Abstract: A system for determining intent in a voice signal receives a first voice signal that indicates to perform a task. The system sends a first response that comprises a hyperlink associated with a particular webpage used to perform the task. The system receives a second voice signal that indicates whether to access the hyperlink. The system determines intent of the second voice signal by comparing keywords of the second voice signal with keywords of the first response. The system activates the hyperlink in response to determining that the keywords of the second voice signal correspond to the keywords of the first response.

Abstract: A hybrid system for natural language processing is provided. The system may include a transceiver operable to receive a query from a user. The query may include a stock portion integrated with a set of real-time conditions specific to the user. The transceiver may transmit the query to a model and to a response application. The model may receive the query and communicate with a data store. Based on historical data stored at the data store, the model may separate the stock portion from the set of real-time conditions, formulate a response to the stock portion and insert placeholders into the response for responses to the real-time conditions portion. The response application may receive the query and the response. The response application may communicate with private data stores to formulate responsive elements for the placeholders. The response application may insert the responsive elements into the placeholders to complete the response.

Abstract: Methods for generating test case scenarios in response to natural language requests may be provided. Methods may receive a natural language formatted request for a test case scenario. The natural language formatted request may include a plurality of parameters. The plurality of parameters may define a scope of the test case scenario. Methods may extract, utilizing natural language processing and artificial intelligence, the plurality of parameters from the request. Methods may query logging applications for log entries that are similar to the role of the test case scenario. Methods may construct, using artificial intelligence, operating on the log entries, a test case scenario in computer language. The test case scenario may correspond to the natural language formatted request. Methods may test the scope of the test case scenario by executing the test case scenario.

Abstract: An interactive voice response system for use with a real-time complaint identification system and dashboard may be provided. The interactive voice response system may receive and respond to human callers in real-time. Simultaneously, the interactive voice response system may use a model to transcribe the audio call and identify complaints within the audio calls. Identified complaints may be resolved by one of a plurality of resolution executables. One of the resolution executables may include registering the identified complaint with a complaint register.

Abstract: Systems are provided for building semantic frames. Systems may include building a semantic frame using a machine learning algorithm. The algorithm may identify: an index number of a token, a semantic role classifier assigned to the token, a corresponding correlation value and an index number of one or more related tokens. The algorithm may also create a semantic frame using the identified information. Systems may include building semantic frames for multiple tokens within an utterance. Systems may include building semantic frames for a plurality of tokens within a plurality of utterances. The plurality of utterances may be components of a conversation. Systems may also include summarizing the conversation using the semantic frames.

Abstract: A hybrid explainable artificial intelligence system may include a shallow learning model and a deep learning model. The shallow learning model may be a machine learning system. The deep learning model may be a neural network. The system may input a data set into both the shallow learning model and the deep learning model. Both the shallow learning model and the deep learning model may produce an output. When there is a common output between the shallow learning model and the deep learning model, the process performed by the shallow learning model may be used to formulate an explanation of the process performed by the deep learning model. The explanation of the process performed by the deep learning model may be used to raise the sensitivity of one or more components of the data set. Such components may include a word or phrase within a transcript.

Abstract: A hybrid explainable artificial intelligence system may include a shallow learning model and a deep learning model. The shallow learning model may be a machine learning system. The deep learning model may be a neural network. The system may input a data set into both the shallow learning model and the deep learning model. Both the shallow learning model and the deep learning model may produce an output. When there is a common output between the shallow learning model and the deep learning model, the process performed by the shallow learning model may be used to formulate an explanation of the process performed by the deep learning model. The explanation of the process performed by the deep learning model may be used to raise the sensitivity of one or more components of the data set. Such components may include a word or phrase within a transcript.

Abstract: A hybrid system for natural language processing is provided. The system may include a transceiver operable to receive a query from a user. The query may include a stock portion integrated with a set of real-time conditions specific to the user. The transceiver may transmit the query to a model and to a response application. The model may receive the query and communicate with a data store. Based on historical data stored at the data store, the model may separate the stock portion from the set of real-time conditions, formulate a response to the stock portion and insert placeholders into the response for responses to the real-time conditions portion. The response application may receive the query and the response. The response application may communicate with private data stores to formulate responsive elements for the placeholders. The response application may insert the responsive elements into the placeholders to complete the response.

Abstract: A system for dynamic semantic role classification, through an entity's natural language process (NLP) pipeline is provided. The system may include assigning semantic role classifiers to tokens included in utterances received from user nodes. The system may include using a machine learning algorithm to assign the semantic role classifiers. The machine learning algorithm may assign the semantic role classifiers based on a calculated correlation value. The machine learning algorithm may use training and testing data sets to dynamically update the semantic role classifiers.

