Abstract: A system for reducing computational load for training machine learning models is provided. The system may provide an end-to-end-solution for automating development, testing and updating of machine learning models in various operational environments. The system may determine which machine learning models included in a computer program product need to be retrained in response to a change in training data. For a computer program product that includes multiple models, the system only retrains target models, resulting in significant savings in computing resources. The system may also reduce the number of machine learning models that need to be generated for testing environments, further reducing consumption of computational resources.

Abstract: Chatbots may be integrated into a customer service workflow and assist a user before, during and after a user-agent interaction. The chatbot may assist an agent during a user-agent interaction. The chatbot may provide customized responses for a target agent or user. Customized responses may be formulated based on conversation context, account information, sentiment and diagnostic tools. Chabot responses may be customized to meet habits and patterns of a target agent or user. The chatbot may crowdsource questions to other agents or users. The chatbot may employ search engines, entity and slot extraction and heat maps and clustering analysis to generate relevant responses for the agent or user.

Abstract: An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.

Abstract: This application describes apparatus and methods for providing artificial intelligence (“AI”) autocomplete that locates functionality within an application based on a search string entered by a user. The AI system may track activity of the user within one or more applications. Based on the user activity, the AI system may formulate a set of training data. The AI system may train an AI model based on the training data. After training the AI model, the AI system may detect user entry of the search string into a search function. The AI system may apply the AI model to present a target output associated with the application that is personalized for the user.

Abstract: Systems, methods, and apparatus are provided for integrating data from multiple computer-based communication platforms within a single user session at a mobile device application. The mobile device application may interact with a first platform in a first format. The first platform may include an interactive response system such as a chatbot. The mobile device application may receive a request to interact with a live agent and may interact with a second platform in a second format. The second platform may include an agent interface. In response to the transfer request, the first interface may capture the interactions with the first platform, convert the interactions from the first format to the second format, and transmit them to the second platform. The second platform may display the session history to the agent on the same screen as real-time, ongoing agent interactions with the user.

Abstract: An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. This determination indicates that the text is associated with a probability that the automatically generated reply includes the requested information that is greater than a threshold. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.

Abstract: An apparatus includes a memory and processor. The memory stores previous requests and corresponding previous responses. The processor determines that a user device transmitted a new voice request, converts the voice request into a first set of text, and transmits the text to an agent device. The processor applies the machine learning algorithm to the first set of text to generate suggested responses, by identifying patterns shared by the first set of text and a subset of the previous requests that are associated with the suggested responses. The processor transmits the suggested responses to the agent device. The processor then determines that the agent device transmitted voice signals responding to the new request. The processor converts these voice signals into a second set of text. The processor stores the first set of text as a previous request, and the second set of text as a corresponding previous response.

Abstract: Aspects of the disclosure relate to systems and methods for increasing the speed, accuracy, and efficiency of language processing systems. A provided method may include storing a plurality of modules in a database. The method may include configuring the plurality of modules in a multi-tier tree architecture. The method may include receiving an utterance. The method may include processing the utterance via a natural language processing (NLP) engine. The method may include routing the utterance. The routing may include identifying a highest tier module that matches a predetermined portion of the utterance. The method may include compiling a result set of modules. The method may include transmitting the result set of modules to the system user. The result set of modules may include a comprehensive and narrowly tailored response to the user request.

Abstract: A device that is configured to establish a network connection between a user and an agent. The device is further configured to identify a first issue type for the user and to identify a first resolution type provided by the agent based on a conversation between the user and the agent. The device is further configured to identify a performance score from a resolution mapping based on a combination of the first issue type and the first resolution type. The device is further configured to identify a first knowledge area that is associated with the first issue type and to update a first knowledge score that is associated with the first knowledge area in a performance record for the agent based on the performance score. The device is further configured to send a recommendation to the agent based at least in part on the performance score.

Abstract: An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to select between forwarding a request to an agent device and transmitting an automatically generated reply to the request. The processor receives feedback for a decision made by the algorithm, indicating whether the automatically generated reply includes the information sought by the request. If the algorithm decided to forward the request to the agent device, a reward is assigned to feedback that indicates that the reply does not include the information, while a punishment is assigned to feedback that indicates that the reply includes the information. If the algorithm decided to transmit the reply, a reward is assigned to feedback that indicates that the reply includes the information, and a punishment is assigned to feedback that indicates that the reply does not include the information. The processor updates the algorithm using the reward/punishment.

Abstract: When a caller initiates a conversation with an interactive voice response (“IVR”) system, the caller may be transferred to a live agent. Apparatus and methods are provided for integrating automated tools and artificial intelligence (“AI”) into the interaction with the IVR system. The automated tools and AI may track the conversation to decipher when to transfer the caller to the agent. The agent may determine which machine generated responses are appropriate for the caller. AI may be leveraged to suggest responses for both caller and agent while they are interacting with each other. The agent may transfer back the caller to the IVR system along with the appropriate machine generated response to maintain efficiency and shorten time of human agent interaction.

