System and method for multi-task learning

Patent metadata

Field	Value
Publication number	US-2025139435-A1
Application number	US-202519007905-A
Country	US
Kind code	A1
Filing date	Jan 2, 2025
Priority date	Mar 1, 2019
Publication date	May 1, 2025
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

What is claimed:

1 . A method for generating a trained prediction model, the method comprising:
receiving, by one or more processors, event data for a plurality of events from a data store, wherein the event data includes spatial event data and non-spatial event data; transforming, by the one or more processors, the event data into one or more segmented data sets; creating, by the one or more processors, an embedding vector of sparse data included in the one or more segmented data sets; generating, by the one or more processors, one or more input data sets based on the embedding vector; and training, by the one or more processors, a mixture density network to generate a multi-modal predication based on the one or more input data sets.

2 . The method of claim 1 , the method further comprising:
reducing, by the one or more processors, a loss between a predicted value of the multi-modal predication and an actual value.

3 . The method of claim 1 , the method further comprising:
parsing, by the one or more processors, the event data to generate one or more sets of data corresponding to each event in a match.

4 . The method of claim 1 , wherein the one or more segmented data sets include a playing surface position, a play-by-play event sequence, one or more players, one or more teams, a possession team, or a game context.

5 . The method of claim 1 , the method further comprising:
providing. by the one or more processors, the sparse data to one or more embedding layers, wherein the one or more embedding layers are configured to output the embedding vector of the sparse data.

6 . The method of claim 1 , wherein the generating the one or more input data sets based on the embedding vector includes:
concatenating, by the one or more processors, the embedding vector with one or more continuous features.

7 . The method of claim 6 , wherein the one or more continuous features include a score difference, a remaining time, or a playing surface position.

8 . The method of claim 1 , the method further comprising:
outputting, by the one or more processors, a game state vector based on the one or more input data sets.

9 . 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, event data for a plurality of events from a data store, wherein the event data includes spatial event data and non-spatial event data; transforming, by the computing system, the event data into one or more segmented data sets; creating, by the computing system, an embedding vector of sparse data included in the one or more segmented data sets; generating, by the computing system, one or more input data sets based on the embedding vector; and training, by the computing system, a mixture density network to generate a multi-modal predication based on the one or more input data sets.

10 . The non-transitory computer readable medium of claim 9 , the operations further comprising:
reducing, by the computing system, a loss between a predicted value of the multi-modal predication and an actual value.

11 . The non-transitory computer readable medium of claim 9 , the operations further comprising:
parsing, by the computing system, the event data to generate one or more sets of data corresponding to each event in a match.

12 . The non-transitory computer readable medium of claim 9 , wherein the one or more segmented data sets include a playing surface position, a play-by-play event sequence, one or more players, one or more teams, a possession team, or a game context.

13 . The non-transitory computer readable medium of claim 9 , the operations further comprising:
providing. by the computing system, the sparse data to one or more embedding layers, wherein the one or more embedding layers are configured to output the embedding vector of the sparse data.

14 . The non-transitory computer readable medium of claim 9 , wherein the generating the one or more input data sets based on the embedding vector includes:
concatenating, by the computing system, the embedding vector with one or more continuous features.

15 . The non-transitory computer readable medium of claim 14 , wherein the one or more continuous features include a score difference, a remaining time, or a playing surface position.

16 . 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 event data for a plurality of events from a data store, wherein the event data includes spatial event data and non-spatial event data;
transforming the event data into one or more segmented data sets;
creating an embedding vector of sparse data included in the one or more segmented data sets;
generating one or more input data sets based on the embedding vector; and
training a mixture density network to generate a multi-modal predication based on the one or more input data sets.

17 . The computer system of claim 16 , the operations further comprising:
reducing a loss between a predicted value of the multi-modal predication and an actual value.

18 . The computer system of claim 16 , the operations further comprising:
parsing the event data to generate one or more sets of data corresponding to each event in a match.

19 . The computer system of claim 16 , wherein the one or more segmented data sets include a playing surface position, a play-by-play event sequence, one or more players, one or more teams, a possession team, or a game context.

20 . The computer system of claim 16 , the operations further comprising:
providing the sparse data to one or more embedding layers, wherein the one or more embedding layers are configured to output the embedding vector of the sparse data.

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

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• A63F13/67

  adaptively or by learning from player actions, e.g. skill level adjustment or by storing successful combat sequences for re-use · CPC title