Abstract: A system for horizontal scaling of retraining machine learning models across operational domains is provided. The system may reduce computational overhead associated model retraining. The system may include an artificial intelligence (“AI”) engine that determines target machine learning models that need to be retrained in response to changed training data. The AI engine may assign daemons to the target models. The daemons may gather retraining requirements such as source code and training data. The daemons may schedule the target models for retraining on a CPU or a GPU based model training system.

Abstract: Methods and apparatus for parallel intent processing at a network of chatbots may be provided. A query may be received at a user interface. The query may be transmitted to multiple chatbots at the same time. Each chatbot may identify an intent for the query. Each chatbot may also identify a percentage of accuracy that the chatbot considers that the intent corresponds to the query. The chatbots may present the intents to the user interface. The user interface may rank the intents. The user interface may display the ranked intents to the user. The user interface may receive an intent selection from the user. The user interface may direct the user to the chatbot that identified the selected intent.

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: Methods for building and training a network of chatbots are provided. Methods may perform data analysis on a first chatbot in the network. The first chatbot may include a first domain of intents. The data analysis may identify and rank the intents in the first domain. The first domain may represent answers to a first domain of user queries. Methods may select a predetermined number of highest-ranking intents from the first domain based on the data analysis. Methods may input the selected intents into a second chatbot in the network. The second chatbot may include a second domain of intents. The second domain may represent answers to a second domain of user queries. Methods may input a hook into the second chatbot. The hook may include a trigger word and may correspond to the first domain. When the hook is triggered, the second chatbot may invoke the first chatbot.

Abstract: A method for simulating a historical call is provided. Methods may receive a communication between a human caller and an interactive voice response system. The communication may have occurred within a production environment. The communication may include utterances communicated by the human caller, responses presented to the human caller by the response system and/or an original outcome of the communication. Methods may receive original components used by the response system to form the responses. The original components may include prediction models and/or parameters. Methods may input the communication and the original components into a simulator. Methods may enable a user to swap out the original components for modified components. Methods may modify the original outcome based on the swap out of the original components for the modified components. Methods may present the modified outcome and the modified components.

Abstract: Methods and systems for transcribing communications are provided. Methods may include receiving a communication. Methods may include splitting the communication into a plurality of communication segments. Each communication segment may include two or more words. Methods may include transcribing each segment included in the plurality of communication segments, in parallel. The transcribing may include using a transformer neural network to transcribe each segment included in the plurality of communication segments. Methods may include generating a transcription from the transcribing. The transcription may be generated by combining the transcription of each of the communication segments into a combined transcription. Methods may include correcting the combined transcription.

Abstract: A hybrid system for natural language processing is provided. The system may include a transceiver operable to receive a query from a user. The query may include a stock portion integrated with a set of real-time conditions specific to the user. The transceiver may transmit the query to a model and to a response application. The model may receive the query and communicate with a data store. Based on historical data stored at the data store, the model may separate the stock portion from the set of real-time conditions, formulate a response to the stock portion and insert placeholders into the response for responses to the real-time conditions portion. The response application may receive the query and the response. The response application may communicate with private data stores to formulate responsive elements for the placeholders. The response application may insert the responsive elements into the placeholders to complete the response.

Abstract: A method for training a model is provided. Methods may, at a first pre-step, receive a base model. Methods may, at a first step, create a label using the base model to be applied to unlabeled data, label a first set of unlabeled data with the label and generate a first set of labeled data from the first set of unlabeled data. Methods may, at a second step, receive a second set of labeled data, separate the second set into a first group and a second group, classify the first group and the first set as training data. Methods may, at a third step, train a deep learning model using the training data, validate the deep learning model using the second group and generate a set of model parameters. Methods may, at a fourth step, save the model parameters. Methods may, at a fifth step, train the deep learning, initialized from the parameters, using the first group. Methods may, at a sixth step, replace the base model with the deep learning model and repeat steps one through six.

Abstract: Methods for generating test case scenarios in response to natural language requests may be provided. Methods may receive a natural language formatted request for a test case scenario. The natural language formatted request may include a plurality of parameters. The plurality of parameters may define a scope of the test case scenario. Methods may extract, utilizing natural language processing and artificial intelligence, the plurality of parameters from the request. Methods may query logging applications for log entries that are similar to the role of the test case scenario. Methods may construct, using artificial intelligence, operating on the log entries, a test case scenario in computer language. The test case scenario may correspond to the natural language formatted request. Methods may test the scope of the test case scenario by executing the test case scenario.

Abstract: An interactive voice response system for use with a real-time complaint identification system and dashboard may be provided. The interactive voice response system may receive and respond to human callers in real-time. Simultaneously, the interactive voice response system may use a model to transcribe the audio call and identify complaints within the audio calls. Identified complaints may be resolved by one of a plurality of resolution executables. One of the resolution executables may include registering the identified complaint with a complaint register.

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.