Abstract: There is a need for more accurate and more efficient predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.

Abstract: As described herein, various embodiments of the present invention disclose techniques that improve efficiency of performing image-based machine learning operations on large images while limiting accuracy drawbacks of partial processing of those large images by using composite-tiled image embeddings for composite-tiled images generated by merging multiple tiled images that are generated using multiple tiling mechanisms. For example, in some embodiments, given an input image that comprises R image regions, each tiled image comprises N selected image regions of the R image regions that are selected in accordance with a tiling mechanism (where N<R). In this way, given T tiling mechansisms, T tiled images are generated, and the T tiled images are merged to generate a composite-tiled image. Accordingly, by using T tiling mechansisms, various embodiments enable reducing the size of feature data provided to an image processing machine learning model by selecting non-holistic subsets of an input image.

Abstract: There is a need for more accurate and more efficient predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.

Abstract: A system for predicting multiple-sequential-event based outcomes may include: a first deep neural network configured to predict a first decision, and that includes: a first input layer configured to receive as input less than an entirety of a plurality of variables; and an internal layer configured to receive as input a remainder of the plurality of variables appended to an output of a preceding layer, such that the first deep neural network is configured generate a prediction value for the first decision; and a second deep neural network configured to predict a second decision subsequent to the first decision, and that includes: a second input layer configured to receive as input a representation of output from a penultimate layer appended to at least a portion of the plurality of variables, wherein the second deep neural network is configured to generate a prediction value for the second decision.

Abstract: Systems and methods are disclosed for processing medical claims to determine vector embeddings for predictive analytics and fraud detection. The method includes receiving claim data for a medical claim, the claim data comprising a plurality of medical codes. A plurality of embedding vectors is determined based on the plurality of medical codes, each embedding vector determined based on a corresponding medical code. A multi-layered matrix is determined based on the plurality of embedding vectors determined for the plurality of medical codes, wherein the multi-layered matrix is representative of the medical claim. One or more actions such as predictive analytics and fraud detection are performed based on the multi-layered matrix.

Abstract: Systems and methods are disclosed for processing medical claims to determine vector embeddings for predictive analytics and fraud detection. The method includes receiving claim data for a medical claim, the claim data comprising a plurality of medical codes. A plurality of embedding vectors is determined based on the plurality of medical codes, each embedding vector determined based on a corresponding medical code. A multi-layered matrix is determined based on the plurality of embedding vectors determined for the plurality of medical codes, wherein the multi-layered matrix is representative of the medical claim. One or more actions such as predictive analytics and fraud detection are performed based on the multi-layered matrix.

Abstract: As described herein, various embodiments of the present invention disclose techniques that improve efficiency of performing image-based machine learning operations on large images while limiting accuracy drawbacks of partial processing of those large images by using composite-tiled image embeddings for composite-tiled images generated by merging multiple tiled images that are generated using multiple tiling mechanisms. For example, in some embodiments, given an input image that comprises R image regions, each tiled image comprises N selected image regions of the R image regions that are selected in accordance with a tiling mechanism (where N<R). In this way, given T tiling mechansisms, T tiled images are generated, and the T tiled images are merged to generate a composite-tiled image. Accordingly, by using T tiling mechansisms, various embodiments enable reducing the size of feature data provided to an image processing machine learning model by selecting non-holistic subsets of an input image.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive structural analysis using at least one of techniques using time bound code transition likelihood data objects, techniques using cross-code relationship values, techniques using augmented entity-code occurrence data objects, techniques using per-pathway text representations of inferred occurrence pathways of a one or more individual historic code occurrences, techniques using polygenic risk score (PRS) measures, and/or the like.

Abstract: A system includes a controller with processors configured to execute an auto-correlation module embodied in one or more sets of program instructions stored in memory. The auto-correlation module is configured to cause the processors to receive one or more patterned wafer geometry metrics, receive wafer characterization data from one or more characterization tools, determine a correlation between the one or more patterned wafer geometry metrics and the wafer characterization data, generate a ranking of the one or more patterned wafer geometry metrics based on the determined correlation, construct a composite metric model from a subset of the one or more patterned wafer geometry metrics based on the ranking of the one or more patterned wafer geometry metrics, generate one or more composite wafer metrics from the composite metric model, and generate a statistical process control output based on the one or more composite wafer metrics.

Abstract: Using data about the geometry of the wafer, the geometry of the wafer is measured along at least three diameters originating at different points along a circumference of the wafer. A characterization of the geometry of the wafer is determined using the three diameters. A probability of wafer clamping failure for the wafer can be determined based on the characterization.

Abstract: There is a need for more accurate and more efficient predictive data analysis steps/operations. This need can be addressed by, for example, techniques for efficient predictive data analysis steps/operations. In one example, a method includes mapping a primary event having a primary event code to a related subset of a plurality of candidate secondary events by at least processing one or more lifecycle-related attributes for the primary event code using a lifecycle inference machine learning model to detect an inferred lifecycle for the primary event.

Abstract: Using data about the geometry of the wafer, the geometry of the wafer is measured along at least three diameters originating at different points along a circumference of the wafer. A characterization of the geometry of the wafer is determined using the three diameters. A probability of wafer clamping failure for the wafer can be determined based on the characterization.

Abstract: A system includes a controller with processors configured to execute an auto-correlation module embodied in one or more sets of program instructions stored in memory. The auto-correlation module is configured to cause the processors to receive one or more patterned wafer geometry metrics, receive wafer characterization data from one or more characterization tools, determine a correlation between the one or more patterned wafer geometry metrics and the wafer characterization data, generate a ranking of the one or more patterned wafer geometry metrics based on the determined correlation, construct a composite metric model from a subset of the one or more patterned wafer geometry metrics based on the ranking of the one or more patterned wafer geometry metrics, generate one or more composite wafer metrics from the composite metric model, and generate a statistical process control output based on the one or more composite wafer metrics.

Abstract: A computer implemented method and apparatus for remotely delivering software. The method comprises installing a provisioning application on a first device, in response to accessing an advertisement for a software product; determining one or more second devices that are synchronized with the first device, wherein the one or more second devices are remote from the first device, and wherein the one or more synchronized second devices are not registered with a software provider of the software product; registering the one or more synchronized second devices with the software provider of the software product; receiving, from the first device, a selection of one or more of the registered devices for installation of the software product; and directing installation of the software product on the one or more selected registered devices.

Abstract: A computer implemented method and apparatus for remotely delivering software. The method comprises installing a provisioning application on a first device, in response to accessing an advertisement for a software product; determining one or more second devices that are synchronized with the first device, wherein the one or more second devices are remote from the first device, and wherein the one or more synchronized second devices are not registered with a software provider of the software product; registering the one or more synchronized second devices with the software provider of the software product; receiving, from the first device, a selection of one or more of the registered devices for installation of the software product; and directing installation of the software product on the one or more selected registered devices.