conditions based on the input and pauses the conversation based on the detected one or more escalation conditions. The CCS outputs a conversation transcript and the identified one or more intents, the one or more entities, or the one or more entity values to an agent device. The CCS receives agent-identified-information or agent-modified-information from the agent device. Subsequently, the CCS resumes the conversation by providing a response to the input based on the received agent-identified-information or the agent-modified-information.

A virtual assistant server receives an utterance provided by an end user via a channel of a virtual assistant rendered in a client device. The virtual assistant server identifies a current-node of execution from a plurality of nodes of a conversation definition of the virtual assistant and identifies a first set of language models from a group of language models of the virtual assistant to interpret the utterance. Further, the virtual assistant server executes the first set of language models in an order based on the current-node until an intent of the utterance is determined. Subsequently, the virtual assistant server generates a response based on the intent and outputs the response to the client device.

Systems and methods for natural language processing using a plurality of natural language models

A virtual assistant server extracts concept(s) from a received user query and correlates the extracted concept(s) to a plurality of nodes in a knowledge graph to identify qualifying path(s) in the knowledge graph. Further, when the two or more qualifying paths are identified, the virtual assistant server addresses one or more ambiguities in the knowledge graph by: prompting the user device to resolve a current one of the one or more ambiguities progressing from contextual nodes towards leaf nodes in the knowledge graph; receiving a response to the prompt from the user device; and repeating the prompting and the receiving until each of the one or more ambiguities is resolved and a matching path to the received query is identified. Finally, the virtual assistant server outputs content associated with a leaf node of the matched path to the user device.

Methods and systems for ambiguity resolution in conversations managed by a virtual assistant server

A virtual assistant server receives conversational inputs as part of a conversation from a customer device and generates a version of a data record of the conversation upon: the receiving of each of the conversational inputs, or receiving each output generated by one of a plurality of software modules when the one of the software modules receives a system input from the conversation management framework. The virtual assistant server provides each of the generated versions of the data record to a communication orchestrator and receives for each of the generated versions of the data record, execution instructions from the communication orchestrator. Further, the virtual assistant server communicates with one or more of the software modules based on the received execution instructions, and provides based on the communicating, one or more of the outputs of the software modules to the customer device, when the one or more of the outputs comprise one or more responses to one or more of the conversational inputs.

Systems and methods for conversation orchestration using trained large language models

A contact center server manages conversation data for a plurality of conversations between agent devices and customer devices and provides to the agent devices, one or more automated response recommendations to one or more customer messages by executing one or more dialog flows. The contact center server, using a classification model, determines for each of the one or more customer messages when there are one or more changes between the corresponding one or more automated response recommendations and corresponding one or more agent responses. Further, the contact center server associates one or more tags to the one or more agent responses when the one or more changes are determined. Subsequently, the contact center server identifies one or more patterns in the one or more tags associated to the one or more agent responses and provides one or more flow modification recommendations to the one or more dialog flows to one or more enterprise user devices based on the identified one or more patterns.

Systems and methods for recommending dialog flow modifications at a contact center

A virtual assistant server receives a selection of at least two versions of a virtual assistant for comparison from a client device and compares metadata corresponding to each component of the at least two versions. The virtual assistant server presents to the client device, based on the compared metadata, a list of components of the at least two versions and a type of change made to one or more components of the list of components. The virtual assistant server receives from the client device, a selection corresponding to the one or more components of the list of components and presents to the client device, a comparison of definitions of the at least two versions of the selected one or more components of the list of components.

Systems and methods for comparing versions of a virtual assistant

A virtual assistant server receives a user input from a virtual assistant interface available on a client system associated with a user identified by a user identifier. Upon receiving the user input, the virtual assistant server creates a search network of repositories not associated with the user identifier by determining, for the received user input, a priority score of each repository not associated with the user identifier based on interactions of the user identifier through the virtual assistant interface. Further, the virtual assistant server searches, for the received user input, in a repository associated with the user identifier and in the repositories in the search network. Based on the searching, a group of one or more results corresponding to the received user input are identified and returned to the client system associated with the user identifier.

Methods for managing automated discovery and knowledge sharing in one or more networks and devices thereof

A virtual assistant server receives a web request such as a HTTP request with one or more call parameters corresponding to a call redirected from an interactive voice response server. The virtual assistant server inputs the received one or more call parameters to a predictive model, which identifies, based on the one or more call parameters, an intelligent communication mode to route the redirected call to. Subsequently, the virtual assistant server routes the redirected call to the intelligent communication mode.

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Hyderabad Telangana, India

Prasanna Kumar Arikala Gunalan