Oracle International Corporation

Guru Selvaraj

Kyasaram Vishwa Prasad

Naveen Jafer Nizar

Srinivasan Sankaranarayanan

Chidambaram Gopal Karthik Murugan

Techniques are described for providing personalized content in applications without revealing personal details to content providers. A base machine learning model is loaded to a cloud environment that is private to a tenant organization. The base machine learning model is used to generate a custom machine learning model specific to the tenant organization based on user interactions within the tenant organization and content categories specific to the tenant organization. The custom machine learning model is used to select content categories for which to provide instances of content items to users based on user interactions with applications. Engagement with the instances of content items is tracked in a user-specific manner privately by the tenant organization, in a tenant-specific manner privately by a cloud services provider, and in a content-specific manner by the content provider. Feedback is provided to the custom machine learning model based on engagement with instances of content items.

Personalized internal services via a privacy-constrained content promotion platform

A computing network includes nodes of different work groups. Nodes of a work group are dedicated to transactions of the work group. If a node of a first work group is predicted to have an idleness window, a second work group may borrow the node to execute a transaction of the second work group. At least a subset of steps of the transaction may be categorized into a step group. Trees of a transaction may be categorized into one or more tree groups. A node is selected for executing a transaction, if the predicted idleness duration of the node is sufficient relative to the predicted runtime of the transaction, the step group, and/or tree group. A credit system is maintained. A first work group transfers a credit to a second work group when borrowing a node of the second work group for executing a transaction of the first work group.

Selecting a node of a work group for executing a target transaction of another work group to execute skippable steps prior to a predicted interruption

Selecting a node group of a work group for executing a target transaction of another work group to optimize parallel execution of steps of the target transaction

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Hyderabad Telangana, India

Guru Selvaraj