Abstract: The present disclosure relates to systems and methods of autonomously or semi-autonomously generating a graph based upon ingested data, triggered by a user or a machine driven request, including for organizations that do not possess a fully ingested/inflated graph. Nodes of a graph may comprise expressions tied to specific data and/or data fields of the organization. Dimensional hierarchies may also be mapped to the specific data and/or data fields. Also disclosed are systems and methods for extrapolating and inflating ingested data and providing analysis to a user. The present disclosure also: (1) provides autonomous (or semi-autonomous) ingestion of data and inflation of a graph across potentially billions of nodes attributable to a particular organization; (2) retrieving insights and analysis on demand at various levels of detail; and (3) permits system resources to identify and analyze patterns, trends and anomalies in the graph for making adjustments and enhancements.

Abstract: The present disclosure relates to systems for providing information in an automated or semi-automated manner, through use of one or more autonomous virtual analysts. In one embodiment, the autonomous virtual analysts may perform many of the same functions as a human analyst, and provide business intelligence relating to revenue, income, profit, loss, expenses, historical data, projections, trends, comparative analysis, etc. In embodiments, the autonomous virtual analyst may be employed through use of natural language dialog with a user, and further configured to capture and appropriately respond to the context of the dialog by supplying a user with information pertinent to the request. According to varying embodiments, a variety of decision trees comprising a plurality of nodes is disclosed. In varying embodiments, the system may comprise one or more distinct modules, including a driver graph module.