Sap Se

Vijay G

Sivakumar N

Raghavendra D

Praveenkumar A K

Pratik Shenoy

Kishan Kumar Kedia

Frank Brunswig

Rakesh Kumar

Vinay C

Zainul Abidin Mulla

Pranjal Chugh

Preeta K

Srinivas Bhaskara

Miguel Rolando Heineken

A retention service registers a target system as a consumer service. A cloud platform provides services to each of multiple consumer service. Retention rule information about the target system is received and reflects a data retention requirement. A rule is created that corresponds to the data retention requirement. A configuration is defined that specifies recurring future invocations of the rule. The rule is invoked per the configuration and a query is sent to the target system, the query configured to extract data. Extracted data is received and the rule is applied thereto. An action specified by the rule is taken with regard to the data.

Providing data protection and privacy as a service in a cloud platform

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.

Dynamic self-learning system

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.

Computer vision architecture with machine learned image recognition models

A method to model a business object is disclosed. An analysis module analyzes elements of a business object. A modeling module models business object logic based on the elements of the business object. An implementation module implements the modeled business object logic. A repository stores the modeled business object logic. A business object runtime module executes the modeled business object logic.

Rule-based determination and validation in business object processing

Growing community of inventors

Bengaluru, India

Vijay G