Oracle International Corporation

Google Inc.

Xiao Liu

Ying Gao

Jeffrey R Doering

Hailun Yang

Emmanouil Koukoumidis

Evan Huang

Yu Liu

Michael E Lanning

Lukas Rutishauser

Shengda Ding

Raghuram Kudaravalli

Timothy Meyer

Nikolaos Kofinas

Kiran Bellare

A computer-implemented method includes receiving a document insight request that requests document insights for a corpus of documents. The document insight request includes the corpus of documents, a set of entities contained within each document of the corpus of documents, and document insight request parameters that includes a confidence value threshold. The method also includes generating the document insights for the corpus of documents based on the confidence value threshold. Here, the document insights include an accuracy target and a user review rate target. The method also includes transmitting the document insights to the user device causing a graphical user interface to display the document insights on the user device.

Human-augmented artificial intelligence configuration and optimization insights

In certain embodiments, a Service Deployment Infrastructure (SDI) request engine is disclosed. The SDI request engine can perform tracking, management and provisioning of services subscribed to by users of a computer infrastructure system. The SDI request engine can be deployed to process large volumes of provisioning requests and deliver time critical applications for customers. The SDI request engine can translate each request into a list of tasks of various sizes based on the requirement and configuration of the request, and store each task in a queue. In certain embodiments, the SDI request engine may evaluate a dynamic weight associated with each task to prioritize the processing of tasks, which may improve the overall throughput of request processing.

Dynamic weighting for cloud-based provisioning systems

In certain embodiments, a Service Deployment Infrastructure (SDI) instance is disclosed. The SDI instance can intelligently allocate resources of a rack to one or more virtual machines as part of provisioning a requested service. The SDI instance can use machine-learning techniques to evaluate historical data associated with a requested service. The machine-learning techniques can be used to generate one or more models that simulate the possible configurations of sizes for virtual machines for a particular service. The one or more models can be accessed at runtime to fit virtual machines of a requested service into servers of a rack in a manner that minimizes unused resources with multi-dimensional constraints.

Enhancing resource allocation for application deployment

In certain embodiments, a Service Deployment Infrastructure (SDI) request engine is disclosed. The SDI request engine performs the tracking, management and provisioning of services subscribed to by customers of the cloud infrastructure system. The SDI request engine is deployed to process large volumes of provisioning requests and deliver time critical applications for customers. The SDI request engine translates each request into a list of tasks of various sizes based on the requirement and configuration of the request. In some embodiments, the SDI request engine imposes control and management on both request and task levels in order to execute, rollback, retry or fail a task automatically and accurately.

Service deployment infrastructure request provisioning

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San Francisco, CA, United States of America

Xiao Liu