Noise interference for capacitive input

Abstract

The invention relates to a capacitor module, which may include: a set of electrodes; a controller in communication with the set of electrodes; and a memory in communication with the controller. The memory may include programming instructions that, when executed, cause the controller to: receive a user input; sending a command to cause noise interference to the user input; making a capacitance measurement during application of noise interference to the user input; storing a noise-affected attribute associated with the capacitance measurement; and classifying the non-hinting user input by comparing the non-hinting user input to the stored noise-affected attributes.

Description

[0001] Cross-reference to related applications

[0002] This application is a partial continuation of U.S. Patent Application No. 18 / 809,924, filed August 20, 2024, entitled “Determining Non-Prompt Input”. The entire contents of U.S. Patent Application No. 18 / 809,924 are incorporated herein by reference. Technical Field

[0003] This disclosure generally relates to systems and methods for enhancing the accuracy of touch input in capacitive touch devices. Specifically, this disclosure relates to systems and methods for improving palm rejection prevention and distinguishing between intentional and unintentional touch input. Background Technology

[0004] Touchpads are frequently integrated into laptops and other devices to provide a mechanism for input. One problem with capacitive touch input devices is accidental touches, particularly touches from the palm, causing unintentional activation. This can happen when the device mistakenly interprets a palm resting on the touch surface as intentional input. Such accidental touches can lead to unintended actions, frustrating the user and degrading the overall user experience.

[0005] Existing methods for preventing accidental hand touches often rely on processing steps such as ignoring large touch areas or ignoring input detected near the edges of the touch surface. While these methods can reduce the frequency of accidental touches, they are not always effective, especially in complex usage scenarios. Furthermore, these methods may fail to accurately distinguish between intentional and unintentional touches, leading to missed inputs or false activations.

[0006] Examples of preventing accidental palm touches are disclosed in U.S. Patent No. 11,886,699 granted to Wayne Carl Westerman. It discloses a method for selectively rejecting touch contacts in the edge area of ​​a touch sensor panel. Furthermore, by setting certain exceptions for excluding edge contacts, the functionality of the touch sensor panel can be maximized. Contacts within the peripheral edge band of the touch sensor panel can be ignored. However, if the contact within the edge band moves beyond a threshold distance or speed, it can be recognized as part of a gesture. To accommodate different finger sizes, the size of the edge band can be modified based on the recognition of a finger or thumb. Additionally, if a contact in the central area of ​​the touch sensor panel follows the movement of a contact within the edge band, the contact within the edge band can be recognized as part of a gesture.

[0007] Another example of palm-touch prevention is disclosed in U.S. Patent No. 6,246,395 to Gregg S. Goyins et al. This reference discloses a method and apparatus for classifying substantially simultaneous inputs on a touchscreen. The method is described within the scope of a computer device having a display screen adapted to receive touchscreen input. First, the display screen is divided into multiple sectors. Next, the sectors are scanned sequentially to acquire inputs. When multiple substantially simultaneous inputs are sensed in each sector, the sector location of each input is determined. Then, each received input is assigned a unique value, the assigned value corresponding to the temporal order of the individual inputs based on the sequential scanning of the sectors at the time of the input. The apparatus includes a display screen adapted to receive touchscreen input. A touchscreen driver / sensor is provided to divide the display screen into multiple sectors and sense inputs in each sector. A sequence counter is used to actuate the driver / sensor to sequentially scan the display screen sectors at predetermined intervals to acquire inputs and assign a unique value to each received input.

[0008] Examples of touch type classification are disclosed in US Patent No. 11,175,698 granted to Christopher Harrison. This reference provides a method for sensing touch input to a digital device, comprising the steps of: sensing a sound / vibration signal generated by a touch; digitally processing the sensed sound / vibration signal; and determining the touch device type and touch intensity that generated the touch based on characteristics of the processed sound / vibration signal, wherein the characteristics include at least one of the following characteristics of the sound / vibration signal in the time domain: maximum amplitude, average amplitude, average frequency, mean, standard deviation, standard deviation normalized to total amplitude, variance, skewness, kurtosis, sum, absolute sum, root mean square (RMS), crest factor, dispersion, entropy, The above-mentioned characteristics are calculated from the power sum, centroid, coefficient of variation, cross-correlation, zero-crossing rate, seasonality, DC bias, or first, second, third, or higher derivatives of the sound / vibration signal; and the following characteristics of the sound / vibration signal in the frequency domain: spectral centroid, spectral density, spherical harmonic function, total average spectral energy, bandwidth energy ratio per octave, logarithmic bandwidth ratio, cepstral coefficients based on linear prediction (LPCC), perceptual linear prediction (PLP) cepstral coefficients, Mel-frequency cepstral coefficients, frequency topology, or first, second, third, or higher derivatives of the frequency domain representation of the sound / vibration signal. A device for sensing touch input is also provided.

[0009] All publicly available content from each of these documents is incorporated into this paper by reference. Summary of the Invention

[0010] In one embodiment, the capacitance module may include: a set of electrodes; a controller communicating with the set of electrodes; and a memory communicating with the controller. The memory may include programming instructions that, when executed, cause the controller to: receive user input; send a command to induce noise interference to the user input; and perform capacitance measurement when the noise interference is applied to the user input.

[0011] When the programming instructions are executed, the controller can further store noise-affected properties associated with the capacitance measurement.

[0012] When the programming instructions are executed, the controller can further classify the non-prompted user input by comparing it with stored noise-affected attributes.

[0013] Commands may include instructing the user to place water on a reference surface associated with the capacitor module.

[0014] The command can instruct the antenna to emit electromagnetic interference.

[0015] The antenna can be integrated into the capacitor module.

[0016] Antennas can be integrated into electronic devices that house capacitor modules.

[0017] Noise interference can include electromagnetic interference from nearby electronic devices.

[0018] User input can be selected from a group consisting of finger input, thumb input, stylus input, and proximity input.

[0019] The programming instructions can further enable the controller to: determine the presence of noise during the detection of non-prompted user input; and select stored noise-affected attributes for comparison based on the similarity between the determined noise and the noise interference present during the calibration process.

[0020] Noise interference can include a combination of two or more interference sources.

[0021] Programming instructions can further enable the controller to modify stored noise-affected properties based on subsequent non-prompt user input.

[0022] When the programming instructions are executed, the controller can determine whether there is noise interference when receiving user input.

[0023] When the programming instructions are executed, the controller can further determine whether there is noise interference when receiving user input.

[0024] Programming instructions enable the controller to: apply multiple intensity levels to noise interference; perform multiple capacitance measurements corresponding to the multiple intensity levels; and store multiple noise-affected attributes associated with the multiple intensity levels.

[0025] When the programming instructions are executed, the controller can further classify the non-prompted user input by comparing it with both stored noise-affected attributes and stored baseline attributes.

[0026] The command may include applying noise interference at multiple intensity levels; and performing capacitance measurements may include performing multiple capacitance measurements corresponding to the multiple intensity levels.

[0027] When the programming instructions are executed, the controller can further store multiple noise-affected attributes associated with multiple intensity levels.

[0028] In another embodiment, a method for classifying non-prompting user input on a capacitance module may include: receiving user input during calibration; sending a command to induce noise interference on the user input; performing capacitance measurement while the noise interference is applied to the user input; storing noise-affected attributes associated with the capacitance measurement; and classifying the non-prompting user input by comparing it with the stored noise-affected attributes.

[0029] Commands may include instructing the user to place water on a reference surface associated with the capacitor module.

[0030] The command can instruct the antenna to emit electromagnetic interference.

[0031] Noise interference can include electromagnetic interference from nearby devices.

[0032] User input can be selected from groups consisting of finger input, palm input, thumb input, stylus input, and proximity input.

[0033] In another embodiment, a computer program product for classifying unprompted user input on a capacitance module may include a non-transitory computer-readable medium storing instructions executable by a controller to receive user input during calibration; send a command to induce noise interference on the user input; perform capacitance measurement while the noise interference is applied to the user input; store noise-affected attributes associated with the capacitance measurement; and classify the unprompted user input by comparing it with the stored noise-affected attributes. Attached Figure Description

[0034] Figure 1 An example of an electronic device according to this disclosure is shown.

[0035] Figure 2 An example of a substrate having a first set of electrodes and a second set of electrodes according to the present disclosure is shown.

[0036] Figure 3 An example of a touchpad according to this disclosure is shown.

[0037] Figure 4 An example of a touchscreen according to this disclosure is shown.

[0038] Figure 5 An example of a capacitor module according to this disclosure is shown.

[0039] Figure 6 An example of an electronic device according to this disclosure is shown.

[0040] Figure 7 An example of an electronic device according to this disclosure is shown.

[0041] Figure 8 An example of an electronic device according to this disclosure is shown.

[0042] Figure 9 An example of an electronic device according to this disclosure is shown.

[0043] Figure 10 An example of an electronic device according to this disclosure is shown.

[0044] Figure 11 An example of a capacitive input according to this disclosure is shown.

[0045] Figure 12 An example of a capacitive input according to this disclosure is shown.

[0046] Figure 13a An example of a capacitive input according to this disclosure is shown.

[0047] Figure 13b An example of a time-voltage diagram according to this disclosure is shown.

[0048] Figure 14 An example of a capacitive input according to this disclosure is shown.

[0049] Figure 15 An example of a capacitive input according to this disclosure is shown.

[0050] Figure 16a An example of a capacitive input according to this disclosure is shown.

[0051] Figure 16b An example of a capacitive input according to this disclosure is shown.

[0052] Figure 17a An example of a capacitive input according to this disclosure is shown.

[0053] Figure 17b An example of a capacitive input according to this disclosure is shown.

[0054] Figure 18 An example of an input classification method according to this disclosure is shown.

[0055] Figure 19 An example of an input classification method according to this disclosure is shown.

[0056] Figure 20 An example of an input classification method according to this disclosure is shown.

[0057] Figure 21 An example of a palm input classification method according to this disclosure is shown.

[0058] Figure 22 An example of a palm input classification method according to this disclosure is shown.

[0059] Figure 23 An example of an input classification method according to this disclosure is shown.

[0060] Figure 24 An example of an input classification method according to this disclosure is shown.

[0061] Figure 25 An example of a method for modifying storage attributes according to this disclosure is shown.

[0062] Figure 26 An example of a method for modifying the classification process according to this disclosure is shown.

[0063] Figure 27 An example of a user input classification method according to this disclosure is shown.

[0064] Figure 28 An example of an electronic device with water-induced noise interference according to this disclosure is shown.

