MITIGATING DISTURBANCES AT A CALL CENTER
An example operation may include one or more of receiving, via a telephone network, a Voice over IP (VOIP) call from a network device, detecting a disturbance value from a tag that has been added to a VOIP message of the VOIP call, determining an answer priority of the VOIP call based on the disturbance value, and processing the VOIP call based on the determined answer priority.
1 - 20 . (canceled)
21 . An apparatus, comprising:
a network interface that receives a call via a telephone network; and
a processor configured
to determine a score that indicates a likelihood that the call is a disturbance, and
to add a tag to the call, at least in part based on the score, to produce an enhanced call, wherein
the network interface transmits the enhanced call.
22 . The apparatus of claim 21 , wherein the processor is further configured to determine the score, at least in part based on machine learning.
23 . The apparatus of claim 22 , wherein the machine learning is at least in part based on a call history of a phone number of the call, a geographical area of the call, a time of day of the call, or a frequency of calls.
24 . The apparatus of claim 22 , wherein the machine learning clusters a plurality of calls to identify a pattern to predict whether the call is a disturbance.
25 . The apparatus of claim 21 , wherein the tag is added to a header of the enhanced call.
26 . The apparatus of claim 25 , wherein the enhanced call adheres to a Session Initiation Protocol (SIP).
27 . The apparatus of claim 26 , wherein the call is a public switch telephone (PSTN) call, and the apparatus converts the PSTN call to the enhanced call.
28 . A method, comprising:
receiving a call via a telephone network;
determining a score that indicates a likelihood that the call is a disturbance;
adding a tag to the call, at least in part based on the score, to produce an enhanced call; and
transmitting the enhanced call.
29 . The method of claim 28 , wherein the score is determined, at least in part based on machine learning.
30 . The method of claim 29 , wherein the machine learning is at least in part based on a call history of a phone number of the call, a geographical area of the call, a time of day of the call, or a frequency of calls.
31 . The method of claim 29 , wherein the machine learning clusters a plurality of calls to identify a pattern to predict whether the call is a disturbance.
32 . The method of claim 28 , wherein the tag is added to a header of the enhanced call.
33 . The method of claim 32 , wherein the enhanced call adheres to a Session Initiation Protocol (SIP).
34 . The method of claim 33 , further comprising:
converting the call to the enhanced call, wherein the call is a public switch telephone (PSTN) call.
35 . A computer-readable medium encoded with instructions that, when executed by a processor of a computer, cause the computer to perform a method comprising:
receiving a call via a telephone network;
determining a score that indicates a likelihood that the call is a disturbance;
adding a tag to the call, at least in part based on the score, to produce an enhanced call; and
transmitting the enhanced call.
36 . The medium of claim 35 , wherein the score is determined, at least in part based on machine learning.
37 . The medium of claim 36 , wherein the machine learning is at least in part based on a call history of a phone number of the call, a geographical area of the call, a time of day of the call, or a frequency of calls.
38 . The medium of claim 36 , wherein the machine learning clusters a plurality of calls to identify a pattern to predict whether the call is a disturbance.
39 . The medium of claim 35 wherein the tag is added to a header of the enhanced call.
40 . The medium of claim 33 , the method further comprising:
converting the call to the enhanced call, wherein the enhanced call adheres to a Session Initiation Protocol (SIP), and the call is a public switch telephone (PSTN) call.