公开(公告)号：US20230065889A1

公开(公告)日：2023-03-02

申请号：US17462602

申请日：2021-08-31

Applicant: Verizon Patent and Licensing Inc.

Inventor： Subham Biswas ,  Bharatwaaj Shankar ,  Sudhakar X. Lanka ,  Eswar P. Somarouthu ,  Keerthi Gudur

IPC: H04W24/08 ,  H04W24/10 ,  H04W28/02 ,  H04W72/04 ,  H04W64/00 ,  H04W4/24 ,  H04W12/06 ,  G06N20/00

Abstract: One or more computing devices, systems, and/or methods for identifying anomalous behavior of users are provided. In an example, users of a telecommunication service provider may be segmented into a plurality of user segments based upon telecommunication service metrics associated with the users. A machine learning model may be trained using telecommunication service information associated with users of the first user segment to generate a trained machine learning model. Using the trained machine learning model, a forecast of telecommunication service usage associated with a first user segment of the plurality of user segments. A telecommunication service usage metric, associated with a user belonging to the first user segment, may be compared with a range indicated by the forecast. The user may be flagged as having anomalous behavior based upon a determination that one or more telecommunication usage metrics, associated with the user, are outside one or more ranges indicated by the forecast.

公开(公告)号：US20230342696A1

公开(公告)日：2023-10-26

申请号：US17725736

申请日：2022-04-21

Applicant: Verizon Patent and Licensing Inc.

Inventor： Bharatwaaj Shankar ,  Eswar P. Somarouthu ,  Pothireddy Munemma ,  Vinoth Kuppathamottur Ghanappan

IPC: G06Q10/06 ,  H04L41/0894

CPC classification number: G06Q10/06375 ,  G06Q10/06315 ,  H04L41/0894

Abstract: A framework uses artificial intelligence to provide suggestions for minimizing stagnant resources in contracts across multiple business relationships. A network device receives multiple smart contracts governing relationships between a telecommunications carrier and partner entities; extracts delivery timelines from the multiple smart contracts; generates an embedding layer based on historical vendor data, in-house procedural data; predicts one or more windows of stagnant resources in the delivery timelines; and generates a policy suggestion to optimize stagnant resources during the one or more windows.