[0065] Figure 29 An example of a capacitor module according to this disclosure is shown.

[0066] Figure 30 An example of a capacitive input with antenna-induced noise interference is shown according to this disclosure.

[0067] Figure 31 An example of a capacitance signal according to this disclosure is shown.

[0068] Figure 32 An example of a capacitance signal according to this disclosure is shown.

[0069] Figure 33 An example of an input classification method according to this disclosure is shown.

[0070] While this disclosure may have various modifications and substitutions, specific embodiments have been shown by way of example in the accompanying drawings and will be described in detail herein. However, it should be understood that this disclosure is not intended to limit it to the specific forms disclosed. Rather, this disclosure is intended to cover all modifications, equivalents, and substitutions falling within the spirit and scope of the invention as defined by the appended claims. Detailed Implementation

[0071] This specification provides examples but is not intended to limit the scope, applicability, or configuration of the invention. Rather, the following description will provide those skilled in the art with an advantageous description for implementing embodiments of the invention. Various changes can be made to the function and arrangement of the elements.

[0072] Therefore, various embodiments may omit, substitute, or add various procedures or components as appropriate. For example, it should be understood that these methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, aspects and elements described with respect to certain embodiments may be combined in various other embodiments. It should also be understood that the following systems, methods, apparatuses, and software may be individually or collectively components of a larger system, wherein the application of other procedures may take precedence over or otherwise modify the application of these components.

[0073] For the purposes of this disclosure, the term "aligned" generally refers to parallel, substantially parallel, or forming an angle of less than 35.0 degrees. For the purposes of this disclosure, the term "lateral" generally refers to perpendicular, substantially perpendicular, or forming an angle between 55.0 and 125.0 degrees. For the purposes of this disclosure, the term "length" generally refers to the longest dimension of the object. For the purposes of this disclosure, the term "width" generally refers to the dimension of the object from one side to the other, and may refer to a measurement perpendicular to the length of the object and spanning the object.

[0074] For the purposes of this disclosure, the term "electrode" generally refers to a portion of an electrical conductor used for measurement, while the terms "path" and "trace" generally refer to portions of an electrical conductor not used for measurement. For the purposes of this disclosure, referring to a circuit, the term "line" generally refers to a combination of an electrode and a portion of a "path" or "trace" of an electrical conductor. For the purposes of this disclosure, the term "Tx" generally refers to a transmitting line, electrode, or a portion of a transmitting line or electrode, and the term "Rx" generally refers to a sensing line, electrode, or a portion of a sensing line or electrode.

[0075] For the purposes of this disclosure, the term "electronic device" generally refers to a device that can be transported and includes a battery and electronic components. Examples may include laptops, desktop computers, mobile phones, tablet computers, personal digital devices, watches, game controllers, gaming wearables, wearable devices, measuring devices, wall detectors, automation devices, security devices, displays, computer mice, vehicles, infotainment systems, audio systems, control panels, other types of devices, motion tracking devices, tracking devices, card readers, point-of-sale stations, kiosks, buttons, sliders, or combinations thereof.

[0076] It should be understood that the terms "capacitive module," "touchpad," and "touch sensor" used herein are interchangeable with "capacitive touch sensor," "capacitive sensor," "capacitive sensor," "capacitive touch and proximity sensor," "proximity sensor," "touch and proximity sensor," "touch panel," "touchpad," "touchpad," and "touchscreen." Capacitive modules can be integrated into electronic devices.

[0077] It should also be understood that, as used herein, the terms “vertical,” “horizontal,” “lateral,” “up,” “down,” “left,” “right,” “inner,” “outer,” etc., can refer to the relative orientation or position of features in the disclosed devices and / or components shown in the figures. For example, “up” or “topmost” can refer to a feature that is closer to the top of the page than another feature. However, these terms should be interpreted broadly to include devices and / or components with other orientations, such as inverted or tilted orientations, where top / bottom, above / below, above / below, up / down, and left / right can be interchanged according to orientation.

[0078] In some cases, the capacitor module is located within a housing. The capacitor module can be located below the housing and capable of detecting objects outside the housing. In an example where the capacitor module can detect capacitance changes through the housing, the housing is a capacitive reference surface. For example, the capacitor module can be disposed within a cavity formed by the keyboard housing of a computer, such as a laptop or other type of computing device, and the sensor can be disposed below the surface of the keyboard housing. In such an example, the keyboard housing adjacent to the capacitor module is a capacitive reference surface. In some examples, an opening can be formed in the housing, and a cover layer can be positioned within the opening. In this example, the cover layer is a capacitive reference surface. In such an example, the capacitor module can be positioned adjacent to the back side of the cover layer, and the capacitor module can sense the presence of an object by the thickness of the cover layer. For the purposes of this disclosure, the term "reference surface" can generally refer to a surface through which a pressure sensor, capacitive sensor, or other type of sensor is positioned to sense pressure, presence, position, touch, proximity, capacitance, magnetic properties, electrical properties, other types of properties, or other characteristics or combinations thereof indicating input. For example, the reference surface can be a housing, a cover layer, or other type of surface through which input is sensed. In some examples, the reference surface does not have a moving portion. In some examples, the reference surface may be made of any suitable type of material, including but not limited to plastics, glass, dielectric materials, metals, other types of materials, or combinations thereof.

[0079] For the purposes of this disclosure, the term "display" can generally refer to a display or screen that is not shown in the same area as the capacitive reference surface. In some cases, the display is integrated into a laptop computer, with the keyboard located between the display and the capacitive reference surface. In some examples where the capacitive reference surface is integrated into the laptop computer, the capacitive reference surface may be part of a touchpad. Pressure sensors may be integrated into the stack that constitutes the capacitive module. However, in some cases, pressure sensors may be located in other parts of the laptop computer, such as under the keyboard housing but outside the area used for sensing touch input, on the side of the laptop computer, above the keyboard, on the side of the keyboard, at another location on the laptop computer, or at other locations. In examples where these elements are integrated into the laptop computer, the display may be pivotally connected to the keyboard housing. The display may be a digital screen, a touchscreen, other types of screens, or a combination thereof. In some cases, the display is located on the same device as the device where the capacitive reference surface is located, while in other examples, the display is located on a different device than the device where the capacitive reference surface is located. For example, the display may be projected onto a different surface such as a wall or a projection screen. In some examples, the reference surface may be located on an input or game controller, and the display may be located on a wearable device such as a virtual reality or augmented reality screen. In some cases, the reference surface and the display are located on the same surface, but at different positions on that surface. In other examples, the reference surface and the display may be integrated into the same device, but located on different surfaces. In some cases, the reference surface and the display may be oriented at different angular directions relative to each other.

[0080] For the purposes of this disclosure, the term "dimensional attribute" generally refers to the dimension of a measured object (e.g., a finger, thumb, palm, stylus, etc.). In some examples, a dimensional attribute may include length, width, surface area, distance between features of the object, diagonal measurement of the object, diagonal measurement of a feature of the object, curvature of an object's edge, length of an object's edge, cross-section of the object, cross-section of a portion of the object, cross-section of a feature of the object, length of a feature of the object, length of the object's central axis, angular orientation of the object's central axis, position of the central axis of a feature of the object, angular orientation of a feature of the object, other dimensions, or combinations thereof. Features of the object may include protrusions of the object, discontinuities of the object, appendages of the object, other features, or combinations thereof. A dimensional attribute may be a finger dimensional attribute, a thumb dimensional attribute, a palm dimensional attribute, a stylus dimensional attribute, a proximity dimensional attribute, other types of dimensional attributes, or combinations thereof.

[0081] For the purposes of this disclosure, the term "motion attribute" generally refers to the motion of a measured object (e.g., a finger, thumb, palm, stylus, etc.). In some examples, motion attributes may include the distance the object moves, the object's rotation, the angular distance of the object's rotation, the object's nutation, the object's direction of motion, the object's motion pattern, the object's motion speed, the object's initial motion speed, the object's sustained speed (i.e., the speed after the initial speed), the object's rolling pattern, the object's motion duration, the number of motion cycles of the object within a predetermined time period, the sliding distance, the sliding speed, the sliding angle, the number of sliding cycles, the sliding rotation, the object's oscillation, the change in the object's oscillation, the object's stability, the object's stationary position, the duration of the object's stationary position, the rolling distance, the rolling speed, the rolling angle, the number of rolling cycles, the rolling rotation, the curvature of the motion, the trajectory of the motion, the position of the motion, the zoom distance, the zoom speed, the zoom speed, the zoom pinch angle, the number of zoom cycles, the zoom pinch rotation, and the zoom... The motion properties include: curvature of motion, trajectory of scaling motion, position of scaling motion, velocity differences between different parts of an object, angular velocity differences between different parts of an object, rotational differences between different parts of an object, distal velocity of an object, proximal velocity of an object, rotational speed of an object, shape formed by object motion, straightness of lines formed by motion, changes in object length, changes in object width, changes in object rotation, changes in object surface area, changes in object size, changes in object shape, changes in object edge curvature, changes in the position of the object's central axis, changes in the position of the central axis of an object's features, changes in the orientation of an object or feature, frequency of positional changes of an object or feature, frequency of motion of an object or feature, changes in the relative angular positions between object features, changes in the relative angular positions between the central axes of object features, other types of motion properties, or combinations thereof. Motion properties can be finger motion properties, thumb motion properties, palm motion properties, stylus motion properties, proximity motion properties, motion differences between different parts of an object, relative motion, absolute motion, other types of motion properties, or combinations thereof.

[0082] For the purposes of this disclosure, the term "signal attribute" generally refers to the signal of a capacitance measurement. In some examples, signal attributes may include signal strength, signal duration, signal amplitude, noise associated with the signal, noise patterns accompanying the signal, interference with the signal, interference patterns associated with the signal, signal resonance, signal frequency, signal polarity, signal reflection, signal voltage, signal strength variation over time, signal frequency variation over time, signal amplitude variation over time, signal polarity variation over time, other variations of the signal over time, signal peak value, signal edge, processed signal attribute, analog signal attribute, other signal attributes, or combinations thereof.

[0083] For the purposes of this disclosure, the term "image attribute" generally refers to an image of a measured object (e.g., a finger, thumb, palm, stylus, etc.). In some examples, image attributes may include image length, image width, image surface area, distance between image features, image interpolation, image splines, spline shape, spline curvature, number of nodes in a spline, relative angles between different parts of a spline, distance between spline nodes, image edge attributes, image centroid, distance between image edges and image centroid, signal intensity variation on the image, edge location, image corner location, length of linear portion of image edge, location of linear portion of image edge, image symmetry, image asymmetry, dimension of image asymmetry, repetition pattern in the image, dimension of image segmentation, image contour, a portion of image contour, derivative of image contour or a portion of image contour, number of features of interest identified in the image, spacing pattern of image features, spacing distance of image features, image density, other image attributes, or combinations thereof.

