System and method for multi-task learning

Patent metadata

Field	Value
Publication number	US-2024160921-A1
Application number	US-202418405218-A
Country	US
Kind code	A1
Filing date	Jan 5, 2024
Priority date	Mar 1, 2019
Publication date	May 16, 2024
Grant date	—

Abstract

A method of generating a multi-modal prediction is disclosed herein. A computing system retrieves event data from a data store. The event data includes information for a plurality of events across a plurality of seasons. Computing system generates a predictive model using a mixture density network, by generating an input vector from the event data learning, by the mixture density network, a plurality of values associated with a next play following each play in the event data. The mixture density network is trained to output the plurality of values near simultaneously. Computing system receives a set of event data directed to an event in a match. The set of event data includes information directed to at least playing surface position and current score. Computing system generates, via the predictive model, a plurality of values associated with a next event following the event based on the set of event data.

First claim

1 - 20 . (canceled)

21 . A computer-implemented method for generating a multi-modal prediction, the computer-implemented method comprising:
receiving, by one or more processors, match data for a sporting match; extracting, by the one or more processors, one or more parameters associated with an event from the match data; generating, by the one or more processors, an input data set from the one or more extracted parameters; generating, by the one or more processors, a multi-modal prediction based on the input data set; and generating, by the one or more processors, one or more graphical representations of the multi-modal prediction.

22 . The computer-implemented method of claim 21 , wherein the one or more parameters include a playing surface position, a subsequent play-by-play event sequence, a plurality of players, at least one team, a team in possession of a ball, or a game context.

23 . The computer-implemented method of claim 22 , wherein generating, by the one or more processors, the multi-modal prediction based on the input data set comprises:
providing, by the one or more processors, the one or more extracted parameters to a mixture density network.

24 . The computer-implemented method of claim 21 , wherein generating, by the one or more processors, the multi-modal prediction based on the input data set includes generating predications for at least one of: one or more expected meters, an expected try tackle, an expected try set, a win probability, an expected play selection, or a final score line.

25 . The computer-implemented method of claim 21 , the computer-implemented method further comprising:
receiving, by the one or more processors, the match data from one or more tracking systems.

26 . The computer-implemented method of claim 21 , wherein generating the input data set includes:
transforming, by the one or more processors, the match data into one or more segmented data sets; selecting, by the one or more processors, a subset of one or more segmented data sets; and creating, by the one or more processors, a dense representation of the subset of one or more segmented data sets.

27 . The computer-implemented method of claim 26 , wherein generating the input data set further comprises:
providing, by the one or more processors, each of the one or more segmented data sets to one or more embedding layers; receiving, by the one or more processors, dense output from the one or more embedding layers; and generating, by the one or more processors, the input data set by concatenating the dense output, one or more continuous features, and spatial information.

28 . The computer-implemented method of claim 27 , wherein the one or more continuous features include a score difference, a remaining time, or a playing surface position.

29 . A non-transitory computer readable medium comprising one or more sequences of instructions, that, when executed by one or more processors, causes a computing system to perform operations comprising:
receiving, by the computing system, match data for a sporting match; extracting, by the computing system, one or more parameters associated with an event from the match data; generating, by the computing system, an input data set from the one or more extracted parameters; generating, by the computing system, a multi-modal prediction based on the input data set; and generating, by the computing system, one or more graphical representations of the multi-modal prediction.

30 . The non-transitory computer readable medium of claim 29 , wherein the one or more parameters include a playing surface position, a subsequent play-by-play event sequence, a plurality of players, at least one team, a team in possession of a ball, or a game context.

31 . The non-transitory computer readable medium of claim 30 , wherein generating, by the computing system, the multi-modal prediction based on the input data set comprises:
providing, by the one or more processors, the one or more extracted parameters to a mixture density network.

32 . The non-transitory computer readable medium of claim 29 , wherein generating, by the computing system, the multi-modal prediction based on the input data set includes generating predications for at least one of: one or more expected meters, an expected try tackle, an expected try set, a win probability, an expected play selection, or a final score line.

33 . The non-transitory computer readable medium of claim 29 , the operations further comprising:
receiving, by the computing system, the match data from one or more tracking systems.

34 . The non-transitory computer readable medium of claim 29 , wherein generating the input data set includes:
transforming, by the computing system, the match data into one or more segmented data sets; selecting, by the computing system, a subset of one or more segmented data sets; and creating, by the computing system, a dense representation of the subset of one or more segmented data sets.

35 . The non-transitory computer readable medium of claim 34 , wherein generating the input data set further comprises:
providing, by the computing system, each of the one or more segmented data sets to one or more embedding layers; receiving, by the computing system, dense output from the one or more embedding layers; and generating, by the computing system, the input data set by concatenating the dense output, one or more continuous features, and spatial information.

36 . A computer system comprising:
a processor; and a memory having programming instructions stored thereon, which, when executed by the processor, causes a computing system to perform operations comprising:
receiving match data for a sporting match;
extracting one or more parameters associated with an event from the match data;
generating an input data set from the one or more extracted parameters;
generating a multi-modal prediction based on the input data set; and
generating one or more graphical representations of the multi-modal prediction.

37 . The computer system of claim 36 , the operations further comprising:
receiving the match data from one or more tracking systems.

38 . The computer system of claim 36 , wherein generating the input data set includes:
transforming the match data into one or more segmented data sets; selecting a subset of one or more segmented data sets; and creating a dense representation of the subset of one or more segmented data sets.

39 . The computer system of claim 38 , wherein generating the input data set further comprises:
providing each of the one or more segmented data sets to one or more embedding layers; receiving dense output from the one or more embedding layers; and generating the input data set by concatenating the dense output, one or more continuous features, and spatial information.

40 . The computer system of claim 39 , wherein the one or more continuous features include a score difference, a remaining time, or a playing surface position.

Assignees

• Stats Llc

Inventors

• Holbrook Matthew
• Hobbs Jennifer
• Lucey Patrick Joseph

Classifications

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/08 Primary

  Learning methods · CPC title

• A63F13/812

  Ball games, e.g. soccer or baseball · CPC title