Abstract: In some examples, computer-implemented systems and processes facilitate a prediction of occurrences of future events using trained artificial intelligence processes and normalized feature data. For instance, an apparatus may generate an input dataset based on elements of interaction data that characterize an occurrence of a first event during a first temporal interval, and that include at least one element of normalized data. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of a second event associated with during a second temporal interval. The apparatus may also transmit at least a portion of the output data to a computing system, which may perform operations consistent with the portion of the output data.

Abstract: The disclosed embodiments relate to computer-implemented systems and processes that facilitate a prediction of occurrences of product-specific events during targeted temporal intervals using trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with an occurrence of a first event. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate an element of output data representative of a predicted likelihood of an occurrence of each of a plurality of second events during a target temporal interval associated with the first event. The apparatus may also transmit the elements of output data to a computing system, which may perform operations that are consistent with the elements of output data.

Abstract: The disclosed embodiments include computer-implemented systems and processes that predict activity-specific engagement events using trained artificial-intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with an activity and a first temporal interval. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an engagement event associated with a cessation of the activity during a second temporal interval, which may be disposed subsequent to the first temporal interval and separated from the first temporal interval by a corresponding buffer interval. The apparatus may transmit at least a portion of the generated output data to a computing system, which may perform operations based on the portion of the output data.

Abstract: The disclosed embodiments include computer-implemented systems and processes that predict occurrences of targeted attrition events using trained artificial-intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a targeted participant during a first temporal interval. Based on an application of a trained artificial-intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an attrition event involving the targeted participant during a second temporal interval that is disposed subsequent to the first temporal interval, and that is separated from the first temporal interval by a buffer interval.

Abstract: The disclosed embodiments include computer-implemented processes that predict service-specific attrition events using trained artificial-intelligence processes. For example, an apparatus may generate an input dataset based on elements of first interaction data associated with a first temporal interval. The elements of first interaction data includes an element of geographic data or an element of engagement data. Based on an application of a trained artificial-intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an attrition event during a second temporal interval that is subsequent to the first temporal interval, and separated from the first temporal interval by a corresponding buffer interval. The apparatus may transmit at least a portion of the generated output data to a computing system, which may perform operations based on the portion of the output data.

Abstract: The disclosed embodiments include computer-implemented systems and methods that predicts targeted, agency-specific recovery events using a trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on elements of interaction data associated with an occurrence of a first event. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate elements of output data indicative of an expected occurrence of a corresponding one of a plurality of targeted second events involving each of a plurality of candidate event assignments during a future temporal interval. The apparatus may transmit at least a portion of the generated output data to a computing system via the communications interface, the computing system may perform operations that assign the first event to a corresponding one of the candidate event assignments based on the elements of output data.

Abstract: The disclosed embodiments include computer-implemented systems and methods that dynamically predict future occurrences of events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on elements of interaction data associated with an extraction interval. Based on an application of a trained artificial intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of a first event during a first portion of a target interval, which may be separated from the extraction interval by a second portion of the target interval. The first event may be associated with a predetermined temporal duration within the first portion of the target interval. The apparatus may transmit a portion of the generated output data to a computing system, and the computing system may be configured to perform operations based on the portion of the output data.

Abstract: A computing device configured to communicate with a central server in order to predict likelihood of fraud in current transactions for a target claim. The computing device then extracts from information stored in the central server (relating to the target claim and past transactions for past claims including those marked as fraud), a plurality of distinct sets of features: text-based features derived from the descriptions of communications between the requesting device and the endpoint device, graph-based features derived from information relating to a network of claims and policies connected through shared information, and tabular features derived from the details related to claim information and exposure details. The features are input into a machine learning model for generating a likelihood of fraud in the current transactions and triggering an action based on the likelihood of fraud (e.g. stopping subsequent related transactions to the target claim).

Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of targeted events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on interaction data associated with a prior temporal interval, and may apply a trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of each of a plurality of targeted events during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval. The apparatus may also transmit the output data to a computing system, and the computing system may transmit digital content to a device based on at least a portion of the output data.

Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of targeted classes of events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on interaction data associated with a prior temporal interval, and may apply a trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of an expected occurrence of a corresponding one of a plurality of targeted events during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval.

Abstract: The disclosed embodiments include computer-implemented processes that determine, in real time, a likelihood of a targeted future engagement using trained artificial intelligence processes. For example, an apparatus may generate a first input dataset based on elements of first interaction data associated with a first temporal interval, and based on an application of a trained first artificial intelligence process to the first input dataset, generate output data representative of a predicted likelihood of an occurrence of each of a plurality of target events during a second temporal interval. The second temporal interval is subsequent to the first temporal interval and is separated from the first temporal interval by a corresponding buffer interval. Further, the apparatus may transmit at least a portion of the output data to a computing system, which may generate notification data associated with the predicted likelihood, and provision the notification data to a device.

Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of events using adaptively trained machine-learning or artificial-intelligence processes. For example, an apparatus may generate an input dataset based on first interaction data associated with a prior temporal interval, and may apply an adaptively trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the adaptively trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval. The apparatus may also transmit a portion of the generated output data to a computing system, and the computing system may be configured to generate or modify second interaction data based on the portion of the output data.

Abstract: The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of events using adaptively trained artificial-intelligence processes and contextual data. For example, an apparatus may generate an input dataset based on first interaction data and contextual data associated with a prior temporal interval, and may apply an adaptively trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the adaptively trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval.

Abstract: The disclosed embodiments include computer-implemented apparatuses and methods that predict occurrences of temporally separated events using adaptively trained artificial intelligence processes. For example, an apparatus may generate an input dataset based on first interaction data that characterizes an occurrence of a first event, and may apply a trained artificial intelligence process to the input dataset. Based on the application of the trained artificial intelligence process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of a second event within a predetermined time period subsequent to the occurrence of the first event, and may transmit the output data to a computing system. The computing system may generate second interaction data specifying an operation associated with the occurrence of the first event based on the output data, and perform the operation in accordance with the second interaction data.

Abstract: The disclosed embodiments include computer-implemented processes that flexibly and dynamically analyze a machine learning process, and that generate analytical output characterizing an operation of the machine learning process across multiple analytical periods. For example, an apparatus may receive an identifier of a dataset associated with the machine learning process and feature data that specifies an input feature of the machine learning process. The apparatus may access at least a portion of the dataset based on the received identifier, and obtain, from the accessed portion of the dataset, a feature vector associated with the machine learning process.

Abstract: A document analysis system trains a document embedding model configured to receive a set of word embeddings for an ordered set of words in a document and generate a document embedding for the document. The document embedding is a representation of the document in a latent space that characterizes the document with respect to properties such as structure, content, and sentiment. The document embedding may represent a prediction of a set of words that follow the last word in the ordered set of words of the document. The document embedding model may be associated with a convolutional neural network (CNN) architecture that includes one or more convolutional layers. The CNN architecture of the document embedding model allows the document analysis system to overcome various difficulties of existing document embedding models, and allows the document analysis system to easily process variable-length documents that include a variable number of words.

Abstract: A recommendation system generates recommendations for an online system using one or more neural network models that predict preferences of users for items in the online system. The neural network models generate a latent representation of a user and of a user that can be combined to determine the expected preference of the user to the item. By using neural network models, the recommendation system can generate predictions in real-time for new users and items without the need to re-calibrate the models. Moreover, the recommendation system can easily incorporate other forms of information other than preference information to generate improved preference predictions by including the additional information to generate the latent description of the user or item.

Abstract: A recommendation system generates recommendations for an online system using one or more neural network models that predict preferences of users for items in the online system. The neural network models generate a latent representation of a user and of a user that can be combined to determine the expected preference of the user to the item. By using neural network models, the recommendation system can generate predictions in real-time for new users and items without the need to re-calibrate the models. Moreover, the recommendation system can easily incorporate other forms of information other than preference information to generate improved preference predictions by including the additional information to generate the latent description of the user or item.

Abstract: A document analysis system trains a document embedding model configured to receive a set of word embeddings for an ordered set of words in a document and generate a document embedding for the document. The document embedding is a representation of the document in a latent space that characterizes the document with respect to properties such as structure, content, and sentiment. The document embedding may represent a prediction of a set of words that follow the last word in the ordered set of words of the document. The document embedding model may be associated with a convolutional neural network (CNN) architecture that includes one or more convolutional layers. The CNN architecture of the document embedding model allows the document analysis system to overcome various difficulties of existing document embedding models, and allows the document analysis system to easily process variable-length documents that include a variable number of words.

Abstract: A document analysis system trains a document embedding model configured to receive a set of word embeddings for an ordered set of words in a document and generate a document embedding for the document. The document embedding is a representation of the document in a latent space that characterizes the document with respect to properties such as structure, content, and sentiment. The document embedding may represent a prediction of a set of words that follow the last word in the ordered set of words of the document. The document embedding model may be associated with a convolutional neural network (CNN) architecture that includes one or more convolutional layers. The CNN architecture of the document embedding model allows the document analysis system to overcome various difficulties of existing document embedding models, and allows the document analysis system to easily process variable-length documents that include a variable number of words.