[0084] For the purposes of this disclosure, the term "keying prompt" generally refers to a prompt to press one or more keys associated with an electronic device having an integrated capacitive module. In some examples, the key is a slider, a mechanical switch key, a virtual key, a key integrated into a touchpad, a key integrated into a touchscreen, a key integrated into a touch surface, or a combination thereof. A prompt may include requesting the pressing of a specific key, a single key, multiple keys simultaneously, multiple keys in a specific order, or a combination thereof, or hovering over a specific key, a single key, multiple keys simultaneously, multiple keys in a specific order, or a combination thereof. A prompt may include prompting the user to type a specific alphanumeric character, a specific word or phrase, a specific code, or a combination thereof. A prompt may include prompting the user to type a series of keys, which generally relate to typing with both hands, typing with one hand, typing on the right side of the keyboard, typing on the left side of the keyboard, typing in the central area of ​​the keyboard, or a combination thereof.

[0085] For the purposes of this disclosure, the term "typing attribute" generally refers to dimensional attributes, motion attributes, signal attributes, image attributes, proximity attributes, processed attributes, raw data attributes, other types of attributes, or combinations thereof. In some cases, typing prompts may allow a user to bring their hand, palm, thumb, and / or fingers close to a capacitive sensor. In such examples, the system can recognize combinations of palm, fingers, and thumb that may be hovering above, placed on, touching, located beside, or combined on a capacitive reference surface. Typing actions can also cause multiple simultaneous or overlapping movements of the fingers, thumb, and palm. Therefore, typing attributes can include aspects of attributes derived from the fingers, thumb, and palm.

[0086] In cases where capacitive reference surfaces differ significantly from the keyboard and are spaced at a distance, assuming a user is typing and does not intend to provide touch or proximity input via capacitive sensors, typing attributes can be associated with unintentional user input. In other examples, such as when keys are integrated into the capacitive reference surface, the system can determine that keystroke input is intentional, but palm input is unintentional. In this case, the system can distinguish between intentional and unintentional inputs. In such situations, it is possible that some intentional and unintentional inputs are provided to the system simultaneously or within overlapping timeframes.

[0087] Figure 1 An example of an electronic device 100 is shown. In this example, the electronic device is a laptop computer. In the example shown, the electronic device 100 includes input components such as a keyboard 102 and a capacitive module such as a touchpad 104 integrated into a housing 103. The electronic device 100 also includes a display 106. Programs operated by the electronic device 100 can be displayed on the display 106 and controlled by a sequence of instructions provided by the user via the keyboard 102 and / or via the touchpad 104. An internal battery (not shown) can be used to power the operation of the electronic device 100.

[0088] Keyboard 102 includes an arrangement of keys 108 that can be individually selected when a user presses a key with sufficient force to press the key 108 against a switch located below keyboard 102. In response to selecting key 108, a program can receive instructions on how to operate, such as a word processing program determining which types of text to process. The user can use touchpad 104 to give different types of instructions to programs operating on computing device 100. For example, the cursor displayed on display 106 can be controlled via touchpad 104. The user can control the cursor's position by sliding their hand along the surface of touchpad 104. In some cases, the user can move the cursor to or near an object on the display of the computing device and give a command to select that object via touchpad 104. For example, the user can provide the instruction to select an object by tapping the surface of touchpad 104 once or multiple times.

[0089] Touchpad 104 is a stacked capacitor module comprising a layer disposed beneath the keyboard housing, beneath a cover layer adapted to an opening in the keyboard housing, or beneath another capacitive reference surface. In some examples, the capacitor module is located in an area of ​​the keyboard surface where a user's palm can rest while typing. The capacitor module may include a substrate such as a printed circuit board or other type of substrate. One of the layers of the capacitor module may include a sensor layer comprising a first set of electrodes oriented in a first direction and a second set of electrodes oriented in a second direction transverse to the first direction. These electrodes may be spaced apart and / or electrically isolated from each other. Electrical isolation can be achieved by depositing at least a portion of the electrodes on different sides of the same substrate or by providing a dedicated substrate for each set of electrodes. Capacitance can be measured at the overlapping intersections between different sets of electrodes. However, the capacitance between the electrodes may change when an object with a dielectric value different from that of the surrounding air (e.g., a finger, stylus, etc.) approaches the intersection between the electrodes. This change in capacitance and the relative position of the object with respect to the capacitor module can be calculated to determine the location where the user is touching or hovering the object within the detection range of the capacitor module. In some examples, the first and second sets of electrodes are equidistant from each other. Therefore, in these examples, the sensitivity of the capacitor module is the same in both directions. However, in other examples, the distance between the electrodes can be non-equidistant to provide greater sensitivity for movement in certain directions.

[0090] In some cases, the display 106 is mechanically separate and movable relative to the keyboard via a connecting mechanism 114. In these examples, the display 106 and the keyboard 102 can be interconnected and movable relative to each other. The display 106 can be movable relative to the keyboard 102 within a range of 0 degrees to 180 degrees or greater. In some examples, when the display 106 is in the closed position, it can fold onto the upper surface of the keyboard 102, and when the display 106 is in the operating position, it can fold open from the keyboard 102. In some examples, when in use by the user, the display 106 can be oriented at an angle between 35 degrees and 135 degrees relative to the keyboard 102. However, in these examples, the display 106 can be positioned at any angle desired by the user.

[0091] In some examples, display 106 may be a non-touch-sensitive display. However, in other examples, at least a portion of display 106 is touch-sensitive. In these examples, the touch-sensitive display may also include a capacitive module located behind the outer surface of display 106. When a user's finger or other object approaches the touch-sensitive screen, the capacitive module can detect changes in capacitance as input from the user.

[0092] Although Figure 1The example shown depicts an electronic device as a laptop computer, but capacitive sensors and touch surfaces can be integrated into any suitable device. The non-exhaustive list of devices includes, but is not limited to, desktop computers, monitors, screens, kiosks, computing devices, tablet computers, smartphones, position sensors, card reader sensors, other types of electronic devices, other types of devices, or combinations thereof.

[0093] Figure 2 An example of a portion of a capacitance module 200 is shown. In this example, the capacitance module 200 may include a substrate 202, a first set of electrodes 204, and a second set of electrodes 206. The first set of electrodes 204 and the second set of electrodes 206 may be oriented laterally to each other. Furthermore, the first set of electrodes 204 and the second set of electrodes 206 may be electrically isolated from each other so that the electrodes do not short-circuit with each other. However, capacitance can be measured where the electrodes from the first set of electrodes 204 and the second set of electrodes 206 overlap. The capacitance module 200 may include one or more electrodes from the first set of electrodes 204 or the second set of electrodes 206. Such a substrate 202 and set of electrodes can be integrated into a touchscreen, touchpad, position sensor, game controller, button, and / or detection circuitry.

[0094] In some examples, the capacitor module 200 is a mutual capacitance sensing device. In such examples, the substrate 202 has a set of row electrodes 204 and a set of column electrodes 206 defining the touch / proximity sensitive area of ​​the component. In some cases, the component is configured as a rectangular grid consisting of an appropriate number of electrodes (e.g., 8x6, 16x12, 9x15, etc.).

[0095] like Figure 2 As shown, the capacitor module 200 includes a touch controller 208. The touch controller 208 may include at least one of a central processing unit (CPU), a digital signal processor (DSP), an analog front-end (AFE) including amplifiers, a peripheral interface controller (PIC), other types of microprocessors, and / or combinations thereof, and may be implemented by suitable circuitry, hardware, firmware, and / or software as an integrated circuit, a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a combination of logic gates, other types of digital or analog electrical design components, or combinations thereof, to select from available operating modes.

[0096] In some cases, the touch controller 208 includes at least one multiplexing circuit to select which of the electrode groups 204, 206 is used as both the driving electrode and the sensing electrode. The driving electrodes can be driven one at a time sequentially, randomly, or simultaneously in an coded mode. Other configurations, such as a self-capacitance mode for simultaneously driving and sensing electrodes, are also possible. The electrodes can also be arranged in a non-rectangular array, such as a radial pattern, a linear series, etc. A shielding layer can be provided beneath the electrodes (see [link to documentation]). Figure 3 This reduces noise or other interference. The shielding layer can extend beyond the electrode grid. Other configurations are also possible.

[0097] In some cases, measurements are not performed using a fixed reference point. The touch controller 208 can generate signals that are sent directly to the first set of electrodes 204 or the second set of electrodes 206 in various modes.

[0098] In some cases, the component does not rely on absolute capacitance measurements to determine the position of a finger (or stylus, pointer, or other object) on the surface of the capacitive module 200. The capacitive module 200 can measure charge imbalances on the electrodes that serve as sensing electrodes; in some examples, the sensing electrodes can be any of the electrodes specified in the group electrodes 204, 206, or in other examples, dedicated sensing electrodes. When there is no pointing object on or near the capacitive module 200, the touch controller 208 can be in a balanced state, and there is no signal on the sensing electrodes. When a finger or other pointing object creates an imbalance due to capacitive coupling, changes in capacitance can occur at the intersections between the group electrodes 204, 206 that constitute the touch / proximity sensitive area. In some cases, changes in capacitance are measured. However, in alternative examples, absolute capacitance values ​​can be measured.

[0099] Although this example is described as a capacitor module 200 having the flexibility to switch between groups of electrodes 204, 206 between sensing and transmitting electrodes, in other examples, each group of electrodes is dedicated to either transmitting or sensing functions.

[0100] Figure 3 An example of a substrate 202 having a first set of electrodes 204 and a second set of electrodes 206 deposited on a substrate 202 and integrated into a capacitor module is shown. The first set of electrodes 204 and the second set of electrodes 206 may be spaced apart from each other and electrically isolated from each other. Figure 3 In the example shown, a first set of electrodes 204 is deposited on a first side of a substrate 202, and a second set of electrodes 206 is deposited on a second side of the substrate 202, wherein the second side is opposite to the first side and spaced apart by the thickness of the substrate 202. The substrate may be made of an electrically insulating material to prevent the first set of electrodes 204 and the second set of electrodes 206 from short-circuiting with each other. Figure 2As shown, the first set of electrodes 204 and the second set of electrodes 206 can be oriented laterally to each other. Capacitance measurements can be performed at the intersections where the electrodes from the first set of electrodes 204 and the second set of electrodes 206 overlap. In some examples, a voltage can be applied to the transmitting electrode, and the voltage of the sensing electrode overlapping with the transmitting electrode can be measured. The voltage from the sensing electrode can be used to determine the capacitance at the intersection where the sensing electrode and the transmitting electrode overlap.

