Abstract: Disclosed is a new identifier allocation approach for allocating repository object identifiers beyond a maximum number usually allowed for a single repository. The new identifier allocation approach includes an enhanced identifier structure in which a portion usually reserved for a docbase identifier is modified to combine with a partition identifier. The actual docbase identifier is stored in a server configuration file. When the maximum number of object identifiers has been allocated, a new partition is created and a sequence table is updated with a new entry that reflects the new partition. A new repository object identifier is allocated using the docbase identifier combined with the new partition identifier. In this way, more than 4 billion objects per object type can be addressed uniquely within a docbase by creating new partitions. This new approach is very unique and flexible in accommodating both on premises and multi-tenant environments.

Abstract: Systems and methods are provided for a dynamic and iterative process for determining a weighted decision using a combination of weighted output from multiple, trained machine learning (ML) models. Key data can be identified and efficient decision-based processing can be achieved. In some examples, the system calculates a weighted decision of a repair or total loss determination for a motor vehicle, yet any industry or data set may be implemented with the use of the dynamic and iterative decision process.

Abstract: Systems and methods to maintain amends to an annotation as discrete chronological events are disclosed. Exemplary implementations may: obtain a selection of a first annotation template for a first annotation via a client computing platform; generate a root node based on the selection of the first annotation template; obtain a first command to update to the first annotation; append a first update node to the root node responsive to the first command; obtain a second command to update to the first annotation; append a second update node to the first node responsive to the second command; receive an indication to present the first annotation; generate, responsive to the indication, the first annotation by populating the first annotation template included in the root node based on the first node set and in sequential order indicated by the edges; and effectuate presentation of the first annotation.

Abstract: Disclosed is a new identifier allocation approach for allocating repository object identifiers beyond a maximum number usually allowed for a single repository. The new identifier allocation approach includes an enhanced identifier structure in which a portion usually reserved for a docbase identifier is modified to combine with a partition identifier. The actual docbase identifier is stored in a server configuration file. When the maximum number of object identifiers has been allocated, a new partition is created and a sequence table is updated with a new entry that reflects the new partition. A new repository object identifier is allocated using the docbase identifier combined with the new partition identifier. In this way, more than 4 billion objects per object type can be addressed uniquely within a docbase by creating new partitions. This new approach is very unique and flexible in accommodating both on premises and multi-tenant environments.

Abstract: Disclosed is a new identifier allocation approach for allocating repository object identifiers beyond a maximum number usually allowed for a single repository. The new identifier allocation approach includes an enhanced identifier structure in which a portion usually reserved for a docbase identifier is modified to combine with a partition identifier. The actual docbase identifier is stored in a server configuration file. When the maximum number of object identifiers has been allocated, a new partition is created and a sequence table is updated with a new entry that reflects the new partition. A new repository object identifier is allocated using the docbase identifier combined with the new partition identifier. In this way, more than 4 billion objects per object type can be addressed uniquely within a docbase by creating new partitions. This new approach is very unique and flexible in accommodating both on premises and multi-tenant environments.

Abstract: Disclosed is a new identifier allocation approach for allocating repository object identifiers beyond a maximum number usually allowed for a single repository. The new identifier allocation approach includes an enhanced identifier structure in which a portion usually reserved for a docbase identifier is modified to combine with a partition identifier. The actual docbase identifier is stored in a server configuration file. When the maximum number of object identifiers has been allocated, a new partition is created and a sequence table is updated with a new entry that reflects the new partition. A new repository object identifier is allocated using the docbase identifier combined with the new partition identifier. In this way, more than 4 billion objects per object type can be addressed uniquely within a docbase by creating new partitions. This new approach is very unique and flexible in accommodating both on premises and multi-tenant environments.

Abstract: An embodiment of the present invention is directed to reducing complexities in machine learning application development by providing a drag-and-drop user interface for an entire machine learning process. The innovative system significantly reduces development time and efforts. An embodiment of the present invention is directed to applying optimized common components that follow industry wide best practices thereby improving the time to market as well as the overall code quality. The embodiments of the present invention provide adaptability and extendibility to support various platforms. According to an embodiment of the present invention, a generic platform agnostic code generator may be extended to support various use cases, applications, platforms and environments.

Abstract: An embodiment of the present invention is directed to reducing complexities in machine learning application development by providing a drag-and-drop user interface for an entire machine learning process. The innovative system significantly reduces development time and efforts. An embodiment of the present invention is directed to applying optimized common components that follow industry wide best practices thereby improving the time to market as well as the overall code quality. The embodiments of the present invention provide adaptability and extendibility to support various platforms. According to an embodiment of the present invention, a generic platform agnostic code generator may be extended to support various use cases, applications, platforms and environments.

Abstract: Certain embodiments of the present disclosure include a method for translating an application-level abstraction to a logical network topology. The method includes receiving an event request from a container orchestrator at an orchestrator adaptor. The method also includes translating the event request to a logical network resource via an application programming interface associated with a network virtualization platform. The method includes mapping the event request to the logical network resource. The method also includes deploying the logical network resource in a logical network via the network virtualization platform.

Abstract: Certain embodiments of the present disclosure include a method for translating an application-level abstraction to a logical network topology. The method includes receiving an event request from a container orchestrator at an orchestrator adaptor. The method also includes translating the event request to a logical network resource via an application programming interface associated with a network virtualization platform. The method includes mapping the event request to the logical network resource. The method also includes deploying the logical network resource in a logical network via the network virtualization platform.