IP Library Granted Patent US 12,638,901
Granted Patent B2
US 12,638,901 · App. 17/946,469 · Granted May 26, 2026

Computing system power-on using circuit

Inventors: Robert J Kapinos (Durham, IN); Scott Li (Cary, NC); Robert James Norton, Jr. (Raleigh, NC); Russell Speight VanBlon (Raleigh, NC)
Assignee: Lenovo (Singapore) Pte. Ltd.
G06F1/3231G06F1/3287
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 12,638,901
App. No.
17/946,469
Granted
May 26, 2026
Kind
B2
Abstract

One embodiment provides a method, the method including: detecting, at an information handling device utilizing a input detection system and while the information handling device is in a low power state, a physical user input at an input mechanism of the information handling device, wherein the detecting comprises detecting a force at the input mechanism; determining, utilizing the input detection system, the physical user input comprises a request to power-on the information handling device; and powering on the information handling device. Other aspects are claimed and described.

Claims (31)

1 . A method, comprising:

detecting, at an information handling device utilizing an input detection system employing a physical input detection circuit and while the information handling device is in a low power state, a physical user input at an input mechanism of the information handling device, wherein the detecting comprises detecting a characteristic of the input at the input mechanism utilizing the physical input detection circuit that detects the characteristics of the input and detecting a force comprising an amount of deflection at the input mechanism, wherein at least a portion of the input mechanism remains deactivated while the information handling device is in the low power state;

determining, utilizing the input detection system employing a machine-learning model and subsequent to detecting the characteristic of the input, the physical user input comprises a request to power-on the information handling device, wherein the determining comprises determining that the amount of the force exceeds a predetermined threshold and distinguishes the physical user input as a request to power-on the information handling device as opposed to a movement shock, wherein the machine-learning model associates the characteristic of the input with a desired response; and the predetermined threshold is computed over time based upon user inputs using the machine-learning model; and

in response to the determining, powering on the information handling device, wherein the powering on comprises receiving a signal from the physical input detection circuit at a power-on circuit of the information handling device.

2 . The method of claim 1 , wherein the physical user input comprises pressing at least one key.

3 . The method of claim 1 , wherein the input mechanism is deactivated during the low power state.

4 . The method of claim 1 , wherein the detecting a force comprises measuring, using at least one sensor, an amount of resistance resulting from the physical user input.

5 . The method of claim 4 , wherein the at least one sensor comprises a strain gauge.

6 . The method of claim 4 , wherein the determining comprises determining that the amount of resistance exceeds a predetermined threshold.

7 . The method of claim 1 , comprising determining, utilizing a keyboard deflection system, the physical user input does not comprise a request to power-on the information handling device and keeping the information handling device in the low power state.

8 . The method of claim 1 , wherein the machine-learning model identifies at least one characteristic of an input received at an information handling device in a low power state and predicts an input type corresponding to the input received;

wherein the predicting an input type comprises the machine-learning model associating the at least one characteristic with a desired response based upon a plurality of historical inputs and annotated input types previously received by the input detection system of the information handling device.

9 . The system of claim 1 , wherein the machine-learning model identifies at least one characteristic of an input received at an information handling device in a low power state and predicts an input type corresponding to the input received;

wherein to predict an input type comprises the machine-learning model associating the at least one characteristic with a desired response based upon a plurality of historical inputs and annotated input types previously received by the input detection system of the information handling device.

