Abstract: A device may receive channel data associated with digital channels utilized by a customer, and may identify, in the channel data, potential issues and sequential events for the potential issues. The device may generate a causal relationship graph based on the sequential events and the potential issues, and may process the causal relationship graph, with a machine learning model, to identify issues of the customer and events associated with the issues. The device may apply causal inference to identify causal relationships between the issues and the events, and may calculate an inference score for the issues and the events based on the causal relationships. The device may modify, based on the inference score, a customer journey defined by the events to generate a modified customer journey, and may perform one or more actions based on the modified customer journey.

Abstract: Disclosed are systems and methods for a computerized framework enacted by service contact centers that provides a proactive and adaptive response system that accurately identifies security and/or legal concerns of service requests, and enables artificial intelligence/machine learning (AI/ML)-based mechanisms for dynamically addressing the underlying technical and/or service related concerns of such service requests. The disclosed framework can computationally determine how effective service agents have been, and are currently being in curating solutions/responses to each customer service call, which can enable modified functionality for the customer as well as curated services based on how sufficiently handled the service call was responded to by the agent. The disclosed systems and methods provide a generative service call experience that can improve agent performance while reducing the strain on user experience, both during and/or after service calls.

Abstract: A device may receive taxonomy data and may preprocess the taxonomy data with preprocessing techniques to generate preprocessed data. The device may process the taxonomy data, with a machine learning interpolative-based feedback model, to generate intents, features of each of the intents, and a taxonomy collection, and may process the taxonomy data, with a machine learning-based feedback model, to generate concepts or entities associated with the intents. The device may combine the intents, the features, the taxonomy collection, and the concepts or the entities to generate an association collection, and may train a machine learning model with the association collection and the preprocessed data to generate a trained machine learning model. The device may process text data, the taxonomy collection, and the association collection, with the trained machine learning model, to determine a crux of the text data, and may perform actions based on the crux of the text data.

Abstract: A device may receive video data that includes a text transcript, audio sequences, and image frames, and may detect a network fluctuation. The device may process the text transcript to generate a new phrase, and may generate a response phoneme based on the new phrase. The device may generate a text embedding based on the response phoneme, and may process the audio sequences to generate a target voice sequence. The device may generate an audio embedding based on the target voice sequence, and may process the image frames to generate a target image sequence. The device may generate an image embedding based on the target image sequence, and may combine the embeddings to generate an embedding input vector. The device may generate a final voice response and a final video based on the embedding input vector, and may provide the video data, the final voice response, and the final video.

Abstract: A device may receive and convert audio data to text data in real-time, and may detect a network fluctuation that causes missing voice packets. The device may process partial text and context of the text data, with a model, to generate a new phrase, and may generate a response phoneme for the new phrase. The device may utilize a text embedding model to generate a text embedding for the response phoneme, and may process the audio data, with the model, to generate a target voice sequence. The device may utilize an audio embedding model to generate an audio embedding for the target voice sequence, and may combine the text embedding and the audio embedding to generate an embedding input vector. The device may process the embedding input vector, with an audio synthesis model, to generate a final voice response, and may provide the audio data and the final voice response.

Abstract: A video summary device may generate a textual summary of a transcription of a virtual event. The video summary device may generate a phonemic transcription of the textual summary and generate a text embedding based on the phonemic transcription. The video summary device may generate an audio embedding based on a target voice. The video summary device may generate an audio output of the phonemic transcription uttered by the target voice. The audio output may be generated based on the text embedding and the audio embedding. The video summary device may generate an image embedding based on video data of a target user. The image embedding may include information regarding images of facial movements of the target user. The video summary device may generate a video output of different facial movements of the target user uttering the phonemic transcription, based on the text embedding and the image embedding.

Abstract: An improved search engine is disclosed. The search engine receives search queries from client devices and inputs these queries into a first neural network (an action understanding model) that includes an action embedding layer. The action embedding layer can be a word embedding layer constructed using action terms. The action understanding model outputs a filter match associated with a type of filter and, in some scenarios, an action-condition pair. The action-condition pair includes an action associated with the type of filter and a condition comprising an adaptive value associated with the action. Based on the filter and, if present, action-condition pair(s), the embodiments generate a structured query and issue the structured query to a data repository (e.g., database). The search engine then returns a search results page responsive to the search query that includes the results returned by the data repository in response to the structured query.

Abstract: Techniques for generating emotionally-aware digital content are disclosed. In one embodiment, a method is disclosed comprising obtaining audio input, obtaining a textual representation of the audio input; using the textual representation of the audio input to identify an emotion corresponding to the audio input; generating an emotionally-aware facial representation in accordance with the textual representation and the identified emotion; using the emotionally-aware facial representation to generate one or more images comprising at least one facial expression corresponding to the identified emotion; and providing digital content comprising the one or more images.

Abstract: In some implementations, a device may determine cluster arrangements of clusters of technicians to perform a job. The device may provide, as input to a model, input values corresponding to exogenous factors associated with the job and with the cluster arrangements. The device may receive, as an output from the model, forecast values corresponding to endogenous factor(s) associated with the cluster arrangements. The device may determine combined forecast values associated with the cluster arrangements. A combined forecast value associated with a particular cluster arrangement may be a combination of forecast value(s) corresponding to the endogenous factor(s) associated with the particular cluster arrangement. The device may identify a selected cluster arrangement having a lowest combined forecast value. The device may assign the job to one or more technicians associated with the selected cluster arrangement.

