Jie J Fang

US Patent:

20220331658, Oct 20, 2022

Filed:

Apr 16, 2021

Appl. No.:

17/233246

Inventors:

- San Francisco CA, US
Jess Venticinque - Woodside CA, US
Louis Gutierrez - San Antonio TX, US
Charles Edouard Gibbons - San Francisco CA, US
Jie Fang - Sunnyvale CA, US

International Classification:

A63B 24/00
A63B 71/06
G06N 20/00

Abstract:

An exercise recommendation system determines recommended weights for users to perform exercises with. The exercise recommendation system accesses a plurality of exercise pairs, each labeled with performance statistics of users who performed the exercises. Each exercise in an exercise pair is associated with a weight. The exercise recommendation system trains a machine learning model on the plurality of exercise pairs to determine a weight to recommend to a user for a first exercise based on performance statistics of the user associated with one or more second exercises, which are each in an exercise pair with the first exercise. The exercise recommendation system retrieves performance statistics of a target user including weights for exercises previously performed by the target user. The exercise recommendation system applies the machine learning model to the performance statistics to determine a target weight to recommend and modifies a user interface to include the target weight.

US Patent:

20220331659, Oct 20, 2022

Filed:

Apr 16, 2021

Appl. No.:

17/233254

Inventors:

- San Francisco CA, US
Jess Venticinque - Woodside CA, US
Louis Gutierrez - San Antonio TX, US
Charles Edouard Gibbons - San Francisco CA, US
Jie Fang - Sunnyvale CA, US
Yihui Liu - Seattle WA, US

International Classification:

A63B 24/00

Abstract:

An exercise recommendation system determines a current capability of a user. The exercise recommendation system accesses an exercise history for a user. The exercise history comprises an exercise performed by the user and a capability of the user each time the user performed the exercise. The exercise recommendation system partitions the exercise history into a plurality of time periods, and, for each time period, computes an aggregate capability of the user for the exercise during the time period. The exercise recommendation system calculates a moving average capability of the user for the exercise based on the aggregate capabilities and determines a current capability of the user for the exercise based on the moving average capability. The current capability of the user may be discounted at least in part based on how recently the user performed the exercise.