Autonomous personalized golf recommendation and analysis environment

• US 10,682,562 B2
• Filed: 01/16/2018
• Issued: 06/16/2020
• Est. Priority Date: 01/17/2017
• Status: Active Grant

• Current Assignee: Arccos Golf, LLC
• Original Assignee: Arccos Golf, LLC
• Inventors: Syed, Salman Hussain, Phillips, Colin David
• Primary Examiner(s): Elisca, Pierre E
• Application Number:
  US15/872,601
• Publication Number:
  US 20180200605A1
• Time in Patent Office: 882 Days
• Field of Search: 473223, 473407
• CPC Class Codes: A63B 2071/0691 Maps, e.g. yardage maps or electronic maps
• CPC Class Codes: A63B 2102/32 Golf
• CPC Class Codes: A63B 2220/12 Absolute positions, e.g. by using GPS
• CPC Class Codes: A63B 2220/51 Force
• CPC Class Codes: A63B 2220/56 Pressure
• CPC Class Codes: A63B 2220/72 Temperature
• CPC Class Codes: A63B 2220/74 Atmospheric pressure
• CPC Class Codes: A63B 2220/75 Humidity
• CPC Class Codes: A63B 2220/76 Wind conditions
• CPC Class Codes: A63B 2220/803 Motion sensors
• CPC Class Codes: A63B 2220/807 Photo cameras
• CPC Class Codes: A63B 2220/808 Microphones
• CPC Class Codes: A63B 2220/833 Sensors arranged on the exercise apparatus or sports implement
• CPC Class Codes: A63B 2220/836 Sensors arranged on the body of the user
• CPC Class Codes: A63B 2225/50 Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/06 heartbeat rate only
• CPC Class Codes: A63B 2230/202 glucose
• CPC Class Codes: A63B 2230/207 P-O2, i.e. partial O2 value
• CPC Class Codes: A63B 2230/30 blood pressure A63B2230/04 takes precedence
• CPC Class Codes: A63B 2230/50 temperature
• CPC Class Codes: A63B 24/0003 : Analysing the course of a movement or motion sequences during an exercise or trainings sequence, e.g. swing for golf or tennis
• CPC Class Codes: A63B 60/46 : Measurement devices associated with golf clubs, bats, rackets or the like for measuring physical parameters relating to sporting activity, e.g. baseball bats with impact indicators or bracelets for measuring the golf swing
• CPC Class Codes: A63B 69/3605 : Golf club selection aids informing player of his average or expected shot distance for each club
• CPC Class Codes: A63B 71/0622 : Visual, audio or audio-visual systems for entertaining, instructing or motivating the user
• CPC Class Codes: A63B 71/0669 : Score-keepers or score display devices
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G16H 20/30 : relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising

Abstract

Exemplary embodiments of the present disclosure are directed to systems, methods, and computer-readable media configured to autonomously generate personalized recommendations for a user before, during, or after a round of golf. The systems and methods can utilize course data, environmental data, user data, and/or equipment data in conjunctions with one or more machine learning algorithms to autonomously generate the personalized recommendations.

Claims

1. A method of autonomously generating personalized recommendations for a user before, during, or after a round of golf, the method comprising:

receiving an input from a user associated with a round of golf;

retrieving at least one of course data, environmental data, user data, or equipment data from at least one of one or more sensors or one or more data sources;

autonomously generating a recommendation or analysis for the user based on the course data, the environment data, the user data, or the equipment data in response to executing of one or more machine learning algorithms that consume the course data, the environmental data, the user data, and the equipment data; and

outputting the recommendation or analysis to the user.

2. The method of claim 1, wherein the course data includes data associated with a golf course for the round of golf.

3. The method of claim 1, wherein the environmental data includes environmental conditions associated with the golf course or a geographic region in proximity to or within which the golf course resides.

4. The method of claim 1, wherein the user data includes data that is specific to the golf performance of the user at the golf course or at other golf courses.