[0101] A cross-section of the capacitor module is shown. Figure 3 In this example, substrate 202 may be located between capacitive reference surface 212 and shielding portion 214. Capacitive reference surface 212 may be a covering placed above a first side of substrate 202 and allowing at least partial passage of an electric field. When a user's finger or stylus approaches capacitive reference surface 212, the presence of the finger or stylus can affect the electric field on substrate 202. In the presence of a finger or stylus, the voltage measured from the sensing electrodes may differ from the voltage when the finger or stylus is absent. Therefore, changes in capacitance can be measured.

[0102] The shielding portion 214 may be a conductive layer that shields against electrical noise from internal components of the electronic device. This shielding portion can prevent the influence of electric fields on the substrate 202. In some cases, the shielding portion is a conductive solid material. In other cases, the shielding portion has a substrate and a conductive material disposed on at least one substrate. In another example, the shielding portion is a functional layer in a touchpad and also shields the electrodes from electrical interference noise. For example, in some examples, a pixel layer in a display application can form an image visible through a capacitive reference surface, but also shield the electrodes from electrical noise.

[0103] The voltage applied to the emitting electrode can be transmitted from the touch controller 208 to the appropriate group electrode via electrical connection 216. The voltage applied to the sensing electrode by the electric field generated from the emitting electrode can be detected via electrical connection 218 from the sensing electrode to the touch controller 208.

[0104] Although Figure 3 An example is shown with two sets of electrodes deposited on a substrate, one set of electrodes deposited on the first side and the second set of electrodes deposited on the second side, but in other examples, each set of electrodes may be deposited on its own dedicated substrate.

[0105] Furthermore, although the above examples describe a touchpad with a first set of electrodes and a second set of electrodes, in some examples, the capacitive module has a single set of electrodes. In such examples, the electrodes of the sensor layer can serve as both transmitting and receiving electrodes. In some cases, a voltage can be applied to the electrodes for a period of time, which changes the capacitance around the electrodes. At the end of this period, the applied voltage is interrupted. The voltage from the same electrode can then be measured to determine the capacitance. If there is no object (e.g., a finger, stylus, etc.) on or near the capacitive reference surface, the measured voltage of the electrode after the voltage interruption can be at a value consistent with the baseline capacitance. However, if an object is touching or near the capacitive reference surface, the measured voltage can indicate the change in capacitance relative to the baseline capacitance.

[0106] In some examples, the capacitor module has a first set of electrodes and a second set of electrodes, and communicates with a controller that is configured to perform mutual capacitance measurements (e.g., capacitance measurements using the first and second sets of electrodes) or self-capacitance measurements (e.g., capacitance measurements using only one set of electrodes).

[0107] Figure 4 An example of a capacitive module integrated into a touchscreen is shown. In this example, the substrate 202, electrode groups 204, 206, and electrical connections 216, 218 can be similarly combined. Figure 3 The described layout. Figure 4 In this example, shielding portion 214 is located between substrate 202 and display layer 400. Display layer 400 may be a pixel layer or diode that emits light to generate an image. The display layer may be a liquid crystal display, a light-emitting diode display, an organic light-emitting diode display, an electroluminescent display, a quantum dot light-emitting diode display, an incandescent filament display, a vacuum fluorescent display, a cathode gas display, other types of displays, or combinations thereof. In this example, shielding portion 214, substrate 202, and capacitive reference surface 212 may all be at least partially optically transparent, such that the image displayed in the display layer is visible to the user through capacitive reference surface 212. Such a touchscreen may be included in monitors, display assemblies, laptops, mobile phones, mobile devices, electronic tablets, dashboards, display panels, infotainment devices, other types of electronic devices, or combinations thereof.

[0108] Figure 5 An example of a capacitor module 500 is shown. In this example, the capacitor module 500 is a three-layer stack, including a sensor layer 502, a shielding layer 504, and a component layer 506. Although the capacitor module 500 in this example includes three layers, in other examples, the capacitor module may include a different number of layers. For example, the capacitor module may include four, five, or different numbers of layers.

[0109] Sensor layer 502 may include a first set of electrodes 508 and a second set of electrodes 510, which can be used to detect and / or measure changes in capacitance in a capacitor circuit. While this example shows a sensor layer 502 with two sets of electrodes, in other examples, a sensor layer may include one set of electrodes, three sets of electrodes, or different numbers of sets of electrodes. While this example shows a single sensor layer 502, in other examples, a capacitor module may include multiple sensor layers.

[0110] The first set of electrodes 508 and the second set of electrodes 510 can operate using mutual capacitance, self-capacitance, or a combination thereof. In an example where the sensor layer includes only a single set of electrodes, that single set of electrodes can operate using self-capacitance. In other examples, the first set of electrodes and the second set of electrodes are located on different layers.

[0111] The shielding layer 504 is located within the capacitor module 500, adjacent to the sensor layer 502. In other examples, the shielding layer may be located at a different position relative to other layers in the stack.

[0112] The shielding layer 504 may include materials that block or reduce electromagnetic and / or electrical interference. In some examples, the shielding layer may be made of conductive materials such as copper, aluminum, silver, or combinations thereof. In other examples, the shielding layer may be a composite material such as plastic, glass, other composite structures, or combinations thereof. In still other examples, the shielding layer may be a shielding material coating applied to a substrate, such as indium tin oxide (ITO), graphene, conductive polymers, other coatings, or combinations thereof. In some cases, the material of the shielding layer may be a magnetic material such as iron, ferrite, other metals, their composites, their alloys, their mixtures, or combinations thereof.

[0113] In this example, shielding layer 504 is located between sensor layer 502 and component layer 506. Shielding layer 504 can help prevent electromagnetic interference from external sources such as component 518 or capacitor module on component layer 506 from interfering with the first set of electrodes 508 and / or the second set of electrodes 510 on sensor layer 502.

[0114] Shielding sensor layer 502 with shielding layer 504 can improve the accuracy and stability of capacitance measurements taken by the first set of electrodes 508 and the second set of electrodes 510. Shielding sensor layer 502 can also reduce noise, which can improve the sensitivity and accuracy of user input on capacitance module 500. Shielding layer 504 can be positioned to block interference from batteries, power supplies, storage resources, processing resources, electronic components, other components, or combinations thereof that may be located within the cavity of the electronic device.

[0115] In this example, component layer 506 is adjacent to shielding layer 504. In other examples, the component layer may be located at a different position relative to other layers in the stack or components of capacitor modules. Component layer 506 includes component 512.

[0116] Component layer 506 may include components 512 that contribute to the functionality of capacitor module 500. Components on the component layer may include central processing unit (CPU), microcontroller, operational amplifier, memory unit, field-programmable gate array (FPGA), graphics processing unit (GPU), interface controller, power management integrated circuit, processing resources, antenna, other types of components, or combinations thereof.

[0117] Figure 6 An example of a personal computer 600 according to the present disclosure is shown. In this example, the personal computer 600 is a laptop computer. The personal computer 600 includes an input device 604, which includes a capacitor module.

[0118] During calibration, the personal computer 600 may convey a prompt 602 to the user 606 requesting user input. In this example, the prompt 602 can be conveyed to the user 606 by displaying the prompt on the monitor of the personal computer 600. In other examples, the prompt can be conveyed in different ways. For example, the prompt can be conveyed to the user in the form of an audio notification via a speaker or audio interface, in the form of haptic feedback via vibration and / or tactile feedback, using light or LED signals, by sending a text message to a connected device, or via other communication methods or a combination thereof.

[0119] Upon receiving prompt 602, user 606 may provide input 608 to input device 604. Input 608 may correspond to prompt 602. In this example, input 608 is finger input, corresponding to prompt 602 of placing a finger on the touchpad. In other examples, the user may be prompted to provide different inputs. In some examples, the user may be prompted to provide palm input, i.e., placing their palm on the capacitive module. In other examples, the user may be prompted to provide thumb input or other finger input on the capacitive module. In other examples, the user may be prompted to provide input with multiple fingers and / or palms simultaneously. The user may also be instructed to place the prompted input at a specific location on input device 604. Furthermore, in other examples, the user may be prompted to provide the same input, but rotated to a different angle.

[0120] In the example shown, input 608 can be a single input. In other examples, the input can be a gesture or a combination of gestures. For example, a user may be prompted to provide a proximity gesture, i.e., placing a finger or other input method near the capacitive module without physical contact with the reference surface. In other examples, a user may be prompted to drag a finger from one point on the input device to another. In other examples, a user may be prompted to drag a finger from one point on the input device to another in a rotational motion. In other examples, a user may be prompted to place a finger on the input device for a specified period of time. In other examples, a user may be prompted to provide a combination of gestures, such as a drag gesture, a rotation gesture, and a proximity gesture in sequence. Other combinations of gestures and prompts also exist.

[0121] When user 606 provides input 608, input device 604 can record capacitance measurements corresponding to that input. These measurements may include measurements of input length, input width, input surface area in contact with a reference surface of the input device, or combinations thereof. Measurements of input 608 may include duration elements, such as the duration of contact between the input and the reference surface of input device 604.

[0122] During calibration, the measurement values ​​input by the user (608) can be processed and stored in the capacitor module's storage resources. These measurement values ​​can form a reference dataset corresponding to input (608).

[0123] After prompting, user input, and measurement recording, the calibration process can repeat these steps to collect measurements and create a reference dataset of different types of user input. For example, a user may initially be prompted to provide finger input, palm input, thumb input, proximity input, touch input, stylus input, other types of input, or combinations thereof.

[0124] Finger input may include touching a reference surface of the input device with a finger. In response to detecting finger input, the input device may record the capacitance signal strength, multiple capacitance signal strengths at selected locations corresponding to the finger shape, finger length, finger width, multiple finger widths along the finger length, finger shape, surface area associated with the finger, finger size, other dimensions of the finger shape, other attributes associated with the measurement signal from the finger input, or combinations thereof.

[0125] Palm input may include touching a reference surface of the input device with the user's palm. In response to detecting palm input, the input device may record the capacitance signal strength, multiple capacitance signal strengths at selected locations corresponding to the palm shape, palm length, palm width, multiple palm widths along the palm length, multiple palm lengths along the palm width, palm shape, surface area associated with the palm, palm size, the position of one or more fingers and / or thumb extending from the palm, other dimensions of the palm shape, other attributes associated with the measurement signal from the palm input, or combinations thereof.

