Systems and methods for prioritizing emergency calls

1. A call priority analysis system comprising:

a processor and a computer readable medium operably coupled thereto, 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:

receiving a call interaction from a call center;

validating, by a validation and transcription engine, that the call interaction is authentic;

converting, by the validation and transcription engine, the call interaction into text;

extracting, by a data calculation engine, organization, location, and time information from the text;

calculating, by the data calculation engine, a priority of the call interaction from the extracted information and the text, wherein calculating the priority of the call interaction comprises determining an importance of words in the text and correlating the words in the text to a priority class by using a trained gradient-boosted tree algorithm;

determining that the call interaction should be transmitted to a queue of the call center for initial handling by a call center agent; and

transmitting the call interaction, the calculated priority, and the extracted information to the call center.

2. The call priority analysis system of claim 1 , wherein the operations further comprise saving the calculated priority and the extracted information in a database.

3. The call priority analysis system of claim 1 , wherein the operations further comprise transmitting the calculated priority and the extracted information to an emergency service.

4. The call priority analysis system of claim 1 , wherein validating the call interaction comprises analyzing an amount of noise on the call interaction, detecting whether there is laughter on the call interaction, evaluating words in the call interaction, or any combination thereof.

5. The call priority analysis system of claim 1 , wherein converting the call interaction into text comprises detecting a word, a phoneme, or both.

6. The call priority analysis system of claim 1 , wherein extracting the organization, location, and time information comprises applying a named entity recognition (NER) model to the text.

7. The call priority analysis system of claim 1 , wherein determining the importance of the words in the text comprises applying term frequency-inverse document frequency (TFIDF) to the text.

8. The call priority analysis system of claim 1 , wherein calculating the priority of the call interaction further comprises training a gradient-boosted tree algorithm to weigh the importance of the words in the text.

9. A method of determining the priority of a call interaction, which comprises:

receiving a call interaction from a call center;

validating, by a validation and transcription engine, that the call interaction is authentic;

converting, by the validation and transcription engine, the call interaction into text;

extracting, by a data calculation engine, organization, location, and time information from the text;

calculating, by the data calculation engine, a priority of the call interaction from the extracted information and the text, wherein calculating the priority of the call interaction comprises determining an importance of words in the text and correlating the words in the text to a priority class by using a trained gradient-boosted tree algorithm;

determining that the call interaction should be transmitted to a queue of the call center for initial handling by a call center agent; and

transmitting the call interaction, the calculated priority, and the extracted information to the call center.

10. The method of claim 9 , wherein validating the call interaction comprises analyzing an amount of noise on the call interaction, detecting whether there is laughter on the call interaction, evaluating words in the call interaction, or any combination thereof.

11. The method of claim 9 , wherein converting the call interaction into text comprises detecting a word, a phoneme, or both.

12. The method of claim 9 , wherein extracting the organization, location, and time information comprises applying a named entity recognition (NER) model to the text.

13. The method of claim 9 , wherein calculating the priority of the call interaction further comprises training a gradient-boosted tree algorithm to weigh the importance of the words in the text.

14. The method of claim 9 , wherein determining the importance of the words in the text comprises applying term frequency-inverse document frequency (TFIDF) to the text.

15. A non-transitory computer-readable medium having stored thereon computer-readable instructions executable by a processor to perform operations which comprise:

receiving a call interaction from a call center;

validating, by a validation and transcription engine, that the call interaction is authentic;

converting, by the validation and transcription engine, the call interaction into text;

extracting, by a data calculation engine, organization, location, and time information from the text;

calculating, by the data calculation engine, a priority of the call interaction from the extracted information and the text, wherein calculating the priority of the call interaction comprises determining an importance of words in the text and correlating the words in the text to a priority class by using a trained gradient-boosted tree algorithm;

determining that the call interaction should be transmitted to a queue of the call center for initial handling by a call center agent; and

transmitting the call interaction, the calculated priority, and the extracted information to the call center.

16. The non-transitory computer-readable medium of claim 15 , wherein validating the call interaction comprises analyzing an amount of noise on the call interaction, detecting whether there is laughter on the call interaction, evaluating words in the call interaction, or any combination thereof.

17. The non-transitory computer-readable medium of claim 15 , wherein converting the call interaction into text comprises detecting a word, a phoneme, or both.

18. The non-transitory computer-readable medium of claim 15 , wherein extracting the organization, location, and time information comprises applying a named entity recognition (NER) model to the text.

19. The non-transitory computer-readable medium of claim 15 , wherein calculating the priority of the call interaction further comprises applying weights learned and saved from a gradient-boosted tree algorithm to the words of the text.