Abstract: Embodiments disclosed herein may extract trending topics from phone call transcripts or any type of text data. The phone call transcripts may be collected for a time period and the time period may be divided into time spans. For each time span having more than a threshold number of phone call transcripts, n-grams from the phone call transcripts may be extracted. The extracted n-grams may be contextually clustered by converting the n-grams into their embedding vectors, reducing the dimensionality of the embedding vectors, and clustering similar reduced dimensionality embedding vectors. Normalized occurrences of one or more clusters may be generated. The recent mean of the number of occurrences of the normalized clusters may be compared with the historical mean and offset by historical standard deviation to generate a modified Z-score. N-grams corresponding to the clusters with high Z-scores may be identified as trending topics.

Abstract: A method including generating a captured facial object and a captured pose from a captured image. The method also includes obtaining a base facial object and a base pose from a base image. The method also includes generating base pose angles using the captured pose, and captured pose angles using the captured pose. The method also includes obtaining selected base images using the base pose angles and the base facial object. The method also includes generating selected captured images using the captured pose angles and the captured facial object. The method also includes comparing the selected base images to the selected captured images to establish a comparison. The method also includes outputting a match output using the comparison.

Abstract: Systems and methods for training machine learning models are disclosed. An example method includes receiving historical event timing data including event data for a first portion including events from a first time period, and a second portion comprising events from a second time period not including the first time period, predicting, based on the first portion of the historical event timing data, a first plurality of predicted events, the first plurality of predicted events corresponding to the second time period, determining a first subset of predicted events to be accurate predictions based at least in part on comparing the first plurality of predicted events to the historical events occurring within the second time period, generating training data based at least in part on the first subset of the first plurality of predicted events, and training the machine learning model based at least in part on the training data.

Abstract: Aspects of the present disclosure provide techniques for automated health scoring through meta-learning. Embodiments include retrieving text data related to an entity that was provided by a user and providing one or more first inputs to a first machine learning model based on a subset of the text data. Embodiments include determining, based on an output from the first machine learning model, whether the text data includes an address. Embodiments include determining that the text data includes a name and determining, based on the address and the name, that one or more text results from one or more data sources relate to the entity. Embodiments include providing one or more second inputs to a second machine learning model based on the one or more text results and determining, based on an output from the second machine learning model, a health score for the entity.

Abstract: A method services competing updates from multiple servicing instances. An update message is received by a class of a plurality of classes that service an object. The update message includes an update to the object and an expected version for the object. A version match is determined between the expected version and a current version of the object. When the version match is successful, the update is applied to the object. A response message including the current version of the object is generated. The response message is transmitted. When the update is successfully applied, the response message is transmitted as a success message. When the update is not successfully applied, the response message is transmitted as a failure message.

Abstract: A method including transcribing, into digital tokens, utterances from a conversation between an agent and a person. The method also includes embedding the digital tokens into an utterances tensor including sequences of the digital tokens. The method also includes obtaining a metadata tensor by encoding metadata related to the utterances into the metadata tensor. The method also includes executing a machine learning model which takes, as input, the utterances tensor and the metadata tensor, and which outputs a predicted source article predicted to be related to the utterances. The method also includes generating an interactive link to the predicted source article.

Abstract: The present disclosure provides techniques for data retrieval using machine learning. One example method includes receiving a plurality of training episodes associated with different environments, wherein each training episode of the plurality of training episodes includes a sequence of states, computing, based on the plurality of training episodes, total counts of a plurality of values in the states, initializing, for each state of the sequence of states in each training episode of the plurality of training episodes, a reward based on the total counts of the plurality of values, and training a reinforcement learning agent using the rewards.

Abstract: An access graph is constructed based on access policy data from user accounts, data lake buckets, and/or access policy statements from any other location. Access logs are analyzed to determine actual access to the data tables. For a given user role, an initial set of data tables that are actually accessed is generated forming the baseline of data tables for which access privileges are to be maintained. User roles that are similar to the given user role are identified and additional data tables accessed by the similar user roles are added to the initial set of data tables to generate a final set of data tables. Access privileges to the final set of data tables are maintained for the given user role, while access privileges to the remaining data tables may be revoked.

Abstract: Disclosed are techniques for implementing an intelligent system with dynamic configurability. These techniques identifying a plurality of flow nodes for a software application and determine a dynamic flow for executions of the intelligent system with the plurality of flow nodes, one or more dynamic conditions, and one or more dynamic actions, without hard coded inter-dependency between two or more flow nodes of the plurality of flow nodes. The intelligent system is transformed into a dynamically configured intelligent system at least by performing a modification pertaining to one or more flow nodes in the dynamic flow, without affecting remaining flow nodes in the dynamic flow.

