IP Library Granted Patent US 12,623,117
Granted Patent B2
US 12,623,117 · App. 18/215,149 · Granted May 12, 2026

Cold start calibration

Inventors: Joshua Ben Shapiro (Toronto, CA); Giuseppe Barbalinardo (Berkeley, CA)
Assignee: Tonal Systems, Inc.
A63B24/0062A63B2024/0068A63B2220/20A63B2220/30A63B2220/52A63B2225/02
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Quick Facts
Patent No.
US 12,623,117
App. No.
18/215,149
Granted
May 12, 2026
Kind
B2
Abstract

A first set of performance information pertaining to a previous performance of a first exercise movement is received, the first set of performance information comprising a first weight, a first velocity, and a first range of motion. Target parameters for a target exercise movement based at least in part on the first set of performance information is predicted, wherein the target exercise movement is different from the first exercise movement. An exercise machine is configured to facilitate performing of the target exercise movement based at least in part on the predicted target parameters.

Claims (34)

1 . A system, comprising:

a processor configured to:

receive a first set of performance information pertaining to a previous performance of a first exercise movement, the first set of performance information comprising a first weight, a first velocity, and a first range of motion;

predict target parameters for a target exercise movement based at least in part on the first set of performance information, wherein the target exercise movement is different from the first exercise movement; and

wherein an exercise machine motor torque is adjusted to facilitate performing of the target exercise movement based at least in part on the predicted target parameters and wherein the exercise machine motor torque is associated with user resistance for an exercise machine;

and

a memory coupled to the processor and configured to provide the processor with instructions.

2 . The system recited in claim 1 , wherein the processor is further configured to:

receive a second set of performance information pertaining to a previous performance of a second exercise movement, the second set of performance information comprising a second weight, a second velocity, and a second range of motion; and

predict the target parameters for the target exercise movement based at least in part on both the first set of performance information and the second set of performance information.

3 . The system recited in claim 1 , wherein the target parameters comprise at least one of weight, velocity, and range of motion.

4 . The system of claim 1 , wherein the first range of motion comprises an aggregate range of motion value across a set comprising performance of a plurality of repetitions of the first exercise movement.

5 . The system of claim 1 , wherein the first set of performance information comprises an indication of a first movement family that the first exercise movement is included in.

6 . The system of claim 1 , wherein the first set of performance information comprises an indication of a first movement family that the first exercise movement is included in and wherein the target exercise movement is not included in the first movement family.

7 . The system of claim 1 , wherein the first set of performance information comprises a first muscle utilization.

8 . The system of claim 1 , wherein the predicting is triggered prior to a workout comprising one or more exercise movements to be performed, the workout comprising the target exercise movement.

9 . The system of claim 1 , wherein the first set of performance information is associated with a calibration set.

10 . The system of claim 1 , wherein the first set of performance information is received based at least in part on a determination that the previous performance of the first exercise movement is within a threshold period of time.

11 . The system of claim 1 , wherein the predicting is performed using a machine learning model, and wherein the first set of performance information is included in an input feature vector to the machine learning model.

12 . The system of claim 1 , wherein the predicting is performed using a machine learning model, and wherein a first set of population performance information associated with the first exercise movement is included in an input feature vector to the machine learning model.

13 . The system of claim 1 , wherein the predicting is performed using a machine learning model, and wherein an output label of the machine learning model comprises a suggested weight for the target exercise movement.

14 . The system of claim 1 , wherein the first weight is associated with a one rep maximum determined for the first exercise movement.

15 . The system of claim 1 , wherein the first weight and the first velocity are associated with a determination from a progressive weight mode for the first exercise movement, wherein the progressive weight mode progressively increases weight for a user at least in part to determine a user force-velocity profile.

16 . The system of claim 1 , wherein the first weight is associated with a determination from an isokinetic weight mode for the first exercise movement, wherein the isokinetic weight mode matches a user applied force at a constant velocity at least in part to determine a user force-velocity profile.

17 . The system of claim 1 , wherein the system comprises a backend server, and wherein the processor is further configured to transmit, over a network, the predicted target parameters to the exercise machine.

18 . The system of claim 1 , wherein the system comprises the exercise machine.

19 . A method, comprising:

receiving a first set of performance information pertaining to a previous performance of a first exercise movement, the first set of performance information comprising a first weight, a first velocity, and a first range of motion;

predicting target parameters for a target exercise movement based at least in part on the first set of performance information, wherein the target exercise movement is different from the first exercise movement; and

wherein an exercise machine motor torque is adjusted to facilitate performing of the target exercise movement based at least in part on the predicted target parameters and wherein the exercise machine motor torque is associated with user resistance for an exercise machine.

20 . A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:

receiving a first set of performance information pertaining to a previous performance of a first exercise movement, the first set of performance information comprising a first weight, a first velocity, and a first range of motion;

predicting target parameters for a target exercise movement based at least in part on the first set of performance information, wherein the target exercise movement is different from the first exercise movement; and

wherein an exercise machine motor torque is adjusted to facilitate performing of the target exercise movement based at least in part on the predicted target parameters and wherein the exercise machine motor torque is associated with user resistance for an exercise machine.

Assignments (3)
SECURITY INTEREST Recorded Mar 31, 2026
From: TONAL SYSTEMS, INC.
To: HERCULES CAPITAL, INC., AS AGENT
Reel/Frame 075306/0212 →
SECURITY INTEREST Recorded Oct 24, 2025
From: TONAL SYSTEMS, INC.
To: CUSTOMERS BANK
Reel/Frame 073255/0161 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 13, 2023
From: SHAPIRO, JOSHUA BEN; BARBALINARDO, GIUSEPPE
To: TONAL SYSTEMS, INC.
Reel/Frame 064890/0820 →
Continuity (1)
Related Publication 20250001257A1 · Jan 2, 2025
References Cited (6)
US 11621067B1 · Nolan · 2023 [cited by examiner]
US 12347542B2 · Chen · 2025 [cited by examiner]
US 12380984B2 · Rosenberg · 2025 [cited by examiner]
US 20070135264A1 · Rosenberg · 2007 [cited by examiner]
US 20080161733A1 · Einav · 2008 [cited by examiner]
US 20210008413A1 · Asikainen · 2021 [cited by examiner]