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公开(公告)号：US10955161B2
公开(公告)日：2021-03-23
申请号：US16264443
申请日：2019-01-31
申请人： SAP SE
发明人： Ninad Kulkarni , Xuening Wu , Sangeetha Krishnamoorthy , Mario Ponce , Jun Meng , Rui Jin , Wafaa Sabil , Sivakumar N
IPC分类号： F24F11/47 , G01W1/10 , F24F11/64 , F24F11/65 , F24F11/77 , G05B13/04 , F24F140/60 , F24F110/10 , F24F110/12 , F24F120/10 , F24F130/10 , F24F140/50 , F24F11/46
摘要： 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.
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公开(公告)号：US10467501B2
公开(公告)日：2019-11-05
申请号：US15797117
申请日：2017-10-30
申请人： SAP SE
发明人： Sivakumar N , Praveenkumar A K , Raghavendra D , Vijay G , Pratik Shenoy , Kishan Kumar Kedia
IPC分类号： G06K9/62 , G06N3/04 , G06N3/08 , G06K9/00 , G06K9/32 , G06K9/34 , G06K9/46 , G06T7/73 , G06N20/00 , G06T7/60 , G06N3/02 , G06N5/04 , G06Q10/08
摘要： 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.
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公开(公告)号：US10474928B2
公开(公告)日：2019-11-12
申请号：US15812533
申请日：2017-11-14
申请人： SAP SE
发明人： Sivakumar N , Praveenkumar A K , Raghavendra D , Vijay G , Pratik Shenoy , Kishan Kumar Kedia
IPC分类号： G06K9/00 , G06K9/62 , G06N3/04 , G06N3/08 , G06K9/32 , G06K9/34 , G06K9/46 , G06T7/73 , G06N20/00 , G06T7/60 , G06N3/02 , G06N5/04 , G06Q10/08
摘要： 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.
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公开(公告)号：US20190130292A1
公开(公告)日：2019-05-02
申请号：US15812533
申请日：2017-11-14
申请人： SAP SE
发明人： Sivakumar N , Praveenkumar A K , Raghavendra D , Vijay G , Pratik Shenoy , Kishan Kumar Kedia
摘要： 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.
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公开(公告)号：US20190130214A1
公开(公告)日：2019-05-02
申请号：US15797117
申请日：2017-10-30
申请人： SAP SE
发明人： Sivakumar N , Praveenkumar A K , Raghavendra D , Vijay G , Pratik Shenoy , Kishan Kumar Kedia
CPC分类号： G06K9/6256 , G06K9/00208 , G06K9/00771 , G06K9/00973 , G06K9/3241 , G06K9/34 , G06K9/4628 , G06K9/6204 , G06K9/6218 , G06K9/6268 , G06K9/6273 , G06K9/6284 , G06N3/02 , G06N3/04 , G06N3/0454 , G06N3/084 , G06N5/047 , G06N20/00 , G06Q10/087 , G06T7/60 , G06T7/75 , G06T2200/28 , G06T2207/30242
摘要： 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.
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