Method and system for self-correcting match states

1. A computer-implemented method for predicting a match state event by a machine learning platform on a server, the method comprising:

receiving at the server, by the machine learning platform, a live data feed from an application program interface (API);

analyzing at the server, by the machine learning platform, multimedia content on the live data feed;

receiving at the server, by the machine learning platform, current match state data corresponding to the multimedia content, wherein the current match state data is based on a current state of a live match;

predicting at the server, by the machine learning platform, an anomaly in the multimedia content based on the analysis of received multimedia content and an analysis of the current match state data;

resolving at the server, by the machine learning platform, the predicted anomaly by (i) encoding a portion of the multimedia content containing the predicted anomaly, (ii) storing the serialized anomaly and a corresponding serialized function that specifies how to resolve the anomaly, and (iii) executing the serialized function based on the serialized anomaly, wherein executing the serialized function causes extracting a portion of the multimedia content containing the anomaly, ignoring the anomaly, or correcting the anomaly;

predicting at the server, by the machine learning platform, a future match state based on the current match state data and the multimedia content, including the resolved anomaly; and

sending, by a transmitter, the future match state to a communicating device.

2. The computer-implemented method in claim 1 , further comprising:

forwarding the extracted portion of the multimedia content to an intelligent technology platform to analyze and predict a future match state event based, at least in part, on the multimedia content.

3. The computer-implemented method in claim 1 , further comprising:

receiving, by the machine learning platform, historical event data comprising past live data feed updates.

4. The computer-implemented method in claim 1 , further comprising:

receiving, by the machine learning platform, historical event data comprising past match states.

5. The computer-implemented method in claim 1 , further comprising:

receiving, by the machine learning platform, historical event data comprising past live data feed updates and past match states.

6. The computer-implemented method in claim 1 , wherein the predicting, by the machine learning platform, the anomaly in the multimedia content includes analyzing past live data feed updates and past match states.

7. The computer-implemented method in claim 1 , further comprising:

determining processor computing time or memory capacity required to correct the anomaly.

8. The computer-implemented method in claim 1 , wherein resolving the anomaly comprises:

predicting, by the machine learning platform, correctability of the anomaly.

9. The computer-implemented method in claim 8 , wherein correctability of the anomaly comprises waiting for an update of the multimedia content that resolves the anomaly.

10. A system for predicting a match state event by a machine learning platform on a server, the system comprising:

a communication unit that includes a transmitter and a receiver, the receiver being arranged to receive a live data feed;

a match state collector that includes a memory arranged to store match state data;

an anomaly predictor having a machine learning system arranged to

monitor multimedia content on the live data feed,

receive current match state data that is based on a current state of a live match and that corresponds to the multimedia content, and

predict, by a machine learning platform, accuracy of the multimedia content based on the multimedia content and corresponding match state data, utilizing one or more specialized neural network models trained to interpret the multimedia content; and

an error corrector having a computing device arranged to resolve an anomaly in the multimedia content based on the predicted accuracy of the multimedia content, wherein resolving the anomaly includes (i) encoding a portion of the multimedia content containing the predicted anomaly, (ii) storing the serialized anomaly and a corresponding serialized function that specifies how to resolve the anomaly, and (iii) executing the serialized function based on the serialized anomaly, wherein executing the serialized function causes extracting a portion of the multimedia content containing the anomaly, ignoring the anomaly, or correcting the anomaly; and

a match state predictor having a machine learning event (MLE) model arranged to predict a future match state based on the current match state data and the multimedia content, including the resolved anomaly,

wherein the transmitter is configured to send the future match state to a communicating device.

11. The system in claim 10 , wherein the match state predictor is arranged to receive the multimedia content and predict a future event based, at least in part, on the multimedia content.

12. The system in claim 10 , wherein the anomaly predictor is further arranged to receive historical event data comprising past live data feed updates.

13. The system in claim 10 , wherein the anomaly predictor is further arranged to receive historical event data comprising past match states.

14. The system in claim 10 , wherein the anomaly predictor is further arranged to receive historical event data comprising past live data feed updates and past match states.

15. The system in claim 10 , wherein the machine learning system is trained to:

receive historical event data comprising past live data feed updates and past match states; and

predict the anomaly in the multimedia content based, at least in part, on the past live data feed updates and the past match states.

16. The system in claim 10 , wherein the machine learning system is trained to:

determine processor computing time or memory capacity required to correct the anomaly.

17. The system in claim 10 , wherein the machine learning system is trained to:

predict correctability of the anomaly.

18. The system in claim 17 , wherein correctability of the anomaly comprises waiting for an update of the multimedia content that resolves the anomaly.

19. A non-transitory computer-readable storage medium containing computer executable instructions that, when executed by a computing device coupled to a communication unit having a transmitter and a receiver and containing a machine learning system, cause the computing device to perform operations comprising:

receiving, by the receiver, a live data feed;

monitoring, by the machine learning system, multimedia content on the received live data feed;

receiving, from a memory coupled to the computing device, current match state data corresponding to the multimedia content, the current match state data being based on a current state of a live match;

predicting, by the machine learning system, an anomaly in the multimedia content based on an analysis of received multimedia content and the current match state data, wherein the analysis utilizes one or more specialized neural network model trained to interpret the multimedia content;

resolving, by the machine learning system, the predicted anomaly by (i) encoding a portion of the multimedia content containing the predicted anomaly, (ii) storing the serialized anomaly and a corresponding serialized function that specifies how to resolve the anomaly, and (iii) executing the serialized function based on the serialized anomaly, wherein executing the serialized function causes: extracting a portion of the multimedia content containing the anomaly, ignoring the anomaly, or correcting the anomaly;

predicting, by the machine learning system, a future match state based on the current match state data and the multimedia content, including the resolved anomaly; and

sending, by the transmitter, the future match state to a communicating device.

20. The non-transitory computer-readable storage medium in claim 19 , wherein the machine learning system is trained to determine processor computing time or memory capacity required to correct the anomaly.