IP Library › Granted Patent US 12,379,790
Granted Patent B2
US 12,379,790 · App. 17/997,904 · Granted Aug 5, 2025

System and method for detecting handwriting problems

Inventors: Eric Humbert (Boulogne Billancourt, FR); Amélie Caudron (Paris, FR); Arthur Belhomme (Paris, FR); Fabrice Devige (Vanves, FR)
Assignee: SOCIÉTÉ BIC
G06F3/03545A61B5/0015A61B5/1124A61B5/1126A61B5/6887A61B5/7264A61B5/7267G06F3/0346G06F18/214G06V30/228G06V30/347G06V30/373A61B2562/0219G06F3/04883
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,379,790
App. No.
17/997,904
Granted
Aug 5, 2025
Kind
B2
Abstract

A system for detecting handwriting problems may include a handwriting instrument including a body extending longitudinally between a first end and a second end, the first end having a writing tip which is able to write on a support, the handwriting instrument further including at least one motion sensor configured to acquire data on the handwriting of the user when a user is using the handwriting instrument, and one calculating unit communicating with the motion sensor and configured to analyze the data by an artificial intelligence trained to detect whether the user has handwriting problems.

Claims (19)

1. A system for detecting handwriting problems comprising:

a handwriting instrument including a body extending longitudinally between a first end and a second end, the first end having a writing tip which is able to write on a support;

a detection device distinct from the handwriting instrument, the detection device comprising a body configured to be mounted on the second end of the handwriting instrument and a protuberant tip extending from the body of the detection device and configured to be inserted in the body of the handwriting instrument;

wherein the detection device comprises:

two motion sensors configured to acquire data on handwriting of a user when the user is using the handwriting instrument, said two motion sensors being three-axis accelerometers, or one three-axis accelerometer and

one three-axis gyroscope; and-one calculating unit communicating with the two motion sensors and configured to analyze the data by an artificial intelligence trained to detect whether the user has handwriting problems;

wherein a first motion sensor of the two motion sensors is provided on the protuberant tip of the detection device that is configured to be inserted in the body of the handwriting instrument and a second motion sensor of the two motion sensors is provided in the body of the detection device;

wherein the artificial intelligence is at least one neural network configured to be trained by segmentation and classification of strokes to determine timestamps of the strokes on the support based upon motion signals sensed by the two motion sensors.

2. The system according to claim 1 , wherein the detection device further includes a short-range radio communication interface configured to communicate raw data acquired by the two motion sensors and a battery.

3. The system according to claim 1 , wherein the one three-axis gyroscope comprises a wake-up input suited for receiving a wake-up signal from the one calculating unit when a movement is detected by the one three-axis accelerometer, the one three-axis gyroscope being configured for switching into an active state when the wake-up signal is received.

4. The system according to claim 1 , further comprising a pressure sensor, wherein the one calculating unit is configured to receive the data acquired by the pressure sensor.

5. The system according to claim 1 , further comprising a stroke sensor configured to acquire stroke data while the user is using the handwriting instrument, the artificial intelligence being further trained with the stroke data to determine the handwriting problems.

6. The system according to claim 5 , wherein the stroke sensor is the two motion sensors.

7. The system according to claim 1 , wherein the artificial intelligence is further configured to determine when the user is actually using the handwriting instrument on the support and to differentiate data corresponding to an actual use of the handwriting instrument from data acquired while the handwriting instrument is only being held in air.

8. The system according to claim 1 , wherein the handwriting instrument is a pen, a pencil a brush or any other element allowing the user to write or draw with it on the support.

9. The system according to claim 1 , wherein the artificial intelligence is further configured to transcribe raw data acquired by the two motion sensors into handwriting characters depicted on a mobile device.

10. The system according to claim 1 , wherein the support is a non-electronic surface.

11. The system according to claim 1 , wherein the one calculating unit comprises a volatile memory to store data acquired by the two motion sensors and a non-volatile memory to store a model enabling the detection of the handwriting problems.