[0126] Thumb input may include touching a reference surface of the input device with the thumb. In response to detecting thumb input, the input device may record the capacitance signal strength, multiple capacitance signal strengths at selected locations corresponding to the thumb shape, thumb length, thumb width, multiple thumb widths along the thumb length, thumb shape, surface area associated with the thumb, thumb size, other dimensions of the thumb shape, other attributes associated with the measurement signal from the thumb input, or combinations thereof.

[0127] Stylus input may involve touching a reference surface of the input device with one end of a stylus. In response to detecting stylus input, the input device may record capacitive signal strength, multiple capacitive signal strengths at selected locations corresponding to the stylus shape, stylus length, stylus width, multiple stylus widths along the stylus length, stylus shape, surface area associated with the stylus, stylus size, other dimensions of the stylus shape, other attributes associated with measurement signals from the stylus input, or combinations thereof. The user may receive stylus prompts instructing them to use the stylus to write specific alphanumeric symbols, write specific phrases, sign their name, draw shapes, draw images, draw lines, draw circles, draw patterns, perform other types of stylus input, or combinations thereof.

[0128] Proximity input can include hovering over a reference surface of the input device. For example, proximity finger input can include hovering a finger over the reference surface of the input device without touching the input device. For example, proximity thumb input can include hovering a thumb over the reference surface of the input device without touching the input device. For example, proximity palm input can include hovering a palm over the reference surface of the input device without touching the input device. For example, proximity stylus input can include hovering a stylus over the reference surface of the input device without touching the input device. Proximity prompts can prompt the user to wave their hand over the reference surface, make a single-finger gesture, make a multi-finger gesture, make a single-hand gesture, make a multi-hand gesture, make a gesture, move an object horizontally relative to the reference surface, move an object vertically relative to the reference surface, make a circular motion, make other types of movements, or combinations thereof.

[0129] In response to the detection of a proximity input, the input device may record the capacitance signal strength, multiple capacitance signal strengths at selected locations corresponding to the proximity shape, the length of the proximity shape, the width of the proximity shape, multiple widths along the length of the proximity shape, the proximity shape, the surface area associated with the proximity shape, the size of the proximity shape, other dimensions of the proximity shape, other attributes associated with the measurement signal from the proximity input, or combinations thereof.

[0130] In some cases, the raw data from the input can be stored as attributes. In other examples, attributes may include processed data. In some examples, processed attributes may include average length, median length, maximum length, minimum length, length within a first standard deviation, average width, median width, maximum width, minimum width, width within a first standard deviation, average surface area, median surface area, maximum surface area, minimum surface area, surface area within a first standard deviation, average capacitance signal strength, median capacitance signal strength, maximum capacitance signal strength, minimum capacitance signal strength, capacitance signal strength within a first standard deviation, average size, median capacitance signal strength, maximum size, minimum size, size within a first standard deviation, other processed attributes, or combinations thereof. In some cases, both raw and processed attributes may be stored and / or used for comparison with non-prompted user input.

[0131] During operation of the personal computer 600, the input device 604 can classify capacitive input by comparing it with a reference dataset stored in its memory. This comparison may involve assessing the similarity and differences between new measurements and stored attributes. In some examples, the input device 604 may classify the non-cue input as an intentional touch when at least one attribute of the non-cue input matches or is at least similar to a finger attribute. In some examples, the input device 604 may classify the non-cue input as an intentional touch when at least one attribute of the non-cue input matches or is at least similar to a thumb attribute. In some examples, the input device 604 may classify the non-cue input as an accidental touch when at least one attribute of the non-cue input matches or is at least similar to a palm attribute.

[0132] In some cases, if the non-cue input closely matches the stored finger attributes, it can be classified as an intentional touch. In other cases, if the non-cue input differs significantly from the stored finger attributes and / or is closer to the stored palm attributes, it can be classified as an accidental touch.

[0133] This process of prompting, measuring, storing, and comparing inputs enables input devices to distinguish different types of touch input and reduce false alarms.

[0134] Figure 7 An example is shown where a personal computer 600 displays a prompt 702, requesting the user 606 to provide palm input 708 on the input device 604 during the calibration process. When palm input 708 is provided, the input device 604 can measure the capacitance value associated with that input, form a reference dataset, and store that dataset in memory resources. During operation of the personal computer 600, non-prompt capacitance inputs to the input device 604 can be categorized by comparing the measured value of the current input with the reference dataset stored in memory.

[0135] Figure 8 An example of a personal computer 600 is shown. In this example, the personal computer 600 displays a prompt 802, requesting the user 606 to provide thumb input 808 on the input device 604 during the calibration process.

[0136] Figure 9 An example of a personal computer 600 is shown. In this example, the personal computer 600 displays a prompt 902, requesting the user 606 to provide proximity input 908 on the input device 604 during the calibration process. Proximity input may include holding a finger, other fingertips, or palm over the capacitive module.

[0137] Figure 10 An example of a personal computer 600 is shown. In this example, the personal computer 600 displays a prompt 1002, requesting the user 606 to provide input 1008 from a second finger on the input device 604 during the calibration process. Providing input from a second finger can refine the reference dataset of finger inputs created during the calibration process.

[0138] In some cases, the system may require the user to provide repeated user input in subsequent prompts. In some cases, the same user input may be located in the same position on the user's device; be the same finger, thumb, or palm; and / or be in the same orientation. However, in other examples, subsequent prompts may include prompting the user to provide subsequent input in a different orientation, in a different position, with a different finger, with a different thumb, with a different palm, in other different ways, or in a combination thereof.

[0139] Figure 11 This illustrates an example of user 1102 providing physical input 1104 to input device 1100. Figure 12 This illustrates an example of user 1102 providing proximity input 1204 to input device 1100.

[0140] Physical input 1104 may involve user 1102 directly touching a reference surface of input device 1100, enabling the capacitance module to measure attributes such as the capacitance, length, width, and surface area of ​​the user's finger. Proximity input 1204 may involve user 1102 hovering their finger near the reference surface of input device 1100 without direct contact, enabling the capacitance module to measure capacitance changes associated with the presence of the finger near the reference surface.

[0141] During calibration, both proximity and physical inputs can be useful for a variety of reasons. Capturing physical touch data ensures that the capacitive module analyzes direct interaction characteristics, such as the accurate capacitance value when a user's finger, thumb, or palm touches a reference surface. Recording proximity data helps the capacitive module analyze user input without direct contact, which can be applied to gestures such as hovering or near-field interactions. This can help distinguish between a hovering finger or palm and an actual touch of a finger or palm.

[0142] Capturing both proximity and physical inputs enhances palm detection. By understanding the capacitive characteristics of physical touch inputs, the capacitive module can more accurately distinguish between intentional touches and accidental palm contact. Proximity data can also help the input device 1100 identify when a palm approaches its reference surface unintentionally for interaction. This helps prevent false alarms, where the input device 1100 might mistakenly interpret a hovering palm as an intentional touch.

[0143] Figure 13a An example of user 1102 interacting with input device 1100 is shown. In this example, user 1102 approaches input 1304, which is measured by input device 1100 at a first time. At a second time, user 1102 provides physical input 1306, which is measured by input device 1100 at a second time. User 1102 can interact with input device 1100 through a single action, which is classified as a proximity input by capacitance at the first time and as a physical input at the second time.

[0144] Figure 13b A graph showing the change in sensor output over time when the display input device 1100 measures input from user 1102 is shown. The y-axis represents the sensor output, and the x-axis represents time. The graph displays the proximity threshold and contact threshold on the y-axis.

[0145] Initially, as user 1102's finger approaches input device 1100, the sensor output gradually increases, indicating proximity input 1304. The proximity threshold is the minimum sensor output level at which input device 1100 can detect the presence of a nearby object, such as user 1102's finger. As the user's finger continues to approach input device 1100, the sensor output can continue to increase. When user 1102's finger makes physical contact with a reference surface of input device 1100, the sensor output can reach and exceed the contact threshold. The contact threshold is the sensor output level at which input device 1100 detects that the user has made physical contact with the reference surface.

[0146] The area shown between the proximity threshold and the contact threshold represents the proximity signal strength 1308. This shown area can be used to distinguish between hover input and actual touch. In addition to the properties of touch input in contact with the capacitive module, proximity signal properties can also be measured and stored as part of the calibration process. By analyzing proximity signal properties and sensor output when the contact threshold is reached, the capacitive module can create a detailed profile of the user's touch characteristics.

[0147] exist Figure 13a and Figure 13b In the example shown, user 1102's input consists of proximity input 1304 and physical input 1306 associated with a finger. In other examples, proximity and physical inputs associated with the palm, thumb, and other fingers can be measured during calibration to form a reference dataset.

[0148] Figure 14 An example of user input on the input device 1400 is shown. In this example, the user can perform gesture input, which starts at a first position 1402 on the reference surface of the input device 1400 and ends at a second position 1404 on the reference surface. In this example, the gesture input is a swipe gesture.

[0149] When a user performs a swipe gesture, the input device 1400 can perform multiple capacitance measurements to capture dynamic capacitance changes associated with the gesture. These measurements may include attributes such as speed, direction, pressure, touch surface area, or combinations thereof.

[0150] For example, when a user performs a swipe gesture, the input device 1400 can record the position of the initial touch point 1402, the swipe path, and the position of the final touch point 1404. This data can help the input device 1400 form a more comprehensive reference dataset of finger-executed inputs, helping the module 1400 better distinguish between finger input and palm input.

[0151] Figure 15An example of user input on input device 1400 is shown. In this example, the user performs a swipe-rotate gesture, which begins at a first position 1502 and ends at a second position 1504. While the user performs the swipe-rotate gesture, input device 1400 can perform multiple capacitance measurements to capture dynamic capacitance changes associated with the gesture.

[0152] Although Figure 14 and Figure 15 The gestures shown are swipe and rotate gestures, but in other examples, users can perform different gestures during calibration.

[0153] Although Figure 14 and Figure 15 The gestures shown are performed by the user's fingers, but in other examples, gestures performed during calibration can also be performed using the palm, thumb, other fingers, stylus, or a combination thereof. Performing similar gestures with different user inputs enhances the effectiveness of the capacitive module in distinguishing user input during operation.

[0154] Figure 16a An example of user input 1604 on an input device 1600 according to the present disclosure is shown. During calibration, user 1602 may be prompted to provide finger input 1604. Figure 16b The diagram shows finger input 1604. Input device 1600 can measure the length 1606 and width 1608 of user input 1604. The length 1606 and width 1608 of the user input can be used to form a reference dataset of the user input, enabling input device 1600 to better distinguish different types of user input.

