METHODS AND SYSTEMS FOR GENERATING AUTOMATED SPORTS SUMMARIES
Disclosed techniques relate to using machine learning for sports applications. In an example, a method for generating textual summaries using one or more generative machine learning models is disclosed. The method can include receiving, from a client device, a request for a summary of a sporting event. The method can include accessing, from a database, one or more database records including sports related data that is associated with the sporting event. The method can include formulating, from the database records, a machine learning model prompt. The method can include providing the machine learning model prompt to the one or more generative machine learning models. The method can include receiving, from the one or more generative machine learning models, a textual summary of the sporting event. The method can include outputting the textual summary to the client device.
1 . A method for generating textual summaries using one or more generative machine learning models, the method comprising:
receiving, from a client device, a request for a summary of a sporting event;
accessing, from a database, one or more database records comprising sports related data that is associated with the sporting event;
formulating, from the database records, a machine learning model prompt, wherein the machine learning model prompt comprises (i) instructions readable by the one or more generative machine learning models and (ii) sports related data from the one or more database records;
providing the machine learning model prompt to the one or more generative machine learning models;
receiving, from the one or more generative machine learning models, a textual summary of the sporting event; and
outputting the textual summary to the client device.
2 . The method of claim 1 , wherein the sports related data comprises tracking data generated based on a broadcast feed of the sporting event.
3 . The method of claim 2 , wherein the tracking data comprises mathematical representations of one or more of positional information, object information, body pose information, or trend information.
4 . The method of claim 1 , wherein the instructions comprise preferences for a sentiment of the summary and wherein the textual summary aligns with the sentiment received from the client device.
5 . The method of claim 1 , wherein the database records comprise ratings of players on a team associated with the sporting event, standing of the team relative to an associated league, and odds of a predicted performance of the team.
6 . The method of claim 1 , wherein the instructions comprise preferences for one or more of a task, topic, style, tone, or format of the summary, the method further comprising, adding the preferences to the prompt prior to providing the prompt to the one or more generative machine learning models.
7 . The method of claim 1 , wherein the summary is one or more of a preview of a future sporting event, a recap of a past sporting event, and a live report of an ongoing sporting event.
8 . The method of claim 1 , further comprising:
identifying, in the database, one or more preferences associated with a user of the client device; and
filtering the one or more database records according to the preferences.
9 . The method of claim 1 , further comprising:
receiving, from the database, one or more updated database records comprising live data from the sporting event;
formulating, from the updated database records, an updated machine learning model prompt;
applying the one or more machine learning models to the updated machine learning model prompt;
receiving, from the one or more machine learning models, an updated textual summary of the sporting event; and
outputting the updated textual summary to the client device.
10 . A system for generating textual summaries using one or more generative machine learning models, the system comprising:
a non-transitory computer readable medium configured to store processor-readable instructions; and
a processor operatively connected to the non-transitory computer readable medium, and configured to execute the instructions to perform operations comprising:
receiving, from a client device, a request for a summary of a sporting event;
accessing, from a database, one or more database records comprising sports related data that is associated with the sporting event;
formulating, from the database records, a machine learning model prompt, wherein the machine learning model prompt comprises (i) instructions readable by the one or more generative machine learning models and (ii) sports related data from the one or more database records;
providing the machine learning model prompt to the one or more generative machine learning models;
receiving, from the one or more generative machine learning models, a textual summary of the sporting event; and
outputting the textual summary to the client device.
11 . The system of claim 10 , wherein the sports related data comprises tracking data generated based on a broadcast feed of the sporting event.
12 . The system of claim 11 , wherein the tracking data comprises mathematical representations of one or more of positional information, object information, body pose information, or trend information.
13 . The system of claim 10 , wherein the instructions comprise preferences for a sentiment of the summary and wherein the textual summary aligns with the sentiment received from the client device.
14 . The system of claim 10 , wherein the database records comprise ratings of players on a team associated with the sporting event, standing of the team relative to an associated league, and odds of a predicted performance of the team.
15 . The system of claim 10 , wherein the instructions comprise preferences for one or more of a task, topic, style, tone, or format of the summary, the operations further comprising, adding the preferences to the prompt prior to providing the prompt to the one or more generative machine learning models.
16 . The system of claim 10 , wherein the summary is one or more of a preview of a future sporting event, a recap of a past sporting event, and a live report of an ongoing sporting event.
17 . The system of claim 10 , the operations further comprising:
identifying, in the database, one or more preferences associated with a user of the client device; and
filtering the one or more database records according to the preferences.
18 . The system of claim 10 , the operations further comprising:
receiving, from the database, one or more updated database records comprising live data from the sporting event;
formulating, from the updated database records, an updated machine learning model prompt;
applying the one or more machine learning models to the updated machine learning model prompt;
receiving, from the one or more machine learning models, an updated textual summary of the sporting event; and
outputting the updated textual summary to the client device.
19 . 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 request for a summary of a sporting event;
accessing, from a database, one or more database records comprising sports related data that is associated with the sporting event;
formulating, from the database records, a machine learning model prompt, wherein the machine learning model prompt comprises (i) instructions readable by one or more generative machine learning models and (ii) sports related data from the one or more database records;
providing the machine learning model prompt to the one or more generative machine learning models;
receiving, from the one or more generative machine learning models, a textual summary of the sporting event; and
outputting the textual summary to the client device.
20 . The non-transitory computer readable medium of claim 19 , wherein the sports related data comprises tracking data generated based on a broadcast feed of the sporting event.