Abstract: In an example, based upon a first image of a face of a first person, a plurality of augmented images may be generated. Based upon the first image and the plurality of augmented images, a first set of facial feature representations may be generated. A second image comprising a representation of a face of a second person may be identified. A second facial feature representation may be generated based upon the second image. It may be determined, based upon the second facial feature representation and the first set of facial feature representations, that the second person is the first person.

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 is directed to network capacity planning based on denoised user clusters and network element clusters. Collected information representing characteristics and activities of users and characteristics and performance of network elements is used to cluster users and network elements to generate initial user clusters and initial network element clusters, each of which is denoised in an iterative process to derive denoised subclusters that have no impure subclusters therein. Network capacity planning is performed based on correlations identified between denoised user subclusters and denoised network element subclusters.

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 dynamic customer data and static customer data, and may calculate additional customer data based on the dynamic customer data and the static customer data. The device may process the static customer data, the dynamic customer data, and the additional customer data, with a first machine learning model, to determine a next action prediction, and may process the static customer data, the dynamic customer data, and the additional customer data, with a second machine learning model, to determine a next sequence prediction. The device may concatenate the static customer data, the dynamic customer data, the additional customer data, the next action prediction, and the next sequence prediction to generate concatenated data, and may process the concatenated data, with a plurality of machine learning models, to calculate various outputs, and may generate a recommendation for the customer based on the various outputs.

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 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 generate first scores for sentences of text based on a cumulative frequency of words in each sentence, may generate second scores for the sentences based on a cumulative frequency of domain entities in each sentence, and may generate third scores for the sentences based on a sentiment analysis of each sentence. The device may generate a summary of the text, may filter the sentences to extract a first set of sentences, may filter the sentences to extract a second set of sentences, and may filter the sentences to extract a third set of sentences. The device may identify and assign weights to a first group of sentences, a second group of sentences, and a third group of sentences, may generate a ranked list of sentences based on the weighted first group, second group, and third group, and may perform actions based on the final summary.

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: The present teaching is directed to clustering with denoising capability and its use in network capacity planning. Data samples with attributes of network elements and respective key performance indicators are first clustered to obtain initial clusters. Each initial cluster is hierarchically clustered to generate subclusters, each of which is detected as a pure or an impure subcluster based on some criterion. Each impure subcluster is denoised based on a situation detected, with some samples merged with a corresponding pure subcluster, some bootstrapped using additional data samples with consistent properties, and some removed if additional data sample with consistent properties is not available. The denoising is iteratively performed until a denoising criterion is satisfied to obtain denoised clusters corresponding to clusters of network elements. Actions may be performed on the network elements according to their corresponding denoised clusters.

Abstract: A device may generate first scores for sentences of text based on a cumulative frequency of words in each sentence, may generate second scores for the sentences based on a cumulative frequency of domain entities in each sentence, and may generate third scores for the sentences based on a sentiment analysis of each sentence. The device may generate a summary of the text, may filter the sentences to extract a first set of sentences, may filter the sentences to extract a second set of sentences, and may filter the sentences to extract a third set of sentences. The device may identify and assign weights to a first group of sentences, a second group of sentences, and a third group of sentences, may generate a ranked list of sentences based on the weighted first group, second group, and third group, and may perform actions based on the final summary.

Abstract: The present teaching relates to a hierarchical and explainable (HE) similarity and use thereof. A target text is identified based on a source text. A HE similarity characterizes the similarity between the source and target texts in terms of multiple assessment categories and is computed based on source and target phrases generated via linguistic features. A HE feature vector is constructed with similarity scores at phrase, word, and character levels. The HE similarity is computed based on the HE feature vector and used to determine whether the target text related to the source text. The HE similarity is used to determine whether the target text relates to the source text.

