SYSTEM AND METHODS FOR INTEGRATING SPORTS DATA AND MACHINE LEARNING TECHNIQUES TO GENERATE RESPONSES TO USER QUERIES

1 . A method for generating multi-modal response to a query using a generative machine learning model, the method comprising:

receiving, from a client device, a query data object related to a sporting event;

providing the query data object and a first prompt to a machine learning system;

receiving, from the machine learning system, a function, from a set of functions, associated with the query data object;

receiving, from the machine learning system, an output format;

providing a data source mapped to the function, the query data object, and a second prompt to the machine learning system,

receiving, from the machine learning system, a response to the query data object, wherein the response is formatted based on the output format; and

outputting the response to one or more users.

2 . The method of claim 1 , wherein the query data object is a query related to a player, team, graphic, video, prediction, and/or odds of the sporting event.

3 . The method of claim 1 , wherein the first prompt includes:

the set of functions;

a description of each function of the set of functions; and

a machine readable request instructing the machine learning system to associate the query data object with a function from the set of functions based on the description of each function.

4 . The method of claim 1 , wherein the first prompt includes:

a set of output formats;

a description of each output format; and

a machine readable request instructions the machine learning system to associate the query data object with the output format from the set of output formats.

5 . The method of claim 4 , wherein the set of output formats include graphics, audio, images, videos, image overlays, or a textual response.

6 . The method of claim 1 , wherein the set of functions are each mapped to respective data sources and types of information.

7 . The method of claim 1 , wherein the set of functions include:

a current match state function;

a current player state function;

a historical team function;

a historical player function;

a graphic function;

a video function;

a generation function;

a prediction function;

an odds function;

a other sports function; or

a non-sports question.

8 . The method of claim 7 , wherein if the received function, from the set of functions, is the current match state function or the current player state function, then the second prompt includes:

a machine readable request instructing the machine learning system to answer the query data object based on the current match state function or the current player state function.

9 . The method of claim 7 , wherein if the received function from the set of functions, is the historical team function, the historical player function, or other sports function, method further includes:

accessing a database;

requesting historical information from the database;

obtaining the historical information in a structured query language (SQL) Query; and

updating the second prompt to include a machine readable request to adapt the SQL query to extract data that responds to the query data object and form a response to the query data object.

10 . The method of claim 7 , wherein if the received function from the set of functions, is a non-sports question, then the method further includes:

performing a search for the query data object through an internet browser;

saving results from the internet browser; and

updating the second prompt to include a machine readable request to respond to the query data object and form a textual response based on the results from the internet browser.

11 . The method of claim 7 , wherein if the received function from the set of functions, is the graphic function, then the method further includes:

a machine readable request instructing the machine learning system to provide an image related to the query data object based on the graphic function.

12 . The method of claim 7 , wherein if the received function from the set of functions, is the generation function, then the method further includes:

sending a machine readable request instructing a second machine learning system to provide a response to the query data object based on the generation function; and

receiving the response from the second machine learning system.

13 . The method of claim 1 , wherein the query data object related to a sporting event includes preferences for a language, topic, style, tone, or format, the method further comprising providing the preferences to the machine learning system.

14 . A system for generating textual answer to a query using a generative machine learning model, the system comprising:

a memory configured to store processor-readable instructions; and

a processor operatively connected to the memory, and configured to execute the instructions to perform operations comprising:

receiving, from a client device, a query data object related to a sporting event;

providing the query data object and a first prompt to a machine learning system;

receiving, from the machine learning system, a function, from a set of functions, associated with the query data object;

receiving, from the machine learning system, an output format;

providing a data source mapped to the function, the query data object, and a second prompt to the machine learning system,

receiving, from the machine learning system, a response to the query data object, wherein the response is formatted based on the output format; and

outputting the response to one or more users.

15 . The system of claim 14 , wherein the query data object is a query related to a player, team, graphic, video, prediction, and/or odds of the sporting event.

16 . The system of claim 14 , wherein the first prompt includes:

the set of functions;

a description of each function of the set of functions; and

a machine readable request instructing the machine learning system to associate the query data object with a function from the set of functions based on the description of each function.

17 . The system of claim 14 , wherein the first prompt includes:

a set of output formats;

a description of each output format; and

a machine readable request instructions the machine learning system to associate the query data object with the output format from the set of output formats.

18 . A non-transitory computer readable medium configured to store processor-readable instructions, wherein when executed by a processor, the instructions perform operations comprising:

receiving, from a client device, a query data object related to a sporting event;

providing the query data object and a first prompt to a machine learning system;

receiving, from the machine learning system, a function, from a set of functions, associated with the query data object;

receiving, from the machine learning system, an output format;

providing a data source mapped to the function, the query data object, and a second prompt to the machine learning system,

receiving, from the machine learning system, a response to the query data object, wherein the response is formatted based on the output format; and

outputting the response to one or more users.

19 . The non-transitory computer readable medium of claim 18 , wherein the query data object is a query related to a player, team, graphic, video, prediction, and/or odds of the sporting event.

20 . The non-transitory computer readable medium of claim 18 , wherein the first prompt includes:

the set of functions;

a description of each function of the set of functions; and

a machine readable request instructing the machine learning system to associate the query data object with a function from the set of functions based on the description of each function.