[0155] The length 1606 and width 1608 input by the user can be processed to calculate a surface area measurement on a reference surface of the input device 1600. This surface area measurement can be used to form a reference dataset input by the user.

[0156] Figure 17a An example of user input 1704 on input device 1600 according to this disclosure is shown. During calibration, user 1602 may be prompted to provide palm input 1704. Figure 17b The image shows a palm measurement input 1702, which can include a length measurement 1706 and a width measurement 1708. The length measurement 1706 and the width measurement 1708 can be processed to calculate a surface area measurement value.

[0157] During the calibration process, such as Figure 16a As shown, users can first provide finger input on the input device. Figure 16b As shown, finger input can be measured. Then, as... Figure 17aAs shown, users can provide palm input on the input device. For example... Figure 17b As shown, palm input can then be measured. Collecting measurements for each input type during calibration and forming a reference dataset enhances the capacitive module's ability to distinguish input types during normal operation.

[0158] Figure 18 An example of a method 1800 for classifying inputs on a capacitor module is shown. This method 1800 can be based on a reference... Figure 1-1 The description of the device, module, and principle in section 7 is used for execution. During operation, the capacitive module can detect 1802 capacitive input. The capacitive module can perform a first determination 1804, determining whether the input attribute is similar to a finger attribute collected during the calibration process. If the input attribute is similar to a stored finger attribute, the capacitive module can classify the input 1808 as a finger input. If the input attribute is not determined to be similar to a stored finger attribute, the capacitive module can perform a second determination 1806, determining whether the input attribute is similar to a palm attribute collected during the calibration process. If the input attribute is similar to a stored palm attribute, the capacitive module can classify the input 1810 as a palm input. In some cases, classifying an input as a palm input may result in rejection of the input. If the input attribute is not determined to be similar to a stored finger attribute or a stored palm attribute, the capacitive module may fail to classify the input 1812.

[0159] In some cases, in response to determining that the input is intentional user input, the system may move the cursor or respond to the user's intentional input. In some cases, finger input and thumb input can be classified as intentional input. In some cases, proximity input can be classified as intentional input. In some cases, proximity input can be identified as intentional input if a proximity attribute is also recognized. For example, proximity input that includes attributes associated with a specific gesture can be identified as intentional user input. In some cases, palm input can be identified as intentional input if a palm attribute is also recognized. For example, palm input that includes attributes associated with a specific movement recognized as a gesture can be identified as intentional user input.

[0160] In some cases, in response to determining that an input is unintentional user input, the system may ignore the input, reject the input, disable the capacitive sensor for a predetermined time, disable a portion of the capacitive sensor for a predetermined time, change the sensitivity threshold, distrust the user input, provide other responses, or combinations thereof. In some cases, any palm input can be identified as unintentional user input. In some cases, proximity input can be identified as unintentional user input. In some cases, a combination of proximity input followed by finger or thumb input can be classified as part of intentional user input.

[0161] In some cases, finger input and thumb input can be classified as intentional input. In some cases, proximity input can be classified as intentional input. In some cases, proximity input can be identified as intentional input if a proximity attribute is also identified. For example, proximity input that includes attributes associated with a specific gesture can be identified as intentional user input. In some cases, palm input can be identified as intentional input if a palm attribute is also identified. For example, palm input that includes attributes associated with a specific movement identified as a gesture can be identified as intentional user input.

[0162] Figure 19 An example of a decision tree 1900 for classifying inputs on a capacitor module is shown. This method 1900 can be based on a reference... Figure 1-1 The description of the device, module, and principle in section 7 is used for execution. During operation, the capacitive module can detect the capacitive input at 1902. The capacitive module can perform a first determination at 1904, determining whether the input attribute is similar to finger attributes collected during the calibration process. If the input attribute is similar to the stored finger attribute, the capacitive module can classify the input at 1906 as a finger input. If the input attribute is not determined to be similar to the stored finger attribute, the capacitive module can perform a second determination at 1908, determining whether the input attribute is similar to thumb attributes collected during the calibration process. If the input attribute is similar to the stored thumb attribute, the capacitive module can classify the input at 1910 as a thumb input. If the input attribute is not determined to be similar to the stored thumb attribute, the capacitive module can perform a third determination at 1912, determining whether the input attribute is similar to palm attributes collected during the calibration process. If the input attribute is similar to the stored palm attribute, the capacitive module can classify the input at 1914 as a palm input. In some cases, classifying an input as a palm input may mean rejecting the palm input as unintentional input. If the input attribute is not determined to be similar to the stored finger attribute, stored thumb attribute, or stored palm attribute, the capacitive module may fail to classify the input.

[0163] During calibration, the capacitance module can be used to train a machine learning model based on measurements collected during the calibration process. During operation, inputs to the capacitance module can be fed into the machine learning model, and the input can be classified, at least in part, based on the model's output.

[0164] Machine learning models can be k-nearest neighbors, logistic regression, decision tree, random forest, gradient boosting machine, support vector machine, neural network, other machine learning models, or combinations thereof.

[0165] In some examples, machine learning models can be trained and stored on processing resources and memory belonging to the capacitor module itself. In other examples, machine learning models can be trained and stored on device resources belonging to the means of electronically communicating with the capacitor module.

[0166] The capacitor module can initiate the calibration process when a user sets up a profile associated with an electronic device. In some examples, the calibration process can be initiated in response to a user request. In some examples, the calibration process can be initiated in response to an event-based trigger, such as turning on the electronic device, updating software, changing settings associated with an input device, a program request, a user request, opening a program with the electronic device, updating a user profile, other event-based triggers, or combinations thereof. In some examples, the calibration process can be initiated repeatedly based on a recurrence.

[0167] In cases of repeated calibration processes, the dataset collected from a previous calibration process can be replaced by the dataset from the most recent calibration. However, in other examples, the dataset from the most recent calibration can be used to update or refine processed stored properties. In other examples, stored properties may include properties from multiple calibrations.

[0168] In some examples, each unique user of an electronic device can have their own profile. In such examples, each profile can be associated with a unique dataset containing stored attributes unique to each user.

[0169] Figure 20 An example of a decision tree 2000 that uses a machine learning model to classify inputs to a capacitor module is shown. This method 2000 can be based on a reference... Figure 1-1 The process is performed according to the description of the device, modules, and principles in section 7. After the capacitive module detects the input at 2002, the input measurement can be passed at 2004 to a finger machine learning model trained on finger measurements collected during calibration. This machine learning model can perform a first determination at 2006, determining whether the input corresponds to a finger input. Regardless of the result, the input can be passed at 2008 and 2010 to a palm machine learning model. The palm machine learning model can perform a second determination at 2012 and 2014, determining whether the input corresponds to a palm input. If the finger model gives a positive classification and the palm model gives a negative classification, the capacitive module can classify the input at 2016 as a finger input. If the finger model gives a negative classification and the palm model gives a positive classification, the capacitive module can classify the input at 2020 as a palm input. If both the finger and palm models give positive classifications, or both give negative classifications, the capacitive module may fail to classify the input at 2018.

[0170] In this example, the capacitor module references two machine learning models to classify the input. In other examples, the capacitor module may reference a single machine learning model trained on a combination of inputs collected during calibration. For example, the capacitor module may reference a machine learning model trained during calibration that utilizes both finger and palm inputs simultaneously. Using a single machine learning model reduces the amount of processing required for classification.

[0171] Figure 21 An example of a method 2100 for classifying non-cue input is shown. This method 2100 can be based on a reference... Figure 1-20 The method is performed by describing the device, module, and principle. In this example, method 2100 includes: prompting 2102 finger input; storing 2104 finger attributes of finger capacitance measurements associated with the finger input; prompting 2106 palm input; storing 2108 palm attributes of palm capacitance measurements associated with the palm input; and determining 2110 non-prompting palm input by referring to at least one of the stored finger attributes and the stored palm attributes.

[0172] Figure 22 An example of a method 2200 for classifying non-cue input is shown. This method 2200 can be based on a reference... Figure 1-20 The method is performed by describing the device, module, and principle. In this example, method 2200 includes: prompting 2202 finger input; storing 2204 finger attributes of finger capacitance measurements associated with the finger input; prompting 2206 palm input; storing 2208 palm attributes of palm capacitance measurements associated with the palm input; prompting 2210 thumb input; storing 2212 thumb attributes of thumb capacitance measurements associated with the thumb input; and determining 2214 non-prompting palm input by referring to at least one of the stored finger attributes, stored palm attributes, and stored thumb attributes.

[0173] Figure 23 An example of a method 2300 for classifying non-cue input is shown. This method 2300 can be based on a reference... Figure 1-20 The method is performed by describing the device, module, and principle. In this example, method 2300 includes: prompting 2302 for finger or palm input; recording 2304 for a capacitance measurement associated with the finger or palm input; activating 2306 a mechanism to introduce noise into the environment associated with the capacitance module during measurement; and storing 2308 an attribute of the capacitance measurement associated with the finger or palm input.

[0174] In some examples, the mechanism introducing noise can be an antenna, near-field antenna, Wi-Fi antenna, Bluetooth antenna, haptic device, speaker, non-capacitive mechanism, light-emitting diode, light source, optical device, vibration device, radar device, ultrasonic device, other types of device, or a combination thereof. In some cases, the noise generated by the mechanism can affect the capacitance measurement. The system can store attributes specific to when the mechanism is activated. When classifying the type of non-cue input, the system can refer to the stored attributes by comparing the recorded non-cue capacitance measurements with attributes acquired when the mechanism is activated and when the mechanism is not activated.

[0175] For example, a user can be prompted to input a first finger and a second finger. While the system records the capacitance measurement of the second finger input, it can also activate the antenna, which may introduce electromagnetic noise into the environment surrounding the capacitance module. The antenna signal may or may not affect the capacitance measurement of the second finger input. The system can store antenna finger attributes that differ from those of non-antenna fingers.