Abstract: The present teaching relates to extracting insightful phrases from an input text. Independent contexts are first identified from the input text. With respect to each independent context, initial candidate phrases are generated with respect to linguistic features and are then filtered. Various features are then computed for each filtered candidate phrase and used to select top k candidate phrases for each independent context. A most insightful phrase is then selected from the k top candidate phrases using deep learned models. Such selected most insightful phrases for the independent contexts are then used for facilitating an understanding the input text.

Abstract: A device may identify, in multi-context text data, unrelated text and coreference text, and may extract coreference clusters, coreference sentences, and coreference sentiments based on the coreference text. The device may extract unrelated sentences from the unrelated text, and may assign tenses to the coreference sentences and the unrelated sentences. The device may extract phrases and entities from the coreference sentences and unrelated sentences, and may assign tense flags that exclude present tense sentences. The device may select past tense phrases and future tense phrases, and may combine the past tense phrases and the future tense phrases to generate phrases. The device may identify invalid phrases in the phrases, and may identify similarities between the coreference sentences and the invalid phrases. The device may process the coreference text, the coreference tenses, the coreference sentiments, and the similarities, with a reinforcement learning model, to generate final context text.

Abstract: A device may receive dynamic customer data and static customer data, and may calculate additional customer data based on the dynamic customer data and the static customer data. The device may process the static customer data, the dynamic customer data, and the additional customer data, with a first machine learning model, to determine a next action prediction, and may process the static customer data, the dynamic customer data, and the additional customer data, with a second machine learning model, to determine a next sequence prediction. The device may concatenate the static customer data, the dynamic customer data, the additional customer data, the next action prediction, and the next sequence prediction to generate concatenated data, and may process the concatenated data, with a plurality of machine learning models, to calculate various outputs, and may generate a recommendation for the customer based on the various outputs.

Abstract: In some implementations, a transcription system may generate a first transcript based on audio data of a conversation between a first user and a second user. The transcription system may determine, using a first machine learning model of the transcript system, that a portion of the first transcript is incorrect. The transcription system may generate, using a second machine learning model, additional data for transcribing the audio data based on determining that the portion of the first transcript is incorrect. The additional data is generated using a portion of the audio data corresponding to the portion of the first transcript. The transcription system may generate a second transcript based on the audio data and the additional data. The transcription system may provide the second transcript to one or more devices.

Abstract: Disclosed are systems and methods for an anomaly detection framework that operates as an executable analysis tool for devices to operate in order to determine whether the device contains an unresponsive touch screen (e.g., defective or malfunctioning touch screen). The disclosed framework can analyze the capacitance capabilities of the touch screen, inclusive of the touch layers associated with the touch screen panel, and determine when a device's touch screen is unresponsive to user provided input, which can be any type of touch or gesture provided on a touch screen.

Abstract: Techniques for identifying and tracking objects in digital content are disclosed. In one embodiment, a method is disclosed comprising obtaining a frame of digital content, the frame comprising pixel data, detecting an object using the pixel data, determining a set of attributes for the detected object, the set of attributes comprising position, object segment and affine attributes, determining a similarity measurement for the detected object and a second object using the set of attributes corresponding to the detected object and the second object's set of attributes, and using the similarity measurement to make a similarity determination whether or not the detected object and the second object are a same object.

Abstract: In an example, a text message sent by a first user equipment (UE) and addressed to a second UE is received. In response to receiving the text message, a set of information associated with the text message is determined based upon information determined by a first carrier of the first UE and/or the second UE. The text message is classified as spam or not spam based upon the set of information.

Abstract: A device may generate first scores for sentences of text based on a cumulative frequency of words in each sentence, may generate second scores for the sentences based on a cumulative frequency of domain entities in each sentence, and may generate third scores for the sentences based on a sentiment analysis of each sentence. The device may generate a summary of the text, may filter the sentences to extract a first set of sentences, may filter the sentences to extract a second set of sentences, and may filter the sentences to extract a third set of sentences. The device may identify and assign weights to a first group of sentences, a second group of sentences, and a third group of sentences, may generate a ranked list of sentences based on the weighted first group, second group, and third group, and may perform actions based on the final summary.