IP Library › Granted Patent US 11,561,123
Granted Patent B2
US 11,561,123 · App. 17/463,163 · Granted Jan 24, 2023

Smart scale systems and methods of using the same

Inventors: Thomas Serval (Neuilly-sur-Seine, FR); Gauthier de Rouzé (Reims, FR)
Assignee: MATEO
G01G17/08G01G19/50G06N5/022
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Quick Facts
Patent No.
US 11,561,123
App. No.
17/463,163
Granted
Jan 24, 2023
Kind
B2
Abstract

A method for determining a normalized weight of a non-static item is disclosed. Weight data associated with the non-static item is received from a plurality of load cells. A load cell weight for the non-static item is determined based at least in part on the weight data. The load cell weight for the non-static item is received as an input for a machine learning algorithm. The normalized weight for the non-static item is generated as an output for the machine learning algorithm.

Claims (53)

1. A method for determining a normalized weight of a user, the method performed by one or more processors of a smart scale system according to machine-readable instructions stored in a memory coupled to the one or more processors, the method comprising:

receiving, from a plurality of load cells of the smart scale system coupled to the one or more processors, weight data associated with the user;

receiving, as an input for a machine learning algorithm, a load cell weight determined based on the received weight data;

receiving, from an array of pressure sensors of the smart scale system coupled to the one or more processors, pressure data associated with the user;

receiving, from the user, an indication of a reason for adjustment of the weight data associated with the user;

generating, based at least in part on the pressure data, a pressure heat map associated with the user, the pressure heat map, being representative of a pressure gradient associated with feet of the user;

determining based on the pressure heat map an identity of the user; and

generating, by the machine learning algorithm based on (i) the weight data associated with the user, (ii) the reason for adjustment of the weight data associated with the user, and (iii) the identity of the user, the normalized weight for the user.

2. The method of claim 1 ,

wherein the reason for adjustment includes:

(i) a state of the user being dressed or undressed;

(ii) a status of the user's recent use of bathroom;

(iii) a time when the user last ate and/or drank;

(iv) a type of food of the user's last meal;

(v) a shower or wetness status of the user; or

(vi) any combination thereof.

3. The method of claim 2 , further comprising:

displaying a prompt for receiving user information via the user interface in response to determining that the user is a registered user.

4. A smart scale system, comprising:

a plurality of load cells coupled to a first side of a substrate, the plurality of load cells being configured to generate weight data associated with a user;

a control system including one or more processors;

an array of pressure sensors coupled to a second opposing side of the substrate, the array of pressure sensors being configured to generate pressure data associated with the user; and

a memory having stored thereon machine readable instructions;

wherein the control system is coupled to the memory and configured to cause the one or more processors to execute the machine executable instructions to:

receive, from the plurality of load cells, the weight data associated with the user;

receive, as an input for a machine learning algorithm, a load cell weight determined based on the received weight data;

receive, from the array of pressure sensors, the pressure data associated with the user;

receive, from the user, an indication of a reason for adjustment of the weight data associated with the user;

generate, based at least in part on the pressure data, a pressure heat map associated with the user, wherein the pressure heat map being representative of a pressure gradient associated with feet of the user;

determine based on the pressure heat map, an identity of the user; and

generate, by the machine learning algorithm based on (i) the weight data associated with the user, (ii) the reason for adjustment of the weight data associated with the user, and (iii) the identity of the user, the normalized weight for the user.

5. The smart scale system of claim 4 , further comprising a cover layer.

6. The smart scale system of claim 5 , wherein the cover layer includes a sheet of fabric.

7. The smart scale system of claim 6 , wherein the sheet of fabric includes at least two electrically conductive fabric portions spaced from each other.

8. The smart scale system of claim 7 , wherein the at least two electrically conductive fabric portions are spaced from each other at least 3 inches.

9. The smart scale system of claim 4 , wherein the substrate is one or more pieces of glass.

10. The smart scale system of claim 4 , wherein the plurality of load cells includes a four-by-four array of load cells, the four-by-four array of load cells being coupled to an analog to digital converter.

11. The smart scale system of claim 4 , wherein the plurality of load cells includes at least four single load cells, each of the four single load cells being coupled to a respective analog to digital converter.

12. The smart scale system of claim 4 , wherein the array of pressure sensors includes a first sheet and a second sheet.

13. The smart scale system of claim 12 , wherein the first sheet includes a pressure sensitive sheet that is positioned adjacent to the second sheet.

14. The smart scale system of claim 13 , wherein the pressure sensitive sheet includes a piezoresistive sheet that is configured to change its electrical resistance in response to pressure being applied thereto.

15. The smart scale system of claim 12 , wherein the second sheet includes a plurality of electrically conductive trace patterns.

16. The smart scale system of claim 15 , wherein each of the plurality of electrically conductive trace patterns defines a pressure sensor of the array of pressure sensors.

17. The smart scale system of claim 15 , wherein each of the plurality of electrically conductive trace patterns includes an inner disk and an outer ring.

18. The smart scale system of claim 17 , wherein the outer ring is an equilateral polygon or a perfect circle.

19. The smart scale system of claim 4 , wherein the memory and the control system are coupled to the first side of the substrate.

20. The smart scale system of claim 4 , wherein the reason for adjustment includes:

(i) a state of the user being dressed or undressed;

(ii) a status of the user's recent use of bathroom;

(iii) a time when the user last ate and/or drank;

(iv) a type of food of the user's last meal;

(v) a shower or wetness status of the user; or

(vi) any combination thereof.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 8, 2021
From: SERVAL, THOMAS; DE ROUZE, GAUTHIER
To: BARACODA
Reel/Frame 057737/0199 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 8, 2021
From: BARACODA
To: MATEO
Reel/Frame 057737/0326 →
Continuity (4)
Continuation PCTIB2020053686 · Apr 17, 2020
Provisional Application 62957210 · Jan 4, 2020
Provisional Application 62836476 · Apr 19, 2019
Related Publication 20210396569A1 · Dec 23, 2021
Cited By (2)
US 12,681,562 US 12,721,498