[0176] The system can store a non-exhaustive list of attributes, including but not limited to: antenna finger attributes, antenna palm attributes, antenna thumb attributes, antenna stylus attributes, antenna proximity attributes, antenna corner attributes, antenna center area attributes, antenna typing attributes, antenna wet finger attributes, antenna wet palm attributes, antenna wet thumb attributes, antenna wet stylus attributes, antenna wet proximity attributes, antenna wet corner attributes, antenna wet center area attributes, antenna wet typing attributes, non-antenna finger attributes, non-antenna palm attributes, non-antenna thumb attributes, non-antenna stylus attributes, non-antenna proximity attributes, non-antenna corner attributes, non-antenna center area attributes, non-antenna typing attributes, non-antenna wet finger attributes, non-antenna wet palm attributes, non-antenna wet thumb attributes, non-antenna wet stylus attributes, non-antenna wet proximity attributes, non-antenna wet corner attributes, non-antenna wet center area attributes, non-antenna wet typing attributes, other antenna attributes, other non-antenna attributes, other non-antenna wet attributes, other antenna wet attributes, or combinations thereof.

[0177] In some cases, the system may prompt the user to input data when: wearing gloves, when there is water on the input device, wearing a ring, wearing a watch, wearing a bracelet, wearing a metal object, sitting in a chair, standing, using headphones or other wearable devices, performing a task that the user frequently performs when providing input to the user device, or a combination thereof. In some cases, rings, jewelry, watches, etc., may affect the capacitance signal. In other examples, wearing gloves may also weaken the capacitance signal. Some electronic devices worn by the user may exert electronic frequencies on the user, which may be picked up in capacitance measurements. For example, some wearable devices, pacemakers, and other medical devices may exert frequencies that can be conducted through the user's body and detected by capacitance sensors. Such frequencies may affect the corresponding storage properties.

[0178] Figure 24 An example of a method 2400 for classifying non-cue input is shown. This method 2400 can be based on a reference... Figure 1-20 The method is performed by describing the apparatus, modules, and principles. In this example, method 2400 includes: prompting finger input 2402; storing 2404 finger attributes associated with finger capacitance measurements of the finger input; prompting palm input 2406; storing 2408 palm attributes associated with palm capacitance measurements of the palm input; prompting typing input 2410; storing 2412 typing attributes associated with typing capacitance measurements of the typing input; and determining 2414 non-prompting palm input by referring to at least one of the stored finger attributes, the stored palm attributes, and the stored typing attributes.

[0179] Typing prompts may prompt a user to type a specific phrase on a keyboard integrated into an electronic device. In some cases, the prompt may prompt the user to type a specific key or key sequence. This key or key sequence may be located on the right, left, or a combination of both of the keyboard. In some cases, the user can provide the prompted input with one hand, or the user may need to use both hands for comfortable input. In some cases, the prompt may prompt the user to select a virtual key integrated into a touchscreen, touchpad, or other user input device.

[0180] When a user provides prompted input, the user may or may not place their hand on the touchpad or touchscreen. In some cases, users may have a habit of lifting their hand off the touchpad while typing. In other cases, users may have a habit of resting their hand on the edge of the touchpad while typing. In still other cases, users may have a habit of resting their hand on an area not limited to the edge of the touchpad while typing. In yet another example, users may have a habit of placing their hand outside the surface area of ​​the touchpad while typing. Recorded capacitance measurements and therefore stored properties can reflect these typing habits of the user.

[0181] Figure 25 An example of a method 2500 for modifying stored capacitance properties is shown. Method 2500 can be based on a reference... Figure 1-20 The method is performed by describing the device, module, and principle. In this example, method 2500 includes: recording 2502 a capacitance measurement value from non-prompted user input; comparing the non-prompted capacitance measurement value with stored capacitance properties 2504; and modifying 2506 the stored capacitance properties based on the non-prompted capacitance measurement value.

[0182] The stored capacitance attribute can be any suitable type of attribute as described above. In some examples, based on the comparison results, it can be determined that the non-prompt user input has features that match the stored attribute, are similar to the stored attribute, are within the standard deviation range of the stored attribute, or a combination thereof. This comparison can classify the non-prompt user input into a specific type of user input. The non-exhaustive list of user input types includes, but is not limited to: intentional input, accidental input, palm input, finger input, thumb input, stylus input, wet input, glove input, proximity input, other types of input, or combinations thereof.

[0183] In some cases, stored capacitance properties can be modified based on a single unhinted capacitance measurement. In other examples, multiple unhinted capacitance measurements can be collected and / or analyzed to determine a composite unhinted property. This unhinted property can be compared to a stored capacitance property. If the stored capacitance property differs from the unhinted property, the unhinted property can be used to modify the stored capacitance property.

[0184] In some examples, only those non-cue attributes associated with user input of the same category are compared and / or used to modify stored capacitance attributes. For example, only those attributes associated with non-cue finger user input can be used to modify stored finger attributes. In other examples, features of non-cue user input can be used to modify stored attributes associated with the type of user input. For example, attributes associated with non-cue finger input can be used to modify finger attributes, palm attributes, thumb attributes, stylus attributes, wet attributes, proximity attributes, other types of attributes, or combinations thereof.

[0185] Figure 26 An example of a method 2600 for modifying the classification process is shown. This method 2600 can be based on a reference... Figure 1-20The method is performed by describing the device, module, and principle. In this example, method 2600 includes: recording 2602 a capacitance measurement value from unprompted user input; comparing the unprompted capacitance measurement value with stored capacitance properties 2604; classifying the unprompted user input based on the comparison result 2606; and using 2608 the classification result to modify the process of classifying the unprompted input.

[0186] The process of classifying non-cue input may include comparing the attributes of the non-cue input with stored attributes, comparing the features of the stored attributes with the features of the non-cue input, running algorithms, performing calculations, performing other tasks, or a combination thereof.

[0187] Modifying the process for classifying non-prompted user input can include machine learning processes, k-nearest neighbor models, logistic regression models, decision tree models, random forest models, gradient boosting machines, support vector machines, neural networks, other machine learning models, or combinations thereof.

[0188] Figure 27 An example of a method for classifying capacitive inputs in the presence of noise interference is shown. This method 2700 can be implemented by the capacitor module described in the foregoing embodiments and can be based on reference... Figure 1-26 The principles and apparatus discussed in the paper are used to perform this. Method 2700 illustrates an input classification method that takes into account the presence of noise interference and utilizes noise-affected attributes that can be collected during the calibration process.

[0189] In this example, method 2700 includes: detecting 2702 a capacitive input; and determining 2704 whether noise interference exists in the measured capacitance signal. If no noise interference exists, the system can classify the input without using noise-affected attributes 2708. If noise interference exists, the system can select 2706 one or more noise-affected attributes based on the similarity between stored noise conditions and the noise conditions of the measured capacitance signal. Finally, the system can compare the capacitive input with stored noise-affected attributes 2710 and classify the capacitive input based on the comparison result 2712.

[0190] In some examples, the command may include instructing the user to place water on a reference surface associated with the capacitor module. In other examples, the command may include instructing the user to place soda water or other materials / liquids on the reference surface associated with the capacitor module. In other examples, the command may instruct the antenna to emit electromagnetic interference. In other examples, noise interference may include electromagnetic interference from nearby devices. Furthermore, in yet another example, the command may instruct another mechanism integrated into the capacitor module or into the electronics housing the capacitor module to generate a signal that could interfere with the capacitor input.

[0191] In some calibration processes, controlled noise interference can be intentionally introduced during the collection of capacitance measurements. This noise interference can be triggered by various mechanisms, such as electromagnetic interference, environmental interference, or other types of signal interference.

[0192] Controlled noise interference can be electromagnetic interference from an antenna. In this case, the antenna generating the electromagnetic interference can be located inside the capacitor module or is part of a device that includes the capacitor module. During calibration, the system can activate the antenna to generate an electromagnetic field that interacts with the capacitive sensing system. The intensity and characteristics of this interference can be varied to simulate different real-world noise conditions.

[0193] The calibration process can also take advantage of electromagnetic interference from nearby electronic devices. During certain calibration steps, users can be instructed to place their mobile phones or other electronic devices near the capacitor module. This can help capture the effects of real electronic noise that users might encounter during normal use of the device.

[0194] Introducing controlled noise can also include instructing the user to introduce environmental factors that may affect capacitive sensing. For example, the user could be prompted to place a drop of water on a reference surface of the capacitive module. This simulates a situation where the touch surface may come into contact with moisture, which could alter the capacitance measurement.

[0195] In some embodiments, the calibration process may employ a combination of these noise sources, activating multiple interference mechanisms simultaneously or in a specific order. This allows the system to capture data on how different noise sources interact in complex ways and affect capacitance measurements. Furthermore, the system can perform capacitance measurements without intentional noise interference to obtain baseline measurements and store baseline properties free from intentional noise-induced interference.

[0196] For each type of noise interference (and combinations thereof), the calibration process can repeatedly collect input measurements. This can generate multiple sets of attributes for each input type: a set of baseline attributes collected without intentional interference, and one or more sets of "noise-affected" attributes collected under various noise conditions. Specific attributes recorded may include, but are not limited to: the maximum capacitance change detected, the detected input region (e.g., the number of electrodes recording significant changes), the shape characteristics of the input, the temporal distribution of the input, the spatial distribution of capacitance changes across the electrode array, the frequency characteristics of the capacitance changes, any oscillations or instabilities detected during the measurement, and the relative changes between adjacent electrodes.

[0197] Each of these attributes can be affected differently by different types of noise. Collecting such detailed data allows the system to understand in detail how noise interferes with different types of input.

[0198] The final result of this calibration process can be a rich set of reference data capturing the characteristics of various input types under ideal and noisy conditions. This comprehensive dataset can serve as the basis for the advanced input classification methods described earlier in this application, enabling the system to accurately distinguish between intentional input and unintentional touches even in challenging noisy environments.

[0199] By collecting and storing these noise-affected attributes, the system gains the ability to adapt its classification algorithm to real-world environments, significantly improving its ability to prevent accidental palm touches and overall input recognition accuracy. This results in a more robust and reliable user interface, enhancing the overall user experience across a wide range of usage scenarios and environmental conditions.

[0200] By identifying the presence of noise and selecting appropriate noise-affected attributes, the system can maintain high accuracy even in noisy environments that may lead to misclassification. This is particularly useful for improving palm touch prevention in situations where ambient noise may cause palm touches to be misinterpreted as intentional input.

[0201] Furthermore, this method allows for adaptation to various noise conditions. By storing multiple sets of noise-affected attributes during the calibration process (each set associated with a different type or level of noise), the system can cope with a wide range of real-world interference scenarios. This adaptability can be further enhanced if the system is designed to update its stored attributes over time based on real inputs, enabling it to fine-tune its classification accuracy for each specific user and usage environment.

[0202] In some examples, the system can update its stored properties based on the input of a new classification, especially when there is high confidence in the classification. This allows the system to continuously improve its accuracy over time.

