Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: Systems and methods for data ingestion in real time are described herein. In an embodiment, a server computer receives a message comprising one or more client events from a storage device which publishes the message in response to storing the one or more client events. The server computer stores the one or more client events as raw event strings which are then parsed into parsed event strings. Identity resolution methods are performed on the parsed event strings. Feature groups are then identified in the parsed event strings and used to generate aggregation keys which are used to aggregate the feature groups prior to storing aggregated data in one or more aggregation tables.

Abstract: Systems and methods for data ingestion in real time are described herein. In an embodiment, a server computer receives a message comprising one or more client events from a storage device which publishes the message in response to storing the one or more client events. The server computer stores the one or more client events as raw event strings which are then parsed into parsed event strings. Identity resolution methods are performed on the parsed event strings. Feature groups are then identified in the parsed event strings and used to generate aggregation keys which are used to aggregate the feature groups prior to storing aggregated data in one or more aggregation tables.

Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user’s performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media for automated generation and use of a machine learning (ML) model to provide recommendations. In some implementations, a method includes receiving a recommendation specification that includes a content type and an outcome identifier, and determining model parameters for a ML model based on the recommendation specification. The method further includes generating a historical user feature matrix (FM), generating a historical content feature matrix (FM), and transforming the historical user FM and the historical content FM into a suitable format for the ML model. The method further includes obtaining a target dataset that includes historical results for the outcome identifier for a plurality of pairs of user identifiers and content items of the content type. The method further includes training the ML model using supervised learning to generate a ranked list of content items for each user identifier.

Abstract: Systems and methods for data ingestion in real time are described herein. In an embodiment, a server computer receives a message comprising one or more client events from a storage device which publishes the message in response to storing the one or more client events. The server computer stores the one or more client events as raw event strings which are then parsed into parsed event strings. Identity resolution methods are performed on the parsed event strings. Feature groups are then identified in the parsed event strings and used to generate aggregation keys which are used to aggregate the feature groups prior to storing aggregated data in one or more aggregation tables.

Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: A vehicle air intake assembly is disclosed. The assembly includes a housing, a conical filter, and optionally may also include an inlet cowl. The housing and filter decrease in diameter from an inlet or distal end toward a proximal or outlet end. The shape of the housing guides the air into a smaller cross-sectional area and induces a Venturi effect on the airflow passing through the housing and filter. The housing decouples the filter from an engine inlet and the proximal or outlet end of the housing is sized so as to attach to the engine inlet and provide a smooth transition for the air leaving the housing and entering the engine inlet.

Abstract: A vehicle air intake assembly is disclosed. The assembly includes a housing, a conical filter, and optionally may also include an inlet cowl. The housing and filter decrease in diameter from an inlet or distal end toward a proximal or outlet end. The shape of the housing guides the air into a smaller cross-sectional area and induces a Venturi effect on the airflow passing through the housing and filter. The housing decouples the filter from an engine inlet and the proximal or outlet end of the housing is sized so as to attach to the engine inlet and provide a smooth transition for the air leaving the housing and entering the engine inlet.

Abstract: A vehicle air intake assembly is disclosed. The assembly includes a housing, a conical filter, and optionally may also include an inlet cowl. The housing and filter decrease in diameter from an inlet or distal end toward a proximal or outlet end. The shape of the housing guides the air into a smaller cross-sectional area and induces a Venturi effect on the airflow passing through the housing and filter. The housing decouples the filter from an engine inlet and the proximal or outlet end of the housing is sized so as to attach to the engine inlet and provide a smooth transition for the air leaving the housing and entering the engine inlet.

Abstract: Systems and methods for surgical imaging are disclosed herein. A head-mountable device (HMD) can include a display configured to provide an image within a field of view of an environment of the HIVID. At least one fiducial marker can be arranged on a surgical patient. At least one sensor can be configured to track a position of the at least one fiducial marker. Three-dimensional image information indicative of one or more internal features of the patient is provided. Based on information from the at least one sensor, a position of the surgical patient can be determined. Based on the determined position of the surgical patient, the HIVID can display at least a portion of the three-dimensional image information superimposed on at least a portion of the surgical patient within the field of view.

Abstract: A vehicle air intake assembly is disclosed. The assembly includes a housing, a conical filter, and optionally may also include an inlet cowl. The housing and filter decrease in diameter from an inlet or distal end toward a proximal or outlet end. The shape of the housing guides the air into a smaller cross-sectional area and induces a Venturi effect on the airflow passing through the housing and filter. The housing decouples the filter from an engine inlet and the proximal or outlet end of the housing is sized so as to attach to the engine inlet and provide a smooth transition for the air leaving the housing and entering the engine inlet.