Abstract: A video summary device may generate a textual summary of a transcription of a virtual event. The video summary device may generate a phonemic transcription of the textual summary and generate a text embedding based on the phonemic transcription. The video summary device may generate an audio embedding based on a target voice. The video summary device may generate an audio output of the phonemic transcription uttered by the target voice. The audio output may be generated based on the text embedding and the audio embedding. The video summary device may generate an image embedding based on video data of a target user. The image embedding may include information regarding images of facial movements of the target user. The video summary device may generate a video output of different facial movements of the target user uttering the phonemic transcription, based on the text embedding and the image embedding.

Abstract: Techniques for generating emotionally-aware audio, or voice, responses for a user interface of an application, such as an automated voice response application, are disclosed. In one embodiment, a method is disclosed comprising obtaining voice input from a user via an automated voice response user interface of an application, obtaining a textual representation of the voice input, using the textual representation of the voice input from a user to obtain a source emotion of the user, determining a response emotion using the source emotion, generating a response textual representation indicating textual content of the response, generating a frequency spectrum representation of the response in accordance with the response textual representation and the response emotion, using the frequency spectrum representation of the response to generate a voice response reflective of the textual content of the response and the response emotion, and communicating the response to the user via the user interface.

Abstract: A device may receive audio data of a first call between a first user and a second user. The device may generate, based on the audio data, time series data associated with an audio signal of the first call and may process, using a first machine learning model, the time series data to generate first call insight information regarding one or more first insights associated with the first call. The device may process the audio data to generate image data associated with the audio signal and may process, using a second machine learning model, the image data to generate second call insight information regarding one or more second insights associated with the first call. The device may combine the first call insight information and the second call insight information to generate combined call insight information and cause an action to be performed based on the combined call insight information.

Abstract: Disclosed are embodiments for improving training data for machine learning (ML) models. In an embodiment, a method is disclosed where an augmentation engine receives a seed example, the seed example stored in a seed training data set; generates an encoded seed example of the seed example using an encoder; inputs the encoded seed example into a machine learning model and receives a candidate example generated by the machine learning model; determines that the candidate example is similar to the encoded seed example; and augments the seed training data set with the candidate example.

Abstract: Techniques for generating conversational responses for a conversational user interface are disclosed. In one embodiment, a method is disclosed comprising obtaining user input from a user via a conversational user interface, using the user input to obtain a user emotion and a user intent, obtaining candidate probabilities for a fragment of a response to the user input using the obtained user emotion, the obtained user intent and the user input, generating the response to the user input using the candidate probabilities obtained for the fragment to select a candidate for the fragment of the response, and communicating the response to the user via the conversational user interface.

Abstract: Techniques for determining anomalous user behavior in connection with an online application are disclosed. In one embodiment, a method is disclosed comprising obtaining user behavior data in connection with a user of an application, generating feature data using the obtained user behavior data, obtaining one or more user behavior anomaly predictions from one or more anomaly prediction models trained to output a user behavior anomaly prediction in response to the feature data. Each user behavior anomaly prediction indicates a probability that the user behavior is anomalous. A user behavior anomaly determination is made using the user behavior anomaly prediction(s).

Abstract: An improved search engine is disclosed. The search engine receives search queries from client devices and inputs these queries into a first neural network (an action understanding model) that includes an action embedding layer. The action embedding layer can be a word embedding layer constructed using action terms. The action understanding model outputs a filter match associated with a type of filter and, in some scenarios, an action-condition pair. The action-condition pair includes an action associated with the type of filter and a condition comprising an adaptive value associated with the action. Based on the filter and, if present, action-condition pair(s), the embodiments generate a structured query and issue the structured query to a data repository (e.g., database). The search engine then returns a search results page responsive to the search query that includes the results returned by the data repository in response to the structured query.

Abstract: A device may receive historical time series data and output data associated with a forecasting model and may process the historical time series data and the output data, with a proxy regression model, to determine inference data. The device may create perturbed data from the historical time series data, the output data, and the inference data, and may process the perturbed data, with the proxy regression model, to generate labelled data and to identify top features of the labelled data. The device may process subsets of the top features of the labelled data, with the proxy regression model, to determine feature data identifying an importance of each of the subsets of the top features, and may evaluate the proxy regression model and the feature data to calculate validation data for validating the forecasting model. The device may validate the forecasting model with the validation data.

Abstract: Disclosed are embodiments for preventing training data vulnerabilities in training data. In one embodiment, a method comprises receiving a first and second set of importance features for a first and second label output by a machine learning (ML) model; generating a first feature dictionary based on the first set of importance features and a second feature dictionary based on the second set of importance features; identifying a subset of labeled examples in a training dataset used to train the ML model based on the first feature dictionary and second feature dictionary; modifying the subset of labeled examples based on the first feature dictionary and second feature dictionary, the modifying generating a modified training data set; and retraining the ML model using the modified training data set.

Abstract: Techniques for identifying user reaction in connection with an online application are disclosed. In one embodiment, a method is disclosed comprising obtaining activity data in connection with a user of an application, generating feature data using the obtained activity data, obtaining a user reaction prediction from a user reaction prediction model trained to output the user reaction prediction in response to the feature data. The user reaction prediction indicates a probability of the user reaction in connection with the application. A determination is made, using the user reaction prediction, whether or not to take a remedial action in connection with the user and the application.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content providing, streaming and/or hosting systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide a novel machine learning framework that trains classifiers to identify specific features of input data. When implementing these trained classifiers in specific runtime environments, the important features of those specific environments can be identified and leveraged for directing the classifier to the vital information that is relevant to the environment. A customized classifier is thereby dynamically created and deployed which improves how data can be classified, thereby reducing false negatives and positives, and increasing confidence and reliance on how such classifiers can be implemented.