Abstract: A system including an interaction processing component is deployed to automatically validate information exchanged during an interaction.

Abstract: An intelligent virtual assistant (IVA) is deployed to place a call/chat to customer service on behalf of the customer, thus saving them time and frustration. The IVA contacts a specific company over one or more channels, for example, chat, phone call, Application Programming Interface (API), or email, in order to complete open-ended task(s) requested by its user. Before the IVA contacts the company, a specific user profile is injected into an IVA dialog state. The IVA contacts the company or agency and answers customer service agent (CSA) questions by using the specific user profile provided for the call. The IVA then stores the task outcome for the user to review. If something prevents the task from succeeding, the IVA alerts the user that either it needs more information or the user may need to perform some action before the task can be completed, such as filling out or emailing a form.

Abstract: An intelligent virtual assistant (IVA) is deployed to place a call/chat to customer service on behalf of the customer, thus saving them time and frustration. The IVA contacts a specific company over one or more channels, for example, chat, phone call, Application Programming Interface (API), or email, in order to complete open-ended task(s) requested by its user. Before the IVA contacts the company, a specific user profile is injected into an IVA dialog state. The IVA contacts the company or agency and answers customer service agent (CSA) questions by using the specific user profile provided for the call. The IVA then stores the task outcome for the user to review. If something prevents the task from succeeding, the IVA alerts the user that either it needs more information or the user may need to perform some action before the task can be completed, such as filling out or emailing a form.

Abstract: A method for providing automated agent performance improvement includes obtaining a ranking for each agent of a plurality of agents; obtaining customer-agent interactions for each agent; determining a task label for each of the customer-agent interactions; selecting a first set of customer-agent interactions from the customer-agent interactions corresponding to a first task label; receiving application event streams corresponding to the first set of customer-agent interactions and comprising agent application usage data; analyzing the application event streams for statistically relevant differences for one or more activities performed by a first group of agents compared to a second group of agents; determining at least one activity of the one or more activities that is performed by the first group of agents differently than the second group of agents; generating guidance corresponding to the least one activity for the second group of agents; and deploying the guidance to the second group of agents.

Abstract: A method for providing automated agent performance ranking includes obtaining, for each agent of a plurality of agents, a performance score for each feature associated with a plurality of customer-agent interactions, wherein each feature is associated with a key performance indicator (KPI) metric and is under control of the agent; determining a task label for each of the plurality of customer-agent interactions; grouping the plurality of customer-agent interactions into one or more task groups based on the task label for each of the plurality of customer-agent interactions; determining, for each agent of the plurality of agents, a task-feature performance value, wherein the task-feature performance value is a combination of the performance score for each feature associated with the one or more task groups; and generating, for each feature, a report comprising a ranking for each agent of the plurality of agents based on the task-feature performance value.

Abstract: A method for providing a predicted key performance indicator (KPI) value includes receiving a KPI metric and a target value for the KPI metric; receiving customer-agent interaction data; implementing a model corresponding to the KPI metric; predicting, with the model, the value for the KPI metric based on features that the model identifies and measures from the customer-agent interaction data; determining one or more features having a potential for improvement based on measured values of the features determined by the model; determining that improvement in a measured value of the one or more features maintains or increases the value of the KPI metric; and outputting a first indication of whether the value predicted for the KPI metric meets the target value and a second indication of the one or more features that maintains or increases the value of the KPI metric when the one or more features are improved.

Abstract: To allow the human customer service agents to specialize in the instances where human service is preferred, but to scale to the volume of large call centers, systems and methods are provided in which human agents and intelligent virtual assistants (IVAs) co-handle a conversation with a customer. IVAs handle simple or moderate tasks, and human agents are used for those tasks that require or would benefit from human compassion or special handling. Instead of starting the conversation with an IVA and then escalating or passing control of the conversation to a human to complete, the IVAs and human agents work together on a conversation.

Abstract: Certain aspects of the present disclosure provide techniques for receiving, from a user, a command for a virtual assistant to perform a task on behalf of the user; determining a communication channel for the virtual assistant to communicate with a remote service in order to perform the task; registering a communication session with an identity provider service, wherein the communication session is associated with the communication channel; initiating the communication session with the remote service using the communication channel; receiving a communication session authentication query from the remote service; and determining, in response to the communication session authentication query, whether the user is authenticated.

Abstract: An analysis platform combines unsupervised and semi-supervised approaches to quickly surface and organize relevant user intentions from conversational text (e.g., from natural language inputs). An unsupervised and semi-supervised pipeline is provided that integrates the fine-tuning of high performing language models via a language models fine-tuning module, a distributed KNN-graph building method via a KNN-graph building module, and community detection techniques for mining the intentions and topics from texts via an intention mining module.

Abstract: Disclosed embodiments pertain to training an intelligent virtual assistant through phased observational learning tasks. A pre-trained language model can be updated offline to produce a second language model with self-supervised learning based on transcripts of historical interactions between one or more customers, one or more customer service agents, and one or more data stores. The second language model can be evaluated and determined to satisfy a predetermined performance threshold. Subsequently, the second language model can be updated online to produce a third language model with reinforcement learning based on received customer input and similarity between a response provided by a customer service agent and a predicted response generated by the second language model. The third language model can then be deployed with an intelligent virtual assistant to respond to received user input.