10 . A system, comprising:

an input mechanism;

a processor operatively coupled to the input mechanism;

a memory device that stores instructions that when executed by the processor, causes the system to:

detect, an input detection system, employing a physical input detection circuit and while the information handling device is in a low power state, a physical user input at the input mechanism of the information handling device, wherein to detect comprises detecting a characteristic of the input at the input mechanism utilizing the physical input detection circuit that detects the characteristics of the input and detecting a force comprising an amount of deflection at the input mechanism, wherein at least a portion of the input mechanism remains deactivated while the information handling device is in the low power state;

determine, utilizing the input detection system employing a machine-learning model and subsequent to detecting the characteristic of the input, the physical user input comprises a request to power-on the information handling device, wherein to determine comprises determining that the amount of the force exceeds a predetermined threshold and distinguishes the physical user input as a request to power-on the information handling device as opposed to a movement shock, wherein the machine-learning model associates the characteristic of the input with a desired response; and the predetermined threshold is computed over time based upon user inputs using the machine-learning model; and

in response to the determining, power on the information handling device, wherein to power on comprises receiving a signal from the physical input detection circuit at a power-on circuit of the information handling device.

11 . The system of claim 10 , wherein the physical user input comprises pressing at least one key.

12 . The system of claim 10 , wherein the input mechanism is deactivated during the low power state.

13 . The system of claim 10 , wherein the detecting a force comprises measuring, using at least one sensor, an amount of resistance resulting from the physical user input.

14 . The system of claim 13 , wherein the at least one sensor comprises a strain gauge.

15 . The system of claim 13 , wherein the determining comprises determining that the amount of resistance exceeds a predetermined threshold.

16 . A product, the product comprising:

a non-transitory computer-readable storage device that stores executable code that, when executed by the processor, causes the product to:

detect, an input detection system employing a physical input detection circuit and while the information handling device is in a low power state, a physical user input at an input mechanism of the information handling device, wherein to detect comprises detecting a characteristic of the input at the input mechanism utilizing the physical input detection circuit that detects the characteristics of the input and detecting a force comprising an amount of deflection at the input mechanism, wherein at least a portion of the input mechanism remains deactivated while the information handling device is in the low power state;

determine, utilizing the input detection system employing a machine-learning model and subsequent to detecting the characteristic of the input, the physical user input comprises a request to power-on the information handling device, wherein determine comprises determining that the amount of the force exceeds a predetermined threshold and distinguishes the physical user input as a request to power-on the information handling device as opposed to a movement shock, wherein the machine-learning model associates the characteristic of the input with a desired response; and the predetermined threshold is computed over time based upon user inputs using the machine-learning model; and

in response to the determining, power on the information handling device, wherein to power on comprises receiving a signal from the physical input detection circuit at a power-on circuit of the information handling device.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 7, 2022
From: LENOVO (UNITED STATES) INC.
To: LENOVO (SINGAPORE) PTE. LTD
Reel/Frame 062078/0718 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 16, 2022
From: KAPINOS, ROBERT J; LI, SCOTT; NORTON, ROBERT JAMES, JR.; VANBLON, RUSSELL SPEIGHT
To: LENOVO (UNITED STATES) INC.
Reel/Frame 061121/0945 →
Continuity (1)
Related Publication 20240094795A1 · Mar 21, 2024
References Cited (17)
US 20050015636A1 · Chen · 2005 [cited by examiner]
US 20060090088A1 · Choi · 2006 [cited by examiner]
US 20110080367A1 · Marchand · 2011 [cited by examiner]
US 20130314349A1 · Chien · 2013 [cited by examiner]
US 20140176332A1 · Alameh · 2014 [cited by examiner]
US 20150012769A1 · Koga · 2015 [cited by examiner]
US 20150015475A1 · Ely · 2015 [cited by examiner]
US 20160187955A1 · Kawaura · 2016 [cited by examiner]
US 20170031495A1 · Smith · 2017 [cited by examiner]
US 20170302821A1 · Sasa · 2017 [cited by examiner]
US 20170336877A1 · Kämpf · 2017 [cited by examiner]
US 20180239490A1 · Yang · 2018 [cited by examiner]
US 20180314387A1 · Hwang · 2018 [cited by examiner]
US 20180365466A1 · Shim · 2018 [cited by examiner]
US 20190250685A1 · Staude · 2019 [cited by examiner]
US 20200081516A1 · Zyskind · 2020 [cited by examiner]
US 20210250433A1 · Liu · 2021 [cited by examiner]