Machine learning model for sports event data analysis
AssigneeSap SE
InventorsHoang-Vu Nguyen, Michael Truong Ngoc, Anish Umesh
Life of this patent
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Abstract
A computer-implemented method can receive a sport event sequence including a plurality of events ordered sequentially, embed the plurality of events into a plurality of event vectors using an embedding stack, transform the plurality of event vectors into a plurality of encoded event vectors using an encoder stack, and train a machine learning model for predicting one or more subsequent events following a new sport event sequence. The training includes adjusting parameters of the embedding stack and the encoder stack based at least in part on the plurality of encoded event vectors. Related computing system and software are also disclosed.
Claims20
A computing system, comprising:
- memory;
- one or more hardware processors coupled to the memory;
- and
- one or more non-transitory computer readable storage media storing instructions that, when loaded into the memory, cause the one or more hardware processors to perform operations comprising:
- receiving a sport event sequence comprising a plurality of events ordered sequentially, wherein an event comprises a plurality of features with mixed data types;
- embedding the plurality of events into a plurality of event vectors using an embedding stack, wherein the embedding stack applies different embedding schemes for features with different data types;
- transforming the plurality of event vectors into a plurality of encoded event vectors using an encoder stack, wherein the encoder stack comprises at least one encoder layer, wherein the at least one encoder layer is configured to apply a self-attention mechanism to the plurality of event vectors;
- and
- training a machine learning model for predicting one or more subsequent events following a new sport event sequence, wherein the training comprises adjusting parameters of the embedding stack and the encoder stack based at least in part on the plurality of encoded event vectors.
A computer-implemented method, comprising:
- receiving a sport event sequence comprising a plurality of events ordered sequentially, wherein an event comprises a plurality of features with mixed data types;
- embedding the plurality of events into a plurality of event vectors using an embedding stack, wherein the embedding stack applies different embedding schemes for features with different data types;
- transforming the plurality of event vectors into a plurality of encoded event vectors using an encoder stack, wherein the encoder stack comprises at least one encoder layer, wherein the at least one encoder layer is configured to apply a self-attention mechanism to the plurality of event vectors;
- and
- training a machine learning model for predicting one or more subsequent events following a new sport event sequence, wherein the training comprises adjusting parameters of the embedding stack and the encoder stack based at least in part on the plurality of encoded event vectors.
One or more non-transitory computer-readable media having encoded thereon computer-executable instructions causing one or more processors to perform a method, the method comprising:
- receiving a sport event sequence comprising a plurality of events ordered sequentially, wherein an event comprises a plurality of features with mixed data types;
- embedding the plurality of events into a plurality of event vectors using an embedding stack, wherein the embedding stack applies different embedding schemes for features with different data types;
- transforming the plurality of event vectors into a plurality of encoded event vectors using an encoder stack, wherein the encoder stack comprises at least one encoder layer, wherein the at least one encoder layer is configured to apply a self-attention mechanism to the plurality of event vectors;
- and
- training a machine learning model for predicting one or more subsequent events following a new sport event sequence, wherein the training comprises adjusting parameters of the embedding stack and the encoder stack based at least in part on the plurality of encoded event vectors.
Dependencies are inferred from the claim text.