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: One or more computing devices, systems, and/or methods for network equipment solution generation utilizing network signals are provided. Network signals collected by devices within a location are evaluated by a neural network to generate contextually aware pixel values forming a vision map of the location. The contextually aware pixel values and/or the vision map are evaluated to generate a coverage map corresponding to regions of low signal coverage and obstacles proximate the regions. A simulation of network equipment operating at the location is performed using the coverage map to generate a simulation result. The simulation result is used to generate an installation plan to install network equipment at the location.

Abstract: One or more computing devices, systems, and/or methods for privacy data augmentation are provided. An augmentation pipeline is selected to process data based upon a data type of the data. The augmentation pipeline processes the data to generate information that is input into a machine learning model. The machine learning model processes the information and privacy laws to determine a subset of the data to mask. In this way, the subset of the data is masked to create augmented data that complies with the privacy laws.

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 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 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 personalized network update. Information on users' network activities is collected and analyzed to identify indirect and direct relations between each user and others. Each user's network influence is determined and represented based on indirect relation embeddings and direct relation embeddings, obtained to characterize the respective indirect and direct relations. A personalized priority for each user is predicted based on the user's representation. A network update schedule is determined based on users' personalized priorities so that network update is conducted in a personalized manner.

Abstract: A cache cleaner controller is described. In one or more examples, an apparatus includes a cache directory including status bits associated with cache locations within cache storage and a cache cleaner controller. The cache cleaner controller is configured to detect that a cache cleaner threshold has been reached. The cache cleaner threshold defines that a threshold number of the status bits indicate data maintained at the cache locations, respectively, has been changed. The cache cleaner controller is also configured to cause the data indicated as changed by the status bits to be copied from the cache locations within cache storage to the physical volatile memory.

Abstract: The present disclosure relates to a system and a method for stream distribution. The method includes: determining an idle score of a distributor in a live stream of the distributor; and determining a priority score of recommending the live stream to a first viewer according to the idle score of the distributor, wherein the idle score increases as the distributor is less active in the live stream.

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 cache cleaner controller is described. In one or more examples, an apparatus includes a cache directory including status bits associated with cache locations within cache storage and a cache cleaner controller. The cache cleaner controller is configured to detect that a cache cleaner threshold has been reached. The cache cleaner threshold defines that a threshold number of the status bits indicate data maintained at the cache locations, respectively, has been changed. The cache cleaner controller is also configured to cause the data indicated as changed by the status bits to be copied from the cache locations within cache storage to the physical volatile memory.

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: 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 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: The present disclosure relates to a system, a method and a computer-readable medium for tagging live streaming data. The method includes generating a first intermediate tag for the live streaming program, generating a second intermediate tag for the live streaming program, and determining a final tag for the live streaming program according to the first intermediate tag and the second intermediate tag. The present disclosure can categorize contents in a more granular and precise way.

Abstract: The present disclosure relates to a system, a method and a computer-readable medium for tagging live streaming data. The method includes generating a first intermediate tag for the live streaming program, generating a second intermediate tag for the live streaming program, and determining a final tag for the live streaming program according to the first intermediate tag and the second intermediate tag. The present disclosure can categorize contents in a more granular and precise way.

Abstract: The present disclosure relates to a system, a method and a computer-readable medium for recommending live streams. The method includes determining a user to be disengaged, determining a thumbnail of a live stream to be attractive for the user, determining the live stream to be attractive for the user, and displaying the thumbnail of the live stream to the user.