Dzee Solutions, Inc.

Apps Consultants Inc.

Ashwin K Pingali

Ramkumar Narayanswamy

Srikar Appana

Ravi Kiran Ponduri

Venkata Krishna Pradeep Ponduri

Implementations described herein relate to methods, systems, and computer-readable media to generate an alert or recommendation based on an activity log. In some implementations, a computer-implemented method includes receiving, at a processor, an activity log associated with one or more enterprise sub-processes for a first time period, determining, at the processor, change context data based on a comparison of the entity data and the associated interaction data with corresponding entity data and associated interaction data of a second time period, analyzing the change context data to determine atomic event data, providing the atomic event data associated with the enterprise process as input to a trained machine learning model, determining, using the trained machine learned model and the atomic event data, a predicted future state of the enterprise process, and generating and transmitting an alert or recommendation to one or more data sinks associated with the enterprise process.

Process discovery and optimization using time-series databases, graph-analytics, and machine learning

The disclosed technology includes a system for modeling progression of lifetime healthcare expenses, including healthcare events resulting in an out-of-pocket expenditure per a given healthcare plan, wherein each healthcare event is associated with a group of healthcare events. Each group of healthcare events is associated with different sets of certainty with different groups of individuals. A central computing device communicates with healthcare event data generation sources to obtain the healthcare event data. A static database module stores the healthcare event data in hierarchical layered graphs. A dynamic database module dynamically generates data that depicts different future expenditures over a life span based on the healthcare event data in the static database module. A computer modeling module generates the most likely set of future expenditures using the different future expenditures data in the dynamic database module.

Growing community of inventors

Parker, CO, United States of America

Ashwin K Pingali