Systems and methods for communication system intent analysis
The present disclosure relates generally to systems and methods for analyzing intent. Intents may be analyzed to determine to which device or agent to route a communication. The analyzed intent information can also be used to formulate reports and analyze the accuracy of the identified intents with respect to the received communication.
1. A computer-implemented method comprising:
dynamically selecting a set of custom intent values for a list of intent categories;
associating the set of custom intent values with corresponding operative words;
parsing a communication to identify one or more operative words from the communication;
determining an intent value selected from the list of intent categories, wherein the intent value is associated with the one or more operative words in the communication, and wherein the intent value and the one or more operative words are associated with an action for a device associated with the communication;
generating an annotation for the intent value, wherein the annotation describes a qualitative strength of an association between the one or more operative words in the communication and the intent value;
calculating a metric for the intent value based the qualitative strength of the association, wherein the metric is calculated using a plurality of communications associated with the intent value and one or more human agent profiles, and wherein a human agent profile is associated with a human agent;
matching the communication with a terminal device associated with the human agent profile, wherein matching is based on the metric; and
facilitating an interaction with the human agent associated with the human agent profile.
2. The computer-implemented method of claim 1 , further comprising:
receiving user feedback and usage data associated with the interaction; and
updating a machine learning algorithm with the user feedback and usage data, wherein the machine learning algorithm is used to select the intent value using the one or more operative words.
3. The computer-implemented method of claim 1 , further comprising:
training an intent management engine using a set of annotation data, wherein the intent value is determined from the one or more operative words using the intent management engine.
4. The computer-implemented method of claim 1 , wherein the annotation for the intent value includes a confidence score for a strength of the association between the communication and the intent value.
5. The computer-implemented method of claim 1 , further comprising selecting a selected human agent profile of the one or more human agent profiles, wherein the selected human agent profile is selected using a machine learning algorithm trained to identify a correlation of the selected human agent profile to the intent value and the annotation.
6. The computer-implemented method of claim 1 , further comprising routing the communication between the device and the terminal device.
7. The computer-implemented method of claim 1 , further comprising:
receiving user feedback and usage data associated with a plurality of interactions including the interaction;
updating a machine learning algorithm using the user feedback and usage data, wherein the machine learning algorithm is used for matching the communication with the terminal device; and
facilitating a new interaction using the machine learning algorithm as updated using the user feedback and usage data.
8. A machine comprising:
a memory; and
one or more processors coupled to the memory and configured to perform operations comprising:
dynamically selecting a set of custom intent values for a list of intent categories;
associating the set of custom intent values with corresponding operative words;
parsing a communication to identify one or more operative words from the communication;
determining an intent value selected from the list of intent categories, wherein the intent value is associated with the one or more operative words in the communication, and wherein the intent value and the one or more operative words are associated with an action for a device associated with the communication;
generating an annotation for the intent value, wherein the annotation describes a qualitative strength of an association between the one or more operative words in the communication and the intent value;
calculating a metric for the intent value based the qualitative strength of the association, wherein the metric is calculated using a plurality of communications associated with the intent value and one or more human agent profiles, and wherein a human agent profile is associated with a human agent;
matching the communication with a terminal device associated with the human agent profile, wherein matching is based on the metric; and
facilitating an interaction with the human agent associated with the human agent profile.
9. The machine of claim 8 , wherein the one or more processors are further configured to perform operations comprising:
receiving user feedback and usage data associated with the interaction; and
updating a machine learning algorithm with the user feedback and usage data, wherein the machine learning algorithm is used to select the intent value using the one or more operative words.
10. The machine of claim 8 , wherein the one or more processors are further configured to perform operations comprising:
training an intent management engine using a set of annotation data, wherein the intent value is determined from the one or more operative words using the intent management engine.
11. The machine of claim 8 , wherein the annotation for the intent value includes a confidence score for a strength of the association between the communication and the intent value.
12. The machine of claim 8 , wherein the one or more processors are further configured to perform operations comprising:
selecting a selected human agent profile of the one or more human agent profiles, wherein the selected human agent profile is selected using a machine learning algorithm trained to identify a correlation of the selected human agent profile to the intent value and the annotation.
13. The machine of claim 8 , wherein the one or more processors are further configured to perform operations comprising:
routing the communication between the device and the terminal device.
14. The machine of claim 8 , wherein the one or more processors are further configured to perform operations comprising:
receiving user feedback and usage data associated with a plurality of interactions including the interaction;
updating a machine learning algorithm using the user feedback and usage data, wherein the machine learning algorithm is used for matching the communication with the terminal device; and
facilitating a new interaction using the machine learning algorithm as updated using the user feedback and usage data.
15. A non-transitory computer readable medium comprising instructions that, when executed by one or more processors, cause a server computer to perform method comprising:
dynamically selecting a set of custom intent values for a list of intent categories;
associating the set of custom intent values with corresponding operative words;
parsing a communication to identify one or more operative words from the communication;
determining an intent value selected from the list of intent categories, wherein the intent value is associated with the one or more operative words in the communication, and wherein the intent value and the one or more operative words are associated with an action for a device associated with the communication;
generating an annotation for the intent value, wherein the annotation describes a qualitative strength of an association between the one or more operative words in the communication and the intent value;
calculating a metric for the intent value based the qualitative strength of the association, wherein the metric is calculated using a plurality of communications associated with the intent value and one or more human agent profiles, and wherein a human agent profile is associated with a human agent;
matching the communication with a terminal device associated with the human agent profile, wherein matching is based on the metric; and
facilitating an interaction with the human agent associated with the human agent profile.
16. The non-transitory computer readable medium of claim 15 , wherein the instructions further cause the server computer to perform operations comprising:
receiving user feedback and usage data associated with the interaction; and
updating a machine learning algorithm with the user feedback and usage data, wherein the machine learning algorithm is used to select the intent value using the one or more operative words.
17. The non-transitory computer readable medium of claim 15 , wherein the instructions further cause the server computer to perform operations comprising:
training an intent management engine using a set of annotation data, wherein the intent value is determined from the one or more operative words using the intent management engine.
18. The non-transitory computer readable medium of claim 15 , wherein the annotation for the intent value includes a confidence score for a strength of the association between the communication and the intent value.
19. The non-transitory computer readable medium of claim 15 , wherein the instructions further cause the server computer to perform operations comprising:
selecting a selected human agent profile of the one or more human agent profiles, wherein the selected human agent profile is selected using a machine learning algorithm trained to identify a correlation of the selected human agent profile to the intent value and the annotation.
20. The non-transitory computer readable medium of claim 15 , wherein the instructions further cause the server computer to perform operations comprising:
routing the communication between the device and the terminal device.
21. The non-transitory computer readable medium of claim 15 , wherein the instructions further cause the server computer to perform operations comprising:
receiving user feedback and usage data associated with a plurality of interactions including the interaction;
updating a machine learning algorithm using the user feedback and usage data, wherein the machine learning algorithm is used for matching the communication with the terminal device; and
facilitating a new interaction using the machine learning algorithm as updated using the user feedback and usage data.