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: A device may receive user activity data identifying activities of a user, and content data identifying text transcripts associated with the user. The device may generate a first custom embedding associated with the user based on the user activity data, and may process the first custom embedding, with a machine learning model, to generate an intent of the user and a next action for the user based on the intent. The device may process the content data to generate a second custom embedding for the user and an end user vector based on the second custom embedding, and may generate a document vector for the user based on the next action for the user. The device may process the document vector and the end user vector, with a language model, to generate a document for the user, and may perform one or more actions based on the document.

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: Systems and methods described herein utilize synthetic pixel generation using a custom neural network to generate synthetic versions of objects hidden by occlusions for effective detection and tracking. A computing device stores an object detector model and a synthetic image generator model; receives a video feed; detects objects of interest in a current frame of the video feed; identifies an occluded object in the current frame; retrieves a previous frame from the video feed; generates synthetic data based on the previous frame for the occluded object; and forwards a modified version of the current frame to an object tracking system, wherein the modified version of the current frame includes the synthetic data.

Abstract: An illustrative intent classification engine may access a text transcript and determine one or more features associated with the text transcript. Based on the one or more features, the intent classification engine may generate an aggregate embedding vector and provide the aggregate embedding vector as an input to a trained model configured to output an intent classification. Corresponding methods and systems are also disclosed.

Abstract: The present teaching relates to customer service with AI-based automated auditing on agent fraud. Real-time features of a communication between an agent and a customer are obtained. To detect agent fraud, a batch feature vector is computed based on real-time features extracted from communications involving the agent and accumulated over a batch period. Agent fraud is detected based on a model and the detection result is used to audit the agent for service performance.

Abstract: The present teaching relates to conduct persona-adaptive communications with a customer at a geo-locale. Transcripts of a current and historic communications involving the customer are used to characterize the persona of the customer. Transcripts of historic communications with customers at the geo-locale are used to characterize the persona of the geo-locale. Current persona of the customer exhibited in the current communication is combined with the customer's persona and the geo-locale's persona to compute a response input vector, A language model generates, based on the response input vector, a persona-adaptive response, which is then sent to the customer a response.

Abstract: The present teaching relates to compressing a model for an application to generate a compressed model. The model has multiple layers, each of which has multiple nodes. Operating the model utilizing an application-dependent dataset, redundant nodes/layers in the model are identified via a loss-based assessment. The loss-based assessment using aggregated output vectors computed based on output vectors produced by the nodes/layers of the model in response to the data samples of the application-dependent dataset. Removing the redundant nodes/layers yields the compressed model.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a conversation path associated with a revised code segment of a conversational interaction entity is identified by a processor. The conversation path has a predetermined intent. A conversational phrase is generated by the processor for the conversation path. The conversational interaction entity is employed by the processor using the conversation path and the conversational phrase to generate a resultant intent. An issue report is generated by the processor for the conversational interaction entity responsive to the resultant intent not matching the predetermined intent.

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: A device may receive unprocessed images to be labeled, and may utilize a first neural network model to identify objects of interest in the unprocessed images and bounding boxes for the objects of interest. The device may annotate the objects of interest to generate annotated objects of interest, and may utilize a second neural network model to group the annotated objects of interest into clusters. The device may utilize a third neural network model to determine labels for the clusters, and may request manually-generated labels for clusters for which labels are not determined. The device may receive the manually-generated labels, and may label the unprocessed images with the labels and the manually-generated labels to generate labeled images. The device may generate a training dataset based on the labeled images, and may train a computer vision model with the training dataset to generate a trained computer vision model.

Abstract: A device may receive text data associated with a chatbot, a live chat, or an interactive voice response system, and may preprocess the text data with one or more preprocessing techniques to generate preprocessed data and key intents. The device may convert the preprocessed data and the key intents into embeddings, and may combine the embeddings into an input vector. The device may process the input vector, with a language model, to identify relationships between words and phrases of the text data, and may process the input vector and the relationships, with a summary generation model, to generate a summary of the text data. The device may perform one or more actions based on the summary of the text data.

Abstract: The present teaching relates to personalized IVR communications with a customer at a geo-locale. A first set of transcripts of the current and historic communications involving the customer and a second set of transcripts of historic communications associated with the geo-locale are analyzed to compute a personalized contextual vector, a geo-localized contextual vector, and a current text vector. The computed vectors are used by a language model to generate a personalized and geo-locale aware prompt, which is used to generate an IVR communication and is sent to the customer as a response.

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: In an example, an image may be identified. Object detection may be performed on the image to identify a region including a distorted representation of an object. The region may be masked to generate a masked image including a masked region corresponding to the object. Using a machine learning model, the masked region may be replaced with an undistorted representation of the object to generate a modified image.

Abstract: A device may receive unprocessed images to be labeled, and may utilize a first neural network model to identify objects of interest in the unprocessed images and bounding boxes for the objects of interest. The device may annotate the objects of interest to generate annotated objects of interest, and may utilize a second neural network model to group the annotated objects of interest into clusters. The device may utilize a third neural network model to determine labels for the clusters, and may request manually-generated labels for clusters for which labels are not determined. The device may receive the manually-generated labels, and may label the unprocessed images with the labels and the manually-generated labels to generate labeled images. The device may generate a training dataset based on the labeled images, and may train a computer vision model with the training dataset to generate a trained computer vision model.

Abstract: A method may include receiving frames associated with a video stream, identifying a first object image included in at least some of the frames and masking a region, in the at least some of the frames, associated with the first object image. The method may also include receiving information identifying at least one attribute associated with a user and identifying, based on the received information, a second object image to replace the first object image. The method may further include replacing pixel values in the masked region with contextually suitable pixel values associated with the second object image and outputting the video stream with the second object image replacing the first object image in the at least some of the frames.

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: A method may include receiving a number of images to train a first neural network, masking a portion of each of the images and inputting the masked images to the first neural network. The method may also include generating, by the first neural network, probable pixel values for pixels located in the masked portion of each of the plurality of images, forwarding the images including the probable pixel values to a second neural network and determining, by the second neural network, whether each of the probable pixel values is contextually suitable. The method may further include identifying pixels in each of the plurality of images that are not contextually suitable.