Abstract: A device that is configured to assign users to an issue cluster based on issue types for the users. The device is further configured to identify available agents and to assign each available agent to one or more knowledge area clusters based on knowledge scores. A knowledge score indicates an expertise level for an agent in a knowledge area. The device is further configured to identify an issue cluster that is associated with an issue type and to identify a user from the issue cluster. The device is further configured to identify a knowledge area cluster that is associated with the issue type and to identify an agent from the knowledge area cluster. The device is further configured to establish a network connection between a user device associated with the user and a user device associated with the agent.

Abstract: A method for filtering a plurality of agent-customer interactions to determine whether one or more of a plurality of agent-customer interactions should be stored in a library of Artificial Intelligence (AI) files related to an interactive voice response system (IVR) is provided. The method may include receiving an identification of a plurality of IVR flashpoints, monitoring and/or reviewing the plurality of agent-customer interactions, and determining whether one of the plurality of agent-customer interactions meets a threshold number of the IVR flashpoints. For each of the plurality of agent-customer interactions that meets a threshold number of the IVR flashpoints, the method may further direct the IVR to convert the interaction into an IVR workflow and store the IVR workflow in the library of AI related to IVR.

Abstract: Aspects of the disclosure relate to systems and methods for increasing the speed, accuracy, and efficiency of language processing systems. A provided method may include storing a plurality of distinct rule sets in a database. Each of the rule sets may be associated with a different pipeline from a set of pipelines. The method may include receiving the utterance. The method may include tokenizing and/or annotating the utterance, determining a pipeline for the utterance, and comparing the utterance to the rule set that is associated with the pipeline. When a match is achieved between the utterance and the rule set, the method may include resolving the intent of the utterance based on the match. The method may include transmitting a request corresponding to the intent to a central server, receiving a response, and transmitting the response to the system user.

Abstract: A system for determining search results for a search string based on detecting an intent from the search string receives the search string. The system determines the intent of the search string by extracting a first set of features from the search string. The first set of features represent the intent. The system compares the first set of features with a second set of features labeled with an intent tag stored in a training dataset. The system determines whether the first set of features corresponds to the second set of features. In response to determining that the first set of features corresponds to the second set of features, the system determines that the first intent tag corresponds to the intent of the search string. The system produces a search result for the search string, where a first item in the search result is a text associated with the intent tag.

Abstract: An artificial intelligence (“AI”) system for tuning a machine learning interactive voice response system is provided. The AI system may perform analysis of outputs generated by the machine learning models. The AI system may determine an expected model output for a given test input. The AI system may determine accuracy, precision and recall scores for an actual output garneted in response to the test input. The system may determine performance metrics for interim outputs generated by individual machine learning models within the interactive voice response system. The AI system may replace malfunctioning models with replacement models.

Abstract: An apparatus includes a memory and processor. The memory stores previous requests and corresponding previous responses. The processor determines that a user device transmitted a new voice request, converts the voice request into a first set of text, and transmits the text to an agent device. The processor applies the machine learning algorithm to the first set of text to generate suggested responses, by identifying patterns shared by the first set of text and a subset of the previous requests that are associated with the suggested responses. The processor transmits the suggested responses to the agent device. The processor then determines that the agent device transmitted voice signals responding to the new request. The processor converts these voice signals into a second set of text. The processor stores the first set of text as a previous request, and the second set of text as a corresponding previous response.

Abstract: An apparatus includes a memory and a processor. The memory stores a machine learning algorithm configured to select between forwarding a request to an agent device and transmitting an automatically generated reply to the request. The processor receives feedback for a decision made by the algorithm, indicating whether the automatically generated reply includes the information sought by the request. If the algorithm decided to forward the request to the agent device, a reward is assigned to feedback that indicates that the reply does not include the information, while a punishment is assigned to feedback that indicates that the reply includes the information. If the algorithm decided to transmit the reply, a reward is assigned to feedback that indicates that the reply includes the information, and a punishment is assigned to feedback that indicates that the reply does not include the information. The processor updates the algorithm using the reward/punishment.

Abstract: An apparatus includes a memory and a processor. The memory stores first and second machine learning algorithms. The processor receives, from a user, voice signals associated with an information request and converts them into text. The processor uses the first machine learning algorithm to determine, based on the text, to automatically generate a reply to the request, rather than transmitting the request to an agent. This determination indicates that the text is associated with a probability that the automatically generated reply includes the requested information that is greater than a threshold. The processor uses the second machine learning algorithm to generate, based on the set of text, the reply, which it transmits to the user. The processor receives feedback associated with the reply, indicating that the reply does or does not include the requested information. The processor uses the feedback to update either or both machine learning algorithms.

Abstract: A device that is configured to establish a network connection between a user and an agent. The device is further configured to identify a first issue type for the user and to identify a first resolution type provided by the agent based on a conversation between the user and the agent. The device is further configured to identify a performance score from a resolution mapping based on a combination of the first issue type and the first resolution type. The device is further configured to identify a first knowledge area that is associated with the first issue type and to update a first knowledge score that is associated with the first knowledge area in a performance record for the agent based on the performance score. The device is further configured to send a recommendation to the agent based at least in part on the performance score.