[0203] Figure 28 An example of an electronic device 2800 is shown in a calibration process involving the introduction of controlled forms of interference. In this example, the electronic device 2800 is a laptop computer. Although the electronic device 2800 in this example is a laptop computer, the described principles can be applied to any electronic device with a capacitive input surface, such as a tablet computer, smartphone, or touchscreen.

[0204] In this example, when the electronic device 2800 displays a prompt 2802, requesting the user 2806 to place a drop of water 2810 on the input device 2804 while providing user input 2808 during the calibration process.

[0205] In this example, prompt 2802 instructs the user to place a drop of water on a reference surface of input device 2804. Such a request could be intended to simulate the presence of moisture on input surface 2804, which would significantly alter capacitance measurements and potentially lead to input misclassification if not properly considered.

[0206] A water droplet 2810 placed on the reference surface of the input device 2804 can serve as a controlled form of disturbance. Water is conductive and has a different dielectric constant than air, which can significantly alter the capacitive field generated by the electrodes within the capacitor module. By introducing this water droplet 2810 during calibration, the system can measure and record various properties of how the presence of moisture affects the capacitance measurements.

[0207] The location and amount of water placed on the reference surface of the capacitor module can vary. In some examples, the user may be asked to place multiple water droplets at different locations on the reference surface. In other examples, the user may be asked to place a larger amount of water on the reference surface. In still other examples, the user may be asked to immerse the electronic device in water before providing input.

[0208] When user 2806 provides user input 2808 during the calibration process, the system can collect and store capacitance measurements as noise-affected properties. During normal operation of the electronic device 2800, when moisture is detected on the capacitor module, the system can refer to these stored noise-affected properties, thereby improving the accuracy of capacitance measurements detected under these conditions.

[0209] Figure 29 An example of a capacitor module 2900 according to the present disclosure is shown. In this example, the capacitor module 2900 includes a sensor layer 2902, a shielding layer 2904, and a component layer 2906. The sensor layer 2902 may include a first set of electrodes 2908 and a second set of electrodes 2910. The component layer 2906 may include a component 2912 and an antenna 2914. Although this example shows the antenna as being located on the component layer, in other examples, the antenna may be placed on other layers and / or locations of the capacitor module.

[0210] Antenna 2914 can be used to transmit wireless signals. Antenna 2914 can be a simple wire loop, multiple loops, an edge antenna, a more complex antenna configuration, or other suitable structures capable of transmitting wireless signals. Antenna 2914 can be formed on or within a suitable layer of material by etching, stamping, deposition, or other methods. Integrating the antenna into the capacitor module can increase the functionality of the capacitor module and save space previously occupied by a dedicated wireless antenna in electronic devices with integrated capacitor modules.

[0211] During calibration, antenna 2914 can be activated. Antenna 2914 can emit electromagnetic interference that affects the measured values ​​of user input on capacitor module 2900. By measuring how electromagnetic interference affects capacitance measurements during calibration, the system can use these calibration measurements to classify user input during normal operation of capacitor module 2900.

[0212] Figure 30 An example of user input according to this disclosure is shown. In this example, user 3002 performs finger input 3004 on reference surface 3006 of a capacitance module. Reference surface 3006 of the capacitance module is adjacent to shielding layer 3008 and component layer 3010, which includes antenna 3012. When user 3002 provides finger input 3004, antenna 3012 is activated, emitting electromagnetic radiation 3014. This electromagnetic radiation 3014 can interfere with the capacitance signal of the finger input 3004 measured by the capacitance module.

[0213] Figure 31 An example of the capacitance signal 3102 according to this disclosure is shown. Figure 32 An example of another capacitance signal 3202 according to this disclosure is shown. Capacitance signals 3102 and 3202 illustrate the effect that electromagnetic radiation may have on capacitance signals.

[0214] The capacitance signal 3102 is a smooth, continuous oscillation. This type of signal can be a typical pattern for capacitance signals under noise-free conditions. In contrast, the capacitance signal 3202 exhibits the same basic pattern as signal 3102, but shows significant distortion and additional noise due to electromagnetic interference. The difference between the baseline signal 3102 and the noisy signal 3202 demonstrates the effect of electromagnetic interference on the capacitance measurement.

[0215] Noise signals can exhibit several characteristic differences from the baseline signal, such as increased baseline noise, potential shift in overall amplitude, distortion of the shape of signal peaks or valleys, introduction of additional small peaks or oscillations, changes in signal transition rise or fall times, potential masking of smaller features of the input signal, and other differences.

[0216] By capturing both the baseline signal and the noise signal during calibration, the system can construct a comprehensive set of reference data. This data allows the system to better interpret the input during normal operation, even in the presence of electromagnetic interference.

[0217] For example, if the system detects a noise signal similar to 3202 during normal operation, it can compare the signal with both its baseline reference data and the noise reference data. This comparison can show that, despite the noise, the underlying pattern matches a specific type of input (e.g., a finger tap), thus enabling accurate classification even in the presence of interference.

[0218] The ability to generate controlled electromagnetic interference during calibration allows for the collection of noise-affected reference data to more closely match real-world usage conditions where various electromagnetic interference sources may exist. This can improve the accuracy of input detection and classification, especially in challenging electromagnetic environments, thereby enhancing the overall reliability and user experience of devices employing this capacitive sensing technology.

[0219] Figure 33 An example of a method 3300 for classifying user input on a capacitor module is shown. This method 3300 can be based on a reference... Figure 1-32 The method is performed by describing the device, module, and principle. In this example, method 3300 includes: receiving user input during calibration 3302; sending a command 3304 to induce noise interference to the user input; storing 3306 noise-affected attributes associated with the capacitance measurement; and classifying the non-prompted user input 3308 by comparing it with the stored noise-affected attributes.

[0220] It should be noted that the methods, systems, and apparatus described above are merely examples. It must be emphasized that various processes or components can be appropriately omitted, substituted, or added in various embodiments. For example, it should be understood that in alternative embodiments, the methods can be performed in a different order than described, and various steps can be added, omitted, or combined. Furthermore, features described with respect to certain embodiments can be combined in various other embodiments. Different aspects and elements of the embodiments can be combined in a similar manner. Additionally, it must be emphasized that technology is constantly evolving; therefore, many elements are exemplary in nature and should not be construed as limiting the scope of the invention.

[0221] Specific details are set forth in this specification to provide a full understanding of the embodiments. However, it will be understood by those skilled in the art that the embodiments can be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques are shown without unnecessary detail to avoid obscuring the embodiments.

[0222] Additionally, it should be noted that embodiments can be described as processes shown as flowcharts or block diagrams. While each embodiment may be described as a sequential process, many operations can be performed in parallel or simultaneously. Furthermore, the order of operations can be rearranged. The process may have additional steps not included in the figures.

[0223] After describing several embodiments, those skilled in the art will recognize that various modifications, substitutions, and equivalents can be used without departing from the spirit of the invention. For example, the above-described elements may simply be components of a larger system, where other rules may take precedence over or otherwise modify the application of the invention. Furthermore, multiple steps may be performed before, during, or after considering the above-described elements. Therefore, the foregoing description should not be considered as limiting the scope of the invention.

Claims

1. A capacitor module, comprising: A set of electrodes; The controller communicates with the set of electrodes; as well as The memory communicates with the controller. The memory includes programming instructions, which, when executed, cause the controller to: Receive user input; Send a command to cause noise interference to the user input; and Capacitance measurement is performed when the noise interference is applied to the user input.

2. The capacitor module according to claim 1, wherein, When the programming instructions are executed, the controller further causes the controller to store noise-affected properties associated with the capacitance measurement.

3. The capacitor module according to claim 2, wherein, When the programming instructions are executed, they further cause the controller to: The capacitance signal was measured without the applied noise interference; Store at least one baseline attribute associated with the capacitance signal acquired when the noise interference was not applied.

4. The capacitor module according to claim 2, wherein, When the programming instructions are executed, the controller further categorizes the unprompted user input by comparing it with stored noise-affected attributes.

5. The capacitor module according to claim 4, wherein, When the programming instructions are executed, the controller further categorizes the non-prompt user input by comparing it with both stored noise-affected attributes and stored baseline attributes.

6. The capacitor module according to claim 1, wherein, The command includes instructing the user to place water on a reference surface associated with the capacitor module.

7. The capacitor module according to claim 1, wherein, The command instructs the antenna to emit electromagnetic interference.

8. The capacitor module according to claim 7, wherein, The antenna is integrated into the capacitor module.

9. The capacitor module of claim 7, wherein the antenna is integrated into an electronic device housing the capacitor module.

10. The capacitor module according to claim 1, wherein, The noise interference includes electromagnetic interference from nearby electronic devices.

11. The capacitor module according to claim 1, wherein, The programming instructions further enable the controller to determine the presence of noise during the detection of the non-prompting user input, and to select stored noise-affected attributes for comparison based on the similarity between the determined noise and the noise interference present during the calibration process.

12. The capacitor module according to claim 1, wherein, The programming instructions further enable the controller to modify stored noise-affected attributes based on subsequent non-prompt user input.

13. The capacitor module according to claim 1, further comprising programming instructions, which, when executed, cause the controller to determine whether noise interference exists when receiving user input.

14. The capacitor module of claim 1, wherein the command includes applying noise interference of multiple intensity levels; and Performing capacitance measurements includes performing multiple capacitance measurements corresponding to the multiple strength levels.

15. The capacitor module according to claim 14, wherein, When the programming instructions are executed, the controller stores multiple noise-affected attributes associated with the multiple intensity levels.

16. A method for classifying non-prompt user input on a capacitor module, comprising: Receive user input during the calibration process; Send a command to cause noise interference to the user input; Capacitance measurement is performed when the noise interference is applied to the user input; Store noise-affected properties associated with the capacitance measurement; as well as The unprompted user input is classified by comparing it with stored attributes affected by noise.

17. The method according to claim 16, wherein, The command includes instructing the user to place water on a reference surface associated with the capacitor module.

18. The method according to claim 16, wherein, The command instructs the antenna to emit electromagnetic interference.

19. The method of claim 16, wherein, The user input is selected from a group consisting of finger input, palm input, thumb input, stylus input, and proximity input.

20. A computer program product for classifying non-prompt user input on a capacitor module, the computer program product comprising a non-transitory computer-readable medium storing instructions executable by a controller to: Receive user input during the calibration process; Send a command to cause noise interference to the user input; Capacitance measurement is performed when the noise interference is applied to the user input; Store noise-affected properties associated with the capacitance measurement; and The unprompted user input is classified by comparing it with stored attributes affected by noise.

Images