Abstract: Systems and methods are provided for determining a weather forecast corresponding to a location of an air handling unit for a building, generating a foot traffic forecast for a specified time period in the building, and generating a predicted energy consumption curve based on the weather forecast and generated foot traffic forecast for the specified time period. Based on the predicted energy consumption curve, the systems and methods further provide for generating settings for controllable energy devices of the air handling unit to control the air handling unit for the specified time period.

Abstract: Systems and methods are provided for determining a weather forecast corresponding to a location of an air handling unit for a building, generating a foot traffic forecast for a specified time period in the building, and generating a predicted energy consumption curve based on the weather forecast and generated foot traffic forecast for the specified time period. Based on the predicted energy consumption curve, the systems and methods further provide for generating settings for controllable energy devices of the air handling unit to control the air handling unit for the specified time period.

Abstract: In an example, a computerized neural fabric is created by representing each pattern of learned weights of one or more machine learning algorithm-trained models specifying a specific set of products as a column in the computerized neural fabric, each pattern comprising one or more clusters representing combinations of convolutional filters, each cluster learning low level features and sending output via a vertical flow up the corresponding column to a final classification within the corresponding pattern. One or more potential lateral flows between patterns in the computerized neural fabrics is dynamically determined based on resemblance of a new product in a candidate image to the specific sets of products in each of the patterns and identifying possible mutations of the patterns based on the resemblance. Then, one of the one or more potential lateral flows is selected as a new model.

Abstract: In an example, a first machine learning algorithm is used to train a smart contour model to identify contours of product shapes in input images and to identify backgrounds in the input images. A second machine learning algorithm is used to train a plurality of shape-specific classification models to output identifications of products in input images. A candidate image of one or more products is obtained. The candidate image is passed to the smart contour model, obtaining output of one or more tags identifying product contours in the candidate image. The candidate image and the one or more tags are passed to an ultra-large scale multi-hierarchy classification system to identify one or more classification models for one or more individual product shapes in the candidate image. The one or more classification models are used to distinguish between one or more products and one or more unknown products in the image.

Abstract: In an example, a first machine learning algorithm is used to train a smart contour model to identify contours of product shapes in input images and to identify backgrounds in the input images. A second machine learning algorithm is used to train a plurality of shape-specific classification models to output identifications of products in input images. A candidate image of one or more products is obtained. The candidate image is passed to the smart contour model, obtaining output of one or more tags identifying product contours in the candidate image. The candidate image and the one or more tags are passed to an ultra-large scale multi-hierarchy classification system to identify one or more classification models for one or more individual product shapes in the candidate image. The one or more classification models are used to distinguish between one or more products and one or more unknown products in the image.

Abstract: In an example, a computerized neural fabric is created by representing each pattern of learned weights of one or more machine learning algorithm-trained models specifying a specific set of products as a column in the computerized neural fabric, each pattern comprising one or more clusters representing combinations of convolutional filters, each cluster learning low level features and sending output via a vertical flow up the corresponding column to a final classification within the corresponding pattern. One or more potential lateral flows between patterns in the computerized neural fabrics is dynamically determined based on resemblance of a new product in a candidate image to the specific sets of products in each of the patterns and identifying possible mutations of the patterns based on the resemblance. Then, one of the one or more potential lateral flows is selected as a new model.

Abstract: Systems, methods, and apparatuses for activity pattern detection are described herein. Embodiments may process large amounts of data from a plurality of different database sources in order to detect events common to the data of the different database sources. Embodiments further perform data mining operations to detect patterns (e.g., two or more events appearing consecutively or non-consecutively), and present these patterns in a graphical user interface (GUI) to illustrate how a plurality of patterns may comprise a business scenario.

Abstract: Systems, methods, and apparatuses for activity pattern detection are described herein. Embodiments may process large amounts of data from a plurality of different database sources in order to detect events common to the data of the different database sources. Embodiments further perform data mining operations to detect patterns (e.g., two or more events appearing consecutively or non-consecutively), and present these patterns in a graphical user interface (GUI) to illustrate how a plurality of patterns may comprise a business scenario.