Abstract: A system and method are disclosed to generate mixed-reality visualizations and objects to facilitate efficient interaction with large volumes of data. Embodiments include a computer comprising a processor and memory and configured to select data stored in a data table coupled to the computer. Embodiments select one or more key process indicators (KPIs), dimensions by which to display the selected data, and color schemes by which to display the selected data. Embodiments generate a first mixed-reality object displaying the selected data according to the selected one or more KPIs, dimensions, and color schemes, and a second mixed-reality object displaying the selected data as a three-dimensional model of nodes and connections. Embodiments display the first and second mixed-reality objects on a display device, and manipulate one of the first and second mixed-reality objects in response to input received by one or more sensors.

Abstract: A system and method are disclosed to generate one or more mixed-reality visualizations and objects to facilitate efficient interaction with large volumes of data. Embodiments include a computer comprising a processor and memory and a mixed-reality display device comprising a display and one or more sensors. Embodiments generate a mixed-reality object comprising a first cuboid, the first cuboid comprising one or more adjustable display dimensions corresponding to one or more key process indicators (KPIs) and one or more cubelets divided along at least one of the one or more adjustable display dimensions. Embodiments display the mixed-reality object on the display, and adjust at least one of the one or more adjustable display dimensions in response to receiving a first input to the one or more sensors. In response to a second input, embodiments transform the one or more cubelets of the first cuboid.

Abstract: Various disclosed embodiments can resolve output inaccuracies produced by many machine learning models. Embodiments use content order as input to machine learning model systems so that they can process documents according to the position or rank of instances in a document or image. In this way, the model is less likely to misclassify or incorrectly detect instances or the ordering between predicted instances. The content order in various embodiments can be used as an additional signal to classify or make predictions.

Abstract: Various disclosed embodiments can resolve output inaccuracies produced by many machine learning models. Embodiments use content order as input to machine learning model systems so that they can process documents according to the position or rank of instances in a document or image. In this way, the model is less likely to misclassify or incorrectly detect instances or the ordering between predicted instances. The content order in various embodiments can be used as an additional signal to classify or make predictions.

Abstract: Various disclosed embodiments can resolve output inaccuracies produced by many machine learning models. Embodiments use content order as input to machine learning model systems so that they can process documents according to the position or rank of instances in a document or image. In this way, the model is less likely to misclassify or incorrectly detect instances or the ordering between predicted instances. The content order in various embodiments can be used as an additional signal to classify or make predictions.