12. The system according to claim 1 , wherein the at least one neural network is further configured to acquire data during use of the handwriting instrument and to determine if the user is forming letters and numbers correctly.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 6, 2023
From: HUMBERT, ERIC; CAUDRON, AMÉLIE; BELHOMME, ARTHUR; DEVIGE, FABRICE
To: SOCIÉTÉ BIC
Reel/Frame 063247/0193 →
Priority Claims (1)
EP 20305430 · May 4, 2020 · regional
Continuity (1)
Related Publication 20230148911A1 · May 18, 2023
References Cited (35)
US 6304667B1 · Reitano · 2001 [cited by applicant]
US 6422775B1 · Bramlett · 2002 [cited by examiner]
US 6454482B1 · Silverbrook · 2002 [cited by examiner]
US 6697524B1 · Arai · 2004 [cited by examiner]
US 10248652B1 · Venkataraman et al. · 2019 [cited by applicant]
US 11464443B2 · Tsoi et al. · 2022 [cited by applicant]
US 20020181744A1 · Vablais · 2002 [cited by examiner]
US 20090058820A1 · Hinckley · 2009 [cited by examiner]
US 20120331546A1 · Falkenburg et al. · 2012 [cited by applicant]
US 20130060124A1 · Zietsma · 2013 [cited by applicant]
US 20150029164A1 · Liu · 2015 [cited by applicant]
US 20160259436A1 · Nicholson et al. · 2016 [cited by applicant]
US 20160364010A1 · Amma · 2016 [cited by examiner]
US 20160378195A1 · Lefebvre · 2016 [cited by applicant]
US 20180299976A1 · Chiewcharnpipat et al. · 2018 [cited by applicant]
US 20190076078A1 · Davis · 2019 [cited by examiner]
US 20190150837A1 · Dibia · 2019 [cited by examiner]
US 20200202741A1 · Zhong · 2020 [cited by examiner]
US 20200250413A1 · Lu et al. · 2020 [cited by applicant]
US 20200251217A1 · Cassuto et al. · 2020 [cited by applicant]
US 20200356251A1 · Gobera Rubalcava · 2020 [cited by examiner]
US 20210076105A1 · Parmar · 2021 [cited by examiner]
US 20210232235A1 · Regani et al. · 2021 [cited by applicant]
US 20210333875A1 · Horii · 2021 [cited by examiner]
US 20220147791A1 · Yao et al. · 2022 [cited by applicant]
US 20220366713A1 · Humbert · 2022 [cited by examiner]
US 20230126043A1 · Ferrante · 2023 [cited by examiner]
CN 108376086A · 2018 [cited by examiner]
WO 2015175462A1 · 2015 [cited by applicant]
EPO Communication issued in corresponding European Application No. 20305430.9, mailed Sep. 11, 2023. [cited by applicant]
Momina Moetesum, Imran Siddiqi, Nicole Vincent, Florence Cloppet, “Assessing visual attributes of handwriting for prediction of neurological disorders—A case study on Parkinson's disease”, Pattern Recognition Letters, v… [cited by applicant]
Asselborn, Thibault, et al. “Automated human-level diagnosis of dysgraphia using a consumer tablet.” NPJ digital medicine, vol. 1, No. 1, Aug. 31, 2018, pp. 1-9. [cited by applicant]
Bashir, Muzaffar. “A novel multisensoric system recording and analyzing human biometric features for biometric and biomedical applications.” Dissertation, Der Universität Regensburg, Dec. 10, 2010. [cited by applicant]
International Search Report and Written Opinion issued on Oct. 19, 2021 in counterpart International Application No. PCT/EP2021/061489 (19 pages, in English). [cited by applicant]
Kumar Chellapilla, Sidd Puri, Patrice Simard. High Performance Convolutional Neural Networks for Document Processing. Tenth International Workshop on Frontiers in Handwriting Recognition, Université de Rennes 1, Oct. 20… [cited by applicant]