5. The method of claim 1, wherein the equipment data includes data that is specific the use of golf equipment by the user.

6. The method of claim 1, wherein the one or more machine learning algorithms are executed concurrently to each other.

7. The method of claim 6, further comprising:

weighting the outputs of the one or more machine learning algorithms.

8. The method of claim 6, further comprising:

assigning each output of the one or more machine learning algorithms a vote; and

determine the recommendation or analysis to output to be rendered on the display based on a quantity of votes.

9. The method of claim 1, wherein a pre-round analysis is autonomously generated, and the method further comprises:

identifying a golf course associated with the round of golf in response to input received from the user.

10. The method of claim 9, wherein identifying the golf course comprises:

receiving location information generated by an electronic device associated with the user; and

determining whether the location information corresponds to an arrival of the user at a golf course.

11. The method of claim 1, wherein a post-round analysis is autonomously generated, and the method further comprises:

receiving location information and golf data generated by at least one of an electronic device associated with the user or sensors affixed to golf clubs associated with the user; and

determining that the golf data and location information corresponds to a completion of a round of golf at the golf course by the user.

12. The method of claim 1, wherein a contemporaneous recommendation is autonomously generated during the round of golf, and the method comprises:

identifying a current hole, shot number, and location of the golf ball on the golf course.

13. The method of claim 12, further comprising:

tracking the round of golf based on data generated by at least one of an electronic device associated with the user or sensors affixed to golf clubs associated with the user.

14. A system for autonomously generating personalized recommendations for a user before, during, or after a round of golf, the system comprising:

one or more non-transitory computer-readable media storing at least one of course data, environmental data, user data, or equipment data;

one or more servers configured to;

receive an input from a user associated with a round of golf;

retrieve the course data, the environmental data, the user data, or the equipment data from the one or more non-transitory computer-readable media;

autonomously generate a recommendation or analysis for the user based on the course data, the environment data, the user data, or the equipment data in response to executing of one or more machine learning algorithms that consume the course data, the environmental data, the user data, and the equipment data; and

output the recommendation or analysis to the user.

15. The system of claim 14, wherein the course data includes data associated with a golf course for the round of golf, the environmental data includes environmental conditions associated with the golf course or a geographic region in proximity to or within which the golf course resides, the user data includes data that is specific to the golf performance of the user at the golf course or at other golf courses, and the equipment data includes data that is specific the use of golf equipment by the user.

16. The system of claim 14, wherein the one or more servers are configured to execute the one or more machine learning algorithms concurrently with each other.

17. The system of claim 16, wherein the one or more servers are configured to weight the outputs of the one or more machine learning algorithms.

18. The system of claim 16, wherein the one or more servers are configured to assign each output of the one or more machine learning algorithms a vote and determine the recommendation or analysis to output to be rendered on the display based on a quantity of votes.

19. The system of claim 14, wherein a pre-round analysis is autonomously generated, and the one or more servers are configured to:

identify a golf course associated with the round of golf in response to input received from the user.

20. The system of claim 19, wherein the one or more servers are configured to identify the golf course by receiving location information generated by an electronic device associated with the user, and determining whether the location information corresponds to an arrival of the user at a golf course.

21. The system of claim 14, wherein a post-round analysis is autonomously generated, and the one or more servers are configured to:

receive location information and golf data generated by at least one of an electronic device associated with the user or sensors affixed to golf clubs associated with the user; and

determine that the golf data and location information corresponds to a completion of a round of golf at the golf course by the user.

22. The system of claim 14, wherein a contemporaneous recommendation is autonomously generated during the round of golf, and the one or more servers are configured to:

identify a current hole, shot number, and location of the golf ball on the golf course.

23. The system of claim 22, wherein the one or more servers are configured to:

track the round of golf based on data generated by at least one of an electronic device associated with the user or sensors affixed to golf clubs associated with the user.