US 11,909,782 B2 · Systems and methods for using machine learning techniques for network-implemented spam call detection

Abstract

A system described herein may provide a technique for Embodiments described herein provide for the use of machine learning, artificial intelligence, and/or other techniques for network-implemented spam call detection. Calls may be screened prior to notifying a called User Equipment (“UE”) that a call has been placed to the called UE. A Machine Learning Spam Detection Component (“MLSDC”) may screen a call, such as a voice call, by initiating a call session between the MLSDC and a calling UE, from which the call was requested. Via the established call session, the MLSDC may receive communications, such as voice communications, from the UE, and may determine a measure of likelihood that the call request is associated with spam by using machine learning or other techniques to compare the received communications against one or more models that indicate attributes of calls that have been identified as spam.

Claims20

Dependent claims

A device, comprising:

one or more processors configured to:
receive a first request to establish a first call between a first User Equipment (“UE”) and a second UE;
notify the second UE of the first request to establish the first call;
receive, from the second UE, a second request to indicate whether the first request to establish the first call is associated with a particular classification;
determine, based on receiving the second request, whether the first request is associated with the particular classification;
and
output, to the second UE, an indication of whether the first request is associated with the particular classification,
wherein when the indication indicates that the first request is associated with the particular classification, the second UE forgoes outputting a notification, to a user of the second UE, of the first request;
and
wherein when the indication indicates that the first request is not associated with the particular classification, the second UE outputs the notification, to the user of the second UE, of the first request.

7 more elements

A non-transitory computer-readable medium, storing a plurality of processor-executable instructions to:

receive a first request to establish a first call between a first User Equipment (“UE”) and a second UE;
notify the second UE of the first request to establish the first call;
receive, from the second UE, a second request to indicate whether the first request to establish the first call is associated with a particular classification;
determine, based on receiving the second request, whether the first request is associated with the particular classification;
and
output, to the second UE, an indication of whether the first request is associated with the particular classification,
wherein when the indication indicates that the first request is associated with the particular classification, the second UE forgoes outputting a notification, to a user of the second UE, of the first request;
and
wherein when the indication indicates that the first request is not associated with the particular classification, the second UE outputs the notification, to the user of the second UE, of the first request.

6 more elements

Dependencies are inferred from the claim text.

Systems and methods for using machine learning techniques for network-implemented spam call detection

Life of this patent

Systems and methods for using machine learning techniques for network-implemented spam call detection

Life of this patent

Abstract

Claims20