公开(公告)号：US10467501B2

公开(公告)日：2019-11-05

申请号：US15797117

申请日：2017-10-30

Applicant: SAP SE

Inventor： Sivakumar N ,  Praveenkumar A K ,  Raghavendra D ,  Vijay G ,  Pratik Shenoy ,  Kishan Kumar Kedia

IPC: 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

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.

公开(公告)号：US12046019B2

公开(公告)日：2024-07-23

申请号：US17157392

申请日：2021-01-25

Applicant: SAP SE

Inventor： Mithilesh Kumar Singh ,  Anubhav Sadana ,  Deepak Pandian ,  Raghavendra D ,  Satyadeep Dey ,  Philippe Long

IPC: G06V30/40 ,  G06F9/451 ,  G06F18/22 ,  G06V10/75 ,  G06V20/62 ,  G06V30/10

CPC classification number: G06V10/751 ,  G06F9/451 ,  G06F18/22 ,  G06V20/62 ,  G06V30/10

Abstract: Disclosed herein are system, method, and computer program product embodiments for surface automation in black box environments. An embodiment operates by determining scenarios of an application for automation; detecting the scenario during an execution of an application; capturing and storing one or more user interface screenshots of the scenario; identifying and storing user interface information from the user interface screenshot; implementing a sequential set of instructions comprising at least one textual element detection technique and at least one non-textual element detection technique; and executing the sequential set of instructions.

公开(公告)号：US20220237404A1

公开(公告)日：2022-07-28

申请号：US17157392

申请日：2021-01-25

Applicant: SAP SE

Inventor： Mithilesh Kumar Singh ,  Anubhav Sadana ,  Deepak Pandian ,  Raghavendra D ,  Satyadeep Dey ,  Phillippe Long

IPC: G06K9/62 ,  G06K9/78 ,  G06K9/54 ,  G06K9/32 ,  G06F9/451

Abstract: Disclosed herein are system, method, and computer program product embodiments for surface automation in black box environments. An embodiment operates by determining scenarios of an application for automation; detecting the scenario during an execution of an application; capturing and storing one or more user interface screenshots of the scenario; identifying and storing user interface information from the user interface screenshot; implementing a sequential set of instructions comprising at least one textual element detection technique and at least one non-textual element detection technique; and executing the sequential set of instructions.

公开(公告)号：US10474928B2

公开(公告)日：2019-11-12

申请号：US15812533

申请日：2017-11-14

Applicant: SAP SE

Inventor： Sivakumar N ,  Praveenkumar A K ,  Raghavendra D ,  Vijay G ,  Pratik Shenoy ,  Kishan Kumar Kedia

IPC: 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

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.

公开(公告)号：US20190130292A1

公开(公告)日：2019-05-02

申请号：US15812533

申请日：2017-11-14

Applicant: SAP SE

Inventor： Sivakumar N ,  Praveenkumar A K ,  Raghavendra D ,  Vijay G ,  Pratik Shenoy ,  Kishan Kumar Kedia

IPC: G06N5/04 ,  G06N3/02 ,  G06N99/00

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.

公开(公告)号：US20190130214A1

公开(公告)日：2019-05-02

申请号：US15797117

申请日：2017-10-30

Applicant: SAP SE

Inventor： Sivakumar N ,  Praveenkumar A K ,  Raghavendra D ,  Vijay G ,  Pratik Shenoy ,  Kishan Kumar Kedia

IPC: G06K9/62 ,  G06N99/00 ,  G06N3/04 ,  G06T7/73 ,  G06T7/60 ,  G06K9/00

CPC classification number: 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

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.