Abstract: Aspects of the present disclosure relate to watermarks and watermarking techniques for data streaming pipelines. Time stamp and offset timeline data is shared by computing instances along the pipeline to enable improved watermarking of the data stream through the pipeline. The improved watermarks enable better determination of completeness for the data stream and improve materialization of the results. The watermarking techniques can include periodically publishing watermark data by processing units of a vertex, fetching a merged watermark for a vertex by a vertex, and/or watching a data storage for the watermark data for events. Consensus algorithms can be used to maintain consensus among vertices for the watermark data.

Abstract: Systems and methods or determining tax recommendations for a taxpayer by using a tax calculation graph to identify tax variables that a taxpayer can control and modify, including a recommendation engine configured to analyze a tax calculation graph which is calculated using tax data of the taxpayer. An identified tax variable can be analyzed by determining nodes of the graph affecting a value of the identified tax variable, providing a user interface enabling at least one modification to the nodes, and determining an effect on the identified tax variable due to the at least one modification.

Abstract: Methods and computer systems are provided for generating synthetic data. A real vector is generated representing real data. Using a classification model, a first output vector that represents a first class is generated from the real vector. the real vector is mutated to generate a counterfactual vector. using the classification model, the second output vector that represents a second class is generated from the counterfactual vector. the counterfactual vector is then mutated to generate a synthetic vector. Using the classification model, a third output vector that corresponds to the first class is generated from the synthetic vector, synthetic data is generated from the synthetic vector.

Abstract: A method implements efficient real time serving of ensemble models. The method includes receiving an input and processing the input with an abridged model to generate a set of component scores and an abridged score. The method further includes processing the set of component scores with a deviation threshold to select one of the abridged score and an ensemble score as an output and presenting the output.

Abstract: A method including receiving an input and embedding the input into a first data structure that defines first relationships among images and texts. The method also includes comparing the first data structure to an index including a second data structure that defines second relationships among pre-determined texts and pre-determined images. The method also includes returning a subset of images from the pre-determined images. The subset includes those images in the pre-determined images for which matches exist between the first relationships and the second relationships.

Abstract: A computing system generates a plurality of training data sets for generating the NLP model. The computing system trains a teacher network to extract and classify tokens from a document. The training includes a pre-training stage where the teacher network is trained to classify generic data in the plurality of training data sets and a fine-tuning stage where the teacher network is trained to classify targeted data in the plurality of training data sets. The computing system trains a student network to extract and classify tokens from a document by distilling knowledge learned by the teacher network during the fine-tuning stage from the teacher network to the student network. The computing system outputs the NLP model based on the training. The computing system causes the NLP model to be deployed in a remote computing environment.

Abstract: A method is provided for authenticating a user. A request to access a resource is received from a user agent. A cookie associated with the request is identified. The cookie includes a first subset of data that was previously used to authenticate the user. The cookie is validated based on the first subset of the data. Responsive to validating the cookie, a second subset of the data is retrieved from server-side storage. A risk decision is generated based on the first subset and the second subset. When the risk decision meets a threshold, the user is authenticated without presenting an authentication challenge, and access to the resources permitted.

Abstract: A method including building a graph data structure storing network data from a relational data structure that stores sequential data describing object identifiers and relationships between the object identifiers. The method also includes generating, from the sequential data, a features matrix for the object identifiers. The method also includes building a machine learning model layer including a long short-term memory neural network (LSTM) programmed to take, as input, the features matrix and to generate, as output, a prediction vector. The method also includes building machine learning model layers including graph convolutional neural network (GCN) layers. The machine learning model layers is programmed to take, as input, the graph data structure and the prediction vector, and generate, as output, a future prediction regarding the sequential data. The method also includes combining, into a machine learning model ensemble, the machine learning model layer and the machine learning model layers.

Abstract: A transcript of an audio conversation between multiple users (e.g., two users) is generated. The transcript is displayed in real time within a VR environment as the conversation takes place. A virtual selection tool is displayed within the VR environment to allow for a selection of different portions of the transcript. In addition, a virtual keyboard and or virtual panels with characters may be displayed and the virtual selection tool may be used to make selections from these displays as well. These selections are used to generate new text. The new text may form part of a user's notes of the conversation or an entry for a text field within the VR environment.

Abstract: A method including detecting, in a written electronic communication, an input sentence satisfying a readability metric threshold. The method also includes transforming, by a sentence transformer model, the input sentence to output suggested sentences. The method also includes evaluating the suggested sentences along a set of acceptability criteria. The method also includes determining, based on the evaluating, that the set of acceptability criteria is satisfied. The method also includes modifying, based on determining that the set of acceptability criteria is satisfied, the written electronic communication with the suggested sentences to obtain a modified written electronic communication. The method also includes returning the modified written electronic communication.

Abstract: Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.