Abstract: Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for an end-to-end solution to auto-identifying features. In one embodiment, a novel architecture is presented that enables the identification of optimal features and feature processes for use by a machine learning model. The novel architecture introduces a feature orchestrator for managing, routing, and retrieving the data and features associated with analytical job request. The novel architecture also introduces a feature store designed to identify, rank, and store the features and data used in the analysis. To aid in identifying the optimal features and feature processes, a training system may also be included in the solution which can perform some of the training and scoring of the features.

Abstract: Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.

Abstract: Aspects of the present disclosure involve systems, methods, devices, and the like for an end-to-end solution to auto-identifying features. In one embodiment, a novel architecture is presented that enables the identification of optimal features and feature processes for use by a machine learning model. The novel architecture introduces a feature orchestrator for managing, routing, and retrieving the data and features associated with analytical job request. The novel architecture also introduces a feature store designed to identify, rank, and store the features and data used in the analysis. To aid in identifying the optimal features and feature processes, a training system may also be included in the solution which can perform some of the training and scoring of the features.