Abstract: To allow the human customer service agents to specialize in the instances where human service is preferred, but to scale to the volume of large call centers, systems and methods are provided in which human agents and intelligent virtual assistants (IVAs) co-handle a conversation with a customer. IVAs handle simple or moderate tasks, and human agents are used for those tasks that require or would benefit from human compassion or special handling. Instead of starting the conversation with an IVA and then escalating or passing control of the conversation to a human to complete, the IVAs and human agents work together on a conversation.

Abstract: Certain aspects of the present disclosure provide techniques for receiving, from a user, a command for a virtual assistant to perform a task on behalf of the user; determining a communication channel for the virtual assistant to communicate with a remote service in order to perform the task; registering a communication session with an identity provider service, wherein the communication session is associated with the communication channel; initiating the communication session with the remote service using the communication channel; receiving a communication session authentication query from the remote service; and determining, in response to the communication session authentication query, whether the user is authenticated.

Abstract: Certain aspects of the present disclosure provide techniques for receiving audio data comprising a user voice command; determining a task to be completed by a remote service based on the user voice command; determining that a reference voice print associated with the user is stored in a user account; authenticating the user by determining that a sample voice print based on the user voice command matches the reference voice print associated with the user; storing authentication evidence associated with the task; and providing proof of user authentication to the remote service in order to initiate the task with the remote service.

Abstract: To allow the human customer service agents to specialize in the instances where human service is preferred, but to scale to the volume of large call centers, systems and methods are provided in which human agents and intelligent virtual assistants (IVAs) co-handle a conversation with a customer. IVAs handle simple or moderate tasks, and human agents are used for those tasks that require or would benefit from human compassion or special handling. Instead of starting the conversation with an IVA and then escalating or passing control of the conversation to a human to complete, the IVAs and human agents work together on a conversation.

Abstract: A system including an interaction processing component is deployed to automatically validate information exchanged during an interaction.

Abstract: Systems and methods are described to automatically generate candidate questions and responses to speed the process of response creation and editing for commercial IVAs and chatbots. Rather than create the questions and responses from scratch for a new IVA, the system uses existing questions and responses from a previous or related IVA to train a model that can generate proposed responses to provided questions. The model, or a different model, can further be trained to generate responses using data taken from company or entity-specific data sources such as websites and knowledge bases. After a set of questions and responses have been generated for an IVA they may be reviewed by one or more human reviewers to ensure they are of a suitable quality. Where no previous or related IVA exists to provide example responses, the model may be trained solely using the company or entity-specific data.

Abstract: A system and method using blockchain for monitoring and tracking customer service representative actions are disclosed. In the system and method, customer service representative actions are encrypted using an encryption key specific to a company on whose behalf the customer service representative is acting. The customer service representative signs the encrypted action with a private key of a public/private key pair. The signed, encrypted action record is placed on the blockchain, which can later be accessed to review the actions of the customer service representative or actions of any customer service representative acting on behalf of the specific company.

Abstract: As described herein, a system for expanding contractions in electronically stored text includes expanding contractions having only on expanded form. For remaining contractions, a grammar check is performed for all possible expanded forms to determine if an expanded form can be selected based on context and grammar rules. If an expanded form is not evident from the first two steps, all possible expanded forms of the remaining contractions are converted to a vector representation along with the original text. A Word Movers Distance (WMD) for each possible expansion is calculated using the vectors for each possible expansion and the original text. An expanded form is chosen without human intervention based on the grammar score alone or the WMD and the grammar score.

Abstract: An intelligent virtual assistant (IVA) is deployed to place a call/chat to customer service on behalf of the customer, thus saving them time and frustration. The IVA contacts a specific company over one or more channels, for example, chat, phone call, Application Programming Interface (API), or email, in order to complete open-ended task(s) requested by its user. Before the IVA contacts the company, a specific user profile is injected into an IVA dialog state. The IVA contacts the company or agency and answers customer service agent (CSA) questions by using the specific user profile provided for the call. The IVA then stores the task outcome for the user to review. If something prevents the task from succeeding, the IVA alerts the user that either it needs more information or the user may need to perform some action before the task can be completed, such as filling out or emailing a form.

Abstract: An intelligent virtual assistant (IVA) is deployed to place a call/chat to customer service on behalf of the customer, thus saving them time and frustration. The IVA contacts a specific company over one or more channels, for example, chat, phone call, Application Programming Interface (API), or email, in order to complete open-ended task(s) requested by its user. Before the IVA contacts the company, a specific user profile is injected into an IVA dialog state. The IVA contacts the company or agency and answers customer service agent (CSA) questions by using the specific user profile provided for the call. The IVA then stores the task outcome for the user to review. If something prevents the task from succeeding, the IVA alerts the user that either it needs more information or the user may need to perform some action before the task can be completed, such as filling out or emailing a form.