US 11,726,570 B2
Surface classifications
Tai Hsiang Chen, Taipei (TW); Charles J. Stancil, Tomball, TX (US); Wei-Hung Lin, Taipei (TW); and Kun-Hung Lin, Taipei (TW)
Assigned to HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P., Spring, TX (US)
Filed by Hewlett-Packard Development Company, L.P., Spring, TX (US)
Filed on Sep. 15, 2021, as Appl. No. 17/476,077.
Prior Publication US 2023/0077550 A1, Mar. 16, 2023
Int. Cl. G06F 3/048 (2013.01); G06F 3/01 (2006.01); G06F 3/0354 (2013.01); G06F 3/041 (2006.01); G06N 3/02 (2006.01); G06F 3/038 (2013.01)
CPC G06F 3/016 (2013.01) [G06F 3/0383 (2013.01); G06F 3/03546 (2013.01); G06F 3/04162 (2019.05); G06N 3/02 (2013.01); G06F 3/048 (2013.01); G06F 2203/0384 (2013.01)] 13 Claims
OG exemplary drawing
 
1. An electronic device, comprising:
a tip pressure sensor to capture tip pressure data based on a writing surface;
a processor to produce, during a prediction stage, a classification of the writing surface based on the tip pressure data via a machine learning model, the classification of the writing surface including a value indicating a degree of roughness of the writing surface, the machine learning model having weights tuned, during a training stage, to produce the classification of the writing surface, wherein the machine learning model is an artificial neural network; and
a haptic device to control haptic feedback based on the classification,
wherein the haptic device is to control haptic feedback with an inverse relationship relative to the value indicating the degree of roughness of the writing surface.