Automatic application-based exercise tracking system and method

• US 9,737,759 B2
• Filed: 05/23/2016
• Issued: 08/22/2017
• Est. Priority Date: 07/17/2015
• Status: Active Grant

• Current Assignee: Genesant Technologies, Inc.
• Original Assignee: Genesant Technologies, Inc.
• Inventors: Mrowka, James J., Robey, Paola N., Robey, Daniel S., Croswell, Joseph A., Christ, Athanasios G.
• Primary Examiner(s): SPOONER, LAMONT M
• Application Number:
  US15/161,588
• Publication Number:
  US 20170014682A1
• Time in Patent Office: 456 Days
• Field of Search: 704 1, 704 9, 704 10
• CPC Class Codes: A63B 24/0062 Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/279 Recognition of textual entities
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising

Abstract

An automatic application-based exercise tracking system and methods comprising: i) voice-transcribed or typed text natural language processing and automatic tracking to record exercises, comprehensive exercise quantities, and calories burned data, and ii) multi-exercise administration to record multiple exercises and related data in a single user voice-transcribed or typed text submission. Further, such automatic application-based exercise tracking system is usable through computers, tablets, mobile phones, smart watches, wearables and other similar devices.

Claims

1. A computer-implemented method of automatic exercise tracking, comprising:

receiving text from a user that describes an exercise that is to be tracked;

parsing the received text into text segments;

identifying automatically in each parsed text segment an exercise quantity value and an exercise quantity unit for said exercise that is to be tracked, said identifying comprising searching said parsed text segment for a quantity value followed directly by a quantity unit, and assigning said quantity value and said quantity unit to be said exercise quantity value and said exercise quantity unit for the exercise to be tracked, and upon not finding a quantity value followed directly by a quantity unit, selecting as said exercise quantity value and said exercise quantity unit a most frequently occurring quantity value and quantity unit for said exercise to be tracked;

cleaning the parsed text segments to identify and remove words, connected spaces, and punctuation that are not used to identify exercise to produce parsed cleaned text;

processing the parsed cleaned text segments using a text match algorithm to find said exercise that is to be tracked in each parsed cleaned text segment, comprising ranking each exercise text match found using a ranking process, and selecting the ranked exercise with a predetermined rank to be the exercise that is to be tracked; and

reporting exercise tracking information for said exercise to be tracked.

2. The method of claim 1, wherein said parsing further comprises removing all quantity values and quantity units from said parsed text segments, and wherein said ranking said found exercise text matches comprises searching a past history of said user for previous matches, and selecting from said past history a most recent exercise tracked as said exercise to be tracked.

3. The method of claim 2, wherein upon an exercise match not being found in said searching, then searching a user population history for previous matches, and selecting an exercise that has been most often tracked as said exercise to be tracked.

4. The method of claim 3, wherein upon an exercise match not being found in said user population history, processing said parsed text using a search string modifying algorithm to create a modified text string;

performing a fuzzy search on an exercise database for exercise results relevant to said modified text string;

scoring said exercise results to create an exercise text match score; and

selecting as said exercise to be tracked an exercise having the top score.

5. The method of claim 4, wherein said scoring comprises determining from a user data history lookup for said user whether an exercise of said exercise results is designated as a favorite exercise of said user, and, if so, scoring that exercise to have the highest score, otherwise, upon no favorite exercise designation being found, determining from an entire user population history a total count for a number of times each exercise has been tracked, and scoring the exercise with the highest count as the exercise with the highest score.

6. The method of claim 4, wherein said search string modifying algorithm creates said modified search string by performing one or more of:

(i) removing, or replacing with spaces, connecting dashes between words, if any, (ii) removing an apostrophe followed by a letter “

s” and

removing an apostrophe only, if any, and (iii) inserting a space between connected words and numbers that have no space, if any.

7. The method of claim 1, wherein said searching for an exercise quantity value and an exercise quantity unit comprises performing a fuzzy logic search of said parsed text for a textual number or a numeric value, and upon finding said textual number or numeric value, performing a further fuzzy logic search of said parsed text for a match of a quantity unit term directly following said textual number or numeric quantity and, if found, assigning said textual number or numeric quantity to said exercise quantity value and assigning said quantity unit term to said exercise quantity unit.

8. The method of claim 7, wherein upon not finding said match of a quantity unit term, applying numeric value sequence pattern recognition logic rules that identify a sequence of numeric values associated with an exercise type, and if such logic rules are met assigning quantity unit terms to said exercise quantity units and assigning such exercise quantity units to the applicable numeric values.

9. The method of claim 7, wherein upon not finding said written number or numeric quantity, performing a further fuzzy logic search of said parsed text for a match of a quantity unit term, and, if found, assigning said quantity unit term to said exercise quantity unit, and, if not found, searching a user data history for said user for a most recently tracked exercise quantity unit for said exercise to be tracked, and setting the exercise quantity unit for said exercise to be tracked to be the most recently tracked exercise quantity unit.

