Sparsified Capacitive Touch Precision via Statistical Inference

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Solution Overview

Problem

Current touch input technologies become expensive when scaled to large surfaces or non-flat applications, such as large-scale non-flat (e.g., curved) TVs, and lack flexibility and capability for irregular shapes due to the use of rigid glass substrates and high conductive materials like ITO, leading to high costs and limited flexibility in surface design.

Innovation Solution

The implementation of sparsified sensor structures and machine-learning systems that use generative models to determine context-dependent statistics, applying delta changes to detected coordinates for improved accuracy in touch location prediction, and the use of statistical inference to enhance the precision of capacitive-touch devices, allowing for more cost-effective and flexible touch input solutions on non-flat surfaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If sparsified sensor structures are used to reduce cost and improve flexibility, then manufacturing cost and adaptability are improved, but measurement precision deteriorates

Engineering Contradiction:

Improvemanufacturing costVSAvoidtouch detection precision

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

The sensor array is segmented into sparsely distributed individual sensors rather than a continuous matrix. Each sensor independently detects touch events, and the system processes signals from these separated sensors to reconstruct accurate touch location information, resolving the contradiction between sparse structure and precision measurement.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Signal processing algorithms act as intermediaries between the sparsified sensor inputs and the final touch location output. These algorithms process and interpolate the limited sensor data to recover precise touch coordinates, enabling accurate measurement despite the reduced sensor density.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If sparsified sensor structures are used to enable non-flat surfaces, then adaptability is improved, but measurement precision deteriorates

Engineering Contradiction:

Improvesurface shape adaptabilityVSAvoidtouch detection precision

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system dynamically adapts to different surface geometries by processing sensor signals that account for the non-flat topology. The signal processing algorithms adjust for surface curvature and irregularities to maintain measurement precision across varied surface shapes, enabling both adaptability and precision.

Inventive Principle:

Principle #15Dynamics

3Measurement precision

If statistical inference is applied to enhance precision, then measurement precision is improved, but device complexity increases

Engineering Contradiction:

Improvetouch location precisionVSAvoidprocessing system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Complex mechanical or hardware-based precision enhancement systems are replaced with statistical inference algorithms. The computational approach uses probability models and signal processing to achieve precision that would otherwise require complex physical systems, trading hardware complexity for software-based solutions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enhances the accuracy and cost-effectiveness of touch input technologies on large and non-flat surfaces by improving the precision of touch detection and location, enabling flexible and adaptable touch capabilities on unconventional shapes without the need for expensive high-conductive materials.

Implementation Method 1

sparsified capacitive-touch and other human-interface devices

Methodology Applied

Scientific EffectCapacitance: Capacitance

Data Source

PatentUS11755149B1Systems and methods for using statistical inference to enhance the precision of sparsified capacitive-touch and other human-interface devices

Publication Date: 2023.09.12 SAMSUNG ELECTRONICS CO LTD

AI summary

In one embodiment, a method includes by an electronic device: receiving sensor data indicative of a touch input from sensors of a human interface-device (HID) of the electronic device, where the touch input occurs at a set of actual coordinates with respect to the HID, and where the sensor data indicates the touch input occurs at a set of detected coordinates with respect to the HID, determining a context associated with the touch input, determining, by one or more generative models, context-dependent statistics to apply a delta change to the set of detected coordinates, where the context-dependent statistics are based on the context associated with the touch input, and where the one or more generative models comprises one or more system parameters and one or more latent parameters, and determining a set of time-lapsed predicted coordinates of the touch input with respect to the HID based on the delta change.