Method for improving accuracy of touch screen event analysis by use of spatiotemporal touch patterns

Patent metadata

Field	Value
Publication number	US-10606417-B2
Application number	US-201414495041-A
Country	US
Kind code	B2
Filing date	Sep 24, 2014
Priority date	Sep 24, 2014
Publication date	Mar 31, 2020
Grant date	Mar 31, 2020

Abstract

A method of classifying touch screen events uses known non-random patterns of touch events over short periods of time to increase the accuracy of analyzing such events. The method takes advantage of the fact that after one touch event, certain actions are more likely to follow than others. Thus if a touch event is classified as a knock, and then within 500 ms a new event in a similar location occurs, but the classification confidence is low (e.g., 60% nail, 40% knuckle), the classifier may add weight to the knuckle classification since this touch sequence is far more likely. Knowledge about the probabilities of follow-on touch events can be used to bias subsequent classification, adding weight to particular events.

First claim

What is claimed is:

1. A method of analyzing touch events based on characterization of features derived from the touch events, the method comprising:
detecting the touch events, including an initial touch event from an initial hand part and a subsequent touch event, on a touch sensitive surface;
generating vibro-acoustic waveform signals using at least one sensor associated with the touch events;
converting the vibro-acoustic waveform signals into at least one domain signal;
extracting distinguishing features from the at least one domain signal; and
classifying the distinguishing features of the subsequent touch event by employing spatiotemporal data relating to historical patterns for different sequences of hand parts causing historical initial and subsequent touch events, wherein the classifying uses the spatiotemporal data to derive different likelihoods for different hand parts causing the subsequent touch event, which occurs after the initial hand part caused the initial touch event, to determine which subsequent hand part from the different hand parts caused the subsequent touch event, and wherein the different hand parts include a finger knuckle, a fingertip, a palm, and a fingernail.

2. The method recited in claim 1 , wherein the historical patterns associated with the spatiotemporal data comprises data from prior sequence of multiple touch events over a selected time period.

3. The method recited in claim 1 , wherein the spatiotemporal data comprises data suggesting a likelihood of each touch event after a first of a plurality of touch events over a selected time period.

4. The method recited in claim 1 , wherein the historical patterns associated with the spatiotemporal data is accumulated and stored in advance of the classifying.

5. The method recited in claim 1 , wherein the classifying the distinguishing features comprises multiplying spatiotemporal event likelihood by classification confidences.

6. A method of classifying a sequence of multiple touch events in touch screen devices to improve accuracy of determining characteristics of touch events imparted to a touch screen, the method comprising:
analyzing each touch event in said sequence to determine within a level of confidence what hand part caused a touch event;
determining likelihoods of different sequences of possible hand part touch events based on spatiotemporal event data relating to historical patterns of different sequences of hand parts that cause the different sequences of touch events; and
combining the level of confidence for each touch event in said sequence and the likelihoods of different sequences of the possible hand part touch events to produce a most likely result of what hand part actually generated the touch event in the sequence, wherein such hand part is selected from among a fingertip, a finger knuckle, a fingernail, and a hand palm.

7. The method recited in claim 6 , wherein the determining uses elapsed time over the sequence of multiple touch events to determine the likelihoods.

8. The method recited in claim 6 , wherein the combining comprises weighting the confidence levels by the likelihoods.

9. The method recited in claim 6 , wherein the combining comprises multiplication of the confidence levels by the likelihoods.

10. A non-transitory computer readable medium containing instructions for classifying a sequence of multiple touch events in touch screen devices to improve accuracy of determining characteristics of touch events imparted to a touch screen, wherein execution of the instructions by a processor causes the processor to:
analyze each touch event in said sequence to determine within a level of confidence what hand part caused a touch event;
determine likelihoods of different sequences of possible hand part touch events based on spatiotemporal event data relating to historical patterns of different sequences of hand parts that cause the different sequences of touch events; and
combine the level of confidence for each touch event in said sequence and the likelihoods of different sequences of the possible hand part touch events to produce a most likely result of what hand part actually generated the touch event in the sequence, wherein such hand part is selected from among a fingertip, a finger knuckle, a fingernail, and a hand palm.

11. The computer readable medium recited in claim 10 , wherein a subset of the instructions that determines the likelihoods of the possible hand part touch event sequences uses elapsed time over the sequence of multiple touch events to determine the likelihoods.

12. The computer readable medium recited in claim 10 , wherein a subset of the instructions that combines the level of confidence for the multiple touch events in the different sequences of possible hand part touch events and the likelihoods of the possible hand part touch events comprises weighting of the confidence levels by the likelihoods.

13. The computer readable medium recited in claim 10 , wherein a subset of the instructions that combines the level of confidence comprises particular instructions to multiply the confidence levels by the likelihoods.

Assignees

• Qeexo Co

Inventors

• Schwarz Julia
• Harrison Chris

Classifications

• G06F3/043 Primary

  using propagating acoustic waves · CPC title

• G06F2203/04104

  Multi-touch detection in digitiser, i.e. details about the simultaneous detection of a plurality of touching locations, e.g. multiple fingers or pen and finger · CPC title

• G06F3/0416

  Control or interface arrangements specially adapted for digitisers · CPC title

• G06F2203/04106

  Multi-sensing digitiser, i.e. digitiser using at least two different sensing technologies simultaneously or alternatively, e.g. for detecting pen and finger, for saving power or for improving position detection · CPC title