Abstract: Software-based technologies for improving structured data collection, presentation, validation, and workflow management processes are disclosed, including the use of interpretable configuration files to define data objects, generate object-level and sub-object-level specifications for a forms-based interface, validation processes and a workflow-based interface. A back-end integration component maps the data objects, validation and workflow processes, and interface specifications to portions of a back-end database system.

Abstract: Example embodiments relate to a network-based workflow system, employed for receiving workflows, defining one or more data-object types based on the workflows, generating data-objects, assigning a workflow from among the one or more workflows to the data-object, and managing the data-object through various states of based on the workflow. As discussed, a “workflow” refers to orchestrated and repeatable patterns enabled by a systematic organization of resources into processes that transform and modify presentations of data-objects based on corresponding data-object states. A workflow may therefore comprise a set of states, wherein each state is linked to another state by one or more transitions, and wherein the transitions are associated with a set of events which may occur at each state.

Abstract: An example method of enforcing granular access policy for embedded artifacts comprises: detecting an association of an embedded artifact with a resource container; associating the embedded artifact with at least a subset of an access control policy associated with the resource container; and responsive to receiving an access request to access the embedded artifact, applying the access control policy associated with the resource container for determining whether the access request is grantable.

Abstract: Computer implemented systems and methods are disclosed for identifying and categorizing electronic documents through machine learning. In accordance with some embodiments, a seed set of categorized electronic documents may be used to train a document categorizer based on a machine learning algorithm. The trained document categorizer may categorize electronic documents in a large corpus of electronic documents. Performance metrics associated with performance of the trained document categorizer may be tracked, and additional seed sets of categorized electronic documents may be used to improve the performance of document categorizer by retraining the document categorizer on subsequent seed sets. Additional seed sets may and categorizations may be iterated through until a desired document categorization performance is reached.

Abstract: Polymorph "A" a novel polymorphic form of N-t-butyl-androst-3,5-diene-17.beta.-carboxamide-3-carboxylic acid. Novel processes for preparing polymorph "A" are also disclosed.