AUTOMATIC AVAILABILITY PREDICTION FOR A SUBJECT MATTER EXPERT
A system automatically identifies wait times for a subject matter expert (SME). The system includes a cloud server in communication with an agent computer, an SME computer, and a database for storing presence data associated with the SME computer. Over a first period of time, the processor receives the presence data associated with the SME computer; stores the presence data in the database; and trains a custom machine learning network. The processor receives an input from the agent computer requesting contact with the SME computer, and solicits a status from the SME computer. If the status is not “Available”, the processor, using the trained custom machine learning network, predicts a wait time after which the status will be “Available” and reports the predicted wait time to the agent computer. If the status is “Available”, the system establishes a communication link between the agent computer and the SME computer.
1 . A system adapted to automatically identify wait times for a subject matter expert, the system comprising:
a cloud server having at least one processor and a non-transitory computer readable medium operably coupled thereto, the cloud server being in electronic communication with an agent computing device and a subject matter expert (SME) computing device, the processor comprising a presence aggregator module and a presence prediction system, the server being in electronic communication with a database for storing presence data associated with the SME computing device, the computer readable medium comprising a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, to perform operations which comprise:
over a first period of time, with the presence aggregator module:
receiving the presence data associated with the SME computing device;
storing the presence data in the database; and
with the stored presence data, training a custom machine learning network;
receiving an input from the agent computing device requesting contact with the SME computing device;
soliciting a status from the SME computing device;
if the status is not “Available”, then with the presence prediction system:
using the trained custom machine learning network, predicting a wait time after which the status will be “Available” and reporting the predicted wait time to the agent computing device; or
if the status is “Available”, then establishing a communication link between the agent computing device and the SME computing device and transmitting a query to the SME computing device.
2 . The system of claim 1 , wherein the custom machine learning network is a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN).
3 . The system of claim 1 , wherein the operations further comprise, with the agent computing device, displaying the predicted time to an agent.
4 . The system of claim 1 , wherein if the status is not “Available”, then the status is one of “Busy”, “In Call”, “Unavailable”, “Away”, “Do Not Disturb”, or “Offline”.
5 . The system of claim 1 , wherein the operations further comprise:
soliciting a calendar associated with the SME computing device; and
based on the calendar, refining the predicted time.
6 . The system of claim 5 , wherein the calendar contains, for each time in the calendar, a calendar status of “Available”, “Busy”, “Meeting”, or “Out Of Office”.
7 . The system of claim 1 , wherein the presence data comprises statuses of “Available”, “Busy”, “In Call”, “Unavailable”, “Away”, “Do Not Disturb”, or “Offline”, and one or more times associated therewith.
8 . The system of claim 1 , further comprising a communication link between the agent computing device and a patron computing device.
9 . A computer-implemented method for automatically identifying wait times for a subject matter expert, the method which comprises:
with a cloud server having at least one processor and a non-transitory computer readable medium operably coupled thereto, the cloud server being in electronic communication with an agent computing device and a subject matter expert (SME) computing device, the processor comprising a presence aggregator module and a presence prediction system, the server being in electronic communication with a database for storing presence data associated with the SME computing device:
over a first period of time, with the presence aggregator module:
receiving the presence data associated with the SME computing device;
storing the presence data in the database; and
with the stored presence data, training a custom machine learning network;
receiving an input from the agent computing device requesting contact with the SME computing device;
soliciting a status from the SME computing device;
if the status is not “Available”, then with the presence prediction system:
using the trained custom machine learning network, predicting a wait time after which the status will be “Available” and reporting the predicted wait time to the agent computing device; or
if the status is “Available”, then establishing a communication link between the agent computing device and the SME computing device and transmitting a query to the SME computing device.
10 . The method of claim 9 , wherein the custom machine learning network is a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN).
11 . The method of claim 9 , further comprising, with the agent computing device, displaying the predicted time to an agent.
12 . The method of claim 9 , wherein if the status is not “Available”, then the status is one of “Busy”, “In Call”, “Unavailable”, “Away”, “Do Not Disturb”, or “Offline”.
13 . The method of claim 9 , further comprising:
soliciting a calendar associated with the SME computing device; and
based on the calendar, refining the predicted time.
14 . The method of claim 13 , wherein the calendar contains, for each time in the calendar, a calendar status of “Available”, “Busy”, “Meeting”, or “Out Of Office”.
15 . The method of claim 9 , wherein the presence data comprises statuses of “Available”, “Busy”, “In Call”, “Unavailable”, “Away”, “Do Not Disturb”, or “Offline”, and one or more times associated therewith.
16 . The method of claim 9 , further comprising establishing a communication link between the agent computing device and a patron computing device and transmitting a second query to the SME computing device.
17 . A computer-implemented method, comprising:
over a first period of time:
receiving presence data associated with a subject matter expert (SME) via an SME computing device;
storing the presence data in a database; and
with the stored presence data, training a custom machine learning network;
receiving an input from an agent, via the agent computing device, requesting contact with the SME computing device;
soliciting a status from the SME computing device;
if the status is not “Available”, then:
using the trained custom machine learning network, predicting a wait time after which the status will be “Available” and reporting the predicted wait time to the agent via the agent computing device; or
if the status is “Available”, then establishing a communication link between the agent computing device and the SME computing device and transmitting a query to the SME via the SME computing device.
18 . The method of claim 17 , wherein if the status is not “Available”, then the status is one of “Busy”, “In Call”, “Unavailable”, “Away”, “Do Not Disturb”, or “Offline”.
19 . The method of claim 17 , further comprising:
soliciting a calendar associated with the SME computing device; and
based on the calendar, refining the predicted time.
20 . The method of claim 19 , wherein the calendar contains, for each time in the calendar, a calendar status of “Available”, “Busy”, “Meeting”, or “Out Of Office”.