Systems and methods for voice rendering of machine-generated electronic messages
Disclosed are systems and methods for generating voice renderings of machine-generated electronic messages. The disclosed systems and methods provide a novel framework for organizing often fragmented machine-generated electronic messages and providing mechanisms for a virtual assistant to produce voice-renderings data extracted from electronic messages. The disclosed system may implement steps for receiving user queries via virtual assistants, extracting data from machine-generated electronic messages, converting the extracted data to purposeful organizational schemas, and generating human perceivable voice renderings based on the user queries and extracted data.
1. A computer implemented method comprising:
receiving electronic messages at a server in relation to a user;
determining an organizational schema of each of the plurality of electronic messages as it is received at the server;
extracting data from each of the electronic messages according to a predefined template corresponding to the organizational schema determined for each electronic message, the extracted data limited to sub-entity datatypes of the predefined template;
generating, by machine learning, a structured entity type based on determining a need for new entity types corresponding to new subject matter from the electronic messages, wherein the structured entity type is a template comprising the sub-entity data types;
identifying and storing, in relation to each of the electronic messages, one or more sub-entities in the structured entity according to the predefined template;
receiving a query from the user for information contained within one or more of the electronic messages of the user;
generating a reformulated query based on the query from the user, the reformulated query including semantic equivalent phrasing;
identifying an organizational schema based on the reformatted query;
identifying one of the electronic messages received at the server in relation to the user corresponding to the identified organizational schema;
looking up identified and stored sub-entities stored in relation to the identified electronic message;
generating a text-snippet of text data in the one or more sub-entities stored in relation to the identified electronic message; and
producing and playing a machine-voice rendering of the generated text-snippet.
2. The computer implemented method of claim 1 further comprising:
wherein the electronic message is: email, text message, or transcribed video/voice calls.
3. The computer implemented method of claim 1 further comprising:
wherein the query is received via user communication with a virtual assistant.
4. The computer implemented method of claim 1 further comprising:
conducting natural language processing techniques on the received electronic message.
5. The computer implemented method of claim 1 further comprising:
executing text-to-speech techniques in order to produce an human-perceivable audio output of the text snippet.
6. The computer implemented method of claim 1 further comprising:
retrieving sub-entity data in order to generate a text snippet via a structure module.
7. The computer implemented method of claim 1 further comprising:
analyzing the query by executing natural language processing techniques.
8. A system implemented method comprising:
a memory storage device storing instructions and one or more processors configured to execute instructions for:
receiving electronic messages at a server in relation to a user;
determining an organizational schema of each of the plurality of electronic messages as it is received at the server;
extracting data from each of the electronic messages according to a predefined template corresponding to the organizational schema determined for each electronic message, the extracted data limited to sub-entity datatypes of the predefined template;
generating, by machine learning, a structured entity type based on determining a need for new entity types corresponding to new subject matter from the electronic messages, wherein the structured entity type is a template comprising the sub-entity data types;
identifying and storing, in relation to each of the electronic messages, one or more sub-entities in the structured entity according to the template;
receiving a query from the user for information contained within one or more of the electronic messages of the user;
generating a reformulated query based on the query from the user, the reformulated query including semantic equivalent phrasing;
identifying an organizational schema based on the reformatted query;
identifying one of the electronic messages received at the server in relation to the user corresponding to the identified organizational schema;
looking up identified and stored sub-entities stored in relation to the identified electronic message;
generating a text-snippet of text data in the one or more sub-entities stored in relation to the identified electronic message; and
producing and playing a machine-voice rendering of the generated text-snippet.
9. The system of claim 8 further comprising:
wherein the electronic message is: email, text message, or transcribed video/voice calls.
10. The system of claim 8 further comprising:
wherein the query is received via user communication with a virtual assistant.
11. The system of claim 8 further comprising:
conducting natural language processing techniques on the received electronic message.
12. The system of claim 8 further comprising:
executing text-to-speech techniques in order to produce an human-perceivable audio output of the text snippet.
13. The system of claim 8 further comprising:
retrieving sub-entity data in order to generate a text snippet via a structure module.
14. The system of claim 8 further comprising:
analyzing the query by executing natural language processing techniques.
15. A non-transitory computer readable medium comprising:
a memory storage device storing and one or more processors configured to execute instructions for:
receiving electronic messages at a server in relation to a user;
determining an organizational schema of each of the plurality of electronic messages as it is received at the server;
extracting data from each of the electronic messages according to a predefined template corresponding to the organizational schema determined for each electronic message, the extracted data limited to sub-entity datatypes of the predefined template;
generating, by machine learning, a structured entity type based on determining a need for new entity types corresponding to new subject matter from the electronic messages, wherein the structured entity type is a template comprising the sub-entity data types;
generating, by machine learning, a structured entity type based on determining new subject matter from the electronic messages;
identifying and storing, in relation to each of the electronic messages, one or more sub-entities in the structured entity according to the template;
receiving a query from the user for information contained within one or more of the electronic messages of the user;
generating a reformulated query based on the query from the user, the reformulated query including semantic equivalent phrasing;
identifying an organizational schema based on the reformatted query;
identifying one of the electronic messages received at the server in relation to the user corresponding to the identified organizational schema;
looking up identified and stored sub-entities stored in relation to the identified electronic message;
generating a text-snippet of text data in the one or more sub-entities stored in relation to the identified electronic message; and
producing and playing a machine-voice rendering of the generated text-snippet.
16. The non-transitory computer readable medium of claim 15 further comprising:
wherein the electronic message is: email, text message, or transcribed video/voice calls.
17. The non-transitory computer readable medium of claim 15 further comprising:
wherein the query is received via user communication with a virtual assistant.
18. The non-transitory computer readable medium of claim 15 further comprising:
conducting natural language processing techniques on the received electronic message.
19. The non-transitory computer readable medium of claim 15 further comprising:
executing text-to-speech techniques in order to produce an human-perceivable audio output of the text snippet.
20. The non-transitory computer readable medium of claim 15 further comprising:
retrieving sub-entity data in order to generate a text snippet via a structure module.