10. The method of claim 9, wherein upon not finding a most recently tracked exercise quantity unit in said user data history, searching an entire user population history for a most frequently tracked exercise quantity unit for said exercise to be tracked, and setting said exercise quantity unit for the exercise to be tracked to be said most frequently tracked exercise quantity unit.

11. Non-transitory computer readable media embodying executable instructions for controlling a computer for an automatic exercise tracking method, comprising:

receiving text from a user that describes an exercise that is to be tracked;

parsing the received text into text segments;

identifying automatically in each parsed text segment an exercise quantity value and an exercise quantity unit for said exercise that is to be tracked, said identifying comprising searching said parsed text segment for a quantity value followed directly by a quantity unit, and assigning said quantity value and said quantity unit to be said exercise quantity value and said exercise quantity unit for the exercise to be tracked, and upon not finding a quantity value followed directly by a quantity unit, selecting as said exercise quantity value and said exercise quantity unit a most frequently occurring quantity value and quantity unit for said exercise to be tracked;

cleaning the parsed text segments to identify and remove words, connected spaces, and punctuation that are not used to identify exercise and produce parsed cleaned text;

processing the parsed cleaned text segments using a text match algorithm to find said exercise that is to be tracked in each parsed cleaned text segment, comprising ranking each exercise text match found using a ranking process, and selecting the ranked exercise with a predetermined rank to be the exercise that is to be tracked; and

reporting exercise tracking information for said exercise to be tracked.

12. The non-transitory computer readable media of claim 11, wherein said parsing further comprises removing all quantity values and quantity units from said parsed text segments, and wherein said ranking said found exercise text matches comprises searching a past history of said user for previous matches, and selecting from said past history a most recent exercise tracked as said exercise to be tracked.

13. The non-transitory computer readable media of claim 12, wherein upon an exercise match not being found in said searching, then searching a user population history for previous matches, and selecting an exercise that has been most often tracked as said exercise to be tracked.

14. The non-transitory computer readable media of claim 13, wherein upon an exercise match not being found in said user population history, processing said parsed cleaned text using a search string modifying algorithm to create a modified text string;

performing a fuzzy search on an exercise database for exercise results relevant to said modified text string;

scoring said exercise results to create an exercise text match score; and

selecting as said exercise to be tracked an exercise having the top score.

15. The non-transitory computer readable media of claim 14, wherein said scoring comprises determining from a user data history lookup for said user whether an exercise of said exercise results is designated as a favorite exercise of said user, and, if so, scoring that exercise to have the highest score, otherwise, upon no favorite exercise designation being found, determining from an entire user population history a total count for a number of times each exercise has been tracked, and scoring the exercise with the highest count as the exercise with the highest score.

16. The non-transitory computer readable media of claim 14, wherein said search string modifying algorithm creates said modified search string by performing one or more of:

(i) removing, or replacing with spaces, connecting dashes between words, if any, (ii) removing an apostrophe followed by a letter “

s” and

removing an apostrophe only, if any, and (iii) inserting a space between connected words and numbers that have no space, if any.

17. The non-transitory computer readable media of claim 11, wherein said searching for an exercise quantity value and an exercise quantity unit comprises performing a fuzzy logic search of said parsed text for a textual number or a numeric value, and upon finding said textual number or numeric value, performing a further fuzzy logic search of said parsed text for a match of a quantity unit term directly following said textual number or numeric quantity and, if found, assigning said textual number or numeric quantity to said exercise quantity value and assigning said quantity unit term to said exercise quantity unit.

18. The non-transitory computer readable media of claim 17, wherein upon not finding said match of a quantity unit term, applying numeric value sequence pattern recognition logic rules that identify a sequence of numeric values associated with an exercise type, and if such logic rules are met assigning quantity unit terms to said exercise quantity units and assigning such exercise quantity units to the applicable numeric values.

19. The non-transitory computer readable media of claim 17, wherein upon not finding said written number or numeric quantity, performing a further fuzzy logic search of said parsed text for a match of a quantity unit term, and, if found, assigning said quantity unit term to said exercise quantity unit, and, if not found, searching a user data history for said user for a most recently tracked exercise quantity unit for said exercise to be tracked, and setting the exercise quantity unit for said exercise to be tracked to be the most recently tracked exercise quantity unit.

20. The non-transitory computer readable media of claim 19, wherein upon not finding a most recently tracked exercise quantity unit in said user data history, searching an entire user population history for a most frequently tracked exercise quantity unit for said exercise to be tracked, and setting said exercise quantity unit for the exercise to be tracked to be said most frequently tracked exercise quantity unit.