Predicting Whether a User Executed a Golf Shot

Abstract

A method for predicting whether a user executed a golf shot includes (i) receiving a selection of a machine learning model from a plurality of machine learning models, each of the plurality of machine learning models being trained to predict whether the golf shot occurred, (ii) receiving inputs relevant to the machine learning model that was selected; and (iii) predicting, using the machine learning model that was selected, whether the user executed the golf shot using the inputs relevant to the machine learning model that was selected.

1 . A method for predicting whether a user executed a golf shot, the method comprising:
 receiving a selection of a machine learning model from a plurality of machine learning models, each of the plurality of machine learning models being trained to predict whether the golf shot occurred;   receiving inputs relevant to the machine learning model that was selected; and   predicting, using the machine learning model that was selected, whether the user executed the golf shot using the inputs relevant to the machine learning model that was selected.   
     
     
         2 . The method of  claim 1 , further comprising, prior to receiving the selection of the machine learning model, determining whether the user was stationary long enough to potentially hit the golf shot. 
     
     
         3 . The method of  claim 2 , wherein the determining is performed by comparing a time the user was stationary to a threshold, the threshold being based on information about the user and information about a golf course. 
     
     
         4 . The method of  claim 2 , further comprising, responsive to determining that the user was stationary long enough to potentially hit the golf shot, receiving the selection of the machine learning model. 
     
     
         5 . The method of  claim 2 , further comprising, responsive to determining that the user was stationary long enough to potentially hit the golf shot, determining that no golf shot occurred. 
     
     
         6 . The method of  claim 2 , further comprising, prior to determining whether the user was stationary long enough to potentially hit the golf shot, receiving at least one of course information and user positional data. 
     
     
         7 . The method of  claim 1 , wherein the selection of the machine learning model is based at least in part on a position of the user relative to a hole on a golf course. 
     
     
         8 . The method of  claim 1 , wherein the inputs relevant to the machine learning model that was selected comprise at least user profile information for the user, the machine learning model that was selected being trained to take as input the user profile information for the user and to predict whether the user executed the golf shot. 
     
     
         9 . The method of  claim 1 , wherein the inputs relevant to the machine learning model that was selected comprise at least positional and hole history information, the machine learning model that was selected being trained to take as input the positional and hole history information and to predict whether the user executed the golf shot. 
     
     
         10 . The method of  claim 1 , wherein the inputs relevant to the machine learning model that was selected comprise at least a swing detection from a hip movement swing model, the machine learning model that was selected being trained to take as input the swing detection from the hip movement swing model and to predict whether the user executed the golf shot. 
     
     
         11 . The method of  claim 1 , wherein the inputs relevant to the machine learning model that was selected comprise at least a previous shot outcome model output, the machine learning model that was selected being trained to take as input the previous shot outcome model output and to predict whether the user executed the golf shot. 
     
     
         12 . The method of  claim 1 , wherein the inputs relevant to the machine learning model that was selected comprise at least audio hit probability information, the machine learning model that was selected being trained to take as input the audio hit probability information and to predict whether the user executed the golf shot. 
     
     
         13 . The method of  claim 1 , wherein the inputs relevant to the machine learning model that was selected comprise at least head movement data, the machine learning model that was selected being trained to take as input the head movement data and predict whether the user executed the golf shot. 
     
     
         14 . The method of  claim 1 , wherein the inputs relevant to the machine learning model that was selected comprise at least a swing detection from a watch swing model, the machine learning model that was selected being trained to take as input the swing detection from the watch swing model whether the user executed the golf shot. 
     
     
         15 . The method of  claim 1 , wherein the inputs relevant to the machine learning model that was selected comprise at least: user profile information for the user, positional and hole history information, a swing detection from a hip movement swing model, a previous shot outcome model output, audio hit probability information, head movement data, and a swing detection from a watch swing model, the machine learning model that was selected being trained to take as input the inputs relevant to the machine learning model. 
     
     
         16 . The method of  claim 1 , wherein the predicting comprises generating a prediction value and comparing the prediction value to a threshold. 
     
     
         17 . The method of  claim 16 , wherein it is determined that the user executed the golf shot responsive to the prediction value satisfying the threshold. 
     
     
         18 . The method of  claim 16 , wherein it is determined that the user did not execute the golf shot responsive to the prediction value failing to satisfy the threshold. 
     
     
         19 . A system comprising:
 a memory comprising computer readable instructions; and   at least one processor for executing the computer readable instructions, the computer readable instructions controlling the at least one processor to perform operations for predicting whether a user executed a golf shot, the operations comprising:   receiving a selection of a machine learning model from a plurality of machine learning models, each of the plurality of machine learning models being trained to predict whether the golf shot occurred;
 receiving inputs relevant to the machine learning model that was selected; and 
 predicting, using the machine learning model that was selected, whether the user executed the golf shot using the inputs relevant to the machine learning model that was selected. 
   
     
     
         20 . A non-transitory computer-readable medium comprising instructions, wherein execution of the instructions by at least one processor causes the at least one processor to perform operations for predicting whether a user executed a golf shot, the operations comprising:
 receiving a selection of a machine learning model from a plurality of machine learning models, each of the plurality of machine learning models being trained to predict whether the golf shot occurred;   receiving inputs relevant to the machine learning model that was selected; and   predicting, using the machine learning model that was selected, whether the user executed the golf shot using the inputs relevant to the machine learning model that was selected.