Abstract: The present disclosure generally relates to systems and methods for operation research optimization. The systems and methods include receiving, at a data processing system, a payload including a request for optimizing a service and processing the payload using a meta learning classifier. The processing includes extracting a problem and use case characteristics from the payload, predicting at least one machine learning model capable of solving the problem having the use case characteristics, and executing the at least one machine learning model to solve the problem. The systems and methods also include outputting a solution to the problem for optimizing the service from the at least one machine learning model, and providing the solution to a computing device.

Abstract: The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.

Abstract: Various techniques can include systems and methods for using contrastive learning to predict anomalous events in data processing systems. The method can include accessing an unstructured data file and contextual data associated with the unstructured data file. The method can also include generating an event-data input element for the unstructured data file. The event-data input element can include a set of feature vectors. The set of feature vectors can include a first feature vector generated by using a first encoder to process the unstructured file and a second feature vector generated by using a second encoder to process the contextual data. The method can also include generating a classification result of the unstructured data file by using a machine-learning model to process the event-data input element, in which the classification result includes a prediction of whether the particular event corresponds to an anomalous event.

Abstract: Techniques for generating human-readable explanations (also referred to herein as “reasons”) for navigational recommendations are disclosed. Composing a human-readable explanation includes individually selecting words or phrases that are then analyzed, combined, rearranged, modified, or removed to generate the human-readable explanation for a navigational recommendation. A decoder trains a machine learning model to generate the human-readable reasons for the navigational recommendations based on (1) historical recommendation vectors, and (2) historical human-readable reasons associated with the recommendation vectors. The system generates a dictionary of human-readable reasons for recommendations, with each entry of the dictionary including: (1) a recommendation identifier (ID) associated with a recommended navigational target, (2) a reason identifier (ID) associated with a particular reason for the recommendation, and (3) a human-readable reason associated with the reason ID.

Abstract: Techniques for generating human-readable explanations (also referred to herein as “reasons”) for navigational recommendations are disclosed. Composing a human-readable explanation includes individually selecting words or phrases that are then analyzed, combined, rearranged, modified, or removed to generate the human-readable explanation for a navigational recommendation. A decoder trains a machine learning model to generate the human-readable reasons for the navigational recommendations based on (1) historical recommendation vectors, and (2) historical human-readable reasons associated with the recommendation vectors. The system generates a dictionary of human-readable reasons for recommendations, with each entry of the dictionary including: (1) a recommendation identifier (ID) associated with a recommended navigational target, (2) a reason identifier (ID) associated with a particular reason for the recommendation, and (3) a human-readable reason associated with the reason ID.

Abstract: The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.

Abstract: Techniques for generating navigational target recommendations for a user are disclosed. A system propagates sets of user attributes through one neural network and sets of navigational target attributes through another neural network. The neural networks are configured to generate, as outputs, vectors mapped to a same vector space. The system trains the neural networks to identify relationships between the sets of user attributes and the sets of navigational targets. Once the neural networks have been trained, the system generates an embedding for a user by propagating the user's attributes through the trained user attribute neural network. The system also generates embeddings for different navigational targets by propagating the attributes for the different navigational targets through the navigational target neural network. The system identifies relationships between the user and the navigational targets based on the embeddings.

Abstract: The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.

Abstract: The present disclosure relates to an intelligent user interface that predicts tasks for users to complete using a trained machine-learning model. In some implementations, when a user accesses the intelligent user interface, the available tasks and a user profile can be inputted into the trained machine-learning model to output a prediction of one or more tasks for the user to complete. Advantageously, the trained machine-learning model outputs a prediction of tasks that the user will likely need to complete, based at least in part on the user's profile and previous interactions with applications.

Abstract: The present disclosure relates to systems and methods for providing an interface that displays a prediction of remaining code segments of a code comprised of a sequence of code segments. The remaining code segments may be automatically predicted in response to the interface receiving a user's input of at least a portion of a code segment (or a user input of other data elements that are not code segments). Predicting the remaining code segments may be performed using a trained machine-learning model that can generate output(s) predictive of remaining code segments in response to a user inputting at least one code segment of a code into an input element of the interface.

Abstract: The present disclosure relates to systems and methods for providing an interface that displays a prediction of remaining code segments of a code comprised of a sequence of code segments. The remaining code segments may be automatically predicted in response to the interface receiving a user's input of at least a portion of a code segment (or a user input of other data elements that are not code segments). Predicting the remaining code segments may be performed using a trained machine-learning model that can generate output(s) predictive of remaining code segments in response to a user inputting at least one code segment of a code into an input element of the interface.

Abstract: The present disclosure relates to an intelligent user interface that predicts tasks for users to complete using a trained machine-learning model. In some implementations, when a user accesses the intelligent user interface, the available tasks and a user profile can be inputted into the trained machine-learning model to output a prediction of one or more tasks for the user to complete. Advantageously, the trained machine-learning model outputs a prediction of tasks that the user will likely need to complete, based at least in part on the user's profile and previous interactions